System

Abstract

A system is provided.SOLUTION: This system is provided with a means for making a user input family constitution information, a means for analyzing collected advertisement information, and for extracting bargain merchandise information, a means for generating an optimal menu based on the family constitution information and the bargain merchandise information, and a means for presenting the generated menu to the user.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] Planning daily dinner menus is a significant burden for many families. It can be particularly difficult for families with small children to plan meals that suit everyone's tastes and ages. While it's important to shop economically by utilizing sales information from nearby supermarkets, manually collecting that information and incorporating it into menu planning is even more time-consuming. Therefore, there is a need for a system that can efficiently plan dinner menus that satisfy the entire family and effectively utilize sales information. [Means for solving the problem]

[0005] The above problem is solved by a system including a means for having a user input family composition information, a means for analyzing collected advertising information and extracting sale item information, a means for generating an optimal menu based on the family composition information and sale item information, and a means for presenting the generated menu to the user.

[0006] Specifically, the means for prompting a user to input family information includes the name, age, favorite foods, disliked foods, and allergy information for each family member. The means for analyzing the collected advertising information and extracting sale item information uses web scraping and image recognition algorithms. The means for generating an optimal menu based on the family information and sale item information uses natural language processing and machine learning algorithms. The system further includes a means for automatically generating a shopping list based on the generated menu.

[0007] "Family composition information" is information including the name, age, favorite foods, disliked foods, and allergy information of each family member.

[0008] "Advertising information" is image and text information showing special sale items and discount information offered by retail stores such as supermarkets.

[0009] "Special sale product information" is information extracted from advertising information about products that are sold at special sale prices.

[0010] A "menu" is a list of the dishes served at a particular meal and the order in which they are served.

[0011] "Web scraping" is a technique for automatically collecting information from websites.

[0012] An "image recognition algorithm" is an algorithm for extracting and analyzing specific information from image data.

[0013] "Natural language processing" is a computer technology for understanding and generating natural human language.

[0014] A "machine learning algorithm" is an algorithm that learns patterns based on data and makes predictions and decisions.

[0015] "Income means" is a means by which a user inputs family composition information through an interface.

[0016] The "generation means" is a means for generating an optimal menu based on family composition information and sale product information.

[0017] The "presentation means" is a means for displaying the generated menu to the user.

[0018] A "shopping list" is a list of ingredients and products to be purchased based on the generated menu. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] This invention is a system that efficiently plans dinner menus that satisfy the whole family and effectively utilizes sale information. This system inputs family composition information from the user, collects and analyzes advertising information from the nearest supermarket, generates menus based on the family composition information and sale item information, presents the generated menus to the user, and generates shopping lists.

[0041] Program Overview

[0042] The system operates in the following major steps:

[0043] Entering family information

[0044] User

[0045] Family information is entered through a form on the device, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[0046] Examples:

[0047] The user types the following into the terminal:

[0048] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0049] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0050] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0051] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0052] Collection and analysis of advertising information

[0053] server

[0054] We periodically collect advertising information from the websites of nearby supermarkets, specifically by using web scraping technology to obtain advertising images.

[0055] Terminal

[0056] The collected advertising images are analyzed using an image recognition algorithm to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[0057] Menu generation

[0058] server

[0059] Using natural language processing and machine learning algorithms, the system generates optimal menus by taking into account the likes and dislikes of each family member, nutritional balance, ease of preparation, and whether to utilize special sale items.

[0060] Examples:

[0061] Based on the collected information, the following menu is generated:

[0062] Chicken and carrot teriyaki

[0063] Pumpkin soup

[0064] Salad (without peppers)

[0065] Menu suggestions and shopping list generation

[0066] Terminal

[0067] The generated menu is presented to the user, who can make further revisions until satisfied.

[0068] server

[0069] A shopping list is automatically generated based on the confirmed menu.

[0070] Examples:

[0071] This will generate a shopping list like this:

[0072] 400g chicken

[0073] 3 carrots

[0074] 1 / 2 pumpkin

[0075] Salad vegetables (excluding bell peppers)

[0076] Example of program processing flow

[0077] 1. The user enters family information.

[0078] 2. The server collects supermarket advertising information.

[0079] 3. The device analyzes the advertising image and extracts information about special sale items.

[0080] 4. The server generates a menu based on family composition information and special sale item information.

[0081] 5. The device presents the generated menu to the user.

[0082] 6. The user reviews the menu and requests changes if necessary.

[0083] 7. The server generates and confirms the menu again.

[0084] 8. The server generates a shopping list based on the confirmed menu, and the terminal displays it to the user.

[0085] This allows for efficient planning of dinner menus that will satisfy the whole family, reduces food waste, and enables more economical shopping.

[0086] The processing flow will be explained below.

[0087] Step 1:

[0088] The user accesses the interface on the terminal and inputs family information, specifically, the name, age, favorite foods, disliked foods, and allergy information of each family member.

[0089] Step 2:

[0090] The device receives the family composition information entered by the user and sends it to the server, which then processes the data.

[0091] Step 3:

[0092] The server periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[0093] Step 4:

[0094] The device stores the collected advertising images and analyzes them using an AI image recognition algorithm, extracting sale product information from the advertising images and saving it as text data.

[0095] Step 5:

[0096] The server generates optimal menus based on the family composition information sent by the user and the sale product information extracted using an image recognition algorithm. Specifically, it utilizes natural language processing and machine learning algorithms to take into account the preferences of all family members, nutritional balance, ease of preparation, and the use of sale products.

[0097] Step 6:

[0098] The terminal displays the generated menu sent from the server to the user, who can review the menu and request modifications if necessary.

[0099] Step 7:

[0100] If the user requests a modification, the request is resent to the server via the terminal, and the server regenerates the menu and resends it to the terminal.

[0101] Step 8:

[0102] The user confirms and confirms the proposed menu. The server automatically generates a shopping list based on the confirmed menu.

[0103] Step 9:

[0104] The terminal displays the shopping list sent from the server to the user, who can then check the list and go shopping at the supermarket using a terminal such as a smartphone.

[0105] Step 10:

[0106] Users can purchase the necessary ingredients at the supermarket and cook dinner based on the suggested menu, providing an economical and balanced meal for the whole family.

[0107] Example 1

[0108] 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."

[0109] For many families, efficiently planning a daily dinner menu that takes into account the preferences and nutritional balance of each family member is a significant burden. It is also difficult to make the most of sales information and shop economically. A system that can solve these problems is needed.

[0110] 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.

[0111] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale product information, means for generating an optimal menu based on the family composition information and sale product information, means for presenting the generated menu to the user, means for the user to check the presented menu and request revisions as necessary, and means for generating a shopping list based on the finalized menu. This enables the system to propose optimal menus that take into account the preferences and nutritional balance of all family members and to make economical shopping decisions by utilizing sale information.

[0112] A "user" is a person who uses the system to input family information and check and modify menus.

[0113] "Family composition information" is data including the names, ages, favorite foods, disliked foods, and allergy information of family members.

[0114] "Advertising information" is information about special sales and campaigns collected from the nearest supermarket or store.

[0115] "Special sale product information" is information on the price and quantity of a specific product extracted from the advertisement information.

[0116] A "menu" refers to the specific contents and combination of dishes that the whole family will eat.

[0117] A "shopping list" is a list of ingredients and items that need to be purchased based on a menu.

[0118] "Web scraping" is a technique for automatically collecting and analyzing the content of specific web pages.

[0119] An "image recognition algorithm" is a computer algorithm used to extract and analyze specific information from image data.

[0120] "Natural language processing" is a technical field that uses computers to understand, generate, and manipulate human language.

[0121] A "machine learning algorithm" is an algorithm that learns patterns based on data and makes predictions and judgments.

[0122] A "prompt" is an instruction or question that is input to a generative AI model.

[0123] This invention is a system that efficiently plans dinner menus that satisfy the whole family and effectively utilizes sale information. This system inputs family composition information from the user, collects and analyzes advertising information from the nearest supermarket, generates menus based on the family composition information and sale item information, presents the generated menus to the user, and generates shopping lists.

[0124] The system consists of the following main components:

[0125] 1. User Interface (UI)

[0126] 2. Server

[0127] 3. Terminal

[0128] User Interface (UI)

[0129] User

[0130] The user enters family information through an input form on the device. This input form provides fields for entering each family member's name, age, favorite foods, disliked foods, and allergy information. This input information undergoes form validation using HTML5 and JavaScript (registered trademark) before being sent to the server.

[0131] Examples:

[0132] The user enters the following information into an input form:

[0133] Name: Father, Age: 35, Favorite food: Pasta, Dislikes: Fish, Allergies: None

[0134] server

[0135] server

[0136] The server periodically collects advertising information from the websites of nearby supermarkets, using web scraping techniques (e.g., Beautiful Soup or Selenium) to obtain advertising images and text information, and then transmits this data to the device.

[0137] Terminal

[0138] Terminal

[0139] The device receives the advertising image data sent from the server and analyzes it using an image recognition algorithm (e.g., Google (registered trademark) Cloud Vision API). As a result of the analysis, information such as the name, price, and quantity of the special sale item is extracted. This information is saved in text format and used for later processing.

[0140] Menu generation

[0141] server

[0142] The server uses natural language processing and machine learning algorithms (e.g., the GPT-3 (registered trademark) model) to generate optimal menus based on family composition information collected from the user and sale product information analyzed by the device. It references food and recipe databases to propose menus that take into account the preferences and allergies of all family members, nutritional balance, ease of preparation, and other factors.

[0143] Examples:

[0144] Based on family composition information such as "Father likes pasta, mother likes salad, child 1 likes curry, child 2 likes pumpkin" and sale information such as "Sale: 100g of chicken for 50 yen," a menu of "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)" is generated.

[0145] Menu presentation

[0146] Terminal

[0147] The generated menu is sent from the server to the terminal and presented to the user. The user can check the displayed menu and make an adjustment request if they wish to make any changes. An interactive UI is incorporated to allow the user to easily modify the menu.

[0148] Menu revision and confirmation

[0149] server

[0150] The algorithm receives a request for revisions from the user and generates a new menu. This process is repeated until the user is satisfied. The final menu is then confirmed.

[0151] Specific behavior:

[0152] The server generates the menu again and confirms it as "Teriyaki chicken and carrots," "Minestrone soup," and "Salad (without green peppers)."

[0153] Generate a shopping list

[0154] server

[0155] Based on the confirmed menu, a shopping list of the necessary ingredients is automatically generated, listing the ingredients to be purchased and their quantities, and sending it back to the device.

[0156] Specific behavior:

[0157] The server compiles a list of "400g of chicken," "3 carrots," "1 / 2 a pumpkin," "vegetables for salad (excluding bell peppers)," and "vegetables for minestrone soup" and sends it to the terminal.

[0158] Prompt Sentence Examples

[0159] Here are some concrete examples of input prompts for a generative AI model like GPT-3:

[0160] Example prompt sentence:

[0161] Family information:

[0162] Father: 35 years old, Favorite food: Pasta, Dislikes: Fish

[0163] Mother: 33 years old, Favorite food: Salad, Dislikes: Green peppers

[0164] Child 1: 5 years old, Favorite food: curry, Dislikes: carrots

[0165] Child 2: 2 years old, Favorite food: Pumpkin, Disliked food: None

[0166] Special Offer Information:

[0167] Chicken 100g 50 yen

[0168] Carrots 3 pieces 100 yen

[0169] Use this information to come up with a dinner menu that will satisfy the whole family.

[0170] This allows for optimal menu suggestions that will satisfy the whole family, and allows for economical shopping by taking advantage of special sale information.

[0171] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0172] Step 1: Enter family information

[0173] User

[0174] The user accesses the input form on the terminal and fills in the input fields with the names, ages, favorite foods, disliked foods, and allergy information of the family members. This information is sent to the server when the user presses the "Send" button.

[0175] Input: Name, age, favorite foods, disliked foods, allergy information

[0176] Output: Family composition information sent to the server

[0177] Specific behavior: The user enters the following information: "Name: Father, Age: 35, Favorite food: Pasta, Dislikes: Fish, Allergies: None"

[0178] Step 2: Collect advertising information

[0179] server

[0180] The server runs a periodically scheduled job (e.g., a Cron job) to collect advertisement information from the websites of nearby supermarkets, using web scraping tools such as Beautiful Soup or Selenium to analyze the HTML structure of the advertisement pages and retrieve advertisement images and text information using specific CSS selectors and XPaths.

[0181] Input: supermarket website URL

[0182] Output: Advertising images and text information

[0183] Specific operation: The server accesses "https: / / example-supermarket.com / ads" and retrieves the ad image from the HTML page.

[0184] Step 3: Analyze the ad image

[0185] Terminal

[0186] The device receives the advertising image data sent from the server and analyzes it using image recognition algorithms such as Google Cloud Vision API. As a result of the analysis, information such as the product name, price, and quantity of the special sale item is extracted. This information is saved in text format and used for further processing.

[0187] Input: Ad image

[0188] Output: Special sale product information (product name, price, quantity)

[0189] Specific operation: The device sends the advertising image to the Google Cloud Vision API, and extracts the following information as the analysis result: "Special Sale: 100g of chicken for 50 yen" and "Special Sale: 3 carrots for 100 yen."

[0190] Step 4: Create a menu

[0191] server

[0192] The server uses natural language processing and machine learning algorithms (e.g., GPT-3 model) to generate optimal menus based on family composition information collected from the user and sale product information analyzed by the device. It references food and recipe databases to propose menus that take into account the preferences and allergies of all family members, nutritional balance, ease of preparation, and other factors.

[0193] Input: Family composition information, sale product information

[0194] Output: Optimal menu

[0195] Specific operation: Based on family composition information such as "Father likes pasta, mother likes salad, child 1 likes curry, child 2 likes pumpkin" and sale information such as "Special sale: 100g chicken for 50 yen," a menu of "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)" is generated.

[0196] Step 5: Present the menu

[0197] Terminal

[0198] The generated menu is sent from the server to the terminal and presented to the user. The user can check the displayed menu and make an adjustment request if they wish to make any changes. An interactive UI is used to allow the user to easily modify the menu.

[0199] Input: Generated menu

[0200] Output: Menu presented to the user

[0201] Specific operation: The device displays the menu items "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)."

[0202] Step 6: Revise and finalize the menu

[0203] server

[0204] The algorithm receives a request for revisions from the user and generates a new menu. This process is repeated until the user is satisfied, and the final menu is confirmed.

[0205] Input: User modification request

[0206] Output: Final revised menu

[0207] Specific operation: The user sends a request to change "pumpkin soup" to "minestrone soup," and the server generates the menu again, finalizing it as "teriyaki chicken and carrots," "minestrone soup," and "salad (without green peppers)."

[0208] Step 7: Generate a shopping list

[0209] server

[0210] Based on the confirmed menu, a shopping list of necessary ingredients is automatically generated, listing the ingredients to be purchased and their quantities, and sending it to the device.

[0211] Input: Confirmed menu

[0212] Output: Shopping list

[0213] Specific operation: The server compiles a list of "400g of chicken," "3 carrots," "1 / 2 a pumpkin," "vegetables for salad (excluding bell peppers)," and "vegetables for minestrone soup" and sends it to the terminal.

[0214] Step 8: View your shopping list

[0215] Terminal

[0216] The terminal displays the final shopping list to the user, which is designed to be visually appealing and facilitate a smooth shopping experience.

[0217] Input: Shopping list

[0218] Output: Displaying the shopping list to the user

[0219] Specific operation: The device displays the following items in a list format: "400g chicken," "3 carrots," "1 / 2 pumpkin," "Salad vegetables (excluding bell peppers)," and "Vegetables for minestrone soup."

[0220] The above processing flow allows efficient planning of dinner menus that will satisfy the whole family, reduces food waste, and enables economical shopping.

[0221] (Application example 1)

[0222] 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."

[0223] With conventional systems, it was difficult to efficiently plan dinner menus that would satisfy the whole family, and it was also difficult to effectively utilize sales information. Even if menus were generated based on sales information, ordering ingredients and creating shopping lists based on those menus was not automated, placing a heavy burden on users. This resulted in problems such as food waste and increased financial burdens.

[0224] 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.

[0225] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale item information, means for generating an optimal menu based on the family composition information and sale item information, means for presenting the generated menu to the user, means for ordering necessary ingredients and dishes from an external service based on the generated menu, and means for generating a shopping list. This makes it possible to efficiently plan a dinner menu that will satisfy the whole family, and automatically order ingredients and generate a shopping list based on the menu.

[0226] "User" refers to the person who operates the system and inputs family composition information.

[0227] "Family composition information" refers to information including the name, age, favorite foods, disliked foods, and allergy information of each family member.

[0228] "Advertising information" refers to information about products and services offered by retail stores, such as special sales and campaigns.

[0229] "Special sale product information" refers to discount prices and special offers for specific products extracted from advertising information.

[0230] "Menu" refers to a meal plan that satisfies the whole family.

[0231] "Server" refers to a computer system that collects and analyzes user input and advertising information, and generates and presents menus.

[0232] "External services" refer to food delivery services that users use to order ingredients and dishes based on their menu.

[0233] A "shopping list" refers to a list of ingredients to be purchased based on the generated menu.

[0234] "Web scraping" refers to the technology of automatically extracting necessary information from websites on the Internet.

[0235] "Image recognition algorithm" refers to technology for extracting sale item information from images included in advertising information.

[0236] This invention is a food delivery system that efficiently plans dinner menus that will satisfy the whole family and effectively utilizes sale information. The system is composed of users, a server, and external services.

[0237] System configuration and roles

[0238] User

[0239] 1. Enter your family information

[0240] Through a smartphone application, users enter the name, age, favorite foods, disliked foods, and allergy information for each family member.

[0241] 2. Check and modify the menu

[0242] The generated menu is presented to the user, who can review it and request modifications if necessary.

[0243] server

[0244] 1. Collection and analysis of advertising information

[0245] The server periodically collects advertising information from the websites of nearby supermarkets and grocery stores, and analyzes and extracts sale product information using web scraping and image recognition algorithms.

[0246] 2. Menu generation

[0247] The server uses a generative AI model to generate optimal menus based on the family composition information collected from the user and the analyzed sale item information. The generated menus take into account the user's preferences and the use of sale items.

[0248] 3. Generate a shopping list

[0249] The server automatically generates a shopping list of necessary ingredients based on the finalized menu.

[0250] External Services

[0251] 1. Ordering ingredients and dishes

[0252] Based on the generated menu, you can order the necessary ingredients and dishes from external food delivery or ingredient delivery services.

[0253] Program processing flow

[0254] The server performs the following data processing and data calculations.

[0255] Hardware and software: The server is a computer system equipped with a powerful processor and large memory capacity, and uses software for web scraping (e.g., BeautifulSoup), image recognition algorithms (e.g., TENSORFLOW®), and frameworks for running generative AI models (e.g., PyTorch).

[0256] Data processing: Advertising information is scraped from websites, HTML data is analyzed to extract sale product information, and the collected image data is analyzed with a recognition algorithm to obtain sale product information as text.

[0257] Data calculation: The AI ​​model compares the family composition information entered by the user with the sale product information and generates the optimal menu. At this time, the model takes into account the nutritional value of ingredients, cooking time, cost, etc.

[0258] Examples of concrete examples and prompts

[0259] Specific examples

[0260] The user types the following into the terminal:

[0261] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0262] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0263] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0264] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0265] Prompt Sentence Examples

[0266] Use the family composition and special offer information below to generate the perfect dinner menu that will satisfy your family.

[0267] Family information:

[0268] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0269] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0270] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0271] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0272] Special Offer Information:

[0273] Chicken 100g 50 yen

[0274] Carrots 3 pieces 100 yen

[0275] This system allows you to efficiently plan meals that will satisfy the whole family and makes the most of special sale information, making shopping more economical.

[0276] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0277] Step 1:

[0278] The user inputs family information.

[0279] Input: Family members' names, ages, favorite foods, disliked foods, and allergy information.

[0280] Action: A user fills out a form using a smartphone application.

[0281] Output: The family information is sent to the server and stored.

[0282] Step 2:

[0283] The server collects the advertising information.

[0284] Input: Advertisement URL from your local supermarket or grocery store.

[0285] How it works: The server periodically collects advertising information using web scraping techniques, specifically by analyzing web pages using libraries such as BeautifulSoup to retrieve advertising images and text.

[0286] Output: A dataset of collected advertising information.

[0287] Step 3:

[0288] The server analyzes the collected advertising information and extracts sale item information.

[0289] Input: A dataset of advertising information.

[0290] How it works: The server analyzes the advertisement image using an image recognition algorithm (e.g. TensorFlow), converts the sale product information into text format, and extracts the text information using HTML analysis.

[0291] Output: A list of sale items.

[0292] Step 4:

[0293] The server generates an optimal menu based on family composition information and special sale product information.

[0294] Input: Family composition information and special offer information.

