System

Abstract

A system is provided.SOLUTION: This system is provided with a means for analyzing a feeling or desire inputted from a user, a means for proposing a dish based on the analyzed result, a means for enabling the user to select the providing method of the dish and a means for performing a recipe, delivery or restaurant reservation corresponding to the selected providing method.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] In modern life, it is not easy for users to choose an appropriate meal to match their mood and physical condition that day. Furthermore, the wide variety of meal options often makes it difficult to decide what to choose. Furthermore, choosing a food delivery method—cooking at home, ordering food delivery, or eating at a restaurant—each requires time and effort. In addition, there are social issues such as food waste and food loss, as well as support for areas where food security is difficult. The present invention aims to solve these issues and enable users to enjoy appropriate meals without stress. [Means for solving the problem]

[0005] The present invention is a system that analyzes a user's mood and preferences and suggests dishes based on the analysis results. The system includes a means for allowing the user to select a food delivery method, and can provide recipes, delivery, or restaurant reservations depending on the selected delivery method. Specifically, the system includes a means for arranging the necessary ingredients from a nearby retailer or online store when cooking at home, a means for ordering food from a partner restaurant when delivery is selected, and a means for making restaurant reservations when a restaurant is selected. The system also includes a means for users to send money to treat others to meals and a means for users to donate to support activities in areas where food is difficult to secure. Furthermore, the system provides an interface for sharing dining experiences and a means for addressing food waste by sharing surplus ingredients with other users. This invention not only allows users to easily choose a meal that suits their mood that day, but also addresses social issues.

[0006] "User" refers to an individual who uses the system to suggest meals and select how they are served.

[0007] "Mood and desires" refers to dietary requests that the user inputs based on their current feelings, physical condition, and preferences.

[0008] "Means for analysis" refers to algorithms or programs that interpret the information entered by the user and suggest appropriate meals based on that information.

[0009] The "means for suggesting dishes" refers to a mechanism for presenting multiple dish candidates to the user based on the analysis results.

[0010] "Method of delivery" refers to the means of meal delivery that the user can choose, specifically including the options of cook-it-yourself, delivery, and restaurant.

[0011] "Means to make it yourself" refers to a system that provides the user with the recipe and necessary ingredients for the dish they have chosen, and makes arrangements to purchase those ingredients.

[0012] "Delivery method" refers to the system by which the user can have the food they select delivered from affiliated restaurants.

[0013] "Restaurant reservation means" refers to a mechanism for reserving a meal at a restaurant of a user's choice.

[0014] "Money transfer method" refers to a system that allows a user to transfer the amount of money needed to treat someone to a meal.

[0015] "Means of donating to relief activities" refers to a system for making monetary donations to relief activities in areas where food security is difficult.

[0016] An "interface for sharing dining experiences" refers to a system that allows users to share photos and impressions of their meals with other users and on social media.

[0017] "Food waste countermeasures" refers to a system for sharing leftover food ingredients with other users. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention provides a communication-based meal recommendation system that proposes dishes that match the user's mood and preferences, and handles the delivery method and payment in an integrated manner. Specific embodiments of this system will be described below.

[0040] Basic configuration

[0041] This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing. The user inputs their mood and preferences through an application installed on their device. The input information is analyzed by the server, and suggestions are generated. The user then selects how the suggested food is to be served (cooked, delivered, or at a restaurant) and makes payment.

[0042] Program processing (natural language explanation)

[0043] 1. Initial Setup

[0044] The server creates a user database and stores information such as each user's profile, past orders, and preferences.

[0045] The application is installed on the device and the user creates an account.

[0046] 2. Log in and enter your mood

[0047] The user launches the app and logs in by entering their credentials on the login screen.

[0048] The server checks the user's authentication information against a database to verify successful authentication.

[0049] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[0050] Users input their mood or desires in sentences (e.g., "I'd like a quick dinner today").

[0051] 3. Mood analysis and suggestions

[0052] The terminal transmits the user's input to the server.

[0053] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood and wishes.

[0054] For example, extract keywords such as "easy to make" and "dinner."

[0055] The server generates a list of relevant recipes and dishes from the database.

[0056] The terminal displays the generated recipe list to the user.

[0057] 4. Choose the food and how it will be served

[0058] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[0059] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[0060] The user selects the desired delivery method.

[0061] 5. Details and Payment

[0062] Depending on the method of provision, the process branches and the detailed operation is performed:

[0063] If you make it yourself:

[0064] 1. The server generates a recipe and a list of ingredients for the selected dish.

[0065] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[0066] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[0067] 4. The server sends the ingredients list to the online store and arranges for delivery.

[0068] For delivery:

[0069] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[0070] 2. The terminal displays delivery details and prices to the user.

[0071] 3. The user selects the desired restaurant and food and makes payment.

[0072] 4. The server sends the order to the selected restaurant and processes the delivery.

[0073] For restaurants:

[0074] 1. The server generates a list of nearby restaurants where reservations can be made.

[0075] 2. The device will display a list of restaurants and available reservation times.

[0076] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[0077] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[0078] 6. Provision of Additional Features

[0079] The server provides additional functionality available to the user.

[0080] Food waste prevention measures: Provides the option to share excess food with other users, allowing users to choose what food to share and how to share it.

[0081] Treating others: Provides a money transfer function, allowing users to specify the person to treat and the amount, and then transfer the money.

[0082] Donate to relief efforts: Provide users with the option to donate to relief efforts in areas where food security is difficult, and allow them to specify the donation amount and make a payment.

[0083] Sharing dining experiences: Provides an interface for sharing dining experiences, allowing users to upload photos and impressions of their meals and share them on social media.

[0084] Specific examples

[0085] 1. The user types, "It's hot today, so I want to eat something cool."

[0086] 2. The server analyzes the input and suggests options such as Zaru Soba and Hiyashi Chuka.

[0087] 3. The device displays suggested dishes, and the user selects "zaru soba."

[0088] 4. The device displays options for delivery method, and the user selects "Make it myself."

[0089] 5. The server will display the recipe for Zaru Soba noodles, the necessary ingredients, and offer purchasing options from a nearby supermarket and online stores.

[0090] 6. The user selects an online store, orders the materials, and makes payment.

[0091] 7. The server sends the order to the online store and the materials are delivered.

[0092] In this way, the present invention aims to significantly improve user convenience by seamlessly integrating meal suggestions and delivery methods that match the user's mood.It is also a system that can meet a wide range of needs by addressing food waste and contributing to society.

[0093] The processing flow will be explained below.

[0094] Step 1:

[0095] The user launches the app and logs in by entering their credentials on the login screen.

[0096] The server checks the user's authentication information against a database to verify successful authentication.

[0097] Step 2:

[0098] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[0099] Users input their mood and desires in sentences (e.g., "I'm tired today, so I'd like a quick dinner").

[0100] Step 3:

[0101] The terminal transmits the user's input to the server.

[0102] Step 4:

[0103] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood and wishes.

[0104] For example, extract keywords such as "tired," "easy to make," and "dinner."

[0105] Step 5:

[0106] The server generates a list of relevant recipes and dishes from the database.

[0107] The terminal displays the generated recipe list to the user.

[0108] Step 6:

[0109] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[0110] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[0111] Step 7:

[0112] The user selects the desired delivery method.

[0113] Step 8:

[0114] The process will be split depending on the delivery method:

[0115] If you make it yourself:

[0116] 1. The server generates a recipe and a list of ingredients for the selected dish.

[0117] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[0118] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[0119] 4. The server sends the ingredients list to the online store and arranges for delivery.

[0120] For delivery:

[0121] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[0122] 2. The terminal displays delivery details and prices to the user.

[0123] 3. The user selects the desired restaurant and food and makes payment.

[0124] 4. The server sends the order to the selected restaurant and processes the delivery.

[0125] For restaurants:

[0126] 1. The server generates a list of nearby restaurants where reservations can be made.

[0127] 2. The device will display a list of restaurants and available reservation times.

[0128] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[0129] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[0130] Step 9:

[0131] The server provides additional functionality available to the user.

[0132] Users can select and use additional features as needed (such as addressing food waste, treating others, donating to relief efforts, and sharing dining experiences).

[0133] Example 1

[0134] 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."

[0135] Conventional food recommendation systems have difficulty in providing specific food recommendations based on a user's mood and preferences. Furthermore, they lack integration of specific delivery methods and payment methods for the recommended dishes, resulting in low user convenience. Furthermore, there is no way for users to share their dining experiences with other users, limiting opportunities to expand their dining enjoyment.

[0136] 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.

[0137] In this invention, the server includes means for analyzing the mood and preferences input by the user, means for suggesting dishes based on the analysis results, means for allowing the user to select a food delivery method, means for providing a recipe, delivery, or reservation based on the selected delivery method, means for natural language processing of the mood and preferences using a generative AI model, means for displaying detailed information about the dishes, and means for making payments. This allows for specific dish suggestions based on the user's mood and preferences, and further improves user convenience by integrating food delivery methods and payment methods. It also allows users to share their dining experiences with other users.

[0138] A "user" is a person who uses the system to receive cooking suggestions and serving methods.

[0139] "Mood and desire" refers to the type and style of food desired by the user at that time, as well as the user's desires and feelings regarding the meal.

[0140] "Means for analyzing" refers to a function or system that processes the moods and wishes input by the user and deciphers and understands their content.

[0141] The "means for suggesting dishes" refers to a function or system for suggesting appropriate dishes to the user based on the analyzed mood and preferences.

[0142] "Serving method" refers to the options and process of how the proposed dish is to be served to the user.

[0143] A "recipe" is a list of steps and ingredients for making a particular dish.

[0144] "Delivery" is a service that delivers food from affiliated restaurants to users.

[0145] The "means for making a reservation" refers to a function or system for confirming a reservation for a date and time of visit at a restaurant selected by the user.

[0146] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze a user's mood and wishes.

[0147] "Natural language processing" is a technology that mechanically processes text entered by a user and understands its meaning.

[0148] "Means for displaying detailed information" refers to a function or system for visually presenting the user with details of the proposed dish and information about how it will be served.

[0149] "Means for making payment" refers to the functions and systems for carrying out payment processing, including online payment, for the food and services selected by the user.

[0150] "Mood and desired keywords" are important words and phrases related to cuisine that are extracted from the text entered by the user.

[0151] "Means for sharing dining experiences" refers to functions and systems that allow users to share photos and impressions of meals with other users and spread them through social media, etc.

[0152] The present invention is a system that proposes dishes that match the user's mood and preferences, and handles everything from the delivery method to payment. The system consists of a terminal used by the user and a server that performs back-end processing. Specific embodiments of the system are described below.

[0153] 1. Basic system configuration

[0154] Users input their mood and preferences through an application installed on their device. The input information is analyzed by the server, and appropriate dish suggestions are generated. The user then selects how the suggested dishes are to be served (cooked, delivered, or at a restaurant) and makes payment.

[0155] 2. Hardware and Software Configuration

[0156] The system uses the following hardware and software:

[0157] Devices: smartphones, tablets, etc.

[0158] Server: A typical backend server

[0159] Database: Relational database such as MySQL or PostgreSQL

[0160] Generative AI Model: Natural Language Processing (NLP) Engine

[0161] 3. Explanation of data processing and calculation

[0162] Based on the user's input of mood and preferences, the device sends the information to the server. The server uses a generative AI model to analyze the input information with a natural language processing engine to extract the mood and preferences. The server then searches for relevant recipes and cooking information from its database and generates suggestions. The generated suggestions are sent to the device and displayed to the user. The user selects the delivery method and confirms the corresponding details and payment options.

[0163] 4. Specific Examples

[0164] The user inputs, "I'm tired today, so I want to eat something easy to make." This input is sent from the device to the server. The server uses a generative AI model to extract keywords such as "easy to make" and "tired," and searches a database for related recipes. For example, suggestions such as "easy curry" or "microwave-safe pasta" are generated. These suggestions are displayed on the device, and the user can select the desired dish.

[0165] Prompt Sentence Examples

[0166] "I'm tired today, so I want to eat something easy to make."

[0167] "It's hot, so I'd like some cool food."

[0168] "I don't have much time, so please tell me something I can make in 5 minutes or less."

[0169] This allows the present invention to seamlessly integrate meal suggestions and delivery methods tailored to the user's mood, significantly improving user convenience. Furthermore, by performing advanced analysis using a generative AI model, it is possible to propose optimal dishes for the user.

[0170] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0171] Step 1: Initial Setup

[0172] The server creates a user database, which contains information about each user, such as their profile, past orders, and preferences.

[0173] Input: User profile information

[0174] Data processing: storing information in a database

[0175] Output: Database update

[0176] Specific operation: Creates a table using a database management system such as MySQL or PostgreSQL to store user information.

[0177] The application is installed on the device and the user creates an account.

[0178] Input: Name, Email Address, Password

[0179] Data processing: Hashing of input information

[0180] Output: Account information registered in the database

[0181] Specific operation: The user fills in the required information in the form and presses the submit button to create an account.

[0182] Step 2: Log in and enter your mood

[0183] The user launches the app and enters their credentials on the login screen.

[0184] Input: Email address, password

[0185] Data calculation: Matching email addresses with hashed passwords

[0186] Output: Authentication success / failure status

[0187] Specific operation: The user enters authentication information and presses the submit button. The server checks the information against the database and returns the authentication result.

[0188] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[0189] Input: User's dietary preferences

[0190] Output: The input text data

[0191] Specific behavior: A text box and a submit button are displayed and the user can enter input.

[0192] Step 3: Mood analysis and suggestions

[0193] The terminal transmits the user's input to the server.

[0194] Input: User-entered text

[0195] Data processing: Convert to JSON format and send to server

[0196] Output: Status of success / failure of data transmission to the server

[0197] Specific operation: Converts the user's input into JSON format and sends it to the server via an HTTP request.

[0198] The server uses a generative AI model to analyze the user's input and extract their mood and wishes.

[0199] Input: User's text data

[0200] Data Calculation: Analysis using a Natural Language Processing Engine

[0201] Output: Extracted keywords and phrases (e.g., "easy to make," "tired")

[0202] Specific operation: Calls a generative AI model to extract important keywords from the input text.

[0203] The server generates a list of relevant recipes and dishes from the database.

[0204] Input: Analysis results (keywords)

[0205] Data search: Search for relevant recipes using SQL queries

[0206] Output: List of dishes (recipe name, details, image link, etc.)

[0207] Specific operation: A search query is executed against the database to retrieve the relevant recipe information.

[0208] The terminal displays the generated recipe list to the user.

[0209] Input: Recipe list data from the server

[0210] Output: Display a list of dishes

[0211] Specific operation: Displays a list of photos, names, and brief descriptions of dishes on the user's device.

[0212] Step 4: Choose your food and serving method

[0213] The user selects a dish they like from the displayed list of dishes.

[0214] Input: Choose a dish (e.g., "Easy pasta")

[0215] Output: Selected dish data

[0216] Specific operation: The user selects the dish of their choice and presses the confirmation button.

[0217] The device will then present delivery options for the selected dish (make it yourself, delivery, restaurant).

[0218] Input: User's food selection data

[0219] Output: Display of options for delivery method

[0220] Specific operation: Display the delivery method (make it yourself, delivery, restaurant) to the user in the form of buttons.

[0221] The user selects the desired delivery method.

[0222] Input:Select delivery method

[0223] Output: Selected delivery method data

[0224] Specific operation: The user presses the desired delivery method button.

[0225] Step 5: Details and payment

[0226] Processing branches depending on the provision method.

[0227] If you make it yourself

[0228] 1. The server generates a recipe and a list of ingredients for the selected dish.

[0229] Input: User's food selection data

[0230] Data Search: Search for recipes and ingredient lists

[0231] Output: Recipe and ingredients list

[0232] What it does: Searches the database for recipes and ingredient lists and retrieves the results.

[0233] 2. The device displays the recipe and ingredients list, and offers purchasing options at local stores or online.

[0234] Input: Recipe and ingredient list data

[0235] Output: Show purchasing options

[0236] What it does: Displays recipes, ingredient lists, and purchasing options (retail stores, online stores) on the screen.

[0237] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[0238] Input: Purchase options and payment information

[0239] Output: Payment completion status

[0240] Specific actions: Select a purchase option, enter payment information such as credit card information, and complete the payment.

[0241] 4. The server sends the ingredients list to the online store and arranges for delivery.

[0242] Input: Material list and shipping information

[0243] Data submission: API requests to online stores

[0244] Output: Order completion notification

[0245] What it does: Sends the ingredients list via the online store's API and initiates the shipping process.

[0246] For delivery

[0247] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[0248] Input: User's food selection data

[0249] Data Search: Search for partner restaurants and delivery options

[0250] Output: Delivery options list

[0251] Specific operation: Retrieve delivery information for the relevant restaurant from the database and create data to display.

[0252] 2. The terminal displays delivery details and prices to the user.

[0253] Input: Delivery option data

[0254] Output: Display delivery details

[0255] Specific operation: Display the dish name, price, delivery time, etc. and set up a selection button.

[0256] 3. The user selects the desired restaurant and food and makes payment.

[0257] Input: Selected restaurant and dish, payment information

[0258] Output: Payment completion status

[0259] Specific actions: Select a restaurant and food, enter payment information, and complete the payment.

[0260] 4. The server sends the order to the selected restaurant and processes the delivery.

[0261] Input: Order Information

[0262] Data transmission: Sending orders to restaurants

[0263] Output: Order receipt confirmation

[0264] Specific operation: Order information is sent to the selected restaurant's system via API and delivery procedures are carried out.

[0265] In the case of a restaurant

[0266] 1. The server generates a list of nearby restaurants where reservations can be made.

[0267] Input: User's food selection data

[0268] Data search: Search for restaurants that accept reservations

[0269] Output: Restaurant list

[0270] Specific operation: Retrieve a list of restaurants that can be booked from the database and create display data.

[0271] 2. The device will display a list of restaurants and available reservation times.

[0272] Input: Restaurant list data

[0273] Output: Display of available time slots

[0274] What it does: Displays restaurant name, location, and available reservation times.

[0275] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[0276] Input: Restaurant and time slot selection

[0277] Output: Reservation completion status

[0278] Specific actions: Select the desired restaurant and time slot and press the reservation button.

[0279] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[0280] Input: Reservation and prepayment information

[0281] Data transmission: Sending reservation information to restaurants

[0282] Output: Reservation confirmation and advance payment completion notification

[0283] Specific actions: Sends reservation information to the restaurant's system and processes payment if necessary.

[0284] Step 6: Providing additional functionality

[0285] The server provides additional functionality available to the user.

[0286] Input: User request data

[0287] Output: Additional feature availability status

[0288] Specific behavior: Displays a UI that provides users with options such as taking steps to combat food waste, treating others, donating to relief efforts, and sharing their dining experience.

[0289] (Application example 1)

[0290] 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."

[0291] Today's busy consumers require systems that allow them to easily select their preferred meals and flexibly choose how they are served. They also want to improve user convenience and simplify operation by utilizing voice control and smart devices. However, current systems do not fully meet these requirements, and systems utilizing wearable devices such as smart glasses are particularly limited.

[0292] 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.

[0293] In this invention, the server includes means for analyzing the mood and preferences input by the user, means for suggesting dishes based on the analysis results, means for allowing the user to select a food serving method, means for making recipes, delivery, or restaurant reservations according to the selected serving method, and means for voice operation through smart glasses. This allows the user to easily operate the server by voice, and enables the suggestion of a variety of meals that match the user's mood and the seamless selection of the serving method.

[0294] The "means for analyzing the moods and wishes input by the user" is a technology for analyzing the text or voice information input by the user and extracting the user's moods and wishes from the content.

[0295] "Means for suggesting dishes based on the analysis results" refers to technology that lists appropriate dishes based on the analyzed mood and preferences and suggests them to the user.

[0296] "Means for allowing the user to select the delivery method of the food" refers to the interface and technology that allows the user to select the delivery method of the proposed food (cooking, delivery, or restaurant reservation).

[0297] "Means for providing recipes, delivery, or making restaurant reservations according to the selected delivery method" refers to technology for displaying recipe information, arranging delivery, or making restaurant reservations according to the delivery method selected by the user.

[0298] "Voice-controlled means via smart glasses" refers to an interface and technology that accepts voice input using smart glasses and allows the user to control the device through voice.

[0299] This invention is a communication-based meal recommendation system that suggests dishes based on the user's mood and preferences, and handles everything from the serving method to payment. This system includes the following elements and means.

[0300] Basic system configuration

[0301] This system mainly consists of a device used by the user (such as smart glasses) and a server that performs back-end processing. The user inputs their mood and preferences by voice through an application installed on the device. The input information is analyzed by the server and suggestions are generated. The user then selects how the suggested food is to be served and makes payment.

[0302] Hardware and software used

[0303] Hardware: Smart glasses (e.g., Google Glass, Microsoft HoloLens)

[0304] Software: Speech recognition engine (e.g., Google Cloud Speech-to-Text), natural language processing engine (e.g., Google Cloud Natural Language API), server (database: MySQL, server-side script: Python / Django)

[0305] Program processing and data calculation

[0306] 1. The server creates a user database and stores information about each user, such as their profile, past orders, and preferences.

[0307] 2. The application is installed on the smart glasses and the user creates an account.

[0308] 3. The user puts on the smart glasses and launches the app.

[0309] 4. The smart glasses will issue a voice prompt saying "Please log in" and the user will enter their credentials by voice.

[0310] 5. The server checks the user's credentials against its database to verify successful authentication.

[0311] 6. The smart glasses will ask the user aloud, "What kind of meal would you like to have today?"

[0312] 7. The user inputs their mood or desires by voice (e.g., "I want to eat something spicy today").

[0313] 8. The smart glasses convert the voice input into text and send it to the server.

[0314] 9. The server uses a natural language processing (NLP) engine to analyze the user's input and extract keywords such as "spicy" and "cooking."

[0315] 10. The server generates a list of relevant dishes from the database.

[0316] 11. The smart glasses will present the generated recipe list to the user via voice.

[0317] 12. The user selects their favorite dish from the presented list by voice (e.g., "Mapo tofu").

[0318] 13. The smart glasses will then provide audible options for the selected dish (cook it yourself, have it delivered, or book a table at the restaurant).

[0319] 14. The user selects the desired delivery method.

[0320] 15. The process branches depending on the delivery method. In the case of delivery, it is as follows:

[0321] 1. The server will present delivery options from nearby participating restaurants.

[0322] 2. The smart glasses will provide delivery details and prices to the user via voice.

[0323] 3. The user selects the desired restaurant and food by voice and makes payment.

[0324] 4. The server sends the order to the selected restaurant and processes the delivery.

