System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a request from a user; means for obtaining user preferences and past selection history; means for generating a plurality of menus based on the data; means for transmitting the generated menus to a user terminal; means for receiving a menu selected by the user; means for updating a database with the received selection; and means for providing the user with detailed information of the selected menu.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] Traditionally, planning daily menus has been a significant burden for housewives and househusbands, requiring time and effort. Furthermore, the burden increases when considering nutritional balance and the preferences and allergies of each family member. Given this background, there is a demand for a system that can more efficiently suggest menus and reduce the burden on users. [Means for solving the problem]

[0005] The present invention is a system that includes a means for receiving requests from a user, a means for acquiring the user's preferences and past selection history, a means for generating multiple menu plans based on the above data, a means for transmitting the generated menu plans to a user terminal, a means for receiving the menus selected by the user, a means for updating a database with the received selections, and a means for providing the user with detailed information about the selected menu. The system also includes an algorithm for performing statistical analysis based on the user's past preference data to generate appropriate menus, and a means for filtering the user's allergy information and likes and dislikes of specific ingredients and reflecting them in the suggestions. This system reduces the burden of planning daily menus and supports the realization of an efficient and balanced diet.

[0006] "User" refers to a person who uses the menu suggestion system to send a request to determine daily menus.

[0007] The "means for receiving a request" refers to a device or program for receiving a request from a user for menu suggestions.

[0008] "Means for acquiring user preferences and past selection history" refers to a device or program for acquiring information from a database, such as menus selected by the user in the past, the user's food preferences, and specific disliked ingredients.

[0009] "Means for generating menu plans" refers to a device or algorithm for automatically generating multiple menu plans based on acquired data, taking into consideration nutritional balance, seasonal ingredients, the user's past preferences, etc.

[0010] The "means for transmitting a menu plan to a user terminal" refers to a device or program for transmitting the generated menu plan to a terminal used by the user so that the user can check it.

[0011] "Means for receiving a user-selected menu" refers to a device or program for receiving one of the suggested menus selected by the user.

[0012] "Means for updating the database" refers to a device or program for adding or updating the information of the menu selected by the user to the database and reflecting it in future suggestions.

[0013] The "means for providing detailed information" refers to a device or program for generating detailed information such as cooking methods and a list of necessary ingredients for the menu selected by the user and providing it to the user.

[0014] "Statistical analysis algorithm" refers to an algorithm that performs statistical analysis based on a user's past preference data to generate more appropriate menu plans.

[0015] "Means for filtering allergy information and likes and dislikes of specific ingredients" refers to a device or program that generates menu plans by excluding ingredients based on allergy information and likes and dislikes of specific ingredients set by the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] The present invention provides a system for making a user's life more efficient and reducing the burden on the user by automatically proposing daily menus to the user. Specific embodiments of the system are described below.

[0038] How users submit requests

[0039] When users want daily menu suggestions, they use a dedicated application or website. The application or website has a form where users can enter and submit their requests. The user enters "I would like to receive suggestions for today's dinner menu" in the form and clicks the submit button.

[0040] The server receives the request and acquires the user information.

[0041] A request sent by a user is sent to a server via the Internet. When the server receives the request, it analyzes the user ID and request content. The server then accesses a database to obtain the user's past preferences, dietary history, allergy information, etc.

[0042] Menu plan generation format

[0043] The server runs a menu generation algorithm based on the acquired user information. This generates multiple menu plans that take into account nutritional balance, seasonal ingredients, and the user's preferences. For example, the following menu plans may be generated:

[0044] 1. Chicken curry and salad

[0045] 2. Grilled salmon in foil and miso soup

[0046] 3. Stir-fried vegetables and grilled fish

[0047] Menu plans are sent to the user's terminal and the user selects the menu.

[0048] The server generates multiple menu plans, which are then sent to the user's device. The device receives the plans and displays them on the screen in a format that is easy for the user to view. The user then selects the menu that best suits their preferences from the proposed menu plans. After selecting, the device sends the selection data to the server.

[0049] The server receives the selection, updates the database, and provides the details.

[0050] The server receives the user's selected menu data and updates the database. This update accumulates new data to be reflected in future suggestions. The server then generates detailed information about the selected menu, such as cooking instructions and a list of ingredients, and sends it to the user's device. The user's device receives this information and displays it for the user to review.

[0051] Specific examples

[0052] A user opens the application, types in "Please suggest a menu for dinner tonight," and submits it. The server receives this request and retrieves the user's past preferences and allergies from a database. The server then runs a menu generation algorithm based on this information and generates a menu like this:

[0053] 1. Chicken curry

[0054] 2. Salmon baked in foil

[0055] 3. Stir-fried vegetables and grilled fish

[0056] These menu suggestions are sent to the user's device and displayed on the device. The user selects "stir-fried vegetables and grilled fish," and this selection is sent back to the server. The server receives the user's selection, updates the database, and then generates detailed cooking instructions and a list of ingredients for "stir-fried vegetables and grilled fish" and sends them to the user's device. The device displays this, and the user can then start cooking based on that information as the next step.

[0057] In this way, the system of the present invention reduces the burden of daily menu decisions for users and supports efficient and balanced dietary habits.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The user submits a request

[0061] A user opens an application or website, enters a request such as "Please suggest a menu for dinner tonight," and clicks the submit button.

[0062] Step 2:

[0063] The server receives the request

[0064] The server receives the request data sent by the user and analyzes the user ID and request content (e.g., "dinner menu").

[0065] Step 3:

[0066] The server retrieves the user information

[0067] The server accesses the database to obtain the user's past preferences, dietary history, allergy information, etc.

[0068] Step 4:

[0069] The server runs the menu planning algorithm

[0070] The server runs a menu generation algorithm based on the user information it has acquired, and generates multiple menu plans taking into account nutritional balance, seasonal ingredients, and the user's preferences.

[0071] Step 5:

[0072] The server sends the menu plan to the user's device.

[0073] The server generates multiple menu plans and sends them to the user's device in JSON format, which is then received by the user's device.

[0074] Step 6:

[0075] The device displays a menu plan

[0076] The user's device parses the received JSON data and displays multiple menu suggestions on the screen in an easy-to-read format for the user.

[0077] Step 7:

[0078] The user selects a menu

[0079] The user selects one of the proposed menus that suits their tastes. For example, they click on "Stir-fried vegetables and grilled fish."

[0080] Step 8:

[0081] The device sends the user's selection to the server

[0082] The data selected by the user is transmitted from the terminal to the server.

[0083] Step 9:

[0084] The server receives the selected data and updates the database

[0085] The server receives the user's selections and adds or updates this information in a database, which is then reflected in future offers.

[0086] Step 10:

[0087] The server generates detailed information

[0088] The server generates detailed information such as cooking instructions and a list of necessary ingredients for the menu selected by the user.

[0089] Step 11:

[0090] The server sends the details to the user terminal.

[0091] The server sends the generated detailed information to the user's terminal, which receives it.

[0092] Step 12:

[0093] The device will display detailed information

[0094] The user's device displays the received detailed information, allowing the user to check the cooking method and necessary ingredients.

[0095] The above is a specific processing flow in the system of the present invention.

[0096] Example 1

[0097] 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."

[0098] Modern users experience significant stress from the time and effort required to plan their daily menus. It is also difficult to plan daily menus that take into account nutritional balance and allergy prevention. Furthermore, existing systems for providing menus tailored to individual users do not adequately reflect the user's preferences or past tastes. Furthermore, they lack the ability to provide suggestions that meet the user's requests in real time, making them inefficient. The purpose of this invention is to provide a system that solves these problems and reduces the burden on users.

[0099] 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.

[0100] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences and past selection history, means for generating various menu plans based on the above data, means for transmitting the generated menu plans to a user terminal, means for receiving the menu selected by the user, means for updating the received selection in a database, means for providing the user with detailed information on the selected menu, means for executing an algorithm for optimizing menu proposals using a generative AI model, and means for generating menu plans in real time based on user input via prompt sentences. This allows the user to efficiently decide on daily menus and receive proposals that fully reflect nutritional balance and requests for specific ingredients.

[0101] "Means for receiving requests from users" refers to the function of obtaining the content of requests or inquiries from users via an interface that allows the user to make specific requests or inquiries to the system.

[0102] "Means for obtaining user preferences and past selection history" refers to a method for retrieving user past behavior and preference data from a database or other storage device.

[0103] "Means for generating diverse menu suggestions" refers to algorithms or software that automatically create multiple meal suggestions based on collected user information.

[0104] "Means for transmitting the generated menu plan to the user terminal" refers to a communication means for transmitting the proposal created by the server to the user's device.

[0105] "Means for receiving a menu selected by the user" refers to a function that allows the system to receive the content selected by the user from the proposed menu plans.

[0106] "Means for updating the database with the received selection" refers to a method for recording the user's selection in the database and updating the data accordingly.

[0107] The "means for providing the user with detailed information about the selected menu" refers to a function that provides the user with information about the menu selected by the user, such as specific cooking methods and necessary ingredients.

[0108] "Means for executing an algorithm that uses a generative AI model to optimize menu suggestions" refers to a method for using AI technology to execute a calculation procedure to suggest optimal menu suggestions while taking into account the user's preferences and nutritional balance.

[0109] "Means for generating menu plans in real time based on user input via prompt sentences" refers to a function that analyzes text information entered by the user in real time and instantly generates an appropriate menu plan based on that information.

[0110] The present invention relates to a system that efficiently suggests daily menus for a user, and specific embodiments thereof will be described in detail below.

[0111] Hardware and software used

[0112] Server: Serves as the central processing unit for running the menu generation algorithm, parsing user requests, and managing data. The server includes a database management system (e.g., MySQL or PostgreSQL) and a program execution environment (e.g., Python or Java).

[0113] User device: An interface for receiving requests from users and displaying the generated menu plans. Devices include mobile devices, tablets, and desktop computers, each with a dedicated application or web browser installed.

[0114] Communications infrastructure: Includes internet connectivity to enable data communication between user devices and servers.

[0115] Specific operation of the system

[0116] 1. Submitting a User Request

[0117] A user opens a dedicated application or website and types in "I want suggestions for dinner tonight," which generates an HTTP request and sends it to the server.

[0118] 2. The server receives and analyzes the request

[0119] The server receives a request from the user over the Internet, which includes the user ID and the specific request content. The server analyzes it and proceeds to the next step.

[0120] 3. Obtaining User Information

[0121] The server accesses the database and retrieves past preferences, dietary history, and allergy information corresponding to the user ID, thereby providing customized information for each user.

[0122] 4. Menu planning

[0123] The server runs a menu generation algorithm implemented using Python etc. based on the acquired user information. Using an AI generation model, multiple menu suggestions are generated that take into account nutritional balance, seasonal ingredients, and the user's preferences.

[0124] 5. Send and display menu plans

[0125] The menu plan generated by the server is sent to the user's device in JSON format, etc. The user's device then uses front-end technologies such as React and Vue.js to display the plan on a user interface based on the received data.

[0126] 6. User menu selection

[0127] The user selects from the displayed menu plans the one that suits his or her taste, and the selection is sent back to the server from the user terminal.

[0128] 7. Server receives selection and updates database

[0129] The server receives the user's selection and records it in a database, which is then used to inform future suggestions.

[0130] 8. Providing more information

[0131] The server generates detailed cooking instructions and a list of ingredients for the selected menu, and sends this information to the user's terminal, allowing the user to start cooking.

[0132] Specific examples

[0133] For example, a user opens an application, types in "Please suggest a menu for dinner tonight," and submits it. The server receives this request and retrieves the user's past preferences and allergies from a database. The server then runs a menu generation algorithm based on this information and generates a menu like this:

[0134] Chicken curry

[0135] Salmon baked in foil

[0136] Stir-fried vegetables and grilled fish

[0137] The generated menu plan is sent to the user's device and displayed on the device. The user selects "stir-fried vegetables and grilled fish," and this selection is sent to the server. The server receives the selection, updates the database, and then generates detailed cooking instructions and an ingredient list for "stir-fried vegetables and grilled fish" and sends them to the user's device. The user's device displays this, and the user can start cooking based on that information as the next step.

[0138] Prompt Sentence Examples

[0139] An example of a prompt for user input is "Please suggest a menu for today's dinner. Please provide multiple menu suggestions taking into consideration past preferences, dietary history, and allergy information."

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1: Submitting a User Request

[0142] The user opens a dedicated application or website, enters information into a form such as "I'd like suggestions for today's dinner menu," and clicks the submit button.

[0143] Input: User request (e.g., "Please suggest what to make for dinner today") and user ID.

[0144] Output: HTTP request (including user ID and request content).

[0145] Specific actions: Open the application on your smartphone or computer, enter your request into the input form, and tap or click the submit button.

[0146] Step 2: Server receives and analyzes the request

[0147] The server receives HTTP requests from users over the Internet.

[0148] Input: HTTP request containing user ID and request content.

[0149] Output: The request is parsed to extract the user ID and the desired content.

[0150] Specific operation: The server analyzes the received request and records the request content and user ID in a log file or memory.

[0151] Step 3: Get user information

[0152] The server accesses the database and retrieves past preferences, dietary history, and allergy information corresponding to the user ID.

[0153] Input: User ID.

[0154] Output: Data including the user's past preferences, dietary history, and allergy information.

[0155] Specific operation: The server accesses the database using an SQL query or similar to obtain user information.

[0156] Step 4: Generate a menu plan

[0157] The server uses an AI model to run a menu generation algorithm based on the acquired user information.

[0158] Input: User's past preferences, dietary history, and allergy information.

[0159] Output: Multiple menu plans.

[0160] Specific operation: The server uses an AI model implemented in Python or other languages ​​to generate menu plans based on the collected information, taking into account nutritional balance and seasonal ingredients.

[0161] Step 5: Send and view menu plans

[0162] The server sends the generated menu plan to the user's terminal.

[0163] Input: Multiple menu ideas.

[0164] Output: HTTP response containing the menu suggestions.

[0165] Specific operation: The server encodes the menu plan in JSON format and sends it to the user's device, which receives it and displays it on the screen.

[0166] Step 6: User menu selection

[0167] The user selects from the displayed menu plans one that suits their tastes.

[0168] Input: Multiple menu ideas.

[0169] Output: Data of selected menu plan.

[0170] Specific actions: The user taps or clicks to select the menu item and clicks the submit button.

[0171] Step 7: Server receives selection and updates database

[0172] The server receives the user's selection and records it in a database.

[0173] Input: Selected menu plan data.

[0174] Output: The updated selections in the database.

[0175] What happens: The server writes the received selection into a database and uses it in future suggestions.

[0176] Step 8: Provide more information

[0177] The server generates detailed cooking instructions and a list of ingredients required for the selected menu and sends them to the user's terminal.

[0178] Input: Selected menu plan data.

[0179] Output: Cooking instructions and ingredients list.

[0180] Specific operation: The server generates information including detailed cooking instructions and necessary ingredients, encodes it in JSON format, and sends it to the user's device.

[0181] Step 9: View detailed information

[0182] The user's terminal receives the detailed information and displays it on the application screen.

[0183] Input: Cooking instructions and ingredient list.

[0184] Output: User-visible recipe and ingredient list.

[0185] Specific operation: The user device analyzes the received data and displays it on the screen in a visually easy-to-understand format. The user can then start cooking based on the displayed information.

[0186] (Application example 1)

[0187] 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."

[0188] Current food delivery services have the challenge of making it difficult for users to receive menu suggestions that take into account their preferences and allergies, and then reflect those menu suggestions in their delivery orders. Furthermore, deciding on daily menus takes a lot of time and effort, making it difficult to maintain a balanced diet efficiently. Therefore, there is a need for a system that can streamline users' lives and reduce their burden.

[0189] 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.

[0190] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences and past selection history, means for generating multiple menu plans based on the above data, means for transmitting the generated menu plans to a user terminal, means for receiving the menu selected by the user, means for updating the received selection in a database, means for providing the user with detailed information about the selected menu, means for linking with an external food delivery service based on the generated menu plan and ordering the suggested menu, and means for executing order processing and tracking delivery status. This allows the user to receive menu suggestions tailored to their preferences and health condition and smoothly order the menu through a food delivery service.

[0191] A "means for receiving a request" is a device or interface that receives input from a user and transmits the content of that input to the system.

[0192] The "means for acquiring user preferences and past selection history" refers to a device or process that can collect information about a user's past behavioral data and preferences and acquire it from a database.

[0193] The "means for generating multiple menu plans" refers to an algorithm or program that creates multiple menu plans based on collected data according to the user's needs and preferences.

[0194] The "means for transmitting to the user terminal" refers to a communication device or protocol for transmitting the generated menu plan to the user's device.

[0195] The "means for receiving the selected menu" is an interface or device for returning the information about the menu selected by the user back to the system.

[0196] A "means for updating the database" is a method or device for adding or updating new user selection information to an existing user database.

[0197] The "means for providing detailed information" refers to a device or system that provides the user with specific information about the selected menu, such as cooking instructions and a list of ingredients.

[0198] The "means for linking with food delivery services" refers to communication methods and protocols for sharing information with external delivery services based on the generated menu plan and placing orders.

[0199] The "means for executing order processing" refers to the process or system that confirms the delivery service order based on the menu selected by the user and manages the processing.

[0200] A "delivery status tracking means" is a system or device that tracks the progress of food delivery after an order is placed and provides that information to the user.

[0201] The present invention is a system that supports users in deciding on a menu and works in conjunction with a food delivery service. This system generates an optimal menu plan based on the user's preferences and allergy information, and allows the user to order the menu through a delivery service. An embodiment of this system is described in detail below.

[0202] Program Overview

[0203] The program of this system mainly consists of the following components:

[0204] 1. User Interface (Smartphone Application)

[0205] 2. Request Processing Module

[0206] 3. Database

[0207] 4. Menu Generation Module

[0208] 5. Food delivery integration module

[0209] 6. Order Module

[0210] Hardware and software used

[0211] 1. User interface: Smartphone application (iOS / Android)

[0212] The application allows users to enter requests, receive menu suggestions, and order selected meals for delivery.

[0213] 2. Request processing module: Flask (Python framework)

[0214] This is the module by which the server receives and analyzes user requests.

[0215] 3. Database: SQLite

[0216] This is a database for storing user preference data and allergy information.

[0217] 4. Menu generation module: Python algorithm (generative AI model can also be applied)

[0218] This is an algorithm that generates optimal menu plans based on user information.

[0219] 5. Food delivery integration module: API integration

[0220] This module proposes and orders food delivery menus based on the generated menu plan.

[0221] 6. Order module: Flask (Python framework)

[0222] This module allows users to order their selected meals from food delivery services and track their status.

[0223] Program processing flow

[0224] When the server receives a request entered by the user, it retrieves past preference data and allergy information from a database based on the user's ID. Using this data, the menu generation module generates an optimal menu plan and sends it to the user interface. The user selects one of the proposed menus, and the selection is sent back to the server, updating the database. The server then orders the selected menu from a delivery service through the food delivery integration module. Once the order is confirmed, its status is tracked and notified to the user interface.

[0225] Specific examples

[0226] A user opens a smartphone application and types "Suggest a menu for dinner today." The server receives this request and retrieves the user's preference data and allergy information from the database. The menu generation module generates a menu like the following and sends it to the user interface:

[0227] 1. Chicken curry and salad

[0228] 2. Grilled salmon in foil and miso soup

[0229] 3. Stir-fried vegetables and grilled fish

[0230] When the user selects "grilled salmon in foil with miso soup," the selection is again sent to the server, which updates the database. The server then orders the selected meal from a delivery service through the food delivery integration module and tracks its status.

[0231] Prompt Sentence Examples

[0232] "Based on user information (preferences, allergy information), please suggest today's dinner menu from the following options:

[0233] 1. Chicken curry and salad

[0234] 2. Grilled salmon in foil and miso soup

[0235] 3. Stir-fried vegetables and grilled fish

[0236] This will make users' lives more efficient and reduce the burden of deciding on daily menus.

[0237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0238] Step 1:

[0239] User enters and submits request

[0240] Specific operation: The user opens the smartphone application, enters "Please suggest a menu for today's dinner," and taps the send button.

[0241] Input: User request ("Please suggest a menu for dinner today").

[0242] Output: The request data is sent to the server.

[0243] Step 2:

[0244] The server receives and analyzes the request

[0245] Specific operation: The server receives a request from the Flask application and analyzes the request content.

[0246] Input: Request data from the user.

[0247] Output: Parsed request content and user ID.

[0248] Step 3:

[0249] Retrieve user preferences and past selection history from a database

[0250] What it does: The server uses an SQL query to retrieve the user's preferences and past selections from an SQLite database.