[0295] How it works: The server uses a generative AI model to generate optimal menus that take into account the preferences of family members, allergies, and special offers. The model uses frameworks such as PyTorch.

[0296] Output: A list of generated menu items.

[0297] Step 5:

[0298] The server presents the generated menu to the user.

[0299] Input: A list of generated menus.

[0300] How it works: The server displays the generated menu on the user's smartphone.

[0301] Output: The user reviews the menu and sends a request to the server to make any necessary modifications.

[0302] Step 6:

[0303] The server receives the modification request and regenerates the menu (if necessary).

[0304] Input: The user's modification request.

[0305] How it works: The server generates a menu using the generative AI model again based on the modification request. If necessary, it may only need to modify parts of the menu generated in the previous step.

[0306] Output: The revised menu list.

[0307] Step 7:

[0308] The server generates a shopping list based on the determined menu.

[0309] Input: A list of confirmed menu items.

[0310] How it works: The server lists the ingredients needed for a meal and generates a shopping list.

[0311] Output: A shopping list of ingredients needed.

[0312] Step 8:

[0313] The server presents the shopping list to the user and supports ordering from external services.

[0314] Input: Shopping list.

[0315] How it works: The server displays the shopping list on the user's smartphone and also works with external food delivery services to help order ingredients and dishes.

[0316] Output: An interface for the user to review the shopping list and place an order with an external service if necessary.

[0317] 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.

[0318] This system efficiently plans dinner menus that satisfy the whole family and effectively utilizes sales information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it can propose menus that take into account the user's psychological state.

[0319] Program Overview

[0320] The system operates in the following major steps:

[0321] Entering family information

[0322] User

[0323] Family information is entered through the device's interface, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[0324] Examples:

[0325] The user types the following into the terminal:

[0326] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0327] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0328] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0329] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0330] Collection and analysis of advertising information

[0331] server

[0332] The system periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[0333] Terminal

[0334] The collected advertising images are analyzed using an image recognition algorithm to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[0335] Menu generation

[0336] server

[0337] Using natural language processing and machine learning algorithms, the system generates optimal menus based on the family composition information collected from the user and the analyzed sale product information. The algorithm takes into account each family member's preferences, nutritional balance, ease of preparation, and the extent to which sale products are utilized.

[0338] Examples:

[0339] Based on the collected information, the following menu is generated:

[0340] Chicken and carrot teriyaki

[0341] Pumpkin soup

[0342] Salad (without peppers)

[0343] Adjusting recommendations with an emotion engine

[0344] Terminal

[0345] The emotion engine analyzes the user's voice and facial expressions and recognizes their emotions, for example, when they are tired or want to relax.

[0346] server

[0347] The emotion engine tailors meal suggestions based on the user's emotional state. For example, if the user is tired, it will suggest meals that are easy to prepare and have a relaxing effect.

[0348] Examples:

[0349] If a user says, "I'm tired today," the system will suggest a menu that includes "easy steamed chicken dishes" and "relaxing herbal tea."

[0350] Menu suggestions and shopping list generation

[0351] Terminal

[0352] The generated menu is presented to the user, who can make further revisions until satisfied.

[0353] server

[0354] A shopping list is automatically generated based on the confirmed menu.

[0355] Examples:

[0356] This will generate a shopping list like this:

[0357] 400g chicken

[0358] 3 carrots

[0359] 1 / 2 pumpkin

[0360] Salad vegetables (excluding bell peppers)

[0361] Example of program processing flow

[0362] 1. The user enters family information.

[0363] 2. The server collects supermarket advertising information.

[0364] 3. The device analyzes the advertising image and extracts information about special sale items.

[0365] 4. The server generates a menu based on family composition information and special sale item information.

[0366] 5. The device recognizes the user's emotions and transmits them to the server.

[0367] 6. Servers adjust menus based on your emotional state.

[0368] 7. The device presents the generated menu to the user.

[0369] 8. The user reviews the menu and requests changes if necessary.

[0370] 9. The server generates and confirms the menu again.

[0371] 10. The server generates a shopping list based on the confirmed menu, and the terminal displays it to the user.

[0372] This allows for efficient planning of dinner menus that satisfy the whole family, allows for menu suggestions that take into consideration the user's feelings, and allows for the provision of economical and balanced meals.

[0373] The processing flow will be explained below.

[0374] Step 1:

[0375] The user accesses the interface on the terminal and inputs family information, specifically, the name, age, favorite foods, disliked foods, and allergy information of each family member.

[0376] Step 2:

[0377] The device receives the family composition information entered by the user and sends it to the server, which can then perform processing based on that data.

[0378] Step 3:

[0379] The server periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[0380] Step 4:

[0381] The device stores the collected advertising images and analyzes them using an AI image recognition algorithm, extracting sale product information from the advertising images and saving it as text data.

[0382] Step 5:

[0383] The server generates optimal menus based on the family composition information sent by the user and the sale product information extracted using an image recognition algorithm. Specifically, it utilizes natural language processing and machine learning algorithms to take into account the preferences of all family members, nutritional balance, ease of preparation, and the use of sale products.

[0384] Step 6:

[0385] The terminal presents the generated menu sent from the server to the user, who can then review the menu and request modifications if necessary.

[0386] Step 7:

[0387] If the user requests a modification, the request is resent through the terminal to the server, which then generates a new menu and sends it to the terminal again.

[0388] Step 8:

[0389] The device analyzes the user's voice and facial expressions and uses an emotion engine to recognize the user's emotions, for example, when the user is tired or wants to relax.

[0390] Step 9:

[0391] The server adjusts the menu suggestions based on the emotional state transmitted by the emotion engine, for example, if the user is tired, it will suggest menus that are easy to prepare and have a relaxing effect.

[0392] Step 10:

[0393] The terminal presents the user with a menu adjusted based on their emotions, allows them to confirm the menu until they are satisfied, and accepts requests for any changes.

[0394] Step 11:

[0395] The user confirms and confirms the proposed menu. The server automatically generates a shopping list based on the confirmed menu.

[0396] Step 12:

[0397] The terminal displays the shopping list sent from the server to the user, who can then check it and go shopping at the supermarket using their smartphone or other device.

[0398] Step 13:

[0399] Users can purchase the necessary ingredients at the supermarket and cook dinner based on the suggested menu, providing an economical and balanced meal for the whole family.

[0400] Example 2

[0401] 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."

[0402] In modern households, it is difficult to efficiently plan a dinner menu that satisfies the entire family. In particular, it is necessary to take into account each family member's preferences, allergies, and nutritional balance, while also utilizing information on special sales items from an economical perspective. Furthermore, there is a demand for flexible menu suggestions that take into account the user's emotional state. The present invention aims to provide a menu suggestion system that simultaneously satisfies these multiple factors.

[0403] 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.

[0404] In this invention, the server includes a means for having the user input family composition information, a means for analyzing collected advertising information and extracting sale item information, a means for generating an optimal meal plan based on the family composition information and sale item information, a means for recognizing the user's emotional state and adjusting the menu, a means for presenting the generated menu to the user, and a means for generating a shopping list based on the confirmed menu. This allows for efficient planning of menus that satisfy the entire family, making it possible to provide economical and balanced meals. It also enables flexible menu suggestions that take the user's emotions into consideration.

[0405] "Family composition information" is information including the name, age, favorite foods, disliked foods, and allergy information of each family member.

[0406] "Advertising information" is data that includes information about special sales and discounts offered by stores such as supermarkets.

[0407] "Special sale product information" refers to information about special sale products and discounted products that has been analyzed and extracted from the collected advertising information.

[0408] The "meal plan" is a plan that includes a menu that is tailored to satisfy the entire family based on family composition information and special sale information.

[0409] "Emotional state" is information that indicates the psychological state of the user analyzed from the user's voice, facial expression, and the like.

[0410] "Web scraping" is a technique for automatically collecting information from websites on the Internet.

[0411] "Image recognition algorithm" refers to an algorithm for analyzing image data and extracting specific information within the image.

[0412] A "shopping list" is a list of all the ingredients and products needed based on a confirmed menu.

[0413] This system efficiently plans dinner menus that satisfy the whole family and effectively utilizes sales information. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it can propose menus that take into account the user's psychological state.

[0414] The implementation of this system uses the following hardware and software:

[0415] Hardware:

[0416] User devices (PCs, tablets, smartphones, etc.)

[0417] Internet connection environment

[0418] Camera and microphone (for emotion recognition)

[0419] software:

[0420] Web scraping tools (such as Python's BeautifulSoup)

[0421] Image recognition algorithms (OpenCV, TensorFlow, etc.)

[0422] Natural language processing and machine learning algorithms (e.g., Python's NLTK, scikit-learn)

[0423] Emotion recognition API (such as Microsoft® Azure® emotion recognition API)

[0424] Entering family information

[0425] The user inputs family information through the interface on the terminal, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[0426] As a concrete example, the user enters the following:

[0427] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0428] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0429] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0430] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0431] Collection and analysis of advertising information

[0432] The server periodically accesses the website of the nearest supermarket and collects advertising information using web scraping technology. The acquired data is analyzed using an image recognition algorithm to extract information about special offers. For example, information such as "Special Offer: 100g of chicken for 50 yen" and "Special Offer: 3 carrots for 100 yen" is collected.

[0433] Menu generation

[0434] The server uses the family composition information collected from the user and the analyzed sale product information as inputs, and generates the optimal menu using natural language processing and machine learning algorithms. The algorithm takes into account each family member's preferences, nutritional balance, ease of preparation, and the use of sale products.

[0435] Specific examples of offerings include the following:

[0436] Chicken and carrot teriyaki

[0437] Pumpkin soup

[0438] Salad (without peppers)

[0439] Adjusting recommendations with an emotion engine

[0440] The device captures the user's voice and facial expressions with a camera and microphone, and the emotion engine analyzes this data. Depending on the user's emotional state, such as fatigue or a desire for relaxation, the server adjusts the menu based on the information sent from the emotion engine.

[0441] For example, if a user says, "I'm tired today," the system will suggest a menu that includes "easy steamed chicken dishes" and "relaxing herbal tea."

[0442] Menu suggestions and shopping list generation

[0443] The terminal presents the generated menu to the user, who can then revise it until he is satisfied. Based on the finalized menu, the server automatically generates a shopping list, which the terminal displays to the user.

[0444] As a concrete example, the following shopping list is generated:

[0445] 400g chicken

[0446] 3 carrots

[0447] 1 / 2 pumpkin

[0448] Salad vegetables (excluding bell peppers)

[0449] This system can efficiently plan dinner menus that satisfy the whole family, providing economical and balanced meals. It can also provide flexible menu suggestions that take into account the user's emotions.

[0450] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0451] Step 1:

[0452] The user inputs family information into the interface on the terminal, including the name, age, favorite foods, disliked foods, and allergy information for each family member.

[0453] Input: Family member's attribute information (name, age, preferences, disliked foods, allergy information)

[0454] Output: Family composition information (format saved in database)

[0455] Specific behavior:

[0456] The user types the following into a text field on the terminal:

[0457] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0458] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0459] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0460] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0461] Step 2:

[0462] The server accesses the website of the nearest supermarket and collects advertising information. Web scraping technology is used to obtain advertising data.

[0463] Input: supermarket website URL

[0464] Output: Advertising information (image data and text data)

[0465] Specific behavior:

[0466] The server periodically accesses the supermarket's website and scrapes advertising information using Python's BeautifulSoup library.

[0467] Step 3:

[0468] The device analyzes the collected advertising images and uses image recognition algorithms to extract sale item information.

[0469] Input: Advertising information (image data or text data)

[0470] Output: Special sale product information (text format data)

[0471] Specific behavior:

[0472] The device uses TensorFlow and OpenCV to analyze images and extract information about special offers, such as "Special Offer: 100g of chicken for 50 yen" and "Special Offer: 3 carrots for 100 yen."

[0473] Step 4:

[0474] The server receives family composition information and sale item information as input, and uses natural language processing and machine learning algorithms to generate the optimal menu.

[0475] Input: Family composition information, sale product information

[0476] Output: Optimal meal plan (list in text format)

[0477] Specific behavior:

[0478] The server uses scikit-learn and NLTK to generate the following menu based on the collected family composition information and sale item information.

[0479] Chicken and carrot teriyaki

[0480] Pumpkin soup

[0481] Salad (without peppers)

[0482] Step 5:

[0483] The device captures the user's voice and facial expressions using a camera and microphone, and the emotion engine analyzes this data.

[0484] Input: User's voice data, facial expression data

[0485] Output: User's emotional state (text data)

[0486] Specific behavior:

[0487] The device uses Microsoft Azure's emotion recognition API to recognize emotions from the user's voice and facial expressions, and extracts emotional states such as "I'm tired" or "I want to relax."

[0488] Step 6:

[0489] The server adjusts the menu suggestions based on the emotional state sent from the emotion engine.

[0490] Input: User's emotional state, optimal menu

[0491] Output: Adjusted menu (list in text format)

[0492] Specific behavior:

[0493] The server takes into account the user's emotional state, and if the user says something like "I'm tired today," it will suggest a menu that includes "simple steamed chicken dishes" and "relaxing herbal tea."

[0494] Step 7:

[0495] The terminal presents the generated menu to the user, who can then make corrections until satisfied.

[0496] Input: Adjusted menu

[0497] Output: Final menu (list in text format)

[0498] Specific behavior:

[0499] The terminal displays the generated menu to the user, and if the user requests a change, the terminal makes the server generate a new menu and presents it.

[0500] Step 8:

[0501] The server automatically generates a shopping list based on the finalized menu, and the terminal displays the generated shopping list to the user.

[0502] Input: Final menu

[0503] Output: Shopping list (list in text format)

[0504] Specific behavior:

[0505] The server lists the necessary ingredients and products based on the final menu, and the terminal displays the following shopping list to the user.

[0506] 400g chicken

[0507] 3 carrots

[0508] 1 / 2 pumpkin

[0509] Salad vegetables (excluding bell peppers)

[0510] (Application example 2)

[0511] 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."

[0512] When planning a typical dinner menu, it is difficult to utilize sale information while taking into account the preferences and nutritional balance of each family member. It is also difficult to adjust menu suggestions based on the user's emotional state. The present invention aims to provide a system that meets these complex requirements, efficiently plans a menu that satisfies the entire family, and also makes suggestions that take into account the user's emotional state.

[0513] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0514] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale product information, means for generating an optimal menu based on the family composition information and sale product information, means for recognizing the user's emotional state and adjusting the menu suggestions, and means for presenting the generated menu and shopping list to the user. This not only enables menu suggestions that make the most of the food preferences and nutritional balance of all family members and sale information, but also makes suggestions that take the user's emotional state into consideration.

[0515] A "user" is an individual or family representative who uses the system to plan menus and receive suggestions.

[0516] "Family composition information" is data entered by the user that includes the names, ages, favorite foods, disliked foods, and allergy information of family members.

[0517] "Advertising information" is data that includes information about special sales and campaigns offered by stores such as supermarkets.

[0518] "Special sale product information" is data relating to the special sale price and sales conditions of a specific product, extracted from the analyzed advertising information.

[0519] A "menu" refers to a combination of dinner menu items that will satisfy the whole family.

[0520] The "emotional state" is a psychological state that is recognized from information such as the user's voice and facial expression.

[0521] "Suggestions" are information that includes menus and associated shopping lists generated by the system, as well as menus that are tailored based on the user's emotional state.

[0522] A "shopping list" is a list of ingredients that need to be purchased based on the generated menu.

[0523] The "server" is a central processing unit that processes various data of this system, generates menus, and recognizes emotional states.

[0524] The present invention is a system that helps users plan dinner menus that take into account the preferences and allergy information of all family members and helps them shop by making the most of sales information. This system can also suggest optimal menus by taking into account the user's emotional state.

[0525] Hardware used

[0526] Smartphones (e.g., iPhone (registered trademark) or ANDROID (registered trademark) devices)

[0527] Servers (cloud servers and on-premise servers)

[0528] Software used

[0529] Web scraping technologies (e.g., Python's Beautiful Soup or Selenium)

[0530] Image recognition algorithms (e.g., Google Cloud Vision API)

[0531] Natural language processing algorithms (e.g., OpenAI® GPT-3, BERT)

[0532] Emotion recognition engine (e.g., Microsoft Azure Emotion API, Affectiva API)

[0533] Specific processing of the system

[0534] 1. Enter family information

[0535] Through a smartphone interface, users enter family information, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[0536] 2. Collection and analysis of advertising information

[0537] The server periodically accesses the websites of each supermarket or store to collect advertising information, specifically by using Python's Beautiful Soup and Selenium to obtain image data for advertisements.

[0538] The collected advertising images are analyzed using the Google Cloud Vision API to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[0539] 3. Menu generation

[0540] The server takes the family composition information collected from the user and the analyzed sale item information as input, and uses natural language processing algorithms such as OpenAI GPT-3 to generate the optimal menu.

[0541] 4. Adjusting recommendations using an emotion engine

[0542] Using the smartphone's camera and microphone, the system analyzes the user's voice and facial expressions, and recognizes the user's emotional state using an emotion recognition engine such as Microsoft Azure Emotion API.

[0543] Based on the recognized emotional state, the server reviews the generated menu and adjusts the proposed content to take into consideration the user's psychological state.

[0544] 5. Present menu and shopping list

[0545] The adjusted menu is presented to the user via their smartphone, and they can make adjustments as necessary until they are satisfied.

[0546] A shopping list is automatically generated based on the confirmed menu and displayed on the smartphone.

[0547] Specific examples

[0548] Consider a scenario where a user launches the app and says, "I'm busy today, so I want to make a simple dinner." The server will suggest menu items such as "stir-fried chicken and vegetables" or "easy steamed chicken" based on the family composition information entered by the user and the sale information of the nearest supermarket. It will also recognize the user's emotional state and suggest even easier dishes to prepare if the user is tired. The generated shopping list will include items such as "400g chicken, carrots, and pumpkin," allowing the user to shop efficiently.

[0549] Prompt Sentence Examples

[0550] This system suggests optimal dinner menus based on the user's family composition information and sales information from physical stores. When a user says, "I want to make something easy to cook today," generate a menu based on the following information:

[0551] Family composition information

[0552] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0553] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0554] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0555] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0556] Special sale information

[0557] Chicken 100g 50 yen

[0558] Carrots 3 pieces 100 yen

[0559] 1 / 2 pumpkin 80 yen

[0560] Proposal Format

[0561] 1. Menu planning

[0562] 2. List of ingredients needed

[0563] In this way, the system can propose optimal menus that meet the user's diverse requirements and emotional state.

[0564] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0565] Step 1:

[0566] User input of family information

[0567] Users enter family information through the interface on their smartphones, including each family member's name, age, favorite foods, disliked foods, and allergy information. The entered family information is sent to the system's server and stored in a database.

[0568] input:

[0569] Family members' names, ages, favorite foods, disliked foods, and allergy information

[0570] output:

[0571] Family composition information stored in a database

[0572] Step 2:

[0573] Collection of advertising information by the server

[0574] The server periodically accesses the websites of each supermarket or store and collects advertising information using web scraping technology. It then uses Python's Beautiful Soup and Selenium to obtain advertising image data, which is then stored on the server.

[0575] input:

[0576] Supermarket or store website URL

[0577] output:

[0578] Advertising image data stored on the server

[0579] Step 3:

[0580] Server analysis of advertising information

[0581] The server analyzes the stored advertising image data using the Google Cloud Vision API to extract sale product information, recognizing text within the image and extracting sale prices and product information as text data.

[0582] input:

[0583] Advertising image data

[0584] output:

[0585] Special sale product information (text data)

[0586] Step 4:

[0587] Server-based menu generation

[0588] The server uses natural language processing algorithms such as OpenAI GPT-3 to generate optimal menus based on family composition information and special sale information. The algorithm takes into account preferences, allergies, and nutritional balance to suggest menus that will satisfy the whole family.

[0589] input:

[0590] Family composition information, special sale product information

[0591] output:

[0592] Generated menu

[0593] Step 5:

[0594] Use of emotion engines on devices

[0595] The device (smartphone) uses a camera and microphone to capture the user's voice and facial expressions. An emotion recognition engine such as Microsoft Azure Emotion API is used to recognize the user's emotional state. The recognized emotional state is sent to the server.

[0596] input:

[0597] Voice and facial expression data

[0598] output:

[0599] Perceived emotional state

[0600] Step 6:

[0601] Server adjustment of proposals

[0602] The server then reviews the generated menu based on the user's emotional state and adjusts the suggestions to take into account the user's psychological state. For example, if the user is tired, the server will prioritize menus that are easy to prepare.

[0603] input:

[0604] Generated menu, emotional state

[0605] output:

[0606] Adjusted menu

[0607] Step 7:

[0608] Menu and shopping list presentation on the device

[0609] The adjusted menu is presented to the user via their smartphone. The user can make any necessary adjustments until they are satisfied. A shopping list is automatically generated based on the finalized menu and displayed on the smartphone.