[0325] Specific examples

[0326] For example, if a user voice-inputs, "I want to eat something spicy today," the server will suggest options such as "spicy chicken, mapo tofu, and dandan noodles." Next, if the user selects "mapo tofu," the smart glasses will ask, "There is a delivery option. Would you like to order mapo tofu?" If the user answers "yes," the server will send the order to the delivery restaurant and process the payment.

[0327] Prompt Sentence Examples

[0328] Develop a smart glasses application that analyzes a user's voice input, such as "I want spicy food," and then suggests appropriate dishes and assists with delivery. Follow these steps:

[0329] 1. The speech recognition engine converts speech into text.

[0330] 2. Use a natural language processing engine to extract the user's mood and preferences.

[0331] 3. We will suggest relevant dishes from our database.

[0332] 4. The user's selected food is sent to the server and delivery is arranged.

[0333] Hardware used: Smart glasses (Google Glass, Microsoft HoloLens)

[0334] Software used: Google Cloud Speech-to-Text, Google Cloud Natural Language API, MySQL database, Python / Django

[0335] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0336] Step 1:

[0337] The server creates a user database and stores information such as each user's profile, past order history, and preferences. Specifically, a database (e.g., MySQL) is used, and a table is created for each user. The input data is the user's basic information and tendencies, and the data is formatted and saved based on this. The output is the stored user's detailed information.

[0338] Step 2:

[0339] The application is installed on the smart glasses and the user creates an account. The user enters basic information and follows prompts to create the account. The input is the information entered by the user directly via voice or text, and the output is that the account is created and authentication information is sent to the server.

[0340] Step 3:

[0341] The user puts on the smart glasses and launches an app. The user turns on the smart glasses, selects an application, and launches it. The input is the user's launch operation, and the output is the application being launched.

[0342] Step 4:

[0343] The smart glasses will issue a voice prompt saying "Please log in" and the user will enter their credentials by voice. A speech recognition engine (Google Cloud Speech-to-Text) is used to convert the speech to text. The input is the user's voice and the output is their credentials in text format.

[0344] Step 5:

[0345] The server checks the user's credentials against a database to see if the authentication was successful. It checks the credentials to see if they match. The input is the credentials in text form and the output is the authentication success or failure status. The specific behavior is a process that uses an SQL query to check against the information in the database.

[0346] Step 6:

[0347] The smart glasses ask the user aloud, "What would you like to eat today?" They issue a voice prompt and wait for the user's response. The input is the text of the question, and the output is the user's voice response.

[0348] Step 7:

[0349] The user inputs their mood or desires through voice (e.g., "I want to eat something spicy today"). The user's voice input is captured through the microphone of the smart glasses. The input is the desired content in voice format, and the output is the content converted into text by the smart glasses.

[0350] Step 8:

[0351] The smart glasses convert voice input into text and send it to the server. They use a voice recognition engine to convert voice into text and send the data to the server. The input is the user's voice data and the output is text data. The specific operation is the conversion of voice into text by the voice recognition engine.

[0352] Step 9:

[0353] The server uses a natural language processing (NLP) engine to analyze the user's input and extract keywords such as "spicy" and "cuisine." The input is user input in text format, and the output is the extracted keywords. Specifically, the text analysis is performed using the Google Cloud Natural Language API.

[0354] Step 10:

[0355] The server generates a list of relevant dishes from the database. It searches the database based on keywords and lists related dishes. The input is the parsed keywords and the output is a list of dishes. The specific operation is the process of executing an SQL query to obtain related dish information.

[0356] Step 11:

[0357] The smart glasses present the generated recipe list to the user by voice. The list is converted from text to speech and presented to the user. The input is the list of recipes, and the output is the audio presentation.

[0358] Step 12:

[0359] The user selects their favorite dish from a presented list of dishes by voice (e.g., "Mapo tofu"). The user's selection is obtained by voice input. The input is a voice-based dish selection, and the output is text data of the selected dish.

[0360] Step 13:

[0361] The smart glasses present options for the selected dish (cook it yourself, have it delivered, or book a restaurant reservation) by voice. The options are presented by voice and the system waits for the user to make a selection. The input is the selected dish, and the output is the options.

[0362] Step 14:

[0363] The user selects the desired delivery method. The user's selection is input by voice. The input is the voice selection of the delivery method, and the output is text data of the selected delivery method.

[0364] Step 15:

[0365] The process will branch depending on the delivery method. For delivery:

[0366] 1. The server presents delivery options from nearby partner restaurants. It references the database to retrieve the relevant delivery options. The input is the selected delivery method, and the output is a list of partner restaurants.

[0367] 2. The smart glasses present delivery details and prices to the user via voice. The list is converted into audio and presented to the user. The input is a list of partner facilities, and the output is an audio presentation.

[0368] 3. The user selects the desired restaurant and food by voice and then pays. The user's selection is captured by voice. The input is the spoken selection of delivery options, and the output is text data with the selected delivery details.

[0369] 4. The server sends the order to the selected restaurant and processes the delivery. The input is the selected delivery details, and the output is sending the order to the restaurant. The specific operation is to send the order via an API call.

[0370] In this way, the entire system works together to make dish suggestions based on the user's mood and preferences, and to select and execute the serving method based on those suggestions.

[0371] 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.

[0372] The present invention provides a communication-based meal recommendation system that analyzes not only a user's mood and desires but also their emotions to recommend appropriate dishes. A specific embodiment of this system will be described below.

[0373] Basic configuration

[0374] This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing. The user inputs their mood, preferences, and emotions through an application installed on their device. The input information is analyzed by the server, which generates suggestions using an emotion engine. The user then selects how the suggested food is to be served (cooked, delivered, or at a restaurant) and makes payment.

[0375] Program processing (natural language explanation)

[0376] 1. Initial Setup

[0377] The server creates a user database and stores information such as each user's profile, past orders, and preferences.

[0378] The application is installed on the device and the user creates an account.

[0379] 2. Log in and enter your mood and emotions

[0380] The user launches the app and logs in by entering their credentials on the login screen.

[0381] The server checks the user's authentication information against a database to verify successful authentication.

[0382] The device displays a text input field to the user and asks, "What would you like to eat today?" It also analyzes the user's facial expressions and voice to present an interface for recognizing emotions.

[0383] Users input their mood and wishes in text, and also input their emotions through facial expressions and voice (for example, they can input "I'm tired today, so I'd like a quick dinner," and facial analysis will recognize their tiredness).

[0384] 3. Mood and emotion analysis and suggestions

[0385] The terminal transmits the user's input and the recognized emotion data to the server.

[0386] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood, desires, and emotions.

[0387] For example, keywords such as "tired," "easy to make," and "dinner" are extracted along with "fatigue."

[0388] The server uses an emotion engine to analyze the user's emotion data and, based on the results, highlights and suggests specific dishes.

[0389] For example, we suggest "easy-to-make pasta" along with "refreshing drinks" to reduce "feelings of fatigue."

[0390] The server generates a list of relevant recipes and dishes from the database.

[0391] The terminal displays the generated recipe list to the user.

[0392] 4. Choose the food and how it will be served

[0393] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[0394] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[0395] The user selects the desired delivery method.

[0396] 5. Details and Payment

[0397] Depending on the method of provision, the process branches and the detailed operation is performed:

[0398] If you make it yourself:

[0399] 1. The server generates a recipe and a list of ingredients for the selected dish.

[0400] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[0401] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[0402] 4. The server sends the ingredients list to the online store and arranges for delivery.

[0403] For delivery:

[0404] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[0405] 2. The terminal displays delivery details and prices to the user.

[0406] 3. The user selects the desired restaurant and food and makes payment.

[0407] 4. The server sends the order to the selected restaurant and processes the delivery.

[0408] For restaurants:

[0409] 1. The server generates a list of nearby restaurants where reservations can be made.

[0410] 2. The device will display a list of restaurants and available reservation times.

[0411] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[0412] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[0413] 6. Provision of Additional Features

[0414] The server provides additional functionality available to the user.

[0415] Food waste prevention measures: Provides the option to share excess food with other users, allowing users to choose what food to share and how to share it.

[0416] Treating others: Provides a money transfer function, allowing users to specify the person to treat and the amount, and then transfer the money.

[0417] Donate to relief efforts: Provide users with the option to donate to relief efforts in areas where food security is difficult, and allow them to specify the donation amount and make a payment.

[0418] Sharing dining experiences: Provides an interface for sharing dining experiences, allowing users to upload photos and impressions of their meals and share them on social media.

[0419] Specific examples

[0420] 1. The user enters, "It's hot today, so I want to eat something cool," and the emotion engine recognizes "joy" and "excitement."

[0421] 2. The server analyzes the input and emotional data and suggests options such as Zaru Soba noodles and Hiyashi Chuka noodles.

[0422] 3. The device displays suggested dishes, and the user selects "zaru soba."

[0423] 4. The device displays options for delivery method, and the user selects "Make it myself."

[0424] 5. The server will display the recipe for Zaru Soba noodles, the necessary ingredients, and offer purchasing options from a nearby supermarket and online stores.

[0425] 6. The user selects an online store, orders the materials, and makes payment.

[0426] 7. The server sends the order to the online store and the materials are delivered.

[0427] In this way, the present invention aims to significantly improve user convenience by seamlessly integrating meal suggestions and delivery methods tailored to the user's mood. It also addresses food waste reduction and social contribution, making it a system that can meet a wide range of needs. Furthermore, by incorporating an emotion engine, it becomes possible to make more personalized suggestions that are in tune with the user's emotions, thereby improving satisfaction.

[0428] The processing flow will be explained below.

[0429] Step 1:

[0430] The user launches the app and logs in by entering their credentials on the login screen.

[0431] The server checks the user's authentication information against a database to verify successful authentication.

[0432] Step 2:

[0433] The device displays a text input field to the user and asks, "What would you like to eat today?" It also analyzes the user's facial expressions and voice to present an interface for recognizing emotions.

[0434] Users input their mood and wishes in text, as well as their emotions through facial expressions and voice (for example, they can input "I'm tired today, so I'd like a quick dinner," and facial analysis will recognize their tiredness).

[0435] Step 3:

[0436] The terminal transmits the user's input and the recognized emotion data to the server.

[0437] Step 4:

[0438] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood, desires, and emotions.

[0439] For example, keywords such as "tired," "easy to make," "dinner," and "fatigue" are extracted.

[0440] Step 5:

[0441] The server uses an emotion engine to analyze the user's emotion data and, based on the results, highlights and suggests specific dishes.

[0442] For example, to reduce fatigue, we suggest combining "easy-to-make pasta" with "a refreshing drink."

[0443] Step 6:

[0444] The server generates a list of relevant recipes and dishes from the database.

[0445] The terminal displays the generated recipe list to the user.

[0446] Step 7:

[0447] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[0448] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[0449] Step 8:

[0450] The user selects the desired delivery method.

[0451] Step 9:

[0452] The process will be split depending on the delivery method:

[0453] If you make it yourself:

[0454] 1. The server generates a recipe and a list of ingredients for the selected dish.

[0455] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[0456] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[0457] 4. The server sends the ingredients list to the online store and arranges for delivery.

[0458] For delivery:

[0459] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[0460] 2. The terminal displays delivery details and prices to the user.

[0461] 3. The user selects the desired restaurant and food and makes payment.

[0462] 4. The server sends the order to the selected restaurant and processes the delivery.

[0463] For restaurants:

[0464] 1. The server generates a list of nearby restaurants where reservations can be made.

[0465] 2. The device will display a list of restaurants and available reservation times.

[0466] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[0467] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[0468] Step 10:

[0469] The server provides additional functionality available to the user.

[0470] Users can select and use additional features as needed (such as addressing food waste, treating others, donating to relief efforts, and sharing dining experiences).

[0471] Example 2

[0472] 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."

[0473] Conventional meal recommendation systems were able to make suggestions based on the user's mood and preferences, but they were unable to analyze the user's emotions and make more personalized suggestions based on those emotions. Furthermore, they lacked a consistent method for providing the suggested food and detailed arrangements, which meant they were unable to fully ensure user convenience. Furthermore, there were only limited systems that could address social issues such as food waste and social contribution.

[0474] 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.

[0475] In this invention, the server includes: a means for analyzing the mood, preferences, and emotions input by the user; a means for suggesting dishes based on the analysis results; a means for allowing the user to select a food delivery method; a means for providing recipes, delivery, or restaurant reservations according to the selected delivery method; a means for collecting the user's specific needs using prompt text; and a means for generating optimal dishes using a generative AI model. This enables personalized food recommendations based on the user's mood and emotions, and realizes consistent management of delivery methods and arrangements. It also addresses social issues such as food waste and social contribution.

[0476] "Mood" refers to the state of mind or feeling a user is experiencing at a particular time.

[0477] "Wishes" refer to the requirements or desires that a user has in a particular situation or condition.

[0478] "Emotions" refer to the psychological state or feelings expressed through the user's facial expressions, tone of voice, etc.

[0479] "Means of analysis" refers to techniques and methods for extracting and understanding moods, desires, and emotions based on information input by the user.

[0480] "Means for suggesting" refers to the technology or method for presenting appropriate food and beverage options to the user based on the analysis results.

[0481] "Means of Choice" refers to interfaces and technologies that allow users to select their preferred delivery method from multiple delivery methods.

[0482] "Delivery method" refers to the means by which food is delivered to the user, and includes options such as cooking it yourself, delivery, or restaurant reservations.

[0483] A "recipe" refers to a list of specific steps and ingredients for making a particular dish.

[0484] "Delivery" refers to a service that delivers food from affiliated restaurants to a location specified by the user.

[0485] "Restaurant reservation" refers to the process of a user making a reservation in advance to eat at a particular restaurant at a particular time.

[0486] A "prompt" is a piece of text or question that guides the user to input a specific need or request.

[0487] A "generative AI model" refers to an algorithm or system that uses large amounts of data to generate food and beverage options that best suit a user's needs.

[0488] The present invention is a communication-based meal recommendation system that analyzes a user's mood, preferences, and even emotions to recommend appropriate dishes. This system is composed of a device used by the user (e.g., a smartphone or tablet) and a server that performs back-end processing. Specific embodiments of this system are described below.

[0489] Basic configuration

[0490] The server uses a database management system (e.g., PostgreSQL) to create a user database and store information such as each user's profile, past order history, and preferences. The server also analyzes user input data using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API) and a sentiment analysis engine. It then uses a generative AI model to suggest appropriate dishes based on the analysis results.

[0491] The device provides the user with an interface through an application that runs on iOS or Android. When the user logs in, they enter their authentication information, and if successful, an input interface for their mood, desires, and emotions is displayed. The device then sends the entered data to the server and displays the server's suggestions.

[0492] The user inputs their mood, wishes, and emotions through the device. For example, they might input a prompt such as, "I'm tired today, so I'd like a quick dinner." At this time, the user's facial expressions and voice are also analyzed and captured as emotional data.

[0493] Specific examples

[0494] 1. Initial Setup: User installs the app and creates an account. The server stores the information in a user database.

[0495] 2. Login: The user logs in and their credentials are verified by the server.

[0496] 3. Information input: The user inputs, "I'm tired today, so I'd like a quick dinner," and facial expression analysis recognizes the "feeling of fatigue."

[0497] 4. Data analysis: The device sends the input data to the server, which then analyzes the data using an NLP engine and a sentiment analysis engine. As a result of the analysis, keywords such as "tired," "easy to make," and "dinner" as well as "feeling tired" are extracted.

[0498] 5. Recommendation Generation: The server uses the generative AI model to suggest a suitable dish (e.g., "Easy-to-make pasta") and a refreshing drink.

[0499] 6. Display: The device displays a list of suggested dishes, and the user selects "Easy Pasta."

[0500] 7. Select delivery method: The device presents delivery options (make it yourself, delivery, restaurant) and the user selects "make it yourself."

[0501] 8. Details and payment: The server generates the recipe and ingredients list for the selected dish, which are then displayed on the device. The user selects an online store to order the ingredients and completes the payment. The server then sends the ingredients list to the online store and arranges for delivery.

[0502] Examples of prompt statements might include, "It's hot today, so I want to eat something cool," or "I'm stressed, so I'd like something to relax me with."

[0503] This allows the present invention to seamlessly integrate meal suggestions and delivery methods tailored to the user's mood and emotions, significantly improving user convenience. It also addresses food waste reduction and social contribution, making it possible to meet a wide range of needs.

[0504] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0505] Step 1: Initial Setup

[0506] The server creates a user database and stores information such as each user's profile, past order history, preferences, etc. Specifically, using a database management system (e.g., PostgreSQL), a user table is created and a unique ID, name, email address, and past order history are stored.

[0507] The device installs an application that runs on iOS or Android and provides an interface for users to create an account. When the user enters their account information and presses the "Register" button, this information is sent to the server and added to the database.

[0508] Input: Account creation information (name, email address, password)

[0509] Output: New entry added to user database

[0510] Step 2: Log in and enter your mood and emotions

[0511] The user launches the app and enters their authentication information on the login screen.

[0512] The server checks the entered email address and password against its database, and if they match, allows the user to log in. It then generates and returns a JSON token.

[0513] After successfully logging in, the device displays a screen for inputting the user's mood, desires, and emotions. The screen includes a text input field, a question such as "What would you like to eat today?", and interfaces for facial expression analysis and voice input.

[0514] Users input their mood, wishes, and emotions through text input and facial and voice analysis.

[0515] Input: Login information (email address, password), mood / emotion information (text, facial expression, voice)

[0516] Output: Request including mood and emotion information (JSON format)

[0517] Step 3: Mood and emotion analysis and suggestions

[0518] The device sends the user's input and emotion data in JSON format to the server.

[0519] The server uses a natural language processing (NLP) engine to analyze the input and extract mood, desires, and emotions. For example, it uses the Google Cloud Natural Language API to extract keywords such as "tired," "easy to make," and "dinner" from the input text. It also uses an emotion engine to recognize "fatigue" from facial expressions and voice data.

[0520] Based on the analysis results, a generative AI model is used to generate dishes suitable for the user. For example, the generative AI model suggests "easy-to-make pasta" or "refreshing drinks."

[0521] The server returns these suggestions to the device in JSON format.

[0522] Input: Mood and emotion information (JSON format)

[0523] Output: Analysis results and suggestions (JSON format)

[0524] Step 4: Choose your food and serving method

[0525] The terminal displays the suggestions received from the server to the user, who then selects the desired dish from the list.

[0526] The device will then present the selected dish with delivery options (make it yourself, delivery, restaurant).

[0527] The user selects the desired delivery method.

[0528] Input: Analysis results and suggestions (JSON format)

[0529] Output: User's choice (dish and serving method)

[0530] Step 5: Details and payment (if you're making it yourself)

[0531] The server generates a recipe and a list of required ingredients for the selected dish and sends them to the terminal in JSON format.

[0532] The device displays the recipe and ingredients list, and offers purchasing options at nearby retailers or online stores.

[0533] The user selects a purchasing option, chooses the materials needed, enters payment information, and clicks the "Pay" button.

[0534] The server uses the online store's API to send the ingredients list and shipping information, and the order is placed.

[0535] Input: User's choice (food and serving method)

[0536] Output: Recipe, ingredient list, purchasing options, order confirmation information

[0537] Step 6: Providing additional functionality

[0538] The server sends a JSON-formatted menu to the device to provide additional features available to the user (food waste reduction measures, treating others, donating to relief efforts, and sharing dining experiences).

[0539] The terminal displays an additional function menu, allowing the user to select each function.

[0540] The server performs the corresponding process according to the additional function selected by the user. For example, in the case of a food waste countermeasure, an interface for sharing ingredients with other users is displayed and the selected ingredient information is sent.

[0541] Input: User's choice (additional function)

[0542] Output: Interface and processing results for additional functions

[0543] (Application example 2)

[0544] 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."

[0545] Conventional meal recommendation systems only analyze the user's mood and preferences, and do not consider the user's emotions, making it impossible to meet the user's true needs. Furthermore, the method of serving the suggested dishes lacks flexibility, making the system insufficient to satisfy user convenience. Therefore, there is a need for a system that can provide more personalized and accurate meal recommendations that also consider the user's emotions, and that can flexibly accommodate different serving methods.

[0546] 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.

[0547] In this invention, the server includes means for analyzing the user's mood, desires, and emotions, means for suggesting dishes based on the analysis results, means for allowing the user to select a food serving method, means for providing a recipe, delivery, or dining reservation in accordance with the selected serving method, and means for highlighting and suggesting dishes based on the user's emotions using an emotion analysis engine. This enables personalized meal suggestions that take into account not only the user's mood and desires but also their emotions, providing a system that allows users to flexibly select food serving methods that meet their needs.

[0548] "Mood" refers to the psychological state that a user temporarily feels.

[0549] "Wishes" refer to what a user wants for a particular condition or situation.

[0550] "Emotions" refer to the user's internal state or sensation, such as emotional reactions like joy or sadness.

[0551] "Analysis" refers to processing data based on input information and interpreting its meaning and patterns.

[0552] "Suggestion" refers to presenting appropriate options to the user based on the analysis results.

[0553] "Providing" refers to actually supplying the suggested dish to the user.

[0554] A "recipe" refers to the steps or list of ingredients for making a particular dish.

[0555] "Delivery" refers to a service that delivers the food selected by the user to a specified location.

[0556] "Dining reservation" refers to making a reservation at a restaurant or eatery of a user's choice.

[0557] An "emotion analysis engine" refers to a software or hardware mechanism that analyzes a user's emotions as data and provides information based on the results.

[0558] "User" refers to an individual who uses this system.

[0559] "Server" refers to a central computer system that receives, analyzes, and processes input data from users.

[0560] A "system" refers to a combination of multiple means or devices that work together to perform a specific function.

[0561] This system analyzes a user's mood, desires, and emotions, suggests appropriate dishes based on the analysis, and then provides recipes, deliveries, or dining reservations according to the selected delivery method. This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing.

[0562] System configuration

[0563] 1. User Device

[0564] Users access the system through applications installed on their terminals.

[0565] The device includes a text input field, a voice input interface, and a camera (for facial expression analysis).

[0566] 2. Server

[0567] The server receives the user's input data and performs analysis.

[0568] Analysis is performed using a natural language processing (NLP) engine (e.g., TextBlob) and an emotion analysis engine (e.g., EmotionRecognizer).

[0569] The server will suggest appropriate dishes based on the analysis results.

[0570] 3. Database

[0571] Stores data such as user profiles, past orders, and preferences.

[0572] Recipe information, information on affiliated restaurants, and delivery service information are also stored.

[0573] Example of operation procedure

[0574] User Input

[0575] The user launches the app and inputs their mood, desires, and emotions. For example, they might input, "It's hot today, so I want to eat something cool," and provide emotional data using voice or a camera.