[0251] Input: User ID.

[0252] Output: User preference data and allergy information.

[0253] Step 4:

[0254] Run the menu generation algorithm

[0255] Specific operation: The server runs a menu generation algorithm using Python, and the generative AI model creates multiple menu plans based on user information.

[0256] Input: User preference data and allergy information.

[0257] Output: Multiple generated menu plans.

[0258] Step 5:

[0259] The generated menu plan is sent to the user's device

[0260] Specific operation: The server sends the generated menu plan to the user's smartphone application.

[0261] Input: Multiple generated menu plans.

[0262] Output: The menu plan is displayed on the user's terminal.

[0263] Step 6:

[0264] User selects menu

[0265] Specific actions: The user selects one of the suggested meal plans and taps that selection in the application.

[0266] Input: Menu plan selected by the user.

[0267] Output: The selected menu data is sent to the server.

[0268] Step 7:

[0269] The server receives the selection and updates the database

[0270] Specific operation: The server receives the user's selection and updates the database with new preference data.

[0271] Input: Menu data selected by the user.

[0272] Output: The database is updated.

[0273] Step 8:

[0274] Generate detailed information and provide it to the user

[0275] Specific operation: The server generates detailed information about the selected menu, such as cooking instructions and a list of necessary ingredients, and sends it to the user's terminal.

[0276] Input: Selected menu data.

[0277] Output: Detailed information is displayed on the user's terminal.

[0278] Step 9:

[0279] The server coordinates with the food delivery service to execute the order

[0280] Specific operation: The server executes an order based on the selected menu using an external food delivery API.

[0281] Input: Selected menu data.

[0282] Output: Order confirmation information.

[0283] Step 10:

[0284] Track delivery status and notify users

[0285] Specific operation: The server tracks the delivery status through the food delivery service's API and notifies the user's smartphone application of that information.

[0286] Input: Delivery status data.

[0287] Output: Delivery status information is displayed on the user's terminal.

[0288] 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.

[0289] The present invention provides a system for making a user's life more efficient and reducing the burden by automatically suggesting daily menus based on the user's emotions. Specific embodiments of the system are described below.

[0290] How users submit requests

[0291] When users want daily menu suggestions, they use a dedicated application or website. The application or website has a form where users can enter and submit their requests. The user enters "I would like to receive suggestions for today's dinner menu" in the form and clicks the submit button.

[0292] The server receives the request and acquires the user information.

[0293] A request sent by a user is sent to a server via the Internet. When the server receives the request, it analyzes the user ID and the request content. The server then accesses a database to obtain the user's past preferences, dietary history, allergy information, and emotional data.

[0294] Emotion analysis using emotion engines

[0295] The server is equipped with an emotion engine that analyzes the user's current emotional state. The emotion engine has an algorithm that infers emotions from the user's input, behavioral patterns, and past preferences. The results of this emotion analysis are reflected in the generation of the next menu.

[0296] Menu plan generation format

[0297] The server runs a menu generation algorithm based on the acquired user information and the results of emotion analysis. This generates multiple menu plans that take into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state. For example, the following menu plans may be generated:

[0298] 1. Chicken curry and salad (for those feeling energized)

[0299] 2. Grilled salmon in foil and miso soup (for those in a relaxing mood)

[0300] 3. Stir-fried vegetables and grilled fish (for those in a healthier mood)

[0301] Menu plans are sent to the user's terminal and the user selects the menu.

[0302] The server generates multiple menu plans, which are then sent to the user's device. The device receives the plans and displays them on the screen in a format that is easy for the user to view. The user then selects the menu that best suits their preferences from the proposed menu plans. After selecting, the device sends the selection data to the server.

[0303] The server receives the selection, updates the database, and provides the details.

[0304] The server receives the user's selected menu data and updates the database. This update accumulates new data to be reflected in future suggestions. The server then generates detailed information about the selected menu, such as cooking instructions and a list of ingredients, and sends it to the user's device. The user's device receives this information and displays it for the user to review.

[0305] Specific examples

[0306] The user types "Please suggest a menu for dinner tonight" into the application and submits it. The server receives this request and retrieves the user's past preferences and allergies from the database. The emotion engine also analyzes the user's current emotional state. For example, if the server determines that the user is in a relaxing mood, it will take this into account and generate the following menu:

[0307] 1. Salmon baked in foil

[0308] 2. Deep-fried Chicken Tatsuta

[0309] 3. Boiled pumpkin

[0310] These menu suggestions are sent to the user's device and displayed on the device. The user selects "Baked Salmon in Foil," and this selection is sent back to the server. The server receives the user's selection, updates the database, and then generates detailed cooking instructions and a list of ingredients for "Baked Salmon in Foil," which are sent to the user's device. The device displays this, and the user can then start cooking based on that information as the next step.

[0311] In this way, the system of the present invention reduces the burden of daily menu decisions for users and supports an efficient and balanced dietary lifestyle that responds to emotions.

[0312] The processing flow will be explained below.

[0313] Step 1:

[0314] The user submits a request

[0315] A user opens an application or website, enters a request such as "Please suggest a menu for dinner tonight," and clicks the submit button.

[0316] Step 2:

[0317] The server receives the request

[0318] The server receives the request data sent by the user and analyzes the user ID and request content (e.g., "dinner menu").

[0319] Step 3:

[0320] The server retrieves the user information

[0321] The server accesses the database and acquires the user's past preferences, dietary history, allergy information, etc. In addition, it also acquires past emotional data.

[0322] Step 4:

[0323] The server runs the emotion engine

[0324] The emotion engine installed on the server analyzes the user's emotional state and infers the user's current emotions by referencing the user's input data, behavioral patterns, and past preference data.

[0325] Step 5:

[0326] The server generates a menu plan based on the emotional state.

[0327] The server runs a menu generation algorithm based on the analysis results of the emotion engine. It generates multiple menu suggestions, taking into account the user's emotional state, past preferences, nutritional balance, seasonal ingredients, etc. For example, based on the analysis result that the user is in the mood to relax, it generates the following menu suggestions:

[0328] 1. Grilled salmon in foil and miso soup

[0329] 2. Deep-fried chicken and salad

[0330] 3. Boiled pumpkin and rice

[0331] Step 6:

[0332] The server sends the menu plan to the user's device.

[0333] The server generates multiple menu plans and sends them to the user's device in JSON format, which is then received by the user's device.

[0334] Step 7:

[0335] The device displays a menu plan

[0336] The user's device parses the received JSON data and displays multiple menu suggestions on the screen in an easy-to-read format for the user.

[0337] Step 8:

[0338] The user selects a menu

[0339] The user selects one of the proposed menus that suits their tastes. For example, they click on "grilled salmon in foil and miso soup."

[0340] Step 9:

[0341] The device sends the user's selection to the server

[0342] The data selected by the user is transmitted from the terminal to the server.

[0343] Step 10:

[0344] The server receives the selected data and updates the database

[0345] The server receives the user's selections and adds or updates this information to a database, accumulating new data to be reflected in future suggestions.

[0346] Step 11:

[0347] The server generates detailed information

[0348] The server generates detailed information such as cooking instructions and a list of necessary ingredients for the menu selected by the user.

[0349] Step 12:

[0350] The server sends the details to the user terminal.

[0351] The server sends the generated detailed information to the user's terminal, which receives it.

[0352] Step 13:

[0353] The device will display detailed information

[0354] The user's device displays the received detailed information, allowing the user to check the cooking method and necessary ingredients.

[0355] The above is a specific processing flow in the system of the present invention.

[0356] Example 2

[0357] 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."

[0358] In today's busy lifestyles, planning daily meals can be a burden for many people. It is also difficult to create satisfying meal plans because it is difficult to propose menus that match individual preferences and feelings. Furthermore, conventional systems often fall short in proposing meals that take into account food allergies and likes and dislikes of specific ingredients.

[0359] 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.

[0360] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences, past selection history, and emotional data, means for generating multiple meal plans based on the above data and the emotion analysis results, means for transmitting the generated meal plans to a user terminal, means for receiving the meal plan selected by the user, means for updating the received selection in the database, and means for providing the user with detailed information on the selected meal plan, thereby making it possible to propose an appropriate meal plan according to the user's individual emotional state and preferences, thereby reducing the burden of meal planning.

[0361] The "means for receiving a request from a user" is a mechanism that allows a user to send a request for a meal plan proposal to a server through a dedicated application or website.

[0362] "Means for acquiring user preferences, past selection history, and emotional data" refers to a function for acquiring data relating to the user's past preferences, selection history, and emotional state from a database.

[0363] The "means for generating multiple meal plans" is a process that includes an algorithm that automatically generates multiple meal plans based on the acquired data and that are tailored to the user's emotions and preferences.

[0364] The "means for sending the generated meal plan to the user's terminal" is a mechanism that has the function of sending the menu plan generated by the server to the user's terminal, such as a smartphone or PC.

[0365] The "means for receiving a user selected meal plan" is a function that transmits the user's selection from the proposed menu plans to the server and receives the selection.

[0366] The "means for updating the selection to the database" is the process by which the user's selection data is recorded in the database and updated to reflect future suggestions.

[0367] The "means for providing detailed information about the selected meal plan to the user" is a mechanism for generating detailed information such as cooking methods and ingredient lists required for the selected meal plan and providing it to the user.

[0368] The present invention provides a system for making a user's life more efficient and reducing their burden by automatically proposing a daily meal plan based on their emotions. Specific embodiments of the system are described below.

[0369] Hardware and Software

[0370] The present invention uses the following major hardware and software:

[0371] Server: A computer system that receives requests from users via the Internet, accesses a database, and processes data.

[0372] User device: A computer device used by a user, such as a smartphone, tablet, or PC.

[0373] Database: A data management system for storing user preferences, past selection history, allergy information, emotional data, etc.

[0374] Emotion engine: Algorithms and software for analyzing the user's emotional state.

[0375] Data acquisition and processing flow

[0376] A user submits a request through a dedicated application or website. Specifically, the user enters a prompt such as "Please suggest a menu for dinner tonight" and clicks the send button. The request is sent to the server via the Internet.

[0377] When the server receives the request, it identifies the user and retrieves the user's past preferences, selection history, allergy information, and emotional data from a database. The emotion engine then analyzes the user's emotional state using algorithms based on the user's input, past behavioral patterns, and preference information.

[0378] The server runs a menu generation algorithm based on this data and the results of sentiment analysis. The generated meal plans take into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state. For example, the following meal plans may be suggested:

[0379] 1. Chicken curry and salad (for those feeling energized)

[0380] 2. Grilled salmon in foil and miso soup (for those in a relaxing mood)

[0381] 3. Stir-fried vegetables and grilled fish (for those in a healthier mood)

[0382] The generated meal plan is sent from the server to the user's device, which receives it and displays it on the screen. The user selects the most preferred menu from the proposed menus, and the selection data is sent back to the server.

[0383] When the server receives the user's selection data, it updates the database and stores the new user information. Next, it generates detailed information about the selected meal plan (e.g., cooking instructions and a list of ingredients) and sends it to the user's device. The device receives this information and displays it on the screen.

[0384] Specific examples

[0385] 1. The user types in the application, "Please suggest what to have for dinner today," and submits it.

[0386] 2. The server receives the request, retrieves the user's past preferences and allergies from the database, and then analyzes the user's current emotional state using the emotion engine.

[0387] 3. If the server determines that you are in a relaxing mood, it generates a menu like this:

[0388] Salmon baked in foil

[0389] Deep-fried Chicken Tatsuta

[0390] Boiled pumpkin

[0391] 4. The generated menu plan is sent from the server to the user's device and displayed.

[0392] 5. The user selects "Baked Salmon" and the selection is sent back to the server.

[0393] 6. The server receives the selection and updates the database. It generates detailed instructions and a list of ingredients for "Baked Salmon in Foil" and sends them to the user's device. The device displays the instructions and the user begins cooking.

[0394] In this way, the present invention reduces the burden of daily meal planning for the user and supports the user's eating habits by proposing appropriate meal plans according to emotions.

[0395] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0396] Step 1:

[0397] The user submits a request.

[0398] Input: A user fills in a form on an application or website with the prompt "Suggest something for dinner tonight" and clicks the submit button.

[0399] Operation: The user terminal generates request data and sends it to the server via the Internet.

[0400] Output: The request data is sent to the server.

[0401] Step 2:

[0402] The server receives the request and retrieves the user information.

[0403] Input: Request data from the user.

[0404] How it works: The server receives the request, identifies the user ID, and then retrieves the user's past preferences, selection history, allergy information, and emotional data from a database.

[0405] Output: The server receives the user's past preferences, selection history, allergy information, and emotional data.

[0406] Step 3:

[0407] Emotion analysis is performed on the server using an emotion engine.

[0408] Input: Captured user's past preferences, choice history, and emotional data.

[0409] How it works: The server uses the emotion engine to analyze the user's current emotional state. The emotion engine uses algorithms to infer emotions based on the user's input, past behavioral patterns, and preferences.

[0410] Output: An analysis of the user's current emotional state.

[0411] Step 4:

[0412] The server runs the menu generation algorithm.

[0413] Input: User's past preferences, selection history, allergy information, and sentiment analysis results.

[0414] How it works: The server runs a menu generation algorithm based on the acquired data and the results of sentiment analysis. The algorithm generates multiple meal plans taking into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state.

[0415] Output: Multiple meal plans will be generated.

[0416] Step 5:

[0417] The server transmits the generated meal plan to the user terminal.

[0418] Input: Multiple generated meal plans.

[0419] How it works: The server generates a meal plan and sends it to the user's device.

[0420] Output: The meal plan is sent to the user's device.

[0421] Step 6:

[0422] The user selects a meal plan.

[0423] Input: Multiple meal plans displayed on a user's device.

[0424] How it works: The user selects the meal plan they like best from the ones displayed on the screen. The device generates the selection data and sends it to the server.

[0425] Output: The selected meal plan data is sent to the server.

[0426] Step 7:

[0427] The server receives the selected plan and updates the database.

[0428] Input: Meal plan data selected by the user.

[0429] Action: The server receives the selection and updates the database, storing the new user information.

[0430] Output: The database is updated.

[0431] Step 8:

[0432] The server transmits detailed information about the selected meal plan to the user terminal.

[0433] Input: The meal plan selected by the user.

[0434] How it works: The server generates detailed information for the selected meal plan (such as cooking instructions and a list of ingredients) and sends it to the user's device.

[0435] Output: Detailed information is sent to the user's terminal and displayed.

[0436] In this way, by clearly indicating the input, operation, and output at each step, the processing flow of the entire system is explained in detail.

[0437] (Application example 2)

[0438] 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."

[0439] While conventional menu suggestion systems generate menu plans based on the user's preferences and past selection history, they have the problem of being unable to suggest menus that reflect the user's emotional state on that day. Furthermore, they lack a means to actually order the suggested menus, requiring the user to manually order again, which is inefficient.

[0440] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server is equipped with an emotion engine that analyzes the user's current emotional state, and includes means for generating a menu plan based on the emotional state, means for ordering food based on the generated menu plan, means for receiving a request from the user, means for acquiring the user's preferences and past selection history, means for generating multiple menu plans, means for transmitting the generated menu plans to the user terminal, means for receiving the menu selected by the user, means for updating the received selection in the database, and means for providing the user with detailed information on the selected menu. This makes it possible to propose an appropriate menu based on the user's emotional state and order it as is.

[0441] The "means for receiving a request from a user" is an interface for receiving an input from the user requesting a desired menu proposal from the system.

[0442] The "means for acquiring user preferences and past selection history" is a function for acquiring information about the user's past food preferences and history from a database.

[0443] A "means for generating multiple menu suggestions" is an algorithm that generates multiple meal menu suggestions based on the user's preferences, past selection history, and emotional state.

[0444] "Means for transmitting the generated menu proposal to the user terminal" is a communication function for transmitting the generated menu proposal to the user's smartphone or other device for display.

[0445] The "means for receiving a menu selected by the user" is a function for receiving a menu selected by the user from the proposed menu.

[0446] The "means for updating the database with the received selections" is a function that adds the user's selected menu information to the database and saves the data for future suggestions.

[0447] The "means for providing the user with detailed information about the selected menu" is a function that provides detailed information about the specific menu selected by the user, such as cooking instructions and a list of necessary ingredients.

[0448] The "emotion engine that analyzes the user's current emotional state" is an algorithm that predicts and analyzes the user's current emotional state from their input and behavioral patterns.

[0449] The "means for generating menu suggestions based on emotional state" is an algorithm that generates appropriate menu suggestions depending on the analyzed emotional state.

[0450] The "means for ordering food based on the generated menu plan" is a function for ordering food and dishes based on a menu selected by the user.

[0451] The present invention provides a system that proposes daily menus based on the user's emotional state and allows the user to order food directly. Specific embodiments of the system are described below.

[0452] Overview of the embodiment

[0453] The system consists of an application installed on the user's smartphone or other device and a server. When the user sends a menu suggestion request through the application, the server receives it and retrieves the user's past preference history and emotional state from a database. The emotion engine analyzes the user's emotional state, and based on that, the server generates a menu plan and suggests it to the user. Food can then be ordered directly based on the menu selected by the user.

[0454] Hardware and software used

[0455] Hardware:

[0456] User devices: smartphones, tablets, etc.

[0457] Server: Receives requests, manages the database, and generates menu plans.

[0458] software:

[0459] Flask: A lightweight Python web application framework that acts as a server, receiving user requests and returning responses.

[0460] Database management system: Manages users' past preference history, allergy information, etc.

[0461] Emotion engine: An algorithm that analyzes a user's current emotional state from their input and behavioral patterns.

[0462] Ordering system: The ability to order food and dishes online.

[0463] Process example

[0464] 1. The user types into the application, "I want suggestions for what to have for dinner today."

[0465] 2. The server receives this request and retrieves past preferences and allergy information from a database based on the user's ID.

[0466] 3. The emotion engine analyzes the user's current emotional state. For example, if it finds that the user is in a "relaxing" mood, the server will generate a menu of relaxation options based on this.

[0467] 4. The server sends the generated menu suggestions to the user's device, which displays them on the screen. For example, the following menu suggestions are proposed:

[0468] Salmon baked in foil

[0469] Deep-fried Chicken Tatsuta

[0470] Boiled pumpkin

[0471] 5. The user selects "Baked Salmon in Foil" and the selection is sent to the server, which updates the database with the selection and generates detailed cooking instructions and an ingredient list for the selected dish.

[0472] 6. The user places a food order based on "Baked Salmon" in the application. The server accepts the order and connects it to the relevant online food ordering system.

[0473] Specific prompt examples

[0474] User input: "I'm feeling a little stressed and want some food to help me relax."

[0475] Server response: "Relaxed menu plan: Baked salmon, deep-fried chicken, and simmered pumpkin. Which would you like?"

[0476] User Choice: "I'd like some grilled salmon in foil. I'll order that."

[0477] Server: "Your order is complete. Please wait a moment for delivery."

[0478] In this way, the system of the present invention can improve the efficiency of the user's daily eating habits and provide appropriate menus according to their emotions.

[0479] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0480] Step 1:

[0481] The user starts the application and inputs and sends a request such as "I want to have today's dinner menu suggested." The input also includes an emotional state such as "I want to relax." The server then receives the user's request.

[0482] Step 2:

[0483] The server analyzes the user request, extracts the user ID and input emotional state, and then retrieves the user's past preference data and allergy information from the database. The input includes the user ID, emotional state, past preference data, and allergy information, and the output is the analyzed user information.

[0484] Step 3:

[0485] The emotion engine further refines the current emotional state from the user's input and behavioral patterns and outputs the result. The input includes the initial input of the emotional state from the user, past behavioral patterns, and preference data, and the output is the refined current emotional state.

[0486] Step 4:

[0487] The server generates multiple menu suggestions based on the emotional state obtained from the emotion engine. The inputs include the examined emotional state, past preference data, and allergy information, and the output is multiple emotion-based menu suggestions.

[0488] Step 5:

[0489] The server sends the generated menu plans to the user's terminal, which receives them and displays them on its screen. The input includes the menu plans, and the output is the menu plans displayed on the user's terminal.

[0490] Step 6:

[0491] The user selects one of the displayed menu plans and sends the selection to the server, with the input including the user-selected menu plan and the output received by the server.

[0492] Step 7:

[0493] The server updates the database with the received selection data, with the selected menu plan as input and updated database information as output.

[0494] Step 8:

[0495] The server generates detailed information about the selected menu, such as cooking instructions and a list of necessary ingredients, and sends it to the user terminal. The input includes the selected menu plan, and the output includes data with the detailed information.