[0610] input:

[0611] Adjusted menu

[0612] output:

[0613] Menu presented to the user, generated shopping list

[0614] 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.

[0615] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0616] 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.

[0617] [Second embodiment]

[0618] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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).

[0624] 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. 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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."

[0630] This invention is a system that efficiently plans dinner menus that satisfy the whole family and effectively utilizes sale information. This system inputs family composition information from the user, collects and analyzes advertising information from the nearest supermarket, generates menus based on the family composition information and sale item information, presents the generated menus to the user, and generates shopping lists.

[0631] Program Overview

[0632] The system operates in the following major steps:

[0633] Entering family information

[0634] User

[0635] Family information is entered through a form on the device, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[0636] Examples:

[0637] The user types the following into the terminal:

[0638] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0639] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0640] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0641] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0642] Collection and analysis of advertising information

[0643] server

[0644] We periodically collect advertising information from the websites of nearby supermarkets, specifically by using web scraping technology to obtain advertising images.

[0645] Terminal

[0646] The collected advertising images are analyzed using an image recognition algorithm to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[0647] Menu generation

[0648] server

[0649] Using natural language processing and machine learning algorithms, the system generates optimal menus by taking into account the likes and dislikes of each family member, nutritional balance, ease of preparation, and whether to utilize special sale items.

[0650] Examples:

[0651] Based on the collected information, the following menu is generated:

[0652] Chicken and carrot teriyaki

[0653] Pumpkin soup

[0654] Salad (without peppers)

[0655] Menu suggestions and shopping list generation

[0656] Terminal

[0657] The generated menu is presented to the user, who can make further revisions until satisfied.

[0658] server

[0659] A shopping list is automatically generated based on the confirmed menu.

[0660] Examples:

[0661] This will generate a shopping list like this:

[0662] 400g chicken

[0663] 3 carrots

[0664] 1 / 2 pumpkin

[0665] Salad vegetables (excluding bell peppers)

[0666] Example of program processing flow

[0667] 1. The user enters family information.

[0668] 2. The server collects supermarket advertising information.

[0669] 3. The device analyzes the advertising image and extracts information about special sale items.

[0670] 4. The server generates a menu based on family composition information and special sale item information.

[0671] 5. The device presents the generated menu to the user.

[0672] 6. The user reviews the menu and requests changes if necessary.

[0673] 7. The server generates and confirms the menu again.

[0674] 8. The server generates a shopping list based on the confirmed menu, and the terminal displays it to the user.

[0675] This allows for efficient planning of dinner menus that will satisfy the whole family, reduces food waste, and enables more economical shopping.

[0676] The processing flow will be explained below.

[0677] Step 1:

[0678] The user accesses the interface on the terminal and inputs family information, specifically, the name, age, favorite foods, disliked foods, and allergy information of each family member.

[0679] Step 2:

[0680] The device receives the family composition information entered by the user and sends it to the server, which then processes the data.

[0681] Step 3:

[0682] The server periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[0683] Step 4:

[0684] The device stores the collected advertising images and analyzes them using an AI image recognition algorithm, extracting sale product information from the advertising images and saving it as text data.

[0685] Step 5:

[0686] The server generates optimal menus based on the family composition information sent by the user and the sale product information extracted using an image recognition algorithm. Specifically, it utilizes natural language processing and machine learning algorithms to take into account the preferences of all family members, nutritional balance, ease of preparation, and the use of sale products.

[0687] Step 6:

[0688] The terminal displays the generated menu sent from the server to the user, who can review the menu and request modifications if necessary.

[0689] Step 7:

[0690] If the user requests a modification, the request is resent to the server via the terminal, and the server regenerates the menu and resends it to the terminal.

[0691] Step 8:

[0692] The user confirms and confirms the proposed menu. The server automatically generates a shopping list based on the confirmed menu.

[0693] Step 9:

[0694] The terminal displays the shopping list sent from the server to the user, who can then check the list and go shopping at the supermarket using a terminal such as a smartphone.

[0695] Step 10:

[0696] Users can purchase the necessary ingredients at the supermarket and cook dinner based on the suggested menu, providing an economical and balanced meal for the whole family.

[0697] Example 1

[0698] 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."

[0699] For many families, efficiently planning a daily dinner menu that takes into account the preferences and nutritional balance of each family member is a significant burden. It is also difficult to make the most of sales information and shop economically. A system that can solve these problems is needed.

[0700] 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.

[0701] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale product information, means for generating an optimal menu based on the family composition information and sale product information, means for presenting the generated menu to the user, means for the user to check the presented menu and request revisions as necessary, and means for generating a shopping list based on the finalized menu. This enables the system to propose optimal menus that take into account the preferences and nutritional balance of all family members and to make economical shopping decisions by utilizing sale information.

[0702] A "user" is a person who uses the system to input family information and check and modify menus.

[0703] "Family composition information" is data including the names, ages, favorite foods, disliked foods, and allergy information of family members.

[0704] "Advertising information" is information about special sales and campaigns collected from the nearest supermarket or store.

[0705] "Special sale product information" is information on the price and quantity of a specific product extracted from the advertisement information.

[0706] A "menu" refers to the specific contents and combination of dishes that the whole family will eat.

[0707] A "shopping list" is a list of ingredients and items that need to be purchased based on a menu.

[0708] "Web scraping" is a technique for automatically collecting and analyzing the content of specific web pages.

[0709] An "image recognition algorithm" is a computer algorithm used to extract and analyze specific information from image data.

[0710] "Natural language processing" is a technical field that uses computers to understand, generate, and manipulate human language.

[0711] A "machine learning algorithm" is an algorithm that learns patterns based on data and makes predictions and judgments.

[0712] A "prompt" is an instruction or question that is input to a generative AI model.

[0713] This invention is a system that efficiently plans dinner menus that satisfy the whole family and effectively utilizes sale information. This system inputs family composition information from the user, collects and analyzes advertising information from the nearest supermarket, generates menus based on the family composition information and sale item information, presents the generated menus to the user, and generates shopping lists.

[0714] The system consists of the following main components:

[0715] 1. User Interface (UI)

[0716] 2. Server

[0717] 3. Terminal

[0718] User Interface (UI)

[0719] User

[0720] The user enters family information through an input form on the device. This input form provides fields for entering each family member's name, age, favorite foods, disliked foods, and allergy information. This input information undergoes form validation using HTML5 and JavaScript before being sent to the server.

[0721] Examples:

[0722] The user enters the following information into an input form:

[0723] Name: Father, Age: 35, Favorite food: Pasta, Dislikes: Fish, Allergies: None

[0724] server

[0725] server

[0726] The server periodically collects advertising information from the websites of nearby supermarkets, using web scraping techniques (e.g., Beautiful Soup or Selenium) to obtain advertising images and text information, and then transmits this data to the device.

[0727] Terminal

[0728] Terminal

[0729] The device receives the advertising image data sent from the server and analyzes it using an image recognition algorithm (e.g., Google Cloud Vision API). As a result of the analysis, information such as the name, price, and quantity of the special sale item is extracted. This information is saved in text format and used for later processing.

[0730] Menu generation

[0731] server

[0732] The server uses natural language processing and machine learning algorithms (e.g., GPT-3 model) to generate optimal menus based on family composition information collected from the user and sale product information analyzed by the device. It references food and recipe databases to propose menus that take into account the preferences and allergies of all family members, nutritional balance, ease of preparation, and other factors.

[0733] Examples:

[0734] Based on family composition information such as "Father likes pasta, mother likes salad, child 1 likes curry, child 2 likes pumpkin" and sale information such as "Sale: 100g of chicken for 50 yen," a menu of "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)" is generated.

[0735] Menu presentation

[0736] Terminal

[0737] The generated menu is sent from the server to the terminal and presented to the user. The user can check the displayed menu and make an adjustment request if they wish to make any changes. An interactive UI is incorporated to allow the user to easily modify the menu.

[0738] Menu revision and confirmation

[0739] server

[0740] The algorithm receives a request for revisions from the user and generates a new menu. This process is repeated until the user is satisfied. The final menu is then confirmed.

[0741] Specific behavior:

[0742] The server generates the menu again and confirms it as "Teriyaki chicken and carrots," "Minestrone soup," and "Salad (without green peppers)."

[0743] Generate a shopping list

[0744] server

[0745] Based on the confirmed menu, a shopping list of the necessary ingredients is automatically generated, listing the ingredients to be purchased and their quantities, and sending it back to the device.

[0746] Specific behavior:

[0747] The server compiles a list of "400g of chicken," "3 carrots," "1 / 2 a pumpkin," "vegetables for salad (excluding bell peppers)," and "vegetables for minestrone soup" and sends it to the terminal.

[0748] Prompt Sentence Examples

[0749] Here are some concrete examples of input prompts for a generative AI model like GPT-3:

[0750] Example prompt sentence:

[0751] Family information:

[0752] Father: 35 years old, Favorite food: Pasta, Dislikes: Fish

[0753] Mother: 33 years old, Favorite food: Salad, Dislikes: Green peppers

[0754] Child 1: 5 years old, Favorite food: curry, Dislikes: carrots

[0755] Child 2: 2 years old, Favorite food: Pumpkin, Disliked food: None

[0756] Special Offer Information:

[0757] Chicken 100g 50 yen

[0758] Carrots 3 pieces 100 yen

[0759] Use this information to come up with a dinner menu that will satisfy the whole family.

[0760] This allows for optimal menu suggestions that will satisfy the whole family, and allows for economical shopping by taking advantage of special sale information.

[0761] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0762] Step 1: Enter family information

[0763] User

[0764] The user accesses the input form on the terminal and fills in the input fields with the names, ages, favorite foods, disliked foods, and allergy information of the family members. This information is sent to the server when the user presses the "Send" button.

[0765] Input: Name, age, favorite foods, disliked foods, allergy information

[0766] Output: Family composition information sent to the server

[0767] Specific behavior: The user enters the following information: "Name: Father, Age: 35, Favorite food: Pasta, Dislikes: Fish, Allergies: None"

[0768] Step 2: Collect advertising information

[0769] server

[0770] The server runs a periodically scheduled job (e.g., a Cron job) to collect advertisement information from the websites of nearby supermarkets, using web scraping tools such as Beautiful Soup or Selenium to analyze the HTML structure of the advertisement pages and retrieve advertisement images and text information using specific CSS selectors and XPaths.

[0771] Input: supermarket website URL

[0772] Output: Advertising images and text information

[0773] Specific operation: The server accesses "https: / / example-supermarket.com / ads" and retrieves the ad image from the HTML page.

[0774] Step 3: Analyze the ad image

[0775] Terminal

[0776] The device receives the advertising image data sent from the server and analyzes it using image recognition algorithms such as Google Cloud Vision API. As a result of the analysis, information such as the product name, price, and quantity of the special sale item is extracted. This information is saved in text format and used for further processing.

[0777] Input: Ad image

[0778] Output: Special sale product information (product name, price, quantity)

[0779] Specific operation: The device sends the advertising image to the Google Cloud Vision API, and extracts the following information as the analysis result: "Special Sale: 100g of chicken for 50 yen" and "Special Sale: 3 carrots for 100 yen."

[0780] Step 4: Create a menu

[0781] server

[0782] The server uses natural language processing and machine learning algorithms (e.g., GPT-3 model) to generate optimal menus based on family composition information collected from the user and sale product information analyzed by the device. It references food and recipe databases to propose menus that take into account the preferences and allergies of all family members, nutritional balance, ease of preparation, and other factors.

[0783] Input: Family composition information, sale product information

[0784] Output: Optimal menu

[0785] Specific operation: Based on family composition information such as "Father likes pasta, mother likes salad, child 1 likes curry, child 2 likes pumpkin" and sale information such as "Special sale: 100g chicken for 50 yen," a menu of "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)" is generated.

[0786] Step 5: Present the menu

[0787] Terminal

[0788] The generated menu is sent from the server to the terminal and presented to the user. The user can check the displayed menu and make an adjustment request if they wish to make any changes. An interactive UI is used to allow the user to easily modify the menu.

[0789] Input: Generated menu

[0790] Output: Menu presented to the user

[0791] Specific operation: The device displays the menu items "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)."

[0792] Step 6: Revise and finalize the menu

[0793] server

[0794] The algorithm receives a request for revisions from the user and generates a new menu. This process is repeated until the user is satisfied, and the final menu is confirmed.

[0795] Input: User modification request

[0796] Output: Final revised menu

[0797] Specific operation: The user sends a request to change "pumpkin soup" to "minestrone soup," and the server generates the menu again, finalizing it as "teriyaki chicken and carrots," "minestrone soup," and "salad (without green peppers)."

[0798] Step 7: Generate a shopping list

[0799] server

[0800] Based on the confirmed menu, a shopping list of necessary ingredients is automatically generated, listing the ingredients to be purchased and their quantities, and sending it to the device.

[0801] Input: Confirmed menu

[0802] Output: Shopping list

[0803] Specific operation: The server compiles a list of "400g of chicken," "3 carrots," "1 / 2 a pumpkin," "vegetables for salad (excluding bell peppers)," and "vegetables for minestrone soup" and sends it to the terminal.

[0804] Step 8: View your shopping list

[0805] Terminal

[0806] The terminal displays the final shopping list to the user, which is designed to be visually appealing and facilitate a smooth shopping experience.

[0807] Input: Shopping list

[0808] Output: Displaying the shopping list to the user

[0809] Specific operation: The device displays the following items in a list format: "400g chicken," "3 carrots," "1 / 2 pumpkin," "Salad vegetables (excluding bell peppers)," and "Vegetables for minestrone soup."

[0810] The above processing flow allows efficient planning of dinner menus that will satisfy the whole family, reduces food waste, and enables economical shopping.

[0811] (Application example 1)

[0812] 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."

[0813] With conventional systems, it was difficult to efficiently plan dinner menus that would satisfy the whole family, and it was also difficult to effectively utilize sales information. Even if menus were generated based on sales information, ordering ingredients and creating shopping lists based on those menus was not automated, placing a heavy burden on users. This resulted in problems such as food waste and increased financial burdens.

[0814] 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.

[0815] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale item information, means for generating an optimal menu based on the family composition information and sale item information, means for presenting the generated menu to the user, means for ordering necessary ingredients and dishes from an external service based on the generated menu, and means for generating a shopping list. This makes it possible to efficiently plan a dinner menu that will satisfy the whole family, and automatically order ingredients and generate a shopping list based on the menu.

[0816] "User" refers to the person who operates the system and inputs family composition information.

[0817] "Family composition information" refers to information including the name, age, favorite foods, disliked foods, and allergy information of each family member.

[0818] "Advertising information" refers to information about products and services offered by retail stores, such as special sales and campaigns.

[0819] "Special sale product information" refers to discount prices and special offers for specific products extracted from advertising information.

[0820] "Menu" refers to a meal plan that satisfies the whole family.

[0821] "Server" refers to a computer system that collects and analyzes user input and advertising information, and generates and presents menus.

[0822] "External services" refer to food delivery services that users use to order ingredients and dishes based on their menu.

[0823] A "shopping list" refers to a list of ingredients to be purchased based on the generated menu.

[0824] "Web scraping" refers to the technology of automatically extracting necessary information from websites on the Internet.

[0825] "Image recognition algorithm" refers to technology for extracting sale item information from images included in advertising information.

[0826] This invention is a food delivery system that efficiently plans dinner menus that will satisfy the whole family and effectively utilizes sale information. The system is composed of users, a server, and external services.

[0827] System configuration and roles

[0828] User

[0829] 1. Enter your family information

[0830] Through a smartphone application, users enter the name, age, favorite foods, disliked foods, and allergy information for each family member.

[0831] 2. Check and modify the menu

[0832] The generated menu is presented to the user, who can review it and request modifications if necessary.

[0833] server

[0834] 1. Collection and analysis of advertising information

[0835] The server periodically collects advertising information from the websites of nearby supermarkets and grocery stores, and analyzes and extracts sale product information using web scraping and image recognition algorithms.

[0836] 2. Menu generation

[0837] The server uses a generative AI model to generate optimal menus based on the family composition information collected from the user and the analyzed sale item information. The generated menus take into account the user's preferences and the use of sale items.

[0838] 3. Generate a shopping list

[0839] The server automatically generates a shopping list of necessary ingredients based on the finalized menu.

[0840] External Services

[0841] 1. Ordering ingredients and dishes

[0842] Based on the generated menu, you can order the necessary ingredients and dishes from external food delivery or ingredient delivery services.

[0843] Program processing flow

[0844] The server performs the following data processing and data calculations.

[0845] Hardware and software: The server is a computer system with a powerful processor and large memory capacity, and uses software for web scraping (e.g., BeautifulSoup), image recognition algorithms (e.g., TensorFlow), and frameworks for running generative AI models (e.g., PyTorch).

[0846] Data processing: Advertising information is scraped from websites, HTML data is analyzed to extract sale product information, and the collected image data is analyzed with a recognition algorithm to obtain sale product information as text.

[0847] Data calculation: The AI ​​model compares the family composition information entered by the user with the sale product information and generates the optimal menu. At this time, the model takes into account the nutritional value of ingredients, cooking time, cost, etc.

[0848] Examples of concrete examples and prompts

[0849] Specific examples

[0850] The user types the following into the terminal:

[0851] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0852] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0853] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0854] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0855] Prompt Sentence Examples

[0856] Use the family composition and special offer information below to generate the perfect dinner menu that will satisfy your family.

[0857] Family information:

[0858] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0859] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0860] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0861] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0862] Special Offer Information:

[0863] Chicken 100g 50 yen

[0864] Carrots 3 pieces 100 yen

[0865] This system allows you to efficiently plan meals that will satisfy the whole family and makes the most of special sale information, making shopping more economical.

[0866] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0867] Step 1:

[0868] The user inputs family information.

[0869] Input: Family members' names, ages, favorite foods, disliked foods, and allergy information.

[0870] Action: A user fills out a form using a smartphone application.

[0871] Output: The family information is sent to the server and stored.

[0872] Step 2:

[0873] The server collects the advertising information.

[0874] Input: Advertisement URL from your local supermarket or grocery store.

[0875] How it works: The server periodically collects advertising information using web scraping techniques, specifically by analyzing web pages using libraries such as BeautifulSoup to retrieve advertising images and text.

[0876] Output: A dataset of collected advertising information.

[0877] Step 3:

[0878] The server analyzes the collected advertising information and extracts sale item information.

[0879] Input: A dataset of advertising information.

[0880] How it works: The server analyzes the advertisement image using an image recognition algorithm (e.g. TensorFlow), converts the sale product information into text format, and extracts the text information using HTML analysis.

[0881] Output: A list of sale items.

[0882] Step 4:

[0883] The server generates an optimal menu based on family composition information and special sale product information.

[0884] Input: Family composition information and special offer information.

[0885] How it works: The server uses a generative AI model to generate optimal menus that take into account the preferences of family members, allergies, and special offers. The model uses frameworks such as PyTorch.

[0886] Output: A list of generated menu items.

[0887] Step 5:

[0888] The server presents the generated menu to the user.

[0889] Input: A list of generated menus.

[0890] How it works: The server displays the generated menu on the user's smartphone.

[0891] Output: The user reviews the menu and sends a request to the server to make any necessary modifications.

[0892] Step 6:

[0893] The server receives the modification request and regenerates the menu (if necessary).

[0894] Input: The user's modification request.

[0895] How it works: The server generates a menu using the generative AI model again based on the modification request. If necessary, it may only need to modify parts of the menu generated in the previous step.

[0896] Output: The revised menu list.

[0897] Step 7:

[0898] The server generates a shopping list based on the determined menu.

[0899] Input: A list of confirmed menu items.

[0900] How it works: The server lists the ingredients needed for a meal and generates a shopping list.

[0901] Output: A shopping list of ingredients needed.

[0902] Step 8:

[0903] The server presents the shopping list to the user and supports ordering from external services.

[0904] Input: Shopping list.

[0905] How it works: The server displays the shopping list on the user's smartphone and also works with external food delivery services to help order ingredients and dishes.

[0906] Output: An interface for the user to review the shopping list and place an order with an external service if necessary.

[0907] 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.

[0908] This system efficiently plans dinner menus that satisfy the whole family and effectively utilizes sales information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it can propose menus that take into account the user's psychological state.

[0909] Program Overview

[0910] The system operates in the following major steps:

[0911] Entering family information

[0912] User

[0913] Family information is entered through the device's interface, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[0914] Examples:

[0915] The user types the following into the terminal:

[0916] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[0917] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[0918] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[0919] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[0920] Collection and analysis of advertising information

[0921] server

[0922] The system periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[0923] Terminal

[0924] The collected advertising images are analyzed using an image recognition algorithm to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[0925] Menu generation

[0926] server

[0927] Using natural language processing and machine learning algorithms, the system generates optimal menus based on the family composition information collected from the user and the analyzed sale product information. The algorithm takes into account each family member's preferences, nutritional balance, ease of preparation, and the extent to which sale products are utilized.

