System

Abstract

A system is provided.SOLUTION: This system is provided with a means for inputting a menu or food materials desired by a user, a means for acquiring price data from a supermarket in real time, a means for analyzing the acquired price data, and for selecting the combination of the most cost-effective food materials, and a means for notifying the user of the generated menu and the purchase list of the food materials.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] Modern consumers find it difficult to shop efficiently and plan balanced meals amid time and budget constraints. In particular, the complexities of gathering information to accommodate food ingredient prices at supermarkets and various dietary restrictions (allergies and religious restrictions) make efficient shopping and meal planning difficult. This often results in wasteful shopping and unnecessary time wastage, which negatively impacts household finances and health. The present invention aims to solve these problems and enable consumers to shop efficiently and plan balanced meals. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to input a desired menu or ingredients, a means for acquiring price data from supermarkets in real time, a means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, and a means for notifying the user of the generated menu and ingredient shopping list. The system further includes a means for personalizing the generated menu taking into consideration the user's preferences, nutritional balance, and restrictions, and a means for displaying the menu and ingredient list generated based on the user's input data in real time. This allows the user to shop efficiently and cost-effectively, realizing a system that allows the user to easily plan a balanced menu.

[0006] "User" refers to the end user who uses the system and is the entity that inputs information about ingredients and menus.

[0007] A "menu" refers to a combination of specific ingredients and dishes, and is a list or menu that a user can refer to when planning a meal.

[0008] "Ingredients" refers to the raw materials used to prepare a dish or meal, including foods and ingredients available for purchase in supermarkets.

[0009] A "supermarket" refers to a retail store that sells food ingredients and daily necessities, and is a place where users can purchase the ingredients they need.

[0010] "Price data" refers to sales price information for specific food ingredients, including price information obtained in real time from supermarkets.

[0011] "Real-time" refers to the instantaneous exchange of information, and means that the latest price data is always obtained and provided.

[0012] "Means for obtaining price data" refers to the methods and technologies used to communicate with supermarket systems and collect the required price information.

[0013] "Means for analyzing price data" refers to methods and techniques for processing the acquired price data and selecting the most cost-effective ingredients.

[0014] An "ingredient combination" refers to the collection of multiple ingredients needed to make a particular dish or menu.

[0015] A "shopping list" refers to information that lists the ingredients that a user should purchase and where to purchase them.

[0016] "Means of notification" refers to the methods and techniques used to communicate the generated menu and shopping list to the user.

[0017] "Preferences" refers to the user's taste preferences and special food preferences, and are information that is taken into consideration when generating a menu.

[0018] "Nutritional balance" refers to the distribution of nutrients that should be considered to achieve a healthy diet.

[0019] "Restrictions" refer to conditions or factors that users must avoid, such as allergies or religious restrictions.

[0020] "Personalization methods" refers to methods and techniques for customizing menus to suit individual user preferences and restrictions.

[0021] "Display means" refers to the methods and techniques used to visually present information to a user, especially for providing real-time information. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] This invention is a system that helps users shop efficiently and plan balanced meals. When a user inputs their desired menu or ingredients, the system acquires and analyzes real-time supermarket price data to suggest the most cost-effective combination of ingredients. Furthermore, the system personalizes menus by taking into account the user's preferences, nutritional balance, and restrictions, and displays them in real time.

[0044] Program processing flow

[0045] User Input Phase

[0046] 1. The user inputs the menu and desired ingredients into the system interface. For example, the user might input, "I want to make a dish using tomatoes."

[0047] 2. The device sends the user input data to the server.

[0048] Data Collection Phase

[0049] 1. The server receives the request and communicates with the APIs of multiple connected supermarkets to obtain price data in real time.

[0050] 2. The terminal collects price data from each supermarket and sends it to the server. For example, the terminal obtains data that the price of a tomato is 100 yen per unit from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0051] Price Analysis Phase

[0052] 1. The server analyzes the collected price data and identifies the most cost-effective combination. For example, it finds that tomatoes are cheapest at Supermarket B.

[0053] 2. The server will also compare prices of other ingredients (onions, garlic, pasta, etc.) and select the best combination of ingredients.

[0054] Menu generation phase

[0055] 1. The server uses generative AI to suggest optimal menu items based on user input and price data, such as "tomato pasta" or "tomato and chicken salad."

[0056] 2. The server determines the list of ingredients needed and where to buy them (cheapest supermarket). For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[0057] Personalization Phase

[0058] 1. The server looks up data to take into account the user's preferences, nutritional balance, and restrictions. For example, if the user is vegan, that information is taken into account.

[0059] 2. The server personalizes the generated menu based on the user's information and suggests vegan-friendly recipes.

[0060] Result notification phase

[0061] 1. The server generates the final menu and ingredients list and sends that information to the device. For example, it might say, "To make tomato pasta, purchase tomatoes, onions, garlic, and pasta. The cheapest places to buy them are supermarkets B, A, and C, respectively."

[0062] 2. The device displays a notification to the user, providing a list of ingredients needed and information on where to purchase them.

[0063] Specific examples

[0064] For example, if a user inputs "I want to make a dish using tomatoes," the server will retrieve tomato price data from multiple nearby supermarkets and select supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) and generate the most cost-effective shopping list overall.

[0065] The system allows users to shop quickly and economically and provides rational choices for a balanced and nutritious diet.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The user inputs the menu and ingredients they want to use on the system interface.

[0069] Specifically, the user inputs, "I want to make a dish using tomatoes."

[0070] Step 2:

[0071] The terminal receives the user's input data and sends it to the server.

[0072] Specifically, the input information is posted to the server via the API.

[0073] Step 3:

[0074] The server analyzes the received input data and generates a search query based on the user's request.

[0075] Specifically, we will construct a query to search for the best menu using "tomato."

[0076] Step 4:

[0077] The server queries the APIs of multiple connected supermarkets to retrieve real-time price data.

[0078] Specifically, it calls the API endpoint of each supermarket to obtain price information for "tomatoes."

[0079] Step 5:

[0080] The terminal collects price data from each supermarket and sends it to the server.

[0081] Specifically, data is obtained that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0082] Step 6:

[0083] The server analyzes the collected price data to identify the most cost-effective ingredient combinations.

[0084] Specifically, select the cheapest supermarket B (tomatoes 90 yen).

[0085] Step 7:

[0086] The server also obtains the prices of other related ingredients (e.g., onions, garlic, pasta) and selects the optimal combination of ingredients.

[0087] Specifically, we analyze onions costing 50 yen (Supermarket A), garlic costing 30 yen (Supermarket C), and pasta costing 120 yen (Supermarket B).

[0088] Step 8:

[0089] The server uses generative AI to generate the optimal menu based on the user's input data and price data.

[0090] Specifically, it generates recipes such as "tomato pasta" and "tomato and chicken salad."

[0091] Step 9:

[0092] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions).

[0093] Specifically, if the user is vegan, it suggests recipes that do not contain animal products.

[0094] Step 10:

[0095] The server generates the final menu, ingredient list, and purchasing information, and sends this information to the terminal.

[0096] Specifically, the notification will say, "To make tomato pasta, you will need 90 yen for tomatoes (Supermarket B), 50 yen for onions (Supermarket A), 30 yen for garlic (Supermarket C), and 120 yen for pasta (Supermarket B)."

[0097] Step 11:

[0098] The device will display a notification to the user, providing a list of ingredients, where to buy them, and meal details.

[0099] Specifically, the user checks "tomato pasta, how to make it, and a list of ingredients needed" on their smartphone.

[0100] Example 1

[0101] 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."

[0102] Conventional shopping support systems have the problem that price data is not collected or analyzed in real time, and the information provided to users is not up-to-date. Furthermore, menu suggestions that take user preferences and nutritional balance into consideration are not adequately taken into account, making it difficult to provide meals that are appropriate for each individual user. Furthermore, there is a lack of a mechanism to utilize generative AI to suggest optimal menus, and these systems are not yet able to support cost-effective shopping.

[0103] 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.

[0104] In this invention, the server includes: means for a user to input a desired menu or ingredients; means for acquiring price data in real time from supermarkets; means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients; means for personalizing the generated menu taking into consideration the user's preferences, nutritional balance, and restrictions; means for notifying the user of the generated menu and ingredient shopping list; means for using a generation AI to suggest an optimal menu based on the user's input data; and means for displaying the generated menu and ingredient list in real time. This allows users to always receive optimal menu suggestions based on the latest price data, enabling them to easily plan meals that suit their individual preferences and nutritional balance while shopping cost-effectively.

[0105] "User" refers to a person who uses this system to input menu items and desired ingredients.

[0106] "Menu" refers to a meal menu or a combination of dishes.

[0107] "Ingredients" refers to foods used as ingredients in cooking.

[0108] A "supermarket" refers to a store that sells food and household goods.

[0109] "Real-time" refers to the acquisition or processing of data immediately at the present time.

[0110] "Price data" refers to information on the selling price of each food ingredient in a supermarket.

[0111] "Server" refers to a computer system that processes information entered by users and manages data.

[0112] "Generative AI" refers to a system that uses artificial intelligence technology to generate appropriate suggestions and predictions from data.

[0113] "Personalization" refers to tailoring content to individual user preferences and needs.

[0114] "Notification" refers to the act of a system conveying information to a user.

[0115] This invention is a system that helps users shop efficiently and plan balanced meals. The system includes a means for inputting a user's desired menu or ingredients, a means for acquiring real-time supermarket price data, a means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, a means for suggesting a menu using a generation AI, and a means for personalizing the generated menu.

[0116] The user uses a terminal to input the menu and desired ingredients into the system interface. For example, the user might input "I want to make a dish using tomatoes" into the terminal. This input data is sent from the terminal to the server. The server uses a program to communicate with the APIs of multiple connected supermarkets and obtain price data in real time. The obtained price data is sent to the server and then forwarded to the terminal.

[0117] The server analyzes the collected price data to identify the most cost-effective combination of ingredients, for example, determining that tomatoes are the cheapest at Supermarket B. The server then analyzes the prices of other related ingredients (onions, garlic, pasta, etc.) to select the optimal combination of ingredients.

[0118] The server then uses a generative AI model to suggest an optimal menu based on the user's input and price data. For example, it might suggest "tomato pasta" or "tomato and chicken salad." It then personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., if the user is vegan). This personalized menu is best suited to the user.

[0119] The final menu and ingredient list are sent from the server to the terminal and notified to the user. The terminal then displays the list of ingredients needed and information on where to purchase them. For example, the terminal may be notified that "To make tomato pasta, purchase tomatoes, onions, garlic, and pasta. The cheapest places to purchase these are supermarkets B, A, and C, respectively," allowing the user to shop efficiently and economically.

[0120] An example prompt might be: "I want to make a dish using tomatoes. Please check the prices at my local supermarket and suggest the most cost-effective recipe."

[0121] The system allows users to make quick, healthy and economical purchases and eat a nutritionally balanced diet.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] The user inputs the menu and desired ingredients.

[0125] Specifically, the user inputs a request into the system interface, such as "I want to make a dish using tomatoes." This input data is sent to the server via the terminal.

[0126] Input: User request (e.g., "I want to make a dish using tomatoes")

[0127] Output: The request data is sent to the server.

[0128] Step 2:

[0129] The device sends the user input data to the server.

[0130] Specifically, the device encodes the user's input data and sends it to the server, which receives the data and prepares it for processing.

[0131] Input: User-entered data

[0132] Output: The server receives the request data.

[0133] Step 3:

[0134] The server receives the request and communicates with the APIs of multiple connected supermarkets to retrieve price data in real time.

[0135] Specifically, the server sends a request to the supermarket's API to obtain price data for tomatoes and related ingredients.

[0136] Input: Request data

[0137] Output: Price data from supermarkets (e.g., tomato price data).

[0138] Step 4:

[0139] The terminal collects price data from each supermarket and sends it to the server.

[0140] Specifically, the terminal aggregates the price data returned by the API and sends it to the server, which stores it for analysis.

[0141] Input: Price Data

[0142] Output: Price data is saved on the server.

[0143] Step 5:

[0144] The server analyzes the collected pricing data and identifies the most cost-effective combination.

[0145] Specifically, the server compares the prices at each supermarket and finds, for example, that tomatoes are the cheapest at supermarket B. Similarly, it identifies the cheapest prices for other ingredients needed.

[0146] Input: Saved price data

[0147] Output: The most cost-effective combination of ingredients.

[0148] Step 6:

[0149] The server uses generative AI to suggest the optimal menu based on the user's input data and price data.

[0150] Specifically, the server uses a generative AI model to generate optimal menu suggestions based on price data and user preferences, such as "tomato pasta" or "tomato and chicken salad."

[0151] Input: User input data, price data

[0152] Output: Suggested optimal menu.

[0153] Step 7:

[0154] The server determines the list of ingredients needed and where to buy them (cheapest supermarket).

[0155] Specifically, the server lists all the ingredients the user needs and where they are sold at the lowest prices, and notifies the user.

[0156] Input: Suggested menu, price data

[0157] Output: Ingredient list and purchasing information.

[0158] Step 8:

[0159] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions.

[0160] Specifically, the server refers to the user's profile information, makes individual adjustments such as veganism or allergies, and personalizes the recipe.

[0161] Input: User profile information, suggested menu items

[0162] Output: Personalized menu.

[0163] Step 9:

[0164] The server generates the final menu and ingredient list and sends this information to the terminal.

[0165] Specifically, the server compiles the final menu and the corresponding ingredient list and sends it to the device. For example, it may notify the user, "To make tomato pasta, please purchase tomatoes, onions, garlic, and pasta. The cheapest places to purchase them are supermarkets B, A, and C, respectively."

[0166] Input: Finalized menu and ingredient list

[0167] Output: The menu and ingredients list are sent to the terminal.

[0168] Step 10:

[0169] The device will display a notification to the user, providing a list of ingredients needed and information on where to purchase them.

[0170] Specifically, the device displays a list of ingredients and their supplier information on the user's interface, helping the user to shop quickly.

[0171] Input: Menu and ingredient list sent from the server

[0172] Output: The ingredient list and supplier information displayed to the user.

[0173] (Application example 1)

[0174] 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."

[0175] The present invention relates to a system that supports users in shopping efficiently and planning balanced meals. Conventional technologies require users to individually check prices at supermarkets and other stores and select optimal ingredient combinations, which is time-consuming. Furthermore, there is a lack of systems that centrally manage this information and propose personalized meals that take into account the user's preferences and nutritional balance. This makes it difficult for users to shop efficiently and economically, and to prepare balanced, nutritious meals.

[0176] 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.

[0177] In this invention, the server includes means for inputting a user's desired menu or ingredients, means for acquiring price data in real time from multiple stores, means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, means for notifying the user of the menu and ingredient shopping list generated using a generative AI model, means for providing a personalized menu taking into consideration the user's preferences, nutritional balance, and restrictions, and means for displaying the menu and ingredient list generated based on the user's input data and their purchasing locations in real time, thereby enabling users to shop efficiently and economically and prepare balanced and nutritious meals.

[0178] A "user" is an individual or corporation that uses the system to create menus and obtain information on purchasing ingredients.

[0179] "Menu" means a meal plan that describes the combination of ingredients and cooking methods.

[0180] "Ingredients" refers to the individual foods or ingredients used to create a menu.

[0181] "Multiple stores" refers to multiple sales areas where users can purchase ingredients, such as supermarkets and specialty stores.

[0182] "Real-time" means instantly acquiring and processing the latest data at the current time.

[0183] "Price data" refers to information regarding the selling prices of ingredients offered by each store.

[0184] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically create optimal menus tailored to the user's needs.

[0185] The "food purchasing list" refers to a list that specifically shows the ingredients that should be prepared based on the generated menu and information on where to purchase them.

[0186] "Notification" refers to the general act of a system providing information to a user.

[0187] "Personalization" means optimizing content to suit individual conditions such as user preferences, nutritional balance, and dietary restrictions.

[0188] "Real-time display" means displaying relevant information immediately in response to a user's request.

[0189] The present invention provides a system for assisting users in shopping efficiently and planning a balanced menu. Specific embodiments will be described below.

[0190] Hardware and Software Configuration

[0191] Hardware used

[0192] 1. Smartphone: Used to provide the user interface.

[0193] 2. Server: Used to collect data, analyze it, and run generative AI models.

[0194] Software used

[0195] 1. API: Used as an interface to get real-time price data from multiple stores.

[0196] 2. Generative AI models (e.g., OpenAI GPT-4): Automatically create optimal menus tailored to user needs.

[0197] 3. Firebase: Used for sending and receiving real-time data and database management.

[0198] Detailed explanation of the process

[0199] The server first receives information about the menu and desired ingredients entered by the user through a smartphone application. This data is then sent to Firebase's real-time database. The server then retrieves price data from multiple stores via API, which is then collected in Firebase.

[0200] The server analyzes the acquired price data and performs price analysis to identify the most cost-effective combination of ingredients. Using this price data and user input, a generative AI model generates an optimal menu. The generated menu and ingredient shopping list are then sent to the user's smartphone in real time.

[0201] The system also takes into account the user's preferences, nutritional balance and dietary restrictions to provide personalized meals. For example, if the user is vegan, the system will take that information into account and suggest meals that exclude animal products.

[0202] As a concrete example, consider a situation where a user requests, "I want to make a dish using tomatoes." The server retrieves real-time pricing data for tomatoes from multiple nearby stores, identifies the store with the lowest price, and then similarly retrieves prices for other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list.

[0203] Prompt Sentence Examples

[0204] The user wants to make a dish using tomatoes. Based on real-time price data, suggest the most cost-effective combination of ingredients. The user is vegan, so avoid animal-based ingredients.

[0205] The invention provides users with rational and economical shopping options and makes it easy to prepare balanced, nutritious meals.

[0206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0207] Step 1:

[0208] Users input their desired menu or ingredients into a smartphone application. For example, the input data might be in the form of "I want to make a dish using tomatoes." This data is then sent from the application to a Firebase real-time database.

[0209] Step 2:

[0210] The device transfers the user input data sent to Firebase to the server. The input data includes a request to "make a dish using tomatoes," and the server prepares for subsequent processing based on that data.

[0211] Step 3:

[0212] The server retrieves price data from multiple stores in real time via API. For example, it retrieves price data for tomatoes, onions, garlic, and pasta from multiple stores. This data is collected in Firebase and stored as price data.

[0213] Step 4:

[0214] The server analyzes the price data stored in Firebase to identify the most cost-effective combination of ingredients. In this process, for example, if tomatoes cost 90 yen at store A and 100 yen at store B, the server checks the price difference and selects the tomatoes from store A, which are the cheapest.

[0215] Step 5:

[0216] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an optimal menu based on the user's input data and price data. For example, it might suggest menu items such as "tomato pasta" or "tomato and chicken salad." Here, a prompt for the dish is constructed and input into the generative AI model.

[0217] Step 6:

[0218] The server creates a list of ingredients based on the generated menu and determines where to buy each ingredient (cheapest). For example, it identifies store A for tomatoes, store B for onions, and store C for garlic.

[0219] Step 7:

[0220] The server personalizes the generated menus by referencing data that takes into account the user's preferences, nutritional balance, and dietary restrictions. If the user is vegan, the server automatically changes the recipes to ones that do not contain animal ingredients.

[0221] Step 8:

[0222] The server generates the final menu, ingredient list, and purchasing information, and sends it to the user's smartphone in real time via Firebase. The user's device receives this data and displays a notification. For example, it might say, "To make tomato pasta, purchase tomatoes from store A, onions from store B, and garlic from store C."