[0496] Step 9:

[0497] The user terminal displays the received detailed information on the screen and notifies the user. The detailed information is included as an input, and information visually provided to the user is obtained as an output.

[0498] Step 10:

[0499] The user confirms the details and completes the food order on the application. The input includes the details and order information, and the output is the order data sent to the online ordering system.

[0500] 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.

[0501] 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.

[0502] 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.

[0503] [Second embodiment]

[0504] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0505] 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.

[0506] 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).

[0507] 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.

[0508] 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.

[0509] 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).

[0510] 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.

[0511] 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.

[0512] 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.

[0513] 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.

[0514] 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.

[0515] 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."

[0516] The present invention provides a system for making a user's life more efficient and reducing the burden on the user by automatically proposing daily menus to the user. Specific embodiments of the system are described below.

[0517] How users submit requests

[0518] When users want daily menu suggestions, they use a dedicated application or website. The application or website has a form where users can enter and submit their requests. The user enters "I would like to receive suggestions for today's dinner menu" in the form and clicks the submit button.

[0519] The server receives the request and acquires the user information.

[0520] A request sent by a user is sent to a server via the Internet. When the server receives the request, it analyzes the user ID and request content. The server then accesses a database to obtain the user's past preferences, dietary history, allergy information, etc.

[0521] Menu plan generation format

[0522] The server runs a menu generation algorithm based on the acquired user information. This generates multiple menu plans that take into account nutritional balance, seasonal ingredients, and the user's preferences. For example, the following menu plans may be generated:

[0523] 1. Chicken curry and salad

[0524] 2. Grilled salmon in foil and miso soup

[0525] 3. Stir-fried vegetables and grilled fish

[0526] Menu plans are sent to the user's terminal and the user selects the menu.

[0527] The server generates multiple menu plans, which are then sent to the user's device. The device receives the plans and displays them on the screen in a format that is easy for the user to view. The user then selects the menu that best suits their preferences from the proposed menu plans. After selecting, the device sends the selection data to the server.

[0528] The server receives the selection, updates the database, and provides the details.

[0529] The server receives the user's selected menu data and updates the database. This update accumulates new data to be reflected in future suggestions. The server then generates detailed information about the selected menu, such as cooking instructions and a list of ingredients, and sends it to the user's device. The user's device receives this information and displays it for the user to review.

[0530] Specific examples

[0531] A user opens the application, types in "Please suggest a menu for dinner tonight," and submits it. The server receives this request and retrieves the user's past preferences and allergies from a database. The server then runs a menu generation algorithm based on this information and generates a menu like this:

[0532] 1. Chicken curry

[0533] 2. Salmon baked in foil

[0534] 3. Stir-fried vegetables and grilled fish

[0535] These menu suggestions are sent to the user's device and displayed on the device. The user selects "stir-fried vegetables and grilled fish," and this selection is sent back to the server. The server receives the user's selection, updates the database, and then generates detailed cooking instructions and a list of ingredients for "stir-fried vegetables and grilled fish" and sends them to the user's device. The device displays this, and the user can then start cooking based on that information as the next step.

[0536] In this way, the system of the present invention reduces the burden of daily menu decisions for users and supports efficient and balanced dietary habits.

[0537] The processing flow will be explained below.

[0538] Step 1:

[0539] The user submits a request

[0540] A user opens an application or website, enters a request such as "Please suggest a menu for dinner tonight," and clicks the submit button.

[0541] Step 2:

[0542] The server receives the request

[0543] The server receives the request data sent by the user and analyzes the user ID and request content (e.g., "dinner menu").

[0544] Step 3:

[0545] The server retrieves the user information

[0546] The server accesses the database to obtain the user's past preferences, dietary history, allergy information, etc.

[0547] Step 4:

[0548] The server runs the menu planning algorithm

[0549] The server runs a menu generation algorithm based on the user information it has acquired, and generates multiple menu plans taking into account nutritional balance, seasonal ingredients, and the user's preferences.

[0550] Step 5:

[0551] The server sends the menu plan to the user's device.

[0552] The server generates multiple menu plans and sends them to the user's device in JSON format, which is then received by the user's device.

[0553] Step 6:

[0554] The device displays a menu plan

[0555] The user's device parses the received JSON data and displays multiple menu suggestions on the screen in an easy-to-read format for the user.

[0556] Step 7:

[0557] The user selects a menu

[0558] The user selects one of the proposed menus that suits their tastes. For example, they click on "Stir-fried vegetables and grilled fish."

[0559] Step 8:

[0560] The device sends the user's selection to the server

[0561] The data selected by the user is transmitted from the terminal to the server.

[0562] Step 9:

[0563] The server receives the selected data and updates the database

[0564] The server receives the user's selections and adds or updates this information in a database, which is then reflected in future offers.

[0565] Step 10:

[0566] The server generates detailed information

[0567] The server generates detailed information such as cooking instructions and a list of necessary ingredients for the menu selected by the user.

[0568] Step 11:

[0569] The server sends the details to the user terminal.

[0570] The server sends the generated detailed information to the user's terminal, which receives it.

[0571] Step 12:

[0572] The device will display detailed information

[0573] The user's device displays the received detailed information, allowing the user to check the cooking method and necessary ingredients.

[0574] The above is a specific processing flow in the system of the present invention.

[0575] Example 1

[0576] 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."

[0577] Modern users experience significant stress from the time and effort required to plan their daily menus. It is also difficult to plan daily menus that take into account nutritional balance and allergy prevention. Furthermore, existing systems for providing menus tailored to individual users do not adequately reflect the user's preferences or past tastes. Furthermore, they lack the ability to provide suggestions that meet the user's requests in real time, making them inefficient. The purpose of this invention is to provide a system that solves these problems and reduces the burden on users.

[0578] 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.

[0579] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences and past selection history, means for generating various menu plans based on the above data, means for transmitting the generated menu plans to a user terminal, means for receiving the menu selected by the user, means for updating the received selection in a database, means for providing the user with detailed information on the selected menu, means for executing an algorithm for optimizing menu proposals using a generative AI model, and means for generating menu plans in real time based on user input via prompt sentences. This allows the user to efficiently decide on daily menus and receive proposals that fully reflect nutritional balance and requests for specific ingredients.

[0580] "Means for receiving requests from users" refers to the function of obtaining the content of requests or inquiries from users via an interface that allows the user to make specific requests or inquiries to the system.

[0581] "Means for obtaining user preferences and past selection history" refers to a method for retrieving user past behavior and preference data from a database or other storage device.

[0582] "Means for generating diverse menu suggestions" refers to algorithms or software that automatically create multiple meal suggestions based on collected user information.

[0583] "Means for transmitting the generated menu plan to the user terminal" refers to a communication means for transmitting the proposal created by the server to the user's device.

[0584] "Means for receiving a menu selected by the user" refers to a function that allows the system to receive the content selected by the user from the proposed menu plans.

[0585] "Means for updating the database with the received selection" refers to a method for recording the user's selection in the database and updating the data accordingly.

[0586] The "means for providing the user with detailed information about the selected menu" refers to a function that provides the user with information about the menu selected by the user, such as specific cooking methods and necessary ingredients.

[0587] "Means for executing an algorithm that uses a generative AI model to optimize menu suggestions" refers to a method for using AI technology to execute a calculation procedure to suggest optimal menu suggestions while taking into account the user's preferences and nutritional balance.

[0588] "Means for generating menu plans in real time based on user input via prompt sentences" refers to a function that analyzes text information entered by the user in real time and instantly generates an appropriate menu plan based on that information.

[0589] The present invention relates to a system that efficiently suggests daily menus for a user, and specific embodiments thereof will be described in detail below.

[0590] Hardware and software used

[0591] Server: Serves as the central processing unit for running the menu generation algorithm, parsing user requests, and managing data. The server includes a database management system (e.g., MySQL or PostgreSQL) and a program execution environment (e.g., Python or Java).

[0592] User device: An interface for receiving requests from users and displaying the generated menu plans. Devices include mobile devices, tablets, and desktop computers, each with a dedicated application or web browser installed.

[0593] Communications infrastructure: Includes internet connectivity to enable data communication between user devices and servers.

[0594] Specific operation of the system

[0595] 1. Submitting a User Request

[0596] A user opens a dedicated application or website and types in "I want suggestions for dinner tonight," which generates an HTTP request and sends it to the server.

[0597] 2. The server receives and analyzes the request

[0598] The server receives a request from the user over the Internet, which includes the user ID and the specific request content. The server analyzes it and proceeds to the next step.

[0599] 3. Obtaining User Information

[0600] The server accesses the database and retrieves past preferences, dietary history, and allergy information corresponding to the user ID, thereby providing customized information for each user.

[0601] 4. Menu planning

[0602] The server runs a menu generation algorithm implemented using Python etc. based on the acquired user information. Using an AI generation model, multiple menu suggestions are generated that take into account nutritional balance, seasonal ingredients, and the user's preferences.

[0603] 5. Send and display menu plans

[0604] The menu plan generated by the server is sent to the user's device in JSON format, etc. The user's device then uses front-end technologies such as React and Vue.js to display the plan on a user interface based on the received data.

[0605] 6. User menu selection

[0606] The user selects from the displayed menu plans the one that suits his or her taste, and the selection is sent back to the server from the user terminal.

[0607] 7. Server receives selection and updates database

[0608] The server receives the user's selection and records it in a database, which is then used to inform future suggestions.

[0609] 8. Providing more information

[0610] The server generates detailed cooking instructions and a list of ingredients for the selected menu, and sends this information to the user's terminal, allowing the user to start cooking.

[0611] Specific examples

[0612] For example, a user opens an application, types in "Please suggest a menu for dinner tonight," and submits it. The server receives this request and retrieves the user's past preferences and allergies from a database. The server then runs a menu generation algorithm based on this information and generates a menu like this:

[0613] Chicken curry

[0614] Salmon baked in foil

[0615] Stir-fried vegetables and grilled fish

[0616] The generated menu plan is sent to the user's device and displayed on the device. The user selects "stir-fried vegetables and grilled fish," and this selection is sent to the server. The server receives the selection, updates the database, and then generates detailed cooking instructions and an ingredient list for "stir-fried vegetables and grilled fish" and sends them to the user's device. The user's device displays this, and the user can start cooking based on that information as the next step.

[0617] Prompt Sentence Examples

[0618] An example of a prompt for user input is "Please suggest a menu for today's dinner. Please provide multiple menu suggestions taking into consideration past preferences, dietary history, and allergy information."

[0619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0620] Step 1: Submitting a User Request

[0621] The user opens a dedicated application or website, enters information into a form such as "I'd like suggestions for today's dinner menu," and clicks the submit button.

[0622] Input: User request (e.g., "Please suggest what to make for dinner today") and user ID.

[0623] Output: HTTP request (including user ID and request content).

[0624] Specific actions: Open the application on your smartphone or computer, enter your request into the input form, and tap or click the submit button.

[0625] Step 2: Server receives and analyzes the request

[0626] The server receives HTTP requests from users over the Internet.

[0627] Input: HTTP request containing user ID and request content.

[0628] Output: The request is parsed to extract the user ID and the desired content.

[0629] Specific operation: The server analyzes the received request and records the request content and user ID in a log file or memory.

[0630] Step 3: Get user information

[0631] The server accesses the database and retrieves past preferences, dietary history, and allergy information corresponding to the user ID.

[0632] Input: User ID.

[0633] Output: Data including the user's past preferences, dietary history, and allergy information.

[0634] Specific operation: The server accesses the database using an SQL query or similar to obtain user information.

[0635] Step 4: Generate a menu plan

[0636] The server uses an AI model to run a menu generation algorithm based on the acquired user information.

[0637] Input: User's past preferences, dietary history, and allergy information.

[0638] Output: Multiple menu plans.

[0639] Specific operation: The server uses an AI model implemented in Python or other languages ​​to generate menu plans based on the collected information, taking into account nutritional balance and seasonal ingredients.

[0640] Step 5: Send and view menu plans

[0641] The server sends the generated menu plan to the user's terminal.

[0642] Input: Multiple menu ideas.

[0643] Output: HTTP response containing the menu suggestions.

[0644] Specific operation: The server encodes the menu plan in JSON format and sends it to the user's device, which receives it and displays it on the screen.

[0645] Step 6: User menu selection

[0646] The user selects from the displayed menu plans one that suits their tastes.

[0647] Input: Multiple menu ideas.

[0648] Output: Data of selected menu plan.

[0649] Specific actions: The user taps or clicks to select the menu item and clicks the submit button.

[0650] Step 7: Server receives selection and updates database

[0651] The server receives the user's selection and records it in a database.

[0652] Input: Selected menu plan data.

[0653] Output: The updated selections in the database.

[0654] What happens: The server writes the received selection into a database and uses it in future suggestions.

[0655] Step 8: Provide more information

[0656] The server generates detailed cooking instructions and a list of ingredients required for the selected menu and sends them to the user's terminal.

[0657] Input: Selected menu plan data.

[0658] Output: Cooking instructions and ingredients list.

[0659] Specific operation: The server generates information including detailed cooking instructions and necessary ingredients, encodes it in JSON format, and sends it to the user's device.

[0660] Step 9: View detailed information

[0661] The user's terminal receives the detailed information and displays it on the application screen.

[0662] Input: Cooking instructions and ingredient list.

[0663] Output: User-visible recipe and ingredient list.

[0664] Specific operation: The user device analyzes the received data and displays it on the screen in a visually easy-to-understand format. The user can then start cooking based on the displayed information.

[0665] (Application example 1)

[0666] 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."

[0667] Current food delivery services have the challenge of making it difficult for users to receive menu suggestions that take into account their preferences and allergies, and then reflect those menu suggestions in their delivery orders. Furthermore, deciding on daily menus takes a lot of time and effort, making it difficult to maintain a balanced diet efficiently. Therefore, there is a need for a system that can streamline users' lives and reduce their burden.

[0668] 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.

[0669] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences and past selection history, means for generating multiple menu plans based on the above data, means for transmitting the generated menu plans to a user terminal, means for receiving the menu selected by the user, means for updating the received selection in a database, means for providing the user with detailed information about the selected menu, means for linking with an external food delivery service based on the generated menu plan and ordering the suggested menu, and means for executing order processing and tracking delivery status. This allows the user to receive menu suggestions tailored to their preferences and health condition and smoothly order the menu through a food delivery service.

[0670] A "means for receiving a request" is a device or interface that receives input from a user and transmits the content of that input to the system.

[0671] The "means for acquiring user preferences and past selection history" refers to a device or process that can collect information about a user's past behavioral data and preferences and acquire it from a database.

[0672] The "means for generating multiple menu plans" refers to an algorithm or program that creates multiple menu plans based on collected data according to the user's needs and preferences.

[0673] The "means for transmitting to the user terminal" refers to a communication device or protocol for transmitting the generated menu plan to the user's device.

[0674] The "means for receiving the selected menu" is an interface or device for returning the information about the menu selected by the user back to the system.

[0675] A "means for updating the database" is a method or device for adding or updating new user selection information to an existing user database.

[0676] The "means for providing detailed information" refers to a device or system that provides the user with specific information about the selected menu, such as cooking instructions and a list of ingredients.

[0677] The "means for linking with food delivery services" refers to communication methods and protocols for sharing information with external delivery services based on the generated menu plan and placing orders.

[0678] The "means for executing order processing" refers to the process or system that confirms the delivery service order based on the menu selected by the user and manages the processing.

[0679] A "delivery status tracking means" is a system or device that tracks the progress of food delivery after an order is placed and provides that information to the user.

[0680] The present invention is a system that supports users in deciding on a menu and works in conjunction with a food delivery service. This system generates an optimal menu plan based on the user's preferences and allergy information, and allows the user to order the menu through a delivery service. An embodiment of this system is described in detail below.

[0681] Program Overview

[0682] The program of this system mainly consists of the following components:

[0683] 1. User Interface (Smartphone Application)

[0684] 2. Request Processing Module

[0685] 3. Database

[0686] 4. Menu Generation Module

[0687] 5. Food delivery integration module

[0688] 6. Order Module

[0689] Hardware and software used

[0690] 1. User interface: Smartphone application (iOS / Android)

[0691] The application allows users to enter requests, receive menu suggestions, and order selected meals for delivery.

[0692] 2. Request processing module: Flask (Python framework)

[0693] This is the module by which the server receives and analyzes user requests.

[0694] 3. Database: SQLite

[0695] This is a database for storing user preference data and allergy information.

[0696] 4. Menu generation module: Python algorithm (generative AI model can also be applied)

[0697] This is an algorithm that generates optimal menu plans based on user information.

[0698] 5. Food delivery integration module: API integration

[0699] This module proposes and orders food delivery menus based on the generated menu plan.

[0700] 6. Order module: Flask (Python framework)

[0701] This module allows users to order their selected meals from food delivery services and track their status.

[0702] Program processing flow

[0703] When the server receives a request entered by the user, it retrieves past preference data and allergy information from a database based on the user's ID. Using this data, the menu generation module generates an optimal menu plan and sends it to the user interface. The user selects one of the proposed menus, and the selection is sent back to the server, updating the database. The server then orders the selected menu from a delivery service through the food delivery integration module. Once the order is confirmed, its status is tracked and notified to the user interface.

[0704] Specific examples

[0705] A user opens a smartphone application and types "Suggest a menu for dinner today." The server receives this request and retrieves the user's preference data and allergy information from the database. The menu generation module generates a menu like the following and sends it to the user interface:

[0706] 1. Chicken curry and salad

[0707] 2. Grilled salmon in foil and miso soup

[0708] 3. Stir-fried vegetables and grilled fish

[0709] When the user selects "grilled salmon in foil with miso soup," the selection is again sent to the server, which updates the database. The server then orders the selected meal from a delivery service through the food delivery integration module and tracks its status.

[0710] Prompt Sentence Examples

[0711] "Based on user information (preferences, allergy information), please suggest today's dinner menu from the following options:

[0712] 1. Chicken curry and salad

[0713] 2. Grilled salmon in foil and miso soup

[0714] 3. Stir-fried vegetables and grilled fish

[0715] This will make users' lives more efficient and reduce the burden of deciding on daily menus.

[0716] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0717] Step 1:

[0718] User enters and submits request

[0719] Specific operation: The user opens the smartphone application, enters "Please suggest a menu for today's dinner," and taps the send button.

[0720] Input: User request ("Please suggest a menu for dinner today").

[0721] Output: The request data is sent to the server.

[0722] Step 2:

[0723] The server receives and analyzes the request

[0724] Specific operation: The server receives a request from the Flask application and analyzes the request content.

[0725] Input: Request data from the user.

[0726] Output: Parsed request content and user ID.

[0727] Step 3:

[0728] Retrieve user preferences and past selection history from a database

[0729] What it does: The server uses an SQL query to retrieve the user's preferences and past selections from an SQLite database.

[0730] Input: User ID.

[0731] Output: User preference data and allergy information.

[0732] Step 4:

[0733] Run the menu generation algorithm

[0734] Specific operation: The server runs a menu generation algorithm using Python, and the generative AI model creates multiple menu plans based on user information.

[0735] Input: User preference data and allergy information.

[0736] Output: Multiple generated menu plans.

[0737] Step 5:

[0738] The generated menu plan is sent to the user's device

[0739] Specific operation: The server sends the generated menu plan to the user's smartphone application.

[0740] Input: Multiple generated menu plans.

[0741] Output: The menu plan is displayed on the user's terminal.

[0742] Step 6:

[0743] User selects menu

[0744] Specific actions: The user selects one of the suggested meal plans and taps that selection in the application.

[0745] Input: Menu plan selected by the user.

[0746] Output: The selected menu data is sent to the server.

[0747] Step 7:

[0748] The server receives the selection and updates the database

[0749] Specific operation: The server receives the user's selection and updates the database with new preference data.

[0750] Input: Menu data selected by the user.

[0751] Output: The database is updated.

[0752] Step 8:

[0753] Generate detailed information and provide it to the user

[0754] Specific operation: The server generates detailed information about the selected menu, such as cooking instructions and a list of necessary ingredients, and sends it to the user's terminal.

[0755] Input: Selected menu data.

[0756] Output: Detailed information is displayed on the user's terminal.

[0757] Step 9:

[0758] The server coordinates with the food delivery service to execute the order

[0759] Specific operation: The server executes an order based on the selected menu using an external food delivery API.

[0760] Input: Selected menu data.

[0761] Output: Order confirmation information.

[0762] Step 10:

[0763] Track delivery status and notify users

[0764] Specific operation: The server tracks the delivery status through the food delivery service's API and notifies the user's smartphone application of that information.

[0765] Input: Delivery status data.