[0576] Server Analysis

[0577] The server uses an NLP engine to analyze the text data and extract keywords. The emotion analysis engine analyzes the user's emotions from voice data and facial expression data. For example, keywords such as "cool" and "want to eat" are extracted, and the emotion analyzed is "joy."

[0578] Cooking suggestions

[0579] Based on the analysis results, the server will suggest dishes such as "zaru soba" (cold soba noodles) or "hiyashi chuka" (cold Chinese noodles).

[0580] Select delivery method

[0581] The user selects one of the proposed dishes and chooses how it will be served. For example, the user selects "hiyashi chuka" (cold Chinese noodles) and chooses "delivery" as the delivery method.

[0582] Delivery arrangements

[0583] The server orders the selected food from affiliated restaurants and arranges for it to be delivered to the user.

[0584] Specific examples

[0585] If a user inputs "It's hot today, so I want to eat something cool," and the server recognizes the emotion of "happiness," it will suggest "zaru soba" (cold noodles) or "hiyashi chuka" (cold Chinese noodles). If the user selects "hiyashi chuka" and chooses "delivery" as the delivery method, the hiyashi chuka will be delivered from the most suitable restaurant.

[0586] Prompt Sentence Examples

[0587] It's hot today, so I want to eat something cool. I'm happy and excited. Please suggest the best dish.

[0588] In this way, the system of the present invention improves user convenience and satisfaction by providing personalized recipe suggestions that take into account the user's mood, desires, and emotions, and by flexibly responding to a variety of delivery methods. Furthermore, by combining it with an emotion analysis engine, it becomes possible to make suggestions that are in line with the user's inner needs.

[0589] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0590] Step 1:

[0591] Initial Setup and User Registration

[0592] A user installs the application and creates an account the first time they launch it.

[0593] Specifically, the user enters registration information using an email address or social media account, and the server receives this information and stores it in a database.

[0594] The input is the user's profile information (name, email address, etc.) and the output is the storage of the successful registration information.

[0595] Step 2:

[0596] Login and User Authentication

[0597] The user launches the application and enters their credentials on the login screen.

[0598] The terminal sends this to the server, which then checks it against a database to confirm whether the authentication was successful.

[0599] The input is the user's authentication information (username, password, etc.), and the output is the authentication result (success or failure).

[0600] Step 3:

[0601] Input of moods, wishes and emotions

[0602] The user enters their feelings and desires in a text input field, and provides emotional data via voice input or a camera. The device then transmits this data to a server.

[0603] Specifically, the user inputs text such as "It's hot today, so I want to eat something cool," and the camera captures their voice and facial expression.

[0604] The input is the user's text, voice and facial expression data, and the output is the data sent to the server.

[0605] Step 4:

[0606] Mood, hope and emotion analysis

[0607] The server analyzes the received data using a natural language processing (NLP) engine and a sentiment analysis engine.

[0608] The NLP engine (TextBlob) extracts keywords from text, and the emotion analysis engine (EmotionRecognizer) analyzes emotions from voice and facial expressions.

[0609] The input is the user's text and emotion data, and the output is keywords and emotion information as the analysis results.

[0610] Step 5:

[0611] Cooking suggestions

[0612] Based on the analysis results, the server performs a database search to suggest appropriate dishes.

[0613] As a specific action, based on the keyword "cool" and the emotion "joy," the user selects cold soba noodles or chilled Chinese noodles.

[0614] The input is the analysis results (keywords and sentiment information) from step 4 above, and the output is a list of suggested dishes.

[0615] Step 6:

[0616] Select delivery method

[0617] The user selects from the suggested dishes and chooses how to serve them (make it yourself, have it delivered, or reserve a place to eat), and the device sends this information to the server.

[0618] As a specific operation, the user selects "hiyashi chuka" and selects "delivery."

[0619] The input is the proposed dish and the selected serving method, and the output is the selection sent to the server.

[0620] Step 7:

[0621] Order processing and fulfillment

[0622] The server makes the necessary arrangements depending on the delivery method selected. In the case of delivery, the server sends the order to the partner restaurant and arranges for delivery.

[0623] Specifically, the selected "hiyashi chuka" is ordered from an affiliated store and delivery service is arranged.

[0624] The input is the selected dish and serving method, and the output is order information and delivery arrangement information for the restaurant.

[0625] Step 8:

[0626] Order status notification and tracking

[0627] The server sends order status updates to the device, allowing users to check the status in real time through the app.

[0628] Specifically, the app will display real-time updates informing you of delivery status.

[0629] The input is delivery status updates and the output is real-time order status notifications.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] [Second embodiment]

[0634] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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."

[0646] The present invention provides a communication-based meal recommendation system that proposes dishes that match the user's mood and preferences, and handles the delivery method and payment in an integrated manner. Specific embodiments of this system will be described below.

[0647] Basic configuration

[0648] This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing. The user inputs their mood and preferences through an application installed on their device. The input information is analyzed by the server, and suggestions are generated. The user then selects how the suggested food is to be served (cooked, delivered, or at a restaurant) and makes payment.

[0649] Program processing (natural language explanation)

[0650] 1. Initial Setup

[0651] The server creates a user database and stores information such as each user's profile, past orders, and preferences.

[0652] The application is installed on the device and the user creates an account.

[0653] 2. Log in and enter your mood

[0654] The user launches the app and logs in by entering their credentials on the login screen.

[0655] The server checks the user's authentication information against a database to verify successful authentication.

[0656] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[0657] Users input their mood or desires in sentences (e.g., "I'd like a quick dinner today").

[0658] 3. Mood analysis and suggestions

[0659] The terminal transmits the user's input to the server.

[0660] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood and wishes.

[0661] For example, extract keywords such as "easy to make" and "dinner."

[0662] The server generates a list of relevant recipes and dishes from the database.

[0663] The terminal displays the generated recipe list to the user.

[0664] 4. Choose the food and how it will be served

[0665] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[0666] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[0667] The user selects the desired delivery method.

[0668] 5. Details and Payment

[0669] Depending on the method of provision, the process branches and the detailed operation is performed:

[0670] If you make it yourself:

[0671] 1. The server generates a recipe and a list of ingredients for the selected dish.

[0672] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[0673] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[0674] 4. The server sends the ingredients list to the online store and arranges for delivery.

[0675] For delivery:

[0676] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[0677] 2. The terminal displays delivery details and prices to the user.

[0678] 3. The user selects the desired restaurant and food and makes payment.

[0679] 4. The server sends the order to the selected restaurant and processes the delivery.

[0680] For restaurants:

[0681] 1. The server generates a list of nearby restaurants where reservations can be made.

[0682] 2. The device will display a list of restaurants and available reservation times.

[0683] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[0684] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[0685] 6. Provision of Additional Features

[0686] The server provides additional functionality available to the user.

[0687] Food waste prevention measures: Provides the option to share excess food with other users, allowing users to choose what food to share and how to share it.

[0688] Treating others: Provides a money transfer function, allowing users to specify the person to treat and the amount, and then transfer the money.

[0689] Donate to relief efforts: Provide users with the option to donate to relief efforts in areas where food security is difficult, and allow them to specify the donation amount and make a payment.

[0690] Sharing dining experiences: Provides an interface for sharing dining experiences, allowing users to upload photos and impressions of their meals and share them on social media.

[0691] Specific examples

[0692] 1. The user types, "It's hot today, so I want to eat something cool."

[0693] 2. The server analyzes the input and suggests options such as Zaru Soba and Hiyashi Chuka.

[0694] 3. The device displays suggested dishes, and the user selects "zaru soba."

[0695] 4. The device displays options for delivery method, and the user selects "Make it myself."

[0696] 5. The server will display the recipe for Zaru Soba noodles, the necessary ingredients, and offer purchasing options from a nearby supermarket and online stores.

[0697] 6. The user selects an online store, orders the materials, and makes payment.

[0698] 7. The server sends the order to the online store and the materials are delivered.

[0699] In this way, the present invention aims to significantly improve user convenience by seamlessly integrating meal suggestions and delivery methods that match the user's mood.It is also a system that can meet a wide range of needs by addressing food waste and contributing to society.

[0700] The processing flow will be explained below.

[0701] Step 1:

[0702] The user launches the app and logs in by entering their credentials on the login screen.

[0703] The server checks the user's authentication information against a database to verify successful authentication.

[0704] Step 2:

[0705] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[0706] Users input their mood and desires in sentences (e.g., "I'm tired today, so I'd like a quick dinner").

[0707] Step 3:

[0708] The terminal transmits the user's input to the server.

[0709] Step 4:

[0710] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood and wishes.

[0711] For example, extract keywords such as "tired," "easy to make," and "dinner."

[0712] Step 5:

[0713] The server generates a list of relevant recipes and dishes from the database.

[0714] The terminal displays the generated recipe list to the user.

[0715] Step 6:

[0716] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[0717] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[0718] Step 7:

[0719] The user selects the desired delivery method.

[0720] Step 8:

[0721] The process will be split depending on the delivery method:

[0722] If you make it yourself:

[0723] 1. The server generates a recipe and a list of ingredients for the selected dish.

[0724] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[0725] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[0726] 4. The server sends the ingredients list to the online store and arranges for delivery.

[0727] For delivery:

[0728] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[0729] 2. The terminal displays delivery details and prices to the user.

[0730] 3. The user selects the desired restaurant and food and makes payment.

[0731] 4. The server sends the order to the selected restaurant and processes the delivery.

[0732] For restaurants:

[0733] 1. The server generates a list of nearby restaurants where reservations can be made.

[0734] 2. The device will display a list of restaurants and available reservation times.

[0735] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[0736] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[0737] Step 9:

[0738] The server provides additional functionality available to the user.

[0739] Users can select and use additional features as needed (such as addressing food waste, treating others, donating to relief efforts, and sharing dining experiences).

[0740] Example 1

[0741] 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."

[0742] Conventional food recommendation systems have difficulty in providing specific food recommendations based on a user's mood and preferences. Furthermore, they lack integration of specific delivery methods and payment methods for the recommended dishes, resulting in low user convenience. Furthermore, there is no way for users to share their dining experiences with other users, limiting opportunities to expand their dining enjoyment.

[0743] 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.

[0744] In this invention, the server includes means for analyzing the mood and preferences input by the user, means for suggesting dishes based on the analysis results, means for allowing the user to select a food delivery method, means for providing a recipe, delivery, or reservation based on the selected delivery method, means for natural language processing of the mood and preferences using a generative AI model, means for displaying detailed information about the dishes, and means for making payments. This allows for specific dish suggestions based on the user's mood and preferences, and further improves user convenience by integrating food delivery methods and payment methods. It also allows users to share their dining experiences with other users.

[0745] A "user" is a person who uses the system to receive cooking suggestions and serving methods.

[0746] "Mood and desire" refers to the type and style of food desired by the user at that time, as well as the user's desires and feelings regarding the meal.

[0747] "Means for analyzing" refers to a function or system that processes the moods and wishes input by the user and deciphers and understands their content.

[0748] The "means for suggesting dishes" refers to a function or system for suggesting appropriate dishes to the user based on the analyzed mood and preferences.

[0749] "Serving method" refers to the options and process of how the proposed dish is to be served to the user.

[0750] A "recipe" is a list of steps and ingredients for making a particular dish.

[0751] "Delivery" is a service that delivers food from affiliated restaurants to users.

[0752] The "means for making a reservation" refers to a function or system for confirming a reservation for a date and time of visit at a restaurant selected by the user.

[0753] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze a user's mood and wishes.

[0754] "Natural language processing" is a technology that mechanically processes text entered by a user and understands its meaning.

[0755] "Means for displaying detailed information" refers to a function or system for visually presenting the user with details of the proposed dish and information about how it will be served.

[0756] "Means for making payment" refers to the functions and systems for carrying out payment processing, including online payment, for the food and services selected by the user.

[0757] "Mood and desired keywords" are important words and phrases related to cuisine that are extracted from the text entered by the user.

[0758] "Means for sharing dining experiences" refers to functions and systems that allow users to share photos and impressions of meals with other users and spread them through social media, etc.

[0759] The present invention is a system that proposes dishes that match the user's mood and preferences, and handles everything from the delivery method to payment. The system consists of a terminal used by the user and a server that performs back-end processing. Specific embodiments of the system are described below.

[0760] 1. Basic system configuration

[0761] Users input their mood and preferences through an application installed on their device. The input information is analyzed by the server, and appropriate dish suggestions are generated. The user then selects how the suggested dishes are to be served (cooked, delivered, or at a restaurant) and makes payment.

[0762] 2. Hardware and Software Configuration

[0763] The system uses the following hardware and software:

[0764] Devices: smartphones, tablets, etc.

[0765] Server: A typical backend server

[0766] Database: Relational database such as MySQL or PostgreSQL

[0767] Generative AI Model: Natural Language Processing (NLP) Engine

[0768] 3. Explanation of data processing and calculation

[0769] Based on the user's input of mood and preferences, the device sends the information to the server. The server uses a generative AI model to analyze the input information with a natural language processing engine to extract the mood and preferences. The server then searches for relevant recipes and cooking information from its database and generates suggestions. The generated suggestions are sent to the device and displayed to the user. The user selects the delivery method and confirms the corresponding details and payment options.

[0770] 4. Specific Examples

[0771] The user inputs, "I'm tired today, so I want to eat something easy to make." This input is sent from the device to the server. The server uses a generative AI model to extract keywords such as "easy to make" and "tired," and searches a database for related recipes. For example, suggestions such as "easy curry" or "microwave-safe pasta" are generated. These suggestions are displayed on the device, and the user can select the desired dish.

[0772] Prompt Sentence Examples

[0773] "I'm tired today, so I want to eat something easy to make."

[0774] "It's hot, so I'd like some cool food."

[0775] "I don't have much time, so please tell me something I can make in 5 minutes or less."

[0776] This allows the present invention to seamlessly integrate meal suggestions and delivery methods tailored to the user's mood, significantly improving user convenience. Furthermore, by performing advanced analysis using a generative AI model, it is possible to propose optimal dishes for the user.

[0777] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0778] Step 1: Initial Setup

[0779] The server creates a user database, which contains information about each user, such as their profile, past orders, and preferences.

[0780] Input: User profile information

[0781] Data processing: storing information in a database

[0782] Output: Database update

[0783] Specific operation: Creates a table using a database management system such as MySQL or PostgreSQL to store user information.

[0784] The application is installed on the device and the user creates an account.

[0785] Input: Name, Email Address, Password

[0786] Data processing: Hashing of input information

[0787] Output: Account information registered in the database

[0788] Specific operation: The user fills in the required information in the form and presses the submit button to create an account.

[0789] Step 2: Log in and enter your mood

[0790] The user launches the app and enters their credentials on the login screen.

[0791] Input: Email address, password

[0792] Data calculation: Matching email addresses with hashed passwords

[0793] Output: Authentication success / failure status

[0794] Specific operation: The user enters authentication information and presses the submit button. The server checks the information against the database and returns the authentication result.

[0795] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[0796] Input: User's dietary preferences

[0797] Output: The input text data

[0798] Specific behavior: A text box and a submit button are displayed and the user can enter input.

[0799] Step 3: Mood analysis and suggestions

[0800] The terminal transmits the user's input to the server.

[0801] Input: User-entered text

[0802] Data processing: Convert to JSON format and send to server

[0803] Output: Status of success / failure of data transmission to the server

[0804] Specific operation: Converts the user's input into JSON format and sends it to the server via an HTTP request.

[0805] The server uses a generative AI model to analyze the user's input and extract their mood and wishes.

[0806] Input: User's text data

[0807] Data Calculation: Analysis using a Natural Language Processing Engine

[0808] Output: Extracted keywords and phrases (e.g., "easy to make," "tired")

[0809] Specific operation: Calls a generative AI model to extract important keywords from the input text.

[0810] The server generates a list of relevant recipes and dishes from the database.

[0811] Input: Analysis results (keywords)

[0812] Data search: Search for relevant recipes using SQL queries

[0813] Output: List of dishes (recipe name, details, image link, etc.)

[0814] Specific operation: A search query is executed against the database to retrieve the relevant recipe information.

[0815] The terminal displays the generated recipe list to the user.

[0816] Input: Recipe list data from the server

[0817] Output: Display a list of dishes

[0818] Specific operation: Displays a list of photos, names, and brief descriptions of dishes on the user's device.

[0819] Step 4: Choose your food and serving method

[0820] The user selects a dish they like from the displayed list of dishes.

[0821] Input: Choose a dish (e.g., "Easy pasta")

[0822] Output: Selected dish data

[0823] Specific operation: The user selects the dish of their choice and presses the confirmation button.

[0824] The device will then present delivery options for the selected dish (make it yourself, delivery, restaurant).

[0825] Input: User's food selection data

[0826] Output: Display of options for delivery method

[0827] Specific operation: Display the delivery method (make it yourself, delivery, restaurant) to the user in the form of buttons.

[0828] The user selects the desired delivery method.

[0829] Input:Select delivery method

[0830] Output: Selected delivery method data

[0831] Specific operation: The user presses the desired delivery method button.

[0832] Step 5: Details and payment

[0833] Processing branches depending on the provision method.

[0834] If you make it yourself

[0835] 1. The server generates a recipe and a list of ingredients for the selected dish.

[0836] Input: User's food selection data

[0837] Data Search: Search for recipes and ingredient lists

[0838] Output: Recipe and ingredients list

[0839] What it does: Searches the database for recipes and ingredient lists and retrieves the results.

[0840] 2. The device displays the recipe and ingredients list, and offers purchasing options at local stores or online.

[0841] Input: Recipe and ingredient list data

[0842] Output: Show purchasing options

[0843] What it does: Displays recipes, ingredient lists, and purchasing options (retail stores, online stores) on the screen.

[0844] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[0845] Input: Purchase options and payment information

[0846] Output: Payment completion status

[0847] Specific actions: Select a purchase option, enter payment information such as credit card information, and complete the payment.

[0848] 4. The server sends the ingredients list to the online store and arranges for delivery.

[0849] Input: Material list and shipping information

[0850] Data submission: API requests to online stores

[0851] Output: Order completion notification

[0852] What it does: Sends the ingredients list via the online store's API and initiates the shipping process.

[0853] For delivery

[0854] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[0855] Input: User's food selection data

[0856] Data Search: Search for partner restaurants and delivery options

[0857] Output: Delivery options list

[0858] Specific operation: Retrieve delivery information for the relevant restaurant from the database and create data to display.

[0859] 2. The terminal displays delivery details and prices to the user.

[0860] Input: Delivery option data

[0861] Output: Display delivery details

[0862] Specific operation: Display the dish name, price, delivery time, etc. and set up a selection button.

[0863] 3. The user selects the desired restaurant and food and makes payment.

[0864] Input: Selected restaurant and dish, payment information

[0865] Output: Payment completion status

[0866] Specific actions: Select a restaurant and food, enter payment information, and complete the payment.

[0867] 4. The server sends the order to the selected restaurant and processes the delivery.

[0868] Input: Order Information

[0869] Data transmission: Sending orders to restaurants

[0870] Output: Order receipt confirmation

[0871] Specific operation: Order information is sent to the selected restaurant's system via API and delivery procedures are carried out.

[0872] In the case of a restaurant

[0873] 1. The server generates a list of nearby restaurants where reservations can be made.

[0874] Input: User's food selection data

[0875] Data search: Search for restaurants that accept reservations

[0876] Output: Restaurant list

[0877] Specific operation: Retrieve a list of restaurants that can be booked from the database and create display data.

[0878] 2. The device will display a list of restaurants and available reservation times.

[0879] Input: Restaurant list data

[0880] Output: Display of available time slots

[0881] What it does: Displays restaurant name, location, and available reservation times.

[0882] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[0883] Input: Restaurant and time slot selection

[0884] Output: Reservation completion status

[0885] Specific actions: Select the desired restaurant and time slot and press the reservation button.

[0886] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[0887] Input: Reservation and prepayment information

[0888] Data transmission: Sending reservation information to restaurants

[0889] Output: Reservation confirmation and advance payment completion notification

[0890] Specific actions: Sends reservation information to the restaurant's system and processes payment if necessary.

[0891] Step 6: Providing additional functionality

[0892] The server provides additional functionality available to the user.

[0893] Input: User request data

[0894] Output: Additional feature availability status

[0895] Specific behavior: Displays a UI that provides users with options such as taking steps to combat food waste, treating others, donating to relief efforts, and sharing their dining experience.

[0896] (Application example 1)

[0897] 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."

[0898] Today's busy consumers require systems that allow them to easily select their preferred meals and flexibly choose how they are served. They also want to improve user convenience and simplify operation by utilizing voice control and smart devices. However, current systems do not fully meet these requirements, and systems utilizing wearable devices such as smart glasses are particularly limited.

[0899] 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.

[0900] In this invention, the server includes means for analyzing the mood and preferences input by the user, means for suggesting dishes based on the analysis results, means for allowing the user to select a food serving method, means for making recipes, delivery, or restaurant reservations according to the selected serving method, and means for voice operation through smart glasses. This allows the user to easily operate the server by voice, and enables the suggestion of a variety of meals that match the user's mood and the seamless selection of the serving method.

[0901] The "means for analyzing the moods and wishes input by the user" is a technology for analyzing the text or voice information input by the user and extracting the user's moods and wishes from the content.

[0902] "Means for suggesting dishes based on the analysis results" refers to technology that lists appropriate dishes based on the analyzed mood and preferences and suggests them to the user.

[0903] "Means for allowing the user to select the delivery method of the food" refers to the interface and technology that allows the user to select the delivery method of the proposed food (cooking, delivery, or restaurant reservation).

[0904] "Means for providing recipes, delivery, or making restaurant reservations according to the selected delivery method" refers to technology for displaying recipe information, arranging delivery, or making restaurant reservations according to the delivery method selected by the user.

[0905] "Voice-controlled means via smart glasses" refers to an interface and technology that accepts voice input using smart glasses and allows the user to control the device through voice.

[0906] This invention is a communication-based meal recommendation system that suggests dishes based on the user's mood and preferences, and handles everything from the serving method to payment. This system includes the following elements and means.

[0907] Basic system configuration

[0908] This system mainly consists of a device used by the user (such as smart glasses) and a server that performs back-end processing. The user inputs their mood and preferences by voice through an application installed on the device. The input information is analyzed by the server and suggestions are generated. The user then selects how the suggested food is to be served and makes payment.

[0909] Hardware and software used

[0910] Hardware: Smart glasses (e.g., Google Glass, Microsoft HoloLens)

[0911] Software: Speech recognition engine (e.g., Google Cloud Speech-to-Text), natural language processing engine (e.g., Google Cloud Natural Language API), server (database: MySQL, server-side script: Python / Django)

[0912] Program processing and data calculation

[0913] 1. The server creates a user database and stores information about each user, such as their profile, past orders, and preferences.