[0928] Examples:

[0929] Based on the collected information, the following menu is generated:

[0930] Chicken and carrot teriyaki

[0931] Pumpkin soup

[0932] Salad (without peppers)

[0933] Adjusting recommendations with an emotion engine

[0934] Terminal

[0935] The emotion engine analyzes the user's voice and facial expressions and recognizes their emotions, for example, when they are tired or want to relax.

[0936] server

[0937] The emotion engine tailors meal suggestions based on the user's emotional state. For example, if the user is tired, it will suggest meals that are easy to prepare and have a relaxing effect.

[0938] Examples:

[0939] If a user says, "I'm tired today," the system will suggest a menu that includes "easy steamed chicken dishes" and "relaxing herbal tea."

[0940] Menu suggestions and shopping list generation

[0941] Terminal

[0942] The generated menu is presented to the user, who can make further revisions until satisfied.

[0943] server

[0944] A shopping list is automatically generated based on the confirmed menu.

[0945] Examples:

[0946] This will generate a shopping list like this:

[0947] 400g chicken

[0948] 3 carrots

[0949] 1 / 2 pumpkin

[0950] Salad vegetables (excluding bell peppers)

[0951] Example of program processing flow

[0952] 1. The user enters family information.

[0953] 2. The server collects supermarket advertising information.

[0954] 3. The device analyzes the advertising image and extracts information about special sale items.

[0955] 4. The server generates a menu based on family composition information and special sale item information.

[0956] 5. The device recognizes the user's emotions and transmits them to the server.

[0957] 6. Servers adjust menus based on your emotional state.

[0958] 7. The device presents the generated menu to the user.

[0959] 8. The user reviews the menu and requests changes if necessary.

[0960] 9. The server generates and confirms the menu again.

[0961] 10. The server generates a shopping list based on the confirmed menu, and the terminal displays it to the user.

[0962] This allows for efficient planning of dinner menus that satisfy the whole family, allows for menu suggestions that take into consideration the user's feelings, and allows for the provision of economical and balanced meals.

[0963] The processing flow will be explained below.

[0964] Step 1:

[0965] The user accesses the interface on the terminal and inputs family information, specifically, the name, age, favorite foods, disliked foods, and allergy information of each family member.

[0966] Step 2:

[0967] The device receives the family composition information entered by the user and sends it to the server, which can then perform processing based on that data.

[0968] Step 3:

[0969] The server periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[0970] Step 4:

[0971] The device stores the collected advertising images and analyzes them using an AI image recognition algorithm, extracting sale product information from the advertising images and saving it as text data.

[0972] Step 5:

[0973] The server generates optimal menus based on the family composition information sent by the user and the sale product information extracted using an image recognition algorithm. Specifically, it utilizes natural language processing and machine learning algorithms to take into account the preferences of all family members, nutritional balance, ease of preparation, and the use of sale products.

[0974] Step 6:

[0975] The terminal presents the generated menu sent from the server to the user, who can then review the menu and request modifications if necessary.

[0976] Step 7:

[0977] If the user requests a modification, the request is resent through the terminal to the server, which then generates a new menu and sends it to the terminal again.

[0978] Step 8:

[0979] The device analyzes the user's voice and facial expressions and uses an emotion engine to recognize the user's emotions, for example, when the user is tired or wants to relax.

[0980] Step 9:

[0981] The server adjusts the menu suggestions based on the emotional state transmitted by the emotion engine, for example, if the user is tired, it will suggest menus that are easy to prepare and have a relaxing effect.

[0982] Step 10:

[0983] The terminal presents the user with a menu adjusted based on their emotions, allows them to confirm the menu until they are satisfied, and accepts requests for any changes.

[0984] Step 11:

[0985] The user confirms and confirms the proposed menu. The server automatically generates a shopping list based on the confirmed menu.

[0986] Step 12:

[0987] The terminal displays the shopping list sent from the server to the user, who can then check it and go shopping at the supermarket using their smartphone or other device.

[0988] Step 13:

[0989] Users can purchase the necessary ingredients at the supermarket and cook dinner based on the suggested menu, providing an economical and balanced meal for the whole family.

[0990] Example 2

[0991] 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."

[0992] In modern households, it is difficult to efficiently plan a dinner menu that satisfies the entire family. In particular, it is necessary to take into account each family member's preferences, allergies, and nutritional balance, while also utilizing information on special sales items from an economical perspective. Furthermore, there is a demand for flexible menu suggestions that take into account the user's emotional state. The present invention aims to provide a menu suggestion system that simultaneously satisfies these multiple factors.

[0993] 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.

[0994] In this invention, the server includes a means for having the user input family composition information, a means for analyzing collected advertising information and extracting sale item information, a means for generating an optimal meal plan based on the family composition information and sale item information, a means for recognizing the user's emotional state and adjusting the menu, a means for presenting the generated menu to the user, and a means for generating a shopping list based on the confirmed menu. This allows for efficient planning of menus that satisfy the entire family, making it possible to provide economical and balanced meals. It also enables flexible menu suggestions that take the user's emotions into consideration.

[0995] "Family composition information" is information including the name, age, favorite foods, disliked foods, and allergy information of each family member.

[0996] "Advertising information" is data that includes information about special sales and discounts offered by stores such as supermarkets.

[0997] "Special sale product information" refers to information about special sale products and discounted products that has been analyzed and extracted from the collected advertising information.

[0998] The "meal plan" is a plan that includes a menu that is tailored to satisfy the entire family based on family composition information and special sale information.

[0999] "Emotional state" is information that indicates the psychological state of the user analyzed from the user's voice, facial expression, and the like.

[1000] "Web scraping" is a technique for automatically collecting information from websites on the Internet.

[1001] "Image recognition algorithm" refers to an algorithm for analyzing image data and extracting specific information within the image.

[1002] A "shopping list" is a list of all the ingredients and products needed based on a confirmed menu.

[1003] This system efficiently plans dinner menus that satisfy the whole family and effectively utilizes sales information. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it can propose menus that take into account the user's psychological state.

[1004] The implementation of this system uses the following hardware and software:

[1005] Hardware:

[1006] User devices (PCs, tablets, smartphones, etc.)

[1007] Internet connection environment

[1008] Camera and microphone (for emotion recognition)

[1009] software:

[1010] Web scraping tools (such as Python's BeautifulSoup)

[1011] Image recognition algorithms (OpenCV, TensorFlow, etc.)

[1012] Natural language processing and machine learning algorithms (e.g., Python's NLTK, scikit-learn)

[1013] Emotion recognition API (such as Microsoft Azure's Emotion Recognition API)

[1014] Entering family information

[1015] The user inputs family information through the interface on the terminal, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[1016] As a concrete example, the user enters the following:

[1017] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1018] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1019] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1020] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1021] Collection and analysis of advertising information

[1022] The server periodically accesses the website of the nearest supermarket and collects advertising information using web scraping technology. The acquired data is analyzed using an image recognition algorithm to extract information about special offers. For example, information such as "Special Offer: 100g of chicken for 50 yen" and "Special Offer: 3 carrots for 100 yen" is collected.

[1023] Menu generation

[1024] The server uses the family composition information collected from the user and the analyzed sale product information as inputs, and generates the optimal menu using natural language processing and machine learning algorithms. The algorithm takes into account each family member's preferences, nutritional balance, ease of preparation, and the use of sale products.

[1025] Specific examples of offerings include the following:

[1026] Chicken and carrot teriyaki

[1027] Pumpkin soup

[1028] Salad (without peppers)

[1029] Adjusting recommendations with an emotion engine

[1030] The device captures the user's voice and facial expressions with a camera and microphone, and the emotion engine analyzes this data. Depending on the user's emotional state, such as fatigue or a desire for relaxation, the server adjusts the menu based on the information sent from the emotion engine.

[1031] For example, if a user says, "I'm tired today," the system will suggest a menu that includes "easy steamed chicken dishes" and "relaxing herbal tea."

[1032] Menu suggestions and shopping list generation

[1033] The terminal presents the generated menu to the user, who can then revise it until he is satisfied. Based on the finalized menu, the server automatically generates a shopping list, which the terminal displays to the user.

[1034] As a concrete example, the following shopping list is generated:

[1035] 400g chicken

[1036] 3 carrots

[1037] 1 / 2 pumpkin

[1038] Salad vegetables (excluding bell peppers)

[1039] This system can efficiently plan dinner menus that satisfy the whole family, providing economical and balanced meals. It can also provide flexible menu suggestions that take into account the user's emotions.

[1040] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1041] Step 1:

[1042] The user inputs family information into the interface on the terminal, including the name, age, favorite foods, disliked foods, and allergy information for each family member.

[1043] Input: Family member's attribute information (name, age, preferences, disliked foods, allergy information)

[1044] Output: Family composition information (format saved in database)

[1045] Specific behavior:

[1046] The user types the following into a text field on the terminal:

[1047] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1048] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1049] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1050] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1051] Step 2:

[1052] The server accesses the website of the nearest supermarket and collects advertising information. Web scraping technology is used to obtain advertising data.

[1053] Input: supermarket website URL

[1054] Output: Advertising information (image data and text data)

[1055] Specific behavior:

[1056] The server periodically accesses the supermarket's website and scrapes advertising information using Python's BeautifulSoup library.

[1057] Step 3:

[1058] The device analyzes the collected advertising images and uses image recognition algorithms to extract sale item information.

[1059] Input: Advertising information (image data or text data)

[1060] Output: Special sale product information (text format data)

[1061] Specific behavior:

[1062] The device uses TensorFlow and OpenCV to analyze images and extract information about special offers, such as "Special Offer: 100g of chicken for 50 yen" and "Special Offer: 3 carrots for 100 yen."

[1063] Step 4:

[1064] The server receives family composition information and sale item information as input, and uses natural language processing and machine learning algorithms to generate the optimal menu.

[1065] Input: Family composition information, sale product information

[1066] Output: Optimal meal plan (list in text format)

[1067] Specific behavior:

[1068] The server uses scikit-learn and NLTK to generate the following menu based on the collected family composition information and sale item information.

[1069] Chicken and carrot teriyaki

[1070] Pumpkin soup

[1071] Salad (without peppers)

[1072] Step 5:

[1073] The device captures the user's voice and facial expressions using a camera and microphone, and the emotion engine analyzes this data.

[1074] Input: User's voice data, facial expression data

[1075] Output: User's emotional state (text data)

[1076] Specific behavior:

[1077] The device uses Microsoft Azure's emotion recognition API to recognize emotions from the user's voice and facial expressions, and extracts emotional states such as "I'm tired" or "I want to relax."

[1078] Step 6:

[1079] The server adjusts the menu suggestions based on the emotional state sent from the emotion engine.

[1080] Input: User's emotional state, optimal menu

[1081] Output: Adjusted menu (list in text format)

[1082] Specific behavior:

[1083] The server takes into account the user's emotional state, and if the user says something like "I'm tired today," it will suggest a menu that includes "simple steamed chicken dishes" and "relaxing herbal tea."

[1084] Step 7:

[1085] The terminal presents the generated menu to the user, who can then make corrections until satisfied.

[1086] Input: Adjusted menu

[1087] Output: Final menu (list in text format)

[1088] Specific behavior:

[1089] The terminal displays the generated menu to the user, and if the user requests a change, the terminal makes the server generate a new menu and presents it.

[1090] Step 8:

[1091] The server automatically generates a shopping list based on the finalized menu, and the terminal displays the generated shopping list to the user.

[1092] Input: Final menu

[1093] Output: Shopping list (list in text format)

[1094] Specific behavior:

[1095] The server lists the necessary ingredients and products based on the final menu, and the terminal displays the following shopping list to the user.

[1096] 400g chicken

[1097] 3 carrots

[1098] 1 / 2 pumpkin

[1099] Salad vegetables (excluding bell peppers)

[1100] (Application example 2)

[1101] 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."

[1102] When planning a typical dinner menu, it is difficult to utilize sale information while taking into account the preferences and nutritional balance of each family member. It is also difficult to adjust menu suggestions based on the user's emotional state. The present invention aims to provide a system that meets these complex requirements, efficiently plans a menu that satisfies the entire family, and also makes suggestions that take into account the user's emotional state.

[1103] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1104] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale product information, means for generating an optimal menu based on the family composition information and sale product information, means for recognizing the user's emotional state and adjusting the menu suggestions, and means for presenting the generated menu and shopping list to the user. This not only enables menu suggestions that make the most of the food preferences and nutritional balance of all family members and sale information, but also makes suggestions that take the user's emotional state into consideration.

[1105] A "user" is an individual or family representative who uses the system to plan menus and receive suggestions.

[1106] "Family composition information" is data entered by the user that includes the names, ages, favorite foods, disliked foods, and allergy information of family members.

[1107] "Advertising information" is data that includes information about special sales and campaigns offered by stores such as supermarkets.

[1108] "Special sale product information" is data relating to the special sale price and sales conditions of a specific product, extracted from the analyzed advertising information.

[1109] A "menu" refers to a combination of dinner menu items that will satisfy the whole family.

[1110] The "emotional state" is a psychological state that is recognized from information such as the user's voice and facial expression.

[1111] "Suggestions" are information that includes menus and associated shopping lists generated by the system, as well as menus that are tailored based on the user's emotional state.

[1112] A "shopping list" is a list of ingredients that need to be purchased based on the generated menu.

[1113] The "server" is a central processing unit that processes various data of this system, generates menus, and recognizes emotional states.

[1114] The present invention is a system that helps users plan dinner menus that take into account the preferences and allergy information of all family members and helps them shop by making the most of sales information. This system can also suggest optimal menus by taking into account the user's emotional state.

[1115] Hardware used

[1116] Smartphones (e.g., iPhones and Android devices)

[1117] Servers (cloud servers and on-premise servers)

[1118] Software used

[1119] Web scraping technologies (e.g., Python's Beautiful Soup or Selenium)

[1120] Image recognition algorithms (e.g., Google Cloud Vision API)

[1121] Natural language processing algorithms (e.g., OpenAI GPT-3, BERT)

[1122] Emotion recognition engine (e.g., Microsoft Azure Emotion API, Affectiva API)

[1123] Specific processing of the system

[1124] 1. Enter family information

[1125] Through a smartphone interface, users enter family information, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[1126] 2. Collection and analysis of advertising information

[1127] The server periodically accesses the websites of each supermarket or store to collect advertising information, specifically by using Python's Beautiful Soup and Selenium to obtain image data for advertisements.

[1128] The collected advertising images are analyzed using the Google Cloud Vision API to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[1129] 3. Menu generation

[1130] The server takes the family composition information collected from the user and the analyzed sale item information as input, and uses natural language processing algorithms such as OpenAI GPT-3 to generate the optimal menu.

[1131] 4. Adjusting recommendations using an emotion engine

[1132] Using the smartphone's camera and microphone, the system analyzes the user's voice and facial expressions, and recognizes the user's emotional state using an emotion recognition engine such as Microsoft Azure Emotion API.

[1133] Based on the recognized emotional state, the server reviews the generated menu and adjusts the proposed content to take into consideration the user's psychological state.

[1134] 5. Present menu and shopping list

[1135] The adjusted menu is presented to the user via their smartphone, and they can make adjustments as necessary until they are satisfied.

[1136] A shopping list is automatically generated based on the confirmed menu and displayed on the smartphone.

[1137] Specific examples

[1138] Consider a scenario where a user launches the app and says, "I'm busy today, so I want to make a simple dinner." The server will suggest menu items such as "stir-fried chicken and vegetables" or "easy steamed chicken" based on the family composition information entered by the user and the sale information of the nearest supermarket. It will also recognize the user's emotional state and suggest even easier dishes to prepare if the user is tired. The generated shopping list will include items such as "400g chicken, carrots, and pumpkin," allowing the user to shop efficiently.

[1139] Prompt Sentence Examples

[1140] This system suggests optimal dinner menus based on the user's family composition information and sales information from physical stores. When a user says, "I want to make something easy to cook today," generate a menu based on the following information:

[1141] Family composition information

[1142] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1143] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1144] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1145] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1146] Special sale information

[1147] Chicken 100g 50 yen

[1148] Carrots 3 pieces 100 yen

[1149] 1 / 2 pumpkin 80 yen

[1150] Proposal Format

[1151] 1. Menu planning

[1152] 2. List of ingredients needed

[1153] In this way, the system can propose optimal menus that meet the user's diverse requirements and emotional state.

[1154] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1155] Step 1:

[1156] User input of family information

[1157] Users enter family information through the interface on their smartphones, including each family member's name, age, favorite foods, disliked foods, and allergy information. The entered family information is sent to the system's server and stored in a database.

[1158] input:

[1159] Family members' names, ages, favorite foods, disliked foods, and allergy information

[1160] output:

[1161] Family composition information stored in a database

[1162] Step 2:

[1163] Collection of advertising information by the server

[1164] The server periodically accesses the websites of each supermarket or store and collects advertising information using web scraping technology. It then uses Python's Beautiful Soup and Selenium to obtain advertising image data, which is then stored on the server.

[1165] input:

[1166] Supermarket or store website URL

[1167] output:

[1168] Advertising image data stored on the server

[1169] Step 3:

[1170] Server analysis of advertising information

[1171] The server analyzes the stored advertising image data using the Google Cloud Vision API to extract sale product information, recognizing text within the image and extracting sale prices and product information as text data.

[1172] input:

[1173] Advertising image data

[1174] output:

[1175] Special sale product information (text data)

[1176] Step 4:

[1177] Server-based menu generation

[1178] The server uses natural language processing algorithms such as OpenAI GPT-3 to generate optimal menus based on family composition information and special sale information. The algorithm takes into account preferences, allergies, and nutritional balance to suggest menus that will satisfy the whole family.

[1179] input:

[1180] Family composition information, special sale product information

[1181] output:

[1182] Generated menu

[1183] Step 5:

[1184] Use of emotion engines on devices

[1185] The device (smartphone) uses a camera and microphone to capture the user's voice and facial expressions. An emotion recognition engine such as Microsoft Azure Emotion API is used to recognize the user's emotional state. The recognized emotional state is sent to the server.

[1186] input:

[1187] Voice and facial expression data

[1188] output:

[1189] Perceived emotional state

[1190] Step 6:

[1191] Server adjustment of proposals

[1192] The server then reviews the generated menu based on the user's emotional state and adjusts the suggestions to take into account the user's psychological state. For example, if the user is tired, the server will prioritize menus that are easy to prepare.

[1193] input:

[1194] Generated menu, emotional state

[1195] output:

[1196] Adjusted menu

[1197] Step 7:

[1198] Menu and shopping list presentation on the device

[1199] The adjusted menu is presented to the user via their smartphone. The user can make any necessary adjustments until they are satisfied. A shopping list is automatically generated based on the finalized menu and displayed on the smartphone.

[1200] input:

[1201] Adjusted menu

[1202] output:

[1203] Menu presented to the user, generated shopping list

[1204] 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.

[1205] 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.

[1206] 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.

[1207] [Third embodiment]

[1208] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1209] 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.

[1210] 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).

[1211] 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.

[1212] 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.

[1213] 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).

[1214] 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. 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.

[1215] 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.

[1216] 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.

[1217] 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.

[1218] 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.

[1219] 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."

[1220] This invention is a system that efficiently plans dinner menus that satisfy the whole family and effectively utilizes sale information. This system inputs family composition information from the user, collects and analyzes advertising information from the nearest supermarket, generates menus based on the family composition information and sale item information, presents the generated menus to the user, and generates shopping lists.

[1221] Program Overview

[1222] The system operates in the following major steps:

[1223] Entering family information

[1224] User

[1225] Family information is entered through a form on the device, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[1226] Examples:

[1227] The user types the following into the terminal:

[1228] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1229] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1230] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1231] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1232] Collection and analysis of advertising information

[1233] server

[1234] We periodically collect advertising information from the websites of nearby supermarkets, specifically by using web scraping technology to obtain advertising images.

[1235] Terminal

[1236] The collected advertising images are analyzed using an image recognition algorithm to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[1237] Menu generation

[1238] server

[1239] Using natural language processing and machine learning algorithms, the system generates optimal menus by taking into account the likes and dislikes of each family member, nutritional balance, ease of preparation, and whether to utilize special sale items.

[1240] Examples:

[1241] Based on the collected information, the following menu is generated:

[1242] Chicken and carrot teriyaki

[1243] Pumpkin soup

[1244] Salad (without peppers)

[1245] Menu suggestions and shopping list generation

[1246] Terminal

[1247] The generated menu is presented to the user, who can make further revisions until satisfied.

[1248] server

[1249] A shopping list is automatically generated based on the confirmed menu.

[1250] Examples:

[1251] This will generate a shopping list like this:

[1252] 400g chicken

[1253] 3 carrots

[1254] 1 / 2 pumpkin

[1255] Salad vegetables (excluding bell peppers)

[1256] Example of program processing flow

[1257] 1. The user enters family information.

[1258] 2. The server collects supermarket advertising information.

[1259] 3. The device analyzes the advertising image and extracts information about special sale items.