[0766] Output: Delivery status information is displayed on the user's terminal.

[0767] 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.

[0768] The present invention provides a system for making a user's life more efficient and reducing the burden by automatically suggesting daily menus based on the user's emotions. Specific embodiments of the system are described below.

[0769] How users submit requests

[0770] When users want daily menu suggestions, they use a dedicated application or website. The application or website has a form where users can enter and submit their requests. The user enters "I would like to receive suggestions for today's dinner menu" in the form and clicks the submit button.

[0771] The server receives the request and acquires the user information.

[0772] A request sent by a user is sent to a server via the Internet. When the server receives the request, it analyzes the user ID and the request content. The server then accesses a database to obtain the user's past preferences, dietary history, allergy information, and emotional data.

[0773] Emotion analysis using emotion engines

[0774] The server is equipped with an emotion engine that analyzes the user's current emotional state. The emotion engine has an algorithm that infers emotions from the user's input, behavioral patterns, and past preferences. The results of this emotion analysis are reflected in the generation of the next menu.

[0775] Menu plan generation format

[0776] The server runs a menu generation algorithm based on the acquired user information and the results of emotion analysis. This generates multiple menu plans that take into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state. For example, the following menu plans may be generated:

[0777] 1. Chicken curry and salad (for those feeling energized)

[0778] 2. Grilled salmon in foil and miso soup (for those in a relaxing mood)

[0779] 3. Stir-fried vegetables and grilled fish (for those in a healthier mood)

[0780] Menu plans are sent to the user's terminal and the user selects the menu.

[0781] The server generates multiple menu plans, which are then sent to the user's device. The device receives the plans and displays them on the screen in a format that is easy for the user to view. The user then selects the menu that best suits their preferences from the proposed menu plans. After selecting, the device sends the selection data to the server.

[0782] The server receives the selection, updates the database, and provides the details.

[0783] The server receives the user's selected menu data and updates the database. This update accumulates new data to be reflected in future suggestions. The server then generates detailed information about the selected menu, such as cooking instructions and a list of ingredients, and sends it to the user's device. The user's device receives this information and displays it for the user to review.

[0784] Specific examples

[0785] The user types "Please suggest a menu for dinner tonight" into the application and submits it. The server receives this request and retrieves the user's past preferences and allergies from the database. The emotion engine also analyzes the user's current emotional state. For example, if the server determines that the user is in a relaxing mood, it will take this into account and generate the following menu:

[0786] 1. Salmon baked in foil

[0787] 2. Deep-fried Chicken Tatsuta

[0788] 3. Boiled pumpkin

[0789] These menu suggestions are sent to the user's device and displayed on the device. The user selects "Baked Salmon in Foil," and this selection is sent back to the server. The server receives the user's selection, updates the database, and then generates detailed cooking instructions and a list of ingredients for "Baked Salmon in Foil," which are sent to the user's device. The device displays this, and the user can then start cooking based on that information as the next step.

[0790] In this way, the system of the present invention reduces the burden of daily menu decisions for users and supports an efficient and balanced dietary lifestyle that responds to emotions.

[0791] The processing flow will be explained below.

[0792] Step 1:

[0793] The user submits a request

[0794] A user opens an application or website, enters a request such as "Please suggest a menu for dinner tonight," and clicks the submit button.

[0795] Step 2:

[0796] The server receives the request

[0797] The server receives the request data sent by the user and analyzes the user ID and request content (e.g., "dinner menu").

[0798] Step 3:

[0799] The server retrieves the user information

[0800] The server accesses the database and acquires the user's past preferences, dietary history, allergy information, etc. In addition, it also acquires past emotional data.

[0801] Step 4:

[0802] The server runs the emotion engine

[0803] The emotion engine installed on the server analyzes the user's emotional state and infers the user's current emotions by referencing the user's input data, behavioral patterns, and past preference data.

[0804] Step 5:

[0805] The server generates a menu plan based on the emotional state.

[0806] The server runs a menu generation algorithm based on the analysis results of the emotion engine. It generates multiple menu suggestions, taking into account the user's emotional state, past preferences, nutritional balance, seasonal ingredients, etc. For example, based on the analysis result that the user is in the mood to relax, it generates the following menu suggestions:

[0807] 1. Grilled salmon in foil and miso soup

[0808] 2. Deep-fried chicken and salad

[0809] 3. Boiled pumpkin and rice

[0810] Step 6:

[0811] The server sends the menu plan to the user's device.

[0812] The server generates multiple menu plans and sends them to the user's device in JSON format, which is then received by the user's device.

[0813] Step 7:

[0814] The device displays a menu plan

[0815] The user's device parses the received JSON data and displays multiple menu suggestions on the screen in an easy-to-read format for the user.

[0816] Step 8:

[0817] The user selects a menu

[0818] The user selects one of the proposed menus that suits their tastes. For example, they click on "grilled salmon in foil and miso soup."

[0819] Step 9:

[0820] The device sends the user's selection to the server

[0821] The data selected by the user is transmitted from the terminal to the server.

[0822] Step 10:

[0823] The server receives the selected data and updates the database

[0824] The server receives the user's selections and adds or updates this information to a database, accumulating new data to be reflected in future suggestions.

[0825] Step 11:

[0826] The server generates detailed information

[0827] The server generates detailed information such as cooking instructions and a list of necessary ingredients for the menu selected by the user.

[0828] Step 12:

[0829] The server sends the details to the user terminal.

[0830] The server sends the generated detailed information to the user's terminal, which receives it.

[0831] Step 13:

[0832] The device will display detailed information

[0833] The user's device displays the received detailed information, allowing the user to check the cooking method and necessary ingredients.

[0834] The above is a specific processing flow in the system of the present invention.

[0835] Example 2

[0836] 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."

[0837] In today's busy lifestyles, planning daily meals can be a burden for many people. It is also difficult to create satisfying meal plans because it is difficult to propose menus that match individual preferences and feelings. Furthermore, conventional systems often fall short in proposing meals that take into account food allergies and likes and dislikes of specific ingredients.

[0838] 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.

[0839] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences, past selection history, and emotional data, means for generating multiple meal plans based on the above data and the emotion analysis results, means for transmitting the generated meal plans to a user terminal, means for receiving the meal plan selected by the user, means for updating the received selection in the database, and means for providing the user with detailed information on the selected meal plan, thereby making it possible to propose an appropriate meal plan according to the user's individual emotional state and preferences, thereby reducing the burden of meal planning.

[0840] The "means for receiving a request from a user" is a mechanism that allows a user to send a request for a meal plan proposal to a server through a dedicated application or website.

[0841] "Means for acquiring user preferences, past selection history, and emotional data" refers to a function for acquiring data relating to the user's past preferences, selection history, and emotional state from a database.

[0842] The "means for generating multiple meal plans" is a process that includes an algorithm that automatically generates multiple meal plans based on the acquired data and that are tailored to the user's emotions and preferences.

[0843] The "means for sending the generated meal plan to the user's terminal" is a mechanism that has the function of sending the menu plan generated by the server to the user's terminal, such as a smartphone or PC.

[0844] The "means for receiving a user selected meal plan" is a function that transmits the user's selection from the proposed menu plans to the server and receives the selection.

[0845] The "means for updating the selection to the database" is the process by which the user's selection data is recorded in the database and updated to reflect future suggestions.

[0846] The "means for providing detailed information about the selected meal plan to the user" is a mechanism for generating detailed information such as cooking methods and ingredient lists required for the selected meal plan and providing it to the user.

[0847] The present invention provides a system for making a user's life more efficient and reducing their burden by automatically proposing a daily meal plan based on their emotions. Specific embodiments of the system are described below.

[0848] Hardware and Software

[0849] The present invention uses the following major hardware and software:

[0850] Server: A computer system that receives requests from users via the Internet, accesses a database, and processes data.

[0851] User device: A computer device used by a user, such as a smartphone, tablet, or PC.

[0852] Database: A data management system for storing user preferences, past selection history, allergy information, emotional data, etc.

[0853] Emotion engine: Algorithms and software for analyzing the user's emotional state.

[0854] Data acquisition and processing flow

[0855] A user submits a request through a dedicated application or website. Specifically, the user enters a prompt such as "Please suggest a menu for dinner tonight" and clicks the send button. The request is sent to the server via the Internet.

[0856] When the server receives the request, it identifies the user and retrieves the user's past preferences, selection history, allergy information, and emotional data from a database. The emotion engine then analyzes the user's emotional state using algorithms based on the user's input, past behavioral patterns, and preference information.

[0857] The server runs a menu generation algorithm based on this data and the results of sentiment analysis. The generated meal plans take into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state. For example, the following meal plans may be suggested:

[0858] 1. Chicken curry and salad (for those feeling energized)

[0859] 2. Grilled salmon in foil and miso soup (for those in a relaxing mood)

[0860] 3. Stir-fried vegetables and grilled fish (for those in a healthier mood)

[0861] The generated meal plan is sent from the server to the user's device, which receives it and displays it on the screen. The user selects the most preferred menu from the proposed menus, and the selection data is sent back to the server.

[0862] When the server receives the user's selection data, it updates the database and stores the new user information. Next, it generates detailed information about the selected meal plan (e.g., cooking instructions and a list of ingredients) and sends it to the user's device. The device receives this information and displays it on the screen.

[0863] Specific examples

[0864] 1. The user types in the application, "Please suggest what to have for dinner today," and submits it.

[0865] 2. The server receives the request, retrieves the user's past preferences and allergies from the database, and then analyzes the user's current emotional state using the emotion engine.

[0866] 3. If the server determines that you are in a relaxing mood, it generates a menu like this:

[0867] Salmon baked in foil

[0868] Deep-fried Chicken Tatsuta

[0869] Boiled pumpkin

[0870] 4. The generated menu plan is sent from the server to the user's device and displayed.

[0871] 5. The user selects "Baked Salmon" and the selection is sent back to the server.

[0872] 6. The server receives the selection and updates the database. It generates detailed instructions and a list of ingredients for "Baked Salmon in Foil" and sends them to the user's device. The device displays the instructions and the user begins cooking.

[0873] In this way, the present invention reduces the burden of daily meal planning for the user and supports the user's eating habits by proposing appropriate meal plans according to emotions.

[0874] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0875] Step 1:

[0876] The user submits a request.

[0877] Input: A user fills in a form on an application or website with the prompt "Suggest something for dinner tonight" and clicks the submit button.

[0878] Operation: The user terminal generates request data and sends it to the server via the Internet.

[0879] Output: The request data is sent to the server.

[0880] Step 2:

[0881] The server receives the request and retrieves the user information.

[0882] Input: Request data from the user.

[0883] How it works: The server receives the request, identifies the user ID, and then retrieves the user's past preferences, selection history, allergy information, and emotional data from a database.

[0884] Output: The server receives the user's past preferences, selection history, allergy information, and emotional data.

[0885] Step 3:

[0886] Emotion analysis is performed on the server using an emotion engine.

[0887] Input: Captured user's past preferences, choice history, and emotional data.

[0888] How it works: The server uses the emotion engine to analyze the user's current emotional state. The emotion engine uses algorithms to infer emotions based on the user's input, past behavioral patterns, and preferences.

[0889] Output: An analysis of the user's current emotional state.

[0890] Step 4:

[0891] The server runs the menu generation algorithm.

[0892] Input: User's past preferences, selection history, allergy information, and sentiment analysis results.

[0893] How it works: The server runs a menu generation algorithm based on the acquired data and the results of sentiment analysis. The algorithm generates multiple meal plans taking into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state.

[0894] Output: Multiple meal plans will be generated.

[0895] Step 5:

[0896] The server transmits the generated meal plan to the user terminal.

[0897] Input: Multiple generated meal plans.

[0898] How it works: The server generates a meal plan and sends it to the user's device.

[0899] Output: The meal plan is sent to the user's device.

[0900] Step 6:

[0901] The user selects a meal plan.

[0902] Input: Multiple meal plans displayed on a user's device.

[0903] How it works: The user selects the meal plan they like best from the ones displayed on the screen. The device generates the selection data and sends it to the server.

[0904] Output: The selected meal plan data is sent to the server.

[0905] Step 7:

[0906] The server receives the selected plan and updates the database.

[0907] Input: Meal plan data selected by the user.

[0908] Action: The server receives the selection and updates the database, storing the new user information.

[0909] Output: The database is updated.

[0910] Step 8:

[0911] The server transmits detailed information about the selected meal plan to the user terminal.

[0912] Input: The meal plan selected by the user.

[0913] How it works: The server generates detailed information for the selected meal plan (such as cooking instructions and a list of ingredients) and sends it to the user's device.

[0914] Output: Detailed information is sent to the user's terminal and displayed.

[0915] In this way, by clearly indicating the input, operation, and output at each step, the processing flow of the entire system is explained in detail.

[0916] (Application example 2)

[0917] 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."

[0918] While conventional menu suggestion systems generate menu plans based on the user's preferences and past selection history, they have the problem of being unable to suggest menus that reflect the user's emotional state on that day. Furthermore, they lack a means to actually order the suggested menus, requiring the user to manually order again, which is inefficient.

[0919] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server is equipped with an emotion engine that analyzes the user's current emotional state, and includes means for generating a menu plan based on the emotional state, means for ordering food based on the generated menu plan, means for receiving a request from the user, means for acquiring the user's preferences and past selection history, means for generating multiple menu plans, means for transmitting the generated menu plans to the user terminal, means for receiving the menu selected by the user, means for updating the received selection in the database, and means for providing the user with detailed information on the selected menu. This makes it possible to propose an appropriate menu based on the user's emotional state and order it as is.

[0920] The "means for receiving a request from a user" is an interface for receiving an input from the user requesting a desired menu proposal from the system.

[0921] The "means for acquiring user preferences and past selection history" is a function for acquiring information about the user's past food preferences and history from a database.

[0922] A "means for generating multiple menu suggestions" is an algorithm that generates multiple meal menu suggestions based on the user's preferences, past selection history, and emotional state.

[0923] "Means for transmitting the generated menu proposal to the user terminal" is a communication function for transmitting the generated menu proposal to the user's smartphone or other device for display.

[0924] The "means for receiving a menu selected by the user" is a function for receiving a menu selected by the user from the proposed menu.

[0925] The "means for updating the database with the received selections" is a function that adds the user's selected menu information to the database and saves the data for future suggestions.

[0926] The "means for providing the user with detailed information about the selected menu" is a function that provides detailed information about the specific menu selected by the user, such as cooking instructions and a list of necessary ingredients.

[0927] The "emotion engine that analyzes the user's current emotional state" is an algorithm that predicts and analyzes the user's current emotional state from their input and behavioral patterns.

[0928] The "means for generating menu suggestions based on emotional state" is an algorithm that generates appropriate menu suggestions depending on the analyzed emotional state.

[0929] The "means for ordering food based on the generated menu plan" is a function for ordering food and dishes based on a menu selected by the user.

[0930] The present invention provides a system that proposes daily menus based on the user's emotional state and allows the user to order food directly. Specific embodiments of the system are described below.

[0931] Overview of the embodiment

[0932] The system consists of an application installed on the user's smartphone or other device and a server. When the user sends a menu suggestion request through the application, the server receives it and retrieves the user's past preference history and emotional state from a database. The emotion engine analyzes the user's emotional state, and based on that, the server generates a menu plan and suggests it to the user. Food can then be ordered directly based on the menu selected by the user.

[0933] Hardware and software used

[0934] Hardware:

[0935] User devices: smartphones, tablets, etc.

[0936] Server: Receives requests, manages the database, and generates menu plans.

[0937] software:

[0938] Flask: A lightweight Python web application framework that acts as a server, receiving user requests and returning responses.

[0939] Database management system: Manages users' past preference history, allergy information, etc.

[0940] Emotion engine: An algorithm that analyzes a user's current emotional state from their input and behavioral patterns.

[0941] Ordering system: The ability to order food and dishes online.

[0942] Process example

[0943] 1. The user types into the application, "I want suggestions for what to have for dinner today."

[0944] 2. The server receives this request and retrieves past preferences and allergy information from a database based on the user's ID.

[0945] 3. The emotion engine analyzes the user's current emotional state. For example, if it finds that the user is in a "relaxing" mood, the server will generate a menu of relaxation options based on this.

[0946] 4. The server sends the generated menu suggestions to the user's device, which displays them on the screen. For example, the following menu suggestions are proposed:

[0947] Salmon baked in foil

[0948] Deep-fried Chicken Tatsuta

[0949] Boiled pumpkin

[0950] 5. The user selects "Baked Salmon in Foil" and the selection is sent to the server, which updates the database with the selection and generates detailed cooking instructions and an ingredient list for the selected dish.

[0951] 6. The user places a food order based on "Baked Salmon" in the application. The server accepts the order and connects it to the relevant online food ordering system.

[0952] Specific prompt examples

[0953] User input: "I'm feeling a little stressed and want some food to help me relax."

[0954] Server response: "Relaxed menu plan: Baked salmon, deep-fried chicken, and simmered pumpkin. Which would you like?"

[0955] User Choice: "I'd like some grilled salmon in foil. I'll order that."

[0956] Server: "Your order is complete. Please wait a moment for delivery."

[0957] In this way, the system of the present invention can improve the efficiency of the user's daily eating habits and provide appropriate menus according to their emotions.

[0958] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0959] Step 1:

[0960] The user starts the application and inputs and sends a request such as "I want to have today's dinner menu suggested." The input also includes an emotional state such as "I want to relax." The server then receives the user's request.

[0961] Step 2:

[0962] The server analyzes the user request, extracts the user ID and input emotional state, and then retrieves the user's past preference data and allergy information from the database. The input includes the user ID, emotional state, past preference data, and allergy information, and the output is the analyzed user information.

[0963] Step 3:

[0964] The emotion engine further refines the current emotional state from the user's input and behavioral patterns and outputs the result. The input includes the initial input of the emotional state from the user, past behavioral patterns, and preference data, and the output is the refined current emotional state.

[0965] Step 4:

[0966] The server generates multiple menu suggestions based on the emotional state obtained from the emotion engine. The inputs include the examined emotional state, past preference data, and allergy information, and the output is multiple emotion-based menu suggestions.

[0967] Step 5:

[0968] The server sends the generated menu plans to the user's terminal, which receives them and displays them on its screen. The input includes the menu plans, and the output is the menu plans displayed on the user's terminal.

[0969] Step 6:

[0970] The user selects one of the displayed menu plans and sends the selection to the server, with the input including the user-selected menu plan and the output received by the server.

[0971] Step 7:

[0972] The server updates the database with the received selection data, with the selected menu plan as input and updated database information as output.

[0973] Step 8:

[0974] The server generates detailed information about the selected menu, such as cooking instructions and a list of necessary ingredients, and sends it to the user terminal. The input includes the selected menu plan, and the output includes data with the detailed information.

[0975] Step 9:

[0976] The user terminal displays the received detailed information on the screen and notifies the user. The detailed information is included as an input, and information visually provided to the user is obtained as an output.

[0977] Step 10:

[0978] The user confirms the details and completes the food order on the application. The input includes the details and order information, and the output is the order data sent to the online ordering system.

[0979] 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.

[0980] 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.

[0981] 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.

[0982] [Third embodiment]

[0983] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0984] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0985] 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).

[0986] 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.

[0987] 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.

[0988] 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).

[0989] 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.

[0990] 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.

[0991] 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.

[0992] 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.

[0993] 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.

[0994] 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."

[0995] The present invention provides a system for making a user's life more efficient and reducing the burden on the user by automatically proposing daily menus to the user. Specific embodiments of the system are described below.

[0996] How users submit requests

[0997] When users want daily menu suggestions, they use a dedicated application or website. The application or website has a form where users can enter and submit their requests. The user enters "I would like to receive suggestions for today's dinner menu" in the form and clicks the submit button.

[0998] The server receives the request and acquires the user information.

[0999] A request sent by a user is sent to a server via the Internet. When the server receives the request, it analyzes the user ID and request content. The server then accesses a database to obtain the user's past preferences, dietary history, allergy information, etc.

[1000] Menu plan generation format

[1001] The server runs a menu generation algorithm based on the acquired user information. This generates multiple menu plans that take into account nutritional balance, seasonal ingredients, and the user's preferences. For example, the following menu plans may be generated:

[1002] 1. Chicken curry and salad

[1003] 2. Grilled salmon in foil and miso soup

[1004] 3. Stir-fried vegetables and grilled fish

[1005] Menu plans are sent to the user's terminal and the user selects the menu.

[1006] The server generates multiple menu plans, which are then sent to the user's device. The device receives the plans and displays them on the screen in a format that is easy for the user to view. The user then selects the menu that best suits their preferences from the proposed menu plans. After selecting, the device sends the selection data to the server.