[0223] Step 9:

[0224] Based on the information provided, users can purchase ingredients from the most cost-effective stores and create balanced meals.

[0225] Through these steps, the present invention helps users shop and prepare balanced meals efficiently and economically.

[0226] 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.

[0227] This invention is a system that helps users shop efficiently and plan balanced meals. The system inputs the user's desired menu or ingredients, acquires and analyzes real-time supermarket price data, and suggests the most cost-effective combination of ingredients. It can also personalize menus by taking into account the user's preferences, nutritional balance, and restrictions. It also incorporates an emotion engine that recognizes the user's emotions, suggesting menus and ingredients according to the user's emotional state.

[0228] Program processing flow

[0229] User Input Phase

[0230] 1. The user inputs the menu and ingredients they want to use on the system interface. For example, they might input, "I want to make a dish using tomatoes."

[0231] 2. The device receives the user input data and sends it to the server.

[0232] Data Collection Phase

[0233] 1. The server receives the request and queries the APIs of multiple connected supermarkets to retrieve real-time price data.

[0234] 2. The terminal collects price data from each supermarket and sends it to the server. For example, it obtains data that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0235] Price Analysis Phase

[0236] 1. The server analyzes the collected price data and identifies the most cost-effective combination of ingredients. For example, it finds that tomatoes are the cheapest at Supermarket B.

[0237] 2. The server also obtains the prices of other ingredients (onions, garlic, pasta, etc.) involved and selects the optimal combination of ingredients.

[0238] Menu generation phase

[0239] 1. The server uses generative AI to suggest optimal menu items based on user input and price data, such as "tomato pasta" or "tomato and chicken salad."

[0240] 2. The server determines the list of ingredients needed and where to buy them, for example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[0241] Personalization Phase

[0242] 1. The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes that are appropriate for that.

[0243] Emotion Recognition Phase

[0244] 1. The server uses an emotion engine to analyze the user's emotional state from their facial expressions, tone of voice, and input text to recognize their emotions. For example, if the user is feeling stressed, it will recognize that.

[0245] 2. The server selects specific ingredients and types of dishes based on the user's emotional state and generates a menu that is optimal for the user's emotional state. For example, it suggests recipes using ingredients that have a relaxing effect.

[0246] Result notification phase

[0247] 1. The server generates the final menu, ingredient list, and supplier information, and sends this information to the terminal. For example, it may notify the user that "To make tomato pasta, you will need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[0248] 2. The device displays a notification to the user, providing a list of ingredients, where to buy them, and details of the meal. For example, the user sees on their smartphone "Tomato pasta, how to make it, and a list of ingredients needed."

[0249] Specific examples

[0250] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve tomato price data from multiple nearby supermarkets and select Supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list. Based on the results of the emotion engine, it will also suggest "tomato pasta," a dish that has a relaxing effect.

[0251] The system allows users to shop quickly and economically, and provides menus that correspond to their emotional state, resulting in a balanced diet and increasing user satisfaction.

[0252] The processing flow will be explained below.

[0253] Step 1:

[0254] The user inputs the menu and ingredients they want to use into the system interface. For example, they might input, "I want to make a dish using tomatoes."

[0255] Step 2:

[0256] The terminal receives the user's input data and sends it to the server.

[0257] Step 3:

[0258] The server analyzes the input data it receives and generates a search query based on the user's request. For example, it constructs a query to search for the best menu using "tomato."

[0259] Step 4:

[0260] The server sends queries to the APIs of multiple connected supermarkets to get price data in real time. For example, it sends an API request to get price data of tomatoes from supermarkets A, B, and C.

[0261] Step 5:

[0262] The terminal collects price data from each supermarket and sends it to the server. For example, data is obtained that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0263] Step 6:

[0264] The server analyzes the collected price data to identify the most cost-effective combination of ingredients, for example, finding that tomatoes are cheapest at Supermarket B.

[0265] Step 7:

[0266] The server also collects the prices of other ingredients (e.g., onions, garlic, and pasta) and selects the optimal combination of ingredients. For example, it analyzes the following: onions 50 yen (supermarket A), garlic 30 yen (supermarket C), and pasta 120 yen (supermarket B).

[0267] Step 8:

[0268] The server uses generative AI to generate optimal menu suggestions based on user input and price data, suggesting, for example, "tomato pasta" or "tomato and chicken salad."

[0269] Step 9:

[0270] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes that cater to that.

[0271] Step 10:

[0272] To recognize the user's emotions, the server activates an emotion engine and analyzes the user's emotional state from their facial expressions, tone of voice, and input text. For example, the emotion engine recognizes that the user is tired.

[0273] Step 11:

[0274] Based on the user's emotional state, the server selects specific ingredients and types of dishes to create a menu that best suits the user's emotional state. For example, it suggests "tomato pasta," a dish that has a relaxing effect.

[0275] Step 12:

[0276] The server generates the final menu, ingredient list, and purchasing information, and sends this information to the terminal. For example, it may notify the user that "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[0277] Step 13:

[0278] The device will display a notification to the user, providing a list of ingredients, where to buy them, and details of the meal. For example, the user will see on their smartphone, "Tomato pasta, how to make it, and a list of ingredients needed."

[0279] Specific examples

[0280] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve price data for tomatoes from multiple nearby supermarkets and select Supermarket B with the lowest price. The server then similarly retrieves price data for other related ingredients (e.g., onions, garlic, pasta) and generates the most cost-effective shopping list. Furthermore, taking into account the user's recognized emotional state, it will suggest "tomato pasta," a dish that has a relaxing effect. The device will then notify the user of the list of necessary ingredients and where to purchase them, as well as provide detailed instructions on how to prepare the dish.

[0281] Example 2

[0282] 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."

[0283] Today's consumers find it difficult to plan balanced meals while shopping efficiently. It is also time-consuming to collect price information from each supermarket and then select the most suitable ingredients based on that information. Furthermore, there are currently no systems that can suggest meals that take into account the user's emotional state, individual preferences, and nutritional balance. Therefore, there is a need for a system that can reduce the burden on consumers and provide healthier, more economical meals.

[0284] 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.

[0285] In this invention, the server includes means for a user to input a desired menu or ingredients, means for acquiring price data from retailers in real time, means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, means for generating an optimal menu using a generative AI model, means for recognizing the user's emotional state, means for adjusting the menu based on the recognized emotional state, and means for notifying the user of the generated menu and ingredient shopping list. This allows users to not only shop efficiently but also easily plan an optimal menu that takes into account their emotional state and nutritional balance.

[0286] "User" refers to an individual who uses the system to select menus and ingredients.

[0287] "Menu" means a meal plan and the combination of ingredients used in it.

[0288] "Ingredients" refers to the individual ingredients and seasonings needed to make a dish.

[0289] "Retail store" refers to a store or supermarket that sells food and other consumer goods to consumers.

[0290] "Real-time" refers to information updates and processing that are occurring in real time, meaning that they are reflected immediately and without delay.

[0291] "Price data" refers to information regarding the selling prices of each ingredient or product obtained from retailers.

[0292] A "generative AI model" refers to an algorithm or system that uses machine learning and artificial intelligence techniques to process data and generate optimal results.

[0293] "Emotional state" refers to the user's current emotional and psychological state, and is analyzed from facial expressions, tone of voice, etc.

[0294] "Notification means" refers to the method or device by which the system communicates information to the user, including websites, emails, app notifications, etc.

[0295] "Personalization" means customizing content according to the preferences and requirements of individual users.

[0296] "Optimal menu" refers to a meal plan generated to meet multiple conditions, such as price-effectiveness, nutritional balance, and user preferences.

[0297] This invention is a system that helps users shop efficiently and plan balanced meals. The system inputs the user's desired menu or ingredients, acquires and analyzes real-time price data from retailers, and suggests the most cost-effective combination of ingredients. It can also suggest meals that take into account the user's preferences, nutritional balance, and restrictions, and are also tailored to the user's emotional state.

[0298] The system mainly consists of a user device, a server, a retailer's API, a generative AI model, and an emotion recognition engine.

[0299] The user inputs the desired menu and ingredients through the interface. For example, they might input "I want to make a dish using tomatoes." The device receives this input data and sends it to the server.

[0300] When the server receives a request, it sends a query to the APIs of multiple connected retailers to obtain price data in real time. For example, it obtains data that a tomato costs 100 yen from retailer A, 90 yen from retailer B, and 95 yen from retailer C. This price data is collected and stored on the server.

[0301] The server then analyzes the price data to identify the most cost-effective combination of ingredients, for example, finding that tomatoes are the cheapest at Retailer B. It also analyzes the prices of other related ingredients (onions, garlic, pasta, etc.) to identify the optimal combination.

[0302] The server uses a generative AI model (such as GPT-4) to generate an optimal menu based on the user's input data and the acquired price data. For example, it might suggest "tomato pasta" or "tomato and chicken salad." It also determines the list of ingredients needed and where to purchase them. For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[0303] The server then personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes accordingly.

[0304] The server then uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotions. It analyzes the user's facial expressions, tone of voice, and input text to determine their emotional state. For example, if the user is feeling stressed, it will recognize this. Based on the recognized emotional state, specific ingredients and types of dishes will be suggested, such as recipes using ingredients with a relaxing effect.

[0305] Finally, the server generates the final menu, ingredients list, and supplier information, and sends this information to the device. The device then displays a notification to the user, providing the ingredients list, supplier information, and menu details. For example, the user can see "Tomato pasta, how to make it, and the list of ingredients needed" on their smartphone.

[0306] As a concrete example, if a user inputs "I want to make a dish using tomatoes" and the emotion engine recognizes the user's emotional state as "tired," the following process will occur: The server obtains price data for tomatoes from multiple nearby retailers and selects Retailer B with the lowest price. It then similarly obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and generates the most cost-effective shopping list. Based on the results of the emotion engine, it also suggests "tomato pasta," a dish that has a relaxing effect. This system allows users to shop quickly and economically, receive menus that correspond to their emotional state, and achieve a balanced diet.

[0307] An example prompt might be, "The user says they want to make a dish using tomatoes. Furthermore, analysis from the emotion engine indicates that the user is tired. Taking this into consideration, please suggest a dish using tomatoes that has a relaxing effect."

[0308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0309] Step 1:

[0310] The user inputs the menu and ingredients they want to use on the system interface. For example, they input "I want to make a dish using tomatoes." The input is the menu or ingredients the user wants, and the output is the user input data received by the terminal.

[0311] Step 2:

[0312] The terminal receives the user input data and sends it to the server. In concrete terms, the terminal sends the user input data to the server via the network. The input is the user input data, and the output is the user input data sent to the server.

[0313] Step 3:

[0314] The server receives the request and sends a query to the APIs of multiple connected retailers to obtain price data in real time. For example, it obtains data on tomatoes at 100 yen from retailer A, 90 yen from retailer B, and 95 yen from retailer C. The input is the user-entered data, and the output is the price data obtained from each retailer.

[0315] Step 4:

[0316] The server collects price data from each retailer and stores it internally. Specifically, the server records the query results in a database. The input is the price data obtained from the retailer, and the output is the price data stored internally on the server.

[0317] Step 5:

[0318] The server analyzes the collected price data and identifies the most cost-effective combination of ingredients. For example, it finds that tomatoes are cheapest at retailer B. The input is the price data stored inside the server, and the output is the most cost-effective combination of ingredients.

[0319] Step 6:

[0320] The server also obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and selects the optimal ingredient combination. The input is the price data of other ingredients obtained from each retailer, and the output is the optimal ingredient combination.

[0321] Step 7:

[0322] The server generates an optimal menu using a generative AI model (e.g., GPT-4) based on the user's input data and price data. Specifically, the server inputs a prompt to the AI ​​model to obtain the optimal menu. The input is the user's input data and price data, and the output is the generated optimal menu.

[0323] Step 8:

[0324] The server determines the list of ingredients needed and where to buy them. For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc. The input is the generated menu and price data, and the output is the list of ingredients and where to buy them.

[0325] Step 9:

[0326] The server personalizes the generated menu by taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). The input is the user's preferences and restrictions, and the output is a personalized menu.

[0327] Step 10:

[0328] To recognize the user's emotions, the server uses an emotion engine to analyze the user's emotional state from their facial expressions, tone of voice, and input text. For example, it uses the Microsoft Azure Emotion API. The input is the user's facial expressions, tone of voice, and input text, and the output is the analysis result of the emotional state.

[0329] Step 11:

[0330] The server selects specific ingredients and types of dishes based on the recognized emotional state, and generates a menu that is optimal for the user's emotional state. Specifically, the server inputs prompts that take the emotional state into account into the generative AI model and obtains the menu. The input is the analysis result of the emotional state, and the output is an optimal menu based on the emotional state.

[0331] Step 12:

[0332] The server generates the final menu, ingredient list, and supplier information and sends that information to the terminal. The input is the final menu, ingredient list, and supplier information, and the output is notification data sent to the terminal.

[0333] Step 13:

[0334] The device displays a notification to the user, providing a list of ingredients, where to buy them, and details of the menu. For example, a user can see "Tomato pasta, how to make it, and a list of ingredients" on their smartphone. The input is the notification data sent from the server, and the output is the detailed information displayed to the user.

[0335] (Application example 2)

[0336] 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."

[0337] Modern consumers face challenges in efficiently shopping and preparing balanced meals within limited time and resources. It's especially difficult to compare prices in real time and select the most cost-effective ingredients when shopping. There's also a demand for personalized menu suggestions that take into account the user's emotional state and individual preferences and allergies.

[0338] 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.

[0339] In this invention, the server includes: means for inputting a user's desired menu or ingredients; means for acquiring price data in real time from supermarkets; means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients; means for notifying the user of the generated menu and ingredient shopping list; emotion recognition means for analyzing the user's emotional state; means for suggesting a menu and ingredients according to the user's emotional state; means for saving the user's food preferences and allergy information to personalize the menu; and a terminal for displaying information in real time on a smartphone or smart glasses.

[0340] This allows users to shop efficiently and cost-effectively, while also suggesting balanced meals that are tailored to individual tastes and emotional states.

[0341] "Means for users to input their desired menu or ingredients" refers to the method by which users input the dishes they want to make or the ingredients they want to use into the interface through the system.

[0342] "Means of obtaining real-time price data from supermarkets" refers to a method of instantly collecting current food price information via the APIs and networks of multiple supermarkets.

[0343] "Means for analyzing acquired price data and selecting the most cost-effective combination of ingredients" refers to a method for calculating cost performance based on collected price information and generating a rational shopping list.

[0344] The "means for notifying the user of the generated menu and ingredient purchasing list" refers to a method for displaying or notifying the user of the optimal menu and ingredient purchasing list.

[0345] "Emotion recognition means for analyzing the user's emotional state" refers to technology for analyzing the user's facial expressions, voice, etc. to identify their current emotional state.

[0346] "Means for suggesting menus and ingredients according to the user's emotional state" refers to a method for taking into account the analyzed emotional state of the user and suggesting dishes and ingredients that are appropriate at that time.

[0347] "Means for saving a user's food preferences and allergy information to personalize menus" refers to a method for saving a user's individual preferences and allergy information and customizing menus based on that information.

[0348] "Devices that display information in real time on smartphones or smart glasses" refers to equipment that displays information on portable or wearable devices so that users can view the information instantly.

[0349] This invention relates to a system that allows users to shop efficiently and proposes balanced menus that are tailored to individual preferences and emotional states, while also taking into consideration cost-effectiveness. The functions of the server, terminal, and user, as well as their specific operations, are explained below.

[0350] Hardware and Software Used

[0351] Hardware: Smartphone (iOS / Android), smart glasses (general wearable device)

[0352] Software: Python, TensorFlow, OpenCV, Google Cloud Vision API, Super API, Emotion AI (for emotion recognition)

[0353] Program processing

[0354] 1. User Input Phase

[0355] The user uses the interface on their smartphone or smart glasses to input the desired menu and ingredients, for example, "I want to make a dish using tomatoes." The input data is then sent to the server via a Python script.

[0356] 2. Data Collection Phase

[0357] The server queries the APIs of multiple supermarkets to get real-time price data, for example, a tomato costs 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0358] 3. Price Analysis Phase

[0359] The server uses Python and Pandas to analyze the obtained price data and select the most cost-effective combination of ingredients. For example, it confirms that tomatoes are the cheapest at Supermarket B. It also obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and generates the most cost-effective shopping list overall.

[0360] 4. Menu generation phase

[0361] The server uses a generative AI model (using TensorFlow) to generate optimal menu suggestions based on user input data and price information, such as "tomato pasta" or "tomato and chicken salad."

[0362] 5. Personalization Phase

[0363] It stores your food preferences and allergies and then creates personalized meal suggestions based on them. For example, if you're vegan, it will generate vegan-friendly recipes.

[0364] 6. Emotion Recognition Phase

[0365] The server analyzes the user's facial expressions and voice using emotion recognition (using OpenCV and Google Cloud Vision API). For example, if the server recognizes that the user is "tired," it will suggest ingredients and dishes that have a relaxing effect based on that emotional state.

[0366] 7. Result notification phase

[0367] The server generates the final menu, ingredient list, and supplier information, and displays this information in real time on the smartphone or smart glasses. For example, it might say, "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[0368] Examples and prompts

[0369] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve tomato price data from multiple nearby supermarkets and select Supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list. Based on the results of the emotion engine, it will also suggest "tomato pasta," a dish that has a relaxing effect.

[0370] Example prompt sentence:

[0371] "We've identified your emotional state as 'tired.' We'll suggest a meal that will help you relax. Search for 'pasta with tomatoes' to find the perfect ingredients."

[0372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0373] Step 1:

[0374] The user uses the interface of their smartphone or smart glasses to input the desired menu or ingredients, for example, "I want to make a dish using tomatoes." This input data is stored in an SQL database and sent to the server.

[0375] Input: User input (e.g. "dishes with tomatoes")

[0376] Output: User request data

[0377] Step 2:

[0378] The server receives the user's request data, queries the APIs of multiple supermarkets to obtain real-time price data, and then analyzes the responses from each supermarket to extract the required price information.

[0379] Input: User request data

[0380] Output: Real-time price data

[0381] Step 3:

[0382] The server analyzes the acquired price data using Python and Pandas. The server compares the price data and calculates the most cost-effective combination of ingredients. For example, the server analyzes the price data for tomatoes and selects the most cost-effective supermarket.

[0383] Input: Real-time price data

[0384] Output: Most cost-effective ingredient combination

[0385] Step 4:

[0386] The server uses a generative AI model (using TensorFlow) to generate optimal menu suggestions based on user input and price information. The generated menu suggestions are formatted using a Python script. For example, the server might suggest "tomato pasta" or "tomato and chicken salad."

[0387] Input: User input data, price information

[0388] Output: Generated menu

[0389] Step 5:

[0390] The server personalizes the generated menu by taking into account the user's food preferences and allergy information, and adjusts the recipe content based on the user's nutritional balance and restrictions.

[0391] Input: Generated menu, user preferences and allergy information

[0392] Output: Personalized menu

[0393] Step 6:

[0394] The server uses emotion recognition (using OpenCV and Google Cloud Vision API) to analyze the user's facial expressions and voice. The analysis results identify the user's emotional state. For example, if the user is recognized as "tired," that information is fed back to the server.

[0395] Input: User's facial expressions and voice data

[0396] Output: User's emotional state

[0397] Step 7:

[0398] The server suggests ingredients and dishes that have a relaxing effect based on the user's emotional state. It comprehensively analyzes the user's emotional state and other data to generate an appropriate menu. For example, it suggests "tomato pasta."