[1260] 4. The server generates a menu based on family composition information and special sale item information.

[1261] 5. The device presents the generated menu to the user.

[1262] 6. The user reviews the menu and requests changes if necessary.

[1263] 7. The server generates and confirms the menu again.

[1264] 8. The server generates a shopping list based on the confirmed menu, and the terminal displays it to the user.

[1265] This allows for efficient planning of dinner menus that will satisfy the whole family, reduces food waste, and enables more economical shopping.

[1266] The processing flow will be explained below.

[1267] Step 1:

[1268] The user accesses the interface on the terminal and inputs family information, specifically, the name, age, favorite foods, disliked foods, and allergy information of each family member.

[1269] Step 2:

[1270] The device receives the family composition information entered by the user and sends it to the server, which then processes the data.

[1271] Step 3:

[1272] The server periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[1273] Step 4:

[1274] The device stores the collected advertising images and analyzes them using an AI image recognition algorithm, extracting sale product information from the advertising images and saving it as text data.

[1275] Step 5:

[1276] The server generates optimal menus based on the family composition information sent by the user and the sale product information extracted using an image recognition algorithm. Specifically, it utilizes natural language processing and machine learning algorithms to take into account the preferences of all family members, nutritional balance, ease of preparation, and the use of sale products.

[1277] Step 6:

[1278] The terminal displays the generated menu sent from the server to the user, who can review the menu and request modifications if necessary.

[1279] Step 7:

[1280] If the user requests a modification, the request is resent to the server via the terminal, and the server regenerates the menu and resends it to the terminal.

[1281] Step 8:

[1282] The user confirms and confirms the proposed menu. The server automatically generates a shopping list based on the confirmed menu.

[1283] Step 9:

[1284] The terminal displays the shopping list sent from the server to the user, who can then check the list and go shopping at the supermarket using a terminal such as a smartphone.

[1285] Step 10:

[1286] Users can purchase the necessary ingredients at the supermarket and cook dinner based on the suggested menu, providing an economical and balanced meal for the whole family.

[1287] Example 1

[1288] 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."

[1289] For many families, efficiently planning a daily dinner menu that takes into account the preferences and nutritional balance of each family member is a significant burden. It is also difficult to make the most of sales information and shop economically. A system that can solve these problems is needed.

[1290] 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.

[1291] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale product information, means for generating an optimal menu based on the family composition information and sale product information, means for presenting the generated menu to the user, means for the user to check the presented menu and request revisions as necessary, and means for generating a shopping list based on the finalized menu. This enables the system to propose optimal menus that take into account the preferences and nutritional balance of all family members and to make economical shopping decisions by utilizing sale information.

[1292] A "user" is a person who uses the system to input family information and check and modify menus.

[1293] "Family composition information" is data including the names, ages, favorite foods, disliked foods, and allergy information of family members.

[1294] "Advertising information" is information about special sales and campaigns collected from the nearest supermarket or store.

[1295] "Special sale product information" is information on the price and quantity of a specific product extracted from the advertisement information.

[1296] A "menu" refers to the specific contents and combination of dishes that the whole family will eat.

[1297] A "shopping list" is a list of ingredients and items that need to be purchased based on a menu.

[1298] "Web scraping" is a technique for automatically collecting and analyzing the content of specific web pages.

[1299] An "image recognition algorithm" is a computer algorithm used to extract and analyze specific information from image data.

[1300] "Natural language processing" is a technical field that uses computers to understand, generate, and manipulate human language.

[1301] A "machine learning algorithm" is an algorithm that learns patterns based on data and makes predictions and judgments.

[1302] A "prompt" is an instruction or question that is input to a generative AI model.

[1303] This invention is a system that efficiently plans dinner menus that satisfy the whole family and effectively utilizes sale information. This system inputs family composition information from the user, collects and analyzes advertising information from the nearest supermarket, generates menus based on the family composition information and sale item information, presents the generated menus to the user, and generates shopping lists.

[1304] The system consists of the following main components:

[1305] 1. User Interface (UI)

[1306] 2. Server

[1307] 3. Terminal

[1308] User Interface (UI)

[1309] User

[1310] The user enters family information through an input form on the device. This input form provides fields for entering each family member's name, age, favorite foods, disliked foods, and allergy information. This input information undergoes form validation using HTML5 and JavaScript before being sent to the server.

[1311] Examples:

[1312] The user enters the following information into an input form:

[1313] Name: Father, Age: 35, Favorite food: Pasta, Dislikes: Fish, Allergies: None

[1314] server

[1315] server

[1316] The server periodically collects advertising information from the websites of nearby supermarkets, using web scraping techniques (e.g., Beautiful Soup or Selenium) to obtain advertising images and text information, and then transmits this data to the device.

[1317] Terminal

[1318] Terminal

[1319] The device receives the advertising image data sent from the server and analyzes it using an image recognition algorithm (e.g., Google Cloud Vision API). As a result of the analysis, information such as the name, price, and quantity of the special sale item is extracted. This information is saved in text format and used for later processing.

[1320] Menu generation

[1321] server

[1322] The server uses natural language processing and machine learning algorithms (e.g., GPT-3 model) to generate optimal menus based on family composition information collected from the user and sale product information analyzed by the device. It references food and recipe databases to propose menus that take into account the preferences and allergies of all family members, nutritional balance, ease of preparation, and other factors.

[1323] Examples:

[1324] Based on family composition information such as "Father likes pasta, mother likes salad, child 1 likes curry, child 2 likes pumpkin" and sale information such as "Sale: 100g of chicken for 50 yen," a menu of "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)" is generated.

[1325] Menu presentation

[1326] Terminal

[1327] The generated menu is sent from the server to the terminal and presented to the user. The user can check the displayed menu and make an adjustment request if they wish to make any changes. An interactive UI is incorporated to allow the user to easily modify the menu.

[1328] Menu revision and confirmation

[1329] server

[1330] The algorithm receives a request for revisions from the user and generates a new menu. This process is repeated until the user is satisfied. The final menu is then confirmed.

[1331] Specific behavior:

[1332] The server generates the menu again and confirms it as "Teriyaki chicken and carrots," "Minestrone soup," and "Salad (without green peppers)."

[1333] Generate a shopping list

[1334] server

[1335] Based on the confirmed menu, a shopping list of the necessary ingredients is automatically generated, listing the ingredients to be purchased and their quantities, and sending it back to the device.

[1336] Specific behavior:

[1337] The server compiles a list of "400g of chicken," "3 carrots," "1 / 2 a pumpkin," "vegetables for salad (excluding bell peppers)," and "vegetables for minestrone soup" and sends it to the terminal.

[1338] Prompt Sentence Examples

[1339] Here are some concrete examples of input prompts for a generative AI model like GPT-3:

[1340] Example prompt sentence:

[1341] Family information:

[1342] Father: 35 years old, Favorite food: Pasta, Dislikes: Fish

[1343] Mother: 33 years old, Favorite food: Salad, Dislikes: Green peppers

[1344] Child 1: 5 years old, Favorite food: curry, Dislikes: carrots

[1345] Child 2: 2 years old, Favorite food: Pumpkin, Disliked food: None

[1346] Special Offer Information:

[1347] Chicken 100g 50 yen

[1348] Carrots 3 pieces 100 yen

[1349] Use this information to come up with a dinner menu that will satisfy the whole family.

[1350] This allows for optimal menu suggestions that will satisfy the whole family, and allows for economical shopping by taking advantage of special sale information.

[1351] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1352] Step 1: Enter family information

[1353] User

[1354] The user accesses the input form on the terminal and fills in the input fields with the names, ages, favorite foods, disliked foods, and allergy information of the family members. This information is sent to the server when the user presses the "Send" button.

[1355] Input: Name, age, favorite foods, disliked foods, allergy information

[1356] Output: Family composition information sent to the server

[1357] Specific behavior: The user enters the following information: "Name: Father, Age: 35, Favorite food: Pasta, Dislikes: Fish, Allergies: None"

[1358] Step 2: Collect advertising information

[1359] server

[1360] The server runs a periodically scheduled job (e.g., a Cron job) to collect advertisement information from the websites of nearby supermarkets, using web scraping tools such as Beautiful Soup or Selenium to analyze the HTML structure of the advertisement pages and retrieve advertisement images and text information using specific CSS selectors and XPaths.

[1361] Input: supermarket website URL

[1362] Output: Advertising images and text information

[1363] Specific operation: The server accesses "https: / / example-supermarket.com / ads" and retrieves the ad image from the HTML page.

[1364] Step 3: Analyze the ad image

[1365] Terminal

[1366] The device receives the advertising image data sent from the server and analyzes it using image recognition algorithms such as Google Cloud Vision API. As a result of the analysis, information such as the product name, price, and quantity of the special sale item is extracted. This information is saved in text format and used for further processing.

[1367] Input: Ad image

[1368] Output: Special sale product information (product name, price, quantity)

[1369] Specific operation: The device sends the advertising image to the Google Cloud Vision API, and extracts the following information as the analysis result: "Special Sale: 100g of chicken for 50 yen" and "Special Sale: 3 carrots for 100 yen."

[1370] Step 4: Create a menu

[1371] server

[1372] The server uses natural language processing and machine learning algorithms (e.g., GPT-3 model) to generate optimal menus based on family composition information collected from the user and sale product information analyzed by the device. It references food and recipe databases to propose menus that take into account the preferences and allergies of all family members, nutritional balance, ease of preparation, and other factors.

[1373] Input: Family composition information, sale product information

[1374] Output: Optimal menu

[1375] Specific operation: Based on family composition information such as "Father likes pasta, mother likes salad, child 1 likes curry, child 2 likes pumpkin" and sale information such as "Special sale: 100g chicken for 50 yen," a menu of "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)" is generated.

[1376] Step 5: Present the menu

[1377] Terminal

[1378] The generated menu is sent from the server to the terminal and presented to the user. The user can check the displayed menu and make an adjustment request if they wish to make any changes. An interactive UI is used to allow the user to easily modify the menu.

[1379] Input: Generated menu

[1380] Output: Menu presented to the user

[1381] Specific operation: The device displays the menu items "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)."

[1382] Step 6: Revise and finalize the menu

[1383] server

[1384] The algorithm receives a request for revisions from the user and generates a new menu. This process is repeated until the user is satisfied, and the final menu is confirmed.

[1385] Input: User modification request

[1386] Output: Final revised menu

[1387] Specific operation: The user sends a request to change "pumpkin soup" to "minestrone soup," and the server generates the menu again, finalizing it as "teriyaki chicken and carrots," "minestrone soup," and "salad (without green peppers)."

[1388] Step 7: Generate a shopping list

[1389] server

[1390] Based on the confirmed menu, a shopping list of necessary ingredients is automatically generated, listing the ingredients to be purchased and their quantities, and sending it to the device.

[1391] Input: Confirmed menu

[1392] Output: Shopping list

[1393] Specific operation: The server compiles a list of "400g of chicken," "3 carrots," "1 / 2 a pumpkin," "vegetables for salad (excluding bell peppers)," and "vegetables for minestrone soup" and sends it to the terminal.

[1394] Step 8: View your shopping list

[1395] Terminal

[1396] The terminal displays the final shopping list to the user, which is designed to be visually appealing and facilitate a smooth shopping experience.

[1397] Input: Shopping list

[1398] Output: Displaying the shopping list to the user

[1399] Specific operation: The device displays the following items in a list format: "400g chicken," "3 carrots," "1 / 2 pumpkin," "Salad vegetables (excluding bell peppers)," and "Vegetables for minestrone soup."

[1400] The above processing flow allows efficient planning of dinner menus that will satisfy the whole family, reduces food waste, and enables economical shopping.

[1401] (Application example 1)

[1402] 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."

[1403] With conventional systems, it was difficult to efficiently plan dinner menus that would satisfy the whole family, and it was also difficult to effectively utilize sales information. Even if menus were generated based on sales information, ordering ingredients and creating shopping lists based on those menus was not automated, placing a heavy burden on users. This resulted in problems such as food waste and increased financial burdens.

[1404] 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.

[1405] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale item information, means for generating an optimal menu based on the family composition information and sale item information, means for presenting the generated menu to the user, means for ordering necessary ingredients and dishes from an external service based on the generated menu, and means for generating a shopping list. This makes it possible to efficiently plan a dinner menu that will satisfy the whole family, and automatically order ingredients and generate a shopping list based on the menu.

[1406] "User" refers to the person who operates the system and inputs family composition information.

[1407] "Family composition information" refers to information including the name, age, favorite foods, disliked foods, and allergy information of each family member.

[1408] "Advertising information" refers to information about products and services offered by retail stores, such as special sales and campaigns.

[1409] "Special sale product information" refers to discount prices and special offers for specific products extracted from advertising information.

[1410] "Menu" refers to a meal plan that satisfies the whole family.

[1411] "Server" refers to a computer system that collects and analyzes user input and advertising information, and generates and presents menus.

[1412] "External services" refer to food delivery services that users use to order ingredients and dishes based on their menu.

[1413] A "shopping list" refers to a list of ingredients to be purchased based on the generated menu.

[1414] "Web scraping" refers to the technology of automatically extracting necessary information from websites on the Internet.

[1415] "Image recognition algorithm" refers to technology for extracting sale item information from images included in advertising information.

[1416] This invention is a food delivery system that efficiently plans dinner menus that will satisfy the whole family and effectively utilizes sale information. The system is composed of users, a server, and external services.

[1417] System configuration and roles

[1418] User

[1419] 1. Enter your family information

[1420] Through a smartphone application, users enter the name, age, favorite foods, disliked foods, and allergy information for each family member.

[1421] 2. Check and modify the menu

[1422] The generated menu is presented to the user, who can review it and request modifications if necessary.

[1423] server

[1424] 1. Collection and analysis of advertising information

[1425] The server periodically collects advertising information from the websites of nearby supermarkets and grocery stores, and analyzes and extracts sale product information using web scraping and image recognition algorithms.

[1426] 2. Menu generation

[1427] The server uses a generative AI model to generate optimal menus based on the family composition information collected from the user and the analyzed sale item information. The generated menus take into account the user's preferences and the use of sale items.

[1428] 3. Generate a shopping list

[1429] The server automatically generates a shopping list of necessary ingredients based on the finalized menu.

[1430] External Services

[1431] 1. Ordering ingredients and dishes

[1432] Based on the generated menu, you can order the necessary ingredients and dishes from external food delivery or ingredient delivery services.

[1433] Program processing flow

[1434] The server performs the following data processing and data calculations.

[1435] Hardware and software: The server is a computer system with a powerful processor and large memory capacity, and uses software for web scraping (e.g., BeautifulSoup), image recognition algorithms (e.g., TensorFlow), and frameworks for running generative AI models (e.g., PyTorch).

[1436] Data processing: Advertising information is scraped from websites, HTML data is analyzed to extract sale product information, and the collected image data is analyzed with a recognition algorithm to obtain sale product information as text.

[1437] Data calculation: The AI ​​model compares the family composition information entered by the user with the sale product information and generates the optimal menu. At this time, the model takes into account the nutritional value of ingredients, cooking time, cost, etc.

[1438] Examples of concrete examples and prompts

[1439] Specific examples

[1440] The user types the following into the terminal:

[1441] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1442] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1443] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1444] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1445] Prompt Sentence Examples

[1446] Use the family composition and special offer information below to generate the perfect dinner menu that will satisfy your family.

[1447] Family information:

[1448] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1449] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1450] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1451] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1452] Special Offer Information:

[1453] Chicken 100g 50 yen

[1454] Carrots 3 pieces 100 yen

[1455] This system allows you to efficiently plan meals that will satisfy the whole family and makes the most of special sale information, making shopping more economical.

[1456] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1457] Step 1:

[1458] The user inputs family information.

[1459] Input: Family members' names, ages, favorite foods, disliked foods, and allergy information.

[1460] Action: A user fills out a form using a smartphone application.

[1461] Output: The family information is sent to the server and stored.

[1462] Step 2:

[1463] The server collects the advertising information.

[1464] Input: Advertisement URL from your local supermarket or grocery store.

[1465] How it works: The server periodically collects advertising information using web scraping techniques, specifically by analyzing web pages using libraries such as BeautifulSoup to retrieve advertising images and text.

[1466] Output: A dataset of collected advertising information.

[1467] Step 3:

[1468] The server analyzes the collected advertising information and extracts sale item information.

[1469] Input: A dataset of advertising information.

[1470] How it works: The server analyzes the advertisement image using an image recognition algorithm (e.g. TensorFlow), converts the sale product information into text format, and extracts the text information using HTML analysis.

[1471] Output: A list of sale items.

[1472] Step 4:

[1473] The server generates an optimal menu based on family composition information and special sale product information.

[1474] Input: Family composition information and special offer information.

[1475] How it works: The server uses a generative AI model to generate optimal menus that take into account the preferences of family members, allergies, and special offers. The model uses frameworks such as PyTorch.

[1476] Output: A list of generated menu items.

[1477] Step 5:

[1478] The server presents the generated menu to the user.

[1479] Input: A list of generated menus.

[1480] How it works: The server displays the generated menu on the user's smartphone.

[1481] Output: The user reviews the menu and sends a request to the server to make any necessary modifications.

[1482] Step 6:

[1483] The server receives the modification request and regenerates the menu (if necessary).

[1484] Input: The user's modification request.

[1485] How it works: The server generates a menu using the generative AI model again based on the modification request. If necessary, it may only need to modify parts of the menu generated in the previous step.

[1486] Output: The revised menu list.

[1487] Step 7:

[1488] The server generates a shopping list based on the determined menu.

[1489] Input: A list of confirmed menu items.

[1490] How it works: The server lists the ingredients needed for a meal and generates a shopping list.

[1491] Output: A shopping list of ingredients needed.

[1492] Step 8:

[1493] The server presents the shopping list to the user and supports ordering from external services.

[1494] Input: Shopping list.

[1495] How it works: The server displays the shopping list on the user's smartphone and also works with external food delivery services to help order ingredients and dishes.

[1496] Output: An interface for the user to review the shopping list and place an order with an external service if necessary.

[1497] 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.

[1498] This system efficiently plans dinner menus that satisfy the whole family and effectively utilizes sales information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it can propose menus that take into account the user's psychological state.

[1499] Program Overview

[1500] The system operates in the following major steps:

[1501] Entering family information

[1502] User

[1503] Family information is entered through the device's interface, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[1504] Examples:

[1505] The user types the following into the terminal:

[1506] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1507] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1508] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1509] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1510] Collection and analysis of advertising information

[1511] server

[1512] The system periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[1513] Terminal

[1514] The collected advertising images are analyzed using an image recognition algorithm to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[1515] Menu generation

[1516] server

[1517] Using natural language processing and machine learning algorithms, the system generates optimal menus based on the family composition information collected from the user and the analyzed sale product information. The algorithm takes into account each family member's preferences, nutritional balance, ease of preparation, and the extent to which sale products are utilized.

[1518] Examples:

[1519] Based on the collected information, the following menu is generated:

[1520] Chicken and carrot teriyaki

[1521] Pumpkin soup

[1522] Salad (without peppers)

[1523] Adjusting recommendations with an emotion engine

[1524] Terminal

[1525] The emotion engine analyzes the user's voice and facial expressions and recognizes their emotions, for example, when they are tired or want to relax.

[1526] server

[1527] The emotion engine tailors meal suggestions based on the user's emotional state. For example, if the user is tired, it will suggest meals that are easy to prepare and have a relaxing effect.

[1528] Examples:

[1529] If a user says, "I'm tired today," the system will suggest a menu that includes "easy steamed chicken dishes" and "relaxing herbal tea."

[1530] Menu suggestions and shopping list generation

[1531] Terminal

[1532] The generated menu is presented to the user, who can make further revisions until satisfied.

[1533] server

[1534] A shopping list is automatically generated based on the confirmed menu.

[1535] Examples:

[1536] This will generate a shopping list like this:

[1537] 400g chicken

[1538] 3 carrots

[1539] 1 / 2 pumpkin

[1540] Salad vegetables (excluding bell peppers)

[1541] Example of program processing flow

[1542] 1. The user enters family information.

[1543] 2. The server collects supermarket advertising information.

[1544] 3. The device analyzes the advertising image and extracts information about special sale items.

[1545] 4. The server generates a menu based on family composition information and special sale item information.

[1546] 5. The device recognizes the user's emotions and transmits them to the server.

[1547] 6. Servers adjust menus based on your emotional state.

[1548] 7. The device presents the generated menu to the user.

[1549] 8. The user reviews the menu and requests changes if necessary.

[1550] 9. The server generates and confirms the menu again.

[1551] 10. The server generates a shopping list based on the confirmed menu, and the terminal displays it to the user.

[1552] This allows for efficient planning of dinner menus that satisfy the whole family, allows for menu suggestions that take into consideration the user's feelings, and allows for the provision of economical and balanced meals.

[1553] The processing flow will be explained below.

