System

Abstract

A system is provided.SOLUTION: A system, comprising: means for inputting physical condition information of a user; means for inputting mood information of the user; means for inputting food ingredient information in a refrigerator of the user; means for transmitting the physical condition information, the mood information, and the food ingredient information input by the user to a server; means for generating a recipe based on the physical condition information, the mood information, and the food ingredient information using a generative AI model in the server; SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Busy modern people are often overwhelmed by their daily work and find it difficult to find time to prepare nutritionally balanced meals at home. It is also difficult to efficiently use the ingredients in their refrigerators, resulting in food waste. This leads to a deterioration in health and a worsening food waste problem. Therefore, there is a need for a system that provides easy and efficient nutritionally balanced recipes that take into account the user's physical condition, mood, and the ingredients in their refrigerator. [Means for solving the problem]

[0005] The present invention provides a system that includes means for inputting information about the user's physical condition, mood, and ingredients in the refrigerator, and a means for transmitting this information to a server. The server uses a generative AI model to generate nutritionally balanced recipes based on the transmitted information. The generated recipes are transmitted to the user's device and displayed on the device. In this way, the system provides a system that allows users to easily prepare nutritionally balanced meals and efficiently use ingredients in the refrigerator, thereby contributing to reducing food waste.

[0006] "User" refers to a person who uses this system.

[0007] "Physical condition information" refers to information about the user's current health condition, such as whether they are tired, healthy, or have a cold.

[0008] "Mood information" refers to the user's current mental state and food preferences, such as a desire for spicy food or a desire for a light meal.

[0009] "Information about ingredients in the refrigerator" refers to information about the types and amounts of ingredients currently stored in the user's refrigerator or kitchen.

[0010] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate recipes based on a user's physical condition, mood, and ingredient information.

[0011] "Server" refers to a computer system that receives information sent from a user's device and generates a recipe using a generative AI model.

[0012] "Terminal" refers to an electronic device for a user to input information and display generated recipes, including, for example, a smartphone or a personal computer.

[0013] A "recipe" refers to information generated by a generative AI model that describes how to make a dish and the ingredients needed.

[0014] "Means for transmitting" refers to a communication method for transmitting the information entered by the user to the server, including, for example, data transmission using an Internet connection.

[0015] The "means for displaying" refers to a method for visually presenting the recipe information received from the server to the user, for example, by displaying the information on the screen of a terminal. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that inputs information about the user's physical condition and mood, as well as information about the ingredients in the refrigerator, and uses a generative AI model to provide nutritionally balanced recipes based on this information.

[0038] System Operation Overview

[0039] 1. Enter your user information

[0040] Using a device such as a smartphone or PC, a user inputs information about their physical condition (e.g., tired, feeling a bit like a cold), mood (e.g., desire for spicy food), and information about ingredients in the refrigerator (e.g., chicken, carrots, potatoes, tomatoes). This information is then entered into an application on the device.

[0041] 2. Transmission of information

[0042] The device sends the input information to the server via an internet connection, and the information is structured in a standard format such as JSON.

[0043] 3. Receiving and analyzing information

[0044] The server analyzes the received information. First, it checks the input data for consistency and for missing values. If there is incomplete information, it either prompts the user for completion or sets default values.

[0045] 4. Recipe Generation

[0046] The server launches a generative AI model and generates recipes using the analyzed data as input. This generative AI model considers the necessary nutrients based on the user's physical condition and creates recipes that suit the user's preferences based on their mood. It also suggests dishes that can be made quickly based on the ingredients in the refrigerator.

[0047] 5. Sending and Viewing Recipes

[0048] The recipe information generated by the server is sent to the user's device, which displays the received recipe information on the screen, allowing the user to easily prepare a dish based on the recipe.

[0049] Specific examples

[0050] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the refrigerator," the system will operate as follows:

[0051] 1. Enter your user information:

[0052] The user inputs information about their physical condition, mood, and ingredients into the application.

[0053] 2. Transmission of Information:

[0054] The terminal sends the input data to the server.

[0055] 3. Receiving and analyzing information:

[0056] The server receives the information and verifies the integrity of the data.

[0057] 4. Generate the recipe:

[0058] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry."

[0059] 5. Submit and view recipes:

[0060] The server sends the generated recipe information to the terminal, which displays it.

[0061] Users can follow these recipes and use ingredients they have in their refrigerator to quickly prepare delicious meals that are suited to their physical condition. This system allows even busy modern people to easily enjoy nutritionally balanced meals, and also contributes to reducing food waste.

[0062] As a result, the present invention provides support for users to lead a healthy life in their busy daily lives and provides an efficient means for achieving a sustainable dietary lifestyle.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The user starts up the device and launches the application. The user inputs information about their physical condition (e.g., "I'm tired"), their mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes").

[0066] Step 2:

[0067] The user presses the "Send" button to send the entered information from the device to the server, which then sends the information to the server in a structured format such as JSON.

[0068] Step 3:

[0069] The server analyzes the data it receives, specifically checking its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[0070] Step 4:

[0071] The server launches the AI ​​model and sets the received information on physical condition, mood, and ingredients as input parameters for the AI ​​model. This model compares the information with past data and generates an appropriate recipe.

[0072] Step 5:

[0073] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, cooking instructions, etc.

[0074] Step 6:

[0075] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking instructions to cook the food.

[0076] Step 7:

[0077] After cooking, the user enters feedback into the device. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated in a way that better suits the user's preferences and physical condition.

[0078] Example 1

[0079] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0080] In recent years, as people's lives have become busier, it has become more difficult to easily prepare nutritionally balanced meals. Furthermore, many people lack the time to consider meals that suit their physical condition and mood, resulting in unhealthy food choices. A method that solves this problem and allows users to easily prepare healthy meals is needed. Furthermore, reducing food waste by effectively utilizing ingredients in the home refrigerator is also an important issue.

[0081] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0082] In this invention, the server includes means for inputting a user's physical condition information, means for inputting a user's mental condition information, means for inputting information about ingredients in the user's refrigerator, means for transmitting the physical condition information, mental condition information, and ingredient information input by the user to the server via a computer network, means in the server for using a generative artificial intelligence model to generate a cooking recipe based on the physical condition information, mental condition information, and ingredient information, means for transmitting the generated cooking recipe to the user's information terminal, and means for displaying the received cooking recipe on the user's information terminal. This enables the user to quickly prepare a nutritionally balanced recipe that suits their physical condition and mood using ingredients in the refrigerator.

[0083] "User" refers to an individual or group that uses the system and is the entity that inputs physical condition information, mental condition information, and material information.

[0084] "Physical condition information" refers to information about the user's current health condition and physical condition, such as "I'm tired" or "I'm feeling a bit sick."

[0085] "Mental state information" refers to information about the mood or emotion the user is currently feeling, such as "I would like spicy food."

[0086] "Ingredient information" refers to information about ingredients and materials in the user's refrigerator or pantry, such as "chicken," "carrots," "potatoes," and "tomatoes."

[0087] "Information terminal" refers to an electronic device that a user uses to operate the system, including, for example, a smartphone, a personal computer, or a tablet.

[0088] A "computer network" refers to a network infrastructure for communicating information between different electronic devices and servers, including the Internet and local area networks (LANs).

[0089] "Server" refers to a remote computer system that receives and analyzes information sent by users and generates cooking recipes based thereon.

[0090] "Generative artificial intelligence model" refers to a machine learning model that generates a specific result, in this case a cooking recipe, based on input data, and includes natural language processing models such as GPT-3.

[0091] "Cooking method" refers to a recipe or cooking method generated based on information input by the user, and includes specific ingredients and cooking steps.

[0092] This invention is a system that inputs information about a user's physical condition, mood, and the ingredients in the refrigerator, and then uses a generative artificial intelligence model to provide nutritionally balanced recipes based on this information.

[0093] Entering user information

[0094] Using a device such as a smartphone or PC, users input information about their physical condition (e.g., tired, feeling a bit under the weather), mood (e.g., prefer spicy food), and the ingredients in their refrigerator (e.g., chicken, carrots, potatoes, tomatoes). Specific applications used include the "Healthy Cooking App" for iOS and the "Nutritional Chef App" for Android. These applications provide user-friendly interfaces and are designed to make it easy to input information.

[0095] Sending information

[0096] The device structures the input information in JSON format and sends it to the server via the Internet. The communication method is an HTTP POST request, so a stable Internet connection is required. The information sent has the following JSON structure:

[0097] json

[0098] {

[0099] "condition": "tired",

[0100] "mood": "Spicy food preferred",

[0101] "ingredients": ["chicken", "carrot", "potato", "tomato"]

[0102] }

[0103] Receiving and analyzing information

[0104] The server analyzes the received information, checking the integrity of the received data and whether there are any missing values. If there is incomplete information, the server sets a default value or asks the user for additional information. The server used is built on an EC2 instance of AWS (Amazon Web Services), for example, and uses Python libraries such as Pandas and NumPy for data analysis.

[0105] Recipe Generation

[0106] The server generates a recipe using a generative artificial intelligence model (e.g., OpenAI's GPT-3) based on the received information. The model is given a prompt like this:

[0107] User's health information:

[0108] tired

[0109] User Mood Information:

[0110] Desire spicy food

[0111] Ingredients in the refrigerator:

[0112] chicken meat

[0113] Carrots

[0114] potatoes

[0115] tomato

[0116] Based on the above information, please suggest a nutritionally balanced recipe.

[0117] Based on this prompt, the generative AI model generates an appropriate recipe (e.g., "Spicy Chicken and Vegetable Stir-fry").

[0118] Sending and Viewing Recipes

[0119] The server restructures the generated recipe information into JSON format and sends it to the user's device. The device parses the received recipe information and displays it to the user using the application interface. The user can then easily cook the dish based on the displayed recipe.

[0120] Specific examples

[0121] For example, if a user inputs "I'm tired," "I'd like something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," the system will do the following:

[0122] 1. The user enters information about their physical condition, mood, and ingredients into the "Healthy Cooking App" application.

[0123] 2. The device sends the data in JSON format to the server.

[0124] 3. The server receives and analyzes the data.

[0125] 4. The server inputs the data into a generative AI model to generate a recipe such as "Spicy Chicken and Vegetable Stir-fry."

[0126] 5. The server sends the generated recipe information to the terminal, which displays it.

[0127] This system allows users to quickly prepare nutritionally balanced meals that suit their physical condition and mood using ingredients available in the refrigerator.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1: Enter your user information

[0130] The user uses a device such as a smartphone or PC to input information about their physical condition, mood, and the ingredients in their refrigerator into the application. The input information is saved in text format as shown below.

[0131] Input: A user fills in a form with the following inputs: "I'm tired," "I'd like something spicy," "Chicken, carrots, potatoes, tomatoes."

[0132] Output: The input information is saved in variables or a database on the device. Specifically, physical condition information, mood information, and ingredient information are each stored in separate fields.

[0133] Step 2: Submit your information

[0134] The terminal structures the information entered by the user into JSON format and sends it over the internet to the server using an HTTP POST request, using standard HTTP libraries.

[0135] Input: Physical condition information, mood information, and food information stored on the device.

[0136] Data processing: Convert this information into JSON format.

[0137] Output: The following JSON data is generated and sent as an HTTP request:

[0138] json

[0139] {

[0140] "condition": "tired",

[0141] "mood": "Spicy food preferred",

[0142] "ingredients": ["chicken", "carrot", "potato", "tomato"]

[0143] }

[0144] Step 3: Receiving and analyzing information

[0145] The server receives the HTTP request and parses the JSON data sent. First, it checks the data for integrity and missing values. It then parses the JSON data using the Python Flask framework and a data analysis library (e.g., Pandas).

[0146] Input: The JSON data sent to the server.

[0147] Data processing and calculation: Convert JSON data into Python objects, extract data for each field, and perform consistency checks.

[0148] Output: Data checked for consistency. If there are problems an error message is returned, otherwise proceed to the next step.

[0149] Step 4: Generate the recipe

[0150] The server generates a recipe using a generative AI model (e.g., OpenAI's GPT-3) based on the data whose integrity has been confirmed. The server constructs a predefined prompt sentence and inputs it into the generative AI model.

[0151] Input: Physical condition information, mood information, and food ingredient information whose consistency has been confirmed.

[0152] Data processing and calculation: Generate the following prompt sentence and input it into the generative AI model.

[0153] User's health information:

[0154] tired

[0155] User Mood Information:

[0156] Desire spicy food

[0157] Ingredients in the refrigerator:

[0158] chicken meat

[0159] Carrots

[0160] potatoes

[0161] tomato

[0162] Based on the above information, please suggest a nutritionally balanced recipe.

[0163] Output: The recipe text returned by the generative AI model (e.g., "Spicy Chicken and Vegetable Stir-Fry").

[0164] Step 5: Submit and view the recipe

[0165] The server restructures the generated recipe information into JSON format and sends it to the user's device as an HTTP response. The device then analyzes the received recipe information and displays it to the user through the application interface.

[0166] Input: The generated recipe text.

[0167] Data processing: Convert recipe text into JSON format.

[0168] Output: The following JSON data is generated and sent to the terminal:

[0169] json

[0170] {

[0171] "recipe": "Spicy Chicken and Vegetable Stir-fry"

[0172] }

[0173] Specific behavior: The device receives this data and displays the recipe in the application UI. The user can check the displayed information and follow the instructions to cook the dish.

[0174] (Application example 1)

[0175] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0176] In traditional restaurants, it was difficult to provide a menu that matched the physical condition or mood of the customer, and even in that case, it was dependent on the chef's experience and knowledge, making it difficult to respond to individual requests. Furthermore, while there is a need to make effective use of leftover ingredients at home, it is generally difficult to come up with a dish that matches the physical condition or mood of the customer using only the ingredients in the refrigerator. For this reason, there is a demand for a way to effectively utilize ingredients available to customers and at home, while also easily providing healthy meals.

[0177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0178] In this invention, the server includes a means for users and store staff to input customer physical condition information, mood information, and ingredient information, a means for generating recipes based on the physical condition information, mood information, and ingredient information using a generation AI model, and a means for transmitting and displaying the generated recipes on the user's terminal and the store staff's terminal, thereby enabling users and store staff to easily generate nutritionally balanced recipes and provide dishes tailored to customer preferences.

[0179] The "means for inputting user's physical condition information" refers to a device or software that provides an interface for the user to input information about their physical condition (for example, whether they are tired or coming down with a cold).

[0180] "Means for inputting user's mood information" refers to a device or software that provides an interface for a user to input information about their mood (e.g., craving spicy food, craving something sweet, etc.).

[0181] "Means for inputting information about ingredients possessed by the user" refers to a device or software that provides an interface for the user to input information about ingredients possessed by the user (for example, ingredients in the refrigerator).

[0182] The "means for transmitting the physical condition information, mood information, and ingredient information input by the user to the server" refers to a communication means for transmitting the information input by the user to the server via the Internet.

[0183] "Means for store staff to input customer health information, mood information, and food ingredient information" refers to a device or software that provides an interface for store staff to input information about the health and mood of customers visiting the store, as well as information about the food ingredients available in the store.

[0184] "Means for using a generative AI model to generate a recipe based on the physical condition information, mood information, and ingredient information" refers to an algorithm or system that uses the received physical condition information, mood information, and ingredient information to make full use of an AI model to generate an appropriate recipe.

[0185] "Means for transmitting the generated recipe to the user's terminal or the terminal of the store staff" refers to a communication means for transmitting the recipe generated by the server to the corresponding terminal via the Internet.

[0186] The "means for displaying the received recipe on the terminal of the user and the store staff" refers to a display device or software for displaying the received recipe information so that it can be viewed by the user and the store staff.

[0187] This invention is a system that consists of terminals and a server used by users and store staff, and generates and displays recipes effectively through communication between these. Specifically, it includes the terminals of users and store staff, the server, the generation AI model, and the internet communication that connects them.

[0188] Hardware and software used

[0189] Hardware

[0190] User devices: smartphones, tablets, etc.

[0191] Store staff devices: smartphones, tablets, etc.

[0192] Server: A high performance computer server.

[0193] software

[0194] Data sending and receiving: Communication and requests library using the HTTP protocol.

[0195] Data analysis and generation: Generative AI models (e.g., GPT-3).

[0196] Data format: JSON (used to send and receive data).

[0197] Server-side framework: Flask (used for server-side processing).

[0198] Interface: The user interface is a mobile application (iOS / Android).

[0199] Operation overview

[0200] 1. Enter your user information:

[0201] Users and store staff use smartphones or tablets to input information about their own or their customers' physical condition (e.g., tired, feeling a bit under the weather), mood (e.g., wanting spicy food), and ingredients they currently have on hand (e.g., chicken, carrots, potatoes, tomatoes).

[0202] 2. Transmission of Information:

[0203] The device sends the input information to the server using an internet connection, with the data structured in JSON format.

[0204] 3. Receiving and analyzing information:

[0205] The server analyzes the received information. First, it checks the input data for consistency and for missing values. If there is incomplete information, it prompts the user to complete the data or sets default values.

[0206] 4. Generate the recipe:

[0207] The server launches a generative AI model and generates recipes using the analyzed data as input. This generative AI model considers the necessary nutrients based on the user's and customer's physical condition information, creates recipes that suit their preferences based on their mood information, and suggests dishes that can be made quickly based on the ingredients they have on hand.

[0208] 5. Submit and view recipes:

[0209] The recipe information generated by the server is sent to the terminals of the user and the store staff and displayed on the terminal screen. The user and the store staff can then serve or prepare healthy, balanced meals according to the recipe.

[0210] Specific examples

[0211] For example, if a customer visits a physical store and says, "I'm feeling a bit under the weather and would like some hot soup," the staff member will enter the information as follows:

[0212] Health status: Feeling a bit like a cold

[0213] Mood info: Hot soup

[0214] Ingredients: Chicken, carrots, potatoes, tomatoes

[0215] The prompt for the generative AI model then becomes:

[0216] "Generate a recipe for a warm soup perfect for someone with a cold. The ingredients are chicken, carrots, potatoes, and tomatoes."

[0217] Based on this, the generative AI model proposes recipes, such as "Chicken and Vegetable Medicinal Soup," and displays them to store staff or users on their devices, allowing customers to enjoy a meal that suits their physical condition on the spot.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] Users and store staff enter health information, mood information, and ingredient information on their devices. An example of this input is "feeling a bit sick," "prefer hot soup" as mood information, and "chicken, carrots, potatoes, tomatoes" as ingredient information. The input data is converted directly into JSON format.

[0221] Step 2:

[0222] The device sends the entered information to the server. The data is sent to the server via the Internet in JSON format. Specifically, it is sent using an HTTP POST request. The data entered at this stage is received by the server.

