System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting family preferences and health information, a means for collecting price information and bargain information from nearby supermarkets, a means for generating an optimal menu based on the collected information, a means for generating a shopping list based on the generated menu, and a means for proposing an optimal shopping route.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] When planning meals at home, creating menus that take into account budget management and nutritional balance is a difficult task for many families. Furthermore, busy schedules often lead to monotonous meal menus, making it difficult to provide meals that suit each family member's preferences and health status. The present invention addresses these challenges by providing a system that offers economical and nutritionally balanced meal plans and enables families to enjoy a variety of meals. [Means for solving the problem]

[0005] The present invention solves the above problems with a system that includes a means for collecting family preference and health information, a means for collecting price information and sale information from nearby supermarkets, a means for generating an optimal menu based on the collected information, a means for generating a shopping list based on the generated menu, and a means for suggesting an optimal shopping route.Furthermore, by including a means for customizing the menu taking into account the likes and dislikes of each family member and the results of health checkups, and a means for calculating nutritional balance based on the proposed menu and providing it to the user, the system realizes family health management and provides a variety of meal plans.

[0006] "Family preferences" refers to the types of ingredients, dishes, and tastes that each family member prefers.

[0007] "Health information" refers to data related to the health status of family members, and specifically includes health checkup results, allergy information, and information on nutritional restrictions.

[0008] A "neighborhood supermarket" refers to a store that sells food and daily necessities located near the home that the system targets.

[0009] "Price information" refers to data showing the prices of products offered at each supermarket.

[0010] "Special sale information" refers to information about products that supermarkets offer at discounted prices for specific periods or under specific conditions.

[0011] An "optimal menu" is an economical and nutritionally balanced meal plan created taking into account family preferences, health information, and the prices of ingredients you plan to purchase.

[0012] "Means of collection" refers to the system for obtaining supermarket prices, sale information, family preferences, and health information via the Internet.

[0013] "Means of generation" refers to a system that uses specific algorithms to create menus and shopping lists based on collected data.

[0014] "Shopping List" means a list of ingredients and other items needed to prepare a suggested meal plan.

[0015] A "shopping route" refers to a plan that shows the order and route to visit supermarkets in order to purchase ingredients efficiently.

[0016] "Means for customization" refers to a system that adjusts menus individually based on each family member's preferences and health information.

[0017] "Nutritional balance" refers to the type and amount of nutrients contained in the proposed menu being in an appropriate balance for a healthy diet.

[0018] "Means for providing" refers to the interface for displaying nutritional information, shopping lists, and meal suggestions to the user. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

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

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby supermarkets, and generates shopping lists and routes. This system is composed of a server, terminals, and users, and its specific operation and embodiments are described below.

[0041] Gathering family preferences and health information

[0042] Through the application, users input their family's food preferences, allergy information, and health checkup results. Specifically, data on each family member's likes and dislikes and health status is entered, and the device collects this information. For example, if a user enters information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," the device sends this information to the server.

[0043] Gathering price information and special offers from nearby supermarkets

[0044] The server sends requests to the websites and APIs of nearby supermarkets to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database. For example, it might collect information that Supermarket A is selling cabbage for 98 yen and chicken breast for 200 yen.

[0045] Menu generation

[0046] The server generates optimal menus based on collected family preferences, health information, and price information from nearby supermarkets. The server uses a specific algorithm to calculate meal plans that will satisfy the entire family and sends the results to the device. For example, it suggests a balanced menu that takes into account the mother's calorie restrictions while also reflecting the second daughter's dislike of fish.

[0047] Nutritional Balance Calculation

[0048] The server calculates the nutritional content and calories of the proposed menu and sends the results to the device. The device then displays information about the calories and nutrients per meal to the user. For example, it may display information such as "This menu contains 500 kcal and 25 g of protein per meal."

[0049] Generate a shopping list

[0050] The server generates a list of ingredients based on the generated menu and sends it to the device. The user can purchase ingredients efficiently based on this shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" is generated.

[0051] Proposal of the best shopping route

[0052] The server compares prices from multiple supermarkets and calculates the most economical shopping route. The results are sent to the device, and suggestions are made to help the user shop efficiently. For example, a route such as "Purchase cabbage at supermarket A and chicken breast at supermarket B" may be suggested.

[0053] With these features, the system supports daily meal planning at home, providing economical and nutritionally balanced meal plans that also reflect the preferences and health status of each family member, ensuring meals that satisfy all members.

[0054] The processing flow will be explained below.

[0055] Specific program flow

[0056] Gathering family preferences and health information

[0057] Step 1:

[0058] The user opens the application's settings screen and enters each family member's food preferences, allergy information, health check results, etc.

[0059] Step 2:

[0060] The terminal transmits the input information to the server.

[0061] Step 3:

[0062] The server stores the received family preferences and health information in a database.

[0063] Gathering price information and special offers from nearby supermarkets

[0064] Step 1:

[0065] The server periodically sends data collection requests to the local supermarket's website or API.

[0066] Step 2:

[0067] The server extracts product name, price, and sale information from the response.

[0068] Step 3:

[0069] The server stores the extracted price and sale information in a database.

[0070] Menu generation

[0071] Step 1:

[0072] The server uses an algorithm to generate the optimal menu based on the family's preferences, health information, and price information from nearby supermarkets.

[0073] Step 2:

[0074] The server sends the contents of the generated menu to the terminal.

[0075] Step 3:

[0076] The terminal displays the contents of the proposed menu to the user.

[0077] Nutritional Balance Calculation

[0078] Step 1:

[0079] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the menu it proposes.

[0080] Step 2:

[0081] The server formats the results and sends them to the terminal.

[0082] Step 3:

[0083] The device displays information about calories and nutrients per meal to the user.

[0084] Generate a shopping list

[0085] Step 1:

[0086] The server runs an algorithm that lists the ingredients needed based on the proposed menu.

[0087] Step 2:

[0088] The server sends the generated shopping list to the terminal.

[0089] Step 3:

[0090] The terminal displays the shopping list to the user.

[0091] Proposal of the best shopping route

[0092] Step 1:

[0093] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs.

[0094] Step 2:

[0095] The server sends the calculated optimal shopping route to the terminal.

[0096] Step 3:

[0097] The terminal displays an efficient shopping route to the user.

[0098] Through the above process, users can receive menu suggestions that suit their family's preferences and health status, and enjoy an economical shopping experience.

[0099] Example 1

[0100] 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."

[0101] The present invention aims to solve the problem of efficiently collecting family preferences, health information, and price information from nearby stores, and proposing economical and nutritionally balanced menus based on this information. It also aims to solve the problem of a lack of means for users to shop efficiently and the difficulty of generating menus that correspond to specific family health conditions and preferences by optimizing shopping lists and shopping routes.

[0102] 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.

[0103] In this invention, the server includes a means for collecting family preference and health information, a means for collecting price information and sale information from nearby stores, a means for generating an optimal menu based on the collected information, a means for calculating nutritional components and calories for the generated menu, a means for generating a shopping list based on the generated menu, and a means for suggesting an optimal shopping route. This allows for economical and nutritionally balanced menu suggestions while taking into account the dietary preferences and health status of all family members. Furthermore, by supporting efficient shopping, the server can save users time and money.

[0104] "Family preferences and health information" is detailed data entered by the user, such as food preferences, allergy information, and health checkup results of family members.

[0105] "Price information and sale information from nearby stores" refers to product price and sale information obtained from supermarket and store websites and APIs.

[0106] The "means for generating optimal menus" is a process that uses an algorithm to automatically create economical and nutritionally balanced menus based on user input information and store information.

[0107] "Means for calculating nutritional components and calories" refers to a system that calculates and quantifies the amount of nutrients and energy contained in the proposed menu.

[0108] The "means for generating a shopping list" is a function that compiles the necessary ingredients and their quantities in list form based on the generated menu.

[0109] The "means for proposing the optimal shopping route" is a function that compares price information from multiple stores, calculates the most cost-effective shopping route, and presents it to the user.

[0110] The present invention relates to a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby stores, and generates shopping lists and routes. This system is composed of a server, terminals, and users.

[0111] Gathering family preferences and health information

[0112] The user enters information about their family's food preferences, allergies, and health checkup results through a dedicated application. Specifically, the user enters information such as "My mother has diabetes and needs a low-carb diet" or "My second daughter is allergic to fish" into the application form. The device collects this information and sends it to a server to be stored in a database.

[0113] Gathering price information and special offers from nearby stores

[0114] The server sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. Specifically, it uses web scraping technology or a specified API to collect product information from each store. For example, the server periodically performs a process such as "collecting information on cabbage for 98 yen from store A and chicken breast for 200 yen from store B." The collected information is then saved in a database in the latest state.

[0115] Menu generation

[0116] The server generates optimal menus based on collected family preferences, health information, and price information. Using an AI algorithm, it proposes economical and nutritionally balanced menus that will satisfy the whole family. For example, it generates meal plans that meet conditions such as "fish-free dishes because the second daughter dislikes fish" and "low-carb menus in consideration of the mother's diabetes."

[0117] Nutritional Balance Calculation

[0118] The server calculates the nutritional content and calories for the generated menu. Specifically, it uses nutrition calculation software to automatically calculate the nutrient and energy content of each dish. For example, it calculates information such as "the calories per meal of the proposed menu are 510 kcal, 30 g of protein, and 60 g of carbohydrates," and sends the results to the device.

[0119] Generate a shopping list

[0120] The server generates a list of the ingredients needed based on the menu that has been generated. Specifically, it lists all the ingredients included in the menu and calculates the required quantities. For example, it creates a shopping list such as "1 cabbage, 300g chicken breast, 200g broccoli" and sends it to the terminal. The terminal displays this list to the user.

[0121] Proposal of the best shopping route

[0122] The server calculates the most economical shopping route based on price information from multiple stores. Specifically, it compares product prices at each store and calculates the most cost-effective shopping route. For example, it suggests a route such as "buy cabbage at store A and chicken breast at store B." The calculation results are sent to the terminal and displayed to the user.

[0123] Specific examples and examples of prompts for generative AI models

[0124] Examples:

[0125] 1. The user fills in the app form with information such as "My mother has diabetes and needs to limit her carbohydrate intake" and "My second daughter is allergic to fish" and submits it.

[0126] 2. The server collects information such as "cabbage 98 yen" from nearby store A and "chicken breast 200 yen" from store B.

[0127] 3. Based on this information, the server uses an AI algorithm to suggest a "low-carb stir-fried vegetable and chicken breast dish."

[0128] 4. The nutritional balance of the menu is calculated as 510 kcal, 28 g of protein, and 20 g of carbohydrates per meal.

[0129] 5. The server generates a shopping list such as "1 cabbage, 300g chicken breast, 2 bell peppers."

[0130] 6. Propose an efficient route to visit Supermarket A and Supermarket B.

[0131] Example prompt for a generative AI model:

[0132] "Enter your family's food preferences and health information. For example, your oldest son has a milk allergy, and your father has high blood pressure."

[0133] "Gather shrimp and broccoli prices at nearby stores."

[0134] "Please suggest an economical and nutritionally balanced menu for my mother, who needs to limit carbohydrates, and my second daughter, who has a fish allergy."

[0135] "Calculate the nutritional content and calories of the suggested meals and provide per-serving information to the user."

[0136] "Generate a list of ingredients based on the menu you create."

[0137] "Based on the price information collected, suggest the most economical shopping route."

[0138] This will support daily meal planning at home, making it possible to provide economical and nutritionally balanced meal plans that also reflect the preferences and health conditions of each family member, ensuring meals that satisfy all members.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1: Enter your family's preferences and health information

[0141] Input: The user opens the application and enters their family's food preferences, allergy information, and health check results.

[0142] Specific operation: The user enters information such as "My mother has diabetes and needs a low-carb diet" or "My second daughter is allergic to fish" into a special form in the app.

[0143] Data processing: Input data is collected by the terminal and stored in a database in an appropriate format.

[0144] Output: The user's input information is sent to the server and stored in a database.

[0145] Step 2: Gather price information and special offers from nearby stores

[0146] Input: The server sends a request to get information about the websites or APIs of the specified nearby stores.

[0147] Specific operations: Use web scraping technology and APIs to collect product names, prices, and special offers from each store.

[0148] Data processing: The collected information is stored in the database in the most up-to-date state.

[0149] Output: The latest price information and special offers from the stores are stored in a database.

[0150] Step 3: Generate the optimal menu

[0151] Input: The server receives the family preference and health information collected in step 1 and the store price information collected in step 2.

[0152] How it works: Using AI algorithms, it analyzes the collected data and generates economical and nutritionally balanced meals that will satisfy the whole family.

[0153] Data calculation: Algorithms are run based on user preferences, health information, and price information to calculate the most appropriate combination.

[0154] Output: A menu optimized to the family's needs is generated and passed on to the next processing step.

[0155] Step 4: Calculate your nutritional balance

[0156] Input: The server receives the menu information generated in step 3.

[0157] Specific Actions: Use nutrition calculation software to calculate the nutritional content and calories of each dish.

[0158] Data calculation: Calculate the nutrients and calories for all ingredients included in the menu and calculate the total value.

[0159] Output: The nutritional information and calories for each meal are calculated and the results are ready to be provided to the user.

[0160] Step 5: Generate a shopping list

[0161] Input: The server receives the menu information generated in step 3.

[0162] Specific actions: List all ingredients included in the menu, calculate the required quantities, and compile them in list form.

[0163] Data processing: Calculate the required amount of each ingredient and format it into a shopping list.

[0164] Output: A specific shopping list (e.g., 1 cabbage, 300g chicken breast, 200g broccoli) is generated and sent to the device.

[0165] Step 6: Optimal shopping route suggestions

[0166] Input: The server re-uses the price information for each store collected in step 2.

[0167] Specific operation: Calculates the most economical shopping route based on price information from multiple stores.

[0168] Data calculation: Compare product prices at each store and calculate the most cost-effective shopping route.

[0169] Output: The optimal shopping route based on the shopping list (e.g., store A for cabbage, store B for chicken breast) is proposed and sent to the terminal.

[0170] This makes it clear what specific data processing and calculations are performed at each processing step and how the system operates.

[0171] (Application example 1)

[0172] 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."

[0173] In modern households, preparing economical and nutritionally balanced meals while taking into account the family's food preferences and health status is a significant burden. Furthermore, there is no system that efficiently collects price information from nearby stores and food delivery services and proposes optimal shopping lists and routes. Furthermore, while there is a need for menu suggestions using generative AI models and flexible menu generation using prompts, there is no comprehensive solution available.

[0174] 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.

[0175] In this invention, the server includes means for collecting family preference and health information, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information, means for generating a shopping list based on the generated menu, means for suggesting an optimal shopping route, means for collecting price information from food delivery services and suggesting menus that can be delivered, means for generating a menu using a generative AI model, and means for inputting prompt text and generating a menu. This makes it easy to prepare economical and nutritionally balanced meals based on family preferences and health status, and also makes it possible to suggest optimal shopping lists, shopping routes, and even food delivery options.

[0176] "Family preferences and health information" refers to each family member's food preferences, allergy information, health checkup results, etc.

[0177] "Price Information" refers to price data for products at nearby stores and food delivery services.

[0178] "Special sale information" refers to sales and discount information offered by stores and services.

[0179] A "menu" refers to a meal plan consisting of a combination of multiple dishes and ingredients.

[0180] A "shopping list" refers to a list of ingredients and products that should be purchased based on a specific menu.

[0181] A "shopping route" refers to the optimal shopping route for efficiently visiting multiple stores.

[0182] A "food delivery service" refers to a service that allows you to order meals via the internet or an app and have them delivered to your home or other location.

[0183] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate new information and ideas from data.

[0184] A "prompt" is an instruction entered into a generative AI model, and refers to text that guides a specific output result.

[0185] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, price information and sale information from nearby stores, and also provides shopping lists, shopping routes and even food delivery options. This system is composed of a server, terminals and users, and its specific operation and embodiments are described below.

[0186] Gathering family preferences and health information

[0187] Users use a smartphone app to input information about their family's food preferences, allergies, and health checkup results. For example, if a family member inputs information such as "I need to limit my calories" or "I don't like fish," the device collects this information and sends it to the server.