[1554] Step 1:

[1555] The user accesses the interface on the terminal and inputs family information, specifically, the name, age, favorite foods, disliked foods, and allergy information of each family member.

[1556] Step 2:

[1557] The device receives the family composition information entered by the user and sends it to the server, which can then perform processing based on that data.

[1558] Step 3:

[1559] The server periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[1560] Step 4:

[1561] The device stores the collected advertising images and analyzes them using an AI image recognition algorithm, extracting sale product information from the advertising images and saving it as text data.

[1562] Step 5:

[1563] The server generates optimal menus based on the family composition information sent by the user and the sale product information extracted using an image recognition algorithm. Specifically, it utilizes natural language processing and machine learning algorithms to take into account the preferences of all family members, nutritional balance, ease of preparation, and the use of sale products.

[1564] Step 6:

[1565] The terminal presents the generated menu sent from the server to the user, who can then review the menu and request modifications if necessary.

[1566] Step 7:

[1567] If the user requests a modification, the request is resent through the terminal to the server, which then generates a new menu and sends it to the terminal again.

[1568] Step 8:

[1569] The device analyzes the user's voice and facial expressions and uses an emotion engine to recognize the user's emotions, for example, when the user is tired or wants to relax.

[1570] Step 9:

[1571] The server adjusts the menu suggestions based on the emotional state transmitted by the emotion engine, for example, if the user is tired, it will suggest menus that are easy to prepare and have a relaxing effect.

[1572] Step 10:

[1573] The terminal presents the user with a menu adjusted based on their emotions, allows them to confirm the menu until they are satisfied, and accepts requests for any changes.

[1574] Step 11:

[1575] The user confirms and confirms the proposed menu. The server automatically generates a shopping list based on the confirmed menu.

[1576] Step 12:

[1577] The terminal displays the shopping list sent from the server to the user, who can then check it and go shopping at the supermarket using their smartphone or other device.

[1578] Step 13:

[1579] Users can purchase the necessary ingredients at the supermarket and cook dinner based on the suggested menu, providing an economical and balanced meal for the whole family.

[1580] Example 2

[1581] 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."

[1582] In modern households, it is difficult to efficiently plan a dinner menu that satisfies the entire family. In particular, it is necessary to take into account each family member's preferences, allergies, and nutritional balance, while also utilizing information on special sales items from an economical perspective. Furthermore, there is a demand for flexible menu suggestions that take into account the user's emotional state. The present invention aims to provide a menu suggestion system that simultaneously satisfies these multiple factors.

[1583] 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.

[1584] In this invention, the server includes a means for having the user input family composition information, a means for analyzing collected advertising information and extracting sale item information, a means for generating an optimal meal plan based on the family composition information and sale item information, a means for recognizing the user's emotional state and adjusting the menu, a means for presenting the generated menu to the user, and a means for generating a shopping list based on the confirmed menu. This allows for efficient planning of menus that satisfy the entire family, making it possible to provide economical and balanced meals. It also enables flexible menu suggestions that take the user's emotions into consideration.

[1585] "Family composition information" is information including the name, age, favorite foods, disliked foods, and allergy information of each family member.

[1586] "Advertising information" is data that includes information about special sales and discounts offered by stores such as supermarkets.

[1587] "Special sale product information" refers to information about special sale products and discounted products that has been analyzed and extracted from the collected advertising information.

[1588] The "meal plan" is a plan that includes a menu that is tailored to satisfy the entire family based on family composition information and special sale information.

[1589] "Emotional state" is information that indicates the psychological state of the user analyzed from the user's voice, facial expression, and the like.

[1590] "Web scraping" is a technique for automatically collecting information from websites on the Internet.

[1591] "Image recognition algorithm" refers to an algorithm for analyzing image data and extracting specific information within the image.

[1592] A "shopping list" is a list of all the ingredients and products needed based on a confirmed menu.

[1593] This system efficiently plans dinner menus that satisfy the whole family and effectively utilizes sales information. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it can propose menus that take into account the user's psychological state.

[1594] The implementation of this system uses the following hardware and software:

[1595] Hardware:

[1596] User devices (PCs, tablets, smartphones, etc.)

[1597] Internet connection environment

[1598] Camera and microphone (for emotion recognition)

[1599] software:

[1600] Web scraping tools (such as Python's BeautifulSoup)

[1601] Image recognition algorithms (OpenCV, TensorFlow, etc.)

[1602] Natural language processing and machine learning algorithms (e.g., Python's NLTK, scikit-learn)

[1603] Emotion recognition API (such as Microsoft Azure's Emotion Recognition API)

[1604] Entering family information

[1605] The user inputs family information through the interface on the terminal, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[1606] As a concrete example, the user enters the following:

[1607] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1608] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1609] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1610] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1611] Collection and analysis of advertising information

[1612] The server periodically accesses the website of the nearest supermarket and collects advertising information using web scraping technology. The acquired data is analyzed using an image recognition algorithm to extract information about special offers. For example, information such as "Special Offer: 100g of chicken for 50 yen" and "Special Offer: 3 carrots for 100 yen" is collected.

[1613] Menu generation

[1614] The server uses the family composition information collected from the user and the analyzed sale product information as inputs, and generates the optimal menu using natural language processing and machine learning algorithms. The algorithm takes into account each family member's preferences, nutritional balance, ease of preparation, and the use of sale products.

[1615] Specific examples of offerings include the following:

[1616] Chicken and carrot teriyaki

[1617] Pumpkin soup

[1618] Salad (without peppers)

[1619] Adjusting recommendations with an emotion engine

[1620] The device captures the user's voice and facial expressions with a camera and microphone, and the emotion engine analyzes this data. Depending on the user's emotional state, such as fatigue or a desire for relaxation, the server adjusts the menu based on the information sent from the emotion engine.

[1621] For example, if a user says, "I'm tired today," the system will suggest a menu that includes "easy steamed chicken dishes" and "relaxing herbal tea."

[1622] Menu suggestions and shopping list generation

[1623] The terminal presents the generated menu to the user, who can then revise it until he is satisfied. Based on the finalized menu, the server automatically generates a shopping list, which the terminal displays to the user.

[1624] As a concrete example, the following shopping list is generated:

[1625] 400g chicken

[1626] 3 carrots

[1627] 1 / 2 pumpkin

[1628] Salad vegetables (excluding bell peppers)

[1629] This system can efficiently plan dinner menus that satisfy the whole family, providing economical and balanced meals. It can also provide flexible menu suggestions that take into account the user's emotions.

[1630] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1631] Step 1:

[1632] The user inputs family information into the interface on the terminal, including the name, age, favorite foods, disliked foods, and allergy information for each family member.

[1633] Input: Family member's attribute information (name, age, preferences, disliked foods, allergy information)

[1634] Output: Family composition information (format saved in database)

[1635] Specific behavior:

[1636] The user types the following into a text field on the terminal:

[1637] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1638] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1639] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1640] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1641] Step 2:

[1642] The server accesses the website of the nearest supermarket and collects advertising information. Web scraping technology is used to obtain advertising data.

[1643] Input: supermarket website URL

[1644] Output: Advertising information (image data and text data)

[1645] Specific behavior:

[1646] The server periodically accesses the supermarket's website and scrapes advertising information using Python's BeautifulSoup library.

[1647] Step 3:

[1648] The device analyzes the collected advertising images and uses image recognition algorithms to extract sale item information.

[1649] Input: Advertising information (image data or text data)

[1650] Output: Special sale product information (text format data)

[1651] Specific behavior:

[1652] The device uses TensorFlow and OpenCV to analyze images and extract information about special offers, such as "Special Offer: 100g of chicken for 50 yen" and "Special Offer: 3 carrots for 100 yen."

[1653] Step 4:

[1654] The server receives family composition information and sale item information as input, and uses natural language processing and machine learning algorithms to generate the optimal menu.

[1655] Input: Family composition information, sale product information

[1656] Output: Optimal meal plan (list in text format)

[1657] Specific behavior:

[1658] The server uses scikit-learn and NLTK to generate the following menu based on the collected family composition information and sale item information.

[1659] Chicken and carrot teriyaki

[1660] Pumpkin soup

[1661] Salad (without peppers)

[1662] Step 5:

[1663] The device captures the user's voice and facial expressions using a camera and microphone, and the emotion engine analyzes this data.

[1664] Input: User's voice data, facial expression data

[1665] Output: User's emotional state (text data)

[1666] Specific behavior:

[1667] The device uses Microsoft Azure's emotion recognition API to recognize emotions from the user's voice and facial expressions, and extracts emotional states such as "I'm tired" or "I want to relax."

[1668] Step 6:

[1669] The server adjusts the menu suggestions based on the emotional state sent from the emotion engine.

[1670] Input: User's emotional state, optimal menu

[1671] Output: Adjusted menu (list in text format)

[1672] Specific behavior:

[1673] The server takes into account the user's emotional state, and if the user says something like "I'm tired today," it will suggest a menu that includes "simple steamed chicken dishes" and "relaxing herbal tea."

[1674] Step 7:

[1675] The terminal presents the generated menu to the user, who can then make corrections until satisfied.

[1676] Input: Adjusted menu

[1677] Output: Final menu (list in text format)

[1678] Specific behavior:

[1679] The terminal displays the generated menu to the user, and if the user requests a change, the terminal makes the server generate a new menu and presents it.

[1680] Step 8:

[1681] The server automatically generates a shopping list based on the finalized menu, and the terminal displays the generated shopping list to the user.

[1682] Input: Final menu

[1683] Output: Shopping list (list in text format)

[1684] Specific behavior:

[1685] The server lists the necessary ingredients and products based on the final menu, and the terminal displays the following shopping list to the user.

[1686] 400g chicken

[1687] 3 carrots

[1688] 1 / 2 pumpkin

[1689] Salad vegetables (excluding bell peppers)

[1690] (Application example 2)

[1691] 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."

[1692] When planning a typical dinner menu, it is difficult to utilize sale information while taking into account the preferences and nutritional balance of each family member. It is also difficult to adjust menu suggestions based on the user's emotional state. The present invention aims to provide a system that meets these complex requirements, efficiently plans a menu that satisfies the entire family, and also makes suggestions that take into account the user's emotional state.

[1693] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1694] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale product information, means for generating an optimal menu based on the family composition information and sale product information, means for recognizing the user's emotional state and adjusting the menu suggestions, and means for presenting the generated menu and shopping list to the user. This not only enables menu suggestions that make the most of the food preferences and nutritional balance of all family members and sale information, but also makes suggestions that take the user's emotional state into consideration.

[1695] A "user" is an individual or family representative who uses the system to plan menus and receive suggestions.

[1696] "Family composition information" is data entered by the user that includes the names, ages, favorite foods, disliked foods, and allergy information of family members.

[1697] "Advertising information" is data that includes information about special sales and campaigns offered by stores such as supermarkets.

[1698] "Special sale product information" is data relating to the special sale price and sales conditions of a specific product, extracted from the analyzed advertising information.

[1699] A "menu" refers to a combination of dinner menu items that will satisfy the whole family.

[1700] The "emotional state" is a psychological state that is recognized from information such as the user's voice and facial expression.

[1701] "Suggestions" are information that includes menus and associated shopping lists generated by the system, as well as menus that are tailored based on the user's emotional state.

[1702] A "shopping list" is a list of ingredients that need to be purchased based on the generated menu.

[1703] The "server" is a central processing unit that processes various data of this system, generates menus, and recognizes emotional states.

[1704] The present invention is a system that helps users plan dinner menus that take into account the preferences and allergy information of all family members and helps them shop by making the most of sales information. This system can also suggest optimal menus by taking into account the user's emotional state.

[1705] Hardware used

[1706] Smartphones (e.g., iPhones and Android devices)

[1707] Servers (cloud servers and on-premise servers)

[1708] Software used

[1709] Web scraping technologies (e.g., Python's Beautiful Soup or Selenium)

[1710] Image recognition algorithms (e.g., Google Cloud Vision API)

[1711] Natural language processing algorithms (e.g., OpenAI GPT-3, BERT)

[1712] Emotion recognition engine (e.g., Microsoft Azure Emotion API, Affectiva API)

[1713] Specific processing of the system

[1714] 1. Enter family information

[1715] Through a smartphone interface, users enter family information, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[1716] 2. Collection and analysis of advertising information

[1717] The server periodically accesses the websites of each supermarket or store to collect advertising information, specifically by using Python's Beautiful Soup and Selenium to obtain image data for advertisements.

[1718] The collected advertising images are analyzed using the Google Cloud Vision API to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[1719] 3. Menu generation

[1720] The server takes the family composition information collected from the user and the analyzed sale item information as input, and uses natural language processing algorithms such as OpenAI GPT-3 to generate the optimal menu.

[1721] 4. Adjusting recommendations using an emotion engine

[1722] Using the smartphone's camera and microphone, the system analyzes the user's voice and facial expressions, and recognizes the user's emotional state using an emotion recognition engine such as Microsoft Azure Emotion API.

[1723] Based on the recognized emotional state, the server reviews the generated menu and adjusts the proposed content to take into consideration the user's psychological state.

[1724] 5. Present menu and shopping list

[1725] The adjusted menu is presented to the user via their smartphone, and they can make adjustments as necessary until they are satisfied.

[1726] A shopping list is automatically generated based on the confirmed menu and displayed on the smartphone.

[1727] Specific examples

[1728] Consider a scenario where a user launches the app and says, "I'm busy today, so I want to make a simple dinner." The server will suggest menu items such as "stir-fried chicken and vegetables" or "easy steamed chicken" based on the family composition information entered by the user and the sale information of the nearest supermarket. It will also recognize the user's emotional state and suggest even easier dishes to prepare if the user is tired. The generated shopping list will include items such as "400g chicken, carrots, and pumpkin," allowing the user to shop efficiently.

[1729] Prompt Sentence Examples

[1730] This system suggests optimal dinner menus based on the user's family composition information and sales information from physical stores. When a user says, "I want to make something easy to cook today," generate a menu based on the following information:

[1731] Family composition information

[1732] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1733] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1734] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1735] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1736] Special sale information

[1737] Chicken 100g 50 yen

[1738] Carrots 3 pieces 100 yen

[1739] 1 / 2 pumpkin 80 yen

[1740] Proposal Format

[1741] 1. Menu planning

[1742] 2. List of ingredients needed

[1743] In this way, the system can propose optimal menus that meet the user's diverse requirements and emotional state.

[1744] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1745] Step 1:

[1746] User input of family information

[1747] Users enter family information through the interface on their smartphones, including each family member's name, age, favorite foods, disliked foods, and allergy information. The entered family information is sent to the system's server and stored in a database.

[1748] input:

[1749] Family members' names, ages, favorite foods, disliked foods, and allergy information

[1750] output:

[1751] Family composition information stored in a database

[1752] Step 2:

[1753] Collection of advertising information by the server

[1754] The server periodically accesses the websites of each supermarket or store and collects advertising information using web scraping technology. It then uses Python's Beautiful Soup and Selenium to obtain advertising image data, which is then stored on the server.

[1755] input:

[1756] Supermarket or store website URL

[1757] output:

[1758] Advertising image data stored on the server

[1759] Step 3:

[1760] Server analysis of advertising information

[1761] The server analyzes the stored advertising image data using the Google Cloud Vision API to extract sale product information, recognizing text within the image and extracting sale prices and product information as text data.

[1762] input:

[1763] Advertising image data

[1764] output:

[1765] Special sale product information (text data)

[1766] Step 4:

[1767] Server-based menu generation

[1768] The server uses natural language processing algorithms such as OpenAI GPT-3 to generate optimal menus based on family composition information and special sale information. The algorithm takes into account preferences, allergies, and nutritional balance to suggest menus that will satisfy the whole family.

[1769] input:

[1770] Family composition information, special sale product information

[1771] output:

[1772] Generated menu

[1773] Step 5:

[1774] Use of emotion engines on devices

[1775] The device (smartphone) uses a camera and microphone to capture the user's voice and facial expressions. An emotion recognition engine such as Microsoft Azure Emotion API is used to recognize the user's emotional state. The recognized emotional state is sent to the server.

[1776] input:

[1777] Voice and facial expression data

[1778] output:

[1779] Perceived emotional state

[1780] Step 6:

[1781] Server adjustment of proposals

[1782] The server then reviews the generated menu based on the user's emotional state and adjusts the suggestions to take into account the user's psychological state. For example, if the user is tired, the server will prioritize menus that are easy to prepare.

[1783] input:

[1784] Generated menu, emotional state

[1785] output:

[1786] Adjusted menu

[1787] Step 7:

[1788] Menu and shopping list presentation on the device

[1789] The adjusted menu is presented to the user via their smartphone. The user can make any necessary adjustments until they are satisfied. A shopping list is automatically generated based on the finalized menu and displayed on the smartphone.

[1790] input:

[1791] Adjusted menu

[1792] output:

[1793] Menu presented to the user, generated shopping list

[1794] 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.

[1795] 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.

[1796] 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.

[1797] [Fourth embodiment]

[1798] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1799] 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.

[1800] 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).

[1801] 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.

[1802] 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.

[1803] 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).

[1804] 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. 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.

[1805] 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.

[1806] 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.

[1807] 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.

[1808] 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.

[1809] 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.

[1810] 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."

[1811] This invention is a system that efficiently plans dinner menus that satisfy the whole family and effectively utilizes sale information. This system inputs family composition information from the user, collects and analyzes advertising information from the nearest supermarket, generates menus based on the family composition information and sale item information, presents the generated menus to the user, and generates shopping lists.

[1812] Program Overview

[1813] The system operates in the following major steps:

[1814] Entering family information

[1815] User

[1816] Family information is entered through a form on the device, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[1817] Examples:

[1818] The user types the following into the terminal:

[1819] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[1820] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[1821] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[1822] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[1823] Collection and analysis of advertising information

[1824] server

[1825] We periodically collect advertising information from the websites of nearby supermarkets, specifically by using web scraping technology to obtain advertising images.

[1826] Terminal

[1827] The collected advertising images are analyzed using an image recognition algorithm to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[1828] Menu generation

[1829] server

[1830] Using natural language processing and machine learning algorithms, the system generates optimal menus by taking into account the likes and dislikes of each family member, nutritional balance, ease of preparation, and whether to utilize special sale items.

[1831] Examples:

[1832] Based on the collected information, the following menu is generated:

[1833] Chicken and carrot teriyaki

[1834] Pumpkin soup

[1835] Salad (without peppers)

[1836] Menu suggestions and shopping list generation

[1837] Terminal

[1838] The generated menu is presented to the user, who can make further revisions until satisfied.

[1839] server

[1840] A shopping list is automatically generated based on the confirmed menu.

[1841] Examples:

[1842] This will generate a shopping list like this:

[1843] 400g chicken

[1844] 3 carrots

[1845] 1 / 2 pumpkin

[1846] Salad vegetables (excluding bell peppers)

[1847] Example of program processing flow

[1848] 1. The user enters family information.

[1849] 2. The server collects supermarket advertising information.

[1850] 3. The device analyzes the advertising image and extracts information about special sale items.

[1851] 4. The server generates a menu based on family composition information and special sale item information.

[1852] 5. The device presents the generated menu to the user.

[1853] 6. The user reviews the menu and requests changes if necessary.

[1854] 7. The server generates and confirms the menu again.

[1855] 8. The server generates a shopping list based on the confirmed menu, and the terminal displays it to the user.

[1856] This allows for efficient planning of dinner menus that will satisfy the whole family, reduces food waste, and enables more economical shopping.

[1857] The processing flow will be explained below.

[1858] Step 1:

[1859] The user accesses the interface on the terminal and inputs family information, specifically, the name, age, favorite foods, disliked foods, and allergy information of each family member.

[1860] Step 2:

[1861] The device receives the family composition information entered by the user and sends it to the server, which then processes the data.

[1862] Step 3:

[1863] The server periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[1864] Step 4:

[1865] The device stores the collected advertising images and analyzes them using an AI image recognition algorithm, extracting sale product information from the advertising images and saving it as text data.

[1866] Step 5:

[1867] The server generates optimal menus based on the family composition information sent by the user and the sale product information extracted using an image recognition algorithm. Specifically, it utilizes natural language processing and machine learning algorithms to take into account the preferences of all family members, nutritional balance, ease of preparation, and the use of sale products.

[1868] Step 6:

[1869] The terminal displays the generated menu sent from the server to the user, who can review the menu and request modifications if necessary.

[1870] Step 7:

[1871] If the user requests a modification, the request is resent to the server via the terminal, and the server regenerates the menu and resends it to the terminal.

[1872] Step 8:

[1873] The user confirms and confirms the proposed menu. The server automatically generates a shopping list based on the confirmed menu.

[1874] Step 9:

[1875] The terminal displays the shopping list sent from the server to the user, who can then check the list and go shopping at the supermarket using a terminal such as a smartphone.

[1876] Step 10:

[1877] Users can purchase the necessary ingredients at the supermarket and cook dinner based on the suggested menu, providing an economical and balanced meal for the whole family.