[0399] Input: User's emotional state, other data (pricing information, preferences, etc.)

[0400] Output: Menu suggestions based on emotional state

[0401] Step 8:

[0402] The server generates the final menu, ingredient list, and supplier information, which are then displayed in real time on a smartphone or smart glasses. The user is given specific instructions, such as, "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[0403] Input: Menu suggestions based on emotional state, purchasing information

[0404] Output: Notification to user (menu and shopping list)

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Second embodiment]

[0409] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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."

[0421] This invention is a system that helps users shop efficiently and plan balanced meals. When a user inputs their desired menu or ingredients, the system acquires and analyzes real-time supermarket price data to suggest the most cost-effective combination of ingredients. Furthermore, the system personalizes menus by taking into account the user's preferences, nutritional balance, and restrictions, and displays them in real time.

[0422] Program processing flow

[0423] User Input Phase

[0424] 1. The user inputs the menu and desired ingredients into the system interface. For example, the user might input, "I want to make a dish using tomatoes."

[0425] 2. The device sends the user input data to the server.

[0426] Data Collection Phase

[0427] 1. The server receives the request and communicates with the APIs of multiple connected supermarkets to obtain price data in real time.

[0428] 2. The terminal collects price data from each supermarket and sends it to the server. For example, the terminal obtains data that the price of a tomato is 100 yen per unit from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0429] Price Analysis Phase

[0430] 1. The server analyzes the collected price data and identifies the most cost-effective combination. For example, it finds that tomatoes are cheapest at Supermarket B.

[0431] 2. The server will also compare prices of other ingredients (onions, garlic, pasta, etc.) and select the best combination of ingredients.

[0432] Menu generation phase

[0433] 1. The server uses generative AI to suggest optimal menu items based on user input and price data, such as "tomato pasta" or "tomato and chicken salad."

[0434] 2. The server determines the list of ingredients needed and where to buy them (cheapest supermarket). For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[0435] Personalization Phase

[0436] 1. The server looks up data to take into account the user's preferences, nutritional balance, and restrictions. For example, if the user is vegan, that information is taken into account.

[0437] 2. The server personalizes the generated menu based on the user's information and suggests vegan-friendly recipes.

[0438] Result notification phase

[0439] 1. The server generates the final menu and ingredients list and sends that information to the device. For example, it might say, "To make tomato pasta, purchase tomatoes, onions, garlic, and pasta. The cheapest places to buy them are supermarkets B, A, and C, respectively."

[0440] 2. The device displays a notification to the user, providing a list of ingredients needed and information on where to purchase them.

[0441] Specific examples

[0442] For example, if a user inputs "I want to make a dish using tomatoes," the server will retrieve tomato price data from multiple nearby supermarkets and select supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) and generate the most cost-effective shopping list overall.

[0443] The system allows users to shop quickly and economically and provides rational choices for a balanced and nutritious diet.

[0444] The processing flow will be explained below.

[0445] Step 1:

[0446] The user inputs the menu and ingredients they want to use on the system interface.

[0447] Specifically, the user inputs, "I want to make a dish using tomatoes."

[0448] Step 2:

[0449] The terminal receives the user's input data and sends it to the server.

[0450] Specifically, the input information is posted to the server via the API.

[0451] Step 3:

[0452] The server analyzes the received input data and generates a search query based on the user's request.

[0453] Specifically, we will construct a query to search for the best menu using "tomato."

[0454] Step 4:

[0455] The server queries the APIs of multiple connected supermarkets to retrieve real-time price data.

[0456] Specifically, it calls the API endpoint of each supermarket to obtain price information for "tomatoes."

[0457] Step 5:

[0458] The terminal collects price data from each supermarket and sends it to the server.

[0459] Specifically, data is obtained that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0460] Step 6:

[0461] The server analyzes the collected price data to identify the most cost-effective ingredient combinations.

[0462] Specifically, select the cheapest supermarket B (tomatoes 90 yen).

[0463] Step 7:

[0464] The server also obtains the prices of other related ingredients (e.g., onions, garlic, pasta) and selects the optimal combination of ingredients.

[0465] Specifically, we analyze onions costing 50 yen (Supermarket A), garlic costing 30 yen (Supermarket C), and pasta costing 120 yen (Supermarket B).

[0466] Step 8:

[0467] The server uses generative AI to generate the optimal menu based on the user's input data and price data.

[0468] Specifically, it generates recipes such as "tomato pasta" and "tomato and chicken salad."

[0469] Step 9:

[0470] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions).

[0471] Specifically, if the user is vegan, it suggests recipes that do not contain animal products.

[0472] Step 10:

[0473] The server generates the final menu, ingredient list, and purchasing information, and sends this information to the terminal.

[0474] Specifically, the notification will say, "To make tomato pasta, you will need 90 yen for tomatoes (Supermarket B), 50 yen for onions (Supermarket A), 30 yen for garlic (Supermarket C), and 120 yen for pasta (Supermarket B)."

[0475] Step 11:

[0476] The device will display a notification to the user, providing a list of ingredients, where to buy them, and meal details.

[0477] Specifically, the user checks "tomato pasta, how to make it, and a list of ingredients needed" on their smartphone.

[0478] Example 1

[0479] 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."

[0480] Conventional shopping support systems have the problem that price data is not collected or analyzed in real time, and the information provided to users is not up-to-date. Furthermore, menu suggestions that take user preferences and nutritional balance into consideration are not adequately taken into account, making it difficult to provide meals that are appropriate for each individual user. Furthermore, there is a lack of a mechanism to utilize generative AI to suggest optimal menus, and these systems are not yet able to support cost-effective shopping.

[0481] 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.

[0482] In this invention, the server includes: means for a user to input a desired menu or ingredients; means for acquiring price data in real time from supermarkets; means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients; means for personalizing the generated menu taking into consideration the user's preferences, nutritional balance, and restrictions; means for notifying the user of the generated menu and ingredient shopping list; means for using a generation AI to suggest an optimal menu based on the user's input data; and means for displaying the generated menu and ingredient list in real time. This allows users to always receive optimal menu suggestions based on the latest price data, enabling them to easily plan meals that suit their individual preferences and nutritional balance while shopping cost-effectively.

[0483] "User" refers to a person who uses this system to input menu items and desired ingredients.

[0484] "Menu" refers to a meal menu or a combination of dishes.

[0485] "Ingredients" refers to foods used as ingredients in cooking.

[0486] A "supermarket" refers to a store that sells food and household goods.

[0487] "Real-time" refers to the acquisition or processing of data immediately at the present time.

[0488] "Price data" refers to information on the selling price of each food ingredient in a supermarket.

[0489] "Server" refers to a computer system that processes information entered by users and manages data.

[0490] "Generative AI" refers to a system that uses artificial intelligence technology to generate appropriate suggestions and predictions from data.

[0491] "Personalization" refers to tailoring content to individual user preferences and needs.

[0492] "Notification" refers to the act of a system conveying information to a user.

[0493] This invention is a system that helps users shop efficiently and plan balanced meals. The system includes a means for inputting a user's desired menu or ingredients, a means for acquiring real-time supermarket price data, a means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, a means for suggesting a menu using a generation AI, and a means for personalizing the generated menu.

[0494] The user uses a terminal to input the menu and desired ingredients into the system interface. For example, the user might input "I want to make a dish using tomatoes" into the terminal. This input data is sent from the terminal to the server. The server uses a program to communicate with the APIs of multiple connected supermarkets and obtain price data in real time. The obtained price data is sent to the server and then forwarded to the terminal.

[0495] The server analyzes the collected price data to identify the most cost-effective combination of ingredients, for example, determining that tomatoes are the cheapest at Supermarket B. The server then analyzes the prices of other related ingredients (onions, garlic, pasta, etc.) to select the optimal combination of ingredients.

[0496] The server then uses a generative AI model to suggest an optimal menu based on the user's input and price data. For example, it might suggest "tomato pasta" or "tomato and chicken salad." It then personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., if the user is vegan). This personalized menu is best suited to the user.

[0497] The final menu and ingredient list are sent from the server to the terminal and notified to the user. The terminal then displays the list of ingredients needed and information on where to purchase them. For example, the terminal may be notified that "To make tomato pasta, purchase tomatoes, onions, garlic, and pasta. The cheapest places to purchase these are supermarkets B, A, and C, respectively," allowing the user to shop efficiently and economically.

[0498] An example prompt might be: "I want to make a dish using tomatoes. Please check the prices at my local supermarket and suggest the most cost-effective recipe."

[0499] The system allows users to make quick, healthy and economical purchases and eat a nutritionally balanced diet.

[0500] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0501] Step 1:

[0502] The user inputs the menu and desired ingredients.

[0503] Specifically, the user inputs a request into the system interface, such as "I want to make a dish using tomatoes." This input data is sent to the server via the terminal.

[0504] Input: User request (e.g., "I want to make a dish using tomatoes")

[0505] Output: The request data is sent to the server.

[0506] Step 2:

[0507] The device sends the user input data to the server.

[0508] Specifically, the device encodes the user's input data and sends it to the server, which receives the data and prepares it for processing.

[0509] Input: User-entered data

[0510] Output: The server receives the request data.

[0511] Step 3:

[0512] The server receives the request and communicates with the APIs of multiple connected supermarkets to retrieve price data in real time.

[0513] Specifically, the server sends a request to the supermarket's API to obtain price data for tomatoes and related ingredients.

[0514] Input: Request data

[0515] Output: Price data from supermarkets (e.g., tomato price data).

[0516] Step 4:

[0517] The terminal collects price data from each supermarket and sends it to the server.

[0518] Specifically, the terminal aggregates the price data returned by the API and sends it to the server, which stores it for analysis.

[0519] Input: Price Data

[0520] Output: Price data is saved on the server.

[0521] Step 5:

[0522] The server analyzes the collected pricing data and identifies the most cost-effective combination.

[0523] Specifically, the server compares the prices at each supermarket and finds, for example, that tomatoes are the cheapest at supermarket B. Similarly, it identifies the cheapest prices for other ingredients needed.

[0524] Input: Saved price data

[0525] Output: The most cost-effective combination of ingredients.

[0526] Step 6:

[0527] The server uses generative AI to suggest the optimal menu based on the user's input data and price data.

[0528] Specifically, the server uses a generative AI model to generate optimal menu suggestions based on price data and user preferences, such as "tomato pasta" or "tomato and chicken salad."

[0529] Input: User input data, price data

[0530] Output: Suggested optimal menu.

[0531] Step 7:

[0532] The server determines the list of ingredients needed and where to buy them (cheapest supermarket).

[0533] Specifically, the server lists all the ingredients the user needs and where they are sold at the lowest prices, and notifies the user.

[0534] Input: Suggested menu, price data

[0535] Output: Ingredient list and purchasing information.

[0536] Step 8:

[0537] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions.

[0538] Specifically, the server refers to the user's profile information, makes individual adjustments such as veganism or allergies, and personalizes the recipe.

[0539] Input: User profile information, suggested menu items

[0540] Output: Personalized menu.

[0541] Step 9:

[0542] The server generates the final menu and ingredient list and sends this information to the terminal.

[0543] Specifically, the server compiles the final menu and the corresponding ingredient list and sends it to the device. For example, it may notify the user, "To make tomato pasta, please purchase tomatoes, onions, garlic, and pasta. The cheapest places to purchase them are supermarkets B, A, and C, respectively."

[0544] Input: Finalized menu and ingredient list

[0545] Output: The menu and ingredients list are sent to the terminal.

[0546] Step 10:

[0547] The device will display a notification to the user, providing a list of ingredients needed and information on where to purchase them.

[0548] Specifically, the device displays a list of ingredients and their supplier information on the user's interface, helping the user to shop quickly.

[0549] Input: Menu and ingredient list sent from the server

[0550] Output: The ingredient list and supplier information displayed to the user.

[0551] (Application example 1)

[0552] 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."

[0553] The present invention relates to a system that supports users in shopping efficiently and planning balanced meals. Conventional technologies require users to individually check prices at supermarkets and other stores and select optimal ingredient combinations, which is time-consuming. Furthermore, there is a lack of systems that centrally manage this information and propose personalized meals that take into account the user's preferences and nutritional balance. This makes it difficult for users to shop efficiently and economically, and to prepare balanced, nutritious meals.

[0554] 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.

[0555] In this invention, the server includes means for inputting a user's desired menu or ingredients, means for acquiring price data in real time from multiple stores, means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, means for notifying the user of the menu and ingredient shopping list generated using a generative AI model, means for providing a personalized menu taking into consideration the user's preferences, nutritional balance, and restrictions, and means for displaying the menu and ingredient list generated based on the user's input data and their purchasing locations in real time, thereby enabling users to shop efficiently and economically and prepare balanced and nutritious meals.

[0556] A "user" is an individual or corporation that uses the system to create menus and obtain information on purchasing ingredients.

[0557] "Menu" means a meal plan that describes the combination of ingredients and cooking methods.

[0558] "Ingredients" refers to the individual foods or ingredients used to create a menu.

[0559] "Multiple stores" refers to multiple sales areas where users can purchase ingredients, such as supermarkets and specialty stores.

[0560] "Real-time" means instantly acquiring and processing the latest data at the current time.

[0561] "Price data" refers to information regarding the selling prices of ingredients offered by each store.

[0562] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically create optimal menus tailored to the user's needs.

[0563] The "food purchasing list" refers to a list that specifically shows the ingredients that should be prepared based on the generated menu and information on where to purchase them.

[0564] "Notification" refers to the general act of a system providing information to a user.

[0565] "Personalization" means optimizing content to suit individual conditions such as user preferences, nutritional balance, and dietary restrictions.

[0566] "Real-time display" means displaying relevant information immediately in response to a user's request.

[0567] The present invention provides a system for assisting users in shopping efficiently and planning a balanced menu. Specific embodiments will be described below.

[0568] Hardware and Software Configuration

[0569] Hardware used

[0570] 1. Smartphone: Used to provide the user interface.

[0571] 2. Server: Used to collect data, analyze it, and run generative AI models.

[0572] Software used

[0573] 1. API: Used as an interface to get real-time price data from multiple stores.

[0574] 2. Generative AI models (e.g., OpenAI GPT-4): Automatically create optimal menus tailored to user needs.

[0575] 3. Firebase: Used for sending and receiving real-time data and database management.

[0576] Detailed explanation of the process

[0577] The server first receives information about the menu and desired ingredients entered by the user through a smartphone application. This data is then sent to Firebase's real-time database. The server then retrieves price data from multiple stores via API, which is then collected in Firebase.

[0578] The server analyzes the acquired price data and performs price analysis to identify the most cost-effective combination of ingredients. Using this price data and user input, a generative AI model generates an optimal menu. The generated menu and ingredient shopping list are then sent to the user's smartphone in real time.

[0579] The system also takes into account the user's preferences, nutritional balance and dietary restrictions to provide personalized meals. For example, if the user is vegan, the system will take that information into account and suggest meals that exclude animal products.

[0580] As a concrete example, consider a situation where a user requests, "I want to make a dish using tomatoes." The server retrieves real-time pricing data for tomatoes from multiple nearby stores, identifies the store with the lowest price, and then similarly retrieves prices for other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list.

[0581] Prompt Sentence Examples

[0582] The user wants to make a dish using tomatoes. Based on real-time price data, suggest the most cost-effective combination of ingredients. The user is vegan, so avoid animal-based ingredients.

[0583] The invention provides users with rational and economical shopping options and makes it easy to prepare balanced, nutritious meals.

[0584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0585] Step 1:

[0586] Users input their desired menu or ingredients into a smartphone application. For example, the input data might be in the form of "I want to make a dish using tomatoes." This data is then sent from the application to a Firebase real-time database.

[0587] Step 2:

[0588] The device transfers the user input data sent to Firebase to the server. The input data includes a request to "make a dish using tomatoes," and the server prepares for subsequent processing based on that data.

[0589] Step 3:

[0590] The server retrieves price data from multiple stores in real time via API. For example, it retrieves price data for tomatoes, onions, garlic, and pasta from multiple stores. This data is collected in Firebase and stored as price data.

[0591] Step 4:

[0592] The server analyzes the price data stored in Firebase to identify the most cost-effective combination of ingredients. In this process, for example, if tomatoes cost 90 yen at store A and 100 yen at store B, the server checks the price difference and selects the tomatoes from store A, which are the cheapest.

[0593] Step 5:

[0594] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an optimal menu based on the user's input data and price data. For example, it might suggest menu items such as "tomato pasta" or "tomato and chicken salad." Here, a prompt for the dish is constructed and input into the generative AI model.

[0595] Step 6:

[0596] The server creates a list of ingredients based on the generated menu and determines where to buy each ingredient (cheapest). For example, it identifies store A for tomatoes, store B for onions, and store C for garlic.

[0597] Step 7:

[0598] The server personalizes the generated menus by referencing data that takes into account the user's preferences, nutritional balance, and dietary restrictions. If the user is vegan, the server automatically changes the recipes to ones that do not contain animal ingredients.

[0599] Step 8:

[0600] The server generates the final menu, ingredient list, and purchasing information, and sends it to the user's smartphone in real time via Firebase. The user's device receives this data and displays a notification. For example, it might say, "To make tomato pasta, purchase tomatoes from store A, onions from store B, and garlic from store C."

[0601] Step 9:

[0602] Based on the information provided, users can purchase ingredients from the most cost-effective stores and create balanced meals.

[0603] Through these steps, the present invention helps users shop and prepare balanced meals efficiently and economically.

[0604] 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.

[0605] This invention is a system that helps users shop efficiently and plan balanced meals. The system inputs the user's desired menu or ingredients, acquires and analyzes real-time supermarket price data, and suggests the most cost-effective combination of ingredients. It can also personalize menus by taking into account the user's preferences, nutritional balance, and restrictions. It also incorporates an emotion engine that recognizes the user's emotions, suggesting menus and ingredients according to the user's emotional state.

[0606] Program processing flow

[0607] User Input Phase

[0608] 1. The user inputs the menu and ingredients they want to use on the system interface. For example, they might input, "I want to make a dish using tomatoes."

[0609] 2. The device receives the user input data and sends it to the server.

[0610] Data Collection Phase

[0611] 1. The server receives the request and queries the APIs of multiple connected supermarkets to retrieve real-time price data.

[0612] 2. The terminal collects price data from each supermarket and sends it to the server. For example, it obtains data that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0613] Price Analysis Phase

[0614] 1. The server analyzes the collected price data and identifies the most cost-effective combination of ingredients. For example, it finds that tomatoes are the cheapest at Supermarket B.

[0615] 2. The server also obtains the prices of other ingredients (onions, garlic, pasta, etc.) involved and selects the optimal combination of ingredients.

[0616] Menu generation phase

[0617] 1. The server uses generative AI to suggest optimal menu items based on user input and price data, such as "tomato pasta" or "tomato and chicken salad."

[0618] 2. The server determines the list of ingredients needed and where to buy them, for example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[0619] Personalization Phase

[0620] 1. The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes that are appropriate for that.

[0621] Emotion Recognition Phase

[0622] 1. The server uses an emotion engine to analyze the user's emotional state from their facial expressions, tone of voice, and input text to recognize their emotions. For example, if the user is feeling stressed, it will recognize that.

[0623] 2. The server selects specific ingredients and types of dishes based on the user's emotional state and generates a menu that is optimal for the user's emotional state. For example, it suggests recipes using ingredients that have a relaxing effect.