[0188] Gathering price information and special offers from nearby stores

[0189] The server sends requests to the websites and APIs of nearby stores to obtain product names, prices, and special offers. The collected information is updated periodically, and the latest price and special offer information is stored in a database. For example, information such as "Store A is selling cabbage for 98 yen and chicken breast for 200 yen" is collected.

[0190] Gathering information on food delivery services

[0191] The server sends requests to the APIs of multiple food delivery services to obtain information on food prices and delivery areas, providing not only information on nearby restaurants but also delivery service options.

[0192] Menu generation

[0193] The server generates optimal menus based on collected family preferences, health information, and pricing information from nearby restaurants and food delivery services. Using a generative AI model, it calculates meal plans that will satisfy the whole family based on all the collected information. For example, it might suggest a "balanced menu that takes into account calorie restrictions and does not include fish."

[0194] Nutritional Balance Calculation

[0195] The server calculates the nutritional content and calories of the proposed menu and sends the results to the device. The device then displays information about the calories and nutrients per meal to the user. For example, it might say, "This menu contains 500 kcal and 25 g of protein per meal."

[0196] Generate a shopping list

[0197] The server generates a list of ingredients based on the generated menu and sends it to the device. The user can purchase ingredients efficiently based on this shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" may be generated.

[0198] Proposal of the best shopping route

[0199] The server compares prices from multiple stores and calculates the most economical shopping route. The results are sent to the device, and suggestions are made to help the user shop efficiently. For example, a route such as "Purchase cabbage from store A and chicken breast from store B" may be suggested.

[0200] This allows users to efficiently prepare economical and healthy meals. An example of a specific prompt would be:

[0201] Prompt Sentence Examples

[0202] "User Information:

[0203] Mother: Calorie restriction, dislike of fish

[0204] Second daughter: I hate fish

[0205] Super API URLs:

[0206] https: / / api.storea.com / products

[0207] https: / / api.storeb.com / products

[0208] Requirements:

[0209] Please suggest economical and nutritionally balanced meals.

[0210] Please limit your calorie intake.

[0211] Do not use fish.

[0212] Generate menus based on pricing information from nearby restaurants and food delivery services.

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] Users input their family's preferences and health information through a smartphone app. Specifically, users input each family member's name, allergy information, likes and dislikes, health checkup results, etc. The input information is stored on the device and sent to the server in real time.

[0216] Input: Family preferences and health information

[0217] Output: Data sent to the server

[0218] Step 2:

[0219] The server sends requests to the websites and APIs of nearby stores to get product names, prices, special offers, etc. The server periodically updates this information and stores it in a database.

[0220] Input: Store API URL

[0221] Output: A database containing pricing and special offers

[0222] Step 3:

[0223] The server sends requests to the APIs of multiple food delivery services to obtain information on the price and delivery area of ​​each dish. The collected information is stored in a database that is updated periodically.

[0224] Input: API URL of food delivery service

[0225] Output: A database containing information about food delivery services

[0226] Step 4:

[0227] The server generates the optimal menu based on collected family preferences, health information, and price information from nearby stores and food delivery services. The server uses a generative AI model to generate a menu based on prompts. This is achieved by using an algorithm to optimally combine each piece of information.

[0228] Input: Family preferences and health information, price information, delivery information, prompt text

[0229] Output: Optimal menu

[0230] Step 5:

[0231] The server calculates the nutritional components and calories for the generated menu. Specifically, it calculates the calories and nutrients such as protein, fat, and carbohydrates per meal based on the nutritional data of each ingredient, and sends the results to the device.

[0232] Input: Generated menu

[0233] Output: Nutritional information

[0234] Step 6:

[0235] The server generates a list of ingredients based on the menu and sends it to the terminal. The user can use this shopping list to purchase ingredients efficiently.

[0236] Input: Generated menu

[0237] Output: Shopping list

[0238] Step 7:

[0239] The server compares prices from multiple stores and calculates the most economical shopping route, which is then sent to the terminal and suggested to the user.

[0240] Input: Price information, shopping list

[0241] Output: Optimal shopping route

[0242] 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.

[0243] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby supermarkets, and further provides meal plans that correspond to the user's emotional state by combining an emotion engine. This system is composed of a server, a terminal, a user, and an emotion engine, and its specific operation and embodiments are described below.

[0244] Gathering family preferences and health information

[0245] Through the application, users input information about their family's food preferences, allergies, health checkup results, etc. Specifically, data about each family member's likes and dislikes and health status is entered, and the device collects this information. For example, if a user enters information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," the device sends this information to the server.

[0246] Collecting Emotional Data

[0247] As users use the application, the emotion engine collects emotional data from their facial expressions, voice tone, and input text, for example, depending on whether they are stressed or relaxed.

[0248] Gathering price information and special offers from nearby supermarkets

[0249] The server sends requests to the websites and APIs of nearby supermarkets to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database. For example, it might collect information that Supermarket A is selling cabbage for 98 yen and chicken breast for 200 yen.

[0250] Menu generation

[0251] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information from nearby supermarkets. By taking emotional data into account, the server can suggest menus that suit the user's mood. For example, if the user is feeling stressed, it can suggest menus that have a relaxing effect.

[0252] Nutritional Balance Calculation

[0253] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the proposed menu. The results are sent to the device and displayed to the user. For example, information such as "This menu has 500 kcal and 25 g of protein per meal" is displayed.

[0254] Generate a shopping list

[0255] The server runs an algorithm to generate a list of ingredients based on the proposed menu. The results are sent to the device and displayed to the user as a shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" might be generated.

[0256] Proposal of the best shopping route

[0257] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs. The results are sent to the device and displayed to the user as an efficient shopping route. For example, a route such as "Purchase cabbage at supermarket A and chicken breast at supermarket B" may be presented.

[0258] Emotion-based menu suggestions

[0259] Based on the emotional data received from the emotion engine, the server generates an optimal menu according to the user's emotional state. For example, if the user is tired, it will suggest nutritious meals, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[0260] Specific examples

[0261] If a user inputs information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," and the emotion engine detects that they have been under a lot of stress recently, the server will use this information to suggest menu items that have a relaxing effect, and will also suggest a "healthy salad with cabbage and chicken breast" based on nutritional balance. This allows for a meal plan that not only satisfies the whole family, but also takes into account their emotional state.

[0262] This system dramatically improves home meal planning, making meals more economical, nutritionally balanced, and emotionally conscious.

[0263] The processing flow will be explained below.

[0264] Specific processing flow of the program

[0265] Gathering family preferences and health information

[0266] Step 1:

[0267] The user opens the application's settings screen and enters each family member's food preferences, allergy information, health check results, etc.

[0268] Step 2:

[0269] The terminal transmits the input information to the server.

[0270] Step 3:

[0271] The server stores the received family preferences and health information in a database.

[0272] Collecting Emotional Data

[0273] Step 1:

[0274] When a user uses the application, the emotion engine captures the user's facial expression data using a camera, and also obtains emotion data from voice tones and text input.

[0275] Step 2:

[0276] The device transmits the collected emotion data to the server.

[0277] Step 3:

[0278] The server analyzes the emotional data to determine the user's current emotional state.

[0279] Gathering price information and special offers from nearby supermarkets

[0280] Step 1:

[0281] The server periodically sends data collection requests to the local supermarket's website or API.

[0282] Step 2:

[0283] The server extracts product name, price, and sale information from the response.

[0284] Step 3:

[0285] The server stores the extracted price and sale information in a database.

[0286] Menu generation

[0287] Step 1:

[0288] The server uses an algorithm to generate the optimal menu based on the family's preferences, health information, emotional data, and price information from nearby supermarkets.

[0289] Step 2:

[0290] The server sends the contents of the generated menu to the terminal.

[0291] Step 3:

[0292] The terminal displays the contents of the proposed menu to the user.

[0293] Nutritional Balance Calculation

[0294] Step 1:

[0295] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the menu it proposes.

[0296] Step 2:

[0297] The server formats the results and sends them to the terminal.

[0298] Step 3:

[0299] The device displays information about calories and nutrients per meal to the user.

[0300] Generate a shopping list

[0301] Step 1:

[0302] The server runs an algorithm that lists the ingredients needed based on the proposed menu.

[0303] Step 2:

[0304] The server sends the generated shopping list to the terminal.

[0305] Step 3:

[0306] The terminal displays the shopping list to the user.

[0307] Proposal of the best shopping route

[0308] Step 1:

[0309] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs.

[0310] Step 2:

[0311] The server sends the calculated optimal shopping route to the terminal.

[0312] Step 3:

[0313] The terminal displays an efficient shopping route to the user.

[0314] Emotion-based menu suggestions

[0315] Step 1:

[0316] Based on the emotion data received from the emotion engine, the server executes an algorithm to generate an optimal menu according to the user's emotional state.

[0317] Step 2:

[0318] The server sends the menu contents based on the emotion data to the terminal.

[0319] Step 3:

[0320] The device will display menu suggestions based on the user's emotions. For example, if the user is feeling stressed, it will suggest a menu with a relaxing effect, or if the user wants to feel energized, it will suggest a menu using their favorite ingredients.

[0321] Through these steps, users can enjoy an economical and nutritionally balanced meal plan tailored to their emotional state.

[0322] Example 2

[0323] 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."

[0324] In modern home cooking, it is difficult to propose economical and nutritionally balanced menus that take into account the preferences and health information of all family members. There is also a demand for meal plans that take into account the user's emotional state, but this is difficult to achieve with conventional methods. Furthermore, there are few systems that suggest efficient shopping routes, which increases the burden of housework.

[0325] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting family member preference and health information, means for collecting user emotion data, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information, means for generating a shopping list based on the generated menu, means for suggesting an optimal shopping route, means for calculating the nutritional balance of the generated menu and providing it to the user, and means for suggesting a menu based on the user's emotional state. This provides an economical and nutritionally balanced meal plan that takes into account the preferences and health information of all family members, as well as the user's emotional state, and also suggests an efficient shopping route, thereby reducing the burden of housework.

[0326] "Means for collecting family preferences and health information" refers to a means by which users input their family's food preferences, allergy information, health checkup results, etc. through an application, which then converts this information into a data format and collects it.

[0327] The "means for collecting user emotional data" refers to a means for acquiring user emotional data from facial expressions, tone of voice, input text, etc. when the user uses an application.

[0328] "Means for collecting price information and sale information from nearby stores" refers to a means in which the server sends requests to the websites or APIs of nearby stores, periodically obtains product names, prices, sale information, etc., and stores them in a database.

[0329] "Means for generating optimal menus based on collected information" refers to means for creating optimal menus using algorithms based on family preferences, health information, user emotional data, store price information, etc.

[0330] The "means for generating a shopping list based on the generated menu" is a means for listing necessary ingredients based on the generated menu and presenting it as a shopping list.

[0331] The "means for proposing the optimal shopping route" is a means for calculating and proposing a shopping route that minimizes costs based on collected price information from multiple stores.

[0332] The "means for calculating the nutritional balance of the generated menu and providing it to the user" is a means for calculating the nutritional components and calories of each ingredient included in the proposed menu and providing the results to the user.

[0333] The "means for proposing a menu according to the emotional state of the user" is a means for creating and proposing an optimal menu according to the emotional state of the user based on the collected emotional data of the user.

[0334] This system proposes economical and nutritionally balanced meals and provides efficient shopping routes based on family preferences, health information, price information from nearby stores, and user emotion data. This system is composed of a server, terminals, users, and an emotion engine.

[0335] First, the user enters information about their family's food preferences, allergies, and health checkup results through the application. For example, they may enter information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories." The device collects this data, converts it into a data format, and sends it to the server. Standard data formats such as JSON are used for this purpose.

[0336] Next, while the user is using the application, the emotion engine collects emotion data from facial expressions, voice tones, input text, etc. For example, if the user inputs text or voice tones that indicate they are feeling stressed, the emotion engine detects this and the device sends the detected emotion data to the server.

[0337] In addition, the server periodically sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. For example, information that cabbage costs 98 yen and chicken breast costs 200 yen at Supermarket A is collected and stored in a database. This process uses web scraping technology and API integration.

[0338] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and store price information. For example, based on information such as "the second daughter doesn't like fish," "the mother needs to limit her calories," and "the user is currently under stress," the server might suggest a "healthy salad with cabbage and chicken breast." This algorithm takes into account nutritional balance and emotional state, so machine learning models and rule-based systems may be used.

[0339] Based on the generated menu, the server runs an algorithm to calculate the nutritional content and calories of each ingredient and sends information such as "This menu contains 500 kcal and 25 g of protein per meal" to the device. This data is important for providing users with a nutritionally balanced diet.

[0340] The server then creates a list of ingredients needed based on the generated menu, generating a shopping list such as "1 cabbage, 300g chicken breast, 200g broccoli," and sends it to the terminal.The server then compares price information from multiple stores, calculates the lowest-cost shopping route, and suggests the optimal route to the user, such as "buy the cabbage at supermarket A and the chicken breast at supermarket B."

[0341] Finally, the server generates and suggests a menu based on the user's emotional state based on the emotional data received from the emotion engine. For example, if the user is tired, it will suggest a menu with a relaxing effect, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[0342] As a specific example of a prompt, the above-mentioned process is performed by inputting sentences such as "The second daughter does not like fish, and the mother needs to limit her calories. She has been feeling stressed recently" into the application.

[0343] As described above, by providing meal plans that take into account the preferences and health information of all family members, as well as the user's emotional state, and also suggesting efficient shopping routes, household meal planning and shopping can be dramatically improved.

[0344] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0345] Step 1:

[0346] The user launches the application and inputs information such as the family's food preferences, allergy information, and health checkup results. The input data is information such as "The second daughter doesn't like fish" and "The mother needs to limit her calories." The device collects this information, converts it to JSON format, and sends it to the server. The data entered here is in text format, which is then processed and sent to the server as the body of the API request.

[0347] Step 2:

[0348] While the user is using the application, the emotion engine collects emotion data from the user's facial expressions, voice tone, input text, etc. Specifically, the user inputs "I've been feeling stressed lately." This data is collected by the device and sent as a request to the server. The input emotion data is used to determine the user's emotional state through text and voice analysis.

[0349] Step 3:

[0350] The server periodically sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. For example, the server obtains price information from Supermarket A, such as "cabbage is 98 yen, chicken breast is 200 yen," and stores this information in a database. The data obtained is updated periodically, so it is necessary to obtain the latest price information.

[0351] Step 4:

[0352] The server uses AI models and algorithms to generate optimal menus based on family preferences, health information, emotional data, and store price information. The input data are family preferences, health information, emotional data, and store price information, and the output is specific menu suggestions. For example, an algorithm might suggest a "healthy salad with cabbage and chicken breast." By taking emotional data into account, a menu suited to the user's mood is generated.

[0353] Step 5:

[0354] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the proposed menu. The input data is the generated menu, and the output is a detailed description of its nutritional balance. For example, the server calculates information such as "This menu contains 500 kcal and 25 g of protein per serving" and sends it to the device.

[0355] Step 6:

[0356] The server creates a shopping list by listing the ingredients needed based on the menu. The input data is the menu and information about each ingredient, and the output is a specific shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" is created and sent to the terminal.

[0357] Step 7:

[0358] The server compares price information from multiple stores and runs an algorithm to calculate a shopping route that minimizes costs. The input data is the price information from each store, and the output is the optimal shopping route. For example, it suggests a route such as "buy cabbage at supermarket A and chicken breast at supermarket B" and sends it to the terminal.

[0359] Step 8:

[0360] Based on the emotional data received by the server from the emotion engine, the server generates and suggests a menu that matches the user's emotional state. The input data is emotional data, preferences, and health information, and the output is a menu that is appropriate for the user's emotional state at the time. For example, if the user is tired, the system suggests "relaxing chamomile tea and avocado salad."

[0361] Through these steps, the system provides meal plans that take into account family preferences, health information, and the user's emotional state, and also suggests efficient shopping routes, dramatically improving household meal planning and shopping.

[0362] (Application example 2)

[0363] 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."