[0223] Step 3:

[0224] The server parses the received information. The server decodes the received JSON data and checks the data integrity. If there are missing values, it sets default values ​​or sends a request to the device for additional information. After parsing, the information is stored in the built-in database.

[0225] Step 4:

[0226] The server launches a generative AI model, which uses the analyzed data as input to generate a recipe. The generative AI model (e.g., GPT-3) receives physical condition information, mood information, and ingredient information as a prompt and generates an appropriate recipe. An example of this prompt is, "Please generate a recipe for a warm soup that is perfect for people who are feeling a bit under the weather. The ingredients used are chicken, carrots, potatoes, and tomatoes." The AI ​​model generates the recipe and outputs it in JSON format.

[0227] Step 5:

[0228] The server sends the generated recipe information to the user's device and the store staff's device. The recipe information is sent to the device again using an HTTP POST request. The recipe information is structured in JSON format.

[0229] Step 6:

[0230] The device displays the received recipe information. The received JSON format recipe information is decoded and a UI is built to display it in an easy-to-read format for users and store staff. As a specific example, the displayed recipe is "Chicken and Vegetable Medicinal Soup."

[0231] Through the above processing steps, users and store staff can quickly obtain recipes for dishes that suit the customer's physical condition and mood.

[0232] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0233] This invention is a system that acquires information on the user's physical condition, mood, and refrigerator ingredients, as well as their emotional information, and uses this information to generate AI models that provide nutritionally balanced recipes. The introduction of an emotion engine makes it possible to provide more personalized recipe suggestions that take into account the user's emotional state.

[0234] System Operation Overview

[0235] 1. Enter your user information

[0236] Using a device such as a smartphone or PC, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). Furthermore, the emotion engine analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[0237] 2. Transmission of information

[0238] The device sends the input and retrieved information to a server over an internet connection, and the information is sent in a structured format such as JSON.

[0239] 3. Receiving and analyzing information

[0240] The server analyzes the received information, specifically checking the consistency and completeness of the data. If the data is incomplete or invalid, the server may return an error message to the terminal and ask the user to re-enter the data.

[0241] 4. Recipe Generation

[0242] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the generative AI model. The model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," it could generate a recipe with a relaxing effect (e.g., a dish that uses a lot of herbs).

[0243] 5. Sending and Viewing Recipes

[0244] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, and cooking instructions.

[0245] 6. Recipe Viewing and Feedback

[0246] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking steps. After cooking, the user enters feedback. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated that is more suited to the user's preferences and physical condition.

[0247] Specific examples

[0248] For example, if a user inputs "I'm tired," "I'd like something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine analyzes the user's facial expressions and voice data and determines that they are "stressed," the system will operate as follows:

[0249] 1. Enter your user information:

[0250] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[0251] 2. Transmission of Information:

[0252] The device sends the input data and analyzed emotion information to the server.

[0253] 3. Receiving and analyzing information:

[0254] The server receives the information and verifies the integrity of the data.

[0255] 4. Generate the recipe:

[0256] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[0257] 5. Submit and view recipes:

[0258] The server transmits the generated recipe information to the terminal, which displays it.

[0259] 6. Enter your feedback:

[0260] After cooking, the user inputs feedback and sends it back to the server.

[0261] The present invention makes it possible to propose nutritionally balanced meals that meet individual needs by taking into consideration the user's physical condition, mood, and emotional state from multiple angles, and also contributes to reducing food waste.

[0262] The processing flow will be explained below.

[0263] Step 1:

[0264] The user starts up the device and launches the application. The user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). The emotion engine then analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[0265] Step 2:

[0266] The device sends the entered information about your physical condition, mood, food in the refrigerator, and emotions to a server via an internet connection in a structured format such as JSON.

[0267] Step 3:

[0268] The server analyzes the data it receives, specifically checking its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[0269] Step 4:

[0270] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the model. The model compares this with past data and generates an appropriate recipe. For example, if the user is "tired, desires spicy food, and is in a state of stress," the model will create a recipe for spicy chicken and vegetable stir-fry using herbs that have a relaxing effect.

[0271] Step 5:

[0272] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, and cooking instructions.

[0273] Step 6:

[0274] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking instructions to cook the food.

[0275] Step 7:

[0276] After cooking, the user inputs feedback into the device. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated in line with the user's preferences, physical condition, and emotions.

[0277] Example 2

[0278] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0279] Conventional recipe recommendation systems provide recipes based only on the user's physical condition and mood, making it difficult to provide personalized recipe recommendations that also take into account the user's mental state. Furthermore, they are unable to effectively utilize user feedback, making it difficult to expect continuous service improvement. Therefore, there is a need for a system that takes into account a wide range of user information and can provide recipe recommendations that are closer to the user's preferences and needs.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0281] In this invention, the server includes a means for acquiring user emotional information, a means for transmitting physical condition information, mood information, ingredient information, and emotional information input by the user to the server, and a means for generating recipes based on the physical condition information, mood information, ingredient information, and emotional information using a generative AI model. This enables more personalized recipe suggestions that also take the user's mental state into consideration. Furthermore, by using user feedback as training data for the generative AI model, continuous service improvement is possible.

[0282] "Physical condition information" is information relating to the physical condition of the user, specifically information such as "tired," "healthy," or "having a cold."

[0283] "Mood information" is information relating to the user's psychological preferences and desires, specifically information such as "I want spicy food" or "I want to eat something sweet."

[0284] "Ingredient information" is information about ingredients in the user's refrigerator, specifically information such as "chicken," "carrots," "potatoes," and "tomatoes."

[0285] "Emotion information" is information relating to the user's emotional state, specifically information such as "stressed," "relaxed," and "anxious."

[0286] A "generative AI model" is an artificial intelligence model that generates optimal recipes based on received input data, and is a system that suggests recipes based on past data and learning results.

[0287] "Feedback" refers to information such as user ratings and opinions on provided recipes, and is information that will be used to improve future recipe suggestions.

[0288] MODE FOR CARRYING OUT THE INVENTION

[0289] This invention is a system that acquires information on the user's physical condition, mood, and ingredients in the refrigerator, as well as their emotional state, and uses this information to generate AI models that provide nutritionally balanced recipes. By introducing an emotion engine, it becomes possible to propose personalized recipes that take the user's emotional state into account.

[0290] System Configuration

[0291] Entering user information

[0292] Using a device such as a smartphone or PC, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). Furthermore, an emotion engine installed in the device analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[0293] Sending information

[0294] The device sends the user-entered information about their physical condition, mood, and food items in the refrigerator, as well as the emotion information acquired by the emotion engine, in a structured format such as JSON, to a server via an internet connection.

[0295] Receiving and analyzing information

[0296] The server receives the information sent from the terminal. The received data is stored in a database and then analyzed. Specifically, the data is checked for consistency and completeness. If the data is incomplete or invalid, the server returns an error message and the terminal prompts the user to re-enter the data.

[0297] Recipe Generation

[0298] The server launches the generative AI model and sets physical condition information, mood information, ingredient information, and emotional information as input parameters. The generative AI model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," a recipe using herbs with a relaxing effect will be generated.

[0299] Sending and Viewing Recipes

[0300] The generated recipe is stored in a database and then sent to the user's device. The device analyzes the received recipe information and displays it in a user-friendly format. The displayed information includes the name of the dish, the ingredients needed, and cooking instructions.

[0301] Enter your feedback

[0302] The user prepares a dish based on the provided recipe. After cooking, the user enters feedback into the device. This feedback information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be more tailored to the user's preferences and physical condition.

[0303] Specific examples

[0304] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine determines that the user is "stressed," the system will act as follows:

[0305] 1. Enter your user information:

[0306] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[0307] 2. Transmission of Information:

[0308] The device sends the input data and analyzed emotion information to the server.

[0309] 3. Receiving and analyzing information:

[0310] The server receives the information and verifies the integrity of the data.

[0311] 4. Generate the recipe:

[0312] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[0313] 5. Submit and view recipes:

[0314] The server transmits the generated recipe information to the terminal, which displays it.

[0315] 6. Enter your feedback:

[0316] After cooking, the user inputs feedback and sends it back to the server.

[0317] Prompt Sentence Examples

[0318] Below are some example input prompts for a generative AI model:

[0319] "The user is tired and wants something spicy. They have chicken, carrots, potatoes, and tomatoes in the fridge. Analysis by the emotion engine indicates that the user is in a stressed state. Based on these conditions, generate a recipe for something relaxing."

[0320] This invention makes it possible to propose personalized recipes that take into consideration the user's physical condition, mood, and emotional state from multiple angles, which can also contribute to reducing food waste.

[0321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0322] Step 1:

[0323] Users use devices such as smartphones or PCs to input information about their physical condition, mood, and the ingredients in their refrigerator. The device, which is equipped with an emotion engine, also analyzes the user's facial expressions and voice data to obtain emotional information. The inputs include physical condition information ("tired"), mood information ("prefer spicy food"), ingredient information ("chicken, carrots, potatoes, tomatoes"), and emotional information ("stressed state"). The output is a dataset that compiles the input information.

[0324] Step 2:

[0325] The device compiles the user-entered information on physical condition, mood, ingredients, and emotions into JSON-formatted data and sends it to the server via an internet connection. The device then processes the input data set to convert it into JSON format, and sends the JSON-formatted data to the server as output.

[0326] Step 3:

[0327] The server receives the JSON data sent from the terminal. It checks the accuracy of the received data, checking its consistency and completeness. The input is the JSON data sent from the terminal, and the output is parseable data after checking the data's consistency. Specifically, if invalid data is included, an error message is generated and returned to the terminal.

[0328] Step 4:

[0329] The server launches a generative AI model based on the received data. Physical condition information, mood information, ingredient information, and emotional information are set as input parameters for the generative AI model. Analyzable data is input into the generative AI model, and an appropriate recipe is generated as output. For example, if the user is in a stressful state, a recipe with a relaxing effect is generated. An example of a prompt sentence is, "The user is tired and would like something spicy. There is chicken, carrots, potatoes, and tomatoes in the refrigerator. Analysis by the emotion engine has determined that the user is in a stressful state. Based on these conditions, please generate a recipe that has a relaxing effect."

[0330] Step 5:

[0331] The generated recipe is stored in a database in the server and then sent to the user's terminal. The generated recipe data is used as input, and the recipe information is sent to the user's terminal as output.

[0332] Step 6:

[0333] The device analyzes the recipe information received from the server and displays it in a format that is easy for the user to view. The input is the recipe information sent from the server, and the output is a display on the screen that the user can view. Specifically, the name of the dish, the necessary ingredients, and the cooking steps are displayed on the screen.

[0334] Step 7:

[0335] The user cooks a dish based on the provided recipe. After the dish is completed, the user inputs feedback about the recipe into the device. The user inputs their rating and opinion on the recipe into the device, and the feedback data is generated as output.

[0336] Step 8:

[0337] The device sends feedback data to the server. The input is the feedback data entered by the user, and the output is the feedback data sent to the server. The server uses the received feedback data as training data for the generative AI model, helping to improve future recipe suggestions.

[0338] (Application example 2)

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

[0340] In modern society, there is a demand for personalized meals tailored to the user's physical condition, mood, and emotional state. In particular, when a user is tired or stressed, they need a nutritionally balanced meal that suits their condition. However, it is difficult for users to choose an appropriate recipe on their own, and they must also consider the availability of ingredients. In addition, there is a problem that efficient food delivery is difficult to achieve because the services that deliver food based on the suggested recipes are not consistently linked.

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

[0342] In this invention, the server includes means for inputting a user's physical condition information, means for inputting a user's mood information, means for inputting information about ingredients in the user's refrigerator, means for inputting a user's emotional information, means for transmitting the physical condition information, mood information, ingredient information, and emotional information input by the user to the server, means for generating a recipe based on the physical condition information, mood information, ingredient information, and emotional information using a generative AI model in the server, means for sending an order to an affiliated food delivery service, means for sending the generated recipe and order information to the user's terminal, and means for displaying the received recipe and order information on the user's terminal. This enables the system to propose nutritionally balanced recipes optimized for the user's physical condition, mood, and emotional state, and to efficiently deliver food based on the recipes.

[0343] "User's physical condition information" is information relating to the user's physical condition, including, for example, how easily fatigued they are, their health condition, and specific physical ailments.

[0344] "User mood information" is information about the user's current psychological or emotional state, including, for example, a desire or preference such as "I want to eat spicy food."

[0345] "Information about ingredients in the user's refrigerator" is information about the types and amounts of ingredients currently stored in the user's refrigerator.

[0346] "User's emotional information" is information about the psychological state obtained by analyzing the user's facial expressions and voice data, and includes, for example, stress state, relaxed state, and the like.

[0347] A "generative AI model" is a model that uses machine learning and artificial intelligence to generate recipes, taking into account the user's physical condition, mood, ingredient information, and emotional information based on input data to generate the optimal recipe.

[0348] The "means for sending an order to a partner food delivery service" is a means for automatically sending order information to a food delivery service that provides ingredients and dishes that match the recipe.

[0349] "Means for sending the generated recipe and order information to the user's terminal" refers to means for sending the recipe generated by the server and the order information sent to the delivery service based on it to the user's terminal such as a smartphone or tablet.

[0350] The "means for displaying the recipe and order information received at the user's terminal" refers to means for visually displaying the recipe information and order information sent from the server at the user's terminal.

[0351] This invention is a system in which a generative AI model provides nutritionally balanced recipes based on the user's physical condition, mood, ingredients in the refrigerator, and emotional information, and then uses affiliated food delivery services to deliver the optimal meal to the user.

[0352] System program generation

[0353] This system operates using the following hardware and software.

[0354] Hardware:

[0355] Smartphones (e.g. iPhone, Android devices)

[0356] Smart refrigerator (food camera inside the refrigerator)

[0357] Smart glasses and head-mounted displays (optional)

[0358] software:

[0359] Application frameworks: React Native, Swift, Kotlin

[0360] Sentiment analysis engine: Azure Cognitive Services, AWS Rekognition

[0361] Data transmission: HTTP, JSON format

[0362] Generative AI models: Natural language processing models such as GPT-4 and BERT

[0363] Database: MySQL, Firebase

[0364] The system operates in the following steps:

[0365] 1. Enter your user information

[0366] Using a device such as a smartphone or smart glasses, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). The emotion engine also uses a camera and microphone to analyze the user's facial expressions and voice data, obtaining emotional information (e.g., "stressed state").

[0367] 2. Transmission of information

[0368] The device sends the input and retrieved information to a server over an internet connection, and the information is sent in a structured format such as JSON.

[0369] 3. Receiving and analyzing information

[0370] The server analyzes the received information and checks the consistency and completeness of the data. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[0371] 4. Recipe Generation

[0372] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the generative AI model. The model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," it will generate a recipe with a relaxing effect (e.g., a dish that makes extensive use of herbs).

[0373] 5. Ordering food delivery

[0374] Based on the generated recipe, the server sends an order to a partner food delivery service, including the ingredients, the dish, and delivery information.

[0375] 6. Submitting and displaying recipe and order information

[0376] The generated recipe and order information are stored in a database on the server and then sent to the user's terminal, which receives the information and displays it to the user.

[0377] Specific examples

[0378] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine analyzes the user's facial expressions and voice data and determines that they are "stressed," the system will operate as follows:

[0379] 1. Enter user information

[0380] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[0381] 2. Transmission of information

[0382] The device sends the input data and analyzed emotion information to the server.

[0383] 3. Receiving and analyzing information

[0384] The server receives the information and verifies the integrity of the data.

[0385] 4. Recipe Generation

[0386] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[0387] 5. Delivery orders

[0388] Orders are sent to affiliated food delivery services, and the best ingredients and dishes are delivered to the user.

[0389] 6. Submitting and displaying recipe and order information

[0390] The generated recipe and order information are sent to the user's device, where the user can confirm it.

[0391] Prompt Sentence Examples

[0392] Design an application that uses a generative AI model to suggest an appropriate recipe and place an order with the nearest restaurant or food delivery service, based on the user's input of "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge." The sentiment analysis engine determines this as a "stressed state."

[0393] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0394] Step 1:

[0395] The user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes") into the device. The emotion engine then uses the camera and microphone to analyze the user's facial expressions and voice data, and obtains emotional information (e.g., "stressed state").

[0396] Input: Physical condition information, Mood information, Food information, Emotion information

[0397] Output: Physical condition information, mood information, food information, and emotion information are collected and ready

[0398] Step 2:

[0399] The device sends the user's physical condition information, mood information, information about ingredients in the refrigerator, and emotional information to a server via an internet connection in a structured format such as JSON.

[0400] Input: Data entered by the user (physical condition information, mood information, food information, emotional information)

[0401] Output: Structured data (JSON format) sent to the server

[0402] Step 3:

[0403] The server analyzes the received data and checks its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks for re-entry.

[0404] Input: Data sent from the terminal

[0405] Output: Data that has been verified to be consistent or an error message

[0406] Step 4:

[0407] The server inputs the data whose consistency has been confirmed into a generative AI model, and generates an optimal recipe based on the patient's physical condition, mood, ingredients, and emotions. For example, if the patient is under stress, a recipe using herbs with a relaxing effect will be generated.

[0408] Input: Data whose integrity has been checked

[0409] Output: The generated recipe

[0410] Step 5:

[0411] The server then sends order data to the partner food delivery service based on the generated recipe, including the ingredients, dishes, and delivery address information.

[0412] Input: Generated recipe

[0413] Output: Order data sent to partner food delivery service

[0414] Step 6:

[0415] The server transmits the generated recipe and order data to the user's terminal.

[0416] Input: Generated recipe and order data

[0417] Output: Recipe and order data sent to user device

[0418] Step 7:

[0419] The user's device displays the received recipe and order data. The user checks the recipe details and waits for delivery from the partner food delivery service.

[0420] Input: Received recipe and order data

[0421] Output: Recipe and order information displayed on the terminal

[0422] Specific examples

[0423] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the sentiment analysis engine determines this to be "stressed," the following steps are executed:

[0424] 1. Enter your user information (Step 1)

[0425] 2. Submitting information (Step 2)

[0426] 3. Receiving and analyzing information (Step 3)

[0427] 4. Generate the recipe (Step 4)

[0428] 5. Order food delivery (Step 5)

[0429] 6. Send and display recipe and order information (Step 6, Step 7)

[0430] Prompt Sentence Examples

[0431] Design an application that uses a generative AI model to suggest an appropriate recipe and place an order with the nearest restaurant or food delivery service, based on the user's input of "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge." The sentiment analysis engine determines this as a "stressed state."

[0432] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0433] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0435] [Second embodiment]

[0436] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0437] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0438] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0439] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0440] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0441] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0442] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0443] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0444] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0445] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0446] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0447] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0448] This invention is a system that inputs information about the user's physical condition and mood, as well as information about the ingredients in the refrigerator, and uses a generative AI model to provide nutritionally balanced recipes based on this information.