[1007] The server receives the selection, updates the database, and provides the details.

[1008] The server receives the user's selected menu data and updates the database. This update accumulates new data to be reflected in future suggestions. The server then generates detailed information about the selected menu, such as cooking instructions and a list of ingredients, and sends it to the user's device. The user's device receives this information and displays it for the user to review.

[1009] Specific examples

[1010] A user opens the application, types in "Please suggest a menu for dinner tonight," and submits it. The server receives this request and retrieves the user's past preferences and allergies from a database. The server then runs a menu generation algorithm based on this information and generates a menu like this:

[1011] 1. Chicken curry

[1012] 2. Salmon baked in foil

[1013] 3. Stir-fried vegetables and grilled fish

[1014] These menu suggestions are sent to the user's device and displayed on the device. The user selects "stir-fried vegetables and grilled fish," and this selection is sent back to the server. The server receives the user's selection, updates the database, and then generates detailed cooking instructions and a list of ingredients for "stir-fried vegetables and grilled fish" and sends them to the user's device. The device displays this, and the user can then start cooking based on that information as the next step.

[1015] In this way, the system of the present invention reduces the burden of daily menu decisions for users and supports efficient and balanced dietary habits.

[1016] The processing flow will be explained below.

[1017] Step 1:

[1018] The user submits a request

[1019] A user opens an application or website, enters a request such as "Please suggest a menu for dinner tonight," and clicks the submit button.

[1020] Step 2:

[1021] The server receives the request

[1022] The server receives the request data sent by the user and analyzes the user ID and request content (e.g., "dinner menu").

[1023] Step 3:

[1024] The server retrieves the user information

[1025] The server accesses the database to obtain the user's past preferences, dietary history, allergy information, etc.

[1026] Step 4:

[1027] The server runs the menu planning algorithm

[1028] The server runs a menu generation algorithm based on the user information it has acquired, and generates multiple menu plans taking into account nutritional balance, seasonal ingredients, and the user's preferences.

[1029] Step 5:

[1030] The server sends the menu plan to the user's device.

[1031] The server generates multiple menu plans and sends them to the user's device in JSON format, which is then received by the user's device.

[1032] Step 6:

[1033] The device displays a menu plan

[1034] The user's device parses the received JSON data and displays multiple menu suggestions on the screen in an easy-to-read format for the user.

[1035] Step 7:

[1036] The user selects a menu

[1037] The user selects one of the proposed menus that suits their tastes. For example, they click on "Stir-fried vegetables and grilled fish."

[1038] Step 8:

[1039] The device sends the user's selection to the server

[1040] The data selected by the user is transmitted from the terminal to the server.

[1041] Step 9:

[1042] The server receives the selected data and updates the database

[1043] The server receives the user's selections and adds or updates this information in a database, which is then reflected in future offers.

[1044] Step 10:

[1045] The server generates detailed information

[1046] The server generates detailed information such as cooking instructions and a list of necessary ingredients for the menu selected by the user.

[1047] Step 11:

[1048] The server sends the details to the user terminal.

[1049] The server sends the generated detailed information to the user's terminal, which receives it.

[1050] Step 12:

[1051] The device will display detailed information

[1052] The user's device displays the received detailed information, allowing the user to check the cooking method and necessary ingredients.

[1053] The above is a specific processing flow in the system of the present invention.

[1054] Example 1

[1055] 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."

[1056] Modern users experience significant stress from the time and effort required to plan their daily menus. It is also difficult to plan daily menus that take into account nutritional balance and allergy prevention. Furthermore, existing systems for providing menus tailored to individual users do not adequately reflect the user's preferences or past tastes. Furthermore, they lack the ability to provide suggestions that meet the user's requests in real time, making them inefficient. The purpose of this invention is to provide a system that solves these problems and reduces the burden on users.

[1057] 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.

[1058] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences and past selection history, means for generating various menu plans based on the above data, means for transmitting the generated menu plans to a user terminal, means for receiving the menu selected by the user, means for updating the received selection in a database, means for providing the user with detailed information on the selected menu, means for executing an algorithm for optimizing menu proposals using a generative AI model, and means for generating menu plans in real time based on user input via prompt sentences. This allows the user to efficiently decide on daily menus and receive proposals that fully reflect nutritional balance and requests for specific ingredients.

[1059] "Means for receiving requests from users" refers to the function of obtaining the content of requests or inquiries from users via an interface that allows the user to make specific requests or inquiries to the system.

[1060] "Means for obtaining user preferences and past selection history" refers to a method for retrieving user past behavior and preference data from a database or other storage device.

[1061] "Means for generating diverse menu suggestions" refers to algorithms or software that automatically create multiple meal suggestions based on collected user information.

[1062] "Means for transmitting the generated menu plan to the user terminal" refers to a communication means for transmitting the proposal created by the server to the user's device.

[1063] "Means for receiving a menu selected by the user" refers to a function that allows the system to receive the content selected by the user from the proposed menu plans.

[1064] "Means for updating the database with the received selection" refers to a method for recording the user's selection in the database and updating the data accordingly.

[1065] The "means for providing the user with detailed information about the selected menu" refers to a function that provides the user with information about the menu selected by the user, such as specific cooking methods and necessary ingredients.

[1066] "Means for executing an algorithm that uses a generative AI model to optimize menu suggestions" refers to a method for using AI technology to execute a calculation procedure to suggest optimal menu suggestions while taking into account the user's preferences and nutritional balance.

[1067] "Means for generating menu plans in real time based on user input via prompt sentences" refers to a function that analyzes text information entered by the user in real time and instantly generates an appropriate menu plan based on that information.

[1068] The present invention relates to a system that efficiently suggests daily menus for a user, and specific embodiments thereof will be described in detail below.

[1069] Hardware and software used

[1070] Server: Serves as the central processing unit for running the menu generation algorithm, parsing user requests, and managing data. The server includes a database management system (e.g., MySQL or PostgreSQL) and a program execution environment (e.g., Python or Java).

[1071] User device: An interface for receiving requests from users and displaying the generated menu plans. Devices include mobile devices, tablets, and desktop computers, each with a dedicated application or web browser installed.

[1072] Communications infrastructure: Includes internet connectivity to enable data communication between user devices and servers.

[1073] Specific operation of the system

[1074] 1. Submitting a User Request

[1075] A user opens a dedicated application or website and types in "I want suggestions for dinner tonight," which generates an HTTP request and sends it to the server.

[1076] 2. The server receives and analyzes the request

[1077] The server receives a request from the user over the Internet, which includes the user ID and the specific request content. The server analyzes it and proceeds to the next step.

[1078] 3. Obtaining User Information

[1079] The server accesses the database and retrieves past preferences, dietary history, and allergy information corresponding to the user ID, thereby providing customized information for each user.

[1080] 4. Menu planning

[1081] The server runs a menu generation algorithm implemented using Python etc. based on the acquired user information. Using an AI generation model, multiple menu suggestions are generated that take into account nutritional balance, seasonal ingredients, and the user's preferences.

[1082] 5. Send and display menu plans

[1083] The menu plan generated by the server is sent to the user's device in JSON format, etc. The user's device then uses front-end technologies such as React and Vue.js to display the plan on a user interface based on the received data.

[1084] 6. User menu selection

[1085] The user selects from the displayed menu plans the one that suits his or her taste, and the selection is sent back to the server from the user terminal.

[1086] 7. Server receives selection and updates database

[1087] The server receives the user's selection and records it in a database, which is then used to inform future suggestions.

[1088] 8. Providing more information

[1089] The server generates detailed cooking instructions and a list of ingredients for the selected menu, and sends this information to the user's terminal, allowing the user to start cooking.

[1090] Specific examples

[1091] For example, a user opens an application, types in "Please suggest a menu for dinner tonight," and submits it. The server receives this request and retrieves the user's past preferences and allergies from a database. The server then runs a menu generation algorithm based on this information and generates a menu like this:

[1092] Chicken curry

[1093] Salmon baked in foil

[1094] Stir-fried vegetables and grilled fish

[1095] The generated menu plan is sent to the user's device and displayed on the device. The user selects "stir-fried vegetables and grilled fish," and this selection is sent to the server. The server receives the selection, updates the database, and then generates detailed cooking instructions and an ingredient list for "stir-fried vegetables and grilled fish" and sends them to the user's device. The user's device displays this, and the user can start cooking based on that information as the next step.

[1096] Prompt Sentence Examples

[1097] An example of a prompt for user input is "Please suggest a menu for today's dinner. Please provide multiple menu suggestions taking into consideration past preferences, dietary history, and allergy information."

[1098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1099] Step 1: Submitting a User Request

[1100] The user opens a dedicated application or website, enters information into a form such as "I'd like suggestions for today's dinner menu," and clicks the submit button.

[1101] Input: User request (e.g., "Please suggest what to make for dinner today") and user ID.

[1102] Output: HTTP request (including user ID and request content).

[1103] Specific actions: Open the application on your smartphone or computer, enter your request into the input form, and tap or click the submit button.

[1104] Step 2: Server receives and analyzes the request

[1105] The server receives HTTP requests from users over the Internet.

[1106] Input: HTTP request containing user ID and request content.

[1107] Output: The request is parsed to extract the user ID and the desired content.

[1108] Specific operation: The server analyzes the received request and records the request content and user ID in a log file or memory.

[1109] Step 3: Get user information

[1110] The server accesses the database and retrieves past preferences, dietary history, and allergy information corresponding to the user ID.

[1111] Input: User ID.

[1112] Output: Data including the user's past preferences, dietary history, and allergy information.

[1113] Specific operation: The server accesses the database using an SQL query or similar to obtain user information.

[1114] Step 4: Generate a menu plan

[1115] The server uses an AI model to run a menu generation algorithm based on the acquired user information.

[1116] Input: User's past preferences, dietary history, and allergy information.

[1117] Output: Multiple menu plans.

[1118] Specific operation: The server uses an AI model implemented in Python or other languages ​​to generate menu plans based on the collected information, taking into account nutritional balance and seasonal ingredients.

[1119] Step 5: Send and view menu plans

[1120] The server sends the generated menu plan to the user's terminal.

[1121] Input: Multiple menu ideas.

[1122] Output: HTTP response containing the menu suggestions.

[1123] Specific operation: The server encodes the menu plan in JSON format and sends it to the user's device, which receives it and displays it on the screen.

[1124] Step 6: User menu selection

[1125] The user selects from the displayed menu plans one that suits their tastes.

[1126] Input: Multiple menu ideas.

[1127] Output: Data of selected menu plan.

[1128] Specific actions: The user taps or clicks to select the menu item and clicks the submit button.

[1129] Step 7: Server receives selection and updates database

[1130] The server receives the user's selection and records it in a database.

[1131] Input: Selected menu plan data.

[1132] Output: The updated selections in the database.

[1133] What happens: The server writes the received selection into a database and uses it in future suggestions.

[1134] Step 8: Provide more information

[1135] The server generates detailed cooking instructions and a list of ingredients required for the selected menu and sends them to the user's terminal.

[1136] Input: Selected menu plan data.

[1137] Output: Cooking instructions and ingredients list.

[1138] Specific operation: The server generates information including detailed cooking instructions and necessary ingredients, encodes it in JSON format, and sends it to the user's device.

[1139] Step 9: View detailed information

[1140] The user's terminal receives the detailed information and displays it on the application screen.

[1141] Input: Cooking instructions and ingredient list.

[1142] Output: User-visible recipe and ingredient list.

[1143] Specific operation: The user device analyzes the received data and displays it on the screen in a visually easy-to-understand format. The user can then start cooking based on the displayed information.

[1144] (Application example 1)

[1145] 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."

[1146] Current food delivery services have the challenge of making it difficult for users to receive menu suggestions that take into account their preferences and allergies, and then reflect those menu suggestions in their delivery orders. Furthermore, deciding on daily menus takes a lot of time and effort, making it difficult to maintain a balanced diet efficiently. Therefore, there is a need for a system that can streamline users' lives and reduce their burden.

[1147] 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.

[1148] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences and past selection history, means for generating multiple menu plans based on the above data, means for transmitting the generated menu plans to a user terminal, means for receiving the menu selected by the user, means for updating the received selection in a database, means for providing the user with detailed information about the selected menu, means for linking with an external food delivery service based on the generated menu plan and ordering the suggested menu, and means for executing order processing and tracking delivery status. This allows the user to receive menu suggestions tailored to their preferences and health condition and smoothly order the menu through a food delivery service.

[1149] A "means for receiving a request" is a device or interface that receives input from a user and transmits the content of that input to the system.

[1150] The "means for acquiring user preferences and past selection history" refers to a device or process that can collect information about a user's past behavioral data and preferences and acquire it from a database.

[1151] The "means for generating multiple menu plans" refers to an algorithm or program that creates multiple menu plans based on collected data according to the user's needs and preferences.

[1152] The "means for transmitting to the user terminal" refers to a communication device or protocol for transmitting the generated menu plan to the user's device.

[1153] The "means for receiving the selected menu" is an interface or device for returning the information about the menu selected by the user back to the system.

[1154] A "means for updating the database" is a method or device for adding or updating new user selection information to an existing user database.

[1155] The "means for providing detailed information" refers to a device or system that provides the user with specific information about the selected menu, such as cooking instructions and a list of ingredients.

[1156] The "means for linking with food delivery services" refers to communication methods and protocols for sharing information with external delivery services based on the generated menu plan and placing orders.

[1157] The "means for executing order processing" refers to the process or system that confirms the delivery service order based on the menu selected by the user and manages the processing.

[1158] A "delivery status tracking means" is a system or device that tracks the progress of food delivery after an order is placed and provides that information to the user.

[1159] The present invention is a system that supports users in deciding on a menu and works in conjunction with a food delivery service. This system generates an optimal menu plan based on the user's preferences and allergy information, and allows the user to order the menu through a delivery service. An embodiment of this system is described in detail below.

[1160] Program Overview

[1161] The program of this system mainly consists of the following components:

[1162] 1. User Interface (Smartphone Application)

[1163] 2. Request Processing Module

[1164] 3. Database

[1165] 4. Menu Generation Module

[1166] 5. Food delivery integration module

[1167] 6. Order Module

[1168] Hardware and software used

[1169] 1. User interface: Smartphone application (iOS / Android)

[1170] The application allows users to enter requests, receive menu suggestions, and order selected meals for delivery.

[1171] 2. Request processing module: Flask (Python framework)

[1172] This is the module by which the server receives and analyzes user requests.

[1173] 3. Database: SQLite

[1174] This is a database for storing user preference data and allergy information.

[1175] 4. Menu generation module: Python algorithm (generative AI model can also be applied)

[1176] This is an algorithm that generates optimal menu plans based on user information.

[1177] 5. Food delivery integration module: API integration

[1178] This module proposes and orders food delivery menus based on the generated menu plan.

[1179] 6. Order module: Flask (Python framework)

[1180] This module allows users to order their selected meals from food delivery services and track their status.

[1181] Program processing flow

[1182] When the server receives a request entered by the user, it retrieves past preference data and allergy information from a database based on the user's ID. Using this data, the menu generation module generates an optimal menu plan and sends it to the user interface. The user selects one of the proposed menus, and the selection is sent back to the server, updating the database. The server then orders the selected menu from a delivery service through the food delivery integration module. Once the order is confirmed, its status is tracked and notified to the user interface.

[1183] Specific examples

[1184] A user opens a smartphone application and types "Suggest a menu for dinner today." The server receives this request and retrieves the user's preference data and allergy information from the database. The menu generation module generates a menu like the following and sends it to the user interface:

[1185] 1. Chicken curry and salad

[1186] 2. Grilled salmon in foil and miso soup

[1187] 3. Stir-fried vegetables and grilled fish

[1188] When the user selects "grilled salmon in foil with miso soup," the selection is again sent to the server, which updates the database. The server then orders the selected meal from a delivery service through the food delivery integration module and tracks its status.

[1189] Prompt Sentence Examples

[1190] "Based on user information (preferences, allergy information), please suggest today's dinner menu from the following options:

[1191] 1. Chicken curry and salad

[1192] 2. Grilled salmon in foil and miso soup

[1193] 3. Stir-fried vegetables and grilled fish

[1194] This will make users' lives more efficient and reduce the burden of deciding on daily menus.

[1195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1196] Step 1:

[1197] User enters and submits request

[1198] Specific operation: The user opens the smartphone application, enters "Please suggest a menu for today's dinner," and taps the send button.

[1199] Input: User request ("Please suggest a menu for dinner today").

[1200] Output: The request data is sent to the server.

[1201] Step 2:

[1202] The server receives and analyzes the request

[1203] Specific operation: The server receives a request from the Flask application and analyzes the request content.

[1204] Input: Request data from the user.

[1205] Output: Parsed request content and user ID.

[1206] Step 3:

[1207] Retrieve user preferences and past selection history from a database

[1208] What it does: The server uses an SQL query to retrieve the user's preferences and past selections from an SQLite database.

[1209] Input: User ID.

[1210] Output: User preference data and allergy information.

[1211] Step 4:

[1212] Run the menu generation algorithm

[1213] Specific operation: The server runs a menu generation algorithm using Python, and the generative AI model creates multiple menu plans based on user information.

[1214] Input: User preference data and allergy information.

[1215] Output: Multiple generated menu plans.

[1216] Step 5:

[1217] The generated menu plan is sent to the user's device

[1218] Specific operation: The server sends the generated menu plan to the user's smartphone application.

[1219] Input: Multiple generated menu plans.

[1220] Output: The menu plan is displayed on the user's terminal.

[1221] Step 6:

[1222] User selects menu

[1223] Specific actions: The user selects one of the suggested meal plans and taps that selection in the application.

[1224] Input: Menu plan selected by the user.

[1225] Output: The selected menu data is sent to the server.

[1226] Step 7:

[1227] The server receives the selection and updates the database

[1228] Specific operation: The server receives the user's selection and updates the database with new preference data.

[1229] Input: Menu data selected by the user.

[1230] Output: The database is updated.

[1231] Step 8:

[1232] Generate detailed information and provide it to the user

[1233] Specific operation: The server generates detailed information about the selected menu, such as cooking instructions and a list of necessary ingredients, and sends it to the user's terminal.

[1234] Input: Selected menu data.

[1235] Output: Detailed information is displayed on the user's terminal.

[1236] Step 9:

[1237] The server coordinates with the food delivery service to execute the order

[1238] Specific operation: The server executes an order based on the selected menu using an external food delivery API.

[1239] Input: Selected menu data.

[1240] Output: Order confirmation information.

[1241] Step 10:

[1242] Track delivery status and notify users

[1243] Specific operation: The server tracks the delivery status through the food delivery service's API and notifies the user's smartphone application of that information.

[1244] Input: Delivery status data.

[1245] Output: Delivery status information is displayed on the user's terminal.

[1246] 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.

[1247] The present invention provides a system for making a user's life more efficient and reducing the burden by automatically suggesting daily menus based on the user's emotions. Specific embodiments of the system are described below.

[1248] How users submit requests

[1249] When users want daily menu suggestions, they use a dedicated application or website. The application or website has a form where users can enter and submit their requests. The user enters "I would like to receive suggestions for today's dinner menu" in the form and clicks the submit button.

[1250] The server receives the request and acquires the user information.

[1251] A request sent by a user is sent to a server via the Internet. When the server receives the request, it analyzes the user ID and the request content. The server then accesses a database to obtain the user's past preferences, dietary history, allergy information, and emotional data.

[1252] Emotion analysis using emotion engines

[1253] The server is equipped with an emotion engine that analyzes the user's current emotional state. The emotion engine has an algorithm that infers emotions from the user's input, behavioral patterns, and past preferences. The results of this emotion analysis are reflected in the generation of the next menu.

[1254] Menu plan generation format

[1255] The server runs a menu generation algorithm based on the acquired user information and the results of emotion analysis. This generates multiple menu plans that take into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state. For example, the following menu plans may be generated:

[1256] 1. Chicken curry and salad (for those feeling energized)

[1257] 2. Grilled salmon in foil and miso soup (for those in a relaxing mood)

[1258] 3. Stir-fried vegetables and grilled fish (for those in a healthier mood)

[1259] Menu plans are sent to the user's terminal and the user selects the menu.

[1260] The server generates multiple menu plans, which are then sent to the user's device. The device receives the plans and displays them on the screen in a format that is easy for the user to view. The user then selects the menu that best suits their preferences from the proposed menu plans. After selecting, the device sends the selection data to the server.

[1261] The server receives the selection, updates the database, and provides the details.