[0364] Conventional menu suggestion systems often generate menus that only take into account family preferences and health information, and are unable to provide meal plans that reflect the user's emotional state. As a result, they are unable to provide appropriate meal suggestions that reflect the user's emotional state or temporary changes in physical condition, making it difficult to provide a satisfying and nutritionally balanced diet. Furthermore, there has been a lack of systems that effectively utilize price information from nearby stores to suggest shopping routes that increase economic efficiency.

[0365] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting family preference and health information, means for analyzing the user's emotional state, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information and the analyzed emotional state, means for generating a shopping list based on the generated menu, and means for suggesting an optimal shopping route. This makes it possible to suggest a nutritionally balanced menu that takes the user's emotional state into consideration, and an optimal shopping route that takes economic efficiency into account.

[0366] "Family preferences" refers to individual preference information regarding favorite and disliked foods and ingredients of each family member.

[0367] "Health Information" means data about the health status of family members, including allergy information, medical test results, and specific nutrient needs or restrictions.

[0368] "Emotional state" refers to the user's current mood or emotional state, including feelings such as stress, relaxation, satisfaction, fatigue, etc.

[0369] "Price information" is data about the prices of products offered at nearby stores and supermarkets.

[0370] "Special sale information" is information about sales and promotions in which specific products are discounted for a limited time.

[0371] "Analyzing" is a process of processing collected data or information to arrive at a specific conclusion or result.

[0372] "Menu" refers to the meal plan and menu composition proposed based on set conditions.

[0373] A "shopping list" is a list of ingredients and products a user needs, including items necessary to create a specific meal plan.

[0374] The "optimal shopping route" refers to the shortest or most efficient route that a user should take to purchase the products they need, taking into consideration cost and efficiency.

[0375] A "system" is a collection of hardware and software that interrelate to accomplish a particular purpose.

[0376] This system proposes optimal menus, generates shopping lists, and suggests optimal shopping routes based on the user's family preferences, health information, price information from nearby stores, and the user's emotional state. This system is composed of a server, terminals, users, and an emotion engine.

[0377] Gathering family preferences and health information

[0378] The user inputs information about their family's food preferences, allergy information, health checkup results, etc. through the device. Specifically, data about each family member's likes and dislikes and health status is entered, and the device collects this information and sends it to the server.

[0379] Collecting Emotional Data

[0380] When a user uses a device (smart glasses or smartphone), the emotion engine collects emotion data from the user's facial expressions, voice tone, input text, etc. For example, data is collected depending on whether the user is stressed or relaxed.

[0381] Gathering price and sale information from nearby stores

[0382] The server sends requests to local stores' websites and APIs to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database.

[0383] Menu generation

[0384] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information from nearby stores. By taking emotional data into account, the server suggests menus that suit the user's mood and health condition.

[0385] Nutritional Balance Calculation

[0386] The server runs an algorithm to calculate the nutritional content and calories of each ingredient in the proposed menu, and the results are sent to the device and displayed to the user.

[0387] Generate a shopping list

[0388] The server creates a list of ingredients based on the proposed menu, and the generated shopping list is sent to the terminal and displayed to the user.

[0389] Proposal of the best shopping route

[0390] The server compares prices from multiple stores and runs an algorithm to calculate the shopping route that minimizes costs. The results are sent to the terminal, and the user is shown an efficient shopping route.

[0391] Emotion-based menu suggestions

[0392] Based on the emotional data received from the emotion engine, the server generates an optimal menu according to the user's emotional state. For example, if the user is tired, it will suggest nutritious meals, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[0393] Specific examples

[0394] If a user inputs "my second daughter doesn't like fish" or "my mother needs to limit her calories," and the emotion engine detects that they have been under a lot of stress recently, the server will use this information to suggest relaxing menu items, and will also consider nutritional balance and suggest a "healthy salad with cabbage and chicken breast." This allows for a meal plan that not only satisfies the whole family, but also takes into account their emotional state.

[0395] Prompts for generative AI models

[0396] For example, use the following prompt:

[0397] "Please suggest a relaxing meal plan that takes into account the user's current emotional state (stress) and the family's preferences (the second daughter doesn't like fish, and the mother needs to limit her calories)."

[0398] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0399] Step 1:

[0400] The user inputs their family's food preferences, allergy information, and health checkup results through the device. This input includes detailed data on each family member's likes and dislikes and health conditions. The device collects this information and sends it to the server. The input information is stored in a database and used for subsequent analysis and menu generation.

[0401] Step 2:

[0402] Through the user's device (smart glasses or smartphone), the emotion engine collects emotional data from the user's facial expressions, voice tone, and input text. The emotion engine uses facial recognition technology to analyze the image data captured by the camera and determine the user's emotional state. The analysis results are sent to the server and used for subsequent processes.

[0403] Step 3:

[0404] The server obtains product name, price, and sale information from nearby stores via their websites and APIs. This information collection process uses a script that accesses the store's database at a specified frequency to continuously obtain the latest price and sale information. The collected data is stored in the server's database and used for subsequent menu generation.

[0405] Step 4:

[0406] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information. The algorithm uses various data to calculate menus that will satisfy the entire family, and the resulting menus are sent to the user's device. The menu generation takes into account multiple parameters, including pre-set nutritional balance, the user's emotional state, and economic efficiency.

[0407] Step 5:

[0408] Based on the generated menu, the server lists the necessary ingredients and generates a shopping list. The shopping list includes detailed information such as the required amount of each ingredient and recommended purchasing locations. The shopping list is sent to the user's device and can be viewed by the user.

[0409] Step 6:

[0410] The server calculates the optimal shopping route that minimizes costs based on price information collected from multiple stores. This process uses a price comparison algorithm to derive the most economical shopping route based on the price information of each store. The calculation results are sent to the terminal and displayed to the user.

[0411] Step 7:

[0412] Based on the suggested shopping route and emotion data, the server makes a final, emotion-based menu suggestion. A nutritionally balanced menu is displayed according to the user's mood and health condition, and the user can shop and cook accordingly.

[0413] This allows users to easily and efficiently plan optimal meals based on each family member's preferences, health status, and real-time emotional state.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] [Second embodiment]

[0418] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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."

[0430] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby supermarkets, and generates shopping lists and routes. This system is composed of a server, terminals, and users, and its specific operation and embodiments are described below.

[0431] Gathering family preferences and health information

[0432] Through the application, users input their family's food preferences, allergy information, and health checkup results. Specifically, data on each family member's likes and dislikes and health status is entered, and the device collects this information. For example, if a user enters information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," the device sends this information to the server.

[0433] Gathering price information and special offers from nearby supermarkets

[0434] The server sends requests to the websites and APIs of nearby supermarkets to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database. For example, it might collect information that Supermarket A is selling cabbage for 98 yen and chicken breast for 200 yen.

[0435] Menu generation

[0436] The server generates optimal menus based on collected family preferences, health information, and price information from nearby supermarkets. The server uses a specific algorithm to calculate meal plans that will satisfy the entire family and sends the results to the device. For example, it suggests a balanced menu that takes into account the mother's calorie restrictions while also reflecting the second daughter's dislike of fish.

[0437] Nutritional Balance Calculation

[0438] The server calculates the nutritional content and calories of the proposed menu and sends the results to the device. The device then displays information about the calories and nutrients per meal to the user. For example, it may display information such as "This menu contains 500 kcal and 25 g of protein per meal."

[0439] Generate a shopping list

[0440] The server generates a list of ingredients based on the generated menu and sends it to the device. The user can purchase ingredients efficiently based on this shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" is generated.

[0441] Proposal of the best shopping route

[0442] The server compares prices from multiple supermarkets and calculates the most economical shopping route. The results are sent to the device, and suggestions are made to help the user shop efficiently. For example, a route such as "Purchase cabbage at supermarket A and chicken breast at supermarket B" may be suggested.

[0443] With these features, the system supports daily meal planning at home, providing economical and nutritionally balanced meal plans that also reflect the preferences and health status of each family member, ensuring meals that satisfy all members.

[0444] The processing flow will be explained below.

[0445] Specific program flow

[0446] Gathering family preferences and health information

[0447] Step 1:

[0448] The user opens the application's settings screen and enters each family member's food preferences, allergy information, health check results, etc.

[0449] Step 2:

[0450] The terminal transmits the input information to the server.

[0451] Step 3:

[0452] The server stores the received family preferences and health information in a database.

[0453] Gathering price information and special offers from nearby supermarkets

[0454] Step 1:

[0455] The server periodically sends data collection requests to the local supermarket's website or API.

[0456] Step 2:

[0457] The server extracts product name, price, and sale information from the response.

[0458] Step 3:

[0459] The server stores the extracted price and sale information in a database.

[0460] Menu generation

[0461] Step 1:

[0462] The server uses an algorithm to generate the optimal menu based on the family's preferences, health information, and price information from nearby supermarkets.

[0463] Step 2:

[0464] The server sends the contents of the generated menu to the terminal.

[0465] Step 3:

[0466] The terminal displays the contents of the proposed menu to the user.

[0467] Nutritional Balance Calculation

[0468] Step 1:

[0469] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the menu it proposes.

[0470] Step 2:

[0471] The server formats the results and sends them to the terminal.

[0472] Step 3:

[0473] The device displays information about calories and nutrients per meal to the user.

[0474] Generate a shopping list

[0475] Step 1:

[0476] The server runs an algorithm that lists the ingredients needed based on the proposed menu.

[0477] Step 2:

[0478] The server sends the generated shopping list to the terminal.

[0479] Step 3:

[0480] The terminal displays the shopping list to the user.

[0481] Proposal of the best shopping route

[0482] Step 1:

[0483] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs.

[0484] Step 2:

[0485] The server sends the calculated optimal shopping route to the terminal.

[0486] Step 3:

[0487] The terminal displays an efficient shopping route to the user.

[0488] Through the above process, users can receive menu suggestions that suit their family's preferences and health status, and enjoy an economical shopping experience.

[0489] Example 1

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

[0491] The present invention aims to solve the problem of efficiently collecting family preferences, health information, and price information from nearby stores, and proposing economical and nutritionally balanced menus based on this information. It also aims to solve the problem of a lack of means for users to shop efficiently and the difficulty of generating menus that correspond to specific family health conditions and preferences by optimizing shopping lists and shopping routes.

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

[0493] In this invention, the server includes a means for collecting family preference and health information, a means for collecting price information and sale information from nearby stores, a means for generating an optimal menu based on the collected information, a means for calculating nutritional components and calories for the generated menu, a means for generating a shopping list based on the generated menu, and a means for suggesting an optimal shopping route. This allows for economical and nutritionally balanced menu suggestions while taking into account the dietary preferences and health status of all family members. Furthermore, by supporting efficient shopping, the server can save users time and money.

[0494] "Family preferences and health information" is detailed data entered by the user, such as food preferences, allergy information, and health checkup results of family members.

[0495] "Price information and sale information from nearby stores" refers to product price and sale information obtained from supermarket and store websites and APIs.

[0496] The "means for generating optimal menus" is a process that uses an algorithm to automatically create economical and nutritionally balanced menus based on user input information and store information.

[0497] "Means for calculating nutritional components and calories" refers to a system that calculates and quantifies the amount of nutrients and energy contained in the proposed menu.

[0498] The "means for generating a shopping list" is a function that compiles the necessary ingredients and their quantities in list form based on the generated menu.

[0499] The "means for proposing the optimal shopping route" is a function that compares price information from multiple stores, calculates the most cost-effective shopping route, and presents it to the user.

[0500] The present invention relates to a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby stores, and generates shopping lists and routes. This system is composed of a server, terminals, and users.

[0501] Gathering family preferences and health information

[0502] The user enters information about their family's food preferences, allergies, and health checkup results through a dedicated application. Specifically, the user enters information such as "My mother has diabetes and needs a low-carb diet" or "My second daughter is allergic to fish" into the application form. The device collects this information and sends it to a server to be stored in a database.

[0503] Gathering price information and special offers from nearby stores

[0504] The server sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. Specifically, it uses web scraping technology or a specified API to collect product information from each store. For example, the server periodically performs a process such as "collecting information on cabbage for 98 yen from store A and chicken breast for 200 yen from store B." The collected information is then saved in a database in the latest state.

[0505] Menu generation

[0506] The server generates optimal menus based on collected family preferences, health information, and price information. Using an AI algorithm, it proposes economical and nutritionally balanced menus that will satisfy the whole family. For example, it generates meal plans that meet conditions such as "fish-free dishes because the second daughter dislikes fish" and "low-carb menus in consideration of the mother's diabetes."

[0507] Nutritional Balance Calculation

[0508] The server calculates the nutritional content and calories for the generated menu. Specifically, it uses nutrition calculation software to automatically calculate the nutrient and energy content of each dish. For example, it calculates information such as "the calories per meal of the proposed menu are 510 kcal, 30 g of protein, and 60 g of carbohydrates," and sends the results to the device.

[0509] Generate a shopping list

[0510] The server generates a list of the ingredients needed based on the menu that has been generated. Specifically, it lists all the ingredients included in the menu and calculates the required quantities. For example, it creates a shopping list such as "1 cabbage, 300g chicken breast, 200g broccoli" and sends it to the terminal. The terminal displays this list to the user.

[0511] Proposal of the best shopping route

[0512] The server calculates the most economical shopping route based on price information from multiple stores. Specifically, it compares product prices at each store and calculates the most cost-effective shopping route. For example, it suggests a route such as "buy cabbage at store A and chicken breast at store B." The calculation results are sent to the terminal and displayed to the user.

[0513] Specific examples and examples of prompts for generative AI models

[0514] Examples:

[0515] 1. The user fills in the app form with information such as "My mother has diabetes and needs to limit her carbohydrate intake" and "My second daughter is allergic to fish" and submits it.

[0516] 2. The server collects information such as "cabbage 98 yen" from nearby store A and "chicken breast 200 yen" from store B.

[0517] 3. Based on this information, the server uses an AI algorithm to suggest a "low-carb stir-fried vegetable and chicken breast dish."

[0518] 4. The nutritional balance of the menu is calculated as 510 kcal, 28 g of protein, and 20 g of carbohydrates per meal.

[0519] 5. The server generates a shopping list such as "1 cabbage, 300g chicken breast, 2 bell peppers."

[0520] 6. Propose an efficient route to visit Supermarket A and Supermarket B.

[0521] Example prompt for a generative AI model:

[0522] "Enter your family's food preferences and health information. For example, your oldest son has a milk allergy, and your father has high blood pressure."

[0523] "Gather shrimp and broccoli prices at nearby stores."

[0524] "Please suggest an economical and nutritionally balanced menu for my mother, who needs to limit carbohydrates, and my second daughter, who has a fish allergy."

[0525] "Calculate the nutritional content and calories of the suggested meals and provide per-serving information to the user."

[0526] "Generate a list of ingredients based on the menu you create."

[0527] "Based on the price information collected, suggest the most economical shopping route."

[0528] This will support daily meal planning at home, making it possible to provide economical and nutritionally balanced meal plans that also reflect the preferences and health conditions of each family member, ensuring meals that satisfy all members.

[0529] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0530] Step 1: Enter your family's preferences and health information

[0531] Input: The user opens the application and enters their family's food preferences, allergy information, and health check results.

[0532] Specific operation: The user enters information such as "My mother has diabetes and needs a low-carb diet" or "My second daughter is allergic to fish" into a special form in the app.

[0533] Data processing: Input data is collected by the terminal and stored in a database in an appropriate format.

[0534] Output: The user's input information is sent to the server and stored in a database.

[0535] Step 2: Gather price information and special offers from nearby stores

[0536] Input: The server sends a request to get information about the websites or APIs of the specified nearby stores.

[0537] Specific operations: Use web scraping technology and APIs to collect product names, prices, and special offers from each store.

[0538] Data processing: The collected information is stored in the database in the most up-to-date state.

[0539] Output: The latest price information and special offers from the stores are stored in a database.

[0540] Step 3: Generate the optimal menu

[0541] Input: The server receives the family preference and health information collected in step 1 and the store price information collected in step 2.

[0542] How it works: Using AI algorithms, it analyzes the collected data and generates economical and nutritionally balanced meals that will satisfy the whole family.

[0543] Data calculation: Algorithms are run based on user preferences, health information, and price information to calculate the most appropriate combination.