[0449] System Operation Overview

[0450] 1. Enter your user information

[0451] Using a device such as a smartphone or PC, a user inputs information about their physical condition (e.g., tired, feeling a bit like a cold), mood (e.g., desire for spicy food), and information about ingredients in the refrigerator (e.g., chicken, carrots, potatoes, tomatoes). This information is then entered into an application on the device.

[0452] 2. Transmission of information

[0453] The device sends the input information to the server via an internet connection, and the information is structured in a standard format such as JSON.

[0454] 3. Receiving and analyzing information

[0455] The server analyzes the received information. First, it checks the input data for consistency and for missing values. If there is incomplete information, it either prompts the user for completion or sets default values.

[0456] 4. Recipe Generation

[0457] The server launches a generative AI model and generates recipes using the analyzed data as input. This generative AI model considers the necessary nutrients based on the user's physical condition and creates recipes that suit the user's preferences based on their mood. It also suggests dishes that can be made quickly based on the ingredients in the refrigerator.

[0458] 5. Sending and Viewing Recipes

[0459] The recipe information generated by the server is sent to the user's device, which displays the received recipe information on the screen, allowing the user to easily prepare a dish based on the recipe.

[0460] Specific examples

[0461] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the refrigerator," the system will operate as follows:

[0462] 1. Enter your user information:

[0463] The user inputs information about their physical condition, mood, and ingredients into the application.

[0464] 2. Transmission of Information:

[0465] The terminal sends the input data to the server.

[0466] 3. Receiving and analyzing information:

[0467] The server receives the information and verifies the integrity of the data.

[0468] 4. Generate the recipe:

[0469] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry."

[0470] 5. Submit and view recipes:

[0471] The server sends the generated recipe information to the terminal, which displays it.

[0472] Users can follow these recipes and use ingredients they have in their refrigerator to quickly prepare delicious meals that are suited to their physical condition. This system allows even busy modern people to easily enjoy nutritionally balanced meals, and also contributes to reducing food waste.

[0473] As a result, the present invention provides support for users to lead a healthy life in their busy daily lives and provides an efficient means for achieving a sustainable dietary lifestyle.

[0474] The processing flow will be explained below.

[0475] Step 1:

[0476] The user starts up the device and launches the application. The user inputs information about their physical condition (e.g., "I'm tired"), their mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes").

[0477] Step 2:

[0478] The user presses the "Send" button to send the entered information from the device to the server, which then sends the information to the server in a structured format such as JSON.

[0479] Step 3:

[0480] The server analyzes the data it receives, specifically checking its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[0481] Step 4:

[0482] The server launches the AI ​​model and sets the received information on physical condition, mood, and ingredients as input parameters for the AI ​​model. This model compares the information with past data and generates an appropriate recipe.

[0483] Step 5:

[0484] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, cooking instructions, etc.

[0485] Step 6:

[0486] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking instructions to cook the food.

[0487] Step 7:

[0488] After cooking, the user enters feedback into the device. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated in a way that better suits the user's preferences and physical condition.

[0489] Example 1

[0490] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0491] In recent years, as people's lives have become busier, it has become more difficult to easily prepare nutritionally balanced meals. Furthermore, many people lack the time to consider meals that suit their physical condition and mood, resulting in unhealthy food choices. A method that solves this problem and allows users to easily prepare healthy meals is needed. Furthermore, reducing food waste by effectively utilizing ingredients in the home refrigerator is also an important issue.

[0492] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0493] In this invention, the server includes means for inputting a user's physical condition information, means for inputting a user's mental condition information, means for inputting information about ingredients in the user's refrigerator, means for transmitting the physical condition information, mental condition information, and ingredient information input by the user to the server via a computer network, means in the server for using a generative artificial intelligence model to generate a cooking recipe based on the physical condition information, mental condition information, and ingredient information, means for transmitting the generated cooking recipe to the user's information terminal, and means for displaying the received cooking recipe on the user's information terminal. This enables the user to quickly prepare a nutritionally balanced recipe that suits their physical condition and mood using ingredients in the refrigerator.

[0494] "User" refers to an individual or group that uses the system and is the entity that inputs physical condition information, mental condition information, and material information.

[0495] "Physical condition information" refers to information about the user's current health condition and physical condition, such as "I'm tired" or "I'm feeling a bit sick."

[0496] "Mental state information" refers to information about the mood or emotion the user is currently feeling, such as "I would like spicy food."

[0497] "Ingredient information" refers to information about ingredients and materials in the user's refrigerator or pantry, such as "chicken," "carrots," "potatoes," and "tomatoes."

[0498] "Information terminal" refers to an electronic device that a user uses to operate the system, including, for example, a smartphone, a personal computer, or a tablet.

[0499] A "computer network" refers to a network infrastructure for communicating information between different electronic devices and servers, including the Internet and local area networks (LANs).

[0500] "Server" refers to a remote computer system that receives and analyzes information sent by users and generates cooking recipes based thereon.

[0501] "Generative artificial intelligence model" refers to a machine learning model that generates a specific result, in this case a cooking recipe, based on input data, and includes natural language processing models such as GPT-3.

[0502] "Cooking method" refers to a recipe or cooking method generated based on information input by the user, and includes specific ingredients and cooking steps.

[0503] This invention is a system that inputs information about a user's physical condition, mood, and the ingredients in the refrigerator, and then uses a generative artificial intelligence model to provide nutritionally balanced recipes based on this information.

[0504] Entering user information

[0505] Using a device such as a smartphone or PC, users input information about their physical condition (e.g., tired, feeling a bit under the weather), mood (e.g., prefer spicy food), and the ingredients in their refrigerator (e.g., chicken, carrots, potatoes, tomatoes). Specific applications used include the "Healthy Cooking App" for iOS and the "Nutritional Chef App" for Android. These applications provide user-friendly interfaces and are designed to make it easy to input information.

[0506] Sending information

[0507] The device structures the input information in JSON format and sends it to the server via the Internet. The communication method is an HTTP POST request, so a stable Internet connection is required. The information sent has the following JSON structure:

[0508] json

[0509] {

[0510] "condition": "tired",

[0511] "mood": "Spicy food preferred",

[0512] "ingredients": ["chicken", "carrot", "potato", "tomato"]

[0513] }

[0514] Receiving and analyzing information

[0515] The server analyzes the received information, checking the integrity of the received data and whether there are any missing values. If there is incomplete information, the server sets a default value or asks the user for additional information. The server used is built on an EC2 instance of AWS (Amazon Web Services), for example, and uses Python libraries such as Pandas and NumPy for data analysis.

[0516] Recipe Generation

[0517] The server generates a recipe using a generative artificial intelligence model (e.g., OpenAI's GPT-3) based on the received information. The model is given a prompt like this:

[0518] User's health information:

[0519] tired

[0520] User Mood Information:

[0521] Desire spicy food

[0522] Ingredients in the refrigerator:

[0523] chicken meat

[0524] Carrots

[0525] potatoes

[0526] tomato

[0527] Based on the above information, please suggest a nutritionally balanced recipe.

[0528] Based on this prompt, the generative AI model generates an appropriate recipe (e.g., "Spicy Chicken and Vegetable Stir-fry").

[0529] Sending and Viewing Recipes

[0530] The server restructures the generated recipe information into JSON format and sends it to the user's device. The device parses the received recipe information and displays it to the user using the application interface. The user can then easily cook the dish based on the displayed recipe.

[0531] Specific examples

[0532] For example, if a user inputs "I'm tired," "I'd like something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," the system will do the following:

[0533] 1. The user enters information about their physical condition, mood, and ingredients into the "Healthy Cooking App" application.

[0534] 2. The device sends the data in JSON format to the server.

[0535] 3. The server receives and analyzes the data.

[0536] 4. The server inputs the data into a generative AI model to generate a recipe such as "Spicy Chicken and Vegetable Stir-fry."

[0537] 5. The server sends the generated recipe information to the terminal, which displays it.

[0538] This system allows users to quickly prepare nutritionally balanced meals that suit their physical condition and mood using ingredients available in the refrigerator.

[0539] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0540] Step 1: Enter your user information

[0541] The user uses a device such as a smartphone or PC to input information about their physical condition, mood, and the ingredients in their refrigerator into the application. The input information is saved in text format as shown below.

[0542] Input: A user fills in a form with the following inputs: "I'm tired," "I'd like something spicy," "Chicken, carrots, potatoes, tomatoes."

[0543] Output: The input information is saved in variables or a database on the device. Specifically, physical condition information, mood information, and ingredient information are each stored in separate fields.

[0544] Step 2: Submit your information

[0545] The terminal structures the information entered by the user into JSON format and sends it over the internet to the server using an HTTP POST request, using standard HTTP libraries.

[0546] Input: Physical condition information, mood information, and food information stored on the device.

[0547] Data processing: Convert this information into JSON format.

[0548] Output: The following JSON data is generated and sent as an HTTP request:

[0549] json

[0550] {

[0551] "condition": "tired",

[0552] "mood": "Spicy food preferred",

[0553] "ingredients": ["chicken", "carrot", "potato", "tomato"]

[0554] }

[0555] Step 3: Receiving and analyzing information

[0556] The server receives the HTTP request and parses the JSON data sent. First, it checks the data for integrity and missing values. It then parses the JSON data using the Python Flask framework and a data analysis library (e.g., Pandas).

[0557] Input: The JSON data sent to the server.

[0558] Data processing and calculation: Convert JSON data into Python objects, extract data for each field, and perform consistency checks.

[0559] Output: Data checked for consistency. If there are problems an error message is returned, otherwise proceed to the next step.

[0560] Step 4: Generate the recipe

[0561] The server generates a recipe using a generative AI model (e.g., OpenAI's GPT-3) based on the data whose integrity has been confirmed. The server constructs a predefined prompt sentence and inputs it into the generative AI model.

[0562] Input: Physical condition information, mood information, and food ingredient information whose consistency has been confirmed.

[0563] Data processing and calculation: Generate the following prompt sentence and input it into the generative AI model.

[0564] User's health information:

[0565] tired

[0566] User Mood Information:

[0567] Desire spicy food

[0568] Ingredients in the refrigerator:

[0569] chicken meat

[0570] Carrots

[0571] potatoes

[0572] tomato

[0573] Based on the above information, please suggest a nutritionally balanced recipe.

[0574] Output: The recipe text returned by the generative AI model (e.g., "Spicy Chicken and Vegetable Stir-Fry").

[0575] Step 5: Submit and view the recipe

[0576] The server restructures the generated recipe information into JSON format and sends it to the user's device as an HTTP response. The device then analyzes the received recipe information and displays it to the user through the application interface.

[0577] Input: The generated recipe text.

[0578] Data processing: Convert recipe text into JSON format.

[0579] Output: The following JSON data is generated and sent to the terminal:

[0580] json

[0581] {

[0582] "recipe": "Spicy Chicken and Vegetable Stir-fry"

[0583] }

[0584] Specific behavior: The device receives this data and displays the recipe in the application UI. The user can check the displayed information and follow the instructions to cook the dish.

[0585] (Application example 1)

[0586] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0587] In traditional restaurants, it was difficult to provide a menu that matched the physical condition or mood of the customer, and even in that case, it was dependent on the chef's experience and knowledge, making it difficult to respond to individual requests. Furthermore, while there is a need to make effective use of leftover ingredients at home, it is generally difficult to come up with a dish that matches the physical condition or mood of the customer using only the ingredients in the refrigerator. For this reason, there is a demand for a way to effectively utilize ingredients available to customers and at home, while also easily providing healthy meals.

[0588] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0589] In this invention, the server includes a means for users and store staff to input customer physical condition information, mood information, and ingredient information, a means for generating recipes based on the physical condition information, mood information, and ingredient information using a generation AI model, and a means for transmitting and displaying the generated recipes on the user's terminal and the store staff's terminal, thereby enabling users and store staff to easily generate nutritionally balanced recipes and provide dishes tailored to customer preferences.

[0590] The "means for inputting user's physical condition information" refers to a device or software that provides an interface for the user to input information about their physical condition (for example, whether they are tired or coming down with a cold).

[0591] "Means for inputting user's mood information" refers to a device or software that provides an interface for a user to input information about their mood (e.g., craving spicy food, craving something sweet, etc.).

[0592] "Means for inputting information about ingredients possessed by the user" refers to a device or software that provides an interface for the user to input information about ingredients possessed by the user (for example, ingredients in the refrigerator).

[0593] The "means for transmitting the physical condition information, mood information, and ingredient information input by the user to the server" refers to a communication means for transmitting the information input by the user to the server via the Internet.

[0594] "Means for store staff to input customer health information, mood information, and food ingredient information" refers to a device or software that provides an interface for store staff to input information about the health and mood of customers visiting the store, as well as information about the food ingredients available in the store.

[0595] "Means for using a generative AI model to generate a recipe based on the physical condition information, mood information, and ingredient information" refers to an algorithm or system that uses the received physical condition information, mood information, and ingredient information to make full use of an AI model to generate an appropriate recipe.

[0596] "Means for transmitting the generated recipe to the user's terminal or the terminal of the store staff" refers to a communication means for transmitting the recipe generated by the server to the corresponding terminal via the Internet.

[0597] The "means for displaying the received recipe on the terminal of the user and the store staff" refers to a display device or software for displaying the received recipe information so that it can be viewed by the user and the store staff.

[0598] This invention is a system that consists of terminals and a server used by users and store staff, and generates and displays recipes effectively through communication between these. Specifically, it includes the terminals of users and store staff, the server, the generation AI model, and the internet communication that connects them.

[0599] Hardware and software used

[0600] Hardware

[0601] User devices: smartphones, tablets, etc.

[0602] Store staff devices: smartphones, tablets, etc.

[0603] Server: A high performance computer server.

[0604] software

[0605] Data sending and receiving: Communication and requests library using the HTTP protocol.

[0606] Data analysis and generation: Generative AI models (e.g., GPT-3).

[0607] Data format: JSON (used to send and receive data).

[0608] Server-side framework: Flask (used for server-side processing).

[0609] Interface: The user interface is a mobile application (iOS / Android).

[0610] Operation overview

[0611] 1. Enter your user information:

[0612] Users and store staff use smartphones or tablets to input information about their own or their customers' physical condition (e.g., tired, feeling a bit under the weather), mood (e.g., wanting spicy food), and ingredients they currently have on hand (e.g., chicken, carrots, potatoes, tomatoes).

[0613] 2. Transmission of Information:

[0614] The device sends the input information to the server using an internet connection, with the data structured in JSON format.

[0615] 3. Receiving and analyzing information:

[0616] The server analyzes the received information. First, it checks the input data for consistency and for missing values. If there is incomplete information, it prompts the user to complete the data or sets default values.

[0617] 4. Generate the recipe:

[0618] The server launches a generative AI model and generates recipes using the analyzed data as input. This generative AI model considers the necessary nutrients based on the user's and customer's physical condition information, creates recipes that suit their preferences based on their mood information, and suggests dishes that can be made quickly based on the ingredients they have on hand.

[0619] 5. Submit and view recipes:

[0620] The recipe information generated by the server is sent to the terminals of the user and the store staff and displayed on the terminal screen. The user and the store staff can then serve or prepare healthy, balanced meals according to the recipe.

[0621] Specific examples

[0622] For example, if a customer visits a physical store and says, "I'm feeling a bit under the weather and would like some hot soup," the staff member will enter the information as follows:

[0623] Health status: Feeling a bit like a cold

[0624] Mood info: Hot soup

[0625] Ingredients: Chicken, carrots, potatoes, tomatoes

[0626] The prompt for the generative AI model then becomes:

[0627] "Generate a recipe for a warm soup perfect for someone with a cold. The ingredients are chicken, carrots, potatoes, and tomatoes."

[0628] Based on this, the generative AI model proposes recipes, such as "Chicken and Vegetable Medicinal Soup," and displays them to store staff or users on their devices, allowing customers to enjoy a meal that suits their physical condition on the spot.

[0629] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0630] Step 1:

[0631] Users and store staff enter health information, mood information, and ingredient information on their devices. An example of this input is "feeling a bit sick," "prefer hot soup" as mood information, and "chicken, carrots, potatoes, tomatoes" as ingredient information. The input data is converted directly into JSON format.

[0632] Step 2:

[0633] The device sends the entered information to the server. The data is sent to the server via the Internet in JSON format. Specifically, it is sent using an HTTP POST request. The data entered at this stage is received by the server.

[0634] Step 3:

[0635] The server parses the received information. The server decodes the received JSON data and checks the data integrity. If there are missing values, it sets default values ​​or sends a request to the device for additional information. After parsing, the information is stored in the built-in database.

[0636] Step 4:

[0637] The server launches a generative AI model, which uses the analyzed data as input to generate a recipe. The generative AI model (e.g., GPT-3) receives physical condition information, mood information, and ingredient information as a prompt and generates an appropriate recipe. An example of this prompt is, "Please generate a recipe for a warm soup that is perfect for people who are feeling a bit under the weather. The ingredients used are chicken, carrots, potatoes, and tomatoes." The AI ​​model generates the recipe and outputs it in JSON format.

[0638] Step 5:

[0639] The server sends the generated recipe information to the user's device and the store staff's device. The recipe information is sent to the device again using an HTTP POST request. The recipe information is structured in JSON format.

[0640] Step 6:

[0641] The device displays the received recipe information. The received JSON format recipe information is decoded and a UI is built to display it in an easy-to-read format for users and store staff. As a specific example, the displayed recipe is "Chicken and Vegetable Medicinal Soup."

[0642] Through the above processing steps, users and store staff can quickly obtain recipes for dishes that suit the customer's physical condition and mood.

[0643] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0644] This invention is a system that acquires information on the user's physical condition, mood, and refrigerator ingredients, as well as their emotional information, and uses this information to generate AI models that provide nutritionally balanced recipes. The introduction of an emotion engine makes it possible to provide more personalized recipe suggestions that take into account the user's emotional state.

[0645] System Operation Overview

[0646] 1. Enter your user information

[0647] Using a device such as a smartphone or PC, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). Furthermore, the emotion engine analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[0648] 2. Transmission of information

[0649] The device sends the input and retrieved information to a server over an internet connection, and the information is sent in a structured format such as JSON.

[0650] 3. Receiving and analyzing information

[0651] The server analyzes the received information, specifically checking the consistency and completeness of the data. If the data is incomplete or invalid, the server may return an error message to the terminal and ask the user to re-enter the data.

[0652] 4. Recipe Generation

[0653] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the generative AI model. The model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," it could generate a recipe with a relaxing effect (e.g., a dish that uses a lot of herbs).

[0654] 5. Sending and Viewing Recipes

[0655] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, and cooking instructions.