[1878] Example 1

[1879] 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."

[1880] For many families, efficiently planning a daily dinner menu that takes into account the preferences and nutritional balance of each family member is a significant burden. It is also difficult to make the most of sales information and shop economically. A system that can solve these problems is needed.

[1881] 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.

[1882] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale product information, means for generating an optimal menu based on the family composition information and sale product information, means for presenting the generated menu to the user, means for the user to check the presented menu and request revisions as necessary, and means for generating a shopping list based on the finalized menu. This enables the system to propose optimal menus that take into account the preferences and nutritional balance of all family members and to make economical shopping decisions by utilizing sale information.

[1883] A "user" is a person who uses the system to input family information and check and modify menus.

[1884] "Family composition information" is data including the names, ages, favorite foods, disliked foods, and allergy information of family members.

[1885] "Advertising information" is information about special sales and campaigns collected from the nearest supermarket or store.

[1886] "Special sale product information" is information on the price and quantity of a specific product extracted from the advertisement information.

[1887] A "menu" refers to the specific contents and combination of dishes that the whole family will eat.

[1888] A "shopping list" is a list of ingredients and items that need to be purchased based on a menu.

[1889] "Web scraping" is a technique for automatically collecting and analyzing the content of specific web pages.

[1890] An "image recognition algorithm" is a computer algorithm used to extract and analyze specific information from image data.

[1891] "Natural language processing" is a technical field that uses computers to understand, generate, and manipulate human language.

[1892] A "machine learning algorithm" is an algorithm that learns patterns based on data and makes predictions and judgments.

[1893] A "prompt" is an instruction or question that is input to a generative AI model.

[1894] This invention is a system that efficiently plans dinner menus that satisfy the whole family and effectively utilizes sale information. This system inputs family composition information from the user, collects and analyzes advertising information from the nearest supermarket, generates menus based on the family composition information and sale item information, presents the generated menus to the user, and generates shopping lists.

[1895] The system consists of the following main components:

[1896] 1. User Interface (UI)

[1897] 2. Server

[1898] 3. Terminal

[1899] User Interface (UI)

[1900] User

[1901] The user enters family information through an input form on the device. This input form provides fields for entering each family member's name, age, favorite foods, disliked foods, and allergy information. This input information undergoes form validation using HTML5 and JavaScript before being sent to the server.

[1902] Examples:

[1903] The user enters the following information into an input form:

[1904] Name: Father, Age: 35, Favorite food: Pasta, Dislikes: Fish, Allergies: None

[1905] server

[1906] server

[1907] The server periodically collects advertising information from the websites of nearby supermarkets, using web scraping techniques (e.g., Beautiful Soup or Selenium) to obtain advertising images and text information, and then transmits this data to the device.

[1908] Terminal

[1909] Terminal

[1910] The device receives the advertising image data sent from the server and analyzes it using an image recognition algorithm (e.g., Google Cloud Vision API). As a result of the analysis, information such as the name, price, and quantity of the special sale item is extracted. This information is saved in text format and used for later processing.

[1911] Menu generation

[1912] server

[1913] The server uses natural language processing and machine learning algorithms (e.g., GPT-3 model) to generate optimal menus based on family composition information collected from the user and sale product information analyzed by the device. It references food and recipe databases to propose menus that take into account the preferences and allergies of all family members, nutritional balance, ease of preparation, and other factors.

[1914] Examples:

[1915] Based on family composition information such as "Father likes pasta, mother likes salad, child 1 likes curry, child 2 likes pumpkin" and sale information such as "Sale: 100g of chicken for 50 yen," a menu of "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)" is generated.

[1916] Menu presentation

[1917] Terminal

[1918] The generated menu is sent from the server to the terminal and presented to the user. The user can check the displayed menu and make an adjustment request if they wish to make any changes. An interactive UI is incorporated to allow the user to easily modify the menu.

[1919] Menu revision and confirmation

[1920] server

[1921] The algorithm receives a request for revisions from the user and generates a new menu. This process is repeated until the user is satisfied. The final menu is then confirmed.

[1922] Specific behavior:

[1923] The server generates the menu again and confirms it as "Teriyaki chicken and carrots," "Minestrone soup," and "Salad (without green peppers)."

[1924] Generate a shopping list

[1925] server

[1926] Based on the confirmed menu, a shopping list of the necessary ingredients is automatically generated, listing the ingredients to be purchased and their quantities, and sending it back to the device.

[1927] Specific behavior:

[1928] The server compiles a list of "400g of chicken," "3 carrots," "1 / 2 a pumpkin," "vegetables for salad (excluding bell peppers)," and "vegetables for minestrone soup" and sends it to the terminal.

[1929] Prompt Sentence Examples

[1930] Here are some concrete examples of input prompts for a generative AI model like GPT-3:

[1931] Example prompt sentence:

[1932] Family information:

[1933] Father: 35 years old, Favorite food: Pasta, Dislikes: Fish

[1934] Mother: 33 years old, Favorite food: Salad, Dislikes: Green peppers

[1935] Child 1: 5 years old, Favorite food: curry, Dislikes: carrots

[1936] Child 2: 2 years old, Favorite food: Pumpkin, Disliked food: None

[1937] Special Offer Information:

[1938] Chicken 100g 50 yen

[1939] Carrots 3 pieces 100 yen

[1940] Use this information to come up with a dinner menu that will satisfy the whole family.

[1941] This allows for optimal menu suggestions that will satisfy the whole family, and allows for economical shopping by taking advantage of special sale information.

[1942] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1943] Step 1: Enter family information

[1944] User

[1945] The user accesses the input form on the terminal and fills in the input fields with the names, ages, favorite foods, disliked foods, and allergy information of the family members. This information is sent to the server when the user presses the "Send" button.

[1946] Input: Name, age, favorite foods, disliked foods, allergy information

[1947] Output: Family composition information sent to the server

[1948] Specific behavior: The user enters the following information: "Name: Father, Age: 35, Favorite food: Pasta, Dislikes: Fish, Allergies: None"

[1949] Step 2: Collect advertising information

[1950] server

[1951] The server runs a periodically scheduled job (e.g., a Cron job) to collect advertisement information from the websites of nearby supermarkets, using web scraping tools such as Beautiful Soup or Selenium to analyze the HTML structure of the advertisement pages and retrieve advertisement images and text information using specific CSS selectors and XPaths.

[1952] Input: supermarket website URL

[1953] Output: Advertising images and text information

[1954] Specific operation: The server accesses "https: / / example-supermarket.com / ads" and retrieves the ad image from the HTML page.

[1955] Step 3: Analyze the ad image

[1956] Terminal

[1957] The device receives the advertising image data sent from the server and analyzes it using image recognition algorithms such as Google Cloud Vision API. As a result of the analysis, information such as the product name, price, and quantity of the special sale item is extracted. This information is saved in text format and used for further processing.

[1958] Input: Ad image

[1959] Output: Special sale product information (product name, price, quantity)

[1960] Specific operation: The device sends the advertising image to the Google Cloud Vision API, and extracts the following information as the analysis result: "Special Sale: 100g of chicken for 50 yen" and "Special Sale: 3 carrots for 100 yen."

[1961] Step 4: Create a menu

[1962] server

[1963] The server uses natural language processing and machine learning algorithms (e.g., GPT-3 model) to generate optimal menus based on family composition information collected from the user and sale product information analyzed by the device. It references food and recipe databases to propose menus that take into account the preferences and allergies of all family members, nutritional balance, ease of preparation, and other factors.

[1964] Input: Family composition information, sale product information

[1965] Output: Optimal menu

[1966] Specific operation: Based on family composition information such as "Father likes pasta, mother likes salad, child 1 likes curry, child 2 likes pumpkin" and sale information such as "Special sale: 100g chicken for 50 yen," a menu of "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)" is generated.

[1967] Step 5: Present the menu

[1968] Terminal

[1969] The generated menu is sent from the server to the terminal and presented to the user. The user can check the displayed menu and make an adjustment request if they wish to make any changes. An interactive UI is used to allow the user to easily modify the menu.

[1970] Input: Generated menu

[1971] Output: Menu presented to the user

[1972] Specific operation: The device displays the menu items "Teriyaki chicken and carrots," "Pumpkin soup," and "Salad (without green peppers)."

[1973] Step 6: Revise and finalize the menu

[1974] server

[1975] The algorithm receives a request for revisions from the user and generates a new menu. This process is repeated until the user is satisfied, and the final menu is confirmed.

[1976] Input: User modification request

[1977] Output: Final revised menu

[1978] Specific operation: The user sends a request to change "pumpkin soup" to "minestrone soup," and the server generates the menu again, finalizing it as "teriyaki chicken and carrots," "minestrone soup," and "salad (without green peppers)."

[1979] Step 7: Generate a shopping list

[1980] server

[1981] Based on the confirmed menu, a shopping list of necessary ingredients is automatically generated, listing the ingredients to be purchased and their quantities, and sending it to the device.

[1982] Input: Confirmed menu

[1983] Output: Shopping list

[1984] Specific operation: The server compiles a list of "400g of chicken," "3 carrots," "1 / 2 a pumpkin," "vegetables for salad (excluding bell peppers)," and "vegetables for minestrone soup" and sends it to the terminal.

[1985] Step 8: View your shopping list

[1986] Terminal

[1987] The terminal displays the final shopping list to the user, which is designed to be visually appealing and facilitate a smooth shopping experience.

[1988] Input: Shopping list

[1989] Output: Displaying the shopping list to the user

[1990] Specific operation: The device displays the following items in a list format: "400g chicken," "3 carrots," "1 / 2 pumpkin," "Salad vegetables (excluding bell peppers)," and "Vegetables for minestrone soup."

[1991] The above processing flow allows efficient planning of dinner menus that will satisfy the whole family, reduces food waste, and enables economical shopping.

[1992] (Application example 1)

[1993] 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."

[1994] With conventional systems, it was difficult to efficiently plan dinner menus that would satisfy the whole family, and it was also difficult to effectively utilize sales information. Even if menus were generated based on sales information, ordering ingredients and creating shopping lists based on those menus was not automated, placing a heavy burden on users. This resulted in problems such as food waste and increased financial burdens.

[1995] 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.

[1996] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale item information, means for generating an optimal menu based on the family composition information and sale item information, means for presenting the generated menu to the user, means for ordering necessary ingredients and dishes from an external service based on the generated menu, and means for generating a shopping list. This makes it possible to efficiently plan a dinner menu that will satisfy the whole family, and automatically order ingredients and generate a shopping list based on the menu.

[1997] "User" refers to the person who operates the system and inputs family composition information.

[1998] "Family composition information" refers to information including the name, age, favorite foods, disliked foods, and allergy information of each family member.

[1999] "Advertising information" refers to information about products and services offered by retail stores, such as special sales and campaigns.

[2000] "Special sale product information" refers to discount prices and special offers for specific products extracted from advertising information.

[2001] "Menu" refers to a meal plan that satisfies the whole family.

[2002] "Server" refers to a computer system that collects and analyzes user input and advertising information, and generates and presents menus.

[2003] "External services" refer to food delivery services that users use to order ingredients and dishes based on their menu.

[2004] A "shopping list" refers to a list of ingredients to be purchased based on the generated menu.

[2005] "Web scraping" refers to the technology of automatically extracting necessary information from websites on the Internet.

[2006] "Image recognition algorithm" refers to technology for extracting sale item information from images included in advertising information.

[2007] This invention is a food delivery system that efficiently plans dinner menus that will satisfy the whole family and effectively utilizes sale information. The system is composed of users, a server, and external services.

[2008] System configuration and roles

[2009] User

[2010] 1. Enter your family information

[2011] Through a smartphone application, users enter the name, age, favorite foods, disliked foods, and allergy information for each family member.

[2012] 2. Check and modify the menu

[2013] The generated menu is presented to the user, who can review it and request modifications if necessary.

[2014] server

[2015] 1. Collection and analysis of advertising information

[2016] The server periodically collects advertising information from the websites of nearby supermarkets and grocery stores, and analyzes and extracts sale product information using web scraping and image recognition algorithms.

[2017] 2. Menu generation

[2018] The server uses a generative AI model to generate optimal menus based on the family composition information collected from the user and the analyzed sale product information. The generated menus take into account the user's preferences and the use of sale products.

[2019] 3. Generate a shopping list

[2020] The server automatically generates a shopping list of necessary ingredients based on the finalized menu.

[2021] External Services

[2022] 1. Ordering ingredients and dishes

[2023] Based on the generated menu, you can order the necessary ingredients and dishes from external food delivery or ingredient delivery services.

[2024] Program processing flow

[2025] The server performs the following data processing and data calculations.

[2026] Hardware and software: The server is a computer system with a powerful processor and large memory capacity, and uses software for web scraping (e.g., BeautifulSoup), image recognition algorithms (e.g., TensorFlow), and frameworks for running generative AI models (e.g., PyTorch).

[2027] Data processing: Advertising information is scraped from websites, HTML data is analyzed to extract sale product information, and the collected image data is analyzed with a recognition algorithm to obtain sale product information as text.

[2028] Data calculation: The AI ​​model compares the family composition information entered by the user with the sale product information and generates the optimal menu. At this time, the model takes into account the nutritional value of ingredients, cooking time, cost, etc.

[2029] Examples of concrete examples and prompts

[2030] Specific examples

[2031] The user types the following into the terminal:

[2032] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[2033] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[2034] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[2035] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[2036] Prompt Sentence Examples

[2037] Use the family composition and special offer information below to generate the perfect dinner menu that will satisfy your family.

[2038] Family information:

[2039] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[2040] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[2041] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[2042] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[2043] Special Offer Information:

[2044] Chicken 100g 50 yen

[2045] Carrots 3 pieces 100 yen

[2046] This system allows you to efficiently plan meals that will satisfy the whole family and makes the most of special sale information, making shopping more economical.

[2047] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2048] Step 1:

[2049] The user inputs family information.

[2050] Input: Family members' names, ages, favorite foods, disliked foods, and allergy information.

[2051] Action: A user fills out a form using a smartphone application.

[2052] Output: The family information is sent to the server and stored.

[2053] Step 2:

[2054] The server collects the advertising information.

[2055] Input: Advertisement URL from your local supermarket or grocery store.

[2056] How it works: The server periodically collects advertising information using web scraping techniques, specifically by analyzing web pages using libraries such as BeautifulSoup to retrieve advertising images and text.

[2057] Output: A dataset of collected advertising information.

[2058] Step 3:

[2059] The server analyzes the collected advertising information and extracts sale item information.

[2060] Input: A dataset of advertising information.

[2061] How it works: The server analyzes the advertisement image using an image recognition algorithm (e.g. TensorFlow), converts the sale product information into text format, and extracts the text information using HTML analysis.

[2062] Output: A list of sale items.

[2063] Step 4:

[2064] The server generates an optimal menu based on family composition information and special sale product information.

[2065] Input: Family composition information and special offer information.

[2066] How it works: The server uses a generative AI model to generate optimal menus that take into account the preferences of family members, allergies, and special offers. The model uses frameworks such as PyTorch.

[2067] Output: A list of generated menu items.

[2068] Step 5:

[2069] The server presents the generated menu to the user.

[2070] Input: A list of generated menus.

[2071] How it works: The server displays the generated menu on the user's smartphone.

[2072] Output: The user reviews the menu and sends a request to the server to make any necessary modifications.

[2073] Step 6:

[2074] The server receives the modification request and regenerates the menu (if necessary).

[2075] Input: The user's modification request.

[2076] How it works: The server generates a menu using the generative AI model again based on the modification request. If necessary, it may only need to modify parts of the menu generated in the previous step.

[2077] Output: The revised menu list.

[2078] Step 7:

[2079] The server generates a shopping list based on the determined menu.

[2080] Input: A list of confirmed menu items.

[2081] How it works: The server lists the ingredients needed for a meal and generates a shopping list.

[2082] Output: A shopping list of ingredients needed.

[2083] Step 8:

[2084] The server presents the shopping list to the user and supports ordering from external services.

[2085] Input: Shopping list.

[2086] How it works: The server displays the shopping list on the user's smartphone and also works with external food delivery services to help order ingredients and dishes.

[2087] Output: An interface for the user to review the shopping list and place an order with an external service if necessary.

[2088] 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.

[2089] This system efficiently plans dinner menus that satisfy the whole family and effectively utilizes sales information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it can propose menus that take into account the user's psychological state.

[2090] Program Overview

[2091] The system operates in the following major steps:

[2092] Entering family information

[2093] User

[2094] Family information is entered through the device's interface, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[2095] Examples:

[2096] The user types the following into the terminal:

[2097] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[2098] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[2099] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[2100] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[2101] Collection and analysis of advertising information

[2102] server

[2103] The system periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[2104] Terminal

[2105] The collected advertising images are analyzed using an image recognition algorithm to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[2106] Menu generation

[2107] server

[2108] Using natural language processing and machine learning algorithms, the system generates optimal menus based on the family composition information collected from the user and the analyzed sale product information. The algorithm takes into account each family member's preferences, nutritional balance, ease of preparation, and the extent to which sale products are utilized.

[2109] Examples:

[2110] Based on the collected information, the following menu is generated:

[2111] Chicken and carrot teriyaki

[2112] Pumpkin soup

[2113] Salad (without peppers)

[2114] Adjusting recommendations with an emotion engine

[2115] Terminal

[2116] The emotion engine analyzes the user's voice and facial expressions and recognizes their emotions, for example, when they are tired or want to relax.

[2117] server

[2118] The emotion engine tailors meal suggestions based on the user's emotional state. For example, if the user is tired, it will suggest meals that are easy to prepare and have a relaxing effect.

[2119] Examples:

[2120] If a user says, "I'm tired today," the system will suggest a menu that includes "easy steamed chicken dishes" and "relaxing herbal tea."

[2121] Menu suggestions and shopping list generation

[2122] Terminal

[2123] The generated menu is presented to the user, who can make further revisions until satisfied.

[2124] server

[2125] A shopping list is automatically generated based on the confirmed menu.

[2126] Examples:

[2127] This will generate a shopping list like this:

[2128] 400g chicken

[2129] 3 carrots

[2130] 1 / 2 pumpkin

[2131] Salad vegetables (excluding bell peppers)

[2132] Example of program processing flow

[2133] 1. The user enters family information.

[2134] 2. The server collects supermarket advertising information.

[2135] 3. The device analyzes the advertising image and extracts information about special sale items.

[2136] 4. The server generates a menu based on family composition information and special sale item information.

[2137] 5. The device recognizes the user's emotions and transmits them to the server.

[2138] 6. Servers adjust menus based on your emotional state.

[2139] 7. The device presents the generated menu to the user.

[2140] 8. The user reviews the menu and requests changes if necessary.

[2141] 9. The server generates and confirms the menu again.

[2142] 10. The server generates a shopping list based on the confirmed menu, and the terminal displays it to the user.

[2143] This allows for efficient planning of dinner menus that satisfy the whole family, allows for menu suggestions that take into consideration the user's feelings, and allows for the provision of economical and balanced meals.

[2144] The processing flow will be explained below.

[2145] Step 1:

[2146] The user accesses the interface on the terminal and inputs family information, specifically, the name, age, favorite foods, disliked foods, and allergy information of each family member.

[2147] Step 2:

[2148] The device receives the family composition information entered by the user and sends it to the server, which can then perform processing based on that data.

[2149] Step 3:

[2150] The server periodically accesses the websites of nearby supermarkets to collect advertising information, specifically by using web scraping technology to obtain image data of advertisements.

[2151] Step 4:

[2152] The device stores the collected advertising images and analyzes them using an AI image recognition algorithm, extracting sale product information from the advertising images and saving it as text data.

[2153] Step 5:

[2154] The server generates optimal menus based on the family composition information sent by the user and the sale product information extracted using an image recognition algorithm. Specifically, it utilizes natural language processing and machine learning algorithms to take into account the preferences of all family members, nutritional balance, ease of preparation, and the use of sale products.

[2155] Step 6:

[2156] The terminal presents the generated menu sent from the server to the user, who can then review the menu and request modifications if necessary.

[2157] Step 7:

[2158] If the user requests a modification, the request is resent through the terminal to the server, which then generates a new menu and sends it to the terminal again.

[2159] Step 8:

[2160] The device analyzes the user's voice and facial expressions and uses an emotion engine to recognize the user's emotions, for example, when the user is tired or wants to relax.

[2161] Step 9:

[2162] The server adjusts the menu suggestions based on the emotional state transmitted by the emotion engine, for example, if the user is tired, it will suggest menus that are easy to prepare and have a relaxing effect.

[2163] Step 10:

[2164] The terminal presents the user with a menu adjusted based on their emotions, allows them to confirm the menu until they are satisfied, and accepts requests for any changes.

[2165] Step 11:

[2166] The user confirms and confirms the proposed menu. The server automatically generates a shopping list based on the confirmed menu.

[2167] Step 12:

[2168] The terminal displays the shopping list sent from the server to the user, who can then check it and go shopping at the supermarket using their smartphone or other device.