[0544] Output: A menu optimized to the family's needs is generated and passed on to the next processing step.

[0545] Step 4: Calculate your nutritional balance

[0546] Input: The server receives the menu information generated in step 3.

[0547] Specific Actions: Use nutrition calculation software to calculate the nutritional content and calories of each dish.

[0548] Data calculation: Calculate the nutrients and calories for all ingredients included in the menu and calculate the total value.

[0549] Output: The nutritional information and calories for each meal are calculated and the results are ready to be provided to the user.

[0550] Step 5: Generate a shopping list

[0551] Input: The server receives the menu information generated in step 3.

[0552] Specific actions: List all ingredients included in the menu, calculate the required quantities, and compile them in list form.

[0553] Data processing: Calculate the required amount of each ingredient and format it into a shopping list.

[0554] Output: A specific shopping list (e.g., 1 cabbage, 300g chicken breast, 200g broccoli) is generated and sent to the device.

[0555] Step 6: Optimal shopping route suggestions

[0556] Input: The server re-uses the price information for each store collected in step 2.

[0557] Specific operation: Calculates the most economical shopping route based on price information from multiple stores.

[0558] Data calculation: Compare product prices at each store and calculate the most cost-effective shopping route.

[0559] Output: The optimal shopping route based on the shopping list (e.g., store A for cabbage, store B for chicken breast) is proposed and sent to the terminal.

[0560] This makes it clear what specific data processing and calculations are performed at each processing step and how the system operates.

[0561] (Application example 1)

[0562] 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."

[0563] In modern households, preparing economical and nutritionally balanced meals while taking into account the family's food preferences and health status is a significant burden. Furthermore, there is no system that efficiently collects price information from nearby stores and food delivery services and proposes optimal shopping lists and routes. Furthermore, while there is a need for menu suggestions using generative AI models and flexible menu generation using prompts, there is no comprehensive solution available.

[0564] 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.

[0565] In this invention, the server includes means for collecting family preference and health information, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information, means for generating a shopping list based on the generated menu, means for suggesting an optimal shopping route, means for collecting price information from food delivery services and suggesting menus that can be delivered, means for generating a menu using a generative AI model, and means for inputting prompt text and generating a menu. This makes it easy to prepare economical and nutritionally balanced meals based on family preferences and health status, and also makes it possible to suggest optimal shopping lists, shopping routes, and even food delivery options.

[0566] "Family preferences and health information" refers to each family member's food preferences, allergy information, health checkup results, etc.

[0567] "Price Information" refers to price data for products at nearby stores and food delivery services.

[0568] "Special sale information" refers to sales and discount information offered by stores and services.

[0569] A "menu" refers to a meal plan consisting of a combination of multiple dishes and ingredients.

[0570] A "shopping list" refers to a list of ingredients and products that should be purchased based on a specific menu.

[0571] A "shopping route" refers to the optimal shopping route for efficiently visiting multiple stores.

[0572] A "food delivery service" refers to a service that allows you to order meals via the internet or an app and have them delivered to your home or other location.

[0573] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate new information and ideas from data.

[0574] A "prompt" is an instruction entered into a generative AI model, and refers to text that guides a specific output result.

[0575] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, price information and sale information from nearby stores, and also provides shopping lists, shopping routes and even food delivery options. This system is composed of a server, terminals and users, and its specific operation and embodiments are described below.

[0576] Gathering family preferences and health information

[0577] Users use a smartphone app to input information about their family's food preferences, allergies, and health checkup results. For example, if a family member inputs information such as "I need to limit my calories" or "I don't like fish," the device collects this information and sends it to the server.

[0578] Gathering price information and special offers from nearby stores

[0579] The server sends requests to the websites and APIs of nearby stores to obtain product names, prices, and special offers. The collected information is updated periodically, and the latest price and special offer information is stored in a database. For example, information such as "Store A is selling cabbage for 98 yen and chicken breast for 200 yen" is collected.

[0580] Gathering information on food delivery services

[0581] The server sends requests to the APIs of multiple food delivery services to obtain information on food prices and delivery areas, providing not only information on nearby restaurants but also delivery service options.

[0582] Menu generation

[0583] The server generates optimal menus based on collected family preferences, health information, and pricing information from nearby restaurants and food delivery services. Using a generative AI model, it calculates meal plans that will satisfy the whole family based on all the collected information. For example, it might suggest a "balanced menu that takes into account calorie restrictions and does not include fish."

[0584] Nutritional Balance Calculation

[0585] The server calculates the nutritional content and calories of the proposed menu and sends the results to the device. The device then displays information about the calories and nutrients per meal to the user. For example, it might say, "This menu contains 500 kcal and 25 g of protein per meal."

[0586] Generate a shopping list

[0587] The server generates a list of ingredients based on the generated menu and sends it to the device. The user can purchase ingredients efficiently based on this shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" may be generated.

[0588] Proposal of the best shopping route

[0589] The server compares prices from multiple stores and calculates the most economical shopping route. The results are sent to the device, and suggestions are made to help the user shop efficiently. For example, a route such as "Purchase cabbage from store A and chicken breast from store B" may be suggested.

[0590] This allows users to efficiently prepare economical and healthy meals. An example of a specific prompt would be:

[0591] Prompt Sentence Examples

[0592] "User Information:

[0593] Mother: Calorie restriction, dislike of fish

[0594] Second daughter: I hate fish

[0595] Super API URLs:

[0596] https: / / api.storea.com / products

[0597] https: / / api.storeb.com / products

[0598] Requirements:

[0599] Please suggest economical and nutritionally balanced meals.

[0600] Please limit your calorie intake.

[0601] Do not use fish.

[0602] Generate menus based on pricing information from nearby restaurants and food delivery services.

[0603] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0604] Step 1:

[0605] Users input their family's preferences and health information through a smartphone app. Specifically, users input each family member's name, allergy information, likes and dislikes, health checkup results, etc. The input information is stored on the device and sent to the server in real time.

[0606] Input: Family preferences and health information

[0607] Output: Data sent to the server

[0608] Step 2:

[0609] The server sends requests to the websites and APIs of nearby stores to get product names, prices, special offers, etc. The server periodically updates this information and stores it in a database.

[0610] Input: Store API URL

[0611] Output: A database containing pricing and special offers

[0612] Step 3:

[0613] The server sends requests to the APIs of multiple food delivery services to obtain information on the price and delivery area of ​​each dish. The collected information is stored in a database that is updated periodically.

[0614] Input: API URL of food delivery service

[0615] Output: A database containing information about food delivery services

[0616] Step 4:

[0617] The server generates the optimal menu based on collected family preferences, health information, and price information from nearby stores and food delivery services. The server uses a generative AI model to generate a menu based on prompts. This is achieved by using an algorithm to optimally combine each piece of information.

[0618] Input: Family preferences and health information, price information, delivery information, prompt text

[0619] Output: Optimal menu

[0620] Step 5:

[0621] The server calculates the nutritional components and calories for the generated menu. Specifically, it calculates the calories and nutrients such as protein, fat, and carbohydrates per meal based on the nutritional data of each ingredient, and sends the results to the device.

[0622] Input: Generated menu

[0623] Output: Nutritional information

[0624] Step 6:

[0625] The server generates a list of ingredients based on the menu and sends it to the terminal. The user can use this shopping list to purchase ingredients efficiently.

[0626] Input: Generated menu

[0627] Output: Shopping list

[0628] Step 7:

[0629] The server compares prices from multiple stores and calculates the most economical shopping route, which is then sent to the terminal and suggested to the user.

[0630] Input: Price information, shopping list

[0631] Output: Optimal shopping route

[0632] 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.

[0633] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby supermarkets, and further provides meal plans that correspond to the user's emotional state by combining an emotion engine. This system is composed of a server, a terminal, a user, and an emotion engine, and its specific operation and embodiments are described below.

[0634] Gathering family preferences and health information

[0635] Through the application, users input information about their family's food preferences, allergies, health checkup results, etc. Specifically, data about each family member's likes and dislikes and health status is entered, and the device collects this information. For example, if a user enters information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," the device sends this information to the server.

[0636] Collecting Emotional Data

[0637] As users use the application, the emotion engine collects emotional data from their facial expressions, voice tone, and input text, for example, depending on whether they are stressed or relaxed.

[0638] Gathering price information and special offers from nearby supermarkets

[0639] The server sends requests to the websites and APIs of nearby supermarkets to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database. For example, it might collect information that Supermarket A is selling cabbage for 98 yen and chicken breast for 200 yen.

[0640] Menu generation

[0641] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information from nearby supermarkets. By taking emotional data into account, the server can suggest menus that suit the user's mood. For example, if the user is feeling stressed, it can suggest menus that have a relaxing effect.

[0642] Nutritional Balance Calculation

[0643] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the proposed menu. The results are sent to the device and displayed to the user. For example, information such as "This menu has 500 kcal and 25 g of protein per meal" is displayed.

[0644] Generate a shopping list

[0645] The server runs an algorithm to generate a list of ingredients based on the proposed menu. The results are sent to the device and displayed to the user as a shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" might be generated.

[0646] Proposal of the best shopping route

[0647] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs. The results are sent to the device and displayed to the user as an efficient shopping route. For example, a route such as "Purchase cabbage at supermarket A and chicken breast at supermarket B" may be presented.

[0648] Emotion-based menu suggestions

[0649] Based on the emotional data received from the emotion engine, the server generates an optimal menu according to the user's emotional state. For example, if the user is tired, it will suggest nutritious meals, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[0650] Specific examples

[0651] If a user inputs information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," and the emotion engine detects that they have been under a lot of stress recently, the server will use this information to suggest menu items that have a relaxing effect, and will also suggest a "healthy salad with cabbage and chicken breast" based on nutritional balance. This allows for a meal plan that not only satisfies the whole family, but also takes into account their emotional state.

[0652] This system dramatically improves home meal planning, making meals more economical, nutritionally balanced, and emotionally conscious.

[0653] The processing flow will be explained below.

[0654] Specific processing flow of the program

[0655] Gathering family preferences and health information

[0656] Step 1:

[0657] The user opens the application's settings screen and enters each family member's food preferences, allergy information, health check results, etc.

[0658] Step 2:

[0659] The terminal transmits the input information to the server.

[0660] Step 3:

[0661] The server stores the received family preferences and health information in a database.

[0662] Collecting Emotional Data

[0663] Step 1:

[0664] When a user uses the application, the emotion engine captures the user's facial expression data using a camera, and also obtains emotion data from voice tones and text input.

[0665] Step 2:

[0666] The device transmits the collected emotion data to the server.

[0667] Step 3:

[0668] The server analyzes the emotional data to determine the user's current emotional state.

[0669] Gathering price information and special offers from nearby supermarkets

[0670] Step 1:

[0671] The server periodically sends data collection requests to the local supermarket's website or API.

[0672] Step 2:

[0673] The server extracts product name, price, and sale information from the response.

[0674] Step 3:

[0675] The server stores the extracted price and sale information in a database.

[0676] Menu generation

[0677] Step 1:

[0678] The server uses an algorithm to generate the optimal menu based on the family's preferences, health information, emotional data, and price information from nearby supermarkets.

[0679] Step 2:

[0680] The server sends the contents of the generated menu to the terminal.

[0681] Step 3:

[0682] The terminal displays the contents of the proposed menu to the user.

[0683] Nutritional Balance Calculation

[0684] Step 1:

[0685] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the menu it proposes.

[0686] Step 2:

[0687] The server formats the results and sends them to the terminal.

[0688] Step 3:

[0689] The device displays information about calories and nutrients per meal to the user.

[0690] Generate a shopping list

[0691] Step 1:

[0692] The server runs an algorithm that lists the ingredients needed based on the proposed menu.

[0693] Step 2:

[0694] The server sends the generated shopping list to the terminal.

[0695] Step 3:

[0696] The terminal displays the shopping list to the user.

[0697] Proposal of the best shopping route

[0698] Step 1:

[0699] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs.

[0700] Step 2:

[0701] The server sends the calculated optimal shopping route to the terminal.

[0702] Step 3:

[0703] The terminal displays an efficient shopping route to the user.

[0704] Emotion-based menu suggestions

[0705] Step 1:

[0706] Based on the emotion data received from the emotion engine, the server executes an algorithm to generate an optimal menu according to the user's emotional state.

[0707] Step 2:

[0708] The server sends the menu contents based on the emotion data to the terminal.

[0709] Step 3:

[0710] The device will display menu suggestions based on the user's emotions. For example, if the user is feeling stressed, it will suggest a menu with a relaxing effect, or if the user wants to feel energized, it will suggest a menu using their favorite ingredients.

[0711] Through these steps, users can enjoy an economical and nutritionally balanced meal plan tailored to their emotional state.

[0712] Example 2

[0713] 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."

[0714] In modern home cooking, it is difficult to propose economical and nutritionally balanced menus that take into account the preferences and health information of all family members. There is also a demand for meal plans that take into account the user's emotional state, but this is difficult to achieve with conventional methods. Furthermore, there are few systems that suggest efficient shopping routes, which increases the burden of housework.

[0715] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting family member preference and health information, means for collecting user emotion data, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information, means for generating a shopping list based on the generated menu, means for suggesting an optimal shopping route, means for calculating the nutritional balance of the generated menu and providing it to the user, and means for suggesting a menu based on the user's emotional state. This provides an economical and nutritionally balanced meal plan that takes into account the preferences and health information of all family members, as well as the user's emotional state, and also suggests an efficient shopping route, thereby reducing the burden of housework.

[0716] "Means for collecting family preferences and health information" refers to a means by which users input their family's food preferences, allergy information, health checkup results, etc. through an application, which then converts this information into a data format and collects it.

[0717] The "means for collecting user emotional data" refers to a means for acquiring user emotional data from facial expressions, tone of voice, input text, etc. when the user uses an application.

[0718] "Means for collecting price information and sale information from nearby stores" refers to a means in which the server sends requests to the websites or APIs of nearby stores, periodically obtains product names, prices, sale information, etc., and stores them in a database.

[0719] "Means for generating optimal menus based on collected information" refers to means for creating optimal menus using algorithms based on family preferences, health information, user emotional data, store price information, etc.

[0720] The "means for generating a shopping list based on the generated menu" is a means for listing necessary ingredients based on the generated menu and presenting it as a shopping list.

[0721] The "means for proposing the optimal shopping route" is a means for calculating and proposing a shopping route that minimizes costs based on collected price information from multiple stores.

[0722] The "means for calculating the nutritional balance of the generated menu and providing it to the user" is a means for calculating the nutritional components and calories of each ingredient included in the proposed menu and providing the results to the user.

[0723] The "means for proposing a menu according to the emotional state of the user" is a means for creating and proposing an optimal menu according to the emotional state of the user based on the collected emotional data of the user.

[0724] This system proposes economical and nutritionally balanced meals and provides efficient shopping routes based on family preferences, health information, price information from nearby stores, and user emotion data. This system is composed of a server, terminals, users, and an emotion engine.

[0725] First, the user enters information about their family's food preferences, allergies, and health checkup results through the application. For example, they may enter information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories." The device collects this data, converts it into a data format, and sends it to the server. Standard data formats such as JSON are used for this purpose.

[0726] Next, while the user is using the application, the emotion engine collects emotion data from facial expressions, voice tones, input text, etc. For example, if the user inputs text or voice tones that indicate they are feeling stressed, the emotion engine detects this and the device sends the detected emotion data to the server.

[0727] In addition, the server periodically sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. For example, information that cabbage costs 98 yen and chicken breast costs 200 yen at Supermarket A is collected and stored in a database. This process uses web scraping technology and API integration.

[0728] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and store price information. For example, based on information such as "the second daughter doesn't like fish," "the mother needs to limit her calories," and "the user is currently under stress," the server might suggest a "healthy salad with cabbage and chicken breast." This algorithm takes into account nutritional balance and emotional state, so machine learning models and rule-based systems may be used.

[0729] Based on the generated menu, the server runs an algorithm to calculate the nutritional content and calories of each ingredient and sends information such as "This menu contains 500 kcal and 25 g of protein per meal" to the device. This data is important for providing users with a nutritionally balanced diet.