[0624] Result notification phase

[0625] 1. The server generates the final menu, ingredient list, and supplier information, and sends this information to the terminal. For example, it may notify the user that "To make tomato pasta, you will need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[0626] 2. The device displays a notification to the user, providing a list of ingredients, where to buy them, and details of the meal. For example, the user sees on their smartphone "Tomato pasta, how to make it, and a list of ingredients needed."

[0627] Specific examples

[0628] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve tomato price data from multiple nearby supermarkets and select Supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list. Based on the results of the emotion engine, it will also suggest "tomato pasta," a dish that has a relaxing effect.

[0629] The system allows users to shop quickly and economically, and provides menus that correspond to their emotional state, resulting in a balanced diet and increasing user satisfaction.

[0630] The processing flow will be explained below.

[0631] Step 1:

[0632] The user inputs the menu and ingredients they want to use into the system interface. For example, they might input, "I want to make a dish using tomatoes."

[0633] Step 2:

[0634] The terminal receives the user's input data and sends it to the server.

[0635] Step 3:

[0636] The server analyzes the input data it receives and generates a search query based on the user's request. For example, it constructs a query to search for the best menu using "tomato."

[0637] Step 4:

[0638] The server sends queries to the APIs of multiple connected supermarkets to get price data in real time. For example, it sends an API request to get price data of tomatoes from supermarkets A, B, and C.

[0639] Step 5:

[0640] The terminal collects price data from each supermarket and sends it to the server. For example, data is obtained that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0641] Step 6:

[0642] The server analyzes the collected price data to identify the most cost-effective combination of ingredients, for example, finding that tomatoes are cheapest at Supermarket B.

[0643] Step 7:

[0644] The server also collects the prices of other ingredients (e.g., onions, garlic, and pasta) and selects the optimal combination of ingredients. For example, it analyzes the following: onions 50 yen (supermarket A), garlic 30 yen (supermarket C), and pasta 120 yen (supermarket B).

[0645] Step 8:

[0646] The server uses generative AI to generate optimal menu suggestions based on user input and price data, suggesting, for example, "tomato pasta" or "tomato and chicken salad."

[0647] Step 9:

[0648] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes that cater to that.

[0649] Step 10:

[0650] To recognize the user's emotions, the server activates an emotion engine and analyzes the user's emotional state from their facial expressions, tone of voice, and input text. For example, the emotion engine recognizes that the user is tired.

[0651] Step 11:

[0652] Based on the user's emotional state, the server selects specific ingredients and types of dishes to create a menu that best suits the user's emotional state. For example, it suggests "tomato pasta," a dish that has a relaxing effect.

[0653] Step 12:

[0654] The server generates the final menu, ingredient list, and purchasing information, and sends this information to the terminal. For example, it may notify the user that "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[0655] Step 13:

[0656] The device will display a notification to the user, providing a list of ingredients, where to buy them, and details of the meal. For example, the user will see on their smartphone, "Tomato pasta, how to make it, and a list of ingredients needed."

[0657] Specific examples

[0658] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve price data for tomatoes from multiple nearby supermarkets and select Supermarket B with the lowest price. The server then similarly retrieves price data for other related ingredients (e.g., onions, garlic, pasta) and generates the most cost-effective shopping list. Furthermore, taking into account the user's recognized emotional state, it will suggest "tomato pasta," a dish that has a relaxing effect. The device will then notify the user of the list of necessary ingredients and where to purchase them, as well as provide detailed instructions on how to prepare the dish.

[0659] Example 2

[0660] 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."

[0661] Today's consumers find it difficult to plan balanced meals while shopping efficiently. It is also time-consuming to collect price information from each supermarket and then select the most suitable ingredients based on that information. Furthermore, there are currently no systems that can suggest meals that take into account the user's emotional state, individual preferences, and nutritional balance. Therefore, there is a need for a system that can reduce the burden on consumers and provide healthier, more economical meals.

[0662] 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.

[0663] In this invention, the server includes means for a user to input a desired menu or ingredients, means for acquiring price data from retailers in real time, means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, means for generating an optimal menu using a generative AI model, means for recognizing the user's emotional state, means for adjusting the menu based on the recognized emotional state, and means for notifying the user of the generated menu and ingredient shopping list. This allows users to not only shop efficiently but also easily plan an optimal menu that takes into account their emotional state and nutritional balance.

[0664] "User" refers to an individual who uses the system to select menus and ingredients.

[0665] "Menu" means a meal plan and the combination of ingredients used in it.

[0666] "Ingredients" refers to the individual ingredients and seasonings needed to make a dish.

[0667] "Retail store" refers to a store or supermarket that sells food and other consumer goods to consumers.

[0668] "Real-time" refers to information updates and processing that are occurring in real time, meaning that they are reflected immediately and without delay.

[0669] "Price data" refers to information regarding the selling prices of each ingredient or product obtained from retailers.

[0670] A "generative AI model" refers to an algorithm or system that uses machine learning and artificial intelligence techniques to process data and generate optimal results.

[0671] "Emotional state" refers to the user's current emotional and psychological state, and is analyzed from facial expressions, tone of voice, etc.

[0672] "Notification means" refers to the method or device by which the system communicates information to the user, including websites, emails, app notifications, etc.

[0673] "Personalization" means customizing content according to the preferences and requirements of individual users.

[0674] "Optimal menu" refers to a meal plan generated to meet multiple conditions, such as price-effectiveness, nutritional balance, and user preferences.

[0675] This invention is a system that helps users shop efficiently and plan balanced meals. The system inputs the user's desired menu or ingredients, acquires and analyzes real-time price data from retailers, and suggests the most cost-effective combination of ingredients. It can also suggest meals that take into account the user's preferences, nutritional balance, and restrictions, and are also tailored to the user's emotional state.

[0676] The system mainly consists of a user device, a server, a retailer's API, a generative AI model, and an emotion recognition engine.

[0677] The user inputs the desired menu and ingredients through the interface. For example, they might input "I want to make a dish using tomatoes." The device receives this input data and sends it to the server.

[0678] When the server receives a request, it sends a query to the APIs of multiple connected retailers to obtain price data in real time. For example, it obtains data that a tomato costs 100 yen from retailer A, 90 yen from retailer B, and 95 yen from retailer C. This price data is collected and stored on the server.

[0679] The server then analyzes the price data to identify the most cost-effective combination of ingredients, for example, finding that tomatoes are the cheapest at Retailer B. It also analyzes the prices of other related ingredients (onions, garlic, pasta, etc.) to identify the optimal combination.

[0680] The server uses a generative AI model (such as GPT-4) to generate an optimal menu based on the user's input data and the acquired price data. For example, it might suggest "tomato pasta" or "tomato and chicken salad." It also determines the list of ingredients needed and where to purchase them. For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[0681] The server then personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes accordingly.

[0682] The server then uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotions. It analyzes the user's facial expressions, tone of voice, and input text to determine their emotional state. For example, if the user is feeling stressed, it will recognize this. Based on the recognized emotional state, specific ingredients and types of dishes will be suggested, such as recipes using ingredients with a relaxing effect.

[0683] Finally, the server generates the final menu, ingredients list, and supplier information, and sends this information to the device. The device then displays a notification to the user, providing the ingredients list, supplier information, and menu details. For example, the user can see "Tomato pasta, how to make it, and the list of ingredients needed" on their smartphone.

[0684] As a concrete example, if a user inputs "I want to make a dish using tomatoes" and the emotion engine recognizes the user's emotional state as "tired," the following process will occur: The server obtains price data for tomatoes from multiple nearby retailers and selects Retailer B with the lowest price. It then similarly obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and generates the most cost-effective shopping list. Based on the results of the emotion engine, it also suggests "tomato pasta," a dish that has a relaxing effect. This system allows users to shop quickly and economically, receive menus that correspond to their emotional state, and achieve a balanced diet.

[0685] An example prompt might be, "The user says they want to make a dish using tomatoes. Furthermore, analysis from the emotion engine indicates that the user is tired. Taking this into consideration, please suggest a dish using tomatoes that has a relaxing effect."

[0686] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0687] Step 1:

[0688] The user inputs the menu and ingredients they want to use on the system interface. For example, they input "I want to make a dish using tomatoes." The input is the menu or ingredients the user wants, and the output is the user input data received by the terminal.

[0689] Step 2:

[0690] The terminal receives the user input data and sends it to the server. In concrete terms, the terminal sends the user input data to the server via the network. The input is the user input data, and the output is the user input data sent to the server.

[0691] Step 3:

[0692] The server receives the request and sends a query to the APIs of multiple connected retailers to obtain price data in real time. For example, it obtains data on tomatoes at 100 yen from retailer A, 90 yen from retailer B, and 95 yen from retailer C. The input is the user-entered data, and the output is the price data obtained from each retailer.

[0693] Step 4:

[0694] The server collects price data from each retailer and stores it internally. Specifically, the server records the query results in a database. The input is the price data obtained from the retailer, and the output is the price data stored internally on the server.

[0695] Step 5:

[0696] The server analyzes the collected price data and identifies the most cost-effective combination of ingredients. For example, it finds that tomatoes are cheapest at retailer B. The input is the price data stored inside the server, and the output is the most cost-effective combination of ingredients.

[0697] Step 6:

[0698] The server also obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and selects the optimal ingredient combination. The input is the price data of other ingredients obtained from each retailer, and the output is the optimal ingredient combination.

[0699] Step 7:

[0700] The server generates an optimal menu using a generative AI model (e.g., GPT-4) based on the user's input data and price data. Specifically, the server inputs a prompt to the AI ​​model to obtain the optimal menu. The input is the user's input data and price data, and the output is the generated optimal menu.

[0701] Step 8:

[0702] The server determines the list of ingredients needed and where to buy them. For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc. The input is the generated menu and price data, and the output is the list of ingredients and where to buy them.

[0703] Step 9:

[0704] The server personalizes the generated menu by taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). The input is the user's preferences and restrictions, and the output is a personalized menu.

[0705] Step 10:

[0706] To recognize the user's emotions, the server uses an emotion engine to analyze the user's emotional state from their facial expressions, tone of voice, and input text. For example, it uses the Microsoft Azure Emotion API. The input is the user's facial expressions, tone of voice, and input text, and the output is the analysis result of the emotional state.

[0707] Step 11:

[0708] The server selects specific ingredients and types of dishes based on the recognized emotional state, and generates a menu that is optimal for the user's emotional state. Specifically, the server inputs prompts that take the emotional state into account into the generative AI model and obtains the menu. The input is the analysis result of the emotional state, and the output is an optimal menu based on the emotional state.

[0709] Step 12:

[0710] The server generates the final menu, ingredient list, and supplier information and sends that information to the terminal. The input is the final menu, ingredient list, and supplier information, and the output is notification data sent to the terminal.

[0711] Step 13:

[0712] The device displays a notification to the user, providing a list of ingredients, where to buy them, and details of the menu. For example, a user can see "Tomato pasta, how to make it, and a list of ingredients" on their smartphone. The input is the notification data sent from the server, and the output is the detailed information displayed to the user.

[0713] (Application example 2)

[0714] 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."

[0715] Modern consumers face challenges in efficiently shopping and preparing balanced meals within limited time and resources. It's especially difficult to compare prices in real time and select the most cost-effective ingredients when shopping. There's also a demand for personalized menu suggestions that take into account the user's emotional state and individual preferences and allergies.

[0716] 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.

[0717] In this invention, the server includes: means for inputting a user's desired menu or ingredients; means for acquiring price data in real time from supermarkets; means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients; means for notifying the user of the generated menu and ingredient shopping list; emotion recognition means for analyzing the user's emotional state; means for suggesting a menu and ingredients according to the user's emotional state; means for saving the user's food preferences and allergy information to personalize the menu; and a terminal for displaying information in real time on a smartphone or smart glasses.

[0718] This allows users to shop efficiently and cost-effectively, while also suggesting balanced meals that are tailored to individual tastes and emotional states.

[0719] "Means for users to input their desired menu or ingredients" refers to the method by which users input the dishes they want to make or the ingredients they want to use into the interface through the system.

[0720] "Means of obtaining real-time price data from supermarkets" refers to a method of instantly collecting current food price information via the APIs and networks of multiple supermarkets.

[0721] "Means for analyzing acquired price data and selecting the most cost-effective combination of ingredients" refers to a method for calculating cost performance based on collected price information and generating a rational shopping list.

[0722] The "means for notifying the user of the generated menu and ingredient purchasing list" refers to a method for displaying or notifying the user of the optimal menu and ingredient purchasing list.

[0723] "Emotion recognition means for analyzing the user's emotional state" refers to technology for analyzing the user's facial expressions, voice, etc. to identify their current emotional state.

[0724] "Means for suggesting menus and ingredients according to the user's emotional state" refers to a method for taking into account the analyzed emotional state of the user and suggesting dishes and ingredients that are appropriate at that time.

[0725] "Means for saving a user's food preferences and allergy information to personalize menus" refers to a method for saving a user's individual preferences and allergy information and customizing menus based on that information.

[0726] "Devices that display information in real time on smartphones or smart glasses" refers to equipment that displays information on portable or wearable devices so that users can view the information instantly.

[0727] This invention relates to a system that allows users to shop efficiently and proposes balanced menus that are tailored to individual preferences and emotional states, while also taking into consideration cost-effectiveness. The functions of the server, terminal, and user, as well as their specific operations, are explained below.

[0728] Hardware and Software Used

[0729] Hardware: Smartphone (iOS / Android), smart glasses (general wearable device)

[0730] Software: Python, TensorFlow, OpenCV, Google Cloud Vision API, Super API, Emotion AI (for emotion recognition)

[0731] Program processing

[0732] 1. User Input Phase

[0733] The user uses the interface on their smartphone or smart glasses to input the desired menu and ingredients, for example, "I want to make a dish using tomatoes." The input data is then sent to the server via a Python script.

[0734] 2. Data Collection Phase

[0735] The server queries the APIs of multiple supermarkets to get real-time price data, for example, a tomato costs 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0736] 3. Price Analysis Phase

[0737] The server uses Python and Pandas to analyze the obtained price data and select the most cost-effective combination of ingredients. For example, it confirms that tomatoes are the cheapest at Supermarket B. It also obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and generates the most cost-effective shopping list overall.

[0738] 4. Menu generation phase

[0739] The server uses a generative AI model (using TensorFlow) to generate optimal menu suggestions based on user input data and price information, such as "tomato pasta" or "tomato and chicken salad."

[0740] 5. Personalization Phase

[0741] It stores your food preferences and allergies and then creates personalized meal suggestions based on them. For example, if you're vegan, it will generate vegan-friendly recipes.

[0742] 6. Emotion Recognition Phase

[0743] The server analyzes the user's facial expressions and voice using emotion recognition (using OpenCV and Google Cloud Vision API). For example, if the server recognizes that the user is "tired," it will suggest ingredients and dishes that have a relaxing effect based on that emotional state.

[0744] 7. Result notification phase

[0745] The server generates the final menu, ingredient list, and supplier information, and displays this information in real time on the smartphone or smart glasses. For example, it might say, "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[0746] Examples and prompts

[0747] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve tomato price data from multiple nearby supermarkets and select Supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list. Based on the results of the emotion engine, it will also suggest "tomato pasta," a dish that has a relaxing effect.

[0748] Example prompt sentence:

[0749] "We've identified your emotional state as 'tired.' We'll suggest a meal that will help you relax. Search for 'pasta with tomatoes' to find the perfect ingredients."

[0750] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0751] Step 1:

[0752] The user uses the interface of their smartphone or smart glasses to input the desired menu or ingredients, for example, "I want to make a dish using tomatoes." This input data is stored in an SQL database and sent to the server.

[0753] Input: User input (e.g. "dishes with tomatoes")

[0754] Output: User request data

[0755] Step 2:

[0756] The server receives the user's request data, queries the APIs of multiple supermarkets to obtain real-time price data, and then analyzes the responses from each supermarket to extract the required price information.

[0757] Input: User request data

[0758] Output: Real-time price data

[0759] Step 3:

[0760] The server analyzes the acquired price data using Python and Pandas. The server compares the price data and calculates the most cost-effective combination of ingredients. For example, the server analyzes the price data for tomatoes and selects the most cost-effective supermarket.

[0761] Input: Real-time price data

[0762] Output: Most cost-effective ingredient combination

[0763] Step 4:

[0764] The server uses a generative AI model (using TensorFlow) to generate optimal menu suggestions based on user input and price information. The generated menu suggestions are formatted using a Python script. For example, the server might suggest "tomato pasta" or "tomato and chicken salad."

[0765] Input: User input data, price information

[0766] Output: Generated menu

[0767] Step 5:

[0768] The server personalizes the generated menu by taking into account the user's food preferences and allergy information, and adjusts the recipe content based on the user's nutritional balance and restrictions.

[0769] Input: Generated menu, user preferences and allergy information

[0770] Output: Personalized menu

[0771] Step 6:

[0772] The server uses emotion recognition (using OpenCV and Google Cloud Vision API) to analyze the user's facial expressions and voice. The analysis results identify the user's emotional state. For example, if the user is recognized as "tired," that information is fed back to the server.

[0773] Input: User's facial expressions and voice data

[0774] Output: User's emotional state

[0775] Step 7:

[0776] The server suggests ingredients and dishes that have a relaxing effect based on the user's emotional state. It comprehensively analyzes the user's emotional state and other data to generate an appropriate menu. For example, it suggests "tomato pasta."

[0777] Input: User's emotional state, other data (pricing information, preferences, etc.)

[0778] Output: Menu suggestions based on emotional state

[0779] Step 8:

[0780] The server generates the final menu, ingredient list, and supplier information, which are then displayed in real time on a smartphone or smart glasses. The user is given specific instructions, such as, "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[0781] Input: Menu suggestions based on emotional state, purchasing information

[0782] Output: Notification to user (menu and shopping list)

[0783] 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.

[0784] 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.

[0785] 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.

[0786] [Third embodiment]

[0787] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0788] 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.

[0789] 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).

[0790] 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.

[0791] 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.

[0792] 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).

[0793] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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."

[0799] This invention is a system that helps users shop efficiently and plan balanced meals. When a user inputs their desired menu or ingredients, the system acquires and analyzes real-time supermarket price data to suggest the most cost-effective combination of ingredients. Furthermore, the system personalizes menus by taking into account the user's preferences, nutritional balance, and restrictions, and displays them in real time.

[0800] Program processing flow

[0801] User Input Phase

[0802] 1. The user inputs the menu and desired ingredients into the system interface. For example, the user might input, "I want to make a dish using tomatoes."

[0803] 2. The device sends the user input data to the server.

[0804] Data Collection Phase

[0805] 1. The server receives the request and communicates with the APIs of multiple connected supermarkets to obtain price data in real time.

[0806] 2. The terminal collects price data from each supermarket and sends it to the server. For example, the terminal obtains data that the price of a tomato is 100 yen per unit from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0807] Price Analysis Phase

[0808] 1. The server analyzes the collected price data and identifies the most cost-effective combination. For example, it finds that tomatoes are cheapest at Supermarket B.

[0809] 2. The server will also compare prices of other ingredients (onions, garlic, pasta, etc.) and select the best combination of ingredients.

[0810] Menu generation phase

[0811] 1. The server uses generative AI to suggest optimal menu items based on user input and price data, such as "tomato pasta" or "tomato and chicken salad."

[0812] 2. The server determines the list of ingredients needed and where to buy them (cheapest supermarket). For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[0813] Personalization Phase

[0814] 1. The server looks up data to take into account the user's preferences, nutritional balance, and restrictions. For example, if the user is vegan, that information is taken into account.