[2169] Step 13:

[2170] Users can purchase the necessary ingredients at the supermarket and cook dinner based on the suggested menu, providing an economical and balanced meal for the whole family.

[2171] Example 2

[2172] 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."

[2173] In modern households, it is difficult to efficiently plan a dinner menu that satisfies the entire family. In particular, it is necessary to take into account each family member's preferences, allergies, and nutritional balance, while also utilizing information on special sales items from an economical perspective. Furthermore, there is a demand for flexible menu suggestions that take into account the user's emotional state. The present invention aims to provide a menu suggestion system that simultaneously satisfies these multiple factors.

[2174] 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.

[2175] In this invention, the server includes a means for having the user input family composition information, a means for analyzing collected advertising information and extracting sale item information, a means for generating an optimal meal plan based on the family composition information and sale item information, a means for recognizing the user's emotional state and adjusting the menu, a means for presenting the generated menu to the user, and a means for generating a shopping list based on the confirmed menu. This allows for efficient planning of menus that satisfy the entire family, making it possible to provide economical and balanced meals. It also enables flexible menu suggestions that take the user's emotions into consideration.

[2176] "Family composition information" is information including the name, age, favorite foods, disliked foods, and allergy information of each family member.

[2177] "Advertising information" is data that includes information about special sales and discounts offered by stores such as supermarkets.

[2178] "Special sale product information" refers to information about special sale products and discounted products that has been analyzed and extracted from the collected advertising information.

[2179] The "meal plan" is a plan that includes a menu that is tailored to satisfy the entire family based on family composition information and special sale information.

[2180] "Emotional state" is information that indicates the psychological state of the user analyzed from the user's voice, facial expression, and the like.

[2181] "Web scraping" is a technique for automatically collecting information from websites on the Internet.

[2182] "Image recognition algorithm" refers to an algorithm for analyzing image data and extracting specific information within the image.

[2183] A "shopping list" is a list of all the ingredients and products needed based on a confirmed menu.

[2184] This system efficiently plans dinner menus that satisfy the whole family and effectively utilizes sales information. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it can propose menus that take into account the user's psychological state.

[2185] The implementation of this system uses the following hardware and software:

[2186] Hardware:

[2187] User devices (PCs, tablets, smartphones, etc.)

[2188] Internet connection environment

[2189] Camera and microphone (for emotion recognition)

[2190] software:

[2191] Web scraping tools (such as Python's BeautifulSoup)

[2192] Image recognition algorithms (OpenCV, TensorFlow, etc.)

[2193] Natural language processing and machine learning algorithms (e.g., Python's NLTK, scikit-learn)

[2194] Emotion recognition API (such as Microsoft Azure's Emotion Recognition API)

[2195] Entering family information

[2196] The user inputs family information through the interface on the terminal, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[2197] As a concrete example, the user enters the following:

[2198] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[2199] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[2200] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[2201] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[2202] Collection and analysis of advertising information

[2203] The server periodically accesses the website of the nearest supermarket and collects advertising information using web scraping technology. The acquired data is analyzed using an image recognition algorithm to extract information about special offers. For example, information such as "Special Offer: 100g of chicken for 50 yen" and "Special Offer: 3 carrots for 100 yen" is collected.

[2204] Menu generation

[2205] The server uses the family composition information collected from the user and the analyzed sale product information as inputs, and generates the optimal menu using natural language processing and machine learning algorithms. The algorithm takes into account each family member's preferences, nutritional balance, ease of preparation, and the use of sale products.

[2206] Specific examples of offerings include the following:

[2207] Chicken and carrot teriyaki

[2208] Pumpkin soup

[2209] Salad (without peppers)

[2210] Adjusting recommendations with an emotion engine

[2211] The device captures the user's voice and facial expressions with a camera and microphone, and the emotion engine analyzes this data. Depending on the user's emotional state, such as fatigue or a desire for relaxation, the server adjusts the menu based on the information sent from the emotion engine.

[2212] For example, if a user says, "I'm tired today," the system will suggest a menu that includes "easy steamed chicken dishes" and "relaxing herbal tea."

[2213] Menu suggestions and shopping list generation

[2214] The terminal presents the generated menu to the user, who can then revise it until he is satisfied. Based on the finalized menu, the server automatically generates a shopping list, which the terminal displays to the user.

[2215] As a concrete example, the following shopping list is generated:

[2216] 400g chicken

[2217] 3 carrots

[2218] 1 / 2 pumpkin

[2219] Salad vegetables (excluding bell peppers)

[2220] This system can efficiently plan dinner menus that satisfy the whole family, providing economical and balanced meals. It can also provide flexible menu suggestions that take into account the user's emotions.

[2221] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2222] Step 1:

[2223] The user inputs family information into the interface on the terminal, including the name, age, favorite foods, disliked foods, and allergy information for each family member.

[2224] Input: Family member's attribute information (name, age, preferences, disliked foods, allergy information)

[2225] Output: Family composition information (format saved in database)

[2226] Specific behavior:

[2227] The user types the following into a text field on the terminal:

[2228] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[2229] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[2230] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[2231] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[2232] Step 2:

[2233] The server accesses the website of the nearest supermarket and collects advertising information. Web scraping technology is used to obtain advertising data.

[2234] Input: supermarket website URL

[2235] Output: Advertising information (image data and text data)

[2236] Specific behavior:

[2237] The server periodically accesses the supermarket's website and scrapes advertising information using Python's BeautifulSoup library.

[2238] Step 3:

[2239] The device analyzes the collected advertising images and uses image recognition algorithms to extract sale item information.

[2240] Input: Advertising information (image data or text data)

[2241] Output: Special sale product information (text format data)

[2242] Specific behavior:

[2243] The device uses TensorFlow and OpenCV to analyze images and extract information about special offers, such as "Special Offer: 100g of chicken for 50 yen" and "Special Offer: 3 carrots for 100 yen."

[2244] Step 4:

[2245] The server receives family composition information and sale item information as input, and uses natural language processing and machine learning algorithms to generate the optimal menu.

[2246] Input: Family composition information, sale product information

[2247] Output: Optimal meal plan (list in text format)

[2248] Specific behavior:

[2249] The server uses scikit-learn and NLTK to generate the following menu based on the collected family composition information and sale item information.

[2250] Chicken and carrot teriyaki

[2251] Pumpkin soup

[2252] Salad (without peppers)

[2253] Step 5:

[2254] The device captures the user's voice and facial expressions using a camera and microphone, and the emotion engine analyzes this data.

[2255] Input: User's voice data, facial expression data

[2256] Output: User's emotional state (text data)

[2257] Specific behavior:

[2258] The device uses Microsoft Azure's emotion recognition API to recognize emotions from the user's voice and facial expressions, and extracts emotional states such as "I'm tired" or "I want to relax."

[2259] Step 6:

[2260] The server adjusts the menu suggestions based on the emotional state sent from the emotion engine.

[2261] Input: User's emotional state, optimal menu

[2262] Output: Adjusted menu (list in text format)

[2263] Specific behavior:

[2264] The server takes into account the user's emotional state, and if the user says something like "I'm tired today," it will suggest a menu that includes "simple steamed chicken dishes" and "relaxing herbal tea."

[2265] Step 7:

[2266] The terminal presents the generated menu to the user, who can then make corrections until satisfied.

[2267] Input: Adjusted menu

[2268] Output: Final menu (list in text format)

[2269] Specific behavior:

[2270] The terminal displays the generated menu to the user, and if the user requests a change, the terminal makes the server generate a new menu and presents it.

[2271] Step 8:

[2272] The server automatically generates a shopping list based on the finalized menu, and the terminal displays the generated shopping list to the user.

[2273] Input: Final menu

[2274] Output: Shopping list (list in text format)

[2275] Specific behavior:

[2276] The server lists the necessary ingredients and products based on the final menu, and the terminal displays the following shopping list to the user.

[2277] 400g chicken

[2278] 3 carrots

[2279] 1 / 2 pumpkin

[2280] Salad vegetables (excluding bell peppers)

[2281] (Application example 2)

[2282] 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."

[2283] When planning a typical dinner menu, it is difficult to utilize sale information while taking into account the preferences and nutritional balance of each family member. It is also difficult to adjust menu suggestions based on the user's emotional state. The present invention aims to provide a system that meets these complex requirements, efficiently plans a menu that satisfies the entire family, and also makes suggestions that take into account the user's emotional state.

[2284] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2285] In this invention, the server includes means for having the user input family composition information, means for analyzing collected advertising information and extracting sale product information, means for generating an optimal menu based on the family composition information and sale product information, means for recognizing the user's emotional state and adjusting the menu suggestions, and means for presenting the generated menu and shopping list to the user. This not only enables menu suggestions that make the most of the food preferences and nutritional balance of all family members and sale information, but also makes suggestions that take the user's emotional state into consideration.

[2286] A "user" is an individual or family representative who uses the system to plan menus and receive suggestions.

[2287] "Family composition information" is data entered by the user that includes the names, ages, favorite foods, disliked foods, and allergy information of family members.

[2288] "Advertising information" is data that includes information about special sales and campaigns offered by stores such as supermarkets.

[2289] "Special sale product information" is data relating to the special sale price and sales conditions of a specific product, extracted from the analyzed advertising information.

[2290] A "menu" refers to a combination of dinner menu items that will satisfy the whole family.

[2291] The "emotional state" is a psychological state that is recognized from information such as the user's voice and facial expression.

[2292] "Suggestions" are information that includes menus and associated shopping lists generated by the system, as well as menus that are tailored based on the user's emotional state.

[2293] A "shopping list" is a list of ingredients that need to be purchased based on the generated menu.

[2294] The "server" is a central processing unit that processes various data of this system, generates menus, and recognizes emotional states.

[2295] The present invention is a system that helps users plan dinner menus that take into account the preferences and allergy information of all family members and helps them shop by making the most of sales information. This system can also suggest optimal menus by taking into account the user's emotional state.

[2296] Hardware used

[2297] Smartphones (e.g., iPhones and Android devices)

[2298] Servers (cloud servers and on-premise servers)

[2299] Software used

[2300] Web scraping technologies (e.g., Python's Beautiful Soup or Selenium)

[2301] Image recognition algorithms (e.g., Google Cloud Vision API)

[2302] Natural language processing algorithms (e.g., OpenAI GPT-3, BERT)

[2303] Emotion recognition engine (e.g., Microsoft Azure Emotion API, Affectiva API)

[2304] Specific processing of the system

[2305] 1. Enter family information

[2306] Through a smartphone interface, users enter family information, including each family member's name, age, favorite foods, disliked foods, and allergy information.

[2307] 2. Collection and analysis of advertising information

[2308] The server periodically accesses the websites of each supermarket or store to collect advertising information, specifically by using Python's Beautiful Soup and Selenium to obtain image data for advertisements.

[2309] The collected advertising images are analyzed using the Google Cloud Vision API to extract special sale information, such as "Special Sale: 100g of chicken for 50 yen" or "Special Sale: 3 carrots for 100 yen."

[2310] 3. Menu generation

[2311] The server takes the family composition information collected from the user and the analyzed sale item information as input, and uses natural language processing algorithms such as OpenAI GPT-3 to generate the optimal menu.

[2312] 4. Adjusting recommendations using an emotion engine

[2313] Using the smartphone's camera and microphone, the system analyzes the user's voice and facial expressions, and recognizes the user's emotional state using an emotion recognition engine such as Microsoft Azure Emotion API.

[2314] Based on the recognized emotional state, the server reviews the generated menu and adjusts the proposed content to take into consideration the user's psychological state.

[2315] 5. Present menu and shopping list

[2316] The adjusted menu is presented to the user via their smartphone, and they can make adjustments as necessary until they are satisfied.

[2317] A shopping list is automatically generated based on the confirmed menu and displayed on the smartphone.

[2318] Specific examples

[2319] Consider a scenario where a user launches the app and says, "I'm busy today, so I want to make a simple dinner." The server will suggest menu items such as "stir-fried chicken and vegetables" or "easy steamed chicken" based on the family composition information entered by the user and the sale information of the nearest supermarket. It will also recognize the user's emotional state and suggest even easier dishes to prepare if the user is tired. The generated shopping list will include items such as "400g chicken, carrots, and pumpkin," allowing the user to shop efficiently.

[2320] Prompt Sentence Examples

[2321] This system suggests optimal dinner menus based on the user's family composition information and sales information from physical stores. When a user says, "I want to make something easy to cook today," generate a menu based on the following information:

[2322] Family composition information

[2323] Father: Age 35, Favorite food: Pasta, Dislikes: Fish

[2324] Mother: Age 33, Favorite food: Salad, Dislikes: Green peppers

[2325] Child 1: Age 5, Favorite food: Curry, Dislikes: Carrots

[2326] Child 2: Age 2, Favorite food: Pumpkin, Disliked food: None

[2327] Special sale information

[2328] Chicken 100g 50 yen

[2329] Carrots 3 pieces 100 yen

[2330] 1 / 2 pumpkin 80 yen

[2331] Proposal Format

[2332] 1. Menu planning

[2333] 2. List of ingredients needed

[2334] In this way, the system can propose optimal menus that meet the user's diverse requirements and emotional state.

[2335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2336] Step 1:

[2337] User input of family information

[2338] Users enter family information through the interface on their smartphones, including each family member's name, age, favorite foods, disliked foods, and allergy information. The entered family information is sent to the system's server and stored in a database.

[2339] input:

[2340] Family members' names, ages, favorite foods, disliked foods, and allergy information

[2341] output:

[2342] Family composition information stored in a database

[2343] Step 2:

[2344] Collection of advertising information by the server

[2345] The server periodically accesses the websites of each supermarket or store and collects advertising information using web scraping technology. It then uses Python's Beautiful Soup and Selenium to obtain advertising image data, which is then stored on the server.

[2346] input:

[2347] Supermarket or store website URL

[2348] output:

[2349] Advertising image data stored on the server

[2350] Step 3:

[2351] Server analysis of advertising information

[2352] The server analyzes the stored advertising image data using the Google Cloud Vision API to extract sale product information, recognizing text within the image and extracting sale prices and product information as text data.

[2353] input:

[2354] Advertising image data

[2355] output:

[2356] Special sale product information (text data)

[2357] Step 4:

[2358] Server-based menu generation

[2359] The server uses natural language processing algorithms such as OpenAI GPT-3 to generate optimal menus based on family composition information and special sale information. The algorithm takes into account preferences, allergies, and nutritional balance to suggest menus that will satisfy the whole family.

[2360] input:

[2361] Family composition information, special sale product information

[2362] output:

[2363] Generated menu

[2364] Step 5:

[2365] Use of emotion engines on devices

[2366] The device (smartphone) uses a camera and microphone to capture the user's voice and facial expressions. An emotion recognition engine such as Microsoft Azure Emotion API is used to recognize the user's emotional state. The recognized emotional state is sent to the server.

[2367] input:

[2368] Voice and facial expression data

[2369] output:

[2370] Perceived emotional state

[2371] Step 6:

[2372] Server adjustment of proposals

[2373] The server then reviews the generated menu based on the user's emotional state and adjusts the suggestions to take into account the user's psychological state. For example, if the user is tired, the server will prioritize menus that are easy to prepare.

[2374] input:

[2375] Generated menu, emotional state

[2376] output:

[2377] Adjusted menu

[2378] Step 7:

[2379] Menu and shopping list presentation on the device

[2380] The adjusted menu is presented to the user via their smartphone. The user can make any necessary adjustments until they are satisfied. A shopping list is automatically generated based on the finalized menu and displayed on the smartphone.

[2381] input:

[2382] Adjusted menu

[2383] output:

[2384] Menu presented to the user, generated shopping list

[2385] 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.

[2386] 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.

[2387] 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.

[2388] 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.

[2389] 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.

[2390] 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.

[2391] 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).

[2392] 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.

[2393] 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."

[2394] 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.

[2395] 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).

[2396] 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.

[2397] 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.

[2398] 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.

[2399] 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.

[2400] 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.

[2401] 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.

[2402] 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.

[2403] 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.

[2404] 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.

[2405] 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.

[2406] The following is further disclosed regarding the above embodiment.

[2407] (Claim 1)

[2408] means for allowing a user to input family composition information;

[2409] A means for analyzing the collected advertising information and extracting sale product information;

[2410] A means for generating an optimal menu based on family composition information and special sale product information;

[2411] A means for presenting the generated menu to a user;

[2412] A system including:

[2413] (Claim 2)

[2414] 2. The system according to claim 1, wherein the means for prompting the user to input family composition information includes the name, age, favorite foods, disliked foods, and allergy information of each family member.

[2415] (Claim 3)

[2416] The system of claim 1, wherein the means for analyzing the collected advertising information and extracting sale product information uses web scraping and image recognition algorithms.

[2417] (Claim 4)

[2418] The system according to claim 1, wherein the means for generating an optimal menu based on family composition information and sale item information uses natural language processing and machine learning algorithms.

[2419] (Claim 5)

[2420] 10. The system of claim 1, further comprising means for automatically generating a shopping list based on the generated menu.

[2421] "Example 1"

[2422] (Claim 1)

[2423] means for allowing a user to input family composition information;

[2424] A means for analyzing the collected advertising information and extracting sale product information;

[2425] A means for generating an optimal menu based on family composition information and special sale product information;

[2426] A means for presenting the generated menu to a user;

[2427] a means for the user to review the proposed menu and request modifications if necessary;

[2428] A means for generating a shopping list based on the finalized menu;

[2429] A system including:

[2430] (Claim 2)

[2431] 2. The system according to claim 1, wherein the means for prompting the user to input family composition information includes the name, age, favorite foods, disliked foods, and allergy information of each family member.

[2432] (Claim 3)

[2433] The system of claim 1, wherein the means for analyzing the collected advertising information and extracting sale product information uses web scraping and image recognition algorithms.

[2434] "Application Example 1"

[2435] (Claim 1)

[2436] means for allowing a user to input family composition information;

[2437] A means for analyzing the collected advertising information and extracting sale product information;

[2438] A means for generating an optimal menu based on family composition information and special sale product information;

[2439] A means for presenting the generated menu to a user;

[2440] A means to order the necessary ingredients and dishes from external services based on the generated menu;

[2441] means for generating a shopping list;

[2442] A system including:

[2443] (Claim 2)

[2444] 2. The system according to claim 1, wherein the means for prompting the user to input family composition information includes the name, age, favorite foods, disliked foods, and allergy information of each family member.

[2445] (Claim 3)

[2446] The system of claim 1, wherein the means for analyzing the collected advertising information and extracting sale product information uses web scraping and image recognition algorithms.

[2447] "Example 2: Combining Emotion Engines"

[2448] (Claim 1)

[2449] means for allowing a user to input family composition information;

[2450] A means for analyzing the collected advertising information and extracting sale product information;

[2451] A means for generating an optimal meal plan based on family composition information and special sale product information;

[2452] means for recognizing the emotional state of a user and adjusting a menu;

[2453] A means for presenting the generated menu to a user;

[2454] A means for generating a shopping list based on the confirmed menu;

[2455] A system including:

[2456] (Claim 2)

[2457] 2. The system according to claim 1, wherein the means for prompting the user to input family composition information includes the name, age, favorite foods, disliked foods, and allergy information of each family member.

[2458] (Claim 3)

[2459] The system of claim 1, wherein the means for analyzing the collected advertising information and extracting sale product information uses web scraping and image recognition algorithms.

[2460] "Application example 2 when combining emotion engines"

[2461] (Claim 1)

[2462] means for allowing a user to input family composition information;

[2463] A means for analyzing the collected advertising information and extracting sale product information;

[2464] A means for generating an optimal menu based on family composition information and special sale product information;

[2465] means for recognizing a user's emotional state and adjusting menu suggestions;

[2466] means for presenting the generated menu and shopping list to a user;

[2467] A system including:

[2468] (Claim 2)

[2469] 2. The system according to claim 1, wherein the means for prompting the user to input family composition information includes the name, age, favorite foods, disliked foods, and allergy information of each family member.

[2470] (Claim 3)

[2471] The system of claim 1, wherein the means for analyzing the collected advertising information and extracting sale product information uses web scraping and image recognition algorithms. [Explanation of symbols]

[2472] 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 allowing a user to input family composition information; A means for analyzing the collected advertising information and extracting sale product information; A means for generating an optimal menu based on family composition information and special sale product information; A means for presenting the generated menu to a user; A system including:

2. 2. The system according to claim 1, wherein the means for prompting the user to input family information includes the name, age, favorite foods, disliked foods, and allergy information of each family member.

3. 2. The system of claim 1, wherein the means for analyzing the collected advertising information and extracting the sale product information uses web scraping and an image recognition algorithm.

4. 2. The system according to claim 1, wherein the means for generating an optimal menu based on family composition information and sale item information uses natural language processing and machine learning algorithms.

5. 10. The system of claim 1, further comprising means for automatically generating a shopping list based on the generated menu.

Images