[0730] The server then creates a list of ingredients needed based on the generated menu, generating a shopping list such as "1 cabbage, 300g chicken breast, 200g broccoli," and sends it to the terminal.The server then compares price information from multiple stores, calculates the lowest-cost shopping route, and suggests the optimal route to the user, such as "buy the cabbage at supermarket A and the chicken breast at supermarket B."

[0731] Finally, the server generates and suggests a menu based on the user's emotional state based on the emotional data received from the emotion engine. For example, if the user is tired, it will suggest a menu with a relaxing effect, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[0732] As a specific example of a prompt, the above-mentioned process is performed by inputting sentences such as "The second daughter does not like fish, and the mother needs to limit her calories. She has been feeling stressed recently" into the application.

[0733] As described above, by providing meal plans that take into account the preferences and health information of all family members, as well as the user's emotional state, and also suggesting efficient shopping routes, household meal planning and shopping can be dramatically improved.

[0734] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0735] Step 1:

[0736] The user launches the application and inputs information such as the family's food preferences, allergy information, and health checkup results. The input data is information such as "The second daughter doesn't like fish" and "The mother needs to limit her calories." The device collects this information, converts it to JSON format, and sends it to the server. The data entered here is in text format, which is then processed and sent to the server as the body of the API request.

[0737] Step 2:

[0738] While the user is using the application, the emotion engine collects emotion data from the user's facial expressions, voice tone, input text, etc. Specifically, the user inputs "I've been feeling stressed lately." This data is collected by the device and sent as a request to the server. The input emotion data is used to determine the user's emotional state through text and voice analysis.

[0739] Step 3:

[0740] The server periodically sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. For example, the server obtains price information from Supermarket A, such as "cabbage is 98 yen, chicken breast is 200 yen," and stores this information in a database. The data obtained is updated periodically, so it is necessary to obtain the latest price information.

[0741] Step 4:

[0742] The server uses AI models and algorithms to generate optimal menus based on family preferences, health information, emotional data, and store price information. The input data are family preferences, health information, emotional data, and store price information, and the output is specific menu suggestions. For example, an algorithm might suggest a "healthy salad with cabbage and chicken breast." By taking emotional data into account, a menu suited to the user's mood is generated.

[0743] Step 5:

[0744] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the proposed menu. The input data is the generated menu, and the output is a detailed description of its nutritional balance. For example, the server calculates information such as "This menu contains 500 kcal and 25 g of protein per serving" and sends it to the device.

[0745] Step 6:

[0746] The server creates a shopping list by listing the ingredients needed based on the menu. The input data is the menu and information about each ingredient, and the output is a specific shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" is created and sent to the terminal.

[0747] Step 7:

[0748] The server compares price information from multiple stores and runs an algorithm to calculate a shopping route that minimizes costs. The input data is the price information from each store, and the output is the optimal shopping route. For example, it suggests a route such as "buy cabbage at supermarket A and chicken breast at supermarket B" and sends it to the terminal.

[0749] Step 8:

[0750] Based on the emotional data received by the server from the emotion engine, the server generates and suggests a menu that matches the user's emotional state. The input data is emotional data, preferences, and health information, and the output is a menu that is appropriate for the user's emotional state at the time. For example, if the user is tired, the system suggests "relaxing chamomile tea and avocado salad."

[0751] Through these steps, the system provides meal plans that take into account family preferences, health information, and the user's emotional state, and also suggests efficient shopping routes, dramatically improving household meal planning and shopping.

[0752] (Application example 2)

[0753] 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."

[0754] Conventional menu suggestion systems often generate menus that only take into account family preferences and health information, and are unable to provide meal plans that reflect the user's emotional state. As a result, they are unable to provide appropriate meal suggestions that reflect the user's emotional state or temporary changes in physical condition, making it difficult to provide a satisfying and nutritionally balanced diet. Furthermore, there has been a lack of systems that effectively utilize price information from nearby stores to suggest shopping routes that increase economic efficiency.

[0755] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting family preference and health information, means for analyzing the user's emotional state, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information and the analyzed emotional state, means for generating a shopping list based on the generated menu, and means for suggesting an optimal shopping route. This makes it possible to suggest a nutritionally balanced menu that takes the user's emotional state into consideration, and an optimal shopping route that takes economic efficiency into account.

[0756] "Family preferences" refers to individual preference information regarding favorite and disliked foods and ingredients of each family member.

[0757] "Health Information" means data about the health status of family members, including allergy information, medical test results, and specific nutrient needs or restrictions.

[0758] "Emotional state" refers to the user's current mood or emotional state, including feelings such as stress, relaxation, satisfaction, fatigue, etc.

[0759] "Price information" is data about the prices of products offered at nearby stores and supermarkets.

[0760] "Special sale information" is information about sales and promotions in which specific products are discounted for a limited time.

[0761] "Analyzing" is a process of processing collected data or information to arrive at a specific conclusion or result.

[0762] "Menu" refers to the meal plan and menu composition proposed based on set conditions.

[0763] A "shopping list" is a list of ingredients and products a user needs, including items necessary to create a specific meal plan.

[0764] The "optimal shopping route" refers to the shortest or most efficient route that a user should take to purchase the products they need, taking into consideration cost and efficiency.

[0765] A "system" is a collection of hardware and software that interrelate to accomplish a particular purpose.

[0766] This system proposes optimal menus, generates shopping lists, and suggests optimal shopping routes based on the user's family preferences, health information, price information from nearby stores, and the user's emotional state. This system is composed of a server, terminals, users, and an emotion engine.

[0767] Gathering family preferences and health information

[0768] The user inputs information about their family's food preferences, allergy information, health checkup results, etc. through the device. Specifically, data about each family member's likes and dislikes and health status is entered, and the device collects this information and sends it to the server.

[0769] Collecting Emotional Data

[0770] When a user uses a device (smart glasses or smartphone), the emotion engine collects emotion data from the user's facial expressions, voice tone, input text, etc. For example, data is collected depending on whether the user is stressed or relaxed.

[0771] Gathering price and sale information from nearby stores

[0772] The server sends requests to local stores' websites and APIs to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database.

[0773] Menu generation

[0774] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information from nearby stores. By taking emotional data into account, the server suggests menus that suit the user's mood and health condition.

[0775] Nutritional Balance Calculation

[0776] The server runs an algorithm to calculate the nutritional content and calories of each ingredient in the proposed menu, and the results are sent to the device and displayed to the user.

[0777] Generate a shopping list

[0778] The server creates a list of ingredients based on the proposed menu, and the generated shopping list is sent to the terminal and displayed to the user.

[0779] Proposal of the best shopping route

[0780] The server compares prices from multiple stores and runs an algorithm to calculate the shopping route that minimizes costs. The results are sent to the terminal, and the user is shown an efficient shopping route.

[0781] Emotion-based menu suggestions

[0782] Based on the emotional data received from the emotion engine, the server generates an optimal menu according to the user's emotional state. For example, if the user is tired, it will suggest nutritious meals, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[0783] Specific examples

[0784] If a user inputs "my second daughter doesn't like fish" or "my mother needs to limit her calories," and the emotion engine detects that they have been under a lot of stress recently, the server will use this information to suggest relaxing menu items, and will also consider nutritional balance and suggest a "healthy salad with cabbage and chicken breast." This allows for a meal plan that not only satisfies the whole family, but also takes into account their emotional state.

[0785] Prompts for generative AI models

[0786] For example, use the following prompt:

[0787] "Please suggest a relaxing meal plan that takes into account the user's current emotional state (stress) and the family's preferences (the second daughter doesn't like fish, and the mother needs to limit her calories)."

[0788] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0789] Step 1:

[0790] The user inputs their family's food preferences, allergy information, and health checkup results through the device. This input includes detailed data on each family member's likes and dislikes and health conditions. The device collects this information and sends it to the server. The input information is stored in a database and used for subsequent analysis and menu generation.

[0791] Step 2:

[0792] Through the user's device (smart glasses or smartphone), the emotion engine collects emotional data from the user's facial expressions, voice tone, and input text. The emotion engine uses facial recognition technology to analyze the image data captured by the camera and determine the user's emotional state. The analysis results are sent to the server and used for subsequent processes.

[0793] Step 3:

[0794] The server obtains product name, price, and sale information from nearby stores via their websites and APIs. This information collection process uses a script that accesses the store's database at a specified frequency to continuously obtain the latest price and sale information. The collected data is stored in the server's database and used for subsequent menu generation.

[0795] Step 4:

[0796] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information. The algorithm uses various data to calculate menus that will satisfy the entire family, and the resulting menus are sent to the user's device. The menu generation takes into account multiple parameters, including pre-set nutritional balance, the user's emotional state, and economic efficiency.

[0797] Step 5:

[0798] Based on the generated menu, the server lists the necessary ingredients and generates a shopping list. The shopping list includes detailed information such as the required amount of each ingredient and recommended purchasing locations. The shopping list is sent to the user's device and can be viewed by the user.

[0799] Step 6:

[0800] The server calculates the optimal shopping route that minimizes costs based on price information collected from multiple stores. This process uses a price comparison algorithm to derive the most economical shopping route based on the price information of each store. The calculation results are sent to the terminal and displayed to the user.

[0801] Step 7:

[0802] Based on the suggested shopping route and emotion data, the server makes a final, emotion-based menu suggestion. A nutritionally balanced menu is displayed according to the user's mood and health condition, and the user can shop and cook accordingly.

[0803] This allows users to easily and efficiently plan optimal meals based on each family member's preferences, health status, and real-time emotional state.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] [Third embodiment]

[0808] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0809] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0810] 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).

[0811] 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.

[0812] 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.

[0813] 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).

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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."

[0820] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby supermarkets, and generates shopping lists and routes. This system is composed of a server, terminals, and users, and its specific operation and embodiments are described below.

[0821] Gathering family preferences and health information

[0822] Through the application, users input their family's food preferences, allergy information, and health checkup results. Specifically, data on each family member's likes and dislikes and health status is entered, and the device collects this information. For example, if a user enters information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," the device sends this information to the server.

[0823] Gathering price information and special offers from nearby supermarkets

[0824] The server sends requests to the websites and APIs of nearby supermarkets to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database. For example, it might collect information that Supermarket A is selling cabbage for 98 yen and chicken breast for 200 yen.

[0825] Menu generation

[0826] The server generates optimal menus based on collected family preferences, health information, and price information from nearby supermarkets. The server uses a specific algorithm to calculate meal plans that will satisfy the entire family and sends the results to the device. For example, it suggests a balanced menu that takes into account the mother's calorie restrictions while also reflecting the second daughter's dislike of fish.

[0827] Nutritional Balance Calculation

[0828] The server calculates the nutritional content and calories of the proposed menu and sends the results to the device. The device then displays information about the calories and nutrients per meal to the user. For example, it may display information such as "This menu contains 500 kcal and 25 g of protein per meal."

[0829] Generate a shopping list

[0830] The server generates a list of ingredients based on the generated menu and sends it to the device. The user can purchase ingredients efficiently based on this shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" is generated.

[0831] Proposal of the best shopping route

[0832] The server compares prices from multiple supermarkets and calculates the most economical shopping route. The results are sent to the device, and suggestions are made to help the user shop efficiently. For example, a route such as "Purchase cabbage at supermarket A and chicken breast at supermarket B" may be suggested.

[0833] With these features, the system supports daily meal planning at home, providing economical and nutritionally balanced meal plans that also reflect the preferences and health status of each family member, ensuring meals that satisfy all members.

[0834] The processing flow will be explained below.

[0835] Specific program flow

[0836] Gathering family preferences and health information

[0837] Step 1:

[0838] The user opens the application's settings screen and enters each family member's food preferences, allergy information, health check results, etc.

[0839] Step 2:

[0840] The terminal transmits the input information to the server.

[0841] Step 3:

[0842] The server stores the received family preferences and health information in a database.

[0843] Gathering price information and special offers from nearby supermarkets

[0844] Step 1:

[0845] The server periodically sends data collection requests to the local supermarket's website or API.

[0846] Step 2:

[0847] The server extracts product name, price, and sale information from the response.

[0848] Step 3:

[0849] The server stores the extracted price and sale information in a database.

[0850] Menu generation

[0851] Step 1:

[0852] The server uses an algorithm to generate the optimal menu based on the family's preferences, health information, and price information from nearby supermarkets.

[0853] Step 2:

[0854] The server sends the contents of the generated menu to the terminal.

[0855] Step 3:

[0856] The terminal displays the contents of the proposed menu to the user.

[0857] Nutritional Balance Calculation

[0858] Step 1:

[0859] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the menu it proposes.

[0860] Step 2:

[0861] The server formats the results and sends them to the terminal.

[0862] Step 3:

[0863] The device displays information about calories and nutrients per meal to the user.

[0864] Generate a shopping list

[0865] Step 1:

[0866] The server runs an algorithm that lists the ingredients needed based on the proposed menu.

[0867] Step 2:

[0868] The server sends the generated shopping list to the terminal.

[0869] Step 3:

[0870] The terminal displays the shopping list to the user.

[0871] Proposal of the best shopping route

[0872] Step 1:

[0873] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs.

[0874] Step 2:

[0875] The server sends the calculated optimal shopping route to the terminal.

[0876] Step 3:

[0877] The terminal displays an efficient shopping route to the user.

[0878] Through the above process, users can receive menu suggestions that suit their family's preferences and health status, and enjoy an economical shopping experience.

[0879] Example 1

[0880] 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."

[0881] The present invention aims to solve the problem of efficiently collecting family preferences, health information, and price information from nearby stores, and proposing economical and nutritionally balanced menus based on this information. It also aims to solve the problem of a lack of means for users to shop efficiently and the difficulty of generating menus that correspond to specific family health conditions and preferences by optimizing shopping lists and shopping routes.

[0882] 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.

[0883] In this invention, the server includes a means for collecting family preference and health information, a means for collecting price information and sale information from nearby stores, a means for generating an optimal menu based on the collected information, a means for calculating nutritional components and calories for the generated menu, a means for generating a shopping list based on the generated menu, and a means for suggesting an optimal shopping route. This allows for economical and nutritionally balanced menu suggestions while taking into account the dietary preferences and health status of all family members. Furthermore, by supporting efficient shopping, the server can save users time and money.

[0884] "Family preferences and health information" is detailed data entered by the user, such as food preferences, allergy information, and health checkup results of family members.

[0885] "Price information and sale information from nearby stores" refers to product price and sale information obtained from supermarket and store websites and APIs.

[0886] The "means for generating optimal menus" is a process that uses an algorithm to automatically create economical and nutritionally balanced menus based on user input information and store information.

[0887] "Means for calculating nutritional components and calories" refers to a system that calculates and quantifies the amount of nutrients and energy contained in the proposed menu.

[0888] The "means for generating a shopping list" is a function that compiles the necessary ingredients and their quantities in list form based on the generated menu.

[0889] The "means for proposing the optimal shopping route" is a function that compares price information from multiple stores, calculates the most cost-effective shopping route, and presents it to the user.

[0890] The present invention relates to a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby stores, and generates shopping lists and routes. This system is composed of a server, terminals, and users.

[0891] Gathering family preferences and health information

[0892] The user enters information about their family's food preferences, allergies, and health checkup results through a dedicated application. Specifically, the user enters information such as "My mother has diabetes and needs a low-carb diet" or "My second daughter is allergic to fish" into the application form. The device collects this information and sends it to a server to be stored in a database.

[0893] Gathering price information and special offers from nearby stores

[0894] The server sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. Specifically, it uses web scraping technology or a specified API to collect product information from each store. For example, the server periodically performs a process such as "collecting information on cabbage for 98 yen from store A and chicken breast for 200 yen from store B." The collected information is then saved in a database in the latest state.

[0895] Menu generation

[0896] The server generates optimal menus based on collected family preferences, health information, and price information. Using an AI algorithm, it proposes economical and nutritionally balanced menus that will satisfy the whole family. For example, it generates meal plans that meet conditions such as "fish-free dishes because the second daughter dislikes fish" and "low-carb menus in consideration of the mother's diabetes."