[0656] 6. Recipe Viewing and Feedback

[0657] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking steps. After cooking, the user enters feedback. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated that is more suited to the user's preferences and physical condition.

[0658] Specific examples

[0659] For example, if a user inputs "I'm tired," "I'd like something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine analyzes the user's facial expressions and voice data and determines that they are "stressed," the system will operate as follows:

[0660] 1. Enter your user information:

[0661] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[0662] 2. Transmission of Information:

[0663] The device sends the input data and analyzed emotion information to the server.

[0664] 3. Receiving and analyzing information:

[0665] The server receives the information and verifies the integrity of the data.

[0666] 4. Generate the recipe:

[0667] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[0668] 5. Submit and view recipes:

[0669] The server transmits the generated recipe information to the terminal, which displays it.

[0670] 6. Enter your feedback:

[0671] After cooking, the user inputs feedback and sends it back to the server.

[0672] The present invention makes it possible to propose nutritionally balanced meals that meet individual needs by taking into consideration the user's physical condition, mood, and emotional state from multiple angles, and also contributes to reducing food waste.

[0673] The processing flow will be explained below.

[0674] Step 1:

[0675] The user starts up the device and launches the application. The user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). The emotion engine then analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[0676] Step 2:

[0677] The device sends the entered information about your physical condition, mood, food in the refrigerator, and emotions to a server via an internet connection in a structured format such as JSON.

[0678] Step 3:

[0679] The server analyzes the data it receives, specifically checking its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[0680] Step 4:

[0681] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the model. The model compares this with past data and generates an appropriate recipe. For example, if the user is "tired, desires spicy food, and is in a state of stress," the model will create a recipe for spicy chicken and vegetable stir-fry using herbs that have a relaxing effect.

[0682] Step 5:

[0683] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, and cooking instructions.

[0684] Step 6:

[0685] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking instructions to cook the food.

[0686] Step 7:

[0687] After cooking, the user inputs feedback into the device. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated in line with the user's preferences, physical condition, and emotions.

[0688] Example 2

[0689] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0690] Conventional recipe recommendation systems provide recipes based only on the user's physical condition and mood, making it difficult to provide personalized recipe recommendations that also take into account the user's mental state. Furthermore, they are unable to effectively utilize user feedback, making it difficult to expect continuous service improvement. Therefore, there is a need for a system that takes into account a wide range of user information and can provide recipe recommendations that are closer to the user's preferences and needs.

[0691] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0692] In this invention, the server includes a means for acquiring user emotional information, a means for transmitting physical condition information, mood information, ingredient information, and emotional information input by the user to the server, and a means for generating recipes based on the physical condition information, mood information, ingredient information, and emotional information using a generative AI model. This enables more personalized recipe suggestions that also take the user's mental state into consideration. Furthermore, by using user feedback as training data for the generative AI model, continuous service improvement is possible.

[0693] "Physical condition information" is information relating to the physical condition of the user, specifically information such as "tired," "healthy," or "having a cold."

[0694] "Mood information" is information relating to the user's psychological preferences and desires, specifically information such as "I want spicy food" or "I want to eat something sweet."

[0695] "Ingredient information" is information about ingredients in the user's refrigerator, specifically information such as "chicken," "carrots," "potatoes," and "tomatoes."

[0696] "Emotion information" is information relating to the user's emotional state, specifically information such as "stressed," "relaxed," and "anxious."

[0697] A "generative AI model" is an artificial intelligence model that generates optimal recipes based on received input data, and is a system that suggests recipes based on past data and learning results.

[0698] "Feedback" refers to information such as user ratings and opinions on provided recipes, and is information that will be used to improve future recipe suggestions.

[0699] MODE FOR CARRYING OUT THE INVENTION

[0700] This invention is a system that acquires information on the user's physical condition, mood, and ingredients in the refrigerator, as well as their emotional state, and uses this information to generate AI models that provide nutritionally balanced recipes. By introducing an emotion engine, it becomes possible to propose personalized recipes that take the user's emotional state into account.

[0701] System Configuration

[0702] Entering user information

[0703] Using a device such as a smartphone or PC, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). Furthermore, an emotion engine installed in the device analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[0704] Sending information

[0705] The device sends the user-entered information about their physical condition, mood, and food items in the refrigerator, as well as the emotion information acquired by the emotion engine, in a structured format such as JSON, to a server via an internet connection.

[0706] Receiving and analyzing information

[0707] The server receives the information sent from the terminal. The received data is stored in a database and then analyzed. Specifically, the data is checked for consistency and completeness. If the data is incomplete or invalid, the server returns an error message and the terminal prompts the user to re-enter the data.

[0708] Recipe Generation

[0709] The server launches the generative AI model and sets physical condition information, mood information, ingredient information, and emotional information as input parameters. The generative AI model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," a recipe using herbs with a relaxing effect will be generated.

[0710] Sending and Viewing Recipes

[0711] The generated recipe is stored in a database and then sent to the user's device. The device analyzes the received recipe information and displays it in a user-friendly format. The displayed information includes the name of the dish, the ingredients needed, and cooking instructions.

[0712] Enter your feedback

[0713] The user prepares a dish based on the provided recipe. After cooking, the user enters feedback into the device. This feedback information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be more tailored to the user's preferences and physical condition.

[0714] Specific examples

[0715] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine determines that the user is "stressed," the system will act as follows:

[0716] 1. Enter your user information:

[0717] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[0718] 2. Transmission of Information:

[0719] The device sends the input data and analyzed emotion information to the server.

[0720] 3. Receiving and analyzing information:

[0721] The server receives the information and verifies the integrity of the data.

[0722] 4. Generate the recipe:

[0723] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[0724] 5. Submit and view recipes:

[0725] The server transmits the generated recipe information to the terminal, which displays it.

[0726] 6. Enter your feedback:

[0727] After cooking, the user inputs feedback and sends it back to the server.

[0728] Prompt Sentence Examples

[0729] Below are some example input prompts for a generative AI model:

[0730] "The user is tired and wants something spicy. They have chicken, carrots, potatoes, and tomatoes in the fridge. Analysis by the emotion engine indicates that the user is in a stressed state. Based on these conditions, generate a recipe for something relaxing."

[0731] This invention makes it possible to propose personalized recipes that take into consideration the user's physical condition, mood, and emotional state from multiple angles, which can also contribute to reducing food waste.

[0732] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0733] Step 1:

[0734] Users use devices such as smartphones or PCs to input information about their physical condition, mood, and the ingredients in their refrigerator. The device, which is equipped with an emotion engine, also analyzes the user's facial expressions and voice data to obtain emotional information. The inputs include physical condition information ("tired"), mood information ("prefer spicy food"), ingredient information ("chicken, carrots, potatoes, tomatoes"), and emotional information ("stressed state"). The output is a dataset that compiles the input information.

[0735] Step 2:

[0736] The device compiles the user-entered information on physical condition, mood, ingredients, and emotions into JSON-formatted data and sends it to the server via an internet connection. The device then processes the input data set to convert it into JSON format, and sends the JSON-formatted data to the server as output.

[0737] Step 3:

[0738] The server receives the JSON data sent from the terminal. It checks the accuracy of the received data, checking its consistency and completeness. The input is the JSON data sent from the terminal, and the output is parseable data after checking the data's consistency. Specifically, if invalid data is included, an error message is generated and returned to the terminal.

[0739] Step 4:

[0740] The server launches a generative AI model based on the received data. Physical condition information, mood information, ingredient information, and emotional information are set as input parameters for the generative AI model. Analyzable data is input into the generative AI model, and an appropriate recipe is generated as output. For example, if the user is in a stressful state, a recipe with a relaxing effect is generated. An example of a prompt sentence is, "The user is tired and would like something spicy. There is chicken, carrots, potatoes, and tomatoes in the refrigerator. Analysis by the emotion engine has determined that the user is in a stressful state. Based on these conditions, please generate a recipe that has a relaxing effect."

[0741] Step 5:

[0742] The generated recipe is stored in a database in the server and then sent to the user's terminal. The generated recipe data is used as input, and the recipe information is sent to the user's terminal as output.

[0743] Step 6:

[0744] The device analyzes the recipe information received from the server and displays it in a format that is easy for the user to view. The input is the recipe information sent from the server, and the output is a display on the screen that the user can view. Specifically, the name of the dish, the necessary ingredients, and the cooking steps are displayed on the screen.

[0745] Step 7:

[0746] The user cooks a dish based on the provided recipe. After the dish is completed, the user inputs feedback about the recipe into the device. The user inputs their rating and opinion on the recipe into the device, and the feedback data is generated as output.

[0747] Step 8:

[0748] The device sends feedback data to the server. The input is the feedback data entered by the user, and the output is the feedback data sent to the server. The server uses the received feedback data as training data for the generative AI model, helping to improve future recipe suggestions.

[0749] (Application example 2)

[0750] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0751] In modern society, there is a demand for personalized meals tailored to the user's physical condition, mood, and emotional state. In particular, when a user is tired or stressed, they need a nutritionally balanced meal that suits their condition. However, it is difficult for users to choose an appropriate recipe on their own, and they must also consider the availability of ingredients. In addition, there is a problem that efficient food delivery is difficult to achieve because the services that deliver food based on the suggested recipes are not consistently linked.

[0752] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0753] In this invention, the server includes means for inputting a user's physical condition information, means for inputting a user's mood information, means for inputting information about ingredients in the user's refrigerator, means for inputting a user's emotional information, means for transmitting the physical condition information, mood information, ingredient information, and emotional information input by the user to the server, means for generating a recipe based on the physical condition information, mood information, ingredient information, and emotional information using a generative AI model in the server, means for sending an order to an affiliated food delivery service, means for sending the generated recipe and order information to the user's terminal, and means for displaying the received recipe and order information on the user's terminal. This enables the system to propose nutritionally balanced recipes optimized for the user's physical condition, mood, and emotional state, and to efficiently deliver food based on the recipes.

[0754] "User's physical condition information" is information relating to the user's physical condition, including, for example, how easily fatigued they are, their health condition, and specific physical ailments.

[0755] "User mood information" is information about the user's current psychological or emotional state, including, for example, a desire or preference such as "I want to eat spicy food."

[0756] "Information about ingredients in the user's refrigerator" is information about the types and amounts of ingredients currently stored in the user's refrigerator.

[0757] "User's emotional information" is information about the psychological state obtained by analyzing the user's facial expressions and voice data, and includes, for example, stress state, relaxed state, and the like.

[0758] A "generative AI model" is a model that uses machine learning and artificial intelligence to generate recipes, taking into account the user's physical condition, mood, ingredient information, and emotional information based on input data to generate the optimal recipe.

[0759] The "means for sending an order to a partner food delivery service" is a means for automatically sending order information to a food delivery service that provides ingredients and dishes that match the recipe.

[0760] "Means for sending the generated recipe and order information to the user's terminal" refers to means for sending the recipe generated by the server and the order information sent to the delivery service based on it to the user's terminal such as a smartphone or tablet.

[0761] The "means for displaying the recipe and order information received at the user's terminal" refers to means for visually displaying the recipe information and order information sent from the server at the user's terminal.

[0762] This invention is a system in which a generative AI model provides nutritionally balanced recipes based on the user's physical condition, mood, ingredients in the refrigerator, and emotional information, and then uses affiliated food delivery services to deliver the optimal meal to the user.

[0763] System program generation

[0764] This system operates using the following hardware and software.

[0765] Hardware:

[0766] Smartphones (e.g. iPhone, Android devices)

[0767] Smart refrigerator (food camera inside the refrigerator)

[0768] Smart glasses and head-mounted displays (optional)

[0769] software:

[0770] Application frameworks: React Native, Swift, Kotlin

[0771] Sentiment analysis engine: Azure Cognitive Services, AWS Rekognition

[0772] Data transmission: HTTP, JSON format

[0773] Generative AI models: Natural language processing models such as GPT-4 and BERT

[0774] Database: MySQL, Firebase

[0775] The system operates in the following steps:

[0776] 1. Enter your user information

[0777] Using a device such as a smartphone or smart glasses, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). The emotion engine also uses a camera and microphone to analyze the user's facial expressions and voice data, obtaining emotional information (e.g., "stressed state").

[0778] 2. Transmission of information

[0779] The device sends the input and retrieved information to a server over an internet connection, and the information is sent in a structured format such as JSON.

[0780] 3. Receiving and analyzing information

[0781] The server analyzes the received information and checks the consistency and completeness of the data. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[0782] 4. Recipe Generation

[0783] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the generative AI model. The model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," it will generate a recipe with a relaxing effect (e.g., a dish that makes extensive use of herbs).

[0784] 5. Ordering food delivery

[0785] Based on the generated recipe, the server sends an order to a partner food delivery service, including the ingredients, the dish, and delivery information.

[0786] 6. Submitting and displaying recipe and order information

[0787] The generated recipe and order information are stored in a database on the server and then sent to the user's terminal, which receives the information and displays it to the user.

[0788] Specific examples

[0789] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine analyzes the user's facial expressions and voice data and determines that they are "stressed," the system will operate as follows:

[0790] 1. Enter user information

[0791] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[0792] 2. Transmission of information

[0793] The device sends the input data and analyzed emotion information to the server.

[0794] 3. Receiving and analyzing information

[0795] The server receives the information and verifies the integrity of the data.

[0796] 4. Recipe Generation

[0797] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[0798] 5. Delivery orders

[0799] Orders are sent to affiliated food delivery services, and the best ingredients and dishes are delivered to the user.

[0800] 6. Submitting and displaying recipe and order information

[0801] The generated recipe and order information are sent to the user's device, where the user can confirm it.

[0802] Prompt Sentence Examples

[0803] Design an application that uses a generative AI model to suggest an appropriate recipe and place an order with the nearest restaurant or food delivery service, based on the user's input of "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge." The sentiment analysis engine determines this as a "stressed state."

[0804] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0805] Step 1:

[0806] The user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes") into the device. The emotion engine then uses the camera and microphone to analyze the user's facial expressions and voice data, and obtains emotional information (e.g., "stressed state").

[0807] Input: Physical condition information, Mood information, Food information, Emotion information

[0808] Output: Physical condition information, mood information, food information, and emotion information are collected and ready

[0809] Step 2:

[0810] The device sends the user's physical condition information, mood information, information about ingredients in the refrigerator, and emotional information to a server via an internet connection in a structured format such as JSON.

[0811] Input: Data entered by the user (physical condition information, mood information, food information, emotional information)

[0812] Output: Structured data (JSON format) sent to the server

[0813] Step 3:

[0814] The server analyzes the received data and checks its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks for re-entry.

[0815] Input: Data sent from the terminal

[0816] Output: Data that has been verified to be consistent or an error message

[0817] Step 4:

[0818] The server inputs the data whose consistency has been confirmed into a generative AI model, and generates an optimal recipe based on the patient's physical condition, mood, ingredients, and emotions. For example, if the patient is under stress, a recipe using herbs with a relaxing effect will be generated.

[0819] Input: Data whose integrity has been checked

[0820] Output: The generated recipe

[0821] Step 5:

[0822] The server then sends order data to the partner food delivery service based on the generated recipe, including the ingredients, dishes, and delivery address information.

[0823] Input: Generated recipe

[0824] Output: Order data sent to partner food delivery service

[0825] Step 6:

[0826] The server transmits the generated recipe and order data to the user's terminal.

[0827] Input: Generated recipe and order data

[0828] Output: Recipe and order data sent to user device

[0829] Step 7:

[0830] The user's device displays the received recipe and order data. The user checks the recipe details and waits for delivery from the partner food delivery service.

[0831] Input: Received recipe and order data

[0832] Output: Recipe and order information displayed on the terminal

[0833] Specific examples

[0834] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the sentiment analysis engine determines this to be "stressed," the following steps are executed:

[0835] 1. Enter your user information (Step 1)

[0836] 2. Submitting information (Step 2)

[0837] 3. Receiving and analyzing information (Step 3)

[0838] 4. Generate the recipe (Step 4)

[0839] 5. Order food delivery (Step 5)

[0840] 6. Send and display recipe and order information (Step 6, Step 7)

[0841] Prompt Sentence Examples

[0842] Design an application that uses a generative AI model to suggest an appropriate recipe and place an order with the nearest restaurant or food delivery service, based on the user's input of "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge." The sentiment analysis engine determines this as a "stressed state."

[0843] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0844] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0845] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0846] [Third embodiment]

[0847] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0848] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0849] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0850] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0851] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0852] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0853] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0854] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0855] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0856] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0857] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0858] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0859] This invention is a system that inputs information about the user's physical condition and mood, as well as information about the ingredients in the refrigerator, and uses a generative AI model to provide nutritionally balanced recipes based on this information.

[0860] System Operation Overview

[0861] 1. Enter your user information

[0862] Using a device such as a smartphone or PC, a user inputs information about their physical condition (e.g., tired, feeling a bit like a cold), mood (e.g., desire for spicy food), and information about ingredients in the refrigerator (e.g., chicken, carrots, potatoes, tomatoes). This information is then entered into an application on the device.

[0863] 2. Transmission of information

[0864] The device sends the input information to the server via an internet connection, and the information is structured in a standard format such as JSON.

[0865] 3. Receiving and analyzing information

[0866] The server analyzes the received information. First, it checks the input data for consistency and for missing values. If there is incomplete information, it either prompts the user for completion or sets default values.

[0867] 4. Recipe Generation

[0868] The server launches a generative AI model and generates recipes using the analyzed data as input. This generative AI model considers the necessary nutrients based on the user's physical condition and creates recipes that suit the user's preferences based on their mood. It also suggests dishes that can be made quickly based on the ingredients in the refrigerator.

[0869] 5. Sending and Viewing Recipes

[0870] The recipe information generated by the server is sent to the user's device, which displays the received recipe information on the screen, allowing the user to easily prepare a dish based on the recipe.

[0871] Specific examples

[0872] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the refrigerator," the system will operate as follows:

[0873] 1. Enter your user information:

[0874] The user inputs information about their physical condition, mood, and ingredients into the application.

[0875] 2. Transmission of Information:

[0876] The terminal sends the input data to the server.

[0877] 3. Receiving and analyzing information:

[0878] The server receives the information and verifies the integrity of the data.

[0879] 4. Generate the recipe:

[0880] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry."

[0881] 5. Submit and view recipes:

[0882] The server sends the generated recipe information to the terminal, which displays it.

[0883] Users can follow these recipes and use ingredients they have in their refrigerator to quickly prepare delicious meals that are suited to their physical condition. This system allows even busy modern people to easily enjoy nutritionally balanced meals, and also contributes to reducing food waste.

[0884] As a result, the present invention provides support for users to lead a healthy life in their busy daily lives and provides an efficient means for achieving a sustainable dietary lifestyle.

[0885] The processing flow will be explained below.