[0815] 2. The server personalizes the generated menu based on the user's information and suggests vegan-friendly recipes.

[0816] Result notification phase

[0817] 1. The server generates the final menu and ingredients list and sends that information to the device. For example, it might say, "To make tomato pasta, purchase tomatoes, onions, garlic, and pasta. The cheapest places to buy them are supermarkets B, A, and C, respectively."

[0818] 2. The device displays a notification to the user, providing a list of ingredients needed and information on where to purchase them.

[0819] Specific examples

[0820] For example, if a user inputs "I want to make a dish using tomatoes," the server will retrieve tomato price data from multiple nearby supermarkets and select supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) and generate the most cost-effective shopping list overall.

[0821] The system allows users to shop quickly and economically and provides rational choices for a balanced and nutritious diet.

[0822] The processing flow will be explained below.

[0823] Step 1:

[0824] The user inputs the menu and ingredients they want to use on the system interface.

[0825] Specifically, the user inputs, "I want to make a dish using tomatoes."

[0826] Step 2:

[0827] The terminal receives the user's input data and sends it to the server.

[0828] Specifically, the input information is posted to the server via the API.

[0829] Step 3:

[0830] The server analyzes the received input data and generates a search query based on the user's request.

[0831] Specifically, we will construct a query to search for the best menu using "tomato."

[0832] Step 4:

[0833] The server queries the APIs of multiple connected supermarkets to retrieve real-time price data.

[0834] Specifically, it calls the API endpoint of each supermarket to obtain price information for "tomatoes."

[0835] Step 5:

[0836] The terminal collects price data from each supermarket and sends it to the server.

[0837] Specifically, data is obtained that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0838] Step 6:

[0839] The server analyzes the collected price data to identify the most cost-effective ingredient combinations.

[0840] Specifically, select the cheapest supermarket B (tomatoes 90 yen).

[0841] Step 7:

[0842] The server also obtains the prices of other related ingredients (e.g., onions, garlic, pasta) and selects the optimal combination of ingredients.

[0843] Specifically, we analyze onions costing 50 yen (Supermarket A), garlic costing 30 yen (Supermarket C), and pasta costing 120 yen (Supermarket B).

[0844] Step 8:

[0845] The server uses generative AI to generate the optimal menu based on the user's input data and price data.

[0846] Specifically, it generates recipes such as "tomato pasta" and "tomato and chicken salad."

[0847] Step 9:

[0848] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions).

[0849] Specifically, if the user is vegan, it suggests recipes that do not contain animal products.

[0850] Step 10:

[0851] The server generates the final menu, ingredient list, and purchasing information, and sends this information to the terminal.

[0852] Specifically, the notification will say, "To make tomato pasta, you will need 90 yen for tomatoes (Supermarket B), 50 yen for onions (Supermarket A), 30 yen for garlic (Supermarket C), and 120 yen for pasta (Supermarket B)."

[0853] Step 11:

[0854] The device will display a notification to the user, providing a list of ingredients, where to buy them, and meal details.

[0855] Specifically, the user checks "tomato pasta, how to make it, and a list of ingredients needed" on their smartphone.

[0856] Example 1

[0857] 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."

[0858] Conventional shopping support systems have the problem that price data is not collected or analyzed in real time, and the information provided to users is not up-to-date. Furthermore, menu suggestions that take user preferences and nutritional balance into consideration are not adequately taken into account, making it difficult to provide meals that are appropriate for each individual user. Furthermore, there is a lack of a mechanism to utilize generative AI to suggest optimal menus, and these systems are not yet able to support cost-effective shopping.

[0859] 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.

[0860] In this invention, the server includes: means for a user to input a desired menu or ingredients; means for acquiring price data in real time from supermarkets; means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients; means for personalizing the generated menu taking into consideration the user's preferences, nutritional balance, and restrictions; means for notifying the user of the generated menu and ingredient shopping list; means for using a generation AI to suggest an optimal menu based on the user's input data; and means for displaying the generated menu and ingredient list in real time. This allows users to always receive optimal menu suggestions based on the latest price data, enabling them to easily plan meals that suit their individual preferences and nutritional balance while shopping cost-effectively.

[0861] "User" refers to a person who uses this system to input menu items and desired ingredients.

[0862] "Menu" refers to a meal menu or a combination of dishes.

[0863] "Ingredients" refers to foods used as ingredients in cooking.

[0864] A "supermarket" refers to a store that sells food and household goods.

[0865] "Real-time" refers to the acquisition or processing of data immediately at the present time.

[0866] "Price data" refers to information on the selling price of each food ingredient in a supermarket.

[0867] "Server" refers to a computer system that processes information entered by users and manages data.

[0868] "Generative AI" refers to a system that uses artificial intelligence technology to generate appropriate suggestions and predictions from data.

[0869] "Personalization" refers to tailoring content to individual user preferences and needs.

[0870] "Notification" refers to the act of a system conveying information to a user.

[0871] This invention is a system that helps users shop efficiently and plan balanced meals. The system includes a means for inputting a user's desired menu or ingredients, a means for acquiring real-time supermarket price data, a means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, a means for suggesting a menu using a generation AI, and a means for personalizing the generated menu.

[0872] The user uses a terminal to input the menu and desired ingredients into the system interface. For example, the user might input "I want to make a dish using tomatoes" into the terminal. This input data is sent from the terminal to the server. The server uses a program to communicate with the APIs of multiple connected supermarkets and obtain price data in real time. The obtained price data is sent to the server and then forwarded to the terminal.

[0873] The server analyzes the collected price data to identify the most cost-effective combination of ingredients, for example, determining that tomatoes are the cheapest at Supermarket B. The server then analyzes the prices of other related ingredients (onions, garlic, pasta, etc.) to select the optimal combination of ingredients.

[0874] The server then uses a generative AI model to suggest an optimal menu based on the user's input and price data. For example, it might suggest "tomato pasta" or "tomato and chicken salad." It then personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., if the user is vegan). This personalized menu is best suited to the user.

[0875] The final menu and ingredient list are sent from the server to the terminal and notified to the user. The terminal then displays the list of ingredients needed and information on where to purchase them. For example, the terminal may be notified that "To make tomato pasta, purchase tomatoes, onions, garlic, and pasta. The cheapest places to purchase these are supermarkets B, A, and C, respectively," allowing the user to shop efficiently and economically.

[0876] An example prompt might be: "I want to make a dish using tomatoes. Please check the prices at my local supermarket and suggest the most cost-effective recipe."

[0877] The system allows users to make quick, healthy and economical purchases and eat a nutritionally balanced diet.

[0878] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0879] Step 1:

[0880] The user inputs the menu and desired ingredients.

[0881] Specifically, the user inputs a request into the system interface, such as "I want to make a dish using tomatoes." This input data is sent to the server via the terminal.

[0882] Input: User request (e.g., "I want to make a dish using tomatoes")

[0883] Output: The request data is sent to the server.

[0884] Step 2:

[0885] The device sends the user input data to the server.

[0886] Specifically, the device encodes the user's input data and sends it to the server, which receives the data and prepares it for processing.

[0887] Input: User-entered data

[0888] Output: The server receives the request data.

[0889] Step 3:

[0890] The server receives the request and communicates with the APIs of multiple connected supermarkets to retrieve price data in real time.

[0891] Specifically, the server sends a request to the supermarket's API to obtain price data for tomatoes and related ingredients.

[0892] Input: Request data

[0893] Output: Price data from supermarkets (e.g., tomato price data).

[0894] Step 4:

[0895] The terminal collects price data from each supermarket and sends it to the server.

[0896] Specifically, the terminal aggregates the price data returned by the API and sends it to the server, which stores it for analysis.

[0897] Input: Price Data

[0898] Output: Price data is saved on the server.

[0899] Step 5:

[0900] The server analyzes the collected pricing data and identifies the most cost-effective combination.

[0901] Specifically, the server compares the prices at each supermarket and finds, for example, that tomatoes are the cheapest at supermarket B. Similarly, it identifies the cheapest prices for other ingredients needed.

[0902] Input: Saved price data

[0903] Output: The most cost-effective combination of ingredients.

[0904] Step 6:

[0905] The server uses generative AI to suggest the optimal menu based on the user's input data and price data.

[0906] Specifically, the server uses a generative AI model to generate optimal menu suggestions based on price data and user preferences, such as "tomato pasta" or "tomato and chicken salad."

[0907] Input: User input data, price data

[0908] Output: Suggested optimal menu.

[0909] Step 7:

[0910] The server determines the list of ingredients needed and where to buy them (cheapest supermarket).

[0911] Specifically, the server lists all the ingredients the user needs and where they are sold at the lowest prices, and notifies the user.

[0912] Input: Suggested menu, price data

[0913] Output: Ingredient list and purchasing information.

[0914] Step 8:

[0915] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions.

[0916] Specifically, the server refers to the user's profile information, makes individual adjustments such as veganism or allergies, and personalizes the recipe.

[0917] Input: User profile information, suggested menu items

[0918] Output: Personalized menu.

[0919] Step 9:

[0920] The server generates the final menu and ingredient list and sends this information to the terminal.

[0921] Specifically, the server compiles the final menu and the corresponding ingredient list and sends it to the device. For example, it may notify the user, "To make tomato pasta, please purchase tomatoes, onions, garlic, and pasta. The cheapest places to purchase them are supermarkets B, A, and C, respectively."

[0922] Input: Finalized menu and ingredient list

[0923] Output: The menu and ingredients list are sent to the terminal.

[0924] Step 10:

[0925] The device will display a notification to the user, providing a list of ingredients needed and information on where to purchase them.

[0926] Specifically, the device displays a list of ingredients and their supplier information on the user's interface, helping the user to shop quickly.

[0927] Input: Menu and ingredient list sent from the server

[0928] Output: The ingredient list and supplier information displayed to the user.

[0929] (Application example 1)

[0930] 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."

[0931] The present invention relates to a system that supports users in shopping efficiently and planning balanced meals. Conventional technologies require users to individually check prices at supermarkets and other stores and select optimal ingredient combinations, which is time-consuming. Furthermore, there is a lack of systems that centrally manage this information and propose personalized meals that take into account the user's preferences and nutritional balance. This makes it difficult for users to shop efficiently and economically, and to prepare balanced, nutritious meals.

[0932] 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.

[0933] In this invention, the server includes means for inputting a user's desired menu or ingredients, means for acquiring price data in real time from multiple stores, means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, means for notifying the user of the menu and ingredient shopping list generated using a generative AI model, means for providing a personalized menu taking into consideration the user's preferences, nutritional balance, and restrictions, and means for displaying the menu and ingredient list generated based on the user's input data and their purchasing locations in real time, thereby enabling users to shop efficiently and economically and prepare balanced and nutritious meals.

[0934] A "user" is an individual or corporation that uses the system to create menus and obtain information on purchasing ingredients.

[0935] "Menu" means a meal plan that describes the combination of ingredients and cooking methods.

[0936] "Ingredients" refers to the individual foods or ingredients used to create a menu.

[0937] "Multiple stores" refers to multiple sales areas where users can purchase ingredients, such as supermarkets and specialty stores.

[0938] "Real-time" means instantly acquiring and processing the latest data at the current time.

[0939] "Price data" refers to information regarding the selling prices of ingredients offered by each store.

[0940] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically create optimal menus tailored to the user's needs.

[0941] The "food purchasing list" refers to a list that specifically shows the ingredients that should be prepared based on the generated menu and information on where to purchase them.

[0942] "Notification" refers to the general act of a system providing information to a user.

[0943] "Personalization" means optimizing content to suit individual conditions such as user preferences, nutritional balance, and dietary restrictions.

[0944] "Real-time display" means displaying relevant information immediately in response to a user's request.

[0945] The present invention provides a system for assisting users in shopping efficiently and planning a balanced menu. Specific embodiments will be described below.

[0946] Hardware and Software Configuration

[0947] Hardware used

[0948] 1. Smartphone: Used to provide the user interface.

[0949] 2. Server: Used to collect data, analyze it, and run generative AI models.

[0950] Software used

[0951] 1. API: Used as an interface to get real-time price data from multiple stores.

[0952] 2. Generative AI models (e.g., OpenAI GPT-4): Automatically create optimal menus tailored to user needs.

[0953] 3. Firebase: Used for sending and receiving real-time data and database management.

[0954] Detailed explanation of the process

[0955] The server first receives information about the menu and desired ingredients entered by the user through a smartphone application. This data is then sent to Firebase's real-time database. The server then retrieves price data from multiple stores via API, which is then collected in Firebase.

[0956] The server analyzes the acquired price data and performs price analysis to identify the most cost-effective combination of ingredients. Using this price data and user input, a generative AI model generates an optimal menu. The generated menu and ingredient shopping list are then sent to the user's smartphone in real time.

[0957] The system also takes into account the user's preferences, nutritional balance and dietary restrictions to provide personalized meals. For example, if the user is vegan, the system will take that information into account and suggest meals that exclude animal products.

[0958] As a concrete example, consider a situation where a user requests, "I want to make a dish using tomatoes." The server retrieves real-time pricing data for tomatoes from multiple nearby stores, identifies the store with the lowest price, and then similarly retrieves prices for other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list.

[0959] Prompt Sentence Examples

[0960] The user wants to make a dish using tomatoes. Based on real-time price data, suggest the most cost-effective combination of ingredients. The user is vegan, so avoid animal-based ingredients.

[0961] The invention provides users with rational and economical shopping options and makes it easy to prepare balanced, nutritious meals.

[0962] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0963] Step 1:

[0964] Users input their desired menu or ingredients into a smartphone application. For example, the input data might be in the form of "I want to make a dish using tomatoes." This data is then sent from the application to a Firebase real-time database.

[0965] Step 2:

[0966] The device transfers the user input data sent to Firebase to the server. The input data includes a request to "make a dish using tomatoes," and the server prepares for subsequent processing based on that data.

[0967] Step 3:

[0968] The server retrieves price data from multiple stores in real time via API. For example, it retrieves price data for tomatoes, onions, garlic, and pasta from multiple stores. This data is collected in Firebase and stored as price data.

[0969] Step 4:

[0970] The server analyzes the price data stored in Firebase to identify the most cost-effective combination of ingredients. In this process, for example, if tomatoes cost 90 yen at store A and 100 yen at store B, the server checks the price difference and selects the tomatoes from store A, which are the cheapest.

[0971] Step 5:

[0972] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an optimal menu based on the user's input data and price data. For example, it might suggest menu items such as "tomato pasta" or "tomato and chicken salad." Here, a prompt for the dish is constructed and input into the generative AI model.

[0973] Step 6:

[0974] The server creates a list of ingredients based on the generated menu and determines where to buy each ingredient (cheapest). For example, it identifies store A for tomatoes, store B for onions, and store C for garlic.

[0975] Step 7:

[0976] The server personalizes the generated menus by referencing data that takes into account the user's preferences, nutritional balance, and dietary restrictions. If the user is vegan, the server automatically changes the recipes to ones that do not contain animal ingredients.

[0977] Step 8:

[0978] The server generates the final menu, ingredient list, and purchasing information, and sends it to the user's smartphone in real time via Firebase. The user's device receives this data and displays a notification. For example, it might say, "To make tomato pasta, purchase tomatoes from store A, onions from store B, and garlic from store C."

[0979] Step 9:

[0980] Based on the information provided, users can purchase ingredients from the most cost-effective stores and create balanced meals.

[0981] Through these steps, the present invention helps users shop and prepare balanced meals efficiently and economically.

[0982] 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.

[0983] This invention is a system that helps users shop efficiently and plan balanced meals. The system inputs the user's desired menu or ingredients, acquires and analyzes real-time supermarket price data, and suggests the most cost-effective combination of ingredients. It can also personalize menus by taking into account the user's preferences, nutritional balance, and restrictions. It also incorporates an emotion engine that recognizes the user's emotions, suggesting menus and ingredients according to the user's emotional state.

[0984] Program processing flow

[0985] User Input Phase

[0986] 1. The user inputs the menu and ingredients they want to use on the system interface. For example, they might input, "I want to make a dish using tomatoes."

[0987] 2. The device receives the user input data and sends it to the server.

[0988] Data Collection Phase

[0989] 1. The server receives the request and queries the APIs of multiple connected supermarkets to retrieve real-time price data.

[0990] 2. The terminal collects price data from each supermarket and sends it to the server. For example, it obtains data that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[0991] Price Analysis Phase

[0992] 1. The server analyzes the collected price data and identifies the most cost-effective combination of ingredients. For example, it finds that tomatoes are the cheapest at Supermarket B.

[0993] 2. The server also obtains the prices of other ingredients (onions, garlic, pasta, etc.) involved and selects the optimal combination of ingredients.

[0994] Menu generation phase

[0995] 1. The server uses generative AI to suggest optimal menu items based on user input and price data, such as "tomato pasta" or "tomato and chicken salad."

[0996] 2. The server determines the list of ingredients needed and where to buy them, for example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[0997] Personalization Phase

[0998] 1. The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes that are appropriate for that.

[0999] Emotion Recognition Phase

[1000] 1. The server uses an emotion engine to analyze the user's emotional state from their facial expressions, tone of voice, and input text to recognize their emotions. For example, if the user is feeling stressed, it will recognize that.

[1001] 2. The server selects specific ingredients and types of dishes based on the user's emotional state and generates a menu that is optimal for the user's emotional state. For example, it suggests recipes using ingredients that have a relaxing effect.

[1002] Result notification phase

[1003] 1. The server generates the final menu, ingredient list, and supplier information, and sends this information to the terminal. For example, it may notify the user that "To make tomato pasta, you will need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[1004] 2. The device displays a notification to the user, providing a list of ingredients, where to buy them, and details of the meal. For example, the user sees on their smartphone "Tomato pasta, how to make it, and a list of ingredients needed."

[1005] Specific examples

[1006] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve tomato price data from multiple nearby supermarkets and select Supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list. Based on the results of the emotion engine, it will also suggest "tomato pasta," a dish that has a relaxing effect.

[1007] The system allows users to shop quickly and economically, and provides menus that correspond to their emotional state, resulting in a balanced diet and increasing user satisfaction.

[1008] The processing flow will be explained below.

[1009] Step 1:

[1010] The user inputs the menu and ingredients they want to use into the system interface. For example, they might input, "I want to make a dish using tomatoes."

[1011] Step 2:

[1012] The terminal receives the user's input data and sends it to the server.

[1013] Step 3:

[1014] The server analyzes the input data it receives and generates a search query based on the user's request. For example, it constructs a query to search for the best menu using "tomato."

[1015] Step 4:

[1016] The server sends queries to the APIs of multiple connected supermarkets to get price data in real time. For example, it sends an API request to get price data of tomatoes from supermarkets A, B, and C.

[1017] Step 5:

[1018] The terminal collects price data from each supermarket and sends it to the server. For example, data is obtained that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[1019] Step 6:

[1020] The server analyzes the collected price data to identify the most cost-effective combination of ingredients, for example, finding that tomatoes are cheapest at Supermarket B.

[1021] Step 7:

[1022] The server also collects the prices of other ingredients (e.g., onions, garlic, and pasta) and selects the optimal combination of ingredients. For example, it analyzes the following: onions 50 yen (supermarket A), garlic 30 yen (supermarket C), and pasta 120 yen (supermarket B).

[1023] Step 8:

[1024] The server uses generative AI to generate optimal menu suggestions based on user input and price data, suggesting, for example, "tomato pasta" or "tomato and chicken salad."