[0897] Nutritional Balance Calculation

[0898] The server calculates the nutritional content and calories for the generated menu. Specifically, it uses nutrition calculation software to automatically calculate the nutrient and energy content of each dish. For example, it calculates information such as "the calories per meal of the proposed menu are 510 kcal, 30 g of protein, and 60 g of carbohydrates," and sends the results to the device.

[0899] Generate a shopping list

[0900] The server generates a list of the ingredients needed based on the menu that has been generated. Specifically, it lists all the ingredients included in the menu and calculates the required quantities. For example, it creates a shopping list such as "1 cabbage, 300g chicken breast, 200g broccoli" and sends it to the terminal. The terminal displays this list to the user.

[0901] Proposal of the best shopping route

[0902] The server calculates the most economical shopping route based on price information from multiple stores. Specifically, it compares product prices at each store and calculates the most cost-effective shopping route. For example, it suggests a route such as "buy cabbage at store A and chicken breast at store B." The calculation results are sent to the terminal and displayed to the user.

[0903] Specific examples and examples of prompts for generative AI models

[0904] Examples:

[0905] 1. The user fills in the app form with information such as "My mother has diabetes and needs to limit her carbohydrate intake" and "My second daughter is allergic to fish" and submits it.

[0906] 2. The server collects information such as "cabbage 98 yen" from nearby store A and "chicken breast 200 yen" from store B.

[0907] 3. Based on this information, the server uses an AI algorithm to suggest a "low-carb stir-fried vegetable and chicken breast dish."

[0908] 4. The nutritional balance of the menu is calculated as 510 kcal, 28 g of protein, and 20 g of carbohydrates per meal.

[0909] 5. The server generates a shopping list such as "1 cabbage, 300g chicken breast, 2 bell peppers."

[0910] 6. Propose an efficient route to visit Supermarket A and Supermarket B.

[0911] Example prompt for a generative AI model:

[0912] "Enter your family's food preferences and health information. For example, your oldest son has a milk allergy, and your father has high blood pressure."

[0913] "Gather shrimp and broccoli prices at nearby stores."

[0914] "Please suggest an economical and nutritionally balanced menu for my mother, who needs to limit carbohydrates, and my second daughter, who has a fish allergy."

[0915] "Calculate the nutritional content and calories of the suggested meals and provide per-serving information to the user."

[0916] "Generate a list of ingredients based on the menu you create."

[0917] "Based on the price information collected, suggest the most economical shopping route."

[0918] This will support daily meal planning at home, making it possible to provide economical and nutritionally balanced meal plans that also reflect the preferences and health conditions of each family member, ensuring meals that satisfy all members.

[0919] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0920] Step 1: Enter your family's preferences and health information

[0921] Input: The user opens the application and enters their family's food preferences, allergy information, and health check results.

[0922] Specific operation: The user enters information such as "My mother has diabetes and needs a low-carb diet" or "My second daughter is allergic to fish" into a special form in the app.

[0923] Data processing: Input data is collected by the terminal and stored in a database in an appropriate format.

[0924] Output: The user's input information is sent to the server and stored in a database.

[0925] Step 2: Gather price information and special offers from nearby stores

[0926] Input: The server sends a request to get information about the websites or APIs of the specified nearby stores.

[0927] Specific operations: Use web scraping technology and APIs to collect product names, prices, and special offers from each store.

[0928] Data processing: The collected information is stored in the database in the most up-to-date state.

[0929] Output: The latest price information and special offers from the stores are stored in a database.

[0930] Step 3: Generate the optimal menu

[0931] Input: The server receives the family preference and health information collected in step 1 and the store price information collected in step 2.

[0932] How it works: Using AI algorithms, it analyzes the collected data and generates economical and nutritionally balanced meals that will satisfy the whole family.

[0933] Data calculation: Algorithms are run based on user preferences, health information, and price information to calculate the most appropriate combination.

[0934] Output: A menu optimized to the family's needs is generated and passed on to the next processing step.

[0935] Step 4: Calculate your nutritional balance

[0936] Input: The server receives the menu information generated in step 3.

[0937] Specific Actions: Use nutrition calculation software to calculate the nutritional content and calories of each dish.

[0938] Data calculation: Calculate the nutrients and calories for all ingredients included in the menu and calculate the total value.

[0939] Output: The nutritional information and calories for each meal are calculated and the results are ready to be provided to the user.

[0940] Step 5: Generate a shopping list

[0941] Input: The server receives the menu information generated in step 3.

[0942] Specific actions: List all ingredients included in the menu, calculate the required quantities, and compile them in list form.

[0943] Data processing: Calculate the required amount of each ingredient and format it into a shopping list.

[0944] Output: A specific shopping list (e.g., 1 cabbage, 300g chicken breast, 200g broccoli) is generated and sent to the device.

[0945] Step 6: Optimal shopping route suggestions

[0946] Input: The server re-uses the price information for each store collected in step 2.

[0947] Specific operation: Calculates the most economical shopping route based on price information from multiple stores.

[0948] Data calculation: Compare product prices at each store and calculate the most cost-effective shopping route.

[0949] Output: The optimal shopping route based on the shopping list (e.g., store A for cabbage, store B for chicken breast) is proposed and sent to the terminal.

[0950] This makes it clear what specific data processing and calculations are performed at each processing step and how the system operates.

[0951] (Application example 1)

[0952] 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."

[0953] In modern households, preparing economical and nutritionally balanced meals while taking into account the family's food preferences and health status is a significant burden. Furthermore, there is no system that efficiently collects price information from nearby stores and food delivery services and proposes optimal shopping lists and routes. Furthermore, while there is a need for menu suggestions using generative AI models and flexible menu generation using prompts, there is no comprehensive solution available.

[0954] 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.

[0955] In this invention, the server includes means for collecting family preference and health information, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information, means for generating a shopping list based on the generated menu, means for suggesting an optimal shopping route, means for collecting price information from food delivery services and suggesting menus that can be delivered, means for generating a menu using a generative AI model, and means for inputting prompt text and generating a menu. This makes it easy to prepare economical and nutritionally balanced meals based on family preferences and health status, and also makes it possible to suggest optimal shopping lists, shopping routes, and even food delivery options.

[0956] "Family preferences and health information" refers to each family member's food preferences, allergy information, health checkup results, etc.

[0957] "Price Information" refers to price data for products at nearby stores and food delivery services.

[0958] "Special sale information" refers to sales and discount information offered by stores and services.

[0959] A "menu" refers to a meal plan consisting of a combination of multiple dishes and ingredients.

[0960] A "shopping list" refers to a list of ingredients and products that should be purchased based on a specific menu.

[0961] A "shopping route" refers to the optimal shopping route for efficiently visiting multiple stores.

[0962] A "food delivery service" refers to a service that allows you to order meals via the internet or an app and have them delivered to your home or other location.

[0963] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate new information and ideas from data.

[0964] A "prompt" is an instruction entered into a generative AI model, and refers to text that guides a specific output result.

[0965] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, price information and sale information from nearby stores, and also provides shopping lists, shopping routes and even food delivery options. This system is composed of a server, terminals and users, and its specific operation and embodiments are described below.

[0966] Gathering family preferences and health information

[0967] Users use a smartphone app to input information about their family's food preferences, allergies, and health checkup results. For example, if a family member inputs information such as "I need to limit my calories" or "I don't like fish," the device collects this information and sends it to the server.

[0968] Gathering price information and special offers from nearby stores

[0969] The server sends requests to the websites and APIs of nearby stores to obtain product names, prices, and special offers. The collected information is updated periodically, and the latest price and special offer information is stored in a database. For example, information such as "Store A is selling cabbage for 98 yen and chicken breast for 200 yen" is collected.

[0970] Gathering information on food delivery services

[0971] The server sends requests to the APIs of multiple food delivery services to obtain information on food prices and delivery areas, providing not only information on nearby restaurants but also delivery service options.

[0972] Menu generation

[0973] The server generates optimal menus based on collected family preferences, health information, and pricing information from nearby restaurants and food delivery services. Using a generative AI model, it calculates meal plans that will satisfy the whole family based on all the collected information. For example, it might suggest a "balanced menu that takes into account calorie restrictions and does not include fish."

[0974] Nutritional Balance Calculation

[0975] The server calculates the nutritional content and calories of the proposed menu and sends the results to the device. The device then displays information about the calories and nutrients per meal to the user. For example, it might say, "This menu contains 500 kcal and 25 g of protein per meal."

[0976] Generate a shopping list

[0977] The server generates a list of ingredients based on the generated menu and sends it to the device. The user can purchase ingredients efficiently based on this shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" may be generated.

[0978] Proposal of the best shopping route

[0979] The server compares prices from multiple stores and calculates the most economical shopping route. The results are sent to the device, and suggestions are made to help the user shop efficiently. For example, a route such as "Purchase cabbage from store A and chicken breast from store B" may be suggested.

[0980] This allows users to efficiently prepare economical and healthy meals. An example of a specific prompt would be:

[0981] Prompt Sentence Examples

[0982] "User Information:

[0983] Mother: Calorie restriction, dislike of fish

[0984] Second daughter: I hate fish

[0985] Super API URLs:

[0986] https: / / api.storea.com / products

[0987] https: / / api.storeb.com / products

[0988] Requirements:

[0989] Please suggest economical and nutritionally balanced meals.

[0990] Please limit your calorie intake.

[0991] Do not use fish.

[0992] Generate menus based on pricing information from nearby restaurants and food delivery services.

[0993] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0994] Step 1:

[0995] Users input their family's preferences and health information through a smartphone app. Specifically, users input each family member's name, allergy information, likes and dislikes, health checkup results, etc. The input information is stored on the device and sent to the server in real time.

[0996] Input: Family preferences and health information

[0997] Output: Data sent to the server

[0998] Step 2:

[0999] The server sends requests to the websites and APIs of nearby stores to get product names, prices, special offers, etc. The server periodically updates this information and stores it in a database.

[1000] Input: Store API URL

[1001] Output: A database containing pricing and special offers

[1002] Step 3:

[1003] The server sends requests to the APIs of multiple food delivery services to obtain information on the price and delivery area of ​​each dish. The collected information is stored in a database that is updated periodically.

[1004] Input: API URL of food delivery service

[1005] Output: A database containing information about food delivery services

[1006] Step 4:

[1007] The server generates the optimal menu based on collected family preferences, health information, and price information from nearby stores and food delivery services. The server uses a generative AI model to generate a menu based on prompts. This is achieved by using an algorithm to optimally combine each piece of information.

[1008] Input: Family preferences and health information, price information, delivery information, prompt text

[1009] Output: Optimal menu

[1010] Step 5:

[1011] The server calculates the nutritional components and calories for the generated menu. Specifically, it calculates the calories and nutrients such as protein, fat, and carbohydrates per meal based on the nutritional data of each ingredient, and sends the results to the device.

[1012] Input: Generated menu

[1013] Output: Nutritional information

[1014] Step 6:

[1015] The server generates a list of ingredients based on the menu and sends it to the terminal. The user can use this shopping list to purchase ingredients efficiently.

[1016] Input: Generated menu

[1017] Output: Shopping list

[1018] Step 7:

[1019] The server compares prices from multiple stores and calculates the most economical shopping route, which is then sent to the terminal and suggested to the user.

[1020] Input: Price information, shopping list

[1021] Output: Optimal shopping route

[1022] 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.

[1023] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby supermarkets, and further provides meal plans that correspond to the user's emotional state by combining an emotion engine. This system is composed of a server, a terminal, a user, and an emotion engine, and its specific operation and embodiments are described below.

[1024] Gathering family preferences and health information

[1025] Through the application, users input information about their family's food preferences, allergies, health checkup results, etc. Specifically, data about each family member's likes and dislikes and health status is entered, and the device collects this information. For example, if a user enters information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," the device sends this information to the server.

[1026] Collecting Emotional Data

[1027] As users use the application, the emotion engine collects emotional data from their facial expressions, voice tone, and input text, for example, depending on whether they are stressed or relaxed.

[1028] Gathering price information and special offers from nearby supermarkets

[1029] The server sends requests to the websites and APIs of nearby supermarkets to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database. For example, it might collect information that Supermarket A is selling cabbage for 98 yen and chicken breast for 200 yen.

[1030] Menu generation

[1031] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information from nearby supermarkets. By taking emotional data into account, the server can suggest menus that suit the user's mood. For example, if the user is feeling stressed, it can suggest menus that have a relaxing effect.

[1032] Nutritional Balance Calculation

[1033] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the proposed menu. The results are sent to the device and displayed to the user. For example, information such as "This menu has 500 kcal and 25 g of protein per meal" is displayed.

[1034] Generate a shopping list

[1035] The server runs an algorithm to generate a list of ingredients based on the proposed menu. The results are sent to the device and displayed to the user as a shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" might be generated.

[1036] Proposal of the best shopping route

[1037] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs. The results are sent to the device and displayed to the user as an efficient shopping route. For example, a route such as "Purchase cabbage at supermarket A and chicken breast at supermarket B" may be presented.

[1038] Emotion-based menu suggestions

[1039] Based on the emotional data received from the emotion engine, the server generates an optimal menu according to the user's emotional state. For example, if the user is tired, it will suggest nutritious meals, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[1040] Specific examples

[1041] If a user inputs information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," and the emotion engine detects that they have been under a lot of stress recently, the server will use this information to suggest menu items that have a relaxing effect, and will also suggest a "healthy salad with cabbage and chicken breast" based on nutritional balance. This allows for a meal plan that not only satisfies the whole family, but also takes into account their emotional state.

[1042] This system dramatically improves home meal planning, making meals more economical, nutritionally balanced, and emotionally conscious.

[1043] The processing flow will be explained below.

[1044] Specific processing flow of the program

[1045] Gathering family preferences and health information

[1046] Step 1:

[1047] The user opens the application's settings screen and enters each family member's food preferences, allergy information, health check results, etc.

[1048] Step 2:

[1049] The terminal transmits the input information to the server.

[1050] Step 3:

[1051] The server stores the received family preferences and health information in a database.

[1052] Collecting Emotional Data

[1053] Step 1:

[1054] When a user uses the application, the emotion engine captures the user's facial expression data using a camera, and also obtains emotion data from voice tones and text input.

[1055] Step 2:

[1056] The device transmits the collected emotion data to the server.

[1057] Step 3:

[1058] The server analyzes the emotional data to determine the user's current emotional state.

[1059] Gathering price information and special offers from nearby supermarkets

[1060] Step 1:

[1061] The server periodically sends data collection requests to the local supermarket's website or API.

[1062] Step 2:

[1063] The server extracts product name, price, and sale information from the response.

[1064] Step 3:

[1065] The server stores the extracted price and sale information in a database.

[1066] Menu generation

[1067] Step 1:

[1068] The server uses an algorithm to generate the optimal menu based on the family's preferences, health information, emotional data, and price information from nearby supermarkets.

[1069] Step 2:

[1070] The server sends the contents of the generated menu to the terminal.

[1071] Step 3:

[1072] The terminal displays the contents of the proposed menu to the user.

[1073] Nutritional Balance Calculation

[1074] Step 1:

[1075] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the menu it proposes.

[1076] Step 2:

[1077] The server formats the results and sends them to the terminal.

[1078] Step 3:

[1079] The device displays information about calories and nutrients per meal to the user.

[1080] Generate a shopping list

[1081] Step 1:

[1082] The server runs an algorithm that lists the ingredients needed based on the proposed menu.

[1083] Step 2:

[1084] The server sends the generated shopping list to the terminal.

[1085] Step 3:

[1086] The terminal displays the shopping list to the user.

[1087] Proposal of the best shopping route

[1088] Step 1:

[1089] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs.

[1090] Step 2:

[1091] The server sends the calculated optimal shopping route to the terminal.

[1092] Step 3:

[1093] The terminal displays an efficient shopping route to the user.

[1094] Emotion-based menu suggestions

[1095] Step 1:

[1096] Based on the emotion data received from the emotion engine, the server executes an algorithm to generate an optimal menu according to the user's emotional state.

[1097] Step 2:

[1098] The server sends the menu contents based on the emotion data to the terminal.

[1099] Step 3:

[1100] The device will display menu suggestions based on the user's emotions. For example, if the user is feeling stressed, it will suggest a menu with a relaxing effect, or if the user wants to feel energized, it will suggest a menu using their favorite ingredients.