[0914] 2. The application is installed on the smart glasses and the user creates an account.

[0915] 3. The user puts on the smart glasses and launches the app.

[0916] 4. The smart glasses will issue a voice prompt saying "Please log in" and the user will enter their credentials by voice.

[0917] 5. The server checks the user's credentials against its database to verify successful authentication.

[0918] 6. The smart glasses will ask the user aloud, "What kind of meal would you like to have today?"

[0919] 7. The user inputs their mood or desires by voice (e.g., "I want to eat something spicy today").

[0920] 8. The smart glasses convert the voice input into text and send it to the server.

[0921] 9. The server uses a natural language processing (NLP) engine to analyze the user's input and extract keywords such as "spicy" and "cooking."

[0922] 10. The server generates a list of relevant dishes from the database.

[0923] 11. The smart glasses will present the generated recipe list to the user via voice.

[0924] 12. The user selects their favorite dish from the presented list by voice (e.g., "Mapo tofu").

[0925] 13. The smart glasses will then provide audible options for the selected dish (cook it yourself, have it delivered, or book a table at the restaurant).

[0926] 14. The user selects the desired delivery method.

[0927] 15. The process branches depending on the delivery method. In the case of delivery, it is as follows:

[0928] 1. The server will present delivery options from nearby participating restaurants.

[0929] 2. The smart glasses will provide delivery details and prices to the user via voice.

[0930] 3. The user selects the desired restaurant and food by voice and makes payment.

[0931] 4. The server sends the order to the selected restaurant and processes the delivery.

[0932] Specific examples

[0933] For example, if a user voice-inputs, "I want to eat something spicy today," the server will suggest options such as "spicy chicken, mapo tofu, and dandan noodles." Next, if the user selects "mapo tofu," the smart glasses will ask, "There is a delivery option. Would you like to order mapo tofu?" If the user answers "yes," the server will send the order to the delivery restaurant and process the payment.

[0934] Prompt Sentence Examples

[0935] Develop a smart glasses application that analyzes a user's voice input, such as "I want spicy food," and then suggests appropriate dishes and assists with delivery. Follow these steps:

[0936] 1. The speech recognition engine converts speech into text.

[0937] 2. Use a natural language processing engine to extract the user's mood and preferences.

[0938] 3. We will suggest relevant dishes from our database.

[0939] 4. The user's selected food is sent to the server and delivery is arranged.

[0940] Hardware used: Smart glasses (Google Glass, Microsoft HoloLens)

[0941] Software used: Google Cloud Speech-to-Text, Google Cloud Natural Language API, MySQL database, Python / Django

[0942] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0943] Step 1:

[0944] The server creates a user database and stores information such as each user's profile, past order history, and preferences. Specifically, a database (e.g., MySQL) is used, and a table is created for each user. The input data is the user's basic information and tendencies, and the data is formatted and saved based on this. The output is the stored user's detailed information.

[0945] Step 2:

[0946] The application is installed on the smart glasses and the user creates an account. The user enters basic information and follows prompts to create the account. The input is the information entered by the user directly via voice or text, and the output is that the account is created and authentication information is sent to the server.

[0947] Step 3:

[0948] The user puts on the smart glasses and launches an app. The user turns on the smart glasses, selects an application, and launches it. The input is the user's launch operation, and the output is the application being launched.

[0949] Step 4:

[0950] The smart glasses will issue a voice prompt saying "Please log in" and the user will enter their credentials by voice. A speech recognition engine (Google Cloud Speech-to-Text) is used to convert the speech to text. The input is the user's voice and the output is their credentials in text format.

[0951] Step 5:

[0952] The server checks the user's credentials against a database to see if the authentication was successful. It checks the credentials to see if they match. The input is the credentials in text form and the output is the authentication success or failure status. The specific behavior is a process that uses an SQL query to check against the information in the database.

[0953] Step 6:

[0954] The smart glasses ask the user aloud, "What would you like to eat today?" They issue a voice prompt and wait for the user's response. The input is the text of the question, and the output is the user's voice response.

[0955] Step 7:

[0956] The user inputs their mood or desires through voice (e.g., "I want to eat something spicy today"). The user's voice input is captured through the microphone of the smart glasses. The input is the desired content in voice format, and the output is the content converted into text by the smart glasses.

[0957] Step 8:

[0958] The smart glasses convert voice input into text and send it to the server. They use a voice recognition engine to convert voice into text and send the data to the server. The input is the user's voice data and the output is text data. The specific operation is the conversion of voice into text by the voice recognition engine.

[0959] Step 9:

[0960] The server uses a natural language processing (NLP) engine to analyze the user's input and extract keywords such as "spicy" and "cuisine." The input is user input in text format, and the output is the extracted keywords. Specifically, the text analysis is performed using the Google Cloud Natural Language API.

[0961] Step 10:

[0962] The server generates a list of relevant dishes from the database. It searches the database based on keywords and lists related dishes. The input is the parsed keywords and the output is a list of dishes. The specific operation is the process of executing an SQL query to obtain related dish information.

[0963] Step 11:

[0964] The smart glasses present the generated recipe list to the user by voice. The list is converted from text to speech and presented to the user. The input is the list of recipes, and the output is the audio presentation.

[0965] Step 12:

[0966] The user selects their favorite dish from a presented list of dishes by voice (e.g., "Mapo tofu"). The user's selection is obtained by voice input. The input is a voice-based dish selection, and the output is text data of the selected dish.

[0967] Step 13:

[0968] The smart glasses present options for the selected dish (cook it yourself, have it delivered, or book a restaurant reservation) by voice. The options are presented by voice and the system waits for the user to make a selection. The input is the selected dish, and the output is the options.

[0969] Step 14:

[0970] The user selects the desired delivery method. The user's selection is input by voice. The input is the voice selection of the delivery method, and the output is text data of the selected delivery method.

[0971] Step 15:

[0972] The process will branch depending on the delivery method. For delivery:

[0973] 1. The server presents delivery options from nearby partner restaurants. It references the database to retrieve the relevant delivery options. The input is the selected delivery method, and the output is a list of partner restaurants.

[0974] 2. The smart glasses present delivery details and prices to the user via voice. The list is converted into audio and presented to the user. The input is a list of partner facilities, and the output is an audio presentation.

[0975] 3. The user selects the desired restaurant and food by voice and then pays. The user's selection is captured by voice. The input is the spoken selection of delivery options, and the output is text data with the selected delivery details.

[0976] 4. The server sends the order to the selected restaurant and processes the delivery. The input is the selected delivery details, and the output is sending the order to the restaurant. The specific operation is to send the order via an API call.

[0977] In this way, the entire system works together to make dish suggestions based on the user's mood and preferences, and to select and execute the serving method based on those suggestions.

[0978] 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.

[0979] The present invention provides a communication-based meal recommendation system that analyzes not only a user's mood and desires but also their emotions to recommend appropriate dishes. A specific embodiment of this system will be described below.

[0980] Basic configuration

[0981] This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing. The user inputs their mood, preferences, and emotions through an application installed on their device. The input information is analyzed by the server, which generates suggestions using an emotion engine. The user then selects how the suggested food is to be served (cooked, delivered, or at a restaurant) and makes payment.

[0982] Program processing (natural language explanation)

[0983] 1. Initial Setup

[0984] The server creates a user database and stores information such as each user's profile, past orders, and preferences.

[0985] The application is installed on the device and the user creates an account.

[0986] 2. Log in and enter your mood and emotions

[0987] The user launches the app and logs in by entering their credentials on the login screen.

[0988] The server checks the user's authentication information against a database to verify successful authentication.

[0989] The device displays a text input field to the user and asks, "What would you like to eat today?" It also analyzes the user's facial expressions and voice to present an interface for recognizing emotions.

[0990] Users input their mood and wishes in text, and also input their emotions through facial expressions and voice (for example, they can input "I'm tired today, so I'd like a quick dinner," and facial analysis will recognize their tiredness).

[0991] 3. Mood and emotion analysis and suggestions

[0992] The terminal transmits the user's input and the recognized emotion data to the server.

[0993] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood, desires, and emotions.

[0994] For example, keywords such as "tired," "easy to make," and "dinner" are extracted along with "fatigue."

[0995] The server uses an emotion engine to analyze the user's emotion data and, based on the results, highlights and suggests specific dishes.

[0996] For example, we suggest "easy-to-make pasta" along with "refreshing drinks" to reduce "feelings of fatigue."

[0997] The server generates a list of relevant recipes and dishes from the database.

[0998] The terminal displays the generated recipe list to the user.

[0999] 4. Choose the food and how it will be served

[1000] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[1001] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[1002] The user selects the desired delivery method.

[1003] 5. Details and Payment

[1004] Depending on the method of provision, the process branches and the detailed operation is performed:

[1005] If you make it yourself:

[1006] 1. The server generates a recipe and a list of ingredients for the selected dish.

[1007] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[1008] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[1009] 4. The server sends the ingredients list to the online store and arranges for delivery.

[1010] For delivery:

[1011] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[1012] 2. The terminal displays delivery details and prices to the user.

[1013] 3. The user selects the desired restaurant and food and makes payment.

[1014] 4. The server sends the order to the selected restaurant and processes the delivery.

[1015] For restaurants:

[1016] 1. The server generates a list of nearby restaurants where reservations can be made.

[1017] 2. The device will display a list of restaurants and available reservation times.

[1018] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[1019] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[1020] 6. Provision of Additional Features

[1021] The server provides additional functionality available to the user.

[1022] Food waste prevention measures: Provides the option to share excess food with other users, allowing users to choose what food to share and how to share it.

[1023] Treating others: Provides a money transfer function, allowing users to specify the person to treat and the amount, and then transfer the money.

[1024] Donate to relief efforts: Provide users with the option to donate to relief efforts in areas where food security is difficult, and allow them to specify the donation amount and make a payment.

[1025] Sharing dining experiences: Provides an interface for sharing dining experiences, allowing users to upload photos and impressions of their meals and share them on social media.

[1026] Specific examples

[1027] 1. The user enters, "It's hot today, so I want to eat something cool," and the emotion engine recognizes "joy" and "excitement."

[1028] 2. The server analyzes the input and emotional data and suggests options such as Zaru Soba noodles and Hiyashi Chuka noodles.

[1029] 3. The device displays suggested dishes, and the user selects "zaru soba."

[1030] 4. The device displays options for delivery method, and the user selects "Make it myself."

[1031] 5. The server will display the recipe for Zaru Soba noodles, the necessary ingredients, and offer purchasing options from a nearby supermarket and online stores.

[1032] 6. The user selects an online store, orders the materials, and makes payment.

[1033] 7. The server sends the order to the online store and the materials are delivered.

[1034] In this way, the present invention aims to significantly improve user convenience by seamlessly integrating meal suggestions and delivery methods tailored to the user's mood. It also addresses food waste reduction and social contribution, making it a system that can meet a wide range of needs. Furthermore, by incorporating an emotion engine, it becomes possible to make more personalized suggestions that are in tune with the user's emotions, thereby improving satisfaction.

[1035] The processing flow will be explained below.

[1036] Step 1:

[1037] The user launches the app and logs in by entering their credentials on the login screen.

[1038] The server checks the user's authentication information against a database to verify successful authentication.

[1039] Step 2:

[1040] The device displays a text input field to the user and asks, "What would you like to eat today?" It also analyzes the user's facial expressions and voice to present an interface for recognizing emotions.

[1041] Users input their mood and wishes in text, as well as their emotions through facial expressions and voice (for example, they can input "I'm tired today, so I'd like a quick dinner," and facial analysis will recognize their tiredness).

[1042] Step 3:

[1043] The terminal transmits the user's input and the recognized emotion data to the server.

[1044] Step 4:

[1045] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood, desires, and emotions.

[1046] For example, keywords such as "tired," "easy to make," "dinner," and "fatigue" are extracted.

[1047] Step 5:

[1048] The server uses an emotion engine to analyze the user's emotion data and, based on the results, highlights and suggests specific dishes.

[1049] For example, to reduce fatigue, we suggest combining "easy-to-make pasta" with "a refreshing drink."

[1050] Step 6:

[1051] The server generates a list of relevant recipes and dishes from the database.

[1052] The terminal displays the generated recipe list to the user.

[1053] Step 7:

[1054] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[1055] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[1056] Step 8:

[1057] The user selects the desired delivery method.

[1058] Step 9:

[1059] The process will be split depending on the delivery method:

[1060] If you make it yourself:

[1061] 1. The server generates a recipe and a list of ingredients for the selected dish.

[1062] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[1063] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[1064] 4. The server sends the ingredients list to the online store and arranges for delivery.

[1065] For delivery:

[1066] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[1067] 2. The terminal displays delivery details and prices to the user.

[1068] 3. The user selects the desired restaurant and food and makes payment.

[1069] 4. The server sends the order to the selected restaurant and processes the delivery.

[1070] For restaurants:

[1071] 1. The server generates a list of nearby restaurants where reservations can be made.

[1072] 2. The device will display a list of restaurants and available reservation times.

[1073] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[1074] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[1075] Step 10:

[1076] The server provides additional functionality available to the user.

[1077] Users can select and use additional features as needed (such as addressing food waste, treating others, donating to relief efforts, and sharing dining experiences).

[1078] Example 2

[1079] 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."

[1080] Conventional meal recommendation systems were able to make suggestions based on the user's mood and preferences, but they were unable to analyze the user's emotions and make more personalized suggestions based on those emotions. Furthermore, they lacked a consistent method for providing the suggested food and detailed arrangements, which meant they were unable to fully ensure user convenience. Furthermore, there were only limited systems that could address social issues such as food waste and social contribution.

[1081] 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.

[1082] In this invention, the server includes: a means for analyzing the mood, preferences, and emotions input by the user; a means for suggesting dishes based on the analysis results; a means for allowing the user to select a food delivery method; a means for providing recipes, delivery, or restaurant reservations according to the selected delivery method; a means for collecting the user's specific needs using prompt text; and a means for generating optimal dishes using a generative AI model. This enables personalized food recommendations based on the user's mood and emotions, and realizes consistent management of delivery methods and arrangements. It also addresses social issues such as food waste and social contribution.

[1083] "Mood" refers to the state of mind or feeling a user is experiencing at a particular time.

[1084] "Wishes" refer to the requirements or desires that a user has in a particular situation or condition.

[1085] "Emotions" refer to the psychological state or feelings expressed through the user's facial expressions, tone of voice, etc.

[1086] "Means of analysis" refers to techniques and methods for extracting and understanding moods, desires, and emotions based on information input by the user.

[1087] "Means for suggesting" refers to the technology or method for presenting appropriate food and beverage options to the user based on the analysis results.

[1088] "Means of Choice" refers to interfaces and technologies that allow users to select their preferred delivery method from multiple delivery methods.

[1089] "Delivery method" refers to the means by which food is delivered to the user, and includes options such as cooking it yourself, delivery, or restaurant reservations.

[1090] A "recipe" refers to a list of specific steps and ingredients for making a particular dish.

[1091] "Delivery" refers to a service that delivers food from affiliated restaurants to a location specified by the user.

[1092] "Restaurant reservation" refers to the process of a user making a reservation in advance to eat at a particular restaurant at a particular time.

[1093] A "prompt" is a piece of text or question that guides the user to input a specific need or request.

[1094] A "generative AI model" refers to an algorithm or system that uses large amounts of data to generate food and beverage options that best suit a user's needs.

[1095] The present invention is a communication-based meal recommendation system that analyzes a user's mood, preferences, and even emotions to recommend appropriate dishes. This system is composed of a device used by the user (e.g., a smartphone or tablet) and a server that performs back-end processing. Specific embodiments of this system are described below.

[1096] Basic configuration

[1097] The server uses a database management system (e.g., PostgreSQL) to create a user database and store information such as each user's profile, past order history, and preferences. The server also analyzes user input data using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API) and a sentiment analysis engine. It then uses a generative AI model to suggest appropriate dishes based on the analysis results.

[1098] The device provides the user with an interface through an application that runs on iOS or Android. When the user logs in, they enter their authentication information, and if successful, an input interface for their mood, desires, and emotions is displayed. The device then sends the entered data to the server and displays the server's suggestions.

[1099] The user inputs their mood, wishes, and emotions through the device. For example, they might input a prompt such as, "I'm tired today, so I'd like a quick dinner." At this time, the user's facial expressions and voice are also analyzed and captured as emotional data.

[1100] Specific examples

[1101] 1. Initial Setup: User installs the app and creates an account. The server stores the information in a user database.

[1102] 2. Login: The user logs in and their credentials are verified by the server.

[1103] 3. Information input: The user inputs, "I'm tired today, so I'd like a quick dinner," and facial expression analysis recognizes the "feeling of fatigue."

[1104] 4. Data analysis: The device sends the input data to the server, which then analyzes the data using an NLP engine and a sentiment analysis engine. As a result of the analysis, keywords such as "tired," "easy to make," and "dinner" as well as "feeling tired" are extracted.

[1105] 5. Recommendation Generation: The server uses the generative AI model to suggest a suitable dish (e.g., "Easy-to-make pasta") and a refreshing drink.

[1106] 6. Display: The device displays a list of suggested dishes, and the user selects "Easy Pasta."

[1107] 7. Select delivery method: The device presents delivery options (make it yourself, delivery, restaurant) and the user selects "make it yourself."

[1108] 8. Details and payment: The server generates the recipe and ingredients list for the selected dish, which are then displayed on the device. The user selects an online store to order the ingredients and completes the payment. The server then sends the ingredients list to the online store and arranges for delivery.

[1109] Examples of prompt statements might include, "It's hot today, so I want to eat something cool," or "I'm stressed, so I'd like something to relax me with."

[1110] This allows the present invention to seamlessly integrate meal suggestions and delivery methods tailored to the user's mood and emotions, significantly improving user convenience. It also addresses food waste reduction and social contribution, making it possible to meet a wide range of needs.

[1111] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1112] Step 1: Initial Setup

[1113] The server creates a user database and stores information such as each user's profile, past order history, preferences, etc. Specifically, using a database management system (e.g., PostgreSQL), a user table is created and a unique ID, name, email address, and past order history are stored.

[1114] The device installs an application that runs on iOS or Android and provides an interface for users to create an account. When the user enters their account information and presses the "Register" button, this information is sent to the server and added to the database.

[1115] Input: Account creation information (name, email address, password)

[1116] Output: New entry added to user database

[1117] Step 2: Log in and enter your mood and emotions

[1118] The user launches the app and enters their authentication information on the login screen.

[1119] The server checks the entered email address and password against its database, and if they match, allows the user to log in. It then generates and returns a JSON token.

[1120] After successfully logging in, the device displays a screen for inputting the user's mood, desires, and emotions. The screen includes a text input field, a question such as "What would you like to eat today?", and interfaces for facial expression analysis and voice input.

[1121] Users input their mood, wishes, and emotions through text input and facial and voice analysis.

[1122] Input: Login information (email address, password), mood / emotion information (text, facial expression, voice)

[1123] Output: Request including mood and emotion information (JSON format)

[1124] Step 3: Mood and emotion analysis and suggestions

[1125] The device sends the user's input and emotion data in JSON format to the server.

[1126] The server uses a natural language processing (NLP) engine to analyze the input and extract mood, desires, and emotions. For example, it uses the Google Cloud Natural Language API to extract keywords such as "tired," "easy to make," and "dinner" from the input text. It also uses an emotion engine to recognize "fatigue" from facial expressions and voice data.

[1127] Based on the analysis results, a generative AI model is used to generate dishes suitable for the user. For example, the generative AI model suggests "easy-to-make pasta" or "refreshing drinks."

[1128] The server returns these suggestions to the device in JSON format.

[1129] Input: Mood and emotion information (JSON format)

[1130] Output: Analysis results and suggestions (JSON format)

[1131] Step 4: Choose your food and serving method

[1132] The terminal displays the suggestions received from the server to the user, who then selects the desired dish from the list.

[1133] The device will then present the selected dish with delivery options (make it yourself, delivery, restaurant).

[1134] The user selects the desired delivery method.

[1135] Input: Analysis results and suggestions (JSON format)

[1136] Output: User's choice (dish and serving method)

[1137] Step 5: Details and payment (if you're making it yourself)

[1138] The server generates a recipe and a list of required ingredients for the selected dish and sends them to the terminal in JSON format.

[1139] The device displays the recipe and ingredients list, and offers purchasing options at nearby retailers or online stores.

[1140] The user selects a purchasing option, chooses the materials needed, enters payment information, and clicks the "Pay" button.

[1141] The server uses the online store's API to send the ingredients list and shipping information, and the order is placed.

[1142] Input: User's choice (food and serving method)

[1143] Output: Recipe, ingredient list, purchasing options, order confirmation information

[1144] Step 6: Providing additional functionality

[1145] The server sends a JSON-formatted menu to the device to provide additional features available to the user (food waste reduction measures, treating others, donating to relief efforts, and sharing dining experiences).

[1146] The terminal displays an additional function menu, allowing the user to select each function.

[1147] The server performs the corresponding process according to the additional function selected by the user. For example, in the case of a food waste countermeasure, an interface for sharing ingredients with other users is displayed and the selected ingredient information is sent.

[1148] Input: User's choice (additional function)

[1149] Output: Interface and processing results for additional functions

[1150] (Application example 2)

[1151] 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."

[1152] Conventional meal recommendation systems only analyze the user's mood and preferences, and do not consider the user's emotions, making it impossible to meet the user's true needs. Furthermore, the method of serving the suggested dishes lacks flexibility, making the system insufficient to satisfy user convenience. Therefore, there is a need for a system that can provide more personalized and accurate meal recommendations that also consider the user's emotions, and that can flexibly accommodate different serving methods.

[1153] 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.

[1154] In this invention, the server includes means for analyzing the user's mood, desires, and emotions, means for suggesting dishes based on the analysis results, means for allowing the user to select a food serving method, means for providing a recipe, delivery, or dining reservation in accordance with the selected serving method, and means for highlighting and suggesting dishes based on the user's emotions using an emotion analysis engine. This enables personalized meal suggestions that take into account not only the user's mood and desires but also their emotions, providing a system that allows users to flexibly select food serving methods that meet their needs.

[1155] "Mood" refers to the psychological state that a user temporarily feels.

[1156] "Wishes" refer to what a user wants for a particular condition or situation.

[1157] "Emotions" refer to the user's internal state or sensation, such as emotional reactions like joy or sadness.

[1158] "Analysis" refers to processing data based on input information and interpreting its meaning and patterns.

[1159] "Suggestion" refers to presenting appropriate options to the user based on the analysis results.

[1160] "Providing" refers to actually supplying the suggested dish to the user.

[1161] A "recipe" refers to the steps or list of ingredients for making a particular dish.