[1262] The server receives the user's selected menu data and updates the database. This update accumulates new data to be reflected in future suggestions. The server then generates detailed information about the selected menu, such as cooking instructions and a list of ingredients, and sends it to the user's device. The user's device receives this information and displays it for the user to review.

[1263] Specific examples

[1264] The user types "Please suggest a menu for dinner tonight" into the application and submits it. The server receives this request and retrieves the user's past preferences and allergies from the database. The emotion engine also analyzes the user's current emotional state. For example, if the server determines that the user is in a relaxing mood, it will take this into account and generate the following menu:

[1265] 1. Salmon baked in foil

[1266] 2. Deep-fried Chicken Tatsuta

[1267] 3. Boiled pumpkin

[1268] These menu suggestions are sent to the user's device and displayed on the device. The user selects "Baked Salmon in Foil," and this selection is sent back to the server. The server receives the user's selection, updates the database, and then generates detailed cooking instructions and a list of ingredients for "Baked Salmon in Foil," which are sent to the user's device. The device displays this, and the user can then start cooking based on that information as the next step.

[1269] In this way, the system of the present invention reduces the burden of daily menu decisions for users and supports an efficient and balanced dietary lifestyle that responds to emotions.

[1270] The processing flow will be explained below.

[1271] Step 1:

[1272] The user submits a request

[1273] A user opens an application or website, enters a request such as "Please suggest a menu for dinner tonight," and clicks the submit button.

[1274] Step 2:

[1275] The server receives the request

[1276] The server receives the request data sent by the user and analyzes the user ID and request content (e.g., "dinner menu").

[1277] Step 3:

[1278] The server retrieves the user information

[1279] The server accesses the database and acquires the user's past preferences, dietary history, allergy information, etc. In addition, it also acquires past emotional data.

[1280] Step 4:

[1281] The server runs the emotion engine

[1282] The emotion engine installed on the server analyzes the user's emotional state and infers the user's current emotions by referencing the user's input data, behavioral patterns, and past preference data.

[1283] Step 5:

[1284] The server generates a menu plan based on the emotional state.

[1285] The server runs a menu generation algorithm based on the analysis results of the emotion engine. It generates multiple menu suggestions, taking into account the user's emotional state, past preferences, nutritional balance, seasonal ingredients, etc. For example, based on the analysis result that the user is in the mood to relax, it generates the following menu suggestions:

[1286] 1. Grilled salmon in foil and miso soup

[1287] 2. Deep-fried chicken and salad

[1288] 3. Boiled pumpkin and rice

[1289] Step 6:

[1290] The server sends the menu plan to the user's device.

[1291] The server generates multiple menu plans and sends them to the user's device in JSON format, which is then received by the user's device.

[1292] Step 7:

[1293] The device displays a menu plan

[1294] The user's device parses the received JSON data and displays multiple menu suggestions on the screen in an easy-to-read format for the user.

[1295] Step 8:

[1296] The user selects a menu

[1297] The user selects one of the proposed menus that suits their tastes. For example, they click on "grilled salmon in foil and miso soup."

[1298] Step 9:

[1299] The device sends the user's selection to the server

[1300] The data selected by the user is transmitted from the terminal to the server.

[1301] Step 10:

[1302] The server receives the selected data and updates the database

[1303] The server receives the user's selections and adds or updates this information to a database, accumulating new data to be reflected in future suggestions.

[1304] Step 11:

[1305] The server generates detailed information

[1306] The server generates detailed information such as cooking instructions and a list of necessary ingredients for the menu selected by the user.

[1307] Step 12:

[1308] The server sends the details to the user terminal.

[1309] The server sends the generated detailed information to the user's terminal, which receives it.

[1310] Step 13:

[1311] The device will display detailed information

[1312] The user's device displays the received detailed information, allowing the user to check the cooking method and necessary ingredients.

[1313] The above is a specific processing flow in the system of the present invention.

[1314] Example 2

[1315] 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."

[1316] In today's busy lifestyles, planning daily meals can be a burden for many people. It is also difficult to create satisfying meal plans because it is difficult to propose menus that match individual preferences and feelings. Furthermore, conventional systems often fall short in proposing meals that take into account food allergies and likes and dislikes of specific ingredients.

[1317] 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.

[1318] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences, past selection history, and emotional data, means for generating multiple meal plans based on the above data and the emotion analysis results, means for transmitting the generated meal plans to a user terminal, means for receiving the meal plan selected by the user, means for updating the received selection in the database, and means for providing the user with detailed information on the selected meal plan, thereby making it possible to propose an appropriate meal plan according to the user's individual emotional state and preferences, thereby reducing the burden of meal planning.

[1319] The "means for receiving a request from a user" is a mechanism that allows a user to send a request for a meal plan proposal to a server through a dedicated application or website.

[1320] "Means for acquiring user preferences, past selection history, and emotional data" refers to a function for acquiring data relating to the user's past preferences, selection history, and emotional state from a database.

[1321] The "means for generating multiple meal plans" is a process that includes an algorithm that automatically generates multiple meal plans based on the acquired data and that are tailored to the user's emotions and preferences.

[1322] The "means for sending the generated meal plan to the user's terminal" is a mechanism that has the function of sending the menu plan generated by the server to the user's terminal, such as a smartphone or PC.

[1323] The "means for receiving a user selected meal plan" is a function that transmits the user's selection from the proposed menu plans to the server and receives the selection.

[1324] The "means for updating the selection to the database" is the process by which the user's selection data is recorded in the database and updated to reflect future suggestions.

[1325] The "means for providing detailed information about the selected meal plan to the user" is a mechanism for generating detailed information such as cooking methods and ingredient lists required for the selected meal plan and providing it to the user.

[1326] The present invention provides a system for making a user's life more efficient and reducing their burden by automatically proposing a daily meal plan based on their emotions. Specific embodiments of the system are described below.

[1327] Hardware and Software

[1328] The present invention uses the following major hardware and software:

[1329] Server: A computer system that receives requests from users via the Internet, accesses a database, and processes data.

[1330] User device: A computer device used by a user, such as a smartphone, tablet, or PC.

[1331] Database: A data management system for storing user preferences, past selection history, allergy information, emotional data, etc.

[1332] Emotion engine: Algorithms and software for analyzing the user's emotional state.

[1333] Data acquisition and processing flow

[1334] A user submits a request through a dedicated application or website. Specifically, the user enters a prompt such as "Please suggest a menu for dinner tonight" and clicks the send button. The request is sent to the server via the Internet.

[1335] When the server receives the request, it identifies the user and retrieves the user's past preferences, selection history, allergy information, and emotional data from a database. The emotion engine then analyzes the user's emotional state using algorithms based on the user's input, past behavioral patterns, and preference information.

[1336] The server runs a menu generation algorithm based on this data and the results of sentiment analysis. The generated meal plans take into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state. For example, the following meal plans may be suggested:

[1337] 1. Chicken curry and salad (for those feeling energized)

[1338] 2. Grilled salmon in foil and miso soup (for those in a relaxing mood)

[1339] 3. Stir-fried vegetables and grilled fish (for those in a healthier mood)

[1340] The generated meal plan is sent from the server to the user's device, which receives it and displays it on the screen. The user selects the most preferred menu from the proposed menus, and the selection data is sent back to the server.

[1341] When the server receives the user's selection data, it updates the database and stores the new user information. Next, it generates detailed information about the selected meal plan (e.g., cooking instructions and a list of ingredients) and sends it to the user's device. The device receives this information and displays it on the screen.

[1342] Specific examples

[1343] 1. The user types in the application, "Please suggest what to have for dinner today," and submits it.

[1344] 2. The server receives the request, retrieves the user's past preferences and allergies from the database, and then analyzes the user's current emotional state using the emotion engine.

[1345] 3. If the server determines that you are in a relaxing mood, it generates a menu like this:

[1346] Salmon baked in foil

[1347] Deep-fried Chicken Tatsuta

[1348] Boiled pumpkin

[1349] 4. The generated menu plan is sent from the server to the user's device and displayed.

[1350] 5. The user selects "Baked Salmon" and the selection is sent back to the server.

[1351] 6. The server receives the selection and updates the database. It generates detailed instructions and a list of ingredients for "Baked Salmon in Foil" and sends them to the user's device. The device displays the instructions and the user begins cooking.

[1352] In this way, the present invention reduces the burden of daily meal planning for the user and supports the user's eating habits by proposing appropriate meal plans according to emotions.

[1353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1354] Step 1:

[1355] The user submits a request.

[1356] Input: A user fills in a form on an application or website with the prompt "Suggest something for dinner tonight" and clicks the submit button.

[1357] Operation: The user terminal generates request data and sends it to the server via the Internet.

[1358] Output: The request data is sent to the server.

[1359] Step 2:

[1360] The server receives the request and retrieves the user information.

[1361] Input: Request data from the user.

[1362] How it works: The server receives the request, identifies the user ID, and then retrieves the user's past preferences, selection history, allergy information, and emotional data from a database.

[1363] Output: The server receives the user's past preferences, selection history, allergy information, and emotional data.

[1364] Step 3:

[1365] Emotion analysis is performed on the server using an emotion engine.

[1366] Input: Captured user's past preferences, choice history, and emotional data.

[1367] How it works: The server uses the emotion engine to analyze the user's current emotional state. The emotion engine uses algorithms to infer emotions based on the user's input, past behavioral patterns, and preferences.

[1368] Output: An analysis of the user's current emotional state.

[1369] Step 4:

[1370] The server runs the menu generation algorithm.

[1371] Input: User's past preferences, selection history, allergy information, and sentiment analysis results.

[1372] How it works: The server runs a menu generation algorithm based on the acquired data and the results of sentiment analysis. The algorithm generates multiple meal plans taking into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state.

[1373] Output: Multiple meal plans will be generated.

[1374] Step 5:

[1375] The server transmits the generated meal plan to the user terminal.

[1376] Input: Multiple generated meal plans.

[1377] How it works: The server generates a meal plan and sends it to the user's device.

[1378] Output: The meal plan is sent to the user's device.

[1379] Step 6:

[1380] The user selects a meal plan.

[1381] Input: Multiple meal plans displayed on a user's device.

[1382] How it works: The user selects the meal plan they like best from the ones displayed on the screen. The device generates the selection data and sends it to the server.

[1383] Output: The selected meal plan data is sent to the server.

[1384] Step 7:

[1385] The server receives the selected plan and updates the database.

[1386] Input: Meal plan data selected by the user.

[1387] Action: The server receives the selection and updates the database, storing the new user information.

[1388] Output: The database is updated.

[1389] Step 8:

[1390] The server transmits detailed information about the selected meal plan to the user terminal.

[1391] Input: The meal plan selected by the user.

[1392] How it works: The server generates detailed information for the selected meal plan (such as cooking instructions and a list of ingredients) and sends it to the user's device.

[1393] Output: Detailed information is sent to the user's terminal and displayed.

[1394] In this way, by clearly indicating the input, operation, and output at each step, the processing flow of the entire system is explained in detail.

[1395] (Application example 2)

[1396] 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."

[1397] While conventional menu suggestion systems generate menu plans based on the user's preferences and past selection history, they have the problem of being unable to suggest menus that reflect the user's emotional state on that day. Furthermore, they lack a means to actually order the suggested menus, requiring the user to manually order again, which is inefficient.

[1398] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server is equipped with an emotion engine that analyzes the user's current emotional state, and includes means for generating a menu plan based on the emotional state, means for ordering food based on the generated menu plan, means for receiving a request from the user, means for acquiring the user's preferences and past selection history, means for generating multiple menu plans, means for transmitting the generated menu plans to the user terminal, means for receiving the menu selected by the user, means for updating the received selection in the database, and means for providing the user with detailed information on the selected menu. This makes it possible to propose an appropriate menu based on the user's emotional state and order it as is.

[1399] The "means for receiving a request from a user" is an interface for receiving an input from the user requesting a desired menu proposal from the system.

[1400] The "means for acquiring user preferences and past selection history" is a function for acquiring information about the user's past food preferences and history from a database.

[1401] A "means for generating multiple menu suggestions" is an algorithm that generates multiple meal menu suggestions based on the user's preferences, past selection history, and emotional state.

[1402] "Means for transmitting the generated menu proposal to the user terminal" is a communication function for transmitting the generated menu proposal to the user's smartphone or other device for display.

[1403] The "means for receiving a menu selected by the user" is a function for receiving a menu selected by the user from the proposed menu.

[1404] The "means for updating the database with the received selections" is a function that adds the user's selected menu information to the database and saves the data for future suggestions.

[1405] The "means for providing the user with detailed information about the selected menu" is a function that provides detailed information about the specific menu selected by the user, such as cooking instructions and a list of necessary ingredients.

[1406] The "emotion engine that analyzes the user's current emotional state" is an algorithm that predicts and analyzes the user's current emotional state from their input and behavioral patterns.

[1407] The "means for generating menu suggestions based on emotional state" is an algorithm that generates appropriate menu suggestions depending on the analyzed emotional state.

[1408] The "means for ordering food based on the generated menu plan" is a function for ordering food and dishes based on a menu selected by the user.

[1409] The present invention provides a system that proposes daily menus based on the user's emotional state and allows the user to order food directly. Specific embodiments of the system are described below.

[1410] Overview of the embodiment

[1411] The system consists of an application installed on the user's smartphone or other device and a server. When the user sends a menu suggestion request through the application, the server receives it and retrieves the user's past preference history and emotional state from a database. The emotion engine analyzes the user's emotional state, and based on that, the server generates a menu plan and suggests it to the user. Food can then be ordered directly based on the menu selected by the user.

[1412] Hardware and software used

[1413] Hardware:

[1414] User devices: smartphones, tablets, etc.

[1415] Server: Receives requests, manages the database, and generates menu plans.

[1416] software:

[1417] Flask: A lightweight Python web application framework that acts as a server, receiving user requests and returning responses.

[1418] Database management system: Manages users' past preference history, allergy information, etc.

[1419] Emotion engine: An algorithm that analyzes a user's current emotional state from their input and behavioral patterns.

[1420] Ordering system: The ability to order food and dishes online.

[1421] Process example

[1422] 1. The user types into the application, "I want suggestions for what to have for dinner today."

[1423] 2. The server receives this request and retrieves past preferences and allergy information from a database based on the user's ID.

[1424] 3. The emotion engine analyzes the user's current emotional state. For example, if it finds that the user is in a "relaxing" mood, the server will generate a menu of relaxation options based on this.

[1425] 4. The server sends the generated menu suggestions to the user's device, which displays them on the screen. For example, the following menu suggestions are proposed:

[1426] Salmon baked in foil

[1427] Deep-fried Chicken Tatsuta

[1428] Boiled pumpkin

[1429] 5. The user selects "Baked Salmon in Foil" and the selection is sent to the server, which updates the database with the selection and generates detailed cooking instructions and an ingredient list for the selected dish.

[1430] 6. The user places a food order based on "Baked Salmon" in the application. The server accepts the order and connects it to the relevant online food ordering system.

[1431] Specific prompt examples

[1432] User input: "I'm feeling a little stressed and want some food to help me relax."

[1433] Server response: "Relaxed menu plan: Baked salmon, deep-fried chicken, and simmered pumpkin. Which would you like?"

[1434] User Choice: "I'd like some grilled salmon in foil. I'll order that."

[1435] Server: "Your order is complete. Please wait a moment for delivery."

[1436] In this way, the system of the present invention can improve the efficiency of the user's daily eating habits and provide appropriate menus according to their emotions.

[1437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1438] Step 1:

[1439] The user starts the application and inputs and sends a request such as "I want to have today's dinner menu suggested." The input also includes an emotional state such as "I want to relax." The server then receives the user's request.

[1440] Step 2:

[1441] The server analyzes the user request, extracts the user ID and input emotional state, and then retrieves the user's past preference data and allergy information from the database. The input includes the user ID, emotional state, past preference data, and allergy information, and the output is the analyzed user information.

[1442] Step 3:

[1443] The emotion engine further refines the current emotional state from the user's input and behavioral patterns and outputs the result. The input includes the initial input of the emotional state from the user, past behavioral patterns, and preference data, and the output is the refined current emotional state.

[1444] Step 4:

[1445] The server generates multiple menu suggestions based on the emotional state obtained from the emotion engine. The inputs include the examined emotional state, past preference data, and allergy information, and the output is multiple emotion-based menu suggestions.

[1446] Step 5:

[1447] The server sends the generated menu plans to the user's terminal, which receives them and displays them on its screen. The input includes the menu plans, and the output is the menu plans displayed on the user's terminal.

[1448] Step 6:

[1449] The user selects one of the displayed menu plans and sends the selection to the server, with the input including the user-selected menu plan and the output received by the server.

[1450] Step 7:

[1451] The server updates the database with the received selection data, with the selected menu plan as input and updated database information as output.

[1452] Step 8:

[1453] The server generates detailed information about the selected menu, such as cooking instructions and a list of necessary ingredients, and sends it to the user terminal. The input includes the selected menu plan, and the output includes data with the detailed information.

[1454] Step 9:

[1455] The user terminal displays the received detailed information on the screen and notifies the user. The detailed information is included as an input, and information visually provided to the user is obtained as an output.

[1456] Step 10:

[1457] The user confirms the details and completes the food order on the application. The input includes the details and order information, and the output is the order data sent to the online ordering system.

[1458] 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.

[1459] 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.

[1460] 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.

[1461] [Fourth embodiment]

[1462] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1463] 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.

[1464] 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).

[1465] 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.

[1466] 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.

[1467] 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).

[1468] 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.

[1469] 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.

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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.

[1474] 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."

[1475] The present invention provides a system for making a user's life more efficient and reducing the burden on the user by automatically proposing daily menus to the user. Specific embodiments of the system are described below.

[1476] How users submit requests

[1477] When users want daily menu suggestions, they use a dedicated application or website. The application or website has a form where users can enter and submit their requests. The user enters "I would like to receive suggestions for today's dinner menu" in the form and clicks the submit button.

[1478] The server receives the request and acquires the user information.

[1479] A request sent by a user is sent to a server via the Internet. When the server receives the request, it analyzes the user ID and request content. The server then accesses a database to obtain the user's past preferences, dietary history, allergy information, etc.

[1480] Menu plan generation format

[1481] The server runs a menu generation algorithm based on the acquired user information. This generates multiple menu plans that take into account nutritional balance, seasonal ingredients, and the user's preferences. For example, the following menu plans may be generated:

[1482] 1. Chicken curry and salad

[1483] 2. Grilled salmon in foil and miso soup

[1484] 3. Stir-fried vegetables and grilled fish

[1485] Menu plans are sent to the user's terminal and the user selects the menu.

[1486] The server generates multiple menu plans, which are then sent to the user's device. The device receives the plans and displays them on the screen in a format that is easy for the user to view. The user then selects the menu that best suits their preferences from the proposed menu plans. After selecting, the device sends the selection data to the server.

[1487] The server receives the selection, updates the database, and provides the details.

[1488] The server receives the user's selected menu data and updates the database. This update accumulates new data to be reflected in future suggestions. The server then generates detailed information about the selected menu, such as cooking instructions and a list of ingredients, and sends it to the user's device. The user's device receives this information and displays it for the user to review.

[1489] Specific examples

[1490] A user opens the application, types in "Please suggest a menu for dinner tonight," and submits it. The server receives this request and retrieves the user's past preferences and allergies from a database. The server then runs a menu generation algorithm based on this information and generates a menu like this:

[1491] 1. Chicken curry

[1492] 2. Salmon baked in foil

[1493] 3. Stir-fried vegetables and grilled fish

[1494] These menu suggestions are sent to the user's device and displayed on the device. The user selects "stir-fried vegetables and grilled fish," and this selection is sent back to the server. The server receives the user's selection, updates the database, and then generates detailed cooking instructions and a list of ingredients for "stir-fried vegetables and grilled fish" and sends them to the user's device. The device displays this, and the user can then start cooking based on that information as the next step.

[1495] In this way, the system of the present invention reduces the burden of daily menu decisions for users and supports efficient and balanced dietary habits.

[1496] The processing flow will be explained below.

[1497] Step 1:

[1498] The user submits a request

[1499] A user opens an application or website, enters a request such as "Please suggest a menu for dinner tonight," and clicks the submit button.

[1500] Step 2:

[1501] The server receives the request

[1502] The server receives the request data sent by the user and analyzes the user ID and request content (e.g., "dinner menu").

[1503] Step 3:

[1504] The server retrieves the user information

[1505] The server accesses the database to obtain the user's past preferences, dietary history, allergy information, etc.

[1506] Step 4:

[1507] The server runs the menu planning algorithm

[1508] The server runs a menu generation algorithm based on the user information it has acquired, and generates multiple menu plans taking into account nutritional balance, seasonal ingredients, and the user's preferences.