[0886] Step 1:

[0887] The user starts up the device and launches the application. The user inputs information about their physical condition (e.g., "I'm tired"), their mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes").

[0888] Step 2:

[0889] The user presses the "Send" button to send the entered information from the device to the server, which then sends the information to the server in a structured format such as JSON.

[0890] Step 3:

[0891] The server analyzes the data it receives, specifically checking its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[0892] Step 4:

[0893] The server launches the AI ​​model and sets the received information on physical condition, mood, and ingredients as input parameters for the AI ​​model. This model compares the information with past data and generates an appropriate recipe.

[0894] Step 5:

[0895] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, cooking instructions, etc.

[0896] Step 6:

[0897] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking instructions to cook the food.

[0898] Step 7:

[0899] After cooking, the user enters feedback into the device. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated in a way that better suits the user's preferences and physical condition.

[0900] Example 1

[0901] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0902] In recent years, as people's lives have become busier, it has become more difficult to easily prepare nutritionally balanced meals. Furthermore, many people lack the time to consider meals that suit their physical condition and mood, resulting in unhealthy food choices. A method that solves this problem and allows users to easily prepare healthy meals is needed. Furthermore, reducing food waste by effectively utilizing ingredients in the home refrigerator is also an important issue.

[0903] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0904] In this invention, the server includes means for inputting a user's physical condition information, means for inputting a user's mental condition information, means for inputting information about ingredients in the user's refrigerator, means for transmitting the physical condition information, mental condition information, and ingredient information input by the user to the server via a computer network, means in the server for using a generative artificial intelligence model to generate a cooking recipe based on the physical condition information, mental condition information, and ingredient information, means for transmitting the generated cooking recipe to the user's information terminal, and means for displaying the received cooking recipe on the user's information terminal. This enables the user to quickly prepare a nutritionally balanced recipe that suits their physical condition and mood using ingredients in the refrigerator.

[0905] "User" refers to an individual or group that uses the system and is the entity that inputs physical condition information, mental condition information, and material information.

[0906] "Physical condition information" refers to information about the user's current health condition and physical condition, such as "I'm tired" or "I'm feeling a bit sick."

[0907] "Mental state information" refers to information about the mood or emotion the user is currently feeling, such as "I would like spicy food."

[0908] "Ingredient information" refers to information about ingredients and materials in the user's refrigerator or pantry, such as "chicken," "carrots," "potatoes," and "tomatoes."

[0909] "Information terminal" refers to an electronic device that a user uses to operate the system, including, for example, a smartphone, a personal computer, or a tablet.

[0910] A "computer network" refers to a network infrastructure for communicating information between different electronic devices and servers, including the Internet and local area networks (LANs).

[0911] "Server" refers to a remote computer system that receives and analyzes information sent by users and generates cooking recipes based thereon.

[0912] "Generative artificial intelligence model" refers to a machine learning model that generates a specific result, in this case a cooking recipe, based on input data, and includes natural language processing models such as GPT-3.

[0913] "Cooking method" refers to a recipe or cooking method generated based on information input by the user, and includes specific ingredients and cooking steps.

[0914] This invention is a system that inputs information about a user's physical condition, mood, and the ingredients in the refrigerator, and then uses a generative artificial intelligence model to provide nutritionally balanced recipes based on this information.

[0915] Entering user information

[0916] Using a device such as a smartphone or PC, users input information about their physical condition (e.g., tired, feeling a bit under the weather), mood (e.g., prefer spicy food), and the ingredients in their refrigerator (e.g., chicken, carrots, potatoes, tomatoes). Specific applications used include the "Healthy Cooking App" for iOS and the "Nutritional Chef App" for Android. These applications provide user-friendly interfaces and are designed to make it easy to input information.

[0917] Sending information

[0918] The device structures the input information in JSON format and sends it to the server via the Internet. The communication method is an HTTP POST request, so a stable Internet connection is required. The information sent has the following JSON structure:

[0919] json

[0920] {

[0921] "condition": "tired",

[0922] "mood": "Spicy food preferred",

[0923] "ingredients": ["chicken", "carrot", "potato", "tomato"]

[0924] }

[0925] Receiving and analyzing information

[0926] The server analyzes the received information, checking the integrity of the received data and whether there are any missing values. If there is incomplete information, the server sets a default value or asks the user for additional information. The server used is built on an EC2 instance of AWS (Amazon Web Services), for example, and uses Python libraries such as Pandas and NumPy for data analysis.

[0927] Recipe Generation

[0928] The server generates a recipe using a generative artificial intelligence model (e.g., OpenAI's GPT-3) based on the received information. The model is given a prompt like this:

[0929] User's health information:

[0930] tired

[0931] User Mood Information:

[0932] Desire spicy food

[0933] Ingredients in the refrigerator:

[0934] chicken meat

[0935] Carrots

[0936] potatoes

[0937] tomato

[0938] Based on the above information, please suggest a nutritionally balanced recipe.

[0939] Based on this prompt, the generative AI model generates an appropriate recipe (e.g., "Spicy Chicken and Vegetable Stir-fry").

[0940] Sending and Viewing Recipes

[0941] The server restructures the generated recipe information into JSON format and sends it to the user's device. The device parses the received recipe information and displays it to the user using the application interface. The user can then easily cook the dish based on the displayed recipe.

[0942] Specific examples

[0943] For example, if a user inputs "I'm tired," "I'd like something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," the system will do the following:

[0944] 1. The user enters information about their physical condition, mood, and ingredients into the "Healthy Cooking App" application.

[0945] 2. The device sends the data in JSON format to the server.

[0946] 3. The server receives and analyzes the data.

[0947] 4. The server inputs the data into a generative AI model to generate a recipe such as "Spicy Chicken and Vegetable Stir-fry."

[0948] 5. The server sends the generated recipe information to the terminal, which displays it.

[0949] This system allows users to quickly prepare nutritionally balanced meals that suit their physical condition and mood using ingredients available in the refrigerator.

[0950] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0951] Step 1: Enter your user information

[0952] The user uses a device such as a smartphone or PC to input information about their physical condition, mood, and the ingredients in their refrigerator into the application. The input information is saved in text format as shown below.

[0953] Input: A user fills in a form with the following inputs: "I'm tired," "I'd like something spicy," "Chicken, carrots, potatoes, tomatoes."

[0954] Output: The input information is saved in variables or a database on the device. Specifically, physical condition information, mood information, and ingredient information are each stored in separate fields.

[0955] Step 2: Submit your information

[0956] The terminal structures the information entered by the user into JSON format and sends it over the internet to the server using an HTTP POST request, using standard HTTP libraries.

[0957] Input: Physical condition information, mood information, and food information stored on the device.

[0958] Data processing: Convert this information into JSON format.

[0959] Output: The following JSON data is generated and sent as an HTTP request:

[0960] json

[0961] {

[0962] "condition": "tired",

[0963] "mood": "Spicy food preferred",

[0964] "ingredients": ["chicken", "carrot", "potato", "tomato"]

[0965] }

[0966] Step 3: Receiving and analyzing information

[0967] The server receives the HTTP request and parses the JSON data sent. First, it checks the data for integrity and missing values. It then parses the JSON data using the Python Flask framework and a data analysis library (e.g., Pandas).

[0968] Input: The JSON data sent to the server.

[0969] Data processing and calculation: Convert JSON data into Python objects, extract data for each field, and perform consistency checks.

[0970] Output: Data checked for consistency. If there are problems an error message is returned, otherwise proceed to the next step.

[0971] Step 4: Generate the recipe

[0972] The server generates a recipe using a generative AI model (e.g., OpenAI's GPT-3) based on the data whose integrity has been confirmed. The server constructs a predefined prompt sentence and inputs it into the generative AI model.

[0973] Input: Physical condition information, mood information, and food ingredient information whose consistency has been confirmed.

[0974] Data processing and calculation: Generate the following prompt sentence and input it into the generative AI model.

[0975] User's health information:

[0976] tired

[0977] User Mood Information:

[0978] Desire spicy food

[0979] Ingredients in the refrigerator:

[0980] chicken meat

[0981] Carrots

[0982] potatoes

[0983] tomato

[0984] Based on the above information, please suggest a nutritionally balanced recipe.

[0985] Output: The recipe text returned by the generative AI model (e.g., "Spicy Chicken and Vegetable Stir-Fry").

[0986] Step 5: Submit and view the recipe

[0987] The server restructures the generated recipe information into JSON format and sends it to the user's device as an HTTP response. The device then analyzes the received recipe information and displays it to the user through the application interface.

[0988] Input: The generated recipe text.

[0989] Data processing: Convert recipe text into JSON format.

[0990] Output: The following JSON data is generated and sent to the terminal:

[0991] json

[0992] {

[0993] "recipe": "Spicy Chicken and Vegetable Stir-fry"

[0994] }

[0995] Specific behavior: The device receives this data and displays the recipe in the application UI. The user can check the displayed information and follow the instructions to cook the dish.

[0996] (Application example 1)

[0997] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0998] In traditional restaurants, it was difficult to provide a menu that matched the physical condition or mood of the customer, and even in that case, it was dependent on the chef's experience and knowledge, making it difficult to respond to individual requests. Furthermore, while there is a need to make effective use of leftover ingredients at home, it is generally difficult to come up with a dish that matches the physical condition or mood of the customer using only the ingredients in the refrigerator. For this reason, there is a demand for a way to effectively utilize ingredients available to customers and at home, while also easily providing healthy meals.

[0999] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1000] In this invention, the server includes a means for users and store staff to input customer physical condition information, mood information, and ingredient information, a means for generating recipes based on the physical condition information, mood information, and ingredient information using a generation AI model, and a means for transmitting and displaying the generated recipes on the user's terminal and the store staff's terminal, thereby enabling users and store staff to easily generate nutritionally balanced recipes and provide dishes tailored to customer preferences.

[1001] The "means for inputting user's physical condition information" refers to a device or software that provides an interface for the user to input information about their physical condition (for example, whether they are tired or coming down with a cold).

[1002] "Means for inputting user's mood information" refers to a device or software that provides an interface for a user to input information about their mood (e.g., craving spicy food, craving something sweet, etc.).

[1003] "Means for inputting information about ingredients possessed by the user" refers to a device or software that provides an interface for the user to input information about ingredients possessed by the user (for example, ingredients in the refrigerator).

[1004] The "means for transmitting the physical condition information, mood information, and ingredient information input by the user to the server" refers to a communication means for transmitting the information input by the user to the server via the Internet.

[1005] "Means for store staff to input customer health information, mood information, and food ingredient information" refers to a device or software that provides an interface for store staff to input information about the health and mood of customers visiting the store, as well as information about the food ingredients available in the store.

[1006] "Means for using a generative AI model to generate a recipe based on the physical condition information, mood information, and ingredient information" refers to an algorithm or system that uses the received physical condition information, mood information, and ingredient information to make full use of an AI model to generate an appropriate recipe.

[1007] "Means for transmitting the generated recipe to the user's terminal or the terminal of the store staff" refers to a communication means for transmitting the recipe generated by the server to the corresponding terminal via the Internet.

[1008] The "means for displaying the received recipe on the terminal of the user and the store staff" refers to a display device or software for displaying the received recipe information so that it can be viewed by the user and the store staff.

[1009] This invention is a system that consists of terminals and a server used by users and store staff, and generates and displays recipes effectively through communication between these. Specifically, it includes the terminals of users and store staff, the server, the generation AI model, and the internet communication that connects them.

[1010] Hardware and software used

[1011] Hardware

[1012] User devices: smartphones, tablets, etc.

[1013] Store staff devices: smartphones, tablets, etc.

[1014] Server: A high performance computer server.

[1015] software

[1016] Data sending and receiving: Communication and requests library using the HTTP protocol.

[1017] Data analysis and generation: Generative AI models (e.g., GPT-3).

[1018] Data format: JSON (used to send and receive data).

[1019] Server-side framework: Flask (used for server-side processing).

[1020] Interface: The user interface is a mobile application (iOS / Android).

[1021] Operation overview

[1022] 1. Enter your user information:

[1023] Users and store staff use smartphones or tablets to input information about their own or their customers' physical condition (e.g., tired, feeling a bit under the weather), mood (e.g., wanting spicy food), and ingredients they currently have on hand (e.g., chicken, carrots, potatoes, tomatoes).

[1024] 2. Transmission of Information:

[1025] The device sends the input information to the server using an internet connection, with the data structured in JSON format.

[1026] 3. Receiving and analyzing information:

[1027] The server analyzes the received information. First, it checks the input data for consistency and for missing values. If there is incomplete information, it prompts the user to complete the data or sets default values.

[1028] 4. Generate the recipe:

[1029] The server launches a generative AI model and generates recipes using the analyzed data as input. This generative AI model considers the necessary nutrients based on the user's and customer's physical condition information, creates recipes that suit their preferences based on their mood information, and suggests dishes that can be made quickly based on the ingredients they have on hand.

[1030] 5. Submit and view recipes:

[1031] The recipe information generated by the server is sent to the terminals of the user and the store staff and displayed on the terminal screen. The user and the store staff can then serve or prepare healthy, balanced meals according to the recipe.

[1032] Specific examples

[1033] For example, if a customer visits a physical store and says, "I'm feeling a bit under the weather and would like some hot soup," the staff member will enter the information as follows:

[1034] Health status: Feeling a bit like a cold

[1035] Mood info: Hot soup

[1036] Ingredients: Chicken, carrots, potatoes, tomatoes

[1037] The prompt for the generative AI model then becomes:

[1038] "Generate a recipe for a warm soup perfect for someone with a cold. The ingredients are chicken, carrots, potatoes, and tomatoes."

[1039] Based on this, the generative AI model proposes recipes, such as "Chicken and Vegetable Medicinal Soup," and displays them to store staff or users on their devices, allowing customers to enjoy a meal that suits their physical condition on the spot.

[1040] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1041] Step 1:

[1042] Users and store staff enter health information, mood information, and ingredient information on their devices. An example of this input is "feeling a bit sick," "prefer hot soup" as mood information, and "chicken, carrots, potatoes, tomatoes" as ingredient information. The input data is converted directly into JSON format.

[1043] Step 2:

[1044] The device sends the entered information to the server. The data is sent to the server via the Internet in JSON format. Specifically, it is sent using an HTTP POST request. The data entered at this stage is received by the server.

[1045] Step 3:

[1046] The server parses the received information. The server decodes the received JSON data and checks the data integrity. If there are missing values, it sets default values ​​or sends a request to the device for additional information. After parsing, the information is stored in the built-in database.

[1047] Step 4:

[1048] The server launches a generative AI model, which uses the analyzed data as input to generate a recipe. The generative AI model (e.g., GPT-3) receives physical condition information, mood information, and ingredient information as a prompt and generates an appropriate recipe. An example of this prompt is, "Please generate a recipe for a warm soup that is perfect for people who are feeling a bit under the weather. The ingredients used are chicken, carrots, potatoes, and tomatoes." The AI ​​model generates the recipe and outputs it in JSON format.

[1049] Step 5:

[1050] The server sends the generated recipe information to the user's device and the store staff's device. The recipe information is sent to the device again using an HTTP POST request. The recipe information is structured in JSON format.

[1051] Step 6:

[1052] The device displays the received recipe information. The received JSON format recipe information is decoded and a UI is built to display it in an easy-to-read format for users and store staff. As a specific example, the displayed recipe is "Chicken and Vegetable Medicinal Soup."

[1053] Through the above processing steps, users and store staff can quickly obtain recipes for dishes that suit the customer's physical condition and mood.

[1054] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1055] This invention is a system that acquires information on the user's physical condition, mood, and refrigerator ingredients, as well as their emotional information, and uses this information to generate AI models that provide nutritionally balanced recipes. The introduction of an emotion engine makes it possible to provide more personalized recipe suggestions that take into account the user's emotional state.

[1056] System Operation Overview

[1057] 1. Enter your user information

[1058] Using a device such as a smartphone or PC, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). Furthermore, the emotion engine analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[1059] 2. Transmission of information

[1060] The device sends the input and retrieved information to a server over an internet connection, and the information is sent in a structured format such as JSON.

[1061] 3. Receiving and analyzing information

[1062] The server analyzes the received information, specifically checking the consistency and completeness of the data. If the data is incomplete or invalid, the server may return an error message to the terminal and ask the user to re-enter the data.

[1063] 4. Recipe Generation

[1064] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the generative AI model. The model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," it could generate a recipe with a relaxing effect (e.g., a dish that uses a lot of herbs).

[1065] 5. Sending and Viewing Recipes

[1066] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, and cooking instructions.

[1067] 6. Recipe Viewing and Feedback

[1068] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking steps. After cooking, the user enters feedback. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated that is more suited to the user's preferences and physical condition.

[1069] Specific examples

[1070] For example, if a user inputs "I'm tired," "I'd like something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine analyzes the user's facial expressions and voice data and determines that they are "stressed," the system will operate as follows:

[1071] 1. Enter your user information:

[1072] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[1073] 2. Transmission of Information:

[1074] The device sends the input data and analyzed emotion information to the server.

[1075] 3. Receiving and analyzing information:

[1076] The server receives the information and verifies the integrity of the data.

[1077] 4. Generate the recipe:

[1078] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[1079] 5. Submit and view recipes:

[1080] The server transmits the generated recipe information to the terminal, which displays it.

[1081] 6. Enter your feedback:

[1082] After cooking, the user inputs feedback and sends it back to the server.

[1083] The present invention makes it possible to propose nutritionally balanced meals that meet individual needs by taking into consideration the user's physical condition, mood, and emotional state from multiple angles, and also contributes to reducing food waste.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] The user starts up the device and launches the application. The user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). The emotion engine then analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[1087] Step 2:

[1088] The device sends the entered information about your physical condition, mood, food in the refrigerator, and emotions to a server via an internet connection in a structured format such as JSON.

[1089] Step 3:

[1090] The server analyzes the data it receives, specifically checking its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[1091] Step 4:

[1092] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the model. The model compares this with past data and generates an appropriate recipe. For example, if the user is "tired, desires spicy food, and is in a state of stress," the model will create a recipe for spicy chicken and vegetable stir-fry using herbs that have a relaxing effect.

[1093] Step 5:

[1094] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, and cooking instructions.

[1095] Step 6:

[1096] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking instructions to cook the food.

[1097] Step 7:

[1098] After cooking, the user inputs feedback into the device. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated in line with the user's preferences, physical condition, and emotions.

[1099] Example 2

[1100] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1101] Conventional recipe recommendation systems provide recipes based only on the user's physical condition and mood, making it difficult to provide personalized recipe recommendations that also take into account the user's mental state. Furthermore, they are unable to effectively utilize user feedback, making it difficult to expect continuous service improvement. Therefore, there is a need for a system that takes into account a wide range of user information and can provide recipe recommendations that are closer to the user's preferences and needs.