[1162] "Delivery" refers to a service that delivers the food selected by the user to a specified location.

[1163] "Dining reservation" refers to making a reservation at a restaurant or eatery of a user's choice.

[1164] An "emotion analysis engine" refers to a software or hardware mechanism that analyzes a user's emotions as data and provides information based on the results.

[1165] "User" refers to an individual who uses this system.

[1166] "Server" refers to a central computer system that receives, analyzes, and processes input data from users.

[1167] A "system" refers to a combination of multiple means or devices that work together to perform a specific function.

[1168] This system analyzes a user's mood, desires, and emotions, suggests appropriate dishes based on the analysis, and then provides recipes, deliveries, or dining reservations according to the selected delivery method. This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing.

[1169] System configuration

[1170] 1. User Device

[1171] Users access the system through applications installed on their terminals.

[1172] The device includes a text input field, a voice input interface, and a camera (for facial expression analysis).

[1173] 2. Server

[1174] The server receives the user's input data and performs analysis.

[1175] Analysis is performed using a natural language processing (NLP) engine (e.g., TextBlob) and an emotion analysis engine (e.g., EmotionRecognizer).

[1176] The server will suggest appropriate dishes based on the analysis results.

[1177] 3. Database

[1178] Stores data such as user profiles, past orders, and preferences.

[1179] Recipe information, information on affiliated restaurants, and delivery service information are also stored.

[1180] Example of operation procedure

[1181] User Input

[1182] The user launches the app and inputs their mood, desires, and emotions. For example, they might input, "It's hot today, so I want to eat something cool," and provide emotional data using voice or a camera.

[1183] Server Analysis

[1184] The server uses an NLP engine to analyze the text data and extract keywords. The emotion analysis engine analyzes the user's emotions from voice data and facial expression data. For example, keywords such as "cool" and "want to eat" are extracted, and the emotion analyzed is "joy."

[1185] Cooking suggestions

[1186] Based on the analysis results, the server will suggest dishes such as "zaru soba" (cold soba noodles) or "hiyashi chuka" (cold Chinese noodles).

[1187] Select delivery method

[1188] The user selects one of the proposed dishes and chooses how it will be served. For example, the user selects "hiyashi chuka" (cold Chinese noodles) and chooses "delivery" as the delivery method.

[1189] Delivery arrangements

[1190] The server orders the selected food from affiliated restaurants and arranges for it to be delivered to the user.

[1191] Specific examples

[1192] If a user inputs "It's hot today, so I want to eat something cool," and the server recognizes the emotion of "happiness," it will suggest "zaru soba" (cold noodles) or "hiyashi chuka" (cold Chinese noodles). If the user selects "hiyashi chuka" and chooses "delivery" as the delivery method, the hiyashi chuka will be delivered from the most suitable restaurant.

[1193] Prompt Sentence Examples

[1194] It's hot today, so I want to eat something cool. I'm happy and excited. Please suggest the best dish.

[1195] In this way, the system of the present invention improves user convenience and satisfaction by providing personalized recipe suggestions that take into account the user's mood, desires, and emotions, and by flexibly responding to a variety of delivery methods. Furthermore, by combining it with an emotion analysis engine, it becomes possible to make suggestions that are in line with the user's inner needs.

[1196] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1197] Step 1:

[1198] Initial Setup and User Registration

[1199] A user installs the application and creates an account the first time they launch it.

[1200] Specifically, the user enters registration information using an email address or social media account, and the server receives this information and stores it in a database.

[1201] The input is the user's profile information (name, email address, etc.) and the output is the storage of the successful registration information.

[1202] Step 2:

[1203] Login and User Authentication

[1204] The user launches the application and enters their credentials on the login screen.

[1205] The terminal sends this to the server, which then checks it against a database to confirm whether the authentication was successful.

[1206] The input is the user's authentication information (username, password, etc.), and the output is the authentication result (success or failure).

[1207] Step 3:

[1208] Input of moods, wishes and emotions

[1209] The user enters their feelings and desires in a text input field, and provides emotional data via voice input or a camera. The device then transmits this data to a server.

[1210] Specifically, the user inputs text such as "It's hot today, so I want to eat something cool," and the camera captures their voice and facial expression.

[1211] The input is the user's text, voice and facial expression data, and the output is the data sent to the server.

[1212] Step 4:

[1213] Mood, hope and emotion analysis

[1214] The server analyzes the received data using a natural language processing (NLP) engine and a sentiment analysis engine.

[1215] The NLP engine (TextBlob) extracts keywords from text, and the emotion analysis engine (EmotionRecognizer) analyzes emotions from voice and facial expressions.

[1216] The input is the user's text and emotion data, and the output is keywords and emotion information as the analysis results.

[1217] Step 5:

[1218] Cooking suggestions

[1219] Based on the analysis results, the server performs a database search to suggest appropriate dishes.

[1220] As a specific action, based on the keyword "cool" and the emotion "joy," the user selects cold soba noodles or chilled Chinese noodles.

[1221] The input is the analysis results (keywords and sentiment information) from step 4 above, and the output is a list of suggested dishes.

[1222] Step 6:

[1223] Select delivery method

[1224] The user selects from the suggested dishes and chooses how to serve them (make it yourself, have it delivered, or reserve a place to eat), and the device sends this information to the server.

[1225] As a specific operation, the user selects "hiyashi chuka" and selects "delivery."

[1226] The input is the proposed dish and the selected serving method, and the output is the selection sent to the server.

[1227] Step 7:

[1228] Order processing and fulfillment

[1229] The server makes the necessary arrangements depending on the delivery method selected. In the case of delivery, the server sends the order to the partner restaurant and arranges for delivery.

[1230] Specifically, the selected "hiyashi chuka" is ordered from an affiliated store and delivery service is arranged.

[1231] The input is the selected dish and serving method, and the output is order information and delivery arrangement information for the restaurant.

[1232] Step 8:

[1233] Order status notification and tracking

[1234] The server sends order status updates to the device, allowing users to check the status in real time through the app.

[1235] Specifically, the app will display real-time updates informing you of delivery status.

[1236] The input is delivery status updates and the output is real-time order status notifications.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] [Third embodiment]

[1241] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1242] 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.

[1243] 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).

[1244] 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.

[1245] 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.

[1246] 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).

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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."

[1253] The present invention provides a communication-based meal recommendation system that proposes dishes that match the user's mood and preferences, and handles the delivery method and payment in an integrated manner. Specific embodiments of this system will be described below.

[1254] Basic configuration

[1255] This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing. The user inputs their mood and preferences through an application installed on their device. The input information is analyzed by the server, and suggestions are generated. The user then selects how the suggested food is to be served (cooked, delivered, or at a restaurant) and makes payment.

[1256] Program processing (natural language explanation)

[1257] 1. Initial Setup

[1258] The server creates a user database and stores information such as each user's profile, past orders, and preferences.

[1259] The application is installed on the device and the user creates an account.

[1260] 2. Log in and enter your mood

[1261] The user launches the app and logs in by entering their credentials on the login screen.

[1262] The server checks the user's authentication information against a database to verify successful authentication.

[1263] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[1264] Users input their mood or desires in sentences (e.g., "I'd like a quick dinner today").

[1265] 3. Mood analysis and suggestions

[1266] The terminal transmits the user's input to the server.

[1267] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood and wishes.

[1268] For example, extract keywords such as "easy to make" and "dinner."

[1269] The server generates a list of relevant recipes and dishes from the database.

[1270] The terminal displays the generated recipe list to the user.

[1271] 4. Choose the food and how it will be served

[1272] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[1273] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[1274] The user selects the desired delivery method.

[1275] 5. Details and Payment

[1276] Depending on the method of provision, the process branches and the detailed operation is performed:

[1277] If you make it yourself:

[1278] 1. The server generates a recipe and a list of ingredients for the selected dish.

[1279] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[1280] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[1281] 4. The server sends the ingredients list to the online store and arranges for delivery.

[1282] For delivery:

[1283] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[1284] 2. The terminal displays delivery details and prices to the user.

[1285] 3. The user selects the desired restaurant and food and makes payment.

[1286] 4. The server sends the order to the selected restaurant and processes the delivery.

[1287] For restaurants:

[1288] 1. The server generates a list of nearby restaurants where reservations can be made.

[1289] 2. The device will display a list of restaurants and available reservation times.

[1290] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[1291] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[1292] 6. Provision of Additional Features

[1293] The server provides additional functionality available to the user.

[1294] Food waste prevention measures: Provides the option to share excess food with other users, allowing users to choose what food to share and how to share it.

[1295] Treating others: Provides a money transfer function, allowing users to specify the person to treat and the amount, and then transfer the money.

[1296] Donate to relief efforts: Provide users with the option to donate to relief efforts in areas where food security is difficult, and allow them to specify the donation amount and make a payment.

[1297] Sharing dining experiences: Provides an interface for sharing dining experiences, allowing users to upload photos and impressions of their meals and share them on social media.

[1298] Specific examples

[1299] 1. The user types, "It's hot today, so I want to eat something cool."

[1300] 2. The server analyzes the input and suggests options such as Zaru Soba and Hiyashi Chuka.

[1301] 3. The device displays suggested dishes, and the user selects "zaru soba."

[1302] 4. The device displays options for delivery method, and the user selects "Make it myself."

[1303] 5. The server will display the recipe for Zaru Soba noodles, the necessary ingredients, and offer purchasing options from a nearby supermarket and online stores.

[1304] 6. The user selects an online store, orders the materials, and makes payment.

[1305] 7. The server sends the order to the online store and the materials are delivered.

[1306] In this way, the present invention aims to significantly improve user convenience by seamlessly integrating meal suggestions and delivery methods that match the user's mood.It is also a system that can meet a wide range of needs by addressing food waste and contributing to society.

[1307] The processing flow will be explained below.

[1308] Step 1:

[1309] The user launches the app and logs in by entering their credentials on the login screen.

[1310] The server checks the user's authentication information against a database to verify successful authentication.

[1311] Step 2:

[1312] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[1313] Users input their mood and desires in sentences (e.g., "I'm tired today, so I'd like a quick dinner").

[1314] Step 3:

[1315] The terminal transmits the user's input to the server.

[1316] Step 4:

[1317] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood and wishes.

[1318] For example, extract keywords such as "tired," "easy to make," and "dinner."

[1319] Step 5:

[1320] The server generates a list of relevant recipes and dishes from the database.

[1321] The terminal displays the generated recipe list to the user.

[1322] Step 6:

[1323] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[1324] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[1325] Step 7:

[1326] The user selects the desired delivery method.

[1327] Step 8:

[1328] The process will be split depending on the delivery method:

[1329] If you make it yourself:

[1330] 1. The server generates a recipe and a list of ingredients for the selected dish.

[1331] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[1332] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[1333] 4. The server sends the ingredients list to the online store and arranges for delivery.

[1334] For delivery:

[1335] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[1336] 2. The terminal displays delivery details and prices to the user.

[1337] 3. The user selects the desired restaurant and food and makes payment.

[1338] 4. The server sends the order to the selected restaurant and processes the delivery.

[1339] For restaurants:

[1340] 1. The server generates a list of nearby restaurants where reservations can be made.

[1341] 2. The device will display a list of restaurants and available reservation times.

[1342] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[1343] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[1344] Step 9:

[1345] The server provides additional functionality available to the user.

[1346] Users can select and use additional features as needed (such as addressing food waste, treating others, donating to relief efforts, and sharing dining experiences).

[1347] Example 1

[1348] 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."

[1349] Conventional food recommendation systems have difficulty in providing specific food recommendations based on a user's mood and preferences. Furthermore, they lack integration of specific delivery methods and payment methods for the recommended dishes, resulting in low user convenience. Furthermore, there is no way for users to share their dining experiences with other users, limiting opportunities to expand their dining enjoyment.

[1350] 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.

[1351] In this invention, the server includes means for analyzing the mood and preferences input by the user, means for suggesting dishes based on the analysis results, means for allowing the user to select a food delivery method, means for providing a recipe, delivery, or reservation based on the selected delivery method, means for natural language processing of the mood and preferences using a generative AI model, means for displaying detailed information about the dishes, and means for making payments. This allows for specific dish suggestions based on the user's mood and preferences, and further improves user convenience by integrating food delivery methods and payment methods. It also allows users to share their dining experiences with other users.

[1352] A "user" is a person who uses the system to receive cooking suggestions and serving methods.

[1353] "Mood and desire" refers to the type and style of food desired by the user at that time, as well as the user's desires and feelings regarding the meal.

[1354] "Means for analyzing" refers to a function or system that processes the moods and wishes input by the user and deciphers and understands their content.

[1355] The "means for suggesting dishes" refers to a function or system for suggesting appropriate dishes to the user based on the analyzed mood and preferences.

[1356] "Serving method" refers to the options and process of how the proposed dish is to be served to the user.

[1357] A "recipe" is a list of steps and ingredients for making a particular dish.

[1358] "Delivery" is a service that delivers food from affiliated restaurants to users.

[1359] The "means for making a reservation" refers to a function or system for confirming a reservation for a date and time of visit at a restaurant selected by the user.

[1360] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze a user's mood and wishes.

[1361] "Natural language processing" is a technology that mechanically processes text entered by a user and understands its meaning.

[1362] "Means for displaying detailed information" refers to a function or system for visually presenting the user with details of the proposed dish and information about how it will be served.

[1363] "Means for making payment" refers to the functions and systems for carrying out payment processing, including online payment, for the food and services selected by the user.

[1364] "Mood and desired keywords" are important words and phrases related to cuisine that are extracted from the text entered by the user.

[1365] "Means for sharing dining experiences" refers to functions and systems that allow users to share photos and impressions of meals with other users and spread them through social media, etc.

[1366] The present invention is a system that proposes dishes that match the user's mood and preferences, and handles everything from the delivery method to payment. The system consists of a terminal used by the user and a server that performs back-end processing. Specific embodiments of the system are described below.

[1367] 1. Basic system configuration

[1368] Users input their mood and preferences through an application installed on their device. The input information is analyzed by the server, and appropriate dish suggestions are generated. The user then selects how the suggested dishes are to be served (cooked, delivered, or at a restaurant) and makes payment.

[1369] 2. Hardware and Software Configuration

[1370] The system uses the following hardware and software:

[1371] Devices: smartphones, tablets, etc.

[1372] Server: A typical backend server

[1373] Database: Relational database such as MySQL or PostgreSQL

[1374] Generative AI Model: Natural Language Processing (NLP) Engine

[1375] 3. Explanation of data processing and calculation

[1376] Based on the user's input of mood and preferences, the device sends the information to the server. The server uses a generative AI model to analyze the input information with a natural language processing engine to extract the mood and preferences. The server then searches for relevant recipes and cooking information from its database and generates suggestions. The generated suggestions are sent to the device and displayed to the user. The user selects the delivery method and confirms the corresponding details and payment options.

[1377] 4. Specific Examples

[1378] The user inputs, "I'm tired today, so I want to eat something easy to make." This input is sent from the device to the server. The server uses a generative AI model to extract keywords such as "easy to make" and "tired," and searches a database for related recipes. For example, suggestions such as "easy curry" or "microwave-safe pasta" are generated. These suggestions are displayed on the device, and the user can select the desired dish.

[1379] Prompt Sentence Examples

[1380] "I'm tired today, so I want to eat something easy to make."

[1381] "It's hot, so I'd like some cool food."

[1382] "I don't have much time, so please tell me something I can make in 5 minutes or less."

[1383] This allows the present invention to seamlessly integrate meal suggestions and delivery methods tailored to the user's mood, significantly improving user convenience. Furthermore, by performing advanced analysis using a generative AI model, it is possible to propose optimal dishes for the user.

[1384] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1385] Step 1: Initial Setup

[1386] The server creates a user database, which contains information about each user, such as their profile, past orders, and preferences.

[1387] Input: User profile information

[1388] Data processing: storing information in a database

[1389] Output: Database update

[1390] Specific operation: Creates a table using a database management system such as MySQL or PostgreSQL to store user information.

[1391] The application is installed on the device and the user creates an account.

[1392] Input: Name, Email Address, Password

[1393] Data processing: Hashing of input information

[1394] Output: Account information registered in the database

[1395] Specific operation: The user fills in the required information in the form and presses the submit button to create an account.

[1396] Step 2: Log in and enter your mood

[1397] The user launches the app and enters their credentials on the login screen.

[1398] Input: Email address, password

[1399] Data calculation: Matching email addresses with hashed passwords

[1400] Output: Authentication success / failure status

[1401] Specific operation: The user enters authentication information and presses the submit button. The server checks the information against the database and returns the authentication result.

[1402] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[1403] Input: User's dietary preferences

[1404] Output: The input text data

[1405] Specific behavior: A text box and a submit button are displayed and the user can enter input.

[1406] Step 3: Mood analysis and suggestions

[1407] The terminal transmits the user's input to the server.

[1408] Input: User-entered text

[1409] Data processing: Convert to JSON format and send to server

[1410] Output: Status of success / failure of data transmission to the server

[1411] Specific operation: Converts the user's input into JSON format and sends it to the server via an HTTP request.

[1412] The server uses a generative AI model to analyze the user's input and extract their mood and wishes.

[1413] Input: User's text data

[1414] Data Calculation: Analysis using a Natural Language Processing Engine

[1415] Output: Extracted keywords and phrases (e.g., "easy to make," "tired")

[1416] Specific operation: Calls a generative AI model to extract important keywords from the input text.

[1417] The server generates a list of relevant recipes and dishes from the database.

[1418] Input: Analysis results (keywords)

[1419] Data search: Search for relevant recipes using SQL queries

[1420] Output: List of dishes (recipe name, details, image link, etc.)

[1421] Specific operation: A search query is executed against the database to retrieve the relevant recipe information.

[1422] The terminal displays the generated recipe list to the user.

[1423] Input: Recipe list data from the server

[1424] Output: Display a list of dishes

[1425] Specific operation: Displays a list of photos, names, and brief descriptions of dishes on the user's device.

[1426] Step 4: Choose your food and serving method

[1427] The user selects a dish they like from the displayed list of dishes.

[1428] Input: Choose a dish (e.g., "Easy pasta")

[1429] Output: Selected dish data

[1430] Specific operation: The user selects the dish of their choice and presses the confirmation button.

[1431] The device will then present delivery options for the selected dish (make it yourself, delivery, restaurant).

[1432] Input: User's food selection data

[1433] Output: Display of options for delivery method

[1434] Specific operation: Display the delivery method (make it yourself, delivery, restaurant) to the user in the form of buttons.

[1435] The user selects the desired delivery method.

[1436] Input:Select delivery method

[1437] Output: Selected delivery method data

[1438] Specific operation: The user presses the desired delivery method button.

[1439] Step 5: Details and payment

[1440] Processing branches depending on the provision method.

[1441] If you make it yourself

[1442] 1. The server generates a recipe and a list of ingredients for the selected dish.

[1443] Input: User's food selection data

[1444] Data Search: Search for recipes and ingredient lists

[1445] Output: Recipe and ingredients list

[1446] What it does: Searches the database for recipes and ingredient lists and retrieves the results.

[1447] 2. The device displays the recipe and ingredients list, and offers purchasing options at local stores or online.

[1448] Input: Recipe and ingredient list data

[1449] Output: Show purchasing options

[1450] What it does: Displays recipes, ingredient lists, and purchasing options (retail stores, online stores) on the screen.

[1451] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[1452] Input: Purchase options and payment information

[1453] Output: Payment completion status

[1454] Specific actions: Select a purchase option, enter payment information such as credit card information, and complete the payment.

[1455] 4. The server sends the ingredients list to the online store and arranges for delivery.

[1456] Input: Material list and shipping information

[1457] Data submission: API requests to online stores

[1458] Output: Order completion notification

[1459] What it does: Sends the ingredients list via the online store's API and initiates the shipping process.

[1460] For delivery

[1461] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[1462] Input: User's food selection data

[1463] Data Search: Search for partner restaurants and delivery options

[1464] Output: Delivery options list

[1465] Specific operation: Retrieve delivery information for the relevant restaurant from the database and create data to display.

[1466] 2. The terminal displays delivery details and prices to the user.

[1467] Input: Delivery option data

[1468] Output: Display delivery details

[1469] Specific operation: Display the dish name, price, delivery time, etc. and set up a selection button.

[1470] 3. The user selects the desired restaurant and food and makes payment.

[1471] Input: Selected restaurant and dish, payment information

[1472] Output: Payment completion status

[1473] Specific actions: Select a restaurant and food, enter payment information, and complete the payment.

[1474] 4. The server sends the order to the selected restaurant and processes the delivery.

[1475] Input: Order Information

[1476] Data transmission: Sending orders to restaurants

[1477] Output: Order receipt confirmation

[1478] Specific operation: Order information is sent to the selected restaurant's system via API and delivery procedures are carried out.

[1479] In the case of a restaurant

[1480] 1. The server generates a list of nearby restaurants where reservations can be made.

[1481] Input: User's food selection data

[1482] Data search: Search for restaurants that accept reservations

[1483] Output: Restaurant list

[1484] Specific operation: Retrieve a list of restaurants that can be booked from the database and create display data.

[1485] 2. The device will display a list of restaurants and available reservation times.

[1486] Input: Restaurant list data

[1487] Output: Display of available time slots

[1488] What it does: Displays restaurant name, location, and available reservation times.

[1489] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[1490] Input: Restaurant and time slot selection

[1491] Output: Reservation completion status

[1492] Specific actions: Select the desired restaurant and time slot and press the reservation button.

[1493] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[1494] Input: Reservation and prepayment information

[1495] Data transmission: Sending reservation information to restaurants

[1496] Output: Reservation confirmation and advance payment completion notification

[1497] Specific actions: Sends reservation information to the restaurant's system and processes payment if necessary.

[1498] Step 6: Providing additional functionality

[1499] The server provides additional functionality available to the user.

[1500] Input: User request data

[1501] Output: Additional feature availability status

[1502] Specific behavior: Displays a UI that provides users with options such as taking steps to combat food waste, treating others, donating to relief efforts, and sharing their dining experience.

[1503] (Application example 1)

[1504] 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."

[1505] Today's busy consumers require systems that allow them to easily select their preferred meals and flexibly choose how they are served. They also want to improve user convenience and simplify operation by utilizing voice control and smart devices. However, current systems do not fully meet these requirements, and systems utilizing wearable devices such as smart glasses are particularly limited.

[1506] 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.

[1507] In this invention, the server includes means for analyzing the mood and preferences input by the user, means for suggesting dishes based on the analysis results, means for allowing the user to select a food serving method, means for making recipes, delivery, or restaurant reservations according to the selected serving method, and means for voice operation through smart glasses. This allows the user to easily operate the server by voice, and enables the suggestion of a variety of meals that match the user's mood and the seamless selection of the serving method.