[1509] Step 5:

[1510] The server sends the menu plan to the user's device.

[1511] The server generates multiple menu plans and sends them to the user's device in JSON format, which is then received by the user's device.

[1512] Step 6:

[1513] The device displays a menu plan

[1514] The user's device parses the received JSON data and displays multiple menu suggestions on the screen in an easy-to-read format for the user.

[1515] Step 7:

[1516] The user selects a menu

[1517] The user selects one of the proposed menus that suits their tastes. For example, they click on "Stir-fried vegetables and grilled fish."

[1518] Step 8:

[1519] The device sends the user's selection to the server

[1520] The data selected by the user is transmitted from the terminal to the server.

[1521] Step 9:

[1522] The server receives the selected data and updates the database

[1523] The server receives the user's selections and adds or updates this information in a database, which is then reflected in future offers.

[1524] Step 10:

[1525] The server generates detailed information

[1526] The server generates detailed information such as cooking instructions and a list of necessary ingredients for the menu selected by the user.

[1527] Step 11:

[1528] The server sends the details to the user terminal.

[1529] The server sends the generated detailed information to the user's terminal, which receives it.

[1530] Step 12:

[1531] The device will display detailed information

[1532] The user's device displays the received detailed information, allowing the user to check the cooking method and necessary ingredients.

[1533] The above is a specific processing flow in the system of the present invention.

[1534] Example 1

[1535] 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."

[1536] Modern users experience significant stress from the time and effort required to plan their daily menus. It is also difficult to plan daily menus that take into account nutritional balance and allergy prevention. Furthermore, existing systems for providing menus tailored to individual users do not adequately reflect the user's preferences or past tastes. Furthermore, they lack the ability to provide suggestions that meet the user's requests in real time, making them inefficient. The purpose of this invention is to provide a system that solves these problems and reduces the burden on users.

[1537] 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.

[1538] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences and past selection history, means for generating various menu plans based on the above data, means for transmitting the generated menu plans to a user terminal, means for receiving the menu selected by the user, means for updating the received selection in a database, means for providing the user with detailed information on the selected menu, means for executing an algorithm for optimizing menu proposals using a generative AI model, and means for generating menu plans in real time based on user input via prompt sentences. This allows the user to efficiently decide on daily menus and receive proposals that fully reflect nutritional balance and requests for specific ingredients.

[1539] "Means for receiving requests from users" refers to the function of obtaining the content of requests or inquiries from users via an interface that allows the user to make specific requests or inquiries to the system.

[1540] "Means for obtaining user preferences and past selection history" refers to a method for retrieving user past behavior and preference data from a database or other storage device.

[1541] "Means for generating diverse menu suggestions" refers to algorithms or software that automatically create multiple meal suggestions based on collected user information.

[1542] "Means for transmitting the generated menu plan to the user terminal" refers to a communication means for transmitting the proposal created by the server to the user's device.

[1543] "Means for receiving a menu selected by the user" refers to a function that allows the system to receive the content selected by the user from the proposed menu plans.

[1544] "Means for updating the database with the received selection" refers to a method for recording the user's selection in the database and updating the data accordingly.

[1545] The "means for providing the user with detailed information about the selected menu" refers to a function that provides the user with information about the menu selected by the user, such as specific cooking methods and necessary ingredients.

[1546] "Means for executing an algorithm that uses a generative AI model to optimize menu suggestions" refers to a method for using AI technology to execute a calculation procedure to suggest optimal menu suggestions while taking into account the user's preferences and nutritional balance.

[1547] "Means for generating menu plans in real time based on user input via prompt sentences" refers to a function that analyzes text information entered by the user in real time and instantly generates an appropriate menu plan based on that information.

[1548] The present invention relates to a system that efficiently suggests daily menus for a user, and specific embodiments thereof will be described in detail below.

[1549] Hardware and software used

[1550] Server: Serves as the central processing unit for running the menu generation algorithm, parsing user requests, and managing data. The server includes a database management system (e.g., MySQL or PostgreSQL) and a program execution environment (e.g., Python or Java).

[1551] User device: An interface for receiving requests from users and displaying the generated menu plans. Devices include mobile devices, tablets, and desktop computers, each with a dedicated application or web browser installed.

[1552] Communications infrastructure: Includes internet connectivity to enable data communication between user devices and servers.

[1553] Specific operation of the system

[1554] 1. Submitting a User Request

[1555] A user opens a dedicated application or website and types in "I want suggestions for dinner tonight," which generates an HTTP request and sends it to the server.

[1556] 2. The server receives and analyzes the request

[1557] The server receives a request from the user over the Internet, which includes the user ID and the specific request content. The server analyzes it and proceeds to the next step.

[1558] 3. Obtaining User Information

[1559] The server accesses the database and retrieves past preferences, dietary history, and allergy information corresponding to the user ID, thereby providing customized information for each user.

[1560] 4. Menu planning

[1561] The server runs a menu generation algorithm implemented using Python etc. based on the acquired user information. Using an AI generation model, multiple menu suggestions are generated that take into account nutritional balance, seasonal ingredients, and the user's preferences.

[1562] 5. Send and display menu plans

[1563] The menu plan generated by the server is sent to the user's device in JSON format, etc. The user's device then uses front-end technologies such as React and Vue.js to display the plan on a user interface based on the received data.

[1564] 6. User menu selection

[1565] The user selects from the displayed menu plans the one that suits his or her taste, and the selection is sent back to the server from the user terminal.

[1566] 7. Server receives selection and updates database

[1567] The server receives the user's selection and records it in a database, which is then used to inform future suggestions.

[1568] 8. Providing more information

[1569] The server generates detailed cooking instructions and a list of ingredients for the selected menu, and sends this information to the user's terminal, allowing the user to start cooking.

[1570] Specific examples

[1571] For example, a user opens an application, types in "Please suggest a menu for dinner tonight," and submits it. The server receives this request and retrieves the user's past preferences and allergies from a database. The server then runs a menu generation algorithm based on this information and generates a menu like this:

[1572] Chicken curry

[1573] Salmon baked in foil

[1574] Stir-fried vegetables and grilled fish

[1575] The generated menu plan is sent to the user's device and displayed on the device. The user selects "stir-fried vegetables and grilled fish," and this selection is sent to the server. The server receives the selection, updates the database, and then generates detailed cooking instructions and an ingredient list for "stir-fried vegetables and grilled fish" and sends them to the user's device. The user's device displays this, and the user can start cooking based on that information as the next step.

[1576] Prompt Sentence Examples

[1577] An example of a prompt for user input is "Please suggest a menu for today's dinner. Please provide multiple menu suggestions taking into consideration past preferences, dietary history, and allergy information."

[1578] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1579] Step 1: Submitting a User Request

[1580] The user opens a dedicated application or website, enters information into a form such as "I'd like suggestions for today's dinner menu," and clicks the submit button.

[1581] Input: User request (e.g., "Please suggest what to make for dinner today") and user ID.

[1582] Output: HTTP request (including user ID and request content).

[1583] Specific actions: Open the application on your smartphone or computer, enter your request into the input form, and tap or click the submit button.

[1584] Step 2: Server receives and analyzes the request

[1585] The server receives HTTP requests from users over the Internet.

[1586] Input: HTTP request containing user ID and request content.

[1587] Output: The request is parsed to extract the user ID and the desired content.

[1588] Specific operation: The server analyzes the received request and records the request content and user ID in a log file or memory.

[1589] Step 3: Get user information

[1590] The server accesses the database and retrieves past preferences, dietary history, and allergy information corresponding to the user ID.

[1591] Input: User ID.

[1592] Output: Data including the user's past preferences, dietary history, and allergy information.

[1593] Specific operation: The server accesses the database using an SQL query or similar to obtain user information.

[1594] Step 4: Generate a menu plan

[1595] The server uses an AI model to run a menu generation algorithm based on the acquired user information.

[1596] Input: User's past preferences, dietary history, and allergy information.

[1597] Output: Multiple menu plans.

[1598] Specific operation: The server uses an AI model implemented in Python or other languages ​​to generate menu plans based on the collected information, taking into account nutritional balance and seasonal ingredients.

[1599] Step 5: Send and view menu plans

[1600] The server sends the generated menu plan to the user's terminal.

[1601] Input: Multiple menu ideas.

[1602] Output: HTTP response containing the menu suggestions.

[1603] Specific operation: The server encodes the menu plan in JSON format and sends it to the user's device, which receives it and displays it on the screen.

[1604] Step 6: User menu selection

[1605] The user selects from the displayed menu plans one that suits their tastes.

[1606] Input: Multiple menu ideas.

[1607] Output: Data of selected menu plan.

[1608] Specific actions: The user taps or clicks to select the menu item and clicks the submit button.

[1609] Step 7: Server receives selection and updates database

[1610] The server receives the user's selection and records it in a database.

[1611] Input: Selected menu plan data.

[1612] Output: The updated selections in the database.

[1613] What happens: The server writes the received selection into a database and uses it in future suggestions.

[1614] Step 8: Provide more information

[1615] The server generates detailed cooking instructions and a list of ingredients required for the selected menu and sends them to the user's terminal.

[1616] Input: Selected menu plan data.

[1617] Output: Cooking instructions and ingredients list.

[1618] Specific operation: The server generates information including detailed cooking instructions and necessary ingredients, encodes it in JSON format, and sends it to the user's device.

[1619] Step 9: View detailed information

[1620] The user's terminal receives the detailed information and displays it on the application screen.

[1621] Input: Cooking instructions and ingredient list.

[1622] Output: User-visible recipe and ingredient list.

[1623] Specific operation: The user device analyzes the received data and displays it on the screen in a visually easy-to-understand format. The user can then start cooking based on the displayed information.

[1624] (Application example 1)

[1625] 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."

[1626] Current food delivery services have the challenge of making it difficult for users to receive menu suggestions that take into account their preferences and allergies, and then reflect those menu suggestions in their delivery orders. Furthermore, deciding on daily menus takes a lot of time and effort, making it difficult to maintain a balanced diet efficiently. Therefore, there is a need for a system that can streamline users' lives and reduce their burden.

[1627] 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.

[1628] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences and past selection history, means for generating multiple menu plans based on the above data, means for transmitting the generated menu plans to a user terminal, means for receiving the menu selected by the user, means for updating the received selection in a database, means for providing the user with detailed information about the selected menu, means for linking with an external food delivery service based on the generated menu plan and ordering the suggested menu, and means for executing order processing and tracking delivery status. This allows the user to receive menu suggestions tailored to their preferences and health condition and smoothly order the menu through a food delivery service.

[1629] A "means for receiving a request" is a device or interface that receives input from a user and transmits the content of that input to the system.

[1630] The "means for acquiring user preferences and past selection history" refers to a device or process that can collect information about a user's past behavioral data and preferences and acquire it from a database.

[1631] The "means for generating multiple menu plans" refers to an algorithm or program that creates multiple menu plans based on collected data according to the user's needs and preferences.

[1632] The "means for transmitting to the user terminal" refers to a communication device or protocol for transmitting the generated menu plan to the user's device.

[1633] The "means for receiving the selected menu" is an interface or device for returning the information about the menu selected by the user back to the system.

[1634] A "means for updating the database" is a method or device for adding or updating new user selection information to an existing user database.

[1635] The "means for providing detailed information" refers to a device or system that provides the user with specific information about the selected menu, such as cooking instructions and a list of ingredients.

[1636] The "means for linking with food delivery services" refers to communication methods and protocols for sharing information with external delivery services based on the generated menu plan and placing orders.

[1637] The "means for executing order processing" refers to the process or system that confirms the delivery service order based on the menu selected by the user and manages the processing.

[1638] A "delivery status tracking means" is a system or device that tracks the progress of food delivery after an order is placed and provides that information to the user.

[1639] The present invention is a system that supports users in deciding on a menu and works in conjunction with a food delivery service. This system generates an optimal menu plan based on the user's preferences and allergy information, and allows the user to order the menu through a delivery service. An embodiment of this system is described in detail below.

[1640] Program Overview

[1641] The program of this system mainly consists of the following components:

[1642] 1. User Interface (Smartphone Application)

[1643] 2. Request Processing Module

[1644] 3. Database

[1645] 4. Menu Generation Module

[1646] 5. Food delivery integration module

[1647] 6. Order Module

[1648] Hardware and software used

[1649] 1. User interface: Smartphone application (iOS / Android)

[1650] The application allows users to enter requests, receive menu suggestions, and order selected meals for delivery.

[1651] 2. Request processing module: Flask (Python framework)

[1652] This is the module by which the server receives and analyzes user requests.

[1653] 3. Database: SQLite

[1654] This is a database for storing user preference data and allergy information.

[1655] 4. Menu generation module: Python algorithm (generative AI model can also be applied)

[1656] This is an algorithm that generates optimal menu plans based on user information.

[1657] 5. Food delivery integration module: API integration

[1658] This module proposes and orders food delivery menus based on the generated menu plan.

[1659] 6. Order module: Flask (Python framework)

[1660] This module allows users to order their selected meals from food delivery services and track their status.

[1661] Program processing flow

[1662] When the server receives a request entered by the user, it retrieves past preference data and allergy information from a database based on the user's ID. Using this data, the menu generation module generates an optimal menu plan and sends it to the user interface. The user selects one of the proposed menus, and the selection is sent back to the server, updating the database. The server then orders the selected menu from a delivery service through the food delivery integration module. Once the order is confirmed, its status is tracked and notified to the user interface.

[1663] Specific examples

[1664] A user opens a smartphone application and types "Suggest a menu for dinner today." The server receives this request and retrieves the user's preference data and allergy information from the database. The menu generation module generates a menu like the following and sends it to the user interface:

[1665] 1. Chicken curry and salad

[1666] 2. Grilled salmon in foil and miso soup

[1667] 3. Stir-fried vegetables and grilled fish

[1668] When the user selects "grilled salmon in foil with miso soup," the selection is again sent to the server, which updates the database. The server then orders the selected meal from a delivery service through the food delivery integration module and tracks its status.

[1669] Prompt Sentence Examples

[1670] "Based on user information (preferences, allergy information), please suggest today's dinner menu from the following options:

[1671] 1. Chicken curry and salad

[1672] 2. Grilled salmon in foil and miso soup

[1673] 3. Stir-fried vegetables and grilled fish

[1674] This will make users' lives more efficient and reduce the burden of deciding on daily menus.

[1675] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1676] Step 1:

[1677] User enters and submits request

[1678] Specific operation: The user opens the smartphone application, enters "Please suggest a menu for today's dinner," and taps the send button.

[1679] Input: User request ("Please suggest a menu for dinner today").

[1680] Output: The request data is sent to the server.

[1681] Step 2:

[1682] The server receives and analyzes the request

[1683] Specific operation: The server receives a request from the Flask application and analyzes the request content.

[1684] Input: Request data from the user.

[1685] Output: Parsed request content and user ID.

[1686] Step 3:

[1687] Retrieve user preferences and past selection history from a database

[1688] What it does: The server uses an SQL query to retrieve the user's preferences and past selections from an SQLite database.

[1689] Input: User ID.

[1690] Output: User preference data and allergy information.

[1691] Step 4:

[1692] Run the menu generation algorithm

[1693] Specific operation: The server runs a menu generation algorithm using Python, and the generative AI model creates multiple menu plans based on user information.

[1694] Input: User preference data and allergy information.

[1695] Output: Multiple generated menu plans.

[1696] Step 5:

[1697] The generated menu plan is sent to the user's device

[1698] Specific operation: The server sends the generated menu plan to the user's smartphone application.

[1699] Input: Multiple generated menu plans.

[1700] Output: The menu plan is displayed on the user's terminal.

[1701] Step 6:

[1702] User selects menu

[1703] Specific actions: The user selects one of the suggested meal plans and taps that selection in the application.

[1704] Input: Menu plan selected by the user.

[1705] Output: The selected menu data is sent to the server.

[1706] Step 7:

[1707] The server receives the selection and updates the database

[1708] Specific operation: The server receives the user's selection and updates the database with new preference data.

[1709] Input: Menu data selected by the user.

[1710] Output: The database is updated.

[1711] Step 8:

[1712] Generate detailed information and provide it to the user

[1713] Specific operation: The server generates detailed information about the selected menu, such as cooking instructions and a list of necessary ingredients, and sends it to the user's terminal.

[1714] Input: Selected menu data.

[1715] Output: Detailed information is displayed on the user's terminal.

[1716] Step 9:

[1717] The server coordinates with the food delivery service to execute the order

[1718] Specific operation: The server executes an order based on the selected menu using an external food delivery API.

[1719] Input: Selected menu data.

[1720] Output: Order confirmation information.

[1721] Step 10:

[1722] Track delivery status and notify users

[1723] Specific operation: The server tracks the delivery status through the food delivery service's API and notifies the user's smartphone application of that information.

[1724] Input: Delivery status data.

[1725] Output: Delivery status information is displayed on the user's terminal.

[1726] 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.

[1727] The present invention provides a system for making a user's life more efficient and reducing the burden by automatically suggesting daily menus based on the user's emotions. Specific embodiments of the system are described below.

[1728] How users submit requests

[1729] When users want daily menu suggestions, they use a dedicated application or website. The application or website has a form where users can enter and submit their requests. The user enters "I would like to receive suggestions for today's dinner menu" in the form and clicks the submit button.

[1730] The server receives the request and acquires the user information.

[1731] A request sent by a user is sent to a server via the Internet. When the server receives the request, it analyzes the user ID and the request content. The server then accesses a database to obtain the user's past preferences, dietary history, allergy information, and emotional data.

[1732] Emotion analysis using emotion engines

[1733] The server is equipped with an emotion engine that analyzes the user's current emotional state. The emotion engine has an algorithm that infers emotions from the user's input, behavioral patterns, and past preferences. The results of this emotion analysis are reflected in the generation of the next menu.

[1734] Menu plan generation format

[1735] The server runs a menu generation algorithm based on the acquired user information and the results of emotion analysis. This generates multiple menu plans that take into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state. For example, the following menu plans may be generated:

[1736] 1. Chicken curry and salad (for those feeling energized)

[1737] 2. Grilled salmon in foil and miso soup (for those in a relaxing mood)

[1738] 3. Stir-fried vegetables and grilled fish (for those in a healthier mood)

[1739] Menu plans are sent to the user's terminal and the user selects the menu.

[1740] The server generates multiple menu plans, which are then sent to the user's device. The device receives the plans and displays them on the screen in a format that is easy for the user to view. The user then selects the menu that best suits their preferences from the proposed menu plans. After selecting, the device sends the selection data to the server.

[1741] The server receives the selection, updates the database, and provides the details.

[1742] The server receives the user's selected menu data and updates the database. This update accumulates new data to be reflected in future suggestions. The server then generates detailed information about the selected menu, such as cooking instructions and a list of ingredients, and sends it to the user's device. The user's device receives this information and displays it for the user to review.

[1743] Specific examples

[1744] The user types "Please suggest a menu for dinner tonight" into the application and submits it. The server receives this request and retrieves the user's past preferences and allergies from the database. The emotion engine also analyzes the user's current emotional state. For example, if the server determines that the user is in a relaxing mood, it will take this into account and generate the following menu:

[1745] 1. Salmon baked in foil

[1746] 2. Deep-fried Chicken Tatsuta

[1747] 3. Boiled pumpkin

[1748] These menu suggestions are sent to the user's device and displayed on the device. The user selects "Baked Salmon in Foil," and this selection is sent back to the server. The server receives the user's selection, updates the database, and then generates detailed cooking instructions and a list of ingredients for "Baked Salmon in Foil," which are sent to the user's device. The device displays this, and the user can then start cooking based on that information as the next step.

[1749] In this way, the system of the present invention reduces the burden of daily menu decisions for users and supports an efficient and balanced dietary lifestyle that responds to emotions.

[1750] The processing flow will be explained below.

[1751] Step 1:

[1752] The user submits a request

[1753] A user opens an application or website, enters a request such as "Please suggest a menu for dinner tonight," and clicks the submit button.

[1754] Step 2:

[1755] The server receives the request

[1756] The server receives the request data sent by the user and analyzes the user ID and request content (e.g., "dinner menu").

[1757] Step 3:

[1758] The server retrieves the user information

[1759] The server accesses the database and acquires the user's past preferences, dietary history, allergy information, etc. In addition, it also acquires past emotional data.

[1760] Step 4:

[1761] The server runs the emotion engine

[1762] The emotion engine installed on the server analyzes the user's emotional state and infers the user's current emotions by referencing the user's input data, behavioral patterns, and past preference data.