[1025] Step 9:

[1026] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes that cater to that.

[1027] Step 10:

[1028] To recognize the user's emotions, the server activates an emotion engine and analyzes the user's emotional state from their facial expressions, tone of voice, and input text. For example, the emotion engine recognizes that the user is tired.

[1029] Step 11:

[1030] Based on the user's emotional state, the server selects specific ingredients and types of dishes to create a menu that best suits the user's emotional state. For example, it suggests "tomato pasta," a dish that has a relaxing effect.

[1031] Step 12:

[1032] The server generates the final menu, ingredient list, and purchasing information, and sends this information to the terminal. For example, it may notify the user that "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[1033] Step 13:

[1034] The device will display a notification to the user, providing a list of ingredients, where to buy them, and details of the meal. For example, the user will see on their smartphone, "Tomato pasta, how to make it, and a list of ingredients needed."

[1035] Specific examples

[1036] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve price data for tomatoes from multiple nearby supermarkets and select Supermarket B with the lowest price. The server then similarly retrieves price data for other related ingredients (e.g., onions, garlic, pasta) and generates the most cost-effective shopping list. Furthermore, taking into account the user's recognized emotional state, it will suggest "tomato pasta," a dish that has a relaxing effect. The device will then notify the user of the list of necessary ingredients and where to purchase them, as well as provide detailed instructions on how to prepare the dish.

[1037] Example 2

[1038] 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."

[1039] Today's consumers find it difficult to plan balanced meals while shopping efficiently. It is also time-consuming to collect price information from each supermarket and then select the most suitable ingredients based on that information. Furthermore, there are currently no systems that can suggest meals that take into account the user's emotional state, individual preferences, and nutritional balance. Therefore, there is a need for a system that can reduce the burden on consumers and provide healthier, more economical meals.

[1040] 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.

[1041] In this invention, the server includes means for a user to input a desired menu or ingredients, means for acquiring price data from retailers in real time, means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, means for generating an optimal menu using a generative AI model, means for recognizing the user's emotional state, means for adjusting the menu based on the recognized emotional state, and means for notifying the user of the generated menu and ingredient shopping list. This allows users to not only shop efficiently but also easily plan an optimal menu that takes into account their emotional state and nutritional balance.

[1042] "User" refers to an individual who uses the system to select menus and ingredients.

[1043] "Menu" means a meal plan and the combination of ingredients used in it.

[1044] "Ingredients" refers to the individual ingredients and seasonings needed to make a dish.

[1045] "Retail store" refers to a store or supermarket that sells food and other consumer goods to consumers.

[1046] "Real-time" refers to information updates and processing that are occurring in real time, meaning that they are reflected immediately and without delay.

[1047] "Price data" refers to information regarding the selling prices of each ingredient or product obtained from retailers.

[1048] A "generative AI model" refers to an algorithm or system that uses machine learning and artificial intelligence techniques to process data and generate optimal results.

[1049] "Emotional state" refers to the user's current emotional and psychological state, and is analyzed from facial expressions, tone of voice, etc.

[1050] "Notification means" refers to the method or device by which the system communicates information to the user, including websites, emails, app notifications, etc.

[1051] "Personalization" means customizing content according to the preferences and requirements of individual users.

[1052] "Optimal menu" refers to a meal plan generated to meet multiple conditions, such as price-effectiveness, nutritional balance, and user preferences.

[1053] This invention is a system that helps users shop efficiently and plan balanced meals. The system inputs the user's desired menu or ingredients, acquires and analyzes real-time price data from retailers, and suggests the most cost-effective combination of ingredients. It can also suggest meals that take into account the user's preferences, nutritional balance, and restrictions, and are also tailored to the user's emotional state.

[1054] The system mainly consists of a user device, a server, a retailer's API, a generative AI model, and an emotion recognition engine.

[1055] The user inputs the desired menu and ingredients through the interface. For example, they might input "I want to make a dish using tomatoes." The device receives this input data and sends it to the server.

[1056] When the server receives a request, it sends a query to the APIs of multiple connected retailers to obtain price data in real time. For example, it obtains data that a tomato costs 100 yen from retailer A, 90 yen from retailer B, and 95 yen from retailer C. This price data is collected and stored on the server.

[1057] The server then analyzes the price data to identify the most cost-effective combination of ingredients, for example, finding that tomatoes are the cheapest at Retailer B. It also analyzes the prices of other related ingredients (onions, garlic, pasta, etc.) to identify the optimal combination.

[1058] The server uses a generative AI model (such as GPT-4) to generate an optimal menu based on the user's input data and the acquired price data. For example, it might suggest "tomato pasta" or "tomato and chicken salad." It also determines the list of ingredients needed and where to purchase them. For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[1059] The server then personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes accordingly.

[1060] The server then uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotions. It analyzes the user's facial expressions, tone of voice, and input text to determine their emotional state. For example, if the user is feeling stressed, it will recognize this. Based on the recognized emotional state, specific ingredients and types of dishes will be suggested, such as recipes using ingredients with a relaxing effect.

[1061] Finally, the server generates the final menu, ingredients list, and supplier information, and sends this information to the device. The device then displays a notification to the user, providing the ingredients list, supplier information, and menu details. For example, the user can see "Tomato pasta, how to make it, and the list of ingredients needed" on their smartphone.

[1062] As a concrete example, if a user inputs "I want to make a dish using tomatoes" and the emotion engine recognizes the user's emotional state as "tired," the following process will occur: The server obtains price data for tomatoes from multiple nearby retailers and selects Retailer B with the lowest price. It then similarly obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and generates the most cost-effective shopping list. Based on the results of the emotion engine, it also suggests "tomato pasta," a dish that has a relaxing effect. This system allows users to shop quickly and economically, receive menus that correspond to their emotional state, and achieve a balanced diet.

[1063] An example prompt might be, "The user says they want to make a dish using tomatoes. Furthermore, analysis from the emotion engine indicates that the user is tired. Taking this into consideration, please suggest a dish using tomatoes that has a relaxing effect."

[1064] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1065] Step 1:

[1066] The user inputs the menu and ingredients they want to use on the system interface. For example, they input "I want to make a dish using tomatoes." The input is the menu or ingredients the user wants, and the output is the user input data received by the terminal.

[1067] Step 2:

[1068] The terminal receives the user input data and sends it to the server. In concrete terms, the terminal sends the user input data to the server via the network. The input is the user input data, and the output is the user input data sent to the server.

[1069] Step 3:

[1070] The server receives the request and sends a query to the APIs of multiple connected retailers to obtain price data in real time. For example, it obtains data on tomatoes at 100 yen from retailer A, 90 yen from retailer B, and 95 yen from retailer C. The input is the user-entered data, and the output is the price data obtained from each retailer.

[1071] Step 4:

[1072] The server collects price data from each retailer and stores it internally. Specifically, the server records the query results in a database. The input is the price data obtained from the retailer, and the output is the price data stored internally on the server.

[1073] Step 5:

[1074] The server analyzes the collected price data and identifies the most cost-effective combination of ingredients. For example, it finds that tomatoes are cheapest at retailer B. The input is the price data stored inside the server, and the output is the most cost-effective combination of ingredients.

[1075] Step 6:

[1076] The server also obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and selects the optimal ingredient combination. The input is the price data of other ingredients obtained from each retailer, and the output is the optimal ingredient combination.

[1077] Step 7:

[1078] The server generates an optimal menu using a generative AI model (e.g., GPT-4) based on the user's input data and price data. Specifically, the server inputs a prompt to the AI ​​model to obtain the optimal menu. The input is the user's input data and price data, and the output is the generated optimal menu.

[1079] Step 8:

[1080] The server determines the list of ingredients needed and where to buy them. For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc. The input is the generated menu and price data, and the output is the list of ingredients and where to buy them.

[1081] Step 9:

[1082] The server personalizes the generated menu by taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). The input is the user's preferences and restrictions, and the output is a personalized menu.

[1083] Step 10:

[1084] To recognize the user's emotions, the server uses an emotion engine to analyze the user's emotional state from their facial expressions, tone of voice, and input text. For example, it uses the Microsoft Azure Emotion API. The input is the user's facial expressions, tone of voice, and input text, and the output is the analysis result of the emotional state.

[1085] Step 11:

[1086] The server selects specific ingredients and types of dishes based on the recognized emotional state, and generates a menu that is optimal for the user's emotional state. Specifically, the server inputs prompts that take the emotional state into account into the generative AI model and obtains the menu. The input is the analysis result of the emotional state, and the output is an optimal menu based on the emotional state.

[1087] Step 12:

[1088] The server generates the final menu, ingredient list, and supplier information and sends that information to the terminal. The input is the final menu, ingredient list, and supplier information, and the output is notification data sent to the terminal.

[1089] Step 13:

[1090] The device displays a notification to the user, providing a list of ingredients, where to buy them, and details of the menu. For example, a user can see "Tomato pasta, how to make it, and a list of ingredients" on their smartphone. The input is the notification data sent from the server, and the output is the detailed information displayed to the user.

[1091] (Application example 2)

[1092] 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."

[1093] Modern consumers face challenges in efficiently shopping and preparing balanced meals within limited time and resources. It's especially difficult to compare prices in real time and select the most cost-effective ingredients when shopping. There's also a demand for personalized menu suggestions that take into account the user's emotional state and individual preferences and allergies.

[1094] 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.

[1095] In this invention, the server includes: means for inputting a user's desired menu or ingredients; means for acquiring price data in real time from supermarkets; means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients; means for notifying the user of the generated menu and ingredient shopping list; emotion recognition means for analyzing the user's emotional state; means for suggesting a menu and ingredients according to the user's emotional state; means for saving the user's food preferences and allergy information to personalize the menu; and a terminal for displaying information in real time on a smartphone or smart glasses.

[1096] This allows users to shop efficiently and cost-effectively, while also suggesting balanced meals that are tailored to individual tastes and emotional states.

[1097] "Means for users to input their desired menu or ingredients" refers to the method by which users input the dishes they want to make or the ingredients they want to use into the interface through the system.

[1098] "Means of obtaining real-time price data from supermarkets" refers to a method of instantly collecting current food price information via the APIs and networks of multiple supermarkets.

[1099] "Means for analyzing acquired price data and selecting the most cost-effective combination of ingredients" refers to a method for calculating cost performance based on collected price information and generating a rational shopping list.

[1100] The "means for notifying the user of the generated menu and ingredient purchasing list" refers to a method for displaying or notifying the user of the optimal menu and ingredient purchasing list.

[1101] "Emotion recognition means for analyzing the user's emotional state" refers to technology for analyzing the user's facial expressions, voice, etc. to identify their current emotional state.

[1102] "Means for suggesting menus and ingredients according to the user's emotional state" refers to a method for taking into account the analyzed emotional state of the user and suggesting dishes and ingredients that are appropriate at that time.

[1103] "Means for saving a user's food preferences and allergy information to personalize menus" refers to a method for saving a user's individual preferences and allergy information and customizing menus based on that information.

[1104] "Devices that display information in real time on smartphones or smart glasses" refers to equipment that displays information on portable or wearable devices so that users can view the information instantly.

[1105] This invention relates to a system that allows users to shop efficiently and proposes balanced menus that are tailored to individual preferences and emotional states, while also taking into consideration cost-effectiveness. The functions of the server, terminal, and user, as well as their specific operations, are explained below.

[1106] Hardware and Software Used

[1107] Hardware: Smartphone (iOS / Android), smart glasses (general wearable device)

[1108] Software: Python, TensorFlow, OpenCV, Google Cloud Vision API, Super API, Emotion AI (for emotion recognition)

[1109] Program processing

[1110] 1. User Input Phase

[1111] The user uses the interface on their smartphone or smart glasses to input the desired menu and ingredients, for example, "I want to make a dish using tomatoes." The input data is then sent to the server via a Python script.

[1112] 2. Data Collection Phase

[1113] The server queries the APIs of multiple supermarkets to get real-time price data, for example, a tomato costs 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[1114] 3. Price Analysis Phase

[1115] The server uses Python and Pandas to analyze the obtained price data and select the most cost-effective combination of ingredients. For example, it confirms that tomatoes are the cheapest at Supermarket B. It also obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and generates the most cost-effective shopping list overall.

[1116] 4. Menu generation phase

[1117] The server uses a generative AI model (using TensorFlow) to generate optimal menu suggestions based on user input data and price information, such as "tomato pasta" or "tomato and chicken salad."

[1118] 5. Personalization Phase

[1119] It stores your food preferences and allergies and then creates personalized meal suggestions based on them. For example, if you're vegan, it will generate vegan-friendly recipes.

[1120] 6. Emotion Recognition Phase

[1121] The server analyzes the user's facial expressions and voice using emotion recognition (using OpenCV and Google Cloud Vision API). For example, if the server recognizes that the user is "tired," it will suggest ingredients and dishes that have a relaxing effect based on that emotional state.

[1122] 7. Result notification phase

[1123] The server generates the final menu, ingredient list, and supplier information, and displays this information in real time on the smartphone or smart glasses. For example, it might say, "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[1124] Examples and prompts

[1125] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve tomato price data from multiple nearby supermarkets and select Supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list. Based on the results of the emotion engine, it will also suggest "tomato pasta," a dish that has a relaxing effect.

[1126] Example prompt sentence:

[1127] "We've identified your emotional state as 'tired.' We'll suggest a meal that will help you relax. Search for 'pasta with tomatoes' to find the perfect ingredients."

[1128] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1129] Step 1:

[1130] The user uses the interface of their smartphone or smart glasses to input the desired menu or ingredients, for example, "I want to make a dish using tomatoes." This input data is stored in an SQL database and sent to the server.

[1131] Input: User input (e.g. "dishes with tomatoes")

[1132] Output: User request data

[1133] Step 2:

[1134] The server receives the user's request data, queries the APIs of multiple supermarkets to obtain real-time price data, and then analyzes the responses from each supermarket to extract the required price information.

[1135] Input: User request data

[1136] Output: Real-time price data

[1137] Step 3:

[1138] The server analyzes the acquired price data using Python and Pandas. The server compares the price data and calculates the most cost-effective combination of ingredients. For example, the server analyzes the price data for tomatoes and selects the most cost-effective supermarket.

[1139] Input: Real-time price data

[1140] Output: Most cost-effective ingredient combination

[1141] Step 4:

[1142] The server uses a generative AI model (using TensorFlow) to generate optimal menu suggestions based on user input and price information. The generated menu suggestions are formatted using a Python script. For example, the server might suggest "tomato pasta" or "tomato and chicken salad."

[1143] Input: User input data, price information

[1144] Output: Generated menu

[1145] Step 5:

[1146] The server personalizes the generated menu by taking into account the user's food preferences and allergy information, and adjusts the recipe content based on the user's nutritional balance and restrictions.

[1147] Input: Generated menu, user preferences and allergy information

[1148] Output: Personalized menu

[1149] Step 6:

[1150] The server uses emotion recognition (using OpenCV and Google Cloud Vision API) to analyze the user's facial expressions and voice. The analysis results identify the user's emotional state. For example, if the user is recognized as "tired," that information is fed back to the server.

[1151] Input: User's facial expressions and voice data

[1152] Output: User's emotional state

[1153] Step 7:

[1154] The server suggests ingredients and dishes that have a relaxing effect based on the user's emotional state. It comprehensively analyzes the user's emotional state and other data to generate an appropriate menu. For example, it suggests "tomato pasta."

[1155] Input: User's emotional state, other data (pricing information, preferences, etc.)

[1156] Output: Menu suggestions based on emotional state

[1157] Step 8:

[1158] The server generates the final menu, ingredient list, and supplier information, which are then displayed in real time on a smartphone or smart glasses. The user is given specific instructions, such as, "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[1159] Input: Menu suggestions based on emotional state, purchasing information

[1160] Output: Notification to user (menu and shopping list)

[1161] 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.

[1162] 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.

[1163] 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.

[1164] [Fourth embodiment]

[1165] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1166] 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.

[1167] 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).

[1168] 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.

[1169] 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.

[1170] 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).

[1171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1172] 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.

[1173] 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.

[1174] 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.

[1175] 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.

[1176] 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.

[1177] 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."

[1178] This invention is a system that helps users shop efficiently and plan balanced meals. When a user inputs their desired menu or ingredients, the system acquires and analyzes real-time supermarket price data to suggest the most cost-effective combination of ingredients. Furthermore, the system personalizes menus by taking into account the user's preferences, nutritional balance, and restrictions, and displays them in real time.

[1179] Program processing flow

[1180] User Input Phase

[1181] 1. The user inputs the menu and desired ingredients into the system interface. For example, the user might input, "I want to make a dish using tomatoes."

[1182] 2. The device sends the user input data to the server.

[1183] Data Collection Phase

[1184] 1. The server receives the request and communicates with the APIs of multiple connected supermarkets to obtain price data in real time.

[1185] 2. The terminal collects price data from each supermarket and sends it to the server. For example, the terminal obtains data that the price of a tomato is 100 yen per unit from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[1186] Price Analysis Phase

[1187] 1. The server analyzes the collected price data and identifies the most cost-effective combination. For example, it finds that tomatoes are cheapest at Supermarket B.

[1188] 2. The server will also compare prices of other ingredients (onions, garlic, pasta, etc.) and select the best combination of ingredients.

[1189] Menu generation phase

[1190] 1. The server uses generative AI to suggest optimal menu items based on user input and price data, such as "tomato pasta" or "tomato and chicken salad."

[1191] 2. The server determines the list of ingredients needed and where to buy them (cheapest supermarket). For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[1192] Personalization Phase

[1193] 1. The server looks up data to take into account the user's preferences, nutritional balance, and restrictions. For example, if the user is vegan, that information is taken into account.

[1194] 2. The server personalizes the generated menu based on the user's information and suggests vegan-friendly recipes.

[1195] Result notification phase

[1196] 1. The server generates the final menu and ingredients list and sends that information to the device. For example, it might say, "To make tomato pasta, purchase tomatoes, onions, garlic, and pasta. The cheapest places to buy them are supermarkets B, A, and C, respectively."

[1197] 2. The device displays a notification to the user, providing a list of ingredients needed and information on where to purchase them.

[1198] Specific examples

[1199] For example, if a user inputs "I want to make a dish using tomatoes," the server will retrieve tomato price data from multiple nearby supermarkets and select supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) and generate the most cost-effective shopping list overall.

[1200] The system allows users to shop quickly and economically and provides rational choices for a balanced and nutritious diet.

[1201] The processing flow will be explained below.

[1202] Step 1:

[1203] The user inputs the menu and ingredients they want to use on the system interface.

[1204] Specifically, the user inputs, "I want to make a dish using tomatoes."

[1205] Step 2:

[1206] The terminal receives the user's input data and sends it to the server.

[1207] Specifically, the input information is posted to the server via the API.

[1208] Step 3:

[1209] The server analyzes the received input data and generates a search query based on the user's request.

[1210] Specifically, we will construct a query to search for the best menu using "tomato."

[1211] Step 4:

[1212] The server queries the APIs of multiple connected supermarkets to retrieve real-time price data.

[1213] Specifically, it calls the API endpoint of each supermarket to obtain price information for "tomatoes."

[1214] Step 5:

[1215] The terminal collects price data from each supermarket and sends it to the server.

[1216] Specifically, data is obtained that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[1217] Step 6:

[1218] The server analyzes the collected price data to identify the most cost-effective ingredient combinations.

[1219] Specifically, select the cheapest supermarket B (tomatoes 90 yen).