[1508] The "means for analyzing the moods and wishes input by the user" is a technology for analyzing the text or voice information input by the user and extracting the user's moods and wishes from the content.

[1509] "Means for suggesting dishes based on the analysis results" refers to technology that lists appropriate dishes based on the analyzed mood and preferences and suggests them to the user.

[1510] "Means for allowing the user to select the delivery method of the food" refers to the interface and technology that allows the user to select the delivery method of the proposed food (cooking, delivery, or restaurant reservation).

[1511] "Means for providing recipes, delivery, or making restaurant reservations according to the selected delivery method" refers to technology for displaying recipe information, arranging delivery, or making restaurant reservations according to the delivery method selected by the user.

[1512] "Voice-controlled means via smart glasses" refers to an interface and technology that accepts voice input using smart glasses and allows the user to control the device through voice.

[1513] This invention is a communication-based meal recommendation system that suggests dishes based on the user's mood and preferences, and handles everything from the serving method to payment. This system includes the following elements and means.

[1514] Basic system configuration

[1515] This system mainly consists of a device used by the user (such as smart glasses) and a server that performs back-end processing. The user inputs their mood and preferences by voice through an application installed on the device. The input information is analyzed by the server and suggestions are generated. The user then selects how the suggested food is to be served and makes payment.

[1516] Hardware and software used

[1517] Hardware: Smart glasses (e.g., Google Glass, Microsoft HoloLens)

[1518] Software: Speech recognition engine (e.g., Google Cloud Speech-to-Text), natural language processing engine (e.g., Google Cloud Natural Language API), server (database: MySQL, server-side script: Python / Django)

[1519] Program processing and data calculation

[1520] 1. The server creates a user database and stores information about each user, such as their profile, past orders, and preferences.

[1521] 2. The application is installed on the smart glasses and the user creates an account.

[1522] 3. The user puts on the smart glasses and launches the app.

[1523] 4. The smart glasses will issue a voice prompt saying "Please log in" and the user will enter their credentials by voice.

[1524] 5. The server checks the user's credentials against its database to verify successful authentication.

[1525] 6. The smart glasses will ask the user aloud, "What kind of meal would you like to have today?"

[1526] 7. The user inputs their mood or desires by voice (e.g., "I want to eat something spicy today").

[1527] 8. The smart glasses convert the voice input into text and send it to the server.

[1528] 9. The server uses a natural language processing (NLP) engine to analyze the user's input and extract keywords such as "spicy" and "cooking."

[1529] 10. The server generates a list of relevant dishes from the database.

[1530] 11. The smart glasses will present the generated recipe list to the user via voice.

[1531] 12. The user selects their favorite dish from the presented list by voice (e.g., "Mapo tofu").

[1532] 13. The smart glasses will then provide audible options for the selected dish (cook it yourself, have it delivered, or book a table at the restaurant).

[1533] 14. The user selects the desired delivery method.

[1534] 15. The process branches depending on the delivery method. In the case of delivery, it is as follows:

[1535] 1. The server will present delivery options from nearby participating restaurants.

[1536] 2. The smart glasses will provide delivery details and prices to the user via voice.

[1537] 3. The user selects the desired restaurant and food by voice and makes payment.

[1538] 4. The server sends the order to the selected restaurant and processes the delivery.

[1539] Specific examples

[1540] For example, if a user voice-inputs, "I want to eat something spicy today," the server will suggest options such as "spicy chicken, mapo tofu, and dandan noodles." Next, if the user selects "mapo tofu," the smart glasses will ask, "There is a delivery option. Would you like to order mapo tofu?" If the user answers "yes," the server will send the order to the delivery restaurant and process the payment.

[1541] Prompt Sentence Examples

[1542] Develop a smart glasses application that analyzes a user's voice input, such as "I want spicy food," and then suggests appropriate dishes and assists with delivery. Follow these steps:

[1543] 1. The speech recognition engine converts speech into text.

[1544] 2. Use a natural language processing engine to extract the user's mood and preferences.

[1545] 3. We will suggest relevant dishes from our database.

[1546] 4. The user's selected food is sent to the server and delivery is arranged.

[1547] Hardware used: Smart glasses (Google Glass, Microsoft HoloLens)

[1548] Software used: Google Cloud Speech-to-Text, Google Cloud Natural Language API, MySQL database, Python / Django

[1549] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1550] Step 1:

[1551] The server creates a user database and stores information such as each user's profile, past order history, and preferences. Specifically, a database (e.g., MySQL) is used, and a table is created for each user. The input data is the user's basic information and tendencies, and the data is formatted and saved based on this. The output is the stored user's detailed information.

[1552] Step 2:

[1553] The application is installed on the smart glasses and the user creates an account. The user enters basic information and follows prompts to create the account. The input is the information entered by the user directly via voice or text, and the output is that the account is created and authentication information is sent to the server.

[1554] Step 3:

[1555] The user puts on the smart glasses and launches an app. The user turns on the smart glasses, selects an application, and launches it. The input is the user's launch operation, and the output is the application being launched.

[1556] Step 4:

[1557] The smart glasses will issue a voice prompt saying "Please log in" and the user will enter their credentials by voice. A speech recognition engine (Google Cloud Speech-to-Text) is used to convert the speech to text. The input is the user's voice and the output is their credentials in text format.

[1558] Step 5:

[1559] The server checks the user's credentials against a database to see if the authentication was successful. It checks the credentials to see if they match. The input is the credentials in text form and the output is the authentication success or failure status. The specific behavior is a process that uses an SQL query to check against the information in the database.

[1560] Step 6:

[1561] The smart glasses ask the user aloud, "What would you like to eat today?" They issue a voice prompt and wait for the user's response. The input is the text of the question, and the output is the user's voice response.

[1562] Step 7:

[1563] The user inputs their mood or desires through voice (e.g., "I want to eat something spicy today"). The user's voice input is captured through the microphone of the smart glasses. The input is the desired content in voice format, and the output is the content converted into text by the smart glasses.

[1564] Step 8:

[1565] The smart glasses convert voice input into text and send it to the server. They use a voice recognition engine to convert voice into text and send the data to the server. The input is the user's voice data and the output is text data. The specific operation is the conversion of voice into text by the voice recognition engine.

[1566] Step 9:

[1567] The server uses a natural language processing (NLP) engine to analyze the user's input and extract keywords such as "spicy" and "cuisine." The input is user input in text format, and the output is the extracted keywords. Specifically, the text analysis is performed using the Google Cloud Natural Language API.

[1568] Step 10:

[1569] The server generates a list of relevant dishes from the database. It searches the database based on keywords and lists related dishes. The input is the parsed keywords and the output is a list of dishes. The specific operation is the process of executing an SQL query to obtain related dish information.

[1570] Step 11:

[1571] The smart glasses present the generated recipe list to the user by voice. The list is converted from text to speech and presented to the user. The input is the list of recipes, and the output is the audio presentation.

[1572] Step 12:

[1573] The user selects their favorite dish from a presented list of dishes by voice (e.g., "Mapo tofu"). The user's selection is obtained by voice input. The input is a voice-based dish selection, and the output is text data of the selected dish.

[1574] Step 13:

[1575] The smart glasses present options for the selected dish (cook it yourself, have it delivered, or book a restaurant reservation) by voice. The options are presented by voice and the system waits for the user to make a selection. The input is the selected dish, and the output is the options.

[1576] Step 14:

[1577] The user selects the desired delivery method. The user's selection is input by voice. The input is the voice selection of the delivery method, and the output is text data of the selected delivery method.

[1578] Step 15:

[1579] The process will branch depending on the delivery method. For delivery:

[1580] 1. The server presents delivery options from nearby partner restaurants. It references the database to retrieve the relevant delivery options. The input is the selected delivery method, and the output is a list of partner restaurants.

[1581] 2. The smart glasses present delivery details and prices to the user via voice. The list is converted into audio and presented to the user. The input is a list of partner facilities, and the output is an audio presentation.

[1582] 3. The user selects the desired restaurant and food by voice and then pays. The user's selection is captured by voice. The input is the spoken selection of delivery options, and the output is text data with the selected delivery details.

[1583] 4. The server sends the order to the selected restaurant and processes the delivery. The input is the selected delivery details, and the output is sending the order to the restaurant. The specific operation is to send the order via an API call.

[1584] In this way, the entire system works together to make dish suggestions based on the user's mood and preferences, and to select and execute the serving method based on those suggestions.

[1585] 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.

[1586] The present invention provides a communication-based meal recommendation system that analyzes not only a user's mood and desires but also their emotions to recommend appropriate dishes. A specific embodiment of this system will be described below.

[1587] Basic configuration

[1588] This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing. The user inputs their mood, preferences, and emotions through an application installed on their device. The input information is analyzed by the server, which generates suggestions using an emotion engine. The user then selects how the suggested food is to be served (cooked, delivered, or at a restaurant) and makes payment.

[1589] Program processing (natural language explanation)

[1590] 1. Initial Setup

[1591] The server creates a user database and stores information such as each user's profile, past orders, and preferences.

[1592] The application is installed on the device and the user creates an account.

[1593] 2. Log in and enter your mood and emotions

[1594] The user launches the app and logs in by entering their credentials on the login screen.

[1595] The server checks the user's authentication information against a database to verify successful authentication.

[1596] The device displays a text input field to the user and asks, "What would you like to eat today?" It also analyzes the user's facial expressions and voice to present an interface for recognizing emotions.

[1597] Users input their mood and wishes in text, and also input their emotions through facial expressions and voice (for example, they can input "I'm tired today, so I'd like a quick dinner," and facial analysis will recognize their tiredness).

[1598] 3. Mood and emotion analysis and suggestions

[1599] The terminal transmits the user's input and the recognized emotion data to the server.

[1600] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood, desires, and emotions.

[1601] For example, keywords such as "tired," "easy to make," and "dinner" are extracted along with "fatigue."

[1602] The server uses an emotion engine to analyze the user's emotion data and, based on the results, highlights and suggests specific dishes.

[1603] For example, we suggest "easy-to-make pasta" along with "refreshing drinks" to reduce "feelings of fatigue."

[1604] The server generates a list of relevant recipes and dishes from the database.

[1605] The terminal displays the generated recipe list to the user.

[1606] 4. Choose the food and how it will be served

[1607] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[1608] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[1609] The user selects the desired delivery method.

[1610] 5. Details and Payment

[1611] Depending on the method of provision, the process branches and the detailed operation is performed:

[1612] If you make it yourself:

[1613] 1. The server generates a recipe and a list of ingredients for the selected dish.

[1614] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[1615] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[1616] 4. The server sends the ingredients list to the online store and arranges for delivery.

[1617] For delivery:

[1618] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[1619] 2. The terminal displays delivery details and prices to the user.

[1620] 3. The user selects the desired restaurant and food and makes payment.

[1621] 4. The server sends the order to the selected restaurant and processes the delivery.

[1622] For restaurants:

[1623] 1. The server generates a list of nearby restaurants where reservations can be made.

[1624] 2. The device will display a list of restaurants and available reservation times.

[1625] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[1626] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[1627] 6. Provision of Additional Features

[1628] The server provides additional functionality available to the user.

[1629] Food waste prevention measures: Provides the option to share excess food with other users, allowing users to choose what food to share and how to share it.

[1630] Treating others: Provides a money transfer function, allowing users to specify the person to treat and the amount, and then transfer the money.

[1631] Donate to relief efforts: Provide users with the option to donate to relief efforts in areas where food security is difficult, and allow them to specify the donation amount and make a payment.

[1632] Sharing dining experiences: Provides an interface for sharing dining experiences, allowing users to upload photos and impressions of their meals and share them on social media.

[1633] Specific examples

[1634] 1. The user enters, "It's hot today, so I want to eat something cool," and the emotion engine recognizes "joy" and "excitement."

[1635] 2. The server analyzes the input and emotional data and suggests options such as Zaru Soba noodles and Hiyashi Chuka noodles.

[1636] 3. The device displays suggested dishes, and the user selects "zaru soba."

[1637] 4. The device displays options for delivery method, and the user selects "Make it myself."

[1638] 5. The server will display the recipe for Zaru Soba noodles, the necessary ingredients, and offer purchasing options from a nearby supermarket and online stores.

[1639] 6. The user selects an online store, orders the materials, and makes payment.

[1640] 7. The server sends the order to the online store and the materials are delivered.

[1641] In this way, the present invention aims to significantly improve user convenience by seamlessly integrating meal suggestions and delivery methods tailored to the user's mood. It also addresses food waste reduction and social contribution, making it a system that can meet a wide range of needs. Furthermore, by incorporating an emotion engine, it becomes possible to make more personalized suggestions that are in tune with the user's emotions, thereby improving satisfaction.

[1642] The processing flow will be explained below.

[1643] Step 1:

[1644] The user launches the app and logs in by entering their credentials on the login screen.

[1645] The server checks the user's authentication information against a database to verify successful authentication.

[1646] Step 2:

[1647] The device displays a text input field to the user and asks, "What would you like to eat today?" It also analyzes the user's facial expressions and voice to present an interface for recognizing emotions.

[1648] Users input their mood and wishes in text, as well as their emotions through facial expressions and voice (for example, they can input "I'm tired today, so I'd like a quick dinner," and facial analysis will recognize their tiredness).

[1649] Step 3:

[1650] The terminal transmits the user's input and the recognized emotion data to the server.

[1651] Step 4:

[1652] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood, desires, and emotions.

[1653] For example, keywords such as "tired," "easy to make," "dinner," and "fatigue" are extracted.

[1654] Step 5:

[1655] The server uses an emotion engine to analyze the user's emotion data and, based on the results, highlights and suggests specific dishes.

[1656] For example, to reduce fatigue, we suggest combining "easy-to-make pasta" with "a refreshing drink."

[1657] Step 6:

[1658] The server generates a list of relevant recipes and dishes from the database.

[1659] The terminal displays the generated recipe list to the user.

[1660] Step 7:

[1661] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[1662] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[1663] Step 8:

[1664] The user selects the desired delivery method.

[1665] Step 9:

[1666] The process will be split depending on the delivery method:

[1667] If you make it yourself:

[1668] 1. The server generates a recipe and a list of ingredients for the selected dish.

[1669] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[1670] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[1671] 4. The server sends the ingredients list to the online store and arranges for delivery.

[1672] For delivery:

[1673] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[1674] 2. The terminal displays delivery details and prices to the user.

[1675] 3. The user selects the desired restaurant and food and makes payment.

[1676] 4. The server sends the order to the selected restaurant and processes the delivery.

[1677] For restaurants:

[1678] 1. The server generates a list of nearby restaurants where reservations can be made.

[1679] 2. The device will display a list of restaurants and available reservation times.

[1680] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[1681] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[1682] Step 10:

[1683] The server provides additional functionality available to the user.

[1684] Users can select and use additional features as needed (such as addressing food waste, treating others, donating to relief efforts, and sharing dining experiences).

[1685] Example 2

[1686] 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."

[1687] Conventional meal recommendation systems were able to make suggestions based on the user's mood and preferences, but they were unable to analyze the user's emotions and make more personalized suggestions based on those emotions. Furthermore, they lacked a consistent method for providing the suggested food and detailed arrangements, which meant they were unable to fully ensure user convenience. Furthermore, there were only limited systems that could address social issues such as food waste and social contribution.

[1688] 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.

[1689] In this invention, the server includes: a means for analyzing the mood, preferences, and emotions input by the user; a means for suggesting dishes based on the analysis results; a means for allowing the user to select a food delivery method; a means for providing recipes, delivery, or restaurant reservations according to the selected delivery method; a means for collecting the user's specific needs using prompt text; and a means for generating optimal dishes using a generative AI model. This enables personalized food recommendations based on the user's mood and emotions, and realizes consistent management of delivery methods and arrangements. It also addresses social issues such as food waste and social contribution.

[1690] "Mood" refers to the state of mind or feeling a user is experiencing at a particular time.

[1691] "Wishes" refer to the requirements or desires that a user has in a particular situation or condition.

[1692] "Emotions" refer to the psychological state or feelings expressed through the user's facial expressions, tone of voice, etc.

[1693] "Means of analysis" refers to techniques and methods for extracting and understanding moods, desires, and emotions based on information input by the user.

[1694] "Means for suggesting" refers to the technology or method for presenting appropriate food and beverage options to the user based on the analysis results.

[1695] "Means of Choice" refers to interfaces and technologies that allow users to select their preferred delivery method from multiple delivery methods.

[1696] "Delivery method" refers to the means by which food is delivered to the user, and includes options such as cooking it yourself, delivery, or restaurant reservations.

[1697] A "recipe" refers to a list of specific steps and ingredients for making a particular dish.

[1698] "Delivery" refers to a service that delivers food from affiliated restaurants to a location specified by the user.

[1699] "Restaurant reservation" refers to the process of a user making a reservation in advance to eat at a particular restaurant at a particular time.

[1700] A "prompt" is a piece of text or question that guides the user to input a specific need or request.

[1701] A "generative AI model" refers to an algorithm or system that uses large amounts of data to generate food and beverage options that best suit a user's needs.

[1702] The present invention is a communication-based meal recommendation system that analyzes a user's mood, preferences, and even emotions to recommend appropriate dishes. This system is composed of a device used by the user (e.g., a smartphone or tablet) and a server that performs back-end processing. Specific embodiments of this system are described below.

[1703] Basic configuration

[1704] The server uses a database management system (e.g., PostgreSQL) to create a user database and store information such as each user's profile, past order history, and preferences. The server also analyzes user input data using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API) and a sentiment analysis engine. It then uses a generative AI model to suggest appropriate dishes based on the analysis results.

[1705] The device provides the user with an interface through an application that runs on iOS or Android. When the user logs in, they enter their authentication information, and if successful, an input interface for their mood, desires, and emotions is displayed. The device then sends the entered data to the server and displays the server's suggestions.

[1706] The user inputs their mood, wishes, and emotions through the device. For example, they might input a prompt such as, "I'm tired today, so I'd like a quick dinner." At this time, the user's facial expressions and voice are also analyzed and captured as emotional data.

[1707] Specific examples

[1708] 1. Initial Setup: User installs the app and creates an account. The server stores the information in a user database.

[1709] 2. Login: The user logs in and their credentials are verified by the server.

[1710] 3. Information input: The user inputs, "I'm tired today, so I'd like a quick dinner," and facial expression analysis recognizes the "feeling of fatigue."

[1711] 4. Data analysis: The device sends the input data to the server, which then analyzes the data using an NLP engine and a sentiment analysis engine. As a result of the analysis, keywords such as "tired," "easy to make," and "dinner" as well as "feeling tired" are extracted.

[1712] 5. Recommendation Generation: The server uses the generative AI model to suggest a suitable dish (e.g., "Easy-to-make pasta") and a refreshing drink.

[1713] 6. Display: The device displays a list of suggested dishes, and the user selects "Easy Pasta."

[1714] 7. Select delivery method: The device presents delivery options (make it yourself, delivery, restaurant) and the user selects "make it yourself."

[1715] 8. Details and payment: The server generates the recipe and ingredients list for the selected dish, which are then displayed on the device. The user selects an online store to order the ingredients and completes the payment. The server then sends the ingredients list to the online store and arranges for delivery.

[1716] Examples of prompt statements might include, "It's hot today, so I want to eat something cool," or "I'm stressed, so I'd like something to relax me with."

[1717] This allows the present invention to seamlessly integrate meal suggestions and delivery methods tailored to the user's mood and emotions, significantly improving user convenience. It also addresses food waste reduction and social contribution, making it possible to meet a wide range of needs.

[1718] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1719] Step 1: Initial Setup

[1720] The server creates a user database and stores information such as each user's profile, past order history, preferences, etc. Specifically, using a database management system (e.g., PostgreSQL), a user table is created and a unique ID, name, email address, and past order history are stored.

[1721] The device installs an application that runs on iOS or Android and provides an interface for users to create an account. When the user enters their account information and presses the "Register" button, this information is sent to the server and added to the database.

[1722] Input: Account creation information (name, email address, password)

[1723] Output: New entry added to user database

[1724] Step 2: Log in and enter your mood and emotions

[1725] The user launches the app and enters their authentication information on the login screen.

[1726] The server checks the entered email address and password against its database, and if they match, allows the user to log in. It then generates and returns a JSON token.

[1727] After successfully logging in, the device displays a screen for inputting the user's mood, desires, and emotions. The screen includes a text input field, a question such as "What would you like to eat today?", and interfaces for facial expression analysis and voice input.

[1728] Users input their mood, wishes, and emotions through text input and facial and voice analysis.

[1729] Input: Login information (email address, password), mood / emotion information (text, facial expression, voice)

[1730] Output: Request including mood and emotion information (JSON format)

[1731] Step 3: Mood and emotion analysis and suggestions

[1732] The device sends the user's input and emotion data in JSON format to the server.

[1733] The server uses a natural language processing (NLP) engine to analyze the input and extract mood, desires, and emotions. For example, it uses the Google Cloud Natural Language API to extract keywords such as "tired," "easy to make," and "dinner" from the input text. It also uses an emotion engine to recognize "fatigue" from facial expressions and voice data.

[1734] Based on the analysis results, a generative AI model is used to generate dishes suitable for the user. For example, the generative AI model suggests "easy-to-make pasta" or "refreshing drinks."

[1735] The server returns these suggestions to the device in JSON format.

[1736] Input: Mood and emotion information (JSON format)

[1737] Output: Analysis results and suggestions (JSON format)

[1738] Step 4: Choose your food and serving method

[1739] The terminal displays the suggestions received from the server to the user, who then selects the desired dish from the list.

[1740] The device will then present the selected dish with delivery options (make it yourself, delivery, restaurant).

[1741] The user selects the desired delivery method.

[1742] Input: Analysis results and suggestions (JSON format)

[1743] Output: User's choice (dish and serving method)

[1744] Step 5: Details and payment (if you're making it yourself)

[1745] The server generates a recipe and a list of required ingredients for the selected dish and sends them to the terminal in JSON format.

[1746] The device displays the recipe and ingredients list, and offers purchasing options at nearby retailers or online stores.

[1747] The user selects a purchasing option, chooses the materials needed, enters payment information, and clicks the "Pay" button.

[1748] The server uses the online store's API to send the ingredients list and shipping information, and the order is placed.

[1749] Input: User's choice (food and serving method)

[1750] Output: Recipe, ingredient list, purchasing options, order confirmation information

[1751] Step 6: Providing additional functionality

[1752] The server sends a JSON-formatted menu to the device to provide additional features available to the user (food waste reduction measures, treating others, donating to relief efforts, and sharing dining experiences).

[1753] The terminal displays an additional function menu, allowing the user to select each function.