[1101] Through these steps, users can enjoy an economical and nutritionally balanced meal plan tailored to their emotional state.

[1102] Example 2

[1103] 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."

[1104] In modern home cooking, it is difficult to propose economical and nutritionally balanced menus that take into account the preferences and health information of all family members. There is also a demand for meal plans that take into account the user's emotional state, but this is difficult to achieve with conventional methods. Furthermore, there are few systems that suggest efficient shopping routes, which increases the burden of housework.

[1105] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting family member preference and health information, means for collecting user emotion data, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information, means for generating a shopping list based on the generated menu, means for suggesting an optimal shopping route, means for calculating the nutritional balance of the generated menu and providing it to the user, and means for suggesting a menu based on the user's emotional state. This provides an economical and nutritionally balanced meal plan that takes into account the preferences and health information of all family members, as well as the user's emotional state, and also suggests an efficient shopping route, thereby reducing the burden of housework.

[1106] "Means for collecting family preferences and health information" refers to a means by which users input their family's food preferences, allergy information, health checkup results, etc. through an application, which then converts this information into a data format and collects it.

[1107] The "means for collecting user emotional data" refers to a means for acquiring user emotional data from facial expressions, tone of voice, input text, etc. when the user uses an application.

[1108] "Means for collecting price information and sale information from nearby stores" refers to a means in which the server sends requests to the websites or APIs of nearby stores, periodically obtains product names, prices, sale information, etc., and stores them in a database.

[1109] "Means for generating optimal menus based on collected information" refers to means for creating optimal menus using algorithms based on family preferences, health information, user emotional data, store price information, etc.

[1110] The "means for generating a shopping list based on the generated menu" is a means for listing necessary ingredients based on the generated menu and presenting it as a shopping list.

[1111] The "means for proposing the optimal shopping route" is a means for calculating and proposing a shopping route that minimizes costs based on collected price information from multiple stores.

[1112] The "means for calculating the nutritional balance of the generated menu and providing it to the user" is a means for calculating the nutritional components and calories of each ingredient included in the proposed menu and providing the results to the user.

[1113] The "means for proposing a menu according to the emotional state of the user" is a means for creating and proposing an optimal menu according to the emotional state of the user based on the collected emotional data of the user.

[1114] This system proposes economical and nutritionally balanced meals and provides efficient shopping routes based on family preferences, health information, price information from nearby stores, and user emotion data. This system is composed of a server, terminals, users, and an emotion engine.

[1115] First, the user enters information about their family's food preferences, allergies, and health checkup results through the application. For example, they may enter information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories." The device collects this data, converts it into a data format, and sends it to the server. Standard data formats such as JSON are used for this purpose.

[1116] Next, while the user is using the application, the emotion engine collects emotion data from facial expressions, voice tones, input text, etc. For example, if the user inputs text or voice tones that indicate they are feeling stressed, the emotion engine detects this and the device sends the detected emotion data to the server.

[1117] In addition, the server periodically sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. For example, information that cabbage costs 98 yen and chicken breast costs 200 yen at Supermarket A is collected and stored in a database. This process uses web scraping technology and API integration.

[1118] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and store price information. For example, based on information such as "the second daughter doesn't like fish," "the mother needs to limit her calories," and "the user is currently under stress," the server might suggest a "healthy salad with cabbage and chicken breast." This algorithm takes into account nutritional balance and emotional state, so machine learning models and rule-based systems may be used.

[1119] Based on the generated menu, the server runs an algorithm to calculate the nutritional content and calories of each ingredient and sends information such as "This menu contains 500 kcal and 25 g of protein per meal" to the device. This data is important for providing users with a nutritionally balanced diet.

[1120] The server then creates a list of ingredients needed based on the generated menu, generating a shopping list such as "1 cabbage, 300g chicken breast, 200g broccoli," and sends it to the terminal.The server then compares price information from multiple stores, calculates the lowest-cost shopping route, and suggests the optimal route to the user, such as "buy the cabbage at supermarket A and the chicken breast at supermarket B."

[1121] Finally, the server generates and suggests a menu based on the user's emotional state based on the emotional data received from the emotion engine. For example, if the user is tired, it will suggest a menu with a relaxing effect, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[1122] As a specific example of a prompt, the above-mentioned process is performed by inputting sentences such as "The second daughter does not like fish, and the mother needs to limit her calories. She has been feeling stressed recently" into the application.

[1123] As described above, by providing meal plans that take into account the preferences and health information of all family members, as well as the user's emotional state, and also suggesting efficient shopping routes, household meal planning and shopping can be dramatically improved.

[1124] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1125] Step 1:

[1126] The user launches the application and inputs information such as the family's food preferences, allergy information, and health checkup results. The input data is information such as "The second daughter doesn't like fish" and "The mother needs to limit her calories." The device collects this information, converts it to JSON format, and sends it to the server. The data entered here is in text format, which is then processed and sent to the server as the body of the API request.

[1127] Step 2:

[1128] While the user is using the application, the emotion engine collects emotion data from the user's facial expressions, voice tone, input text, etc. Specifically, the user inputs "I've been feeling stressed lately." This data is collected by the device and sent as a request to the server. The input emotion data is used to determine the user's emotional state through text and voice analysis.

[1129] Step 3:

[1130] The server periodically sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. For example, the server obtains price information from Supermarket A, such as "cabbage is 98 yen, chicken breast is 200 yen," and stores this information in a database. The data obtained is updated periodically, so it is necessary to obtain the latest price information.

[1131] Step 4:

[1132] The server uses AI models and algorithms to generate optimal menus based on family preferences, health information, emotional data, and store price information. The input data are family preferences, health information, emotional data, and store price information, and the output is specific menu suggestions. For example, an algorithm might suggest a "healthy salad with cabbage and chicken breast." By taking emotional data into account, a menu suited to the user's mood is generated.

[1133] Step 5:

[1134] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the proposed menu. The input data is the generated menu, and the output is a detailed description of its nutritional balance. For example, the server calculates information such as "This menu contains 500 kcal and 25 g of protein per serving" and sends it to the device.

[1135] Step 6:

[1136] The server creates a shopping list by listing the ingredients needed based on the menu. The input data is the menu and information about each ingredient, and the output is a specific shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" is created and sent to the terminal.

[1137] Step 7:

[1138] The server compares price information from multiple stores and runs an algorithm to calculate a shopping route that minimizes costs. The input data is the price information from each store, and the output is the optimal shopping route. For example, it suggests a route such as "buy cabbage at supermarket A and chicken breast at supermarket B" and sends it to the terminal.

[1139] Step 8:

[1140] Based on the emotional data received by the server from the emotion engine, the server generates and suggests a menu that matches the user's emotional state. The input data is emotional data, preferences, and health information, and the output is a menu that is appropriate for the user's emotional state at the time. For example, if the user is tired, the system suggests "relaxing chamomile tea and avocado salad."

[1141] Through these steps, the system provides meal plans that take into account family preferences, health information, and the user's emotional state, and also suggests efficient shopping routes, dramatically improving household meal planning and shopping.

[1142] (Application example 2)

[1143] 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."

[1144] Conventional menu suggestion systems often generate menus that only take into account family preferences and health information, and are unable to provide meal plans that reflect the user's emotional state. As a result, they are unable to provide appropriate meal suggestions that reflect the user's emotional state or temporary changes in physical condition, making it difficult to provide a satisfying and nutritionally balanced diet. Furthermore, there has been a lack of systems that effectively utilize price information from nearby stores to suggest shopping routes that increase economic efficiency.

[1145] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting family preference and health information, means for analyzing the user's emotional state, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information and the analyzed emotional state, means for generating a shopping list based on the generated menu, and means for suggesting an optimal shopping route. This makes it possible to suggest a nutritionally balanced menu that takes the user's emotional state into consideration, and an optimal shopping route that takes economic efficiency into account.

[1146] "Family preferences" refers to individual preference information regarding favorite and disliked foods and ingredients of each family member.

[1147] "Health Information" means data about the health status of family members, including allergy information, medical test results, and specific nutrient needs or restrictions.

[1148] "Emotional state" refers to the user's current mood or emotional state, including feelings such as stress, relaxation, satisfaction, fatigue, etc.

[1149] "Price information" is data about the prices of products offered at nearby stores and supermarkets.

[1150] "Special sale information" is information about sales and promotions in which specific products are discounted for a limited time.

[1151] "Analyzing" is a process of processing collected data or information to arrive at a specific conclusion or result.

[1152] "Menu" refers to the meal plan and menu composition proposed based on set conditions.

[1153] A "shopping list" is a list of ingredients and products a user needs, including items necessary to create a specific meal plan.

[1154] The "optimal shopping route" refers to the shortest or most efficient route that a user should take to purchase the products they need, taking into consideration cost and efficiency.

[1155] A "system" is a collection of hardware and software that interrelate to accomplish a particular purpose.

[1156] This system proposes optimal menus, generates shopping lists, and suggests optimal shopping routes based on the user's family preferences, health information, price information from nearby stores, and the user's emotional state. This system is composed of a server, terminals, users, and an emotion engine.

[1157] Gathering family preferences and health information

[1158] The user inputs information about their family's food preferences, allergy information, health checkup results, etc. through the device. Specifically, data about each family member's likes and dislikes and health status is entered, and the device collects this information and sends it to the server.

[1159] Collecting Emotional Data

[1160] When a user uses a device (smart glasses or smartphone), the emotion engine collects emotion data from the user's facial expressions, voice tone, input text, etc. For example, data is collected depending on whether the user is stressed or relaxed.

[1161] Gathering price and sale information from nearby stores

[1162] The server sends requests to local stores' websites and APIs to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database.

[1163] Menu generation

[1164] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information from nearby stores. By taking emotional data into account, the server suggests menus that suit the user's mood and health condition.

[1165] Nutritional Balance Calculation

[1166] The server runs an algorithm to calculate the nutritional content and calories of each ingredient in the proposed menu, and the results are sent to the device and displayed to the user.

[1167] Generate a shopping list

[1168] The server creates a list of ingredients based on the proposed menu, and the generated shopping list is sent to the terminal and displayed to the user.

[1169] Proposal of the best shopping route

[1170] The server compares prices from multiple stores and runs an algorithm to calculate the shopping route that minimizes costs. The results are sent to the terminal, and the user is shown an efficient shopping route.

[1171] Emotion-based menu suggestions

[1172] Based on the emotional data received from the emotion engine, the server generates an optimal menu according to the user's emotional state. For example, if the user is tired, it will suggest nutritious meals, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[1173] Specific examples

[1174] If a user inputs "my second daughter doesn't like fish" or "my mother needs to limit her calories," and the emotion engine detects that they have been under a lot of stress recently, the server will use this information to suggest relaxing menu items, and will also consider nutritional balance and suggest a "healthy salad with cabbage and chicken breast." This allows for a meal plan that not only satisfies the whole family, but also takes into account their emotional state.

[1175] Prompts for generative AI models

[1176] For example, use the following prompt:

[1177] "Please suggest a relaxing meal plan that takes into account the user's current emotional state (stress) and the family's preferences (the second daughter doesn't like fish, and the mother needs to limit her calories)."

[1178] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1179] Step 1:

[1180] The user inputs their family's food preferences, allergy information, and health checkup results through the device. This input includes detailed data on each family member's likes and dislikes and health conditions. The device collects this information and sends it to the server. The input information is stored in a database and used for subsequent analysis and menu generation.

[1181] Step 2:

[1182] Through the user's device (smart glasses or smartphone), the emotion engine collects emotional data from the user's facial expressions, voice tone, and input text. The emotion engine uses facial recognition technology to analyze the image data captured by the camera and determine the user's emotional state. The analysis results are sent to the server and used for subsequent processes.

[1183] Step 3:

[1184] The server obtains product name, price, and sale information from nearby stores via their websites and APIs. This information collection process uses a script that accesses the store's database at a specified frequency to continuously obtain the latest price and sale information. The collected data is stored in the server's database and used for subsequent menu generation.

[1185] Step 4:

[1186] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information. The algorithm uses various data to calculate menus that will satisfy the entire family, and the resulting menus are sent to the user's device. The menu generation takes into account multiple parameters, including pre-set nutritional balance, the user's emotional state, and economic efficiency.

[1187] Step 5:

[1188] Based on the generated menu, the server lists the necessary ingredients and generates a shopping list. The shopping list includes detailed information such as the required amount of each ingredient and recommended purchasing locations. The shopping list is sent to the user's device and can be viewed by the user.

[1189] Step 6:

[1190] The server calculates the optimal shopping route that minimizes costs based on price information collected from multiple stores. This process uses a price comparison algorithm to derive the most economical shopping route based on the price information of each store. The calculation results are sent to the terminal and displayed to the user.

[1191] Step 7:

[1192] Based on the suggested shopping route and emotion data, the server makes a final, emotion-based menu suggestion. A nutritionally balanced menu is displayed according to the user's mood and health condition, and the user can shop and cook accordingly.

[1193] This allows users to easily and efficiently plan optimal meals based on each family member's preferences, health status, and real-time emotional state.

[1194] 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.

[1195] 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.

[1196] 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.

[1197] [Fourth embodiment]

[1198] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1199] 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.

[1200] 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).

[1201] 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.

[1202] 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.

[1203] 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).

[1204] 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.

[1205] 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.

[1206] 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.

[1207] 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.

[1208] 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.

[1209] 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.

[1210] 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."

[1211] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby supermarkets, and generates shopping lists and routes. This system is composed of a server, terminals, and users, and its specific operation and embodiments are described below.

[1212] Gathering family preferences and health information

[1213] Through the application, users input their family's food preferences, allergy information, and health checkup results. Specifically, data on each family member's likes and dislikes and health status is entered, and the device collects this information. For example, if a user enters information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," the device sends this information to the server.

[1214] Gathering price information and special offers from nearby supermarkets

[1215] The server sends requests to the websites and APIs of nearby supermarkets to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database. For example, it might collect information that Supermarket A is selling cabbage for 98 yen and chicken breast for 200 yen.

[1216] Menu generation

[1217] The server generates optimal menus based on collected family preferences, health information, and price information from nearby supermarkets. The server uses a specific algorithm to calculate meal plans that will satisfy the entire family and sends the results to the device. For example, it suggests a balanced menu that takes into account the mother's calorie restrictions while also reflecting the second daughter's dislike of fish.

[1218] Nutritional Balance Calculation

[1219] The server calculates the nutritional content and calories of the proposed menu and sends the results to the device. The device then displays information about the calories and nutrients per meal to the user. For example, it may display information such as "This menu contains 500 kcal and 25 g of protein per meal."

[1220] Generate a shopping list

[1221] The server generates a list of ingredients based on the generated menu and sends it to the device. The user can purchase ingredients efficiently based on this shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" is generated.

[1222] Proposal of the best shopping route

[1223] The server compares prices from multiple supermarkets and calculates the most economical shopping route. The results are sent to the device, and suggestions are made to help the user shop efficiently. For example, a route such as "Purchase cabbage at supermarket A and chicken breast at supermarket B" may be suggested.

[1224] With these features, the system supports daily meal planning at home, providing economical and nutritionally balanced meal plans that also reflect the preferences and health status of each family member, ensuring meals that satisfy all members.

[1225] The processing flow will be explained below.

[1226] Specific program flow

[1227] Gathering family preferences and health information

[1228] Step 1:

[1229] The user opens the application's settings screen and enters each family member's food preferences, allergy information, health check results, etc.

[1230] Step 2:

[1231] The terminal transmits the input information to the server.

[1232] Step 3:

[1233] The server stores the received family preferences and health information in a database.

[1234] Gathering price information and special offers from nearby supermarkets

[1235] Step 1:

[1236] The server periodically sends data collection requests to the local supermarket's website or API.

[1237] Step 2:

[1238] The server extracts product name, price, and sale information from the response.

[1239] Step 3:

[1240] The server stores the extracted price and sale information in a database.

[1241] Menu generation

[1242] Step 1:

[1243] The server uses an algorithm to generate the optimal menu based on the family's preferences, health information, and price information from nearby supermarkets.

[1244] Step 2:

[1245] The server sends the contents of the generated menu to the terminal.