[1102] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1103] In this invention, the server includes a means for acquiring user emotional information, a means for transmitting physical condition information, mood information, ingredient information, and emotional information input by the user to the server, and a means for generating recipes based on the physical condition information, mood information, ingredient information, and emotional information using a generative AI model. This enables more personalized recipe suggestions that also take the user's mental state into consideration. Furthermore, by using user feedback as training data for the generative AI model, continuous service improvement is possible.

[1104] "Physical condition information" is information relating to the physical condition of the user, specifically information such as "tired," "healthy," or "having a cold."

[1105] "Mood information" is information relating to the user's psychological preferences and desires, specifically information such as "I want spicy food" or "I want to eat something sweet."

[1106] "Ingredient information" is information about ingredients in the user's refrigerator, specifically information such as "chicken," "carrots," "potatoes," and "tomatoes."

[1107] "Emotion information" is information relating to the user's emotional state, specifically information such as "stressed," "relaxed," and "anxious."

[1108] A "generative AI model" is an artificial intelligence model that generates optimal recipes based on received input data, and is a system that suggests recipes based on past data and learning results.

[1109] "Feedback" refers to information such as user ratings and opinions on provided recipes, and is information that will be used to improve future recipe suggestions.

[1110] MODE FOR CARRYING OUT THE INVENTION

[1111] This invention is a system that acquires information on the user's physical condition, mood, and ingredients in the refrigerator, as well as their emotional state, and uses this information to generate AI models that provide nutritionally balanced recipes. By introducing an emotion engine, it becomes possible to propose personalized recipes that take the user's emotional state into account.

[1112] System Configuration

[1113] Entering user information

[1114] Using a device such as a smartphone or PC, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). Furthermore, an emotion engine installed in the device analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[1115] Sending information

[1116] The device sends the user-entered information about their physical condition, mood, and food items in the refrigerator, as well as the emotion information acquired by the emotion engine, in a structured format such as JSON, to a server via an internet connection.

[1117] Receiving and analyzing information

[1118] The server receives the information sent from the terminal. The received data is stored in a database and then analyzed. Specifically, the data is checked for consistency and completeness. If the data is incomplete or invalid, the server returns an error message and the terminal prompts the user to re-enter the data.

[1119] Recipe Generation

[1120] The server launches the generative AI model and sets physical condition information, mood information, ingredient information, and emotional information as input parameters. The generative AI model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," a recipe using herbs with a relaxing effect will be generated.

[1121] Sending and Viewing Recipes

[1122] The generated recipe is stored in a database and then sent to the user's device. The device analyzes the received recipe information and displays it in a user-friendly format. The displayed information includes the name of the dish, the ingredients needed, and cooking instructions.

[1123] Enter your feedback

[1124] The user prepares a dish based on the provided recipe. After cooking, the user enters feedback into the device. This feedback information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be more tailored to the user's preferences and physical condition.

[1125] Specific examples

[1126] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine determines that the user is "stressed," the system will act as follows:

[1127] 1. Enter your user information:

[1128] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[1129] 2. Transmission of Information:

[1130] The device sends the input data and analyzed emotion information to the server.

[1131] 3. Receiving and analyzing information:

[1132] The server receives the information and verifies the integrity of the data.

[1133] 4. Generate the recipe:

[1134] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[1135] 5. Submit and view recipes:

[1136] The server transmits the generated recipe information to the terminal, which displays it.

[1137] 6. Enter your feedback:

[1138] After cooking, the user inputs feedback and sends it back to the server.

[1139] Prompt Sentence Examples

[1140] Below are some example input prompts for a generative AI model:

[1141] "The user is tired and wants something spicy. They have chicken, carrots, potatoes, and tomatoes in the fridge. Analysis by the emotion engine indicates that the user is in a stressed state. Based on these conditions, generate a recipe for something relaxing."

[1142] This invention makes it possible to propose personalized recipes that take into consideration the user's physical condition, mood, and emotional state from multiple angles, which can also contribute to reducing food waste.

[1143] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1144] Step 1:

[1145] Users use devices such as smartphones or PCs to input information about their physical condition, mood, and the ingredients in their refrigerator. The device, which is equipped with an emotion engine, also analyzes the user's facial expressions and voice data to obtain emotional information. The inputs include physical condition information ("tired"), mood information ("prefer spicy food"), ingredient information ("chicken, carrots, potatoes, tomatoes"), and emotional information ("stressed state"). The output is a dataset that compiles the input information.

[1146] Step 2:

[1147] The device compiles the user-entered information on physical condition, mood, ingredients, and emotions into JSON-formatted data and sends it to the server via an internet connection. The device then processes the input data set to convert it into JSON format, and sends the JSON-formatted data to the server as output.

[1148] Step 3:

[1149] The server receives the JSON data sent from the terminal. It checks the accuracy of the received data, checking its consistency and completeness. The input is the JSON data sent from the terminal, and the output is parseable data after checking the data's consistency. Specifically, if invalid data is included, an error message is generated and returned to the terminal.

[1150] Step 4:

[1151] The server launches a generative AI model based on the received data. Physical condition information, mood information, ingredient information, and emotional information are set as input parameters for the generative AI model. Analyzable data is input into the generative AI model, and an appropriate recipe is generated as output. For example, if the user is in a stressful state, a recipe with a relaxing effect is generated. An example of a prompt sentence is, "The user is tired and would like something spicy. There is chicken, carrots, potatoes, and tomatoes in the refrigerator. Analysis by the emotion engine has determined that the user is in a stressful state. Based on these conditions, please generate a recipe that has a relaxing effect."

[1152] Step 5:

[1153] The generated recipe is stored in a database in the server and then sent to the user's terminal. The generated recipe data is used as input, and the recipe information is sent to the user's terminal as output.

[1154] Step 6:

[1155] The device analyzes the recipe information received from the server and displays it in a format that is easy for the user to view. The input is the recipe information sent from the server, and the output is a display on the screen that the user can view. Specifically, the name of the dish, the necessary ingredients, and the cooking steps are displayed on the screen.

[1156] Step 7:

[1157] The user cooks a dish based on the provided recipe. After the dish is completed, the user inputs feedback about the recipe into the device. The user inputs their rating and opinion on the recipe into the device, and the feedback data is generated as output.

[1158] Step 8:

[1159] The device sends feedback data to the server. The input is the feedback data entered by the user, and the output is the feedback data sent to the server. The server uses the received feedback data as training data for the generative AI model, helping to improve future recipe suggestions.

[1160] (Application example 2)

[1161] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1162] In modern society, there is a demand for personalized meals tailored to the user's physical condition, mood, and emotional state. In particular, when a user is tired or stressed, they need a nutritionally balanced meal that suits their condition. However, it is difficult for users to choose an appropriate recipe on their own, and they must also consider the availability of ingredients. In addition, there is a problem that efficient food delivery is difficult to achieve because the services that deliver food based on the suggested recipes are not consistently linked.

[1163] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1164] In this invention, the server includes means for inputting a user's physical condition information, means for inputting a user's mood information, means for inputting information about ingredients in the user's refrigerator, means for inputting a user's emotional information, means for transmitting the physical condition information, mood information, ingredient information, and emotional information input by the user to the server, means for generating a recipe based on the physical condition information, mood information, ingredient information, and emotional information using a generative AI model in the server, means for sending an order to an affiliated food delivery service, means for sending the generated recipe and order information to the user's terminal, and means for displaying the received recipe and order information on the user's terminal. This enables the system to propose nutritionally balanced recipes optimized for the user's physical condition, mood, and emotional state, and to efficiently deliver food based on the recipes.

[1165] "User's physical condition information" is information relating to the user's physical condition, including, for example, how easily fatigued they are, their health condition, and specific physical ailments.

[1166] "User mood information" is information about the user's current psychological or emotional state, including, for example, a desire or preference such as "I want to eat spicy food."

[1167] "Information about ingredients in the user's refrigerator" is information about the types and amounts of ingredients currently stored in the user's refrigerator.

[1168] "User's emotional information" is information about the psychological state obtained by analyzing the user's facial expressions and voice data, and includes, for example, stress state, relaxed state, and the like.

[1169] A "generative AI model" is a model that uses machine learning and artificial intelligence to generate recipes, taking into account the user's physical condition, mood, ingredient information, and emotional information based on input data to generate the optimal recipe.

[1170] The "means for sending an order to a partner food delivery service" is a means for automatically sending order information to a food delivery service that provides ingredients and dishes that match the recipe.

[1171] "Means for sending the generated recipe and order information to the user's terminal" refers to means for sending the recipe generated by the server and the order information sent to the delivery service based on it to the user's terminal such as a smartphone or tablet.

[1172] The "means for displaying the recipe and order information received at the user's terminal" refers to means for visually displaying the recipe information and order information sent from the server at the user's terminal.

[1173] This invention is a system in which a generative AI model provides nutritionally balanced recipes based on the user's physical condition, mood, ingredients in the refrigerator, and emotional information, and then uses affiliated food delivery services to deliver the optimal meal to the user.

[1174] System program generation

[1175] This system operates using the following hardware and software.

[1176] Hardware:

[1177] Smartphones (e.g. iPhone, Android devices)

[1178] Smart refrigerator (food camera inside the refrigerator)

[1179] Smart glasses and head-mounted displays (optional)

[1180] software:

[1181] Application frameworks: React Native, Swift, Kotlin

[1182] Sentiment analysis engine: Azure Cognitive Services, AWS Rekognition

[1183] Data transmission: HTTP, JSON format

[1184] Generative AI models: Natural language processing models such as GPT-4 and BERT

[1185] Database: MySQL, Firebase

[1186] The system operates in the following steps:

[1187] 1. Enter your user information

[1188] Using a device such as a smartphone or smart glasses, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). The emotion engine also uses a camera and microphone to analyze the user's facial expressions and voice data, obtaining emotional information (e.g., "stressed state").

[1189] 2. Transmission of information

[1190] The device sends the input and retrieved information to a server over an internet connection, and the information is sent in a structured format such as JSON.

[1191] 3. Receiving and analyzing information

[1192] The server analyzes the received information and checks the consistency and completeness of the data. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[1193] 4. Recipe Generation

[1194] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the generative AI model. The model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," it will generate a recipe with a relaxing effect (e.g., a dish that makes extensive use of herbs).

[1195] 5. Ordering food delivery

[1196] Based on the generated recipe, the server sends an order to a partner food delivery service, including the ingredients, the dish, and delivery information.

[1197] 6. Submitting and displaying recipe and order information

[1198] The generated recipe and order information are stored in a database on the server and then sent to the user's terminal, which receives the information and displays it to the user.

[1199] Specific examples

[1200] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine analyzes the user's facial expressions and voice data and determines that they are "stressed," the system will operate as follows:

[1201] 1. Enter user information

[1202] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[1203] 2. Transmission of information

[1204] The device sends the input data and analyzed emotion information to the server.

[1205] 3. Receiving and analyzing information

[1206] The server receives the information and verifies the integrity of the data.

[1207] 4. Recipe Generation

[1208] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[1209] 5. Delivery orders

[1210] Orders are sent to affiliated food delivery services, and the best ingredients and dishes are delivered to the user.

[1211] 6. Submitting and displaying recipe and order information

[1212] The generated recipe and order information are sent to the user's device, where the user can confirm it.

[1213] Prompt Sentence Examples

[1214] Design an application that uses a generative AI model to suggest an appropriate recipe and place an order with the nearest restaurant or food delivery service, based on the user's input of "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge." The sentiment analysis engine determines this as a "stressed state."

[1215] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1216] Step 1:

[1217] The user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes") into the device. The emotion engine then uses the camera and microphone to analyze the user's facial expressions and voice data, and obtains emotional information (e.g., "stressed state").

[1218] Input: Physical condition information, Mood information, Food information, Emotion information

[1219] Output: Physical condition information, mood information, food information, and emotion information are collected and ready

[1220] Step 2:

[1221] The device sends the user's physical condition information, mood information, information about ingredients in the refrigerator, and emotional information to a server via an internet connection in a structured format such as JSON.

[1222] Input: Data entered by the user (physical condition information, mood information, food information, emotional information)

[1223] Output: Structured data (JSON format) sent to the server

[1224] Step 3:

[1225] The server analyzes the received data and checks its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks for re-entry.

[1226] Input: Data sent from the terminal

[1227] Output: Data that has been verified to be consistent or an error message

[1228] Step 4:

[1229] The server inputs the data whose consistency has been confirmed into a generative AI model, and generates an optimal recipe based on the patient's physical condition, mood, ingredients, and emotions. For example, if the patient is under stress, a recipe using herbs with a relaxing effect will be generated.

[1230] Input: Data whose integrity has been checked

[1231] Output: The generated recipe

[1232] Step 5:

[1233] The server then sends order data to the partner food delivery service based on the generated recipe, including the ingredients, dishes, and delivery address information.

[1234] Input: Generated recipe

[1235] Output: Order data sent to partner food delivery service

[1236] Step 6:

[1237] The server transmits the generated recipe and order data to the user's terminal.

[1238] Input: Generated recipe and order data

[1239] Output: Recipe and order data sent to user device

[1240] Step 7:

[1241] The user's device displays the received recipe and order data. The user checks the recipe details and waits for delivery from the partner food delivery service.

[1242] Input: Received recipe and order data

[1243] Output: Recipe and order information displayed on the terminal

[1244] Specific examples

[1245] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the sentiment analysis engine determines this to be "stressed," the following steps are executed:

[1246] 1. Enter your user information (Step 1)

[1247] 2. Submitting information (Step 2)

[1248] 3. Receiving and analyzing information (Step 3)

[1249] 4. Generate the recipe (Step 4)

[1250] 5. Order food delivery (Step 5)

[1251] 6. Send and display recipe and order information (Step 6, Step 7)

[1252] Prompt Sentence Examples

[1253] Design an application that uses a generative AI model to suggest an appropriate recipe and place an order with the nearest restaurant or food delivery service, based on the user's input of "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge." The sentiment analysis engine determines this as a "stressed state."

[1254] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1255] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1256] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1257] [Fourth embodiment]

[1258] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1259] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1260] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1261] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1262] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1263] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1264] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1265] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1266] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1267] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1268] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1269] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1270] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1271] This invention is a system that inputs information about the user's physical condition and mood, as well as information about the ingredients in the refrigerator, and uses a generative AI model to provide nutritionally balanced recipes based on this information.

[1272] System Operation Overview

[1273] 1. Enter your user information

[1274] Using a device such as a smartphone or PC, a user inputs information about their physical condition (e.g., tired, feeling a bit like a cold), mood (e.g., desire for spicy food), and information about ingredients in the refrigerator (e.g., chicken, carrots, potatoes, tomatoes). This information is then entered into an application on the device.

[1275] 2. Transmission of information

[1276] The device sends the input information to the server via an internet connection, and the information is structured in a standard format such as JSON.

[1277] 3. Receiving and analyzing information

[1278] The server analyzes the received information. First, it checks the input data for consistency and for missing values. If there is incomplete information, it either prompts the user for completion or sets default values.

[1279] 4. Recipe Generation

[1280] The server launches a generative AI model and generates recipes using the analyzed data as input. This generative AI model considers the necessary nutrients based on the user's physical condition and creates recipes that suit the user's preferences based on their mood. It also suggests dishes that can be made quickly based on the ingredients in the refrigerator.

[1281] 5. Sending and Viewing Recipes

[1282] The recipe information generated by the server is sent to the user's device, which displays the received recipe information on the screen, allowing the user to easily prepare a dish based on the recipe.

[1283] Specific examples

[1284] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the refrigerator," the system will operate as follows:

[1285] 1. Enter your user information:

[1286] The user inputs information about their physical condition, mood, and ingredients into the application.

[1287] 2. Transmission of Information:

[1288] The terminal sends the input data to the server.

[1289] 3. Receiving and analyzing information:

[1290] The server receives the information and verifies the integrity of the data.

[1291] 4. Generate the recipe:

[1292] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry."

[1293] 5. Submit and view recipes:

[1294] The server sends the generated recipe information to the terminal, which displays it.

[1295] Users can follow these recipes and use ingredients they have in their refrigerator to quickly prepare delicious meals that are suited to their physical condition. This system allows even busy modern people to easily enjoy nutritionally balanced meals, and also contributes to reducing food waste.

[1296] As a result, the present invention provides support for users to lead a healthy life in their busy daily lives and provides an efficient means for achieving a sustainable dietary lifestyle.

[1297] The processing flow will be explained below.

[1298] Step 1:

[1299] The user starts up the device and launches the application. The user inputs information about their physical condition (e.g., "I'm tired"), their mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes").

[1300] Step 2:

[1301] The user presses the "Send" button to send the entered information from the device to the server, which then sends the information to the server in a structured format such as JSON.

[1302] Step 3:

[1303] The server analyzes the data it receives, specifically checking its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[1304] Step 4:

[1305] The server launches the AI ​​model and sets the received information on physical condition, mood, and ingredients as input parameters for the AI ​​model. This model compares the information with past data and generates an appropriate recipe.

[1306] Step 5:

[1307] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, cooking instructions, etc.

[1308] Step 6:

[1309] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking instructions to cook the food.

[1310] Step 7:

[1311] After cooking, the user enters feedback into the device. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated in a way that better suits the user's preferences and physical condition.

[1312] Example 1

[1313] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1314] In recent years, as people's lives have become busier, it has become more difficult to easily prepare nutritionally balanced meals. Furthermore, many people lack the time to consider meals that suit their physical condition and mood, resulting in unhealthy food choices. A method that solves this problem and allows users to easily prepare healthy meals is needed. Furthermore, reducing food waste by effectively utilizing ingredients in the home refrigerator is also an important issue.

[1315] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1316] In this invention, the server includes means for inputting a user's physical condition information, means for inputting a user's mental condition information, means for inputting information about ingredients in the user's refrigerator, means for transmitting the physical condition information, mental condition information, and ingredient information input by the user to the server via a computer network, means in the server for using a generative artificial intelligence model to generate a cooking recipe based on the physical condition information, mental condition information, and ingredient information, means for transmitting the generated cooking recipe to the user's information terminal, and means for displaying the received cooking recipe on the user's information terminal. This enables the user to quickly prepare a nutritionally balanced recipe that suits their physical condition and mood using ingredients in the refrigerator.

[1317] "User" refers to an individual or group that uses the system and is the entity that inputs physical condition information, mental condition information, and material information.

[1318] "Physical condition information" refers to information about the user's current health condition and physical condition, such as "I'm tired" or "I'm feeling a bit sick."

[1319] "Mental state information" refers to information about the mood or emotion the user is currently feeling, such as "I would like spicy food."