[1754] The server performs the corresponding process according to the additional function selected by the user. For example, in the case of a food waste countermeasure, an interface for sharing ingredients with other users is displayed and the selected ingredient information is sent.

[1755] Input: User's choice (additional function)

[1756] Output: Interface and processing results for additional functions

[1757] (Application example 2)

[1758] 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."

[1759] Conventional meal recommendation systems only analyze the user's mood and preferences, and do not consider the user's emotions, making it impossible to meet the user's true needs. Furthermore, the method of serving the suggested dishes lacks flexibility, making the system insufficient to satisfy user convenience. Therefore, there is a need for a system that can provide more personalized and accurate meal recommendations that also consider the user's emotions, and that can flexibly accommodate different serving methods.

[1760] 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.

[1761] In this invention, the server includes means for analyzing the user's mood, desires, and emotions, means for suggesting dishes based on the analysis results, means for allowing the user to select a food serving method, means for providing a recipe, delivery, or dining reservation in accordance with the selected serving method, and means for highlighting and suggesting dishes based on the user's emotions using an emotion analysis engine. This enables personalized meal suggestions that take into account not only the user's mood and desires but also their emotions, providing a system that allows users to flexibly select food serving methods that meet their needs.

[1762] "Mood" refers to the psychological state that a user temporarily feels.

[1763] "Wishes" refer to what a user wants for a particular condition or situation.

[1764] "Emotions" refer to the user's internal state or sensation, such as emotional reactions like joy or sadness.

[1765] "Analysis" refers to processing data based on input information and interpreting its meaning and patterns.

[1766] "Suggestion" refers to presenting appropriate options to the user based on the analysis results.

[1767] "Providing" refers to actually supplying the suggested dish to the user.

[1768] A "recipe" refers to the steps or list of ingredients for making a particular dish.

[1769] "Delivery" refers to a service that delivers the food selected by the user to a specified location.

[1770] "Dining reservation" refers to making a reservation at a restaurant or eatery of a user's choice.

[1771] An "emotion analysis engine" refers to a software or hardware mechanism that analyzes a user's emotions as data and provides information based on the results.

[1772] "User" refers to an individual who uses this system.

[1773] "Server" refers to a central computer system that receives, analyzes, and processes input data from users.

[1774] A "system" refers to a combination of multiple means or devices that work together to perform a specific function.

[1775] This system analyzes a user's mood, desires, and emotions, suggests appropriate dishes based on the analysis, and then provides recipes, deliveries, or dining reservations according to the selected delivery method. This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing.

[1776] System configuration

[1777] 1. User Device

[1778] Users access the system through applications installed on their terminals.

[1779] The device includes a text input field, a voice input interface, and a camera (for facial expression analysis).

[1780] 2. Server

[1781] The server receives the user's input data and performs analysis.

[1782] Analysis is performed using a natural language processing (NLP) engine (e.g., TextBlob) and an emotion analysis engine (e.g., EmotionRecognizer).

[1783] The server will suggest appropriate dishes based on the analysis results.

[1784] 3. Database

[1785] Stores data such as user profiles, past orders, and preferences.

[1786] Recipe information, information on affiliated restaurants, and delivery service information are also stored.

[1787] Example of operation procedure

[1788] User Input

[1789] The user launches the app and inputs their mood, desires, and emotions. For example, they might input, "It's hot today, so I want to eat something cool," and provide emotional data using voice or a camera.

[1790] Server Analysis

[1791] The server uses an NLP engine to analyze the text data and extract keywords. The emotion analysis engine analyzes the user's emotions from voice data and facial expression data. For example, keywords such as "cool" and "want to eat" are extracted, and the emotion analyzed is "joy."

[1792] Cooking suggestions

[1793] Based on the analysis results, the server will suggest dishes such as "zaru soba" (cold soba noodles) or "hiyashi chuka" (cold Chinese noodles).

[1794] Select delivery method

[1795] The user selects one of the proposed dishes and chooses how it will be served. For example, the user selects "hiyashi chuka" (cold Chinese noodles) and chooses "delivery" as the delivery method.

[1796] Delivery arrangements

[1797] The server orders the selected food from affiliated restaurants and arranges for it to be delivered to the user.

[1798] Specific examples

[1799] If a user inputs "It's hot today, so I want to eat something cool," and the server recognizes the emotion of "happiness," it will suggest "zaru soba" (cold noodles) or "hiyashi chuka" (cold Chinese noodles). If the user selects "hiyashi chuka" and chooses "delivery" as the delivery method, the hiyashi chuka will be delivered from the most suitable restaurant.

[1800] Prompt Sentence Examples

[1801] It's hot today, so I want to eat something cool. I'm happy and excited. Please suggest the best dish.

[1802] In this way, the system of the present invention improves user convenience and satisfaction by providing personalized recipe suggestions that take into account the user's mood, desires, and emotions, and by flexibly responding to a variety of delivery methods. Furthermore, by combining it with an emotion analysis engine, it becomes possible to make suggestions that are in line with the user's inner needs.

[1803] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1804] Step 1:

[1805] Initial Setup and User Registration

[1806] A user installs the application and creates an account the first time they launch it.

[1807] Specifically, the user enters registration information using an email address or social media account, and the server receives this information and stores it in a database.

[1808] The input is the user's profile information (name, email address, etc.) and the output is the storage of the successful registration information.

[1809] Step 2:

[1810] Login and User Authentication

[1811] The user launches the application and enters their credentials on the login screen.

[1812] The terminal sends this to the server, which then checks it against a database to confirm whether the authentication was successful.

[1813] The input is the user's authentication information (username, password, etc.), and the output is the authentication result (success or failure).

[1814] Step 3:

[1815] Input of moods, wishes and emotions

[1816] The user enters their feelings and desires in a text input field, and provides emotional data via voice input or a camera. The device then transmits this data to a server.

[1817] Specifically, the user inputs text such as "It's hot today, so I want to eat something cool," and the camera captures their voice and facial expression.

[1818] The input is the user's text, voice and facial expression data, and the output is the data sent to the server.

[1819] Step 4:

[1820] Mood, hope and emotion analysis

[1821] The server analyzes the received data using a natural language processing (NLP) engine and a sentiment analysis engine.

[1822] The NLP engine (TextBlob) extracts keywords from text, and the emotion analysis engine (EmotionRecognizer) analyzes emotions from voice and facial expressions.

[1823] The input is the user's text and emotion data, and the output is keywords and emotion information as the analysis results.

[1824] Step 5:

[1825] Cooking suggestions

[1826] Based on the analysis results, the server performs a database search to suggest appropriate dishes.

[1827] As a specific action, based on the keyword "cool" and the emotion "joy," the user selects cold soba noodles or chilled Chinese noodles.

[1828] The input is the analysis results (keywords and sentiment information) from step 4 above, and the output is a list of suggested dishes.

[1829] Step 6:

[1830] Select delivery method

[1831] The user selects from the suggested dishes and chooses how to serve them (make it yourself, have it delivered, or reserve a place to eat), and the device sends this information to the server.

[1832] As a specific operation, the user selects "hiyashi chuka" and selects "delivery."

[1833] The input is the proposed dish and the selected serving method, and the output is the selection sent to the server.

[1834] Step 7:

[1835] Order processing and fulfillment

[1836] The server makes the necessary arrangements depending on the delivery method selected. In the case of delivery, the server sends the order to the partner restaurant and arranges for delivery.

[1837] Specifically, the selected "hiyashi chuka" is ordered from an affiliated store and delivery service is arranged.

[1838] The input is the selected dish and serving method, and the output is order information and delivery arrangement information for the restaurant.

[1839] Step 8:

[1840] Order status notification and tracking

[1841] The server sends order status updates to the device, allowing users to check the status in real time through the app.

[1842] Specifically, the app will display real-time updates informing you of delivery status.

[1843] The input is delivery status updates and the output is real-time order status notifications.

[1844] 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.

[1845] 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.

[1846] 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.

[1847] [Fourth embodiment]

[1848] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1849] 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.

[1850] 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).

[1851] 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.

[1852] 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.

[1853] 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).

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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."

[1861] The present invention provides a communication-based meal recommendation system that proposes dishes that match the user's mood and preferences, and handles the delivery method and payment in an integrated manner. Specific embodiments of this system will be described below.

[1862] Basic configuration

[1863] This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing. The user inputs their mood and preferences through an application installed on their device. The input information is analyzed by the server, and suggestions are generated. The user then selects how the suggested food is to be served (cooked, delivered, or at a restaurant) and makes payment.

[1864] Program processing (natural language explanation)

[1865] 1. Initial Setup

[1866] The server creates a user database and stores information such as each user's profile, past orders, and preferences.

[1867] The application is installed on the device and the user creates an account.

[1868] 2. Log in and enter your mood

[1869] The user launches the app and logs in by entering their credentials on the login screen.

[1870] The server checks the user's authentication information against a database to verify successful authentication.

[1871] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[1872] Users input their mood or desires in sentences (e.g., "I'd like a quick dinner today").

[1873] 3. Mood analysis and suggestions

[1874] The terminal transmits the user's input to the server.

[1875] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood and wishes.

[1876] For example, extract keywords such as "easy to make" and "dinner."

[1877] The server generates a list of relevant recipes and dishes from the database.

[1878] The terminal displays the generated recipe list to the user.

[1879] 4. Choose the food and how it will be served

[1880] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[1881] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[1882] The user selects the desired delivery method.

[1883] 5. Details and Payment

[1884] Depending on the method of provision, the process branches and the detailed operation is performed:

[1885] If you make it yourself:

[1886] 1. The server generates a recipe and a list of ingredients for the selected dish.

[1887] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[1888] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[1889] 4. The server sends the ingredients list to the online store and arranges for delivery.

[1890] For delivery:

[1891] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[1892] 2. The terminal displays delivery details and prices to the user.

[1893] 3. The user selects the desired restaurant and food and makes payment.

[1894] 4. The server sends the order to the selected restaurant and processes the delivery.

[1895] For restaurants:

[1896] 1. The server generates a list of nearby restaurants where reservations can be made.

[1897] 2. The device will display a list of restaurants and available reservation times.

[1898] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[1899] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[1900] 6. Provision of Additional Features

[1901] The server provides additional functionality available to the user.

[1902] Food waste prevention measures: Provides the option to share excess food with other users, allowing users to choose what food to share and how to share it.

[1903] Treating others: Provides a money transfer function, allowing users to specify the person to treat and the amount, and then transfer the money.

[1904] Donate to relief efforts: Provide users with the option to donate to relief efforts in areas where food security is difficult, and allow them to specify the donation amount and make a payment.

[1905] Sharing dining experiences: Provides an interface for sharing dining experiences, allowing users to upload photos and impressions of their meals and share them on social media.

[1906] Specific examples

[1907] 1. The user types, "It's hot today, so I want to eat something cool."

[1908] 2. The server analyzes the input and suggests options such as Zaru Soba and Hiyashi Chuka.

[1909] 3. The device displays suggested dishes, and the user selects "zaru soba."

[1910] 4. The device displays options for delivery method, and the user selects "Make it myself."

[1911] 5. The server will display the recipe for Zaru Soba noodles, the necessary ingredients, and offer purchasing options from a nearby supermarket and online stores.

[1912] 6. The user selects an online store, orders the materials, and makes payment.

[1913] 7. The server sends the order to the online store and the materials are delivered.

[1914] In this way, the present invention aims to significantly improve user convenience by seamlessly integrating meal suggestions and delivery methods that match the user's mood.It is also a system that can meet a wide range of needs by addressing food waste and contributing to society.

[1915] The processing flow will be explained below.

[1916] Step 1:

[1917] The user launches the app and logs in by entering their credentials on the login screen.

[1918] The server checks the user's authentication information against a database to verify successful authentication.

[1919] Step 2:

[1920] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[1921] Users input their mood and desires in sentences (e.g., "I'm tired today, so I'd like a quick dinner").

[1922] Step 3:

[1923] The terminal transmits the user's input to the server.

[1924] Step 4:

[1925] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood and wishes.

[1926] For example, extract keywords such as "tired," "easy to make," and "dinner."

[1927] Step 5:

[1928] The server generates a list of relevant recipes and dishes from the database.

[1929] The terminal displays the generated recipe list to the user.

[1930] Step 6:

[1931] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[1932] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[1933] Step 7:

[1934] The user selects the desired delivery method.

[1935] Step 8:

[1936] The process will be split depending on the delivery method:

[1937] If you make it yourself:

[1938] 1. The server generates a recipe and a list of ingredients for the selected dish.

[1939] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[1940] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[1941] 4. The server sends the ingredients list to the online store and arranges for delivery.

[1942] For delivery:

[1943] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[1944] 2. The terminal displays delivery details and prices to the user.

[1945] 3. The user selects the desired restaurant and food and makes payment.

[1946] 4. The server sends the order to the selected restaurant and processes the delivery.

[1947] For restaurants:

[1948] 1. The server generates a list of nearby restaurants where reservations can be made.

[1949] 2. The device will display a list of restaurants and available reservation times.

[1950] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[1951] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[1952] Step 9:

[1953] The server provides additional functionality available to the user.

[1954] Users can select and use additional features as needed (such as addressing food waste, treating others, donating to relief efforts, and sharing dining experiences).

[1955] Example 1

[1956] 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."

[1957] Conventional food recommendation systems have difficulty in providing specific food recommendations based on a user's mood and preferences. Furthermore, they lack integration of specific delivery methods and payment methods for the recommended dishes, resulting in low user convenience. Furthermore, there is no way for users to share their dining experiences with other users, limiting opportunities to expand their dining enjoyment.

[1958] 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.

[1959] In this invention, the server includes means for analyzing the mood and preferences input by the user, means for suggesting dishes based on the analysis results, means for allowing the user to select a food delivery method, means for providing a recipe, delivery, or reservation based on the selected delivery method, means for natural language processing of the mood and preferences using a generative AI model, means for displaying detailed information about the dishes, and means for making payments. This allows for specific dish suggestions based on the user's mood and preferences, and further improves user convenience by integrating food delivery methods and payment methods. It also allows users to share their dining experiences with other users.

[1960] A "user" is a person who uses the system to receive cooking suggestions and serving methods.

[1961] "Mood and desire" refers to the type and style of food desired by the user at that time, as well as the user's desires and feelings regarding the meal.

[1962] "Means for analyzing" refers to a function or system that processes the moods and wishes input by the user and deciphers and understands their content.

[1963] The "means for suggesting dishes" refers to a function or system for suggesting appropriate dishes to the user based on the analyzed mood and preferences.

[1964] "Serving method" refers to the options and process of how the proposed dish is to be served to the user.

[1965] A "recipe" is a list of steps and ingredients for making a particular dish.

[1966] "Delivery" is a service that delivers food from affiliated restaurants to users.

[1967] The "means for making a reservation" refers to a function or system for confirming a reservation for a date and time of visit at a restaurant selected by the user.

[1968] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze a user's mood and wishes.

[1969] "Natural language processing" is a technology that mechanically processes text entered by a user and understands its meaning.

[1970] "Means for displaying detailed information" refers to a function or system for visually presenting the user with details of the proposed dish and information about how it will be served.

[1971] "Means for making payment" refers to the functions and systems for carrying out payment processing, including online payment, for the food and services selected by the user.

[1972] "Mood and desired keywords" are important words and phrases related to cuisine that are extracted from the text entered by the user.

[1973] "Means for sharing dining experiences" refers to functions and systems that allow users to share photos and impressions of meals with other users and spread them through social media, etc.

[1974] The present invention is a system that proposes dishes that match the user's mood and preferences, and handles everything from the delivery method to payment. The system consists of a terminal used by the user and a server that performs back-end processing. Specific embodiments of the system are described below.

[1975] 1. Basic system configuration

[1976] Users input their mood and preferences through an application installed on their device. The input information is analyzed by the server, and appropriate dish suggestions are generated. The user then selects how the suggested dishes are to be served (cooked, delivered, or at a restaurant) and makes payment.

[1977] 2. Hardware and Software Configuration

[1978] The system uses the following hardware and software:

[1979] Devices: smartphones, tablets, etc.

[1980] Server: A typical backend server

[1981] Database: Relational database such as MySQL or PostgreSQL

[1982] Generative AI Model: Natural Language Processing (NLP) Engine

[1983] 3. Explanation of data processing and calculation

[1984] Based on the user's input of mood and preferences, the device sends the information to the server. The server uses a generative AI model to analyze the input information with a natural language processing engine to extract the mood and preferences. The server then searches for relevant recipes and cooking information from its database and generates suggestions. The generated suggestions are sent to the device and displayed to the user. The user selects the delivery method and confirms the corresponding details and payment options.

[1985] 4. Specific Examples

[1986] The user inputs, "I'm tired today, so I want to eat something easy to make." This input is sent from the device to the server. The server uses a generative AI model to extract keywords such as "easy to make" and "tired," and searches a database for related recipes. For example, suggestions such as "easy curry" or "microwave-safe pasta" are generated. These suggestions are displayed on the device, and the user can select the desired dish.

[1987] Prompt Sentence Examples

[1988] "I'm tired today, so I want to eat something easy to make."

[1989] "It's hot, so I'd like some cool food."

[1990] "I don't have much time, so please tell me something I can make in 5 minutes or less."

[1991] This allows the present invention to seamlessly integrate meal suggestions and delivery methods tailored to the user's mood, significantly improving user convenience. Furthermore, by performing advanced analysis using a generative AI model, it is possible to propose optimal dishes for the user.

[1992] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1993] Step 1: Initial Setup

[1994] The server creates a user database, which contains information about each user, such as their profile, past orders, and preferences.

[1995] Input: User profile information

[1996] Data processing: storing information in a database

[1997] Output: Database update

[1998] Specific operation: Creates a table using a database management system such as MySQL or PostgreSQL to store user information.

[1999] The application is installed on the device and the user creates an account.

[2000] Input: Name, Email Address, Password

[2001] Data processing: Hashing of input information

[2002] Output: Account information registered in the database

[2003] Specific operation: The user fills in the required information in the form and presses the submit button to create an account.

[2004] Step 2: Log in and enter your mood

[2005] The user launches the app and enters their credentials on the login screen.

[2006] Input: Email address, password

[2007] Data calculation: Matching email addresses with hashed passwords

[2008] Output: Authentication success / failure status

[2009] Specific operation: The user enters authentication information and presses the submit button. The server checks the information against the database and returns the authentication result.

[2010] The terminal presents the user with a text entry field and asks, "What would you like to eat today?"

[2011] Input: User's dietary preferences

[2012] Output: The input text data

[2013] Specific behavior: A text box and a submit button are displayed and the user can enter input.

[2014] Step 3: Mood analysis and suggestions

[2015] The terminal transmits the user's input to the server.

[2016] Input: User-entered text

[2017] Data processing: Convert to JSON format and send to server

[2018] Output: Status of success / failure of data transmission to the server

[2019] Specific operation: Converts the user's input into JSON format and sends it to the server via an HTTP request.

[2020] The server uses a generative AI model to analyze the user's input and extract their mood and wishes.

[2021] Input: User's text data

[2022] Data Calculation: Analysis using a Natural Language Processing Engine

[2023] Output: Extracted keywords and phrases (e.g., "easy to make," "tired")

[2024] Specific operation: Calls a generative AI model to extract important keywords from the input text.

[2025] The server generates a list of relevant recipes and dishes from the database.

[2026] Input: Analysis results (keywords)

[2027] Data search: Search for relevant recipes using SQL queries

[2028] Output: List of dishes (recipe name, details, image link, etc.)

[2029] Specific operation: A search query is executed against the database to retrieve the relevant recipe information.

[2030] The terminal displays the generated recipe list to the user.

[2031] Input: Recipe list data from the server

[2032] Output: Display a list of dishes

[2033] Specific operation: Displays a list of photos, names, and brief descriptions of dishes on the user's device.

[2034] Step 4: Choose your food and serving method

[2035] The user selects a dish they like from the displayed list of dishes.

[2036] Input: Choose a dish (e.g., "Easy pasta")

[2037] Output: Selected dish data

[2038] Specific operation: The user selects the dish of their choice and presses the confirmation button.

[2039] The device will then present delivery options for the selected dish (make it yourself, delivery, restaurant).

[2040] Input: User's food selection data

[2041] Output: Display of options for delivery method

[2042] Specific operation: Display the delivery method (make it yourself, delivery, restaurant) to the user in the form of buttons.

[2043] The user selects the desired delivery method.

[2044] Input:Select delivery method

[2045] Output: Selected delivery method data

[2046] Specific operation: The user presses the desired delivery method button.

[2047] Step 5: Details and payment

[2048] Processing branches depending on the provision method.

[2049] If you make it yourself

[2050] 1. The server generates a recipe and a list of ingredients for the selected dish.

[2051] Input: User's food selection data

[2052] Data Search: Search for recipes and ingredient lists

[2053] Output: Recipe and ingredients list

[2054] What it does: Searches the database for recipes and ingredient lists and retrieves the results.

[2055] 2. The device displays the recipe and ingredients list, and offers purchasing options at local stores or online.

[2056] Input: Recipe and ingredient list data

[2057] Output: Show purchasing options

[2058] What it does: Displays recipes, ingredient lists, and purchasing options (retail stores, online stores) on the screen.

[2059] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[2060] Input: Purchase options and payment information

[2061] Output: Payment completion status

[2062] Specific actions: Select a purchase option, enter payment information such as credit card information, and complete the payment.

[2063] 4. The server sends the ingredients list to the online store and arranges for delivery.

[2064] Input: Material list and shipping information

[2065] Data submission: API requests to online stores

[2066] Output: Order completion notification

[2067] What it does: Sends the ingredients list via the online store's API and initiates the shipping process.

[2068] For delivery

[2069] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[2070] Input: User's food selection data

[2071] Data Search: Search for partner restaurants and delivery options

[2072] Output: Delivery options list

[2073] Specific operation: Retrieve delivery information for the relevant restaurant from the database and create data to display.

[2074] 2. The terminal displays delivery details and prices to the user.

[2075] Input: Delivery option data

[2076] Output: Display delivery details

[2077] Specific operation: Display the dish name, price, delivery time, etc. and set up a selection button.

[2078] 3. The user selects the desired restaurant and food and makes payment.

[2079] Input: Selected restaurant and dish, payment information

[2080] Output: Payment completion status

[2081] Specific actions: Select a restaurant and food, enter payment information, and complete the payment.

[2082] 4. The server sends the order to the selected restaurant and processes the delivery.

[2083] Input: Order Information

[2084] Data transmission: Sending orders to restaurants

[2085] Output: Order receipt confirmation

[2086] Specific operation: Order information is sent to the selected restaurant's system via API and delivery procedures are carried out.

[2087] In the case of a restaurant

[2088] 1. The server generates a list of nearby restaurants where reservations can be made.