[1220] Step 7:

[1221] The server also obtains the prices of other related ingredients (e.g., onions, garlic, pasta) and selects the optimal combination of ingredients.

[1222] Specifically, we analyze onions costing 50 yen (Supermarket A), garlic costing 30 yen (Supermarket C), and pasta costing 120 yen (Supermarket B).

[1223] Step 8:

[1224] The server uses generative AI to generate the optimal menu based on the user's input data and price data.

[1225] Specifically, it generates recipes such as "tomato pasta" and "tomato and chicken salad."

[1226] Step 9:

[1227] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions).

[1228] Specifically, if the user is vegan, it suggests recipes that do not contain animal products.

[1229] Step 10:

[1230] The server generates the final menu, ingredient list, and purchasing information, and sends this information to the terminal.

[1231] Specifically, the notification will say, "To make tomato pasta, you will need 90 yen for tomatoes (Supermarket B), 50 yen for onions (Supermarket A), 30 yen for garlic (Supermarket C), and 120 yen for pasta (Supermarket B)."

[1232] Step 11:

[1233] The device will display a notification to the user, providing a list of ingredients, where to buy them, and meal details.

[1234] Specifically, the user checks "tomato pasta, how to make it, and a list of ingredients needed" on their smartphone.

[1235] Example 1

[1236] 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."

[1237] Conventional shopping support systems have the problem that price data is not collected or analyzed in real time, and the information provided to users is not up-to-date. Furthermore, menu suggestions that take user preferences and nutritional balance into consideration are not adequately taken into account, making it difficult to provide meals that are appropriate for each individual user. Furthermore, there is a lack of a mechanism to utilize generative AI to suggest optimal menus, and these systems are not yet able to support cost-effective shopping.

[1238] 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.

[1239] In this invention, the server includes: means for a user to input a desired menu or ingredients; means for acquiring price data in real time from supermarkets; means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients; means for personalizing the generated menu taking into consideration the user's preferences, nutritional balance, and restrictions; means for notifying the user of the generated menu and ingredient shopping list; means for using a generation AI to suggest an optimal menu based on the user's input data; and means for displaying the generated menu and ingredient list in real time. This allows users to always receive optimal menu suggestions based on the latest price data, enabling them to easily plan meals that suit their individual preferences and nutritional balance while shopping cost-effectively.

[1240] "User" refers to a person who uses this system to input menu items and desired ingredients.

[1241] "Menu" refers to a meal menu or a combination of dishes.

[1242] "Ingredients" refers to foods used as ingredients in cooking.

[1243] A "supermarket" refers to a store that sells food and household goods.

[1244] "Real-time" refers to the acquisition or processing of data immediately at the present time.

[1245] "Price data" refers to information on the selling price of each food ingredient in a supermarket.

[1246] "Server" refers to a computer system that processes information entered by users and manages data.

[1247] "Generative AI" refers to a system that uses artificial intelligence technology to generate appropriate suggestions and predictions from data.

[1248] "Personalization" refers to tailoring content to individual user preferences and needs.

[1249] "Notification" refers to the act of a system conveying information to a user.

[1250] This invention is a system that helps users shop efficiently and plan balanced meals. The system includes a means for inputting a user's desired menu or ingredients, a means for acquiring real-time supermarket price data, a means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, a means for suggesting a menu using a generation AI, and a means for personalizing the generated menu.

[1251] The user uses a terminal to input the menu and desired ingredients into the system interface. For example, the user might input "I want to make a dish using tomatoes" into the terminal. This input data is sent from the terminal to the server. The server uses a program to communicate with the APIs of multiple connected supermarkets and obtain price data in real time. The obtained price data is sent to the server and then forwarded to the terminal.

[1252] The server analyzes the collected price data to identify the most cost-effective combination of ingredients, for example, determining that tomatoes are the cheapest at Supermarket B. The server then analyzes the prices of other related ingredients (onions, garlic, pasta, etc.) to select the optimal combination of ingredients.

[1253] The server then uses a generative AI model to suggest an optimal menu based on the user's input and price data. For example, it might suggest "tomato pasta" or "tomato and chicken salad." It then personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., if the user is vegan). This personalized menu is best suited to the user.

[1254] The final menu and ingredient list are sent from the server to the terminal and notified to the user. The terminal then displays the list of ingredients needed and information on where to purchase them. For example, the terminal may be notified that "To make tomato pasta, purchase tomatoes, onions, garlic, and pasta. The cheapest places to purchase these are supermarkets B, A, and C, respectively," allowing the user to shop efficiently and economically.

[1255] An example prompt might be: "I want to make a dish using tomatoes. Please check the prices at my local supermarket and suggest the most cost-effective recipe."

[1256] The system allows users to make quick, healthy and economical purchases and eat a nutritionally balanced diet.

[1257] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1258] Step 1:

[1259] The user inputs the menu and desired ingredients.

[1260] Specifically, the user inputs a request into the system interface, such as "I want to make a dish using tomatoes." This input data is sent to the server via the terminal.

[1261] Input: User request (e.g., "I want to make a dish using tomatoes")

[1262] Output: The request data is sent to the server.

[1263] Step 2:

[1264] The device sends the user input data to the server.

[1265] Specifically, the device encodes the user's input data and sends it to the server, which receives the data and prepares it for processing.

[1266] Input: User-entered data

[1267] Output: The server receives the request data.

[1268] Step 3:

[1269] The server receives the request and communicates with the APIs of multiple connected supermarkets to retrieve price data in real time.

[1270] Specifically, the server sends a request to the supermarket's API to obtain price data for tomatoes and related ingredients.

[1271] Input: Request data

[1272] Output: Price data from supermarkets (e.g., tomato price data).

[1273] Step 4:

[1274] The terminal collects price data from each supermarket and sends it to the server.

[1275] Specifically, the terminal aggregates the price data returned by the API and sends it to the server, which stores it for analysis.

[1276] Input: Price Data

[1277] Output: Price data is saved on the server.

[1278] Step 5:

[1279] The server analyzes the collected pricing data and identifies the most cost-effective combination.

[1280] Specifically, the server compares the prices at each supermarket and finds, for example, that tomatoes are the cheapest at supermarket B. Similarly, it identifies the cheapest prices for other ingredients needed.

[1281] Input: Saved price data

[1282] Output: The most cost-effective combination of ingredients.

[1283] Step 6:

[1284] The server uses generative AI to suggest the optimal menu based on the user's input data and price data.

[1285] Specifically, the server uses a generative AI model to generate optimal menu suggestions based on price data and user preferences, such as "tomato pasta" or "tomato and chicken salad."

[1286] Input: User input data, price data

[1287] Output: Suggested optimal menu.

[1288] Step 7:

[1289] The server determines the list of ingredients needed and where to buy them (cheapest supermarket).

[1290] Specifically, the server lists all the ingredients the user needs and where they are sold at the lowest prices, and notifies the user.

[1291] Input: Suggested menu, price data

[1292] Output: Ingredient list and purchasing information.

[1293] Step 8:

[1294] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions.

[1295] Specifically, the server refers to the user's profile information, makes individual adjustments such as veganism or allergies, and personalizes the recipe.

[1296] Input: User profile information, suggested menu items

[1297] Output: Personalized menu.

[1298] Step 9:

[1299] The server generates the final menu and ingredient list and sends this information to the terminal.

[1300] Specifically, the server compiles the final menu and the corresponding ingredient list and sends it to the device. For example, it may notify the user, "To make tomato pasta, please purchase tomatoes, onions, garlic, and pasta. The cheapest places to purchase them are supermarkets B, A, and C, respectively."

[1301] Input: Finalized menu and ingredient list

[1302] Output: The menu and ingredients list are sent to the terminal.

[1303] Step 10:

[1304] The device will display a notification to the user, providing a list of ingredients needed and information on where to purchase them.

[1305] Specifically, the device displays a list of ingredients and their supplier information on the user's interface, helping the user to shop quickly.

[1306] Input: Menu and ingredient list sent from the server

[1307] Output: The ingredient list and supplier information displayed to the user.

[1308] (Application example 1)

[1309] 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."

[1310] The present invention relates to a system that supports users in shopping efficiently and planning balanced meals. Conventional technologies require users to individually check prices at supermarkets and other stores and select optimal ingredient combinations, which is time-consuming. Furthermore, there is a lack of systems that centrally manage this information and propose personalized meals that take into account the user's preferences and nutritional balance. This makes it difficult for users to shop efficiently and economically, and to prepare balanced, nutritious meals.

[1311] 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.

[1312] In this invention, the server includes means for inputting a user's desired menu or ingredients, means for acquiring price data in real time from multiple stores, means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, means for notifying the user of the menu and ingredient shopping list generated using a generative AI model, means for providing a personalized menu taking into consideration the user's preferences, nutritional balance, and restrictions, and means for displaying the menu and ingredient list generated based on the user's input data and their purchasing locations in real time, thereby enabling users to shop efficiently and economically and prepare balanced and nutritious meals.

[1313] A "user" is an individual or corporation that uses the system to create menus and obtain information on purchasing ingredients.

[1314] "Menu" means a meal plan that describes the combination of ingredients and cooking methods.

[1315] "Ingredients" refers to the individual foods or ingredients used to create a menu.

[1316] "Multiple stores" refers to multiple sales areas where users can purchase ingredients, such as supermarkets and specialty stores.

[1317] "Real-time" means instantly acquiring and processing the latest data at the current time.

[1318] "Price data" refers to information regarding the selling prices of ingredients offered by each store.

[1319] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically create optimal menus tailored to the user's needs.

[1320] The "food purchasing list" refers to a list that specifically shows the ingredients that should be prepared based on the generated menu and information on where to purchase them.

[1321] "Notification" refers to the general act of a system providing information to a user.

[1322] "Personalization" means optimizing content to suit individual conditions such as user preferences, nutritional balance, and dietary restrictions.

[1323] "Real-time display" means displaying relevant information immediately in response to a user's request.

[1324] The present invention provides a system for assisting users in shopping efficiently and planning a balanced menu. Specific embodiments will be described below.

[1325] Hardware and Software Configuration

[1326] Hardware used

[1327] 1. Smartphone: Used to provide the user interface.

[1328] 2. Server: Used to collect data, analyze it, and run generative AI models.

[1329] Software used

[1330] 1. API: Used as an interface to get real-time price data from multiple stores.

[1331] 2. Generative AI models (e.g., OpenAI GPT-4): Automatically create optimal menus tailored to user needs.

[1332] 3. Firebase: Used for sending and receiving real-time data and database management.

[1333] Detailed explanation of the process

[1334] The server first receives information about the menu and desired ingredients entered by the user through a smartphone application. This data is then sent to Firebase's real-time database. The server then retrieves price data from multiple stores via API, which is then collected in Firebase.

[1335] The server analyzes the acquired price data and performs price analysis to identify the most cost-effective combination of ingredients. Using this price data and user input, a generative AI model generates an optimal menu. The generated menu and ingredient shopping list are then sent to the user's smartphone in real time.

[1336] The system also takes into account the user's preferences, nutritional balance and dietary restrictions to provide personalized meals. For example, if the user is vegan, the system will take that information into account and suggest meals that exclude animal products.

[1337] As a concrete example, consider a situation where a user requests, "I want to make a dish using tomatoes." The server retrieves real-time pricing data for tomatoes from multiple nearby stores, identifies the store with the lowest price, and then similarly retrieves prices for other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list.

[1338] Prompt Sentence Examples

[1339] The user wants to make a dish using tomatoes. Based on real-time price data, suggest the most cost-effective combination of ingredients. The user is vegan, so avoid animal-based ingredients.

[1340] The invention provides users with rational and economical shopping options and makes it easy to prepare balanced, nutritious meals.

[1341] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1342] Step 1:

[1343] Users input their desired menu or ingredients into a smartphone application. For example, the input data might be in the form of "I want to make a dish using tomatoes." This data is then sent from the application to a Firebase real-time database.

[1344] Step 2:

[1345] The device transfers the user input data sent to Firebase to the server. The input data includes a request to "make a dish using tomatoes," and the server prepares for subsequent processing based on that data.

[1346] Step 3:

[1347] The server retrieves price data from multiple stores in real time via API. For example, it retrieves price data for tomatoes, onions, garlic, and pasta from multiple stores. This data is collected in Firebase and stored as price data.

[1348] Step 4:

[1349] The server analyzes the price data stored in Firebase to identify the most cost-effective combination of ingredients. In this process, for example, if tomatoes cost 90 yen at store A and 100 yen at store B, the server checks the price difference and selects the tomatoes from store A, which are the cheapest.

[1350] Step 5:

[1351] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate an optimal menu based on the user's input data and price data. For example, it might suggest menu items such as "tomato pasta" or "tomato and chicken salad." Here, a prompt for the dish is constructed and input into the generative AI model.

[1352] Step 6:

[1353] The server creates a list of ingredients based on the generated menu and determines where to buy each ingredient (cheapest). For example, it identifies store A for tomatoes, store B for onions, and store C for garlic.

[1354] Step 7:

[1355] The server personalizes the generated menus by referencing data that takes into account the user's preferences, nutritional balance, and dietary restrictions. If the user is vegan, the server automatically changes the recipes to ones that do not contain animal ingredients.

[1356] Step 8:

[1357] The server generates the final menu, ingredient list, and purchasing information, and sends it to the user's smartphone in real time via Firebase. The user's device receives this data and displays a notification. For example, it might say, "To make tomato pasta, purchase tomatoes from store A, onions from store B, and garlic from store C."

[1358] Step 9:

[1359] Based on the information provided, users can purchase ingredients from the most cost-effective stores and create balanced meals.

[1360] Through these steps, the present invention helps users shop and prepare balanced meals efficiently and economically.

[1361] 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.

[1362] This invention is a system that helps users shop efficiently and plan balanced meals. The system inputs the user's desired menu or ingredients, acquires and analyzes real-time supermarket price data, and suggests the most cost-effective combination of ingredients. It can also personalize menus by taking into account the user's preferences, nutritional balance, and restrictions. It also incorporates an emotion engine that recognizes the user's emotions, suggesting menus and ingredients according to the user's emotional state.

[1363] Program processing flow

[1364] User Input Phase

[1365] 1. The user inputs the menu and ingredients they want to use on the system interface. For example, they might input, "I want to make a dish using tomatoes."

[1366] 2. The device receives the user input data and sends it to the server.

[1367] Data Collection Phase

[1368] 1. The server receives the request and queries the APIs of multiple connected supermarkets to retrieve real-time price data.

[1369] 2. The terminal collects price data from each supermarket and sends it to the server. For example, it obtains data that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[1370] Price Analysis Phase

[1371] 1. The server analyzes the collected price data and identifies the most cost-effective combination of ingredients. For example, it finds that tomatoes are the cheapest at Supermarket B.

[1372] 2. The server also obtains the prices of other ingredients (onions, garlic, pasta, etc.) involved and selects the optimal combination of ingredients.

[1373] Menu generation phase

[1374] 1. The server uses generative AI to suggest optimal menu items based on user input and price data, such as "tomato pasta" or "tomato and chicken salad."

[1375] 2. The server determines the list of ingredients needed and where to buy them, for example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[1376] Personalization Phase

[1377] 1. The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes that are appropriate for that.

[1378] Emotion Recognition Phase

[1379] 1. The server uses an emotion engine to analyze the user's emotional state from their facial expressions, tone of voice, and input text to recognize their emotions. For example, if the user is feeling stressed, it will recognize that.

[1380] 2. The server selects specific ingredients and types of dishes based on the user's emotional state and generates a menu that is optimal for the user's emotional state. For example, it suggests recipes using ingredients that have a relaxing effect.

[1381] Result notification phase

[1382] 1. The server generates the final menu, ingredient list, and supplier information, and sends this information to the terminal. For example, it may notify the user that "To make tomato pasta, you will need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[1383] 2. The device displays a notification to the user, providing a list of ingredients, where to buy them, and details of the meal. For example, the user sees on their smartphone "Tomato pasta, how to make it, and a list of ingredients needed."

[1384] Specific examples

[1385] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve tomato price data from multiple nearby supermarkets and select Supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list. Based on the results of the emotion engine, it will also suggest "tomato pasta," a dish that has a relaxing effect.

[1386] The system allows users to shop quickly and economically, and provides menus that correspond to their emotional state, resulting in a balanced diet and increasing user satisfaction.

[1387] The processing flow will be explained below.

[1388] Step 1:

[1389] The user inputs the menu and ingredients they want to use into the system interface. For example, they might input, "I want to make a dish using tomatoes."

[1390] Step 2:

[1391] The terminal receives the user's input data and sends it to the server.

[1392] Step 3:

[1393] The server analyzes the input data it receives and generates a search query based on the user's request. For example, it constructs a query to search for the best menu using "tomato."

[1394] Step 4:

[1395] The server sends queries to the APIs of multiple connected supermarkets to get price data in real time. For example, it sends an API request to get price data of tomatoes from supermarkets A, B, and C.

[1396] Step 5:

[1397] The terminal collects price data from each supermarket and sends it to the server. For example, data is obtained that tomatoes cost 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[1398] Step 6:

[1399] The server analyzes the collected price data to identify the most cost-effective combination of ingredients, for example, finding that tomatoes are cheapest at Supermarket B.

[1400] Step 7:

[1401] The server also collects the prices of other ingredients (e.g., onions, garlic, and pasta) and selects the optimal combination of ingredients. For example, it analyzes the following: onions 50 yen (supermarket A), garlic 30 yen (supermarket C), and pasta 120 yen (supermarket B).

[1402] Step 8:

[1403] The server uses generative AI to generate optimal menu suggestions based on user input and price data, suggesting, for example, "tomato pasta" or "tomato and chicken salad."

[1404] Step 9:

[1405] The server personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes that cater to that.

[1406] Step 10:

[1407] To recognize the user's emotions, the server activates an emotion engine and analyzes the user's emotional state from their facial expressions, tone of voice, and input text. For example, the emotion engine recognizes that the user is tired.

[1408] Step 11:

[1409] Based on the user's emotional state, the server selects specific ingredients and types of dishes to create a menu that best suits the user's emotional state. For example, it suggests "tomato pasta," a dish that has a relaxing effect.

[1410] Step 12:

[1411] The server generates the final menu, ingredient list, and purchasing information, and sends this information to the terminal. For example, it may notify the user that "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[1412] Step 13:

[1413] The device will display a notification to the user, providing a list of ingredients, where to buy them, and details of the meal. For example, the user will see on their smartphone, "Tomato pasta, how to make it, and a list of ingredients needed."

[1414] Specific examples

[1415] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve price data for tomatoes from multiple nearby supermarkets and select Supermarket B with the lowest price. The server then similarly retrieves price data for other related ingredients (e.g., onions, garlic, pasta) and generates the most cost-effective shopping list. Furthermore, taking into account the user's recognized emotional state, it will suggest "tomato pasta," a dish that has a relaxing effect. The device will then notify the user of the list of necessary ingredients and where to purchase them, as well as provide detailed instructions on how to prepare the dish.

[1416] Example 2

[1417] 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."

[1418] Today's consumers find it difficult to plan balanced meals while shopping efficiently. It is also time-consuming to collect price information from each supermarket and then select the most suitable ingredients based on that information. Furthermore, there are currently no systems that can suggest meals that take into account the user's emotional state, individual preferences, and nutritional balance. Therefore, there is a need for a system that can reduce the burden on consumers and provide healthier, more economical meals.

[1419] 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.