[1763] Step 5:

[1764] The server generates a menu plan based on the emotional state.

[1765] The server runs a menu generation algorithm based on the analysis results of the emotion engine. It generates multiple menu suggestions, taking into account the user's emotional state, past preferences, nutritional balance, seasonal ingredients, etc. For example, based on the analysis result that the user is in the mood to relax, it generates the following menu suggestions:

[1766] 1. Grilled salmon in foil and miso soup

[1767] 2. Deep-fried chicken and salad

[1768] 3. Boiled pumpkin and rice

[1769] Step 6:

[1770] The server sends the menu plan to the user's device.

[1771] The server generates multiple menu plans and sends them to the user's device in JSON format, which is then received by the user's device.

[1772] Step 7:

[1773] The device displays a menu plan

[1774] The user's device parses the received JSON data and displays multiple menu suggestions on the screen in an easy-to-read format for the user.

[1775] Step 8:

[1776] The user selects a menu

[1777] The user selects one of the proposed menus that suits their tastes. For example, they click on "grilled salmon in foil and miso soup."

[1778] Step 9:

[1779] The device sends the user's selection to the server

[1780] The data selected by the user is transmitted from the terminal to the server.

[1781] Step 10:

[1782] The server receives the selected data and updates the database

[1783] The server receives the user's selections and adds or updates this information to a database, accumulating new data to be reflected in future suggestions.

[1784] Step 11:

[1785] The server generates detailed information

[1786] The server generates detailed information such as cooking instructions and a list of necessary ingredients for the menu selected by the user.

[1787] Step 12:

[1788] The server sends the details to the user terminal.

[1789] The server sends the generated detailed information to the user's terminal, which receives it.

[1790] Step 13:

[1791] The device will display detailed information

[1792] The user's device displays the received detailed information, allowing the user to check the cooking method and necessary ingredients.

[1793] The above is a specific processing flow in the system of the present invention.

[1794] Example 2

[1795] 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."

[1796] In today's busy lifestyles, planning daily meals can be a burden for many people. It is also difficult to create satisfying meal plans because it is difficult to propose menus that match individual preferences and feelings. Furthermore, conventional systems often fall short in proposing meals that take into account food allergies and likes and dislikes of specific ingredients.

[1797] 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.

[1798] In this invention, the server includes means for receiving requests from a user, means for acquiring the user's preferences, past selection history, and emotional data, means for generating multiple meal plans based on the above data and the emotion analysis results, means for transmitting the generated meal plans to a user terminal, means for receiving the meal plan selected by the user, means for updating the received selection in the database, and means for providing the user with detailed information on the selected meal plan, thereby making it possible to propose an appropriate meal plan according to the user's individual emotional state and preferences, thereby reducing the burden of meal planning.

[1799] The "means for receiving a request from a user" is a mechanism that allows a user to send a request for a meal plan proposal to a server through a dedicated application or website.

[1800] "Means for acquiring user preferences, past selection history, and emotional data" refers to a function for acquiring data relating to the user's past preferences, selection history, and emotional state from a database.

[1801] The "means for generating multiple meal plans" is a process that includes an algorithm that automatically generates multiple meal plans based on the acquired data and that are tailored to the user's emotions and preferences.

[1802] The "means for sending the generated meal plan to the user's terminal" is a mechanism that has the function of sending the menu plan generated by the server to the user's terminal, such as a smartphone or PC.

[1803] The "means for receiving a user selected meal plan" is a function that transmits the user's selection from the proposed menu plans to the server and receives the selection.

[1804] The "means for updating the selection to the database" is the process by which the user's selection data is recorded in the database and updated to reflect future suggestions.

[1805] The "means for providing detailed information about the selected meal plan to the user" is a mechanism for generating detailed information such as cooking methods and ingredient lists required for the selected meal plan and providing it to the user.

[1806] The present invention provides a system for making a user's life more efficient and reducing their burden by automatically proposing a daily meal plan based on their emotions. Specific embodiments of the system are described below.

[1807] Hardware and Software

[1808] The present invention uses the following major hardware and software:

[1809] Server: A computer system that receives requests from users via the Internet, accesses a database, and processes data.

[1810] User device: A computer device used by a user, such as a smartphone, tablet, or PC.

[1811] Database: A data management system for storing user preferences, past selection history, allergy information, emotional data, etc.

[1812] Emotion engine: Algorithms and software for analyzing the user's emotional state.

[1813] Data acquisition and processing flow

[1814] A user submits a request through a dedicated application or website. Specifically, the user enters a prompt such as "Please suggest a menu for dinner tonight" and clicks the send button. The request is sent to the server via the Internet.

[1815] When the server receives the request, it identifies the user and retrieves the user's past preferences, selection history, allergy information, and emotional data from a database. The emotion engine then analyzes the user's emotional state using algorithms based on the user's input, past behavioral patterns, and preference information.

[1816] The server runs a menu generation algorithm based on this data and the results of sentiment analysis. The generated meal plans take into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state. For example, the following meal plans may be suggested:

[1817] 1. Chicken curry and salad (for those feeling energized)

[1818] 2. Grilled salmon in foil and miso soup (for those in a relaxing mood)

[1819] 3. Stir-fried vegetables and grilled fish (for those in a healthier mood)

[1820] The generated meal plan is sent from the server to the user's device, which receives it and displays it on the screen. The user selects the most preferred menu from the proposed menus, and the selection data is sent back to the server.

[1821] When the server receives the user's selection data, it updates the database and stores the new user information. Next, it generates detailed information about the selected meal plan (e.g., cooking instructions and a list of ingredients) and sends it to the user's device. The device receives this information and displays it on the screen.

[1822] Specific examples

[1823] 1. The user types in the application, "Please suggest what to have for dinner today," and submits it.

[1824] 2. The server receives the request, retrieves the user's past preferences and allergies from the database, and then analyzes the user's current emotional state using the emotion engine.

[1825] 3. If the server determines that you are in a relaxing mood, it generates a menu like this:

[1826] Salmon baked in foil

[1827] Deep-fried Chicken Tatsuta

[1828] Boiled pumpkin

[1829] 4. The generated menu plan is sent from the server to the user's device and displayed.

[1830] 5. The user selects "Baked Salmon" and the selection is sent back to the server.

[1831] 6. The server receives the selection and updates the database. It generates detailed instructions and a list of ingredients for "Baked Salmon in Foil" and sends them to the user's device. The device displays the instructions and the user begins cooking.

[1832] In this way, the present invention reduces the burden of daily meal planning for the user and supports the user's eating habits by proposing appropriate meal plans according to emotions.

[1833] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1834] Step 1:

[1835] The user submits a request.

[1836] Input: A user fills in a form on an application or website with the prompt "Suggest something for dinner tonight" and clicks the submit button.

[1837] Operation: The user terminal generates request data and sends it to the server via the Internet.

[1838] Output: The request data is sent to the server.

[1839] Step 2:

[1840] The server receives the request and retrieves the user information.

[1841] Input: Request data from the user.

[1842] How it works: The server receives the request, identifies the user ID, and then retrieves the user's past preferences, selection history, allergy information, and emotional data from a database.

[1843] Output: The server receives the user's past preferences, selection history, allergy information, and emotional data.

[1844] Step 3:

[1845] Emotion analysis is performed on the server using an emotion engine.

[1846] Input: Captured user's past preferences, choice history, and emotional data.

[1847] How it works: The server uses the emotion engine to analyze the user's current emotional state. The emotion engine uses algorithms to infer emotions based on the user's input, past behavioral patterns, and preferences.

[1848] Output: An analysis of the user's current emotional state.

[1849] Step 4:

[1850] The server runs the menu generation algorithm.

[1851] Input: User's past preferences, selection history, allergy information, and sentiment analysis results.

[1852] How it works: The server runs a menu generation algorithm based on the acquired data and the results of sentiment analysis. The algorithm generates multiple meal plans taking into account nutritional balance, seasonal ingredients, the user's preferences, and their emotional state.

[1853] Output: Multiple meal plans will be generated.

[1854] Step 5:

[1855] The server transmits the generated meal plan to the user terminal.

[1856] Input: Multiple generated meal plans.

[1857] How it works: The server generates a meal plan and sends it to the user's device.

[1858] Output: The meal plan is sent to the user's device.

[1859] Step 6:

[1860] The user selects a meal plan.

[1861] Input: Multiple meal plans displayed on a user's device.

[1862] How it works: The user selects the meal plan they like best from the ones displayed on the screen. The device generates the selection data and sends it to the server.

[1863] Output: The selected meal plan data is sent to the server.

[1864] Step 7:

[1865] The server receives the selected plan and updates the database.

[1866] Input: Meal plan data selected by the user.

[1867] Action: The server receives the selection and updates the database, storing the new user information.

[1868] Output: The database is updated.

[1869] Step 8:

[1870] The server transmits detailed information about the selected meal plan to the user terminal.

[1871] Input: The meal plan selected by the user.

[1872] How it works: The server generates detailed information for the selected meal plan (such as cooking instructions and a list of ingredients) and sends it to the user's device.

[1873] Output: Detailed information is sent to the user's terminal and displayed.

[1874] In this way, by clearly indicating the input, operation, and output at each step, the processing flow of the entire system is explained in detail.

[1875] (Application example 2)

[1876] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1877] While conventional menu suggestion systems generate menu plans based on the user's preferences and past selection history, they have the problem of being unable to suggest menus that reflect the user's emotional state on that day. Furthermore, they lack a means to actually order the suggested menus, requiring the user to manually order again, which is inefficient.

[1878] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server is equipped with an emotion engine that analyzes the user's current emotional state, and includes means for generating a menu plan based on the emotional state, means for ordering food based on the generated menu plan, means for receiving a request from the user, means for acquiring the user's preferences and past selection history, means for generating multiple menu plans, means for transmitting the generated menu plans to the user terminal, means for receiving the menu selected by the user, means for updating the received selection in the database, and means for providing the user with detailed information on the selected menu. This makes it possible to propose an appropriate menu based on the user's emotional state and order it as is.

[1879] The "means for receiving a request from a user" is an interface for receiving an input from the user requesting a desired menu proposal from the system.

[1880] The "means for acquiring user preferences and past selection history" is a function for acquiring information about the user's past food preferences and history from a database.

[1881] A "means for generating multiple menu suggestions" is an algorithm that generates multiple meal menu suggestions based on the user's preferences, past selection history, and emotional state.

[1882] "Means for transmitting the generated menu proposal to the user terminal" is a communication function for transmitting the generated menu proposal to the user's smartphone or other device for display.

[1883] The "means for receiving a menu selected by the user" is a function for receiving a menu selected by the user from the proposed menu.

[1884] The "means for updating the database with the received selections" is a function that adds the user's selected menu information to the database and saves the data for future suggestions.

[1885] The "means for providing the user with detailed information about the selected menu" is a function that provides detailed information about the specific menu selected by the user, such as cooking instructions and a list of necessary ingredients.

[1886] The "emotion engine that analyzes the user's current emotional state" is an algorithm that predicts and analyzes the user's current emotional state from their input and behavioral patterns.

[1887] The "means for generating menu suggestions based on emotional state" is an algorithm that generates appropriate menu suggestions depending on the analyzed emotional state.

[1888] The "means for ordering food based on the generated menu plan" is a function for ordering food and dishes based on a menu selected by the user.

[1889] The present invention provides a system that proposes daily menus based on the user's emotional state and allows the user to order food directly. Specific embodiments of the system are described below.

[1890] Overview of the embodiment

[1891] The system consists of an application installed on the user's smartphone or other device and a server. When the user sends a menu suggestion request through the application, the server receives it and retrieves the user's past preference history and emotional state from a database. The emotion engine analyzes the user's emotional state, and based on that, the server generates a menu plan and suggests it to the user. Food can then be ordered directly based on the menu selected by the user.

[1892] Hardware and software used

[1893] Hardware:

[1894] User devices: smartphones, tablets, etc.

[1895] Server: Receives requests, manages the database, and generates menu plans.

[1896] software:

[1897] Flask: A lightweight Python web application framework that acts as a server, receiving user requests and returning responses.

[1898] Database management system: Manages users' past preference history, allergy information, etc.

[1899] Emotion engine: An algorithm that analyzes a user's current emotional state from their input and behavioral patterns.

[1900] Ordering system: The ability to order food and dishes online.

[1901] Process example

[1902] 1. The user types into the application, "I want suggestions for what to have for dinner today."

[1903] 2. The server receives this request and retrieves past preferences and allergy information from a database based on the user's ID.

[1904] 3. The emotion engine analyzes the user's current emotional state. For example, if it finds that the user is in a "relaxing" mood, the server will generate a menu of relaxation options based on this.

[1905] 4. The server sends the generated menu suggestions to the user's device, which displays them on the screen. For example, the following menu suggestions are proposed:

[1906] Salmon baked in foil

[1907] Deep-fried Chicken Tatsuta

[1908] Boiled pumpkin

[1909] 5. The user selects "Baked Salmon in Foil" and the selection is sent to the server, which updates the database with the selection and generates detailed cooking instructions and an ingredient list for the selected dish.

[1910] 6. The user places a food order based on "Baked Salmon" in the application. The server accepts the order and connects it to the relevant online food ordering system.

[1911] Specific prompt examples

[1912] User input: "I'm feeling a little stressed and want some food to help me relax."

[1913] Server response: "Relaxed menu plan: Baked salmon, deep-fried chicken, and simmered pumpkin. Which would you like?"

[1914] User Choice: "I'd like some grilled salmon in foil. I'll order that."

[1915] Server: "Your order is complete. Please wait a moment for delivery."

[1916] In this way, the system of the present invention can improve the efficiency of the user's daily eating habits and provide appropriate menus according to their emotions.

[1917] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1918] Step 1:

[1919] The user starts the application and inputs and sends a request such as "I want to have today's dinner menu suggested." The input also includes an emotional state such as "I want to relax." The server then receives the user's request.

[1920] Step 2:

[1921] The server analyzes the user request, extracts the user ID and input emotional state, and then retrieves the user's past preference data and allergy information from the database. The input includes the user ID, emotional state, past preference data, and allergy information, and the output is the analyzed user information.

[1922] Step 3:

[1923] The emotion engine further refines the current emotional state from the user's input and behavioral patterns and outputs the result. The input includes the initial input of the emotional state from the user, past behavioral patterns, and preference data, and the output is the refined current emotional state.

[1924] Step 4:

[1925] The server generates multiple menu suggestions based on the emotional state obtained from the emotion engine. The inputs include the examined emotional state, past preference data, and allergy information, and the output is multiple emotion-based menu suggestions.

[1926] Step 5:

[1927] The server sends the generated menu plans to the user's terminal, which receives them and displays them on its screen. The input includes the menu plans, and the output is the menu plans displayed on the user's terminal.

[1928] Step 6:

[1929] The user selects one of the displayed menu plans and sends the selection to the server, with the input including the user-selected menu plan and the output received by the server.

[1930] Step 7:

[1931] The server updates the database with the received selection data, with the selected menu plan as input and updated database information as output.

[1932] Step 8:

[1933] The server generates detailed information about the selected menu, such as cooking instructions and a list of necessary ingredients, and sends it to the user terminal. The input includes the selected menu plan, and the output includes data with the detailed information.

[1934] Step 9:

[1935] The user terminal displays the received detailed information on the screen and notifies the user. The detailed information is included as an input, and information visually provided to the user is obtained as an output.

[1936] Step 10:

[1937] The user confirms the details and completes the food order on the application. The input includes the details and order information, and the output is the order data sent to the online ordering system.

[1938] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1939] 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.

[1940] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1941] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1942] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1943] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1944] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1945] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1946] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1947] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1948] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1949] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1950] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1951] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1952] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1953] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1954] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1955] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1956] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1957] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1958] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1959] The following is further disclosed regarding the above embodiment.

[1960] (Claim 1)

[1961] means for receiving a request from a user;

[1962] a means for obtaining user preferences and past selection history;

[1963] A means for generating a plurality of menu plans based on the data;

[1964] means for transmitting the generated menu plan to a user terminal;

[1965] means for receiving a user selected menu;

[1966] a means for updating the received selections to a database;

[1967] The system includes means for providing detailed information about a selected menu to a user.

[1968] (Claim 2)

[1969] The system of claim 1, further comprising an algorithm that performs statistical analysis based on a user's past preference data to generate more appropriate menus.

[1970] (Claim 3)

[1971] 2. The system according to claim 1, further comprising means for filtering the user's allergy information and likes and dislikes of specific ingredients and reflecting these in the menu suggestions.

[1972] "Example 1"

[1973] (Claim 1)

[1974] means for receiving a request from a user;

[1975] a means for obtaining user preferences and past selection history;

[1976] A means for generating various menu plans based on the above data;

[1977] means for transmitting the generated menu plan to a user terminal;

[1978] means for receiving a user selected menu;

[1979] a means for updating the received selections to a database;

[1980] means for providing the user with detailed information about the selected menu;

[1981] means for executing an algorithm that uses the generative AI model to optimize menu suggestions;

[1982] a means for generating a menu plan in real time based on a user input using a prompt sentence;

[1983] A system including:

[1984] (Claim 2)

[1985] The system of claim 1, further comprising an algorithm that performs statistical analysis based on a user's past preference data to generate more appropriate menus.

[1986] (Claim 3)

[1987] 2. The system according to claim 1, further comprising means for filtering the user's allergy information and likes and dislikes of specific ingredients and reflecting these in the menu suggestions.

[1988] "Application Example 1"

[1989] (Claim 1)

[1990] means for receiving a request from a user;

[1991] a means for obtaining user preferences and past selection history;

[1992] A means for generating a plurality of menu plans based on the data;

[1993] means for transmitting the generated menu plan to a user terminal;

[1994] means for receiving a user selected menu;

[1995] a means for updating the received selections to a database;

[1996] means for providing the user with detailed information about the selected menu;

[1997] A means to link with an external food delivery service and order the proposed menu based on the generated menu plan;

[1998] A system that includes a means to fulfill orders and track delivery status.

[1999] (Claim 2)

[2000] The system of claim 1, further comprising an algorithm that performs statistical analysis based on a user's past preference data to generate more appropriate menus.

[2001] (Claim 3)

[2002] 2. The system according to claim 1, further comprising means for filtering the user's allergy information and likes and dislikes of specific ingredients and reflecting these in the menu suggestions.

[2003] "Example 2: Combining Emotion Engines"

[2004] (Claim 1)

[2005] means for receiving a request from a user;

[2006] means for acquiring user preferences, past selection history, and emotion data;

[2007] A means for generating multiple meal plans based on the above data and sentiment analysis results;

[2008] means for transmitting the generated meal plan to a user terminal;

[2009] means for receiving a user selected meal plan;

[2010] a means for updating the received selections to a database;

[2011] The system includes means for providing detailed information about the selected meal plan to the user.

[2012] (Claim 2)

[2013] The system of claim 1, further comprising an algorithm that performs statistical analysis based on the user's past preference data and emotional data to generate a more appropriate meal plan.

[2014] (Claim 3)

[2015] 2. The system according to claim 1, further comprising means for filtering the user's allergy information and likes and dislikes of specific ingredients and reflecting these in the proposed meal plan.

[2016] "Application example 2 when combining emotion engines"

[2017] (Claim 1)

[2018] means for receiving a request from a user;

[2019] a means for obtaining user preferences and past selection history;

[2020] A means for generating a plurality of menu plans based on the data;

[2021] means for transmitting the generated menu plan to a user terminal;

[2022] means for receiving a user selected menu;

[2023] a means for updating the received selections to a database;

[2024] means for providing the user with detailed information about the selected menu;

[2025] a means for generating a menu plan based on an emotion engine that analyzes a user's current emotional state;

[2026] The system includes means for ordering food based on the generated menu plan.

[2027] (Claim 2)

[2028] The system of claim 1, further comprising an algorithm that performs statistical analysis based on a user's past preference data to generate more appropriate menus.

[2029] (Claim 3)

[2030] 2. The system according to claim 1, further comprising means for filtering the user's allergy information and likes and dislikes of specific ingredients and reflecting these in the menu suggestions. [Explanation of symbols]

[2031] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a request from a user; a means for obtaining user preferences and past selection history; A means for generating a plurality of menu plans based on the data; means for transmitting the generated menu plan to a user terminal; means for receiving a user selected menu; a means for updating the received selections to a database; The system includes means for providing detailed information about a selected menu to a user.

2. The system of claim 1, further comprising an algorithm that performs statistical analysis based on the user's past preference data to generate more appropriate menus.

3. 2. The system according to claim 1, further comprising means for filtering the user's allergy information and likes and dislikes of specific ingredients and reflecting these in the menu proposals.

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