[1320] "Ingredient information" refers to information about ingredients and materials in the user's refrigerator or pantry, such as "chicken," "carrots," "potatoes," and "tomatoes."

[1321] "Information terminal" refers to an electronic device that a user uses to operate the system, including, for example, a smartphone, a personal computer, or a tablet.

[1322] A "computer network" refers to a network infrastructure for communicating information between different electronic devices and servers, including the Internet and local area networks (LANs).

[1323] "Server" refers to a remote computer system that receives and analyzes information sent by users and generates cooking recipes based thereon.

[1324] "Generative artificial intelligence model" refers to a machine learning model that generates a specific result, in this case a cooking recipe, based on input data, and includes natural language processing models such as GPT-3.

[1325] "Cooking method" refers to a recipe or cooking method generated based on information input by the user, and includes specific ingredients and cooking steps.

[1326] This invention is a system that inputs information about a user's physical condition, mood, and the ingredients in the refrigerator, and then uses a generative artificial intelligence model to provide nutritionally balanced recipes based on this information.

[1327] Entering user information

[1328] Using a device such as a smartphone or PC, users input information about their physical condition (e.g., tired, feeling a bit under the weather), mood (e.g., prefer spicy food), and the ingredients in their refrigerator (e.g., chicken, carrots, potatoes, tomatoes). Specific applications used include the "Healthy Cooking App" for iOS and the "Nutritional Chef App" for Android. These applications provide user-friendly interfaces and are designed to make it easy to input information.

[1329] Sending information

[1330] The device structures the input information in JSON format and sends it to the server via the Internet. The communication method is an HTTP POST request, so a stable Internet connection is required. The information sent has the following JSON structure:

[1331] json

[1332] {

[1333] "condition": "tired",

[1334] "mood": "Spicy food preferred",

[1335] "ingredients": ["chicken", "carrot", "potato", "tomato"]

[1336] }

[1337] Receiving and analyzing information

[1338] The server analyzes the received information, checking the integrity of the received data and whether there are any missing values. If there is incomplete information, the server sets a default value or asks the user for additional information. The server used is built on an EC2 instance of AWS (Amazon Web Services), for example, and uses Python libraries such as Pandas and NumPy for data analysis.

[1339] Recipe Generation

[1340] The server generates a recipe using a generative artificial intelligence model (e.g., OpenAI's GPT-3) based on the received information. The model is given a prompt like this:

[1341] User's health information:

[1342] tired

[1343] User Mood Information:

[1344] Desire spicy food

[1345] Ingredients in the refrigerator:

[1346] chicken meat

[1347] Carrots

[1348] potatoes

[1349] tomato

[1350] Based on the above information, please suggest a nutritionally balanced recipe.

[1351] Based on this prompt, the generative AI model generates an appropriate recipe (e.g., "Spicy Chicken and Vegetable Stir-fry").

[1352] Sending and Viewing Recipes

[1353] The server restructures the generated recipe information into JSON format and sends it to the user's device. The device parses the received recipe information and displays it to the user using the application interface. The user can then easily cook the dish based on the displayed recipe.

[1354] Specific examples

[1355] For example, if a user inputs "I'm tired," "I'd like something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," the system will do the following:

[1356] 1. The user enters information about their physical condition, mood, and ingredients into the "Healthy Cooking App" application.

[1357] 2. The device sends the data in JSON format to the server.

[1358] 3. The server receives and analyzes the data.

[1359] 4. The server inputs the data into a generative AI model to generate a recipe such as "Spicy Chicken and Vegetable Stir-fry."

[1360] 5. The server sends the generated recipe information to the terminal, which displays it.

[1361] This system allows users to quickly prepare nutritionally balanced meals that suit their physical condition and mood using ingredients available in the refrigerator.

[1362] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1363] Step 1: Enter your user information

[1364] The user uses a device such as a smartphone or PC to input information about their physical condition, mood, and the ingredients in their refrigerator into the application. The input information is saved in text format as shown below.

[1365] Input: A user fills in a form with the following inputs: "I'm tired," "I'd like something spicy," "Chicken, carrots, potatoes, tomatoes."

[1366] Output: The input information is saved in variables or a database on the device. Specifically, physical condition information, mood information, and ingredient information are each stored in separate fields.

[1367] Step 2: Submit your information

[1368] The terminal structures the information entered by the user into JSON format and sends it over the internet to the server using an HTTP POST request, using standard HTTP libraries.

[1369] Input: Physical condition information, mood information, and food information stored on the device.

[1370] Data processing: Convert this information into JSON format.

[1371] Output: The following JSON data is generated and sent as an HTTP request:

[1372] json

[1373] {

[1374] "condition": "tired",

[1375] "mood": "Spicy food preferred",

[1376] "ingredients": ["chicken", "carrot", "potato", "tomato"]

[1377] }

[1378] Step 3: Receiving and analyzing information

[1379] The server receives the HTTP request and parses the JSON data sent. First, it checks the data for integrity and missing values. It then parses the JSON data using the Python Flask framework and a data analysis library (e.g., Pandas).

[1380] Input: The JSON data sent to the server.

[1381] Data processing and calculation: Convert JSON data into Python objects, extract data for each field, and perform consistency checks.

[1382] Output: Data checked for consistency. If there are problems an error message is returned, otherwise proceed to the next step.

[1383] Step 4: Generate the recipe

[1384] The server generates a recipe using a generative AI model (e.g., OpenAI's GPT-3) based on the data whose integrity has been confirmed. The server constructs a predefined prompt sentence and inputs it into the generative AI model.

[1385] Input: Physical condition information, mood information, and food ingredient information whose consistency has been confirmed.

[1386] Data processing and calculation: Generate the following prompt sentence and input it into the generative AI model.

[1387] User's health information:

[1388] tired

[1389] User Mood Information:

[1390] Desire spicy food

[1391] Ingredients in the refrigerator:

[1392] chicken meat

[1393] Carrots

[1394] potatoes

[1395] tomato

[1396] Based on the above information, please suggest a nutritionally balanced recipe.

[1397] Output: The recipe text returned by the generative AI model (e.g., "Spicy Chicken and Vegetable Stir-Fry").

[1398] Step 5: Submit and view the recipe

[1399] The server restructures the generated recipe information into JSON format and sends it to the user's device as an HTTP response. The device then analyzes the received recipe information and displays it to the user through the application interface.

[1400] Input: The generated recipe text.

[1401] Data processing: Convert recipe text into JSON format.

[1402] Output: The following JSON data is generated and sent to the terminal:

[1403] json

[1404] {

[1405] "recipe": "Spicy Chicken and Vegetable Stir-fry"

[1406] }

[1407] Specific behavior: The device receives this data and displays the recipe in the application UI. The user can check the displayed information and follow the instructions to cook the dish.

[1408] (Application example 1)

[1409] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1410] In traditional restaurants, it was difficult to provide a menu that matched the physical condition or mood of the customer, and even in that case, it was dependent on the chef's experience and knowledge, making it difficult to respond to individual requests. Furthermore, while there is a need to make effective use of leftover ingredients at home, it is generally difficult to come up with a dish that matches the physical condition or mood of the customer using only the ingredients in the refrigerator. For this reason, there is a demand for a way to effectively utilize ingredients available to customers and at home, while also easily providing healthy meals.

[1411] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1412] In this invention, the server includes a means for users and store staff to input customer physical condition information, mood information, and ingredient information, a means for generating recipes based on the physical condition information, mood information, and ingredient information using a generation AI model, and a means for transmitting and displaying the generated recipes on the user's terminal and the store staff's terminal, thereby enabling users and store staff to easily generate nutritionally balanced recipes and provide dishes tailored to customer preferences.

[1413] The "means for inputting user's physical condition information" refers to a device or software that provides an interface for the user to input information about their physical condition (for example, whether they are tired or coming down with a cold).

[1414] "Means for inputting user's mood information" refers to a device or software that provides an interface for a user to input information about their mood (e.g., craving spicy food, craving something sweet, etc.).

[1415] "Means for inputting information about ingredients possessed by the user" refers to a device or software that provides an interface for the user to input information about ingredients possessed by the user (for example, ingredients in the refrigerator).

[1416] The "means for transmitting the physical condition information, mood information, and ingredient information input by the user to the server" refers to a communication means for transmitting the information input by the user to the server via the Internet.

[1417] "Means for store staff to input customer health information, mood information, and food ingredient information" refers to a device or software that provides an interface for store staff to input information about the health and mood of customers visiting the store, as well as information about the food ingredients available in the store.

[1418] "Means for using a generative AI model to generate a recipe based on the physical condition information, mood information, and ingredient information" refers to an algorithm or system that uses the received physical condition information, mood information, and ingredient information to make full use of an AI model to generate an appropriate recipe.

[1419] "Means for transmitting the generated recipe to the user's terminal or the terminal of the store staff" refers to a communication means for transmitting the recipe generated by the server to the corresponding terminal via the Internet.

[1420] The "means for displaying the received recipe on the terminal of the user and the store staff" refers to a display device or software for displaying the received recipe information so that it can be viewed by the user and the store staff.

[1421] This invention is a system that consists of terminals and a server used by users and store staff, and generates and displays recipes effectively through communication between these. Specifically, it includes the terminals of users and store staff, the server, the generation AI model, and the internet communication that connects them.

[1422] Hardware and software used

[1423] Hardware

[1424] User devices: smartphones, tablets, etc.

[1425] Store staff devices: smartphones, tablets, etc.

[1426] Server: A high performance computer server.

[1427] software

[1428] Data sending and receiving: Communication and requests library using the HTTP protocol.

[1429] Data analysis and generation: Generative AI models (e.g., GPT-3).

[1430] Data format: JSON (used to send and receive data).

[1431] Server-side framework: Flask (used for server-side processing).

[1432] Interface: The user interface is a mobile application (iOS / Android).

[1433] Operation overview

[1434] 1. Enter your user information:

[1435] Users and store staff use smartphones or tablets to input information about their own or their customers' physical condition (e.g., tired, feeling a bit under the weather), mood (e.g., wanting spicy food), and ingredients they currently have on hand (e.g., chicken, carrots, potatoes, tomatoes).

[1436] 2. Transmission of Information:

[1437] The device sends the input information to the server using an internet connection, with the data structured in JSON format.

[1438] 3. Receiving and analyzing information:

[1439] The server analyzes the received information. First, it checks the input data for consistency and for missing values. If there is incomplete information, it prompts the user to complete the data or sets default values.

[1440] 4. Generate the recipe:

[1441] The server launches a generative AI model and generates recipes using the analyzed data as input. This generative AI model considers the necessary nutrients based on the user's and customer's physical condition information, creates recipes that suit their preferences based on their mood information, and suggests dishes that can be made quickly based on the ingredients they have on hand.

[1442] 5. Submit and view recipes:

[1443] The recipe information generated by the server is sent to the terminals of the user and the store staff and displayed on the terminal screen. The user and the store staff can then serve or prepare healthy, balanced meals according to the recipe.

[1444] Specific examples

[1445] For example, if a customer visits a physical store and says, "I'm feeling a bit under the weather and would like some hot soup," the staff member will enter the information as follows:

[1446] Health status: Feeling a bit like a cold

[1447] Mood info: Hot soup

[1448] Ingredients: Chicken, carrots, potatoes, tomatoes

[1449] The prompt for the generative AI model then becomes:

[1450] "Generate a recipe for a warm soup perfect for someone with a cold. The ingredients are chicken, carrots, potatoes, and tomatoes."

[1451] Based on this, the generative AI model proposes recipes, such as "Chicken and Vegetable Medicinal Soup," and displays them to store staff or users on their devices, allowing customers to enjoy a meal that suits their physical condition on the spot.

[1452] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1453] Step 1:

[1454] Users and store staff enter health information, mood information, and ingredient information on their devices. An example of this input is "feeling a bit sick," "prefer hot soup" as mood information, and "chicken, carrots, potatoes, tomatoes" as ingredient information. The input data is converted directly into JSON format.

[1455] Step 2:

[1456] The device sends the entered information to the server. The data is sent to the server via the Internet in JSON format. Specifically, it is sent using an HTTP POST request. The data entered at this stage is received by the server.

[1457] Step 3:

[1458] The server parses the received information. The server decodes the received JSON data and checks the data integrity. If there are missing values, it sets default values ​​or sends a request to the device for additional information. After parsing, the information is stored in the built-in database.

[1459] Step 4:

[1460] The server launches a generative AI model, which uses the analyzed data as input to generate a recipe. The generative AI model (e.g., GPT-3) receives physical condition information, mood information, and ingredient information as a prompt and generates an appropriate recipe. An example of this prompt is, "Please generate a recipe for a warm soup that is perfect for people who are feeling a bit under the weather. The ingredients used are chicken, carrots, potatoes, and tomatoes." The AI ​​model generates the recipe and outputs it in JSON format.

[1461] Step 5:

[1462] The server sends the generated recipe information to the user's device and the store staff's device. The recipe information is sent to the device again using an HTTP POST request. The recipe information is structured in JSON format.

[1463] Step 6:

[1464] The device displays the received recipe information. The received JSON format recipe information is decoded and a UI is built to display it in an easy-to-read format for users and store staff. As a specific example, the displayed recipe is "Chicken and Vegetable Medicinal Soup."

[1465] Through the above processing steps, users and store staff can quickly obtain recipes for dishes that suit the customer's physical condition and mood.

[1466] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1467] This invention is a system that acquires information on the user's physical condition, mood, and refrigerator ingredients, as well as their emotional information, and uses this information to generate AI models that provide nutritionally balanced recipes. The introduction of an emotion engine makes it possible to provide more personalized recipe suggestions that take into account the user's emotional state.

[1468] System Operation Overview

[1469] 1. Enter your user information

[1470] Using a device such as a smartphone or PC, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). Furthermore, the emotion engine analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[1471] 2. Transmission of information

[1472] The device sends the input and retrieved information to a server over an internet connection, and the information is sent in a structured format such as JSON.

[1473] 3. Receiving and analyzing information

[1474] The server analyzes the received information, specifically checking the consistency and completeness of the data. If the data is incomplete or invalid, the server may return an error message to the terminal and ask the user to re-enter the data.

[1475] 4. Recipe Generation

[1476] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the generative AI model. The model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," it could generate a recipe with a relaxing effect (e.g., a dish that uses a lot of herbs).

[1477] 5. Sending and Viewing Recipes

[1478] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, and cooking instructions.

[1479] 6. Recipe Viewing and Feedback

[1480] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking steps. After cooking, the user enters feedback. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated that is more suited to the user's preferences and physical condition.

[1481] Specific examples

[1482] For example, if a user inputs "I'm tired," "I'd like something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine analyzes the user's facial expressions and voice data and determines that they are "stressed," the system will operate as follows:

[1483] 1. Enter your user information:

[1484] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[1485] 2. Transmission of Information:

[1486] The device sends the input data and analyzed emotion information to the server.

[1487] 3. Receiving and analyzing information:

[1488] The server receives the information and verifies the integrity of the data.

[1489] 4. Generate the recipe:

[1490] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[1491] 5. Submit and view recipes:

[1492] The server transmits the generated recipe information to the terminal, which displays it.

[1493] 6. Enter your feedback:

[1494] After cooking, the user inputs feedback and sends it back to the server.

[1495] The present invention makes it possible to propose nutritionally balanced meals that meet individual needs by taking into consideration the user's physical condition, mood, and emotional state from multiple angles, and also contributes to reducing food waste.

[1496] The processing flow will be explained below.

[1497] Step 1:

[1498] The user starts up the device and launches the application. The user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). The emotion engine then analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[1499] Step 2:

[1500] The device sends the entered information about your physical condition, mood, food in the refrigerator, and emotions to a server via an internet connection in a structured format such as JSON.

[1501] Step 3:

[1502] The server analyzes the data it receives, specifically checking its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[1503] Step 4:

[1504] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the model. The model compares this with past data and generates an appropriate recipe. For example, if the user is "tired, desires spicy food, and is in a state of stress," the model will create a recipe for spicy chicken and vegetable stir-fry using herbs that have a relaxing effect.

[1505] Step 5:

[1506] The generated recipe is stored in a database on the server and then sent to the user's device. The recipe includes the name of the dish, the ingredients needed, and cooking instructions.

[1507] Step 6:

[1508] The device displays the recipe information received from the server. The user checks the recipe details on the device screen and follows the cooking instructions to cook the food.

[1509] Step 7:

[1510] After cooking, the user inputs feedback into the device. This information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be generated in line with the user's preferences, physical condition, and emotions.

[1511] Example 2

[1512] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1513] Conventional recipe recommendation systems provide recipes based only on the user's physical condition and mood, making it difficult to provide personalized recipe recommendations that also take into account the user's mental state. Furthermore, they are unable to effectively utilize user feedback, making it difficult to expect continuous service improvement. Therefore, there is a need for a system that takes into account a wide range of user information and can provide recipe recommendations that are closer to the user's preferences and needs.

[1514] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1515] In this invention, the server includes a means for acquiring user emotional information, a means for transmitting physical condition information, mood information, ingredient information, and emotional information input by the user to the server, and a means for generating recipes based on the physical condition information, mood information, ingredient information, and emotional information using a generative AI model. This enables more personalized recipe suggestions that also take the user's mental state into consideration. Furthermore, by using user feedback as training data for the generative AI model, continuous service improvement is possible.

[1516] "Physical condition information" is information relating to the physical condition of the user, specifically information such as "tired," "healthy," or "having a cold."

[1517] "Mood information" is information relating to the user's psychological preferences and desires, specifically information such as "I want spicy food" or "I want to eat something sweet."

[1518] "Ingredient information" is information about ingredients in the user's refrigerator, specifically information such as "chicken," "carrots," "potatoes," and "tomatoes."

[1519] "Emotion information" is information relating to the user's emotional state, specifically information such as "stressed," "relaxed," and "anxious."

[1520] A "generative AI model" is an artificial intelligence model that generates optimal recipes based on received input data, and is a system that suggests recipes based on past data and learning results.

[1521] "Feedback" refers to information such as user ratings and opinions on provided recipes, and is information that will be used to improve future recipe suggestions.

[1522] MODE FOR CARRYING OUT THE INVENTION

[1523] This invention is a system that acquires information on the user's physical condition, mood, and ingredients in the refrigerator, as well as their emotional state, and uses this information to generate AI models that provide nutritionally balanced recipes. By introducing an emotion engine, it becomes possible to propose personalized recipes that take the user's emotional state into account.

[1524] System Configuration

[1525] Entering user information

[1526] Using a device such as a smartphone or PC, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). Furthermore, an emotion engine installed in the device analyzes the user's facial expressions and voice data to obtain the user's emotional information (e.g., "stressed state").

[1527] Sending information

[1528] The device sends the user-entered information about their physical condition, mood, and food items in the refrigerator, as well as the emotion information acquired by the emotion engine, in a structured format such as JSON, to a server via an internet connection.