[1246] Step 3:

[1247] The terminal displays the contents of the proposed menu to the user.

[1248] Nutritional Balance Calculation

[1249] Step 1:

[1250] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the menu it proposes.

[1251] Step 2:

[1252] The server formats the results and sends them to the terminal.

[1253] Step 3:

[1254] The device displays information about calories and nutrients per meal to the user.

[1255] Generate a shopping list

[1256] Step 1:

[1257] The server runs an algorithm that lists the ingredients needed based on the proposed menu.

[1258] Step 2:

[1259] The server sends the generated shopping list to the terminal.

[1260] Step 3:

[1261] The terminal displays the shopping list to the user.

[1262] Proposal of the best shopping route

[1263] Step 1:

[1264] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs.

[1265] Step 2:

[1266] The server sends the calculated optimal shopping route to the terminal.

[1267] Step 3:

[1268] The terminal displays an efficient shopping route to the user.

[1269] Through the above process, users can receive menu suggestions that suit their family's preferences and health status, and enjoy an economical shopping experience.

[1270] Example 1

[1271] 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."

[1272] The present invention aims to solve the problem of efficiently collecting family preferences, health information, and price information from nearby stores, and proposing economical and nutritionally balanced menus based on this information. It also aims to solve the problem of a lack of means for users to shop efficiently and the difficulty of generating menus that correspond to specific family health conditions and preferences by optimizing shopping lists and shopping routes.

[1273] 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.

[1274] In this invention, the server includes a means for collecting family preference and health information, a means for collecting price information and sale information from nearby stores, a means for generating an optimal menu based on the collected information, a means for calculating nutritional components and calories for the generated menu, a means for generating a shopping list based on the generated menu, and a means for suggesting an optimal shopping route. This allows for economical and nutritionally balanced menu suggestions while taking into account the dietary preferences and health status of all family members. Furthermore, by supporting efficient shopping, the server can save users time and money.

[1275] "Family preferences and health information" is detailed data entered by the user, such as food preferences, allergy information, and health checkup results of family members.

[1276] "Price information and sale information from nearby stores" refers to product price and sale information obtained from supermarket and store websites and APIs.

[1277] The "means for generating optimal menus" is a process that uses an algorithm to automatically create economical and nutritionally balanced menus based on user input information and store information.

[1278] "Means for calculating nutritional components and calories" refers to a system that calculates and quantifies the amount of nutrients and energy contained in the proposed menu.

[1279] The "means for generating a shopping list" is a function that compiles the necessary ingredients and their quantities in list form based on the generated menu.

[1280] The "means for proposing the optimal shopping route" is a function that compares price information from multiple stores, calculates the most cost-effective shopping route, and presents it to the user.

[1281] The present invention relates to a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby stores, and generates shopping lists and routes. This system is composed of a server, terminals, and users.

[1282] Gathering family preferences and health information

[1283] The user enters information about their family's food preferences, allergies, and health checkup results through a dedicated application. Specifically, the user enters information such as "My mother has diabetes and needs a low-carb diet" or "My second daughter is allergic to fish" into the application form. The device collects this information and sends it to a server to be stored in a database.

[1284] Gathering price information and special offers from nearby stores

[1285] The server sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. Specifically, it uses web scraping technology or a specified API to collect product information from each store. For example, the server periodically performs a process such as "collecting information on cabbage for 98 yen from store A and chicken breast for 200 yen from store B." The collected information is then saved in a database in the latest state.

[1286] Menu generation

[1287] The server generates optimal menus based on collected family preferences, health information, and price information. Using an AI algorithm, it proposes economical and nutritionally balanced menus that will satisfy the whole family. For example, it generates meal plans that meet conditions such as "fish-free dishes because the second daughter dislikes fish" and "low-carb menus in consideration of the mother's diabetes."

[1288] Nutritional Balance Calculation

[1289] The server calculates the nutritional content and calories for the generated menu. Specifically, it uses nutrition calculation software to automatically calculate the nutrient and energy content of each dish. For example, it calculates information such as "the calories per meal of the proposed menu are 510 kcal, 30 g of protein, and 60 g of carbohydrates," and sends the results to the device.

[1290] Generate a shopping list

[1291] The server generates a list of the ingredients needed based on the menu that has been generated. Specifically, it lists all the ingredients included in the menu and calculates the required quantities. For example, it creates a shopping list such as "1 cabbage, 300g chicken breast, 200g broccoli" and sends it to the terminal. The terminal displays this list to the user.

[1292] Proposal of the best shopping route

[1293] The server calculates the most economical shopping route based on price information from multiple stores. Specifically, it compares product prices at each store and calculates the most cost-effective shopping route. For example, it suggests a route such as "buy cabbage at store A and chicken breast at store B." The calculation results are sent to the terminal and displayed to the user.

[1294] Specific examples and examples of prompts for generative AI models

[1295] Examples:

[1296] 1. The user fills in the app form with information such as "My mother has diabetes and needs to limit her carbohydrate intake" and "My second daughter is allergic to fish" and submits it.

[1297] 2. The server collects information such as "cabbage 98 yen" from nearby store A and "chicken breast 200 yen" from store B.

[1298] 3. Based on this information, the server uses an AI algorithm to suggest a "low-carb stir-fried vegetable and chicken breast dish."

[1299] 4. The nutritional balance of the menu is calculated as 510 kcal, 28 g of protein, and 20 g of carbohydrates per meal.

[1300] 5. The server generates a shopping list such as "1 cabbage, 300g chicken breast, 2 bell peppers."

[1301] 6. Propose an efficient route to visit Supermarket A and Supermarket B.

[1302] Example prompt for a generative AI model:

[1303] "Enter your family's food preferences and health information. For example, your oldest son has a milk allergy, and your father has high blood pressure."

[1304] "Gather shrimp and broccoli prices at nearby stores."

[1305] "Please suggest an economical and nutritionally balanced menu for my mother, who needs to limit carbohydrates, and my second daughter, who has a fish allergy."

[1306] "Calculate the nutritional content and calories of the suggested meals and provide per-serving information to the user."

[1307] "Generate a list of ingredients based on the menu you create."

[1308] "Based on the price information collected, suggest the most economical shopping route."

[1309] This will support daily meal planning at home, making it possible to provide economical and nutritionally balanced meal plans that also reflect the preferences and health conditions of each family member, ensuring meals that satisfy all members.

[1310] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1311] Step 1: Enter your family's preferences and health information

[1312] Input: The user opens the application and enters their family's food preferences, allergy information, and health check results.

[1313] Specific operation: The user enters information such as "My mother has diabetes and needs a low-carb diet" or "My second daughter is allergic to fish" into a special form in the app.

[1314] Data processing: Input data is collected by the terminal and stored in a database in an appropriate format.

[1315] Output: The user's input information is sent to the server and stored in a database.

[1316] Step 2: Gather price information and special offers from nearby stores

[1317] Input: The server sends a request to get information about the websites or APIs of the specified nearby stores.

[1318] Specific operations: Use web scraping technology and APIs to collect product names, prices, and special offers from each store.

[1319] Data processing: The collected information is stored in the database in the most up-to-date state.

[1320] Output: The latest price information and special offers from the stores are stored in a database.

[1321] Step 3: Generate the optimal menu

[1322] Input: The server receives the family preference and health information collected in step 1 and the store price information collected in step 2.

[1323] How it works: Using AI algorithms, it analyzes the collected data and generates economical and nutritionally balanced meals that will satisfy the whole family.

[1324] Data calculation: Algorithms are run based on user preferences, health information, and price information to calculate the most appropriate combination.

[1325] Output: A menu optimized to the family's needs is generated and passed on to the next processing step.

[1326] Step 4: Calculate your nutritional balance

[1327] Input: The server receives the menu information generated in step 3.

[1328] Specific Actions: Use nutrition calculation software to calculate the nutritional content and calories of each dish.

[1329] Data calculation: Calculate the nutrients and calories for all ingredients included in the menu and calculate the total value.

[1330] Output: The nutritional information and calories for each meal are calculated and the results are ready to be provided to the user.

[1331] Step 5: Generate a shopping list

[1332] Input: The server receives the menu information generated in step 3.

[1333] Specific actions: List all ingredients included in the menu, calculate the required quantities, and compile them in list form.

[1334] Data processing: Calculate the required amount of each ingredient and format it into a shopping list.

[1335] Output: A specific shopping list (e.g., 1 cabbage, 300g chicken breast, 200g broccoli) is generated and sent to the device.

[1336] Step 6: Optimal shopping route suggestions

[1337] Input: The server re-uses the price information for each store collected in step 2.

[1338] Specific operation: Calculates the most economical shopping route based on price information from multiple stores.

[1339] Data calculation: Compare product prices at each store and calculate the most cost-effective shopping route.

[1340] Output: The optimal shopping route based on the shopping list (e.g., store A for cabbage, store B for chicken breast) is proposed and sent to the terminal.

[1341] This makes it clear what specific data processing and calculations are performed at each processing step and how the system operates.

[1342] (Application example 1)

[1343] 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."

[1344] In modern households, preparing economical and nutritionally balanced meals while taking into account the family's food preferences and health status is a significant burden. Furthermore, there is no system that efficiently collects price information from nearby stores and food delivery services and proposes optimal shopping lists and routes. Furthermore, while there is a need for menu suggestions using generative AI models and flexible menu generation using prompts, there is no comprehensive solution available.

[1345] 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.

[1346] In this invention, the server includes means for collecting family preference and health information, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information, means for generating a shopping list based on the generated menu, means for suggesting an optimal shopping route, means for collecting price information from food delivery services and suggesting menus that can be delivered, means for generating a menu using a generative AI model, and means for inputting prompt text and generating a menu. This makes it easy to prepare economical and nutritionally balanced meals based on family preferences and health status, and also makes it possible to suggest optimal shopping lists, shopping routes, and even food delivery options.

[1347] "Family preferences and health information" refers to each family member's food preferences, allergy information, health checkup results, etc.

[1348] "Price Information" refers to price data for products at nearby stores and food delivery services.

[1349] "Special sale information" refers to sales and discount information offered by stores and services.

[1350] A "menu" refers to a meal plan consisting of a combination of multiple dishes and ingredients.

[1351] A "shopping list" refers to a list of ingredients and products that should be purchased based on a specific menu.

[1352] A "shopping route" refers to the optimal shopping route for efficiently visiting multiple stores.

[1353] A "food delivery service" refers to a service that allows you to order meals via the internet or an app and have them delivered to your home or other location.

[1354] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate new information and ideas from data.

[1355] A "prompt" is an instruction entered into a generative AI model, and refers to text that guides a specific output result.

[1356] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, price information and sale information from nearby stores, and also provides shopping lists, shopping routes and even food delivery options. This system is composed of a server, terminals and users, and its specific operation and embodiments are described below.

[1357] Gathering family preferences and health information

[1358] Users use a smartphone app to input information about their family's food preferences, allergies, and health checkup results. For example, if a family member inputs information such as "I need to limit my calories" or "I don't like fish," the device collects this information and sends it to the server.

[1359] Gathering price information and special offers from nearby stores

[1360] The server sends requests to the websites and APIs of nearby stores to obtain product names, prices, and special offers. The collected information is updated periodically, and the latest price and special offer information is stored in a database. For example, information such as "Store A is selling cabbage for 98 yen and chicken breast for 200 yen" is collected.

[1361] Gathering information on food delivery services

[1362] The server sends requests to the APIs of multiple food delivery services to obtain information on food prices and delivery areas, providing not only information on nearby restaurants but also delivery service options.

[1363] Menu generation

[1364] The server generates optimal menus based on collected family preferences, health information, and pricing information from nearby restaurants and food delivery services. Using a generative AI model, it calculates meal plans that will satisfy the whole family based on all the collected information. For example, it might suggest a "balanced menu that takes into account calorie restrictions and does not include fish."

[1365] Nutritional Balance Calculation

[1366] The server calculates the nutritional content and calories of the proposed menu and sends the results to the device. The device then displays information about the calories and nutrients per meal to the user. For example, it might say, "This menu contains 500 kcal and 25 g of protein per meal."

[1367] Generate a shopping list

[1368] The server generates a list of ingredients based on the generated menu and sends it to the device. The user can purchase ingredients efficiently based on this shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" may be generated.

[1369] Proposal of the best shopping route

[1370] The server compares prices from multiple stores and calculates the most economical shopping route. The results are sent to the device, and suggestions are made to help the user shop efficiently. For example, a route such as "Purchase cabbage from store A and chicken breast from store B" may be suggested.

[1371] This allows users to efficiently prepare economical and healthy meals. An example of a specific prompt would be:

[1372] Prompt Sentence Examples

[1373] "User Information:

[1374] Mother: Calorie restriction, dislike of fish

[1375] Second daughter: I hate fish

[1376] Super API URLs:

[1377] https: / / api.storea.com / products

[1378] https: / / api.storeb.com / products

[1379] Requirements:

[1380] Please suggest economical and nutritionally balanced meals.

[1381] Please limit your calorie intake.

[1382] Do not use fish.

[1383] Generate menus based on pricing information from nearby restaurants and food delivery services.

[1384] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1385] Step 1:

[1386] Users input their family's preferences and health information through a smartphone app. Specifically, users input each family member's name, allergy information, likes and dislikes, health checkup results, etc. The input information is stored on the device and sent to the server in real time.

[1387] Input: Family preferences and health information

[1388] Output: Data sent to the server

[1389] Step 2:

[1390] The server sends requests to the websites and APIs of nearby stores to get product names, prices, special offers, etc. The server periodically updates this information and stores it in a database.

[1391] Input: Store API URL

[1392] Output: A database containing pricing and special offers

[1393] Step 3:

[1394] The server sends requests to the APIs of multiple food delivery services to obtain information on the price and delivery area of ​​each dish. The collected information is stored in a database that is updated periodically.

[1395] Input: API URL of food delivery service

[1396] Output: A database containing information about food delivery services

[1397] Step 4:

[1398] The server generates the optimal menu based on collected family preferences, health information, and price information from nearby stores and food delivery services. The server uses a generative AI model to generate a menu based on prompts. This is achieved by using an algorithm to optimally combine each piece of information.

[1399] Input: Family preferences and health information, price information, delivery information, prompt text

[1400] Output: Optimal menu

[1401] Step 5:

[1402] The server calculates the nutritional components and calories for the generated menu. Specifically, it calculates the calories and nutrients such as protein, fat, and carbohydrates per meal based on the nutritional data of each ingredient, and sends the results to the device.

[1403] Input: Generated menu

[1404] Output: Nutritional information

[1405] Step 6:

[1406] The server generates a list of ingredients based on the menu and sends it to the terminal. The user can use this shopping list to purchase ingredients efficiently.

[1407] Input: Generated menu

[1408] Output: Shopping list

[1409] Step 7:

[1410] The server compares prices from multiple stores and calculates the most economical shopping route, which is then sent to the terminal and suggested to the user.

[1411] Input: Price information, shopping list

[1412] Output: Optimal shopping route

[1413] 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.

[1414] The present invention is a system that proposes economical and nutritionally balanced menus based on family preferences, health information, and price information from nearby supermarkets, and further provides meal plans that correspond to the user's emotional state by combining an emotion engine. This system is composed of a server, a terminal, a user, and an emotion engine, and its specific operation and embodiments are described below.

[1415] Gathering family preferences and health information

[1416] Through the application, users input information about their family's food preferences, allergies, health checkup results, etc. Specifically, data about each family member's likes and dislikes and health status is entered, and the device collects this information. For example, if a user enters information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," the device sends this information to the server.

[1417] Collecting Emotional Data

[1418] As users use the application, the emotion engine collects emotional data from their facial expressions, voice tone, and input text, for example, depending on whether they are stressed or relaxed.

[1419] Gathering price information and special offers from nearby supermarkets

[1420] The server sends requests to the websites and APIs of nearby supermarkets to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database. For example, it might collect information that Supermarket A is selling cabbage for 98 yen and chicken breast for 200 yen.

[1421] Menu generation

[1422] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information from nearby supermarkets. By taking emotional data into account, the server can suggest menus that suit the user's mood. For example, if the user is feeling stressed, it can suggest menus that have a relaxing effect