[1420] In this invention, the server includes means for a user to input a desired menu or ingredients, means for acquiring price data from retailers in real time, means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients, means for generating an optimal menu using a generative AI model, means for recognizing the user's emotional state, means for adjusting the menu based on the recognized emotional state, and means for notifying the user of the generated menu and ingredient shopping list. This allows users to not only shop efficiently but also easily plan an optimal menu that takes into account their emotional state and nutritional balance.

[1421] "User" refers to an individual who uses the system to select menus and ingredients.

[1422] "Menu" means a meal plan and the combination of ingredients used in it.

[1423] "Ingredients" refers to the individual ingredients and seasonings needed to make a dish.

[1424] "Retail store" refers to a store or supermarket that sells food and other consumer goods to consumers.

[1425] "Real-time" refers to information updates and processing that are occurring in real time, meaning that they are reflected immediately and without delay.

[1426] "Price data" refers to information regarding the selling prices of each ingredient or product obtained from retailers.

[1427] A "generative AI model" refers to an algorithm or system that uses machine learning and artificial intelligence techniques to process data and generate optimal results.

[1428] "Emotional state" refers to the user's current emotional and psychological state, and is analyzed from facial expressions, tone of voice, etc.

[1429] "Notification means" refers to the method or device by which the system communicates information to the user, including websites, emails, app notifications, etc.

[1430] "Personalization" means customizing content according to the preferences and requirements of individual users.

[1431] "Optimal menu" refers to a meal plan generated to meet multiple conditions, such as price-effectiveness, nutritional balance, and user preferences.

[1432] This invention is a system that helps users shop efficiently and plan balanced meals. The system inputs the user's desired menu or ingredients, acquires and analyzes real-time price data from retailers, and suggests the most cost-effective combination of ingredients. It can also suggest meals that take into account the user's preferences, nutritional balance, and restrictions, and are also tailored to the user's emotional state.

[1433] The system mainly consists of a user device, a server, a retailer's API, a generative AI model, and an emotion recognition engine.

[1434] The user inputs the desired menu and ingredients through the interface. For example, they might input "I want to make a dish using tomatoes." The device receives this input data and sends it to the server.

[1435] When the server receives a request, it sends a query to the APIs of multiple connected retailers to obtain price data in real time. For example, it obtains data that a tomato costs 100 yen from retailer A, 90 yen from retailer B, and 95 yen from retailer C. This price data is collected and stored on the server.

[1436] The server then analyzes the price data to identify the most cost-effective combination of ingredients, for example, finding that tomatoes are the cheapest at Retailer B. It also analyzes the prices of other related ingredients (onions, garlic, pasta, etc.) to identify the optimal combination.

[1437] The server uses a generative AI model (such as GPT-4) to generate an optimal menu based on the user's input data and the acquired price data. For example, it might suggest "tomato pasta" or "tomato and chicken salad." It also determines the list of ingredients needed and where to purchase them. For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc.

[1438] The server then personalizes the generated menu, taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). For example, if the user is vegan, it will suggest recipes accordingly.

[1439] The server then uses an emotion engine (e.g., Microsoft Azure Emotion API) to recognize the user's emotions. It analyzes the user's facial expressions, tone of voice, and input text to determine their emotional state. For example, if the user is feeling stressed, it will recognize this. Based on the recognized emotional state, specific ingredients and types of dishes will be suggested, such as recipes using ingredients with a relaxing effect.

[1440] Finally, the server generates the final menu, ingredients list, and supplier information, and sends this information to the device. The device then displays a notification to the user, providing the ingredients list, supplier information, and menu details. For example, the user can see "Tomato pasta, how to make it, and the list of ingredients needed" on their smartphone.

[1441] As a concrete example, if a user inputs "I want to make a dish using tomatoes" and the emotion engine recognizes the user's emotional state as "tired," the following process will occur: The server obtains price data for tomatoes from multiple nearby retailers and selects Retailer B with the lowest price. It then similarly obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and generates the most cost-effective shopping list. Based on the results of the emotion engine, it also suggests "tomato pasta," a dish that has a relaxing effect. This system allows users to shop quickly and economically, receive menus that correspond to their emotional state, and achieve a balanced diet.

[1442] An example prompt might be, "The user says they want to make a dish using tomatoes. Furthermore, analysis from the emotion engine indicates that the user is tired. Taking this into consideration, please suggest a dish using tomatoes that has a relaxing effect."

[1443] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1444] Step 1:

[1445] The user inputs the menu and ingredients they want to use on the system interface. For example, they input "I want to make a dish using tomatoes." The input is the menu or ingredients the user wants, and the output is the user input data received by the terminal.

[1446] Step 2:

[1447] The terminal receives the user input data and sends it to the server. In concrete terms, the terminal sends the user input data to the server via the network. The input is the user input data, and the output is the user input data sent to the server.

[1448] Step 3:

[1449] The server receives the request and sends a query to the APIs of multiple connected retailers to obtain price data in real time. For example, it obtains data on tomatoes at 100 yen from retailer A, 90 yen from retailer B, and 95 yen from retailer C. The input is the user-entered data, and the output is the price data obtained from each retailer.

[1450] Step 4:

[1451] The server collects price data from each retailer and stores it internally. Specifically, the server records the query results in a database. The input is the price data obtained from the retailer, and the output is the price data stored internally on the server.

[1452] Step 5:

[1453] The server analyzes the collected price data and identifies the most cost-effective combination of ingredients. For example, it finds that tomatoes are cheapest at retailer B. The input is the price data stored inside the server, and the output is the most cost-effective combination of ingredients.

[1454] Step 6:

[1455] The server also obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and selects the optimal ingredient combination. The input is the price data of other ingredients obtained from each retailer, and the output is the optimal ingredient combination.

[1456] Step 7:

[1457] The server generates an optimal menu using a generative AI model (e.g., GPT-4) based on the user's input data and price data. Specifically, the server inputs a prompt to the AI ​​model to obtain the optimal menu. The input is the user's input data and price data, and the output is the generated optimal menu.

[1458] Step 8:

[1459] The server determines the list of ingredients needed and where to buy them. For example, tomatoes (90 yen), onions (50 yen), garlic (30 yen), pasta (120 yen), etc. The input is the generated menu and price data, and the output is the list of ingredients and where to buy them.

[1460] Step 9:

[1461] The server personalizes the generated menu by taking into account the user's preferences, nutritional balance, and restrictions (e.g., allergies or religious restrictions). The input is the user's preferences and restrictions, and the output is a personalized menu.

[1462] Step 10:

[1463] To recognize the user's emotions, the server uses an emotion engine to analyze the user's emotional state from their facial expressions, tone of voice, and input text. For example, it uses the Microsoft Azure Emotion API. The input is the user's facial expressions, tone of voice, and input text, and the output is the analysis result of the emotional state.

[1464] Step 11:

[1465] The server selects specific ingredients and types of dishes based on the recognized emotional state, and generates a menu that is optimal for the user's emotional state. Specifically, the server inputs prompts that take the emotional state into account into the generative AI model and obtains the menu. The input is the analysis result of the emotional state, and the output is an optimal menu based on the emotional state.

[1466] Step 12:

[1467] The server generates the final menu, ingredient list, and supplier information and sends that information to the terminal. The input is the final menu, ingredient list, and supplier information, and the output is notification data sent to the terminal.

[1468] Step 13:

[1469] The device displays a notification to the user, providing a list of ingredients, where to buy them, and details of the menu. For example, a user can see "Tomato pasta, how to make it, and a list of ingredients" on their smartphone. The input is the notification data sent from the server, and the output is the detailed information displayed to the user.

[1470] (Application example 2)

[1471] 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."

[1472] Modern consumers face challenges in efficiently shopping and preparing balanced meals within limited time and resources. It's especially difficult to compare prices in real time and select the most cost-effective ingredients when shopping. There's also a demand for personalized menu suggestions that take into account the user's emotional state and individual preferences and allergies.

[1473] 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.

[1474] In this invention, the server includes: means for inputting a user's desired menu or ingredients; means for acquiring price data in real time from supermarkets; means for analyzing the acquired price data and selecting the most cost-effective combination of ingredients; means for notifying the user of the generated menu and ingredient shopping list; emotion recognition means for analyzing the user's emotional state; means for suggesting a menu and ingredients according to the user's emotional state; means for saving the user's food preferences and allergy information to personalize the menu; and a terminal for displaying information in real time on a smartphone or smart glasses.

[1475] This allows users to shop efficiently and cost-effectively, while also suggesting balanced meals that are tailored to individual tastes and emotional states.

[1476] "Means for users to input their desired menu or ingredients" refers to the method by which users input the dishes they want to make or the ingredients they want to use into the interface through the system.

[1477] "Means of obtaining real-time price data from supermarkets" refers to a method of instantly collecting current food price information via the APIs and networks of multiple supermarkets.

[1478] "Means for analyzing acquired price data and selecting the most cost-effective combination of ingredients" refers to a method for calculating cost performance based on collected price information and generating a rational shopping list.

[1479] The "means for notifying the user of the generated menu and ingredient purchasing list" refers to a method for displaying or notifying the user of the optimal menu and ingredient purchasing list.

[1480] "Emotion recognition means for analyzing the user's emotional state" refers to technology for analyzing the user's facial expressions, voice, etc. to identify their current emotional state.

[1481] "Means for suggesting menus and ingredients according to the user's emotional state" refers to a method for taking into account the analyzed emotional state of the user and suggesting dishes and ingredients that are appropriate at that time.

[1482] "Means for saving a user's food preferences and allergy information to personalize menus" refers to a method for saving a user's individual preferences and allergy information and customizing menus based on that information.

[1483] "Devices that display information in real time on smartphones or smart glasses" refers to equipment that displays information on portable or wearable devices so that users can view the information instantly.

[1484] This invention relates to a system that allows users to shop efficiently and proposes balanced menus that are tailored to individual preferences and emotional states, while also taking into consideration cost-effectiveness. The functions of the server, terminal, and user, as well as their specific operations, are explained below.

[1485] Hardware and Software Used

[1486] Hardware: Smartphone (iOS / Android), smart glasses (general wearable device)

[1487] Software: Python, TensorFlow, OpenCV, Google Cloud Vision API, Super API, Emotion AI (for emotion recognition)

[1488] Program processing

[1489] 1. User Input Phase

[1490] The user uses the interface on their smartphone or smart glasses to input the desired menu and ingredients, for example, "I want to make a dish using tomatoes." The input data is then sent to the server via a Python script.

[1491] 2. Data Collection Phase

[1492] The server queries the APIs of multiple supermarkets to get real-time price data, for example, a tomato costs 100 yen from supermarket A, 90 yen from supermarket B, and 95 yen from supermarket C.

[1493] 3. Price Analysis Phase

[1494] The server uses Python and Pandas to analyze the obtained price data and select the most cost-effective combination of ingredients. For example, it confirms that tomatoes are the cheapest at Supermarket B. It also obtains the prices of other related ingredients (onions, garlic, pasta, etc.) and generates the most cost-effective shopping list overall.

[1495] 4. Menu generation phase

[1496] The server uses a generative AI model (using TensorFlow) to generate optimal menu suggestions based on user input data and price information, such as "tomato pasta" or "tomato and chicken salad."

[1497] 5. Personalization Phase

[1498] It stores your food preferences and allergies and then creates personalized meal suggestions based on them. For example, if you're vegan, it will generate vegan-friendly recipes.

[1499] 6. Emotion Recognition Phase

[1500] The server analyzes the user's facial expressions and voice using emotion recognition (using OpenCV and Google Cloud Vision API). For example, if the server recognizes that the user is "tired," it will suggest ingredients and dishes that have a relaxing effect based on that emotional state.

[1501] 7. Result notification phase

[1502] The server generates the final menu, ingredient list, and supplier information, and displays this information in real time on the smartphone or smart glasses. For example, it might say, "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[1503] Examples and prompts

[1504] For example, if a user inputs "I want to make a dish using tomatoes," and the emotion engine recognizes the user's emotional state as "tired," the server will retrieve tomato price data from multiple nearby supermarkets and select Supermarket B with the lowest price. It will then similarly retrieve the prices of other related ingredients (onions, garlic, pasta, etc.) to generate the most cost-effective overall shopping list. Based on the results of the emotion engine, it will also suggest "tomato pasta," a dish that has a relaxing effect.

[1505] Example prompt sentence:

[1506] "We've identified your emotional state as 'tired.' We'll suggest a meal that will help you relax. Search for 'pasta with tomatoes' to find the perfect ingredients."

[1507] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1508] Step 1:

[1509] The user uses the interface of their smartphone or smart glasses to input the desired menu or ingredients, for example, "I want to make a dish using tomatoes." This input data is stored in an SQL database and sent to the server.

[1510] Input: User input (e.g. "dishes with tomatoes")

[1511] Output: User request data

[1512] Step 2:

[1513] The server receives the user's request data, queries the APIs of multiple supermarkets to obtain real-time price data, and then analyzes the responses from each supermarket to extract the required price information.

[1514] Input: User request data

[1515] Output: Real-time price data

[1516] Step 3:

[1517] The server analyzes the acquired price data using Python and Pandas. The server compares the price data and calculates the most cost-effective combination of ingredients. For example, the server analyzes the price data for tomatoes and selects the most cost-effective supermarket.

[1518] Input: Real-time price data

[1519] Output: Most cost-effective ingredient combination

[1520] Step 4:

[1521] The server uses a generative AI model (using TensorFlow) to generate optimal menu suggestions based on user input and price information. The generated menu suggestions are formatted using a Python script. For example, the server might suggest "tomato pasta" or "tomato and chicken salad."

[1522] Input: User input data, price information

[1523] Output: Generated menu

[1524] Step 5:

[1525] The server personalizes the generated menu by taking into account the user's food preferences and allergy information, and adjusts the recipe content based on the user's nutritional balance and restrictions.

[1526] Input: Generated menu, user preferences and allergy information

[1527] Output: Personalized menu

[1528] Step 6:

[1529] The server uses emotion recognition (using OpenCV and Google Cloud Vision API) to analyze the user's facial expressions and voice. The analysis results identify the user's emotional state. For example, if the user is recognized as "tired," that information is fed back to the server.

[1530] Input: User's facial expressions and voice data

[1531] Output: User's emotional state

[1532] Step 7:

[1533] The server suggests ingredients and dishes that have a relaxing effect based on the user's emotional state. It comprehensively analyzes the user's emotional state and other data to generate an appropriate menu. For example, it suggests "tomato pasta."

[1534] Input: User's emotional state, other data (pricing information, preferences, etc.)

[1535] Output: Menu suggestions based on emotional state

[1536] Step 8:

[1537] The server generates the final menu, ingredient list, and supplier information, which are then displayed in real time on a smartphone or smart glasses. The user is given specific instructions, such as, "To make tomato pasta, you need tomatoes (90 yen), onions (50 yen), garlic (30 yen), and pasta (120 yen)."

[1538] Input: Menu suggestions based on emotional state, purchasing information

[1539] Output: Notification to user (menu and shopping list)

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] 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.

[1546] 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).

[1547] 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.

[1548] 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."

[1549] 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.

[1550] 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).

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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.

[1556] 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.

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] The following is further disclosed regarding the above embodiment.

[1562] (Claim 1)

[1563] A means for the user to input a desired menu or ingredients;

[1564] a means of obtaining real-time price data from supermarkets;

[1565] A means of analyzing the acquired price data and selecting the most cost-effective combination of ingredients;

[1566] The system includes a means for notifying the user of the generated menu and ingredient shopping list.

[1567] (Claim 2)

[1568] 10. The system of claim 1, further comprising means for personalizing the generated menu taking into account the user's preferences, nutritional balance, and restrictions.

[1569] (Claim 3)

[1570] 10. The system of claim 1, further comprising means for displaying the generated menu and ingredient list in real time based on user input data.

[1571] "Example 1"

[1572] (Claim 1)

[1573] A means for the user to input a desired menu or ingredients;

[1574] a means of obtaining real-time price data from supermarkets;

[1575] A means of analyzing the acquired price data and selecting the most cost-effective combination of ingredients;

[1576] A means to personalize the menu generated based on the user's preferences, nutritional balance, and restrictions;

[1577] a means for notifying the user of the generated menu and food shopping list;

[1578] A method to suggest optimal menus using generative AI based on user input data, and

[1579] A means for displaying the generated menu and ingredient list in real time;

[1580] A system including:

[1581] (Claim 2)

[1582] 10. The system of claim 1, further comprising means for personalizing the generated menu based on user information.

[1583] (Claim 3)

[1584] The system according to claim 1, further comprising means for selecting an optimal combination of ingredients based on price data from multiple supermarkets obtained in real time.

[1585] "Application Example 1"

[1586] (Claim 1)

[1587] A means for the user to input a desired menu or ingredients;

[1588] A means of obtaining real-time price data from multiple stores;

[1589] A means of analyzing the acquired price data and selecting the most cost-effective combination of ingredients;

[1590] A system including a means for notifying a user of a menu and ingredient shopping list generated using a generative AI model.

[1591] (Claim 2)

[1592] 10. The system of claim 1, further comprising means for personalizing the generated menu taking into account the user's preferences, nutritional balance, and restrictions.

[1593] (Claim 3)

[1594] A means for displaying the generated menu and ingredient list in real time based on the user's input data;

[1595] 10. The system of claim 1, further comprising means for notifying a user of the generated menu and ingredient list and where to purchase them.

[1596] "Example 2: Combining Emotion Engines"

[1597] (Claim 1)

[1598] a means for the user to input a desired menu or ingredients;

[1599] A means of obtaining real-time price data from retailers; and

[1600] A means of analyzing the acquired pricing data and selecting the most cost-effective material combination;

[1601] A means for generating an optimal menu using a generative AI model;

[1602] a means for recognizing the emotional state of a user;

[1603] means for adjusting a menu based on the perceived emotional state;

[1604] The system includes means for notifying the user of the generated menu and ingredient shopping list.

[1605] (Claim 2)

[1606] 10. The system of claim 1, further comprising means for personalizing the generated menu taking into account the user's preferences, nutritional balance, and restrictions.

[1607] (Claim 3)

[1608] 10. The system of claim 1, further comprising means for displaying the generated menu and ingredient list in real time based on user input data.

[1609] "Application example 2 when combining emotion engines"

[1610] (Claim 1)

[1611] A means for the user to input a desired menu or ingredients;

[1612] a means of obtaining real-time price data from supermarkets;

[1613] A means of analyzing the acquired price data and selecting the most cost-effective combination of ingredients;

[1614] a means for notifying the user of the generated menu and food shopping list;

[1615] an emotion recognition means for analyzing the emotional state of a user;

[1616] A means of suggesting menus and ingredients according to the user's emotional state,

[1617] A way to personalize menus by storing users' food preferences and allergy information;

[1618] A device that displays real-time information on a smartphone or smart glasses

[1619] A system including:

[1620] (Claim 2)

[1621] 10. The system of claim 1, further comprising means for personalizing the generated menu taking into account the user's preferences, nutritional balance, and restrictions.

[1622] (Claim 3)

[1623] 10. The system of claim 1, further comprising means for displaying the generated menu and ingredient list in real time based on user input data. [Explanation of symbols]

[1624] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for the user to input a desired menu or ingredients; a means of obtaining real-time price data from supermarkets; A means of analyzing the acquired price data and selecting the most cost-effective combination of ingredients; The system includes a means for notifying the user of the generated menu and ingredient shopping list.

2. 10. The system of claim 1, further comprising means for personalizing the generated menu taking into account the user's preferences, nutritional balance, and restrictions.

3. 10. The system of claim 1, further comprising means for displaying the generated menu and ingredient list in real time based on user input data.

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