[1529] Receiving and analyzing information

[1530] The server receives the information sent from the terminal. The received data is stored in a database and then analyzed. Specifically, the data is checked for consistency and completeness. If the data is incomplete or invalid, the server returns an error message and the terminal prompts the user to re-enter the data.

[1531] Recipe Generation

[1532] The server launches the generative AI model and sets physical condition information, mood information, ingredient information, and emotional information as input parameters. The generative AI model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," a recipe using herbs with a relaxing effect will be generated.

[1533] Sending and Viewing Recipes

[1534] The generated recipe is stored in a database and then sent to the user's device. The device analyzes the received recipe information and displays it in a user-friendly format. The displayed information includes the name of the dish, the ingredients needed, and cooking instructions.

[1535] Enter your feedback

[1536] The user prepares a dish based on the provided recipe. After cooking, the user enters feedback into the device. This feedback information is sent back to the server and used as training data for the generative AI model. This allows the next recipe to be more tailored to the user's preferences and physical condition.

[1537] Specific examples

[1538] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine determines that the user is "stressed," the system will act as follows:

[1539] 1. Enter your user information:

[1540] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[1541] 2. Transmission of Information:

[1542] The device sends the input data and analyzed emotion information to the server.

[1543] 3. Receiving and analyzing information:

[1544] The server receives the information and verifies the integrity of the data.

[1545] 4. Generate the recipe:

[1546] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[1547] 5. Submit and view recipes:

[1548] The server transmits the generated recipe information to the terminal, which displays it.

[1549] 6. Enter your feedback:

[1550] After cooking, the user inputs feedback and sends it back to the server.

[1551] Prompt Sentence Examples

[1552] Below are some example input prompts for a generative AI model:

[1553] "The user is tired and wants something spicy. They have chicken, carrots, potatoes, and tomatoes in the fridge. Analysis by the emotion engine indicates that the user is in a stressed state. Based on these conditions, generate a recipe for something relaxing."

[1554] This invention makes it possible to propose personalized recipes that take into consideration the user's physical condition, mood, and emotional state from multiple angles, which can also contribute to reducing food waste.

[1555] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1556] Step 1:

[1557] Users use devices such as smartphones or PCs to input information about their physical condition, mood, and the ingredients in their refrigerator. The device, which is equipped with an emotion engine, also analyzes the user's facial expressions and voice data to obtain emotional information. The inputs include physical condition information ("tired"), mood information ("prefer spicy food"), ingredient information ("chicken, carrots, potatoes, tomatoes"), and emotional information ("stressed state"). The output is a dataset that compiles the input information.

[1558] Step 2:

[1559] The device compiles the user-entered information on physical condition, mood, ingredients, and emotions into JSON-formatted data and sends it to the server via an internet connection. The device then processes the input data set to convert it into JSON format, and sends the JSON-formatted data to the server as output.

[1560] Step 3:

[1561] The server receives the JSON data sent from the terminal. It checks the accuracy of the received data, checking its consistency and completeness. The input is the JSON data sent from the terminal, and the output is parseable data after checking the data's consistency. Specifically, if invalid data is included, an error message is generated and returned to the terminal.

[1562] Step 4:

[1563] The server launches a generative AI model based on the received data. Physical condition information, mood information, ingredient information, and emotional information are set as input parameters for the generative AI model. Analyzable data is input into the generative AI model, and an appropriate recipe is generated as output. For example, if the user is in a stressful state, a recipe with a relaxing effect is generated. An example of a prompt sentence is, "The user is tired and would like something spicy. There is chicken, carrots, potatoes, and tomatoes in the refrigerator. Analysis by the emotion engine has determined that the user is in a stressful state. Based on these conditions, please generate a recipe that has a relaxing effect."

[1564] Step 5:

[1565] The generated recipe is stored in a database in the server and then sent to the user's terminal. The generated recipe data is used as input, and the recipe information is sent to the user's terminal as output.

[1566] Step 6:

[1567] The device analyzes the recipe information received from the server and displays it in a format that is easy for the user to view. The input is the recipe information sent from the server, and the output is a display on the screen that the user can view. Specifically, the name of the dish, the necessary ingredients, and the cooking steps are displayed on the screen.

[1568] Step 7:

[1569] The user cooks a dish based on the provided recipe. After the dish is completed, the user inputs feedback about the recipe into the device. The user inputs their rating and opinion on the recipe into the device, and the feedback data is generated as output.

[1570] Step 8:

[1571] The device sends feedback data to the server. The input is the feedback data entered by the user, and the output is the feedback data sent to the server. The server uses the received feedback data as training data for the generative AI model, helping to improve future recipe suggestions.

[1572] (Application example 2)

[1573] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1574] In modern society, there is a demand for personalized meals tailored to the user's physical condition, mood, and emotional state. In particular, when a user is tired or stressed, they need a nutritionally balanced meal that suits their condition. However, it is difficult for users to choose an appropriate recipe on their own, and they must also consider the availability of ingredients. In addition, there is a problem that efficient food delivery is difficult to achieve because the services that deliver food based on the suggested recipes are not consistently linked.

[1575] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1576] In this invention, the server includes means for inputting a user's physical condition information, means for inputting a user's mood information, means for inputting information about ingredients in the user's refrigerator, means for inputting a user's emotional information, means for transmitting the physical condition information, mood information, ingredient information, and emotional information input by the user to the server, means for generating a recipe based on the physical condition information, mood information, ingredient information, and emotional information using a generative AI model in the server, means for sending an order to an affiliated food delivery service, means for sending the generated recipe and order information to the user's terminal, and means for displaying the received recipe and order information on the user's terminal. This enables the system to propose nutritionally balanced recipes optimized for the user's physical condition, mood, and emotional state, and to efficiently deliver food based on the recipes.

[1577] "User's physical condition information" is information relating to the user's physical condition, including, for example, how easily fatigued they are, their health condition, and specific physical ailments.

[1578] "User mood information" is information about the user's current psychological or emotional state, including, for example, a desire or preference such as "I want to eat spicy food."

[1579] "Information about ingredients in the user's refrigerator" is information about the types and amounts of ingredients currently stored in the user's refrigerator.

[1580] "User's emotional information" is information about the psychological state obtained by analyzing the user's facial expressions and voice data, and includes, for example, stress state, relaxed state, and the like.

[1581] A "generative AI model" is a model that uses machine learning and artificial intelligence to generate recipes, taking into account the user's physical condition, mood, ingredient information, and emotional information based on input data to generate the optimal recipe.

[1582] The "means for sending an order to a partner food delivery service" is a means for automatically sending order information to a food delivery service that provides ingredients and dishes that match the recipe.

[1583] "Means for sending the generated recipe and order information to the user's terminal" refers to means for sending the recipe generated by the server and the order information sent to the delivery service based on it to the user's terminal such as a smartphone or tablet.

[1584] The "means for displaying the recipe and order information received at the user's terminal" refers to means for visually displaying the recipe information and order information sent from the server at the user's terminal.

[1585] This invention is a system in which a generative AI model provides nutritionally balanced recipes based on the user's physical condition, mood, ingredients in the refrigerator, and emotional information, and then uses affiliated food delivery services to deliver the optimal meal to the user.

[1586] System program generation

[1587] This system operates using the following hardware and software.

[1588] Hardware:

[1589] Smartphones (e.g. iPhone, Android devices)

[1590] Smart refrigerator (food camera inside the refrigerator)

[1591] Smart glasses and head-mounted displays (optional)

[1592] software:

[1593] Application frameworks: React Native, Swift, Kotlin

[1594] Sentiment analysis engine: Azure Cognitive Services, AWS Rekognition

[1595] Data transmission: HTTP, JSON format

[1596] Generative AI models: Natural language processing models such as GPT-4 and BERT

[1597] Database: MySQL, Firebase

[1598] The system operates in the following steps:

[1599] 1. Enter your user information

[1600] Using a device such as a smartphone or smart glasses, the user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes"). The emotion engine also uses a camera and microphone to analyze the user's facial expressions and voice data, obtaining emotional information (e.g., "stressed state").

[1601] 2. Transmission of information

[1602] The device sends the input and retrieved information to a server over an internet connection, and the information is sent in a structured format such as JSON.

[1603] 3. Receiving and analyzing information

[1604] The server analyzes the received information and checks the consistency and completeness of the data. If the data is incomplete or invalid, the server returns an error message to the terminal and asks the user to re-enter the data.

[1605] 4. Recipe Generation

[1606] The server launches the generative AI model and sets the received physical condition information, mood information, ingredient information, and emotion information as input parameters for the generative AI model. The model compares this with past data and generates an appropriate recipe. For example, if the user is in a "stressed state," it will generate a recipe with a relaxing effect (e.g., a dish that makes extensive use of herbs).

[1607] 5. Ordering food delivery

[1608] Based on the generated recipe, the server sends an order to a partner food delivery service, including the ingredients, the dish, and delivery information.

[1609] 6. Submitting and displaying recipe and order information

[1610] The generated recipe and order information are stored in a database on the server and then sent to the user's terminal, which receives the information and displays it to the user.

[1611] Specific examples

[1612] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the emotion engine analyzes the user's facial expressions and voice data and determines that they are "stressed," the system will operate as follows:

[1613] 1. Enter user information

[1614] The user inputs information about their physical condition, mood, and ingredients into the application, and the emotion engine analyzes the user's emotional state to obtain emotion information.

[1615] 2. Transmission of information

[1616] The device sends the input data and analyzed emotion information to the server.

[1617] 3. Receiving and analyzing information

[1618] The server receives the information and verifies the integrity of the data.

[1619] 4. Recipe Generation

[1620] The server inputs the data into a generative AI model, which generates recipes such as "spicy chicken and vegetable stir-fry." Based on your emotional state (e.g., stress), it may recommend using herbs with relaxing effects.

[1621] 5. Delivery orders

[1622] Orders are sent to affiliated food delivery services, and the best ingredients and dishes are delivered to the user.

[1623] 6. Submitting and displaying recipe and order information

[1624] The generated recipe and order information are sent to the user's device, where the user can confirm it.

[1625] Prompt Sentence Examples

[1626] Design an application that uses a generative AI model to suggest an appropriate recipe and place an order with the nearest restaurant or food delivery service, based on the user's input of "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge." The sentiment analysis engine determines this as a "stressed state."

[1627] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1628] Step 1:

[1629] The user inputs information about their physical condition (e.g., "I'm tired"), mood (e.g., "I'd like something spicy"), and the ingredients in their refrigerator (e.g., "chicken, carrots, potatoes, tomatoes") into the device. The emotion engine then uses the camera and microphone to analyze the user's facial expressions and voice data, and obtains emotional information (e.g., "stressed state").

[1630] Input: Physical condition information, Mood information, Food information, Emotion information

[1631] Output: Physical condition information, mood information, food information, and emotion information are collected and ready

[1632] Step 2:

[1633] The device sends the user's physical condition information, mood information, information about ingredients in the refrigerator, and emotional information to a server via an internet connection in a structured format such as JSON.

[1634] Input: Data entered by the user (physical condition information, mood information, food information, emotional information)

[1635] Output: Structured data (JSON format) sent to the server

[1636] Step 3:

[1637] The server analyzes the received data and checks its consistency and completeness. If the data is incomplete or invalid, the server returns an error message to the terminal and asks for re-entry.

[1638] Input: Data sent from the terminal

[1639] Output: Data that has been verified to be consistent or an error message

[1640] Step 4:

[1641] The server inputs the data whose consistency has been confirmed into a generative AI model, and generates an optimal recipe based on the patient's physical condition, mood, ingredients, and emotions. For example, if the patient is under stress, a recipe using herbs with a relaxing effect will be generated.

[1642] Input: Data whose integrity has been checked

[1643] Output: The generated recipe

[1644] Step 5:

[1645] The server then sends order data to the partner food delivery service based on the generated recipe, including the ingredients, dishes, and delivery address information.

[1646] Input: Generated recipe

[1647] Output: Order data sent to partner food delivery service

[1648] Step 6:

[1649] The server transmits the generated recipe and order data to the user's terminal.

[1650] Input: Generated recipe and order data

[1651] Output: Recipe and order data sent to user device

[1652] Step 7:

[1653] The user's device displays the received recipe and order data. The user checks the recipe details and waits for delivery from the partner food delivery service.

[1654] Input: Received recipe and order data

[1655] Output: Recipe and order information displayed on the terminal

[1656] Specific examples

[1657] For example, if a user inputs "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge," and the sentiment analysis engine determines this to be "stressed," the following steps are executed:

[1658] 1. Enter your user information (Step 1)

[1659] 2. Submitting information (Step 2)

[1660] 3. Receiving and analyzing information (Step 3)

[1661] 4. Generate the recipe (Step 4)

[1662] 5. Order food delivery (Step 5)

[1663] 6. Send and display recipe and order information (Step 6, Step 7)

[1664] Prompt Sentence Examples

[1665] Design an application that uses a generative AI model to suggest an appropriate recipe and place an order with the nearest restaurant or food delivery service, based on the user's input of "I'm tired," "I want something spicy," and "I have chicken, carrots, potatoes, and tomatoes in the fridge." The sentiment analysis engine determines this as a "stressed state."

[1666] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1667] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1668] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1669] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1670] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1671] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1672] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1673] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1674] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1675] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1676] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1677] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1678] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1679] 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.

[1680] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1681] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1682] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1683] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1684] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1685] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1686] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1687] The following is further disclosed regarding the above embodiment.

[1688] (Claim 1)

[1689] A means for inputting user's physical condition information;

[1690] a means for inputting user mood information;

[1691] A means for inputting information about ingredients in a user's refrigerator;

[1692] means for transmitting physical condition information, mood information, and ingredient information input by a user to a server;

[1693] A means for generating a recipe based on the physical condition information, mood information, and ingredient information by using a generation AI model in a server;

[1694] means for transmitting the generated recipe to a user's terminal;

[1695] means for displaying the received recipe on the user's terminal;

[1696] A system including:

[1697] (Claim 2)

[1698] The system according to claim 1, wherein the generative AI model generates nutritionally balanced recipes based on the user's physical condition information.

[1699] (Claim 3)

[1700] The system according to claim 1, wherein the generative AI model generates recipes that match the user's preferences based on the user's mood information.

[1701] "Example 1"

[1702] (Claim 1)

[1703] A means for inputting user's physical condition information;

[1704] a means for inputting user mental state information;

[1705] a means for inputting information about ingredients in a user's refrigerator;

[1706] means for transmitting physical state information, mental state information, and material information input by a user to a server via a computer network;

[1707] a means for generating a cooking recipe based on the physical state information, mental state information, and ingredient information by utilizing a generative artificial intelligence model in a server;

[1708] means for transmitting the generated recipe to a user's information terminal;

[1709] means for displaying the received recipe on the user's information terminal;

[1710] A system including:

[1711] (Claim 2)

[1712] The system of claim 1, wherein the generative artificial intelligence model generates nutritionally balanced cooking methods based on the user's physical condition information.

[1713] (Claim 3)

[1714] The system according to claim 1, wherein the generative artificial intelligence model generates cooking methods that suit the user's preferences based on the user's mental state information.

[1715] "Application Example 1"

[1716] (Claim 1)

[1717] A means for inputting user's physical condition information;

[1718] a means for inputting user mood information;

[1719] A means for inputting information on ingredients possessed by the user;

[1720] means for transmitting physical condition information, mood information, and ingredient information input by a user to a server;

[1721] A means for a store staff member to input information on the customer's physical condition, mood, and food ingredients;

[1722] A means for generating a recipe based on the physical condition information, mood information, and ingredient information by using a generation AI model in a server;

[1723] A means for transmitting the generated recipe to a user's terminal or a terminal of a store staff member;

[1724] A means for displaying the received recipe on the terminals of the user and the store staff;

[1725] A system including:

[1726] (Claim 2)

[1727] The system according to claim 1, wherein the generative AI model generates nutritionally balanced recipes based on the user's physical condition information.

[1728] (Claim 3)

[1729] The system of claim 1, wherein the generative AI model generates recipes that suit customer preferences based on mood information of the user and store staff.

[1730] "Example 2: Combining Emotion Engines"

[1731] (Claim 1)

[1732] A means for inputting user's physical condition information;

[1733] a means for inputting user mood information;

[1734] A means for inputting information about ingredients in a user's refrigerator;

[1735] A means for acquiring user emotion information;

[1736] means for transmitting physical condition information, mood information, food ingredient information, and emotion information input by a user to a server;

[1737] A means for generating a recipe based on the physical condition information, mood information, ingredient information, and emotion information by using a generation AI model in a server;

[1738] means for transmitting the generated recipe to a user's terminal;

[1739] means for displaying the received recipe on the user's terminal;

[1740] A means for receiving user feedback and using that information as training data for a generative AI model;

[1741] A system including:

[1742] (Claim 2)

[1743] The system according to claim 1, wherein the generative AI model generates nutritionally balanced recipes based on the user's physical condition information.

[1744] (Claim 3)

[1745] The system according to claim 1, wherein the generative AI model generates recipes that match the user's preferences based on the user's mood information.

[1746] "Application example 2 when combining emotion engines"

[1747] (Claim 1)

[1748] A means for inputting user's physical condition information;

[1749] a means for inputting user mood information;

[1750] A means for inputting information about ingredients in a user's refrigerator;

[1751] A means for inputting user emotion information;

[1752] means for transmitting physical condition information, mood information, food ingredient information, and emotion information input by a user to a server;

[1753] A means for generating a recipe based on the physical condition information, mood information, ingredient information, and emotion information by using a generation AI model in a server;

[1754] a means for transmitting orders to a partner food delivery service;

[1755] means for transmitting the generated recipe and order information to a user's terminal;

[1756] a means for displaying the received recipe and order information on a user's terminal;

[1757] A system including:

[1758] (Claim 2)

[1759] The system according to claim 1, wherein the generative AI model generates nutritionally balanced recipes based on the user's physical condition information.

[1760] (Claim 3)

[1761] The system of claim 1, wherein the generative AI model generates recipes that match the user's preferences based on the user's mood information and emotion information. [Explanation of symbols]

[1762] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting user's physical condition information; a means for inputting user mood information; A means for inputting information about ingredients in a user's refrigerator; means for transmitting physical condition information, mood information, and ingredient information input by a user to a server; A means for generating a recipe based on the physical condition information, mood information, and ingredient information by using a generation AI model in a server; means for transmitting the generated recipe to a user's terminal; means for displaying the received recipe on the user's terminal; A system including:

2. The system according to claim 1 , wherein the generative AI model generates nutritionally balanced recipes based on the user's physical condition information.

3. The system according to claim 1 , wherein the generative AI model generates recipes that match the user's preferences based on the user's mood information.

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