[2089] Input: User's food selection data

[2090] Data search: Search for restaurants that accept reservations

[2091] Output: Restaurant list

[2092] Specific operation: Retrieve a list of restaurants that can be booked from the database and create display data.

[2093] 2. The device will display a list of restaurants and available reservation times.

[2094] Input: Restaurant list data

[2095] Output: Display of available time slots

[2096] What it does: Displays restaurant name, location, and available reservation times.

[2097] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[2098] Input: Restaurant and time slot selection

[2099] Output: Reservation completion status

[2100] Specific actions: Select the desired restaurant and time slot and press the reservation button.

[2101] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[2102] Input: Reservation and prepayment information

[2103] Data transmission: Sending reservation information to restaurants

[2104] Output: Reservation confirmation and advance payment completion notification

[2105] Specific actions: Sends reservation information to the restaurant's system and processes payment if necessary.

[2106] Step 6: Providing additional functionality

[2107] The server provides additional functionality available to the user.

[2108] Input: User request data

[2109] Output: Additional feature availability status

[2110] Specific behavior: Displays a UI that provides users with options such as taking steps to combat food waste, treating others, donating to relief efforts, and sharing their dining experience.

[2111] (Application example 1)

[2112] 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."

[2113] Today's busy consumers require systems that allow them to easily select their preferred meals and flexibly choose how they are served. They also want to improve user convenience and simplify operation by utilizing voice control and smart devices. However, current systems do not fully meet these requirements, and systems utilizing wearable devices such as smart glasses are particularly limited.

[2114] 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.

[2115] In this invention, the server includes means for analyzing the mood and preferences input by the user, means for suggesting dishes based on the analysis results, means for allowing the user to select a food serving method, means for making recipes, delivery, or restaurant reservations according to the selected serving method, and means for voice operation through smart glasses. This allows the user to easily operate the server by voice, and enables the suggestion of a variety of meals that match the user's mood and the seamless selection of the serving method.

[2116] The "means for analyzing the moods and wishes input by the user" is a technology for analyzing the text or voice information input by the user and extracting the user's moods and wishes from the content.

[2117] "Means for suggesting dishes based on the analysis results" refers to technology that lists appropriate dishes based on the analyzed mood and preferences and suggests them to the user.

[2118] "Means for allowing the user to select the delivery method of the food" refers to the interface and technology that allows the user to select the delivery method of the proposed food (cooking, delivery, or restaurant reservation).

[2119] "Means for providing recipes, delivery, or making restaurant reservations according to the selected delivery method" refers to technology for displaying recipe information, arranging delivery, or making restaurant reservations according to the delivery method selected by the user.

[2120] "Voice-controlled means via smart glasses" refers to an interface and technology that accepts voice input using smart glasses and allows the user to control the device through voice.

[2121] This invention is a communication-based meal recommendation system that suggests dishes based on the user's mood and preferences, and handles everything from the serving method to payment. This system includes the following elements and means.

[2122] Basic system configuration

[2123] This system mainly consists of a device used by the user (such as smart glasses) and a server that performs back-end processing. The user inputs their mood and preferences by voice through an application installed on the device. The input information is analyzed by the server and suggestions are generated. The user then selects how the suggested food is to be served and makes payment.

[2124] Hardware and software used

[2125] Hardware: Smart glasses (e.g., Google Glass, Microsoft HoloLens)

[2126] Software: Speech recognition engine (e.g., Google Cloud Speech-to-Text), natural language processing engine (e.g., Google Cloud Natural Language API), server (database: MySQL, server-side script: Python / Django)

[2127] Program processing and data calculation

[2128] 1. The server creates a user database and stores information about each user, such as their profile, past orders, and preferences.

[2129] 2. The application is installed on the smart glasses and the user creates an account.

[2130] 3. The user puts on the smart glasses and launches the app.

[2131] 4. The smart glasses will issue a voice prompt saying "Please log in" and the user will enter their credentials by voice.

[2132] 5. The server checks the user's credentials against its database to verify successful authentication.

[2133] 6. The smart glasses will ask the user aloud, "What kind of meal would you like to have today?"

[2134] 7. The user inputs their mood or desires by voice (e.g., "I want to eat something spicy today").

[2135] 8. The smart glasses convert the voice input into text and send it to the server.

[2136] 9. The server uses a natural language processing (NLP) engine to analyze the user's input and extract keywords such as "spicy" and "cooking."

[2137] 10. The server generates a list of relevant dishes from the database.

[2138] 11. The smart glasses will present the generated recipe list to the user via voice.

[2139] 12. The user selects their favorite dish from the presented list by voice (e.g., "Mapo tofu").

[2140] 13. The smart glasses will then provide audible options for the selected dish (cook it yourself, have it delivered, or book a table at the restaurant).

[2141] 14. The user selects the desired delivery method.

[2142] 15. The process branches depending on the delivery method. In the case of delivery, it is as follows:

[2143] 1. The server will present delivery options from nearby participating restaurants.

[2144] 2. The smart glasses will provide delivery details and prices to the user via voice.

[2145] 3. The user selects the desired restaurant and food by voice and makes payment.

[2146] 4. The server sends the order to the selected restaurant and processes the delivery.

[2147] Specific examples

[2148] For example, if a user voice-inputs, "I want to eat something spicy today," the server will suggest options such as "spicy chicken, mapo tofu, and dandan noodles." Next, if the user selects "mapo tofu," the smart glasses will ask, "There is a delivery option. Would you like to order mapo tofu?" If the user answers "yes," the server will send the order to the delivery restaurant and process the payment.

[2149] Prompt Sentence Examples

[2150] Develop a smart glasses application that analyzes a user's voice input, such as "I want spicy food," and then suggests appropriate dishes and assists with delivery. Follow these steps:

[2151] 1. The speech recognition engine converts speech into text.

[2152] 2. Use a natural language processing engine to extract the user's mood and preferences.

[2153] 3. We will suggest relevant dishes from our database.

[2154] 4. The user's selected food is sent to the server and delivery is arranged.

[2155] Hardware used: Smart glasses (Google Glass, Microsoft HoloLens)

[2156] Software used: Google Cloud Speech-to-Text, Google Cloud Natural Language API, MySQL database, Python / Django

[2157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2158] Step 1:

[2159] The server creates a user database and stores information such as each user's profile, past order history, and preferences. Specifically, a database (e.g., MySQL) is used, and a table is created for each user. The input data is the user's basic information and tendencies, and the data is formatted and saved based on this. The output is the stored user's detailed information.

[2160] Step 2:

[2161] The application is installed on the smart glasses and the user creates an account. The user enters basic information and follows prompts to create the account. The input is the information entered by the user directly via voice or text, and the output is that the account is created and authentication information is sent to the server.

[2162] Step 3:

[2163] The user puts on the smart glasses and launches an app. The user turns on the smart glasses, selects an application, and launches it. The input is the user's launch operation, and the output is the application being launched.

[2164] Step 4:

[2165] The smart glasses will issue a voice prompt saying "Please log in" and the user will enter their credentials by voice. A speech recognition engine (Google Cloud Speech-to-Text) is used to convert the speech to text. The input is the user's voice and the output is their credentials in text format.

[2166] Step 5:

[2167] The server checks the user's credentials against a database to see if the authentication was successful. It checks the credentials to see if they match. The input is the credentials in text form and the output is the authentication success or failure status. The specific behavior is a process that uses an SQL query to check against the information in the database.

[2168] Step 6:

[2169] The smart glasses ask the user aloud, "What would you like to eat today?" They issue a voice prompt and wait for the user's response. The input is the text of the question, and the output is the user's voice response.

[2170] Step 7:

[2171] The user inputs their mood or desires through voice (e.g., "I want to eat something spicy today"). The user's voice input is captured through the microphone of the smart glasses. The input is the desired content in voice format, and the output is the content converted into text by the smart glasses.

[2172] Step 8:

[2173] The smart glasses convert voice input into text and send it to the server. They use a voice recognition engine to convert voice into text and send the data to the server. The input is the user's voice data and the output is text data. The specific operation is the conversion of voice into text by the voice recognition engine.

[2174] Step 9:

[2175] The server uses a natural language processing (NLP) engine to analyze the user's input and extract keywords such as "spicy" and "cuisine." The input is user input in text format, and the output is the extracted keywords. Specifically, the text analysis is performed using the Google Cloud Natural Language API.

[2176] Step 10:

[2177] The server generates a list of relevant dishes from the database. It searches the database based on keywords and lists related dishes. The input is the parsed keywords and the output is a list of dishes. The specific operation is the process of executing an SQL query to obtain related dish information.

[2178] Step 11:

[2179] The smart glasses present the generated recipe list to the user by voice. The list is converted from text to speech and presented to the user. The input is the list of recipes, and the output is the audio presentation.

[2180] Step 12:

[2181] The user selects their favorite dish from a presented list of dishes by voice (e.g., "Mapo tofu"). The user's selection is obtained by voice input. The input is a voice-based dish selection, and the output is text data of the selected dish.

[2182] Step 13:

[2183] The smart glasses present options for the selected dish (cook it yourself, have it delivered, or book a restaurant reservation) by voice. The options are presented by voice and the system waits for the user to make a selection. The input is the selected dish, and the output is the options.

[2184] Step 14:

[2185] The user selects the desired delivery method. The user's selection is input by voice. The input is the voice selection of the delivery method, and the output is text data of the selected delivery method.

[2186] Step 15:

[2187] The process will branch depending on the delivery method. For delivery:

[2188] 1. The server presents delivery options from nearby partner restaurants. It references the database to retrieve the relevant delivery options. The input is the selected delivery method, and the output is a list of partner restaurants.

[2189] 2. The smart glasses present delivery details and prices to the user via voice. The list is converted into audio and presented to the user. The input is a list of partner facilities, and the output is an audio presentation.

[2190] 3. The user selects the desired restaurant and food by voice and then pays. The user's selection is captured by voice. The input is the spoken selection of delivery options, and the output is text data with the selected delivery details.

[2191] 4. The server sends the order to the selected restaurant and processes the delivery. The input is the selected delivery details, and the output is sending the order to the restaurant. The specific operation is to send the order via an API call.

[2192] In this way, the entire system works together to make dish suggestions based on the user's mood and preferences, and to select and execute the serving method based on those suggestions.

[2193] 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.

[2194] The present invention provides a communication-based meal recommendation system that analyzes not only a user's mood and desires but also their emotions to recommend appropriate dishes. A specific embodiment of this system will be described below.

[2195] Basic configuration

[2196] This system consists of a device used by the user (such as a smartphone or tablet) and a server that performs back-end processing. The user inputs their mood, preferences, and emotions through an application installed on their device. The input information is analyzed by the server, which generates suggestions using an emotion engine. The user then selects how the suggested food is to be served (cooked, delivered, or at a restaurant) and makes payment.

[2197] Program processing (natural language explanation)

[2198] 1. Initial Setup

[2199] The server creates a user database and stores information such as each user's profile, past orders, and preferences.

[2200] The application is installed on the device and the user creates an account.

[2201] 2. Log in and enter your mood and emotions

[2202] The user launches the app and logs in by entering their credentials on the login screen.

[2203] The server checks the user's authentication information against a database to verify successful authentication.

[2204] The device displays a text input field to the user and asks, "What would you like to eat today?" It also analyzes the user's facial expressions and voice to present an interface for recognizing emotions.

[2205] Users input their mood and wishes in text, and also input their emotions through facial expressions and voice (for example, they can input "I'm tired today, so I'd like a quick dinner," and facial analysis will recognize their tiredness).

[2206] 3. Mood and emotion analysis and suggestions

[2207] The terminal transmits the user's input and the recognized emotion data to the server.

[2208] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood, desires, and emotions.

[2209] For example, keywords such as "tired," "easy to make," and "dinner" are extracted along with "fatigue."

[2210] The server uses an emotion engine to analyze the user's emotion data and, based on the results, highlights and suggests specific dishes.

[2211] For example, we suggest "easy-to-make pasta" along with "refreshing drinks" to reduce "feelings of fatigue."

[2212] The server generates a list of relevant recipes and dishes from the database.

[2213] The terminal displays the generated recipe list to the user.

[2214] 4. Choose the food and how it will be served

[2215] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[2216] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[2217] The user selects the desired delivery method.

[2218] 5. Details and Payment

[2219] Depending on the method of provision, the process branches and the detailed operation is performed:

[2220] If you make it yourself:

[2221] 1. The server generates a recipe and a list of ingredients for the selected dish.

[2222] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[2223] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[2224] 4. The server sends the ingredients list to the online store and arranges for delivery.

[2225] For delivery:

[2226] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[2227] 2. The terminal displays delivery details and prices to the user.

[2228] 3. The user selects the desired restaurant and food and makes payment.

[2229] 4. The server sends the order to the selected restaurant and processes the delivery.

[2230] For restaurants:

[2231] 1. The server generates a list of nearby restaurants where reservations can be made.

[2232] 2. The device will display a list of restaurants and available reservation times.

[2233] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[2234] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[2235] 6. Provision of Additional Features

[2236] The server provides additional functionality available to the user.

[2237] Food waste prevention measures: Provides the option to share excess food with other users, allowing users to choose what food to share and how to share it.

[2238] Treating others: Provides a money transfer function, allowing users to specify the person to treat and the amount, and then transfer the money.

[2239] Donate to relief efforts: Provide users with the option to donate to relief efforts in areas where food security is difficult, and allow them to specify the donation amount and make a payment.

[2240] Sharing dining experiences: Provides an interface for sharing dining experiences, allowing users to upload photos and impressions of their meals and share them on social media.

[2241] Specific examples

[2242] 1. The user enters, "It's hot today, so I want to eat something cool," and the emotion engine recognizes "joy" and "excitement."

[2243] 2. The server analyzes the input and emotional data and suggests options such as Zaru Soba noodles and Hiyashi Chuka noodles.

[2244] 3. The device displays suggested dishes, and the user selects "zaru soba."

[2245] 4. The device displays options for delivery method, and the user selects "Make it myself."

[2246] 5. The server will display the recipe for Zaru Soba noodles, the necessary ingredients, and offer purchasing options from a nearby supermarket and online stores.

[2247] 6. The user selects an online store, orders the materials, and makes payment.

[2248] 7. The server sends the order to the online store and the materials are delivered.

[2249] In this way, the present invention aims to significantly improve user convenience by seamlessly integrating meal suggestions and delivery methods tailored to the user's mood. It also addresses food waste reduction and social contribution, making it a system that can meet a wide range of needs. Furthermore, by incorporating an emotion engine, it becomes possible to make more personalized suggestions that are in tune with the user's emotions, thereby improving satisfaction.

[2250] The processing flow will be explained below.

[2251] Step 1:

[2252] The user launches the app and logs in by entering their credentials on the login screen.

[2253] The server checks the user's authentication information against a database to verify successful authentication.

[2254] Step 2:

[2255] The device displays a text input field to the user and asks, "What would you like to eat today?" It also analyzes the user's facial expressions and voice to present an interface for recognizing emotions.

[2256] Users input their mood and wishes in text, as well as their emotions through facial expressions and voice (for example, they can input "I'm tired today, so I'd like a quick dinner," and facial analysis will recognize their tiredness).

[2257] Step 3:

[2258] The terminal transmits the user's input and the recognized emotion data to the server.

[2259] Step 4:

[2260] The server uses a natural language processing (NLP) engine to analyze the user's input and extract their mood, desires, and emotions.

[2261] For example, keywords such as "tired," "easy to make," "dinner," and "fatigue" are extracted.

[2262] Step 5:

[2263] The server uses an emotion engine to analyze the user's emotion data and, based on the results, highlights and suggests specific dishes.

[2264] For example, to reduce fatigue, we suggest combining "easy-to-make pasta" with "a refreshing drink."

[2265] Step 6:

[2266] The server generates a list of relevant recipes and dishes from the database.

[2267] The terminal displays the generated recipe list to the user.

[2268] Step 7:

[2269] The user selects their favorite dish from the displayed list of dishes (e.g., "Easy-to-make pasta").

[2270] The device will then present the selected dish with delivery options (make it yourself / delivery / restaurant).

[2271] Step 8:

[2272] The user selects the desired delivery method.

[2273] Step 9:

[2274] The process will be split depending on the delivery method:

[2275] If you make it yourself:

[2276] 1. The server generates a recipe and a list of ingredients for the selected dish.

[2277] 2. The device displays the recipe and ingredients list, and offers purchasing options at local retailers or online stores.

[2278] 3. The user selects a purchase option, chooses the materials needed, and makes payment.

[2279] 4. The server sends the ingredients list to the online store and arranges for delivery.

[2280] For delivery:

[2281] 1. The server displays delivery options from nearby restaurants that are partners with the server.

[2282] 2. The terminal displays delivery details and prices to the user.

[2283] 3. The user selects the desired restaurant and food and makes payment.

[2284] 4. The server sends the order to the selected restaurant and processes the delivery.

[2285] For restaurants:

[2286] 1. The server generates a list of nearby restaurants where reservations can be made.

[2287] 2. The device will display a list of restaurants and available reservation times.

[2288] 3. The user selects the desired restaurant, date and time, and confirms the reservation.

[2289] 4. The server sends the reservation information to the restaurant and takes prepayment if necessary.

[2290] Step 10:

[2291] The server provides additional functionality available to the user.

[2292] Users can select and use additional features as needed (such as addressing food waste, treating others, donating to relief efforts, and sharing dining experiences).

[2293] Example 2

[2294] 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."

[2295] Conventional meal recommendation systems were able to make suggestions based on the user's mood and preferences, but they were unable to analyze the user's emotions and make more personalized suggestions based on those emotions. Furthermore, they lacked a consistent method for providing the suggested food and detailed arrangements, which meant they were unable to fully ensure user convenience. Furthermore, there were only limited systems that could address social issues such as food waste and social contribution.

[2296] 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.

[2297] In this invention, the server includes: a means for analyzing the mood, preferences, and emotions input by the user; a means for suggesting dishes based on the analysis results; a means for allowing the user to select a food delivery method; a means for providing recipes, delivery, or restaurant reservations according to the selected delivery method; a means for collecting the user's specific needs using prompt text; and a means for generating optimal dishes using a generative AI model. This enables personalized food recommendations based on the user's mood and emotions, and realizes consistent management of delivery methods and arrangements. It also addresses social issues such as food waste and social contribution.

[2298] "Mood" refers to the state of mind or feeling a user is experiencing at a particular time.

[2299] "Wishes" refer to the requirements or desires that a user has in a particular situation or condition.

[2300] "Emotions" refer to the psychological state or feelings expressed through the user's facial expressions, tone of voice, etc.

[2301] "Means of analysis" refers to techniques and methods for extracting and understanding moods, desires, and emotions based on information input by the user.

[2302] "Means for suggesting" refers to the technology or method for presenting appropriate food and beverage options to the user based on the analysis results.

[2303] "Means of Choice" refers to interfaces and technologies that allow users to select their preferred delivery method from multiple delivery methods.

[2304] "Delivery method" refers to the means by which food is delivered to the user, and includes options such as cooking it yourself, delivery, or restaurant reservations.

[2305] A "recipe" refers to a list of specific steps and ingredients for making a particular dish.

[2306] "Delivery" refers to a service that delivers food from affiliated restaurants to a location specified by the user.

[2307] "Restaurant reservation" refers to the process of a user making a reservation in advance to eat at a particular restaurant at a particular time.

[2308] A "prompt" is a piece of text or question that guides the user to input a specific need or request.

[2309] A "generative AI model" refers to an algorithm or system that uses large amounts of data to generate food and beverage options that best suit a user's needs.

[2310] The present invention is a communication-based meal recommendation system that analyzes a user's mood, preferences, and even emotions to recommend appropriate dishes. This system is composed of a device used by the user (e.g., a smartphone or tablet) and a server that performs back-end processing. Specific embodiments of this system are described below.

[2311] Basic configuration

[2312] The server uses a database management system (e.g., PostgreSQL) to create a user database and store information such as each user's profile, past order history, and preferences. The server also analyzes user input data using a natural language processing (NLP) engine (e.g., Google Cloud Natural Language API) and a sentiment analysis engine. It then uses a generative AI model to suggest appropriate dishes based on the analysis results.

[2313] The device provides the user with an interface through an application that runs on iOS or Android. When the user logs in, they enter their authentication information, and if successful, an input interface for their mood, desires, and emotions is displayed. The device then sends the entered data to the server and displays the server's suggestions.

[2314] The user inputs their mood, wishes, and emotions through the device. For example, they might input a prompt such as, "I'm tired today, so I'd like a quick dinner." At this time, the user's facial expressions and voice are also analyzed and captured as emotional data.

[2315] Specific examples

[2316] 1. Initial Setup: User installs the app and creates an account. The server stores the information in a user database.

[2317] 2. Login: The user logs in and their credentials are verified by the server.

[2318] 3. Information input: The user inputs, "I'm tired today, so I'd like a quick dinner," and facial expression analysis recognizes the "feeling of fatigue."

[2319] 4. Data analysis: The device sends the input data to the server, which then analyzes the data using an NLP engine and a sentiment analysis engine. As a result of the analysis, keywords such as "tired," "easy to make," and "dinner" as well as "feeling tired" are extracted.

[2320] 5. Recommendation Generation: The server uses the generative AI model to suggest a suitable dish (e.g., "Easy-to-make pasta") and a refreshing drink.

[2321] 6. Display: The device displays a list of suggested dishes, and the user selects "Easy Pasta."

[2322] 7. Select delivery method: The device presents delivery options (make it yourself, delivery, restaurant) and the user selects "make it yourself."

[2323] 8. Details and payment: The server generates the recipe and ingredients list for the selected dish, which are then displayed on the device. The user selects an online store to order the ingredients and completes the payment. The server then sends the ingredients list to the onlin...

Claims

1. A means for analyzing moods and preferences input by a user; A means of suggesting dishes based on the analysis results, A means for allowing the user to select how the food is served; Depending on the delivery method selected, recipes, delivery or restaurant reservations can be made. A system including:

2. The system of claim 1, wherein the options for how to prepare the meal include a means for ordering the necessary ingredients from a nearby retail store or an online store if the meal is to be prepared by the user.

3. The system according to claim 1, further comprising means for ordering food from an affiliated restaurant when delivery is selected as an option for the food delivery method.

4. 2. The system according to claim 1, further comprising means for making a reservation at a restaurant when the restaurant is selected as an option for the food delivery method.

5. 10. The system of claim 1, further comprising means for a user to send money to treat others to a meal.

6. 10. The system of claim 1, further comprising means for making a donation to support food insecurity in communities.

7. The system of claim 1 , further comprising: means for providing an interface for sharing a dining experience.

8. The system according to claim 1, further comprising a food waste countermeasure means for sharing surplus ingredients with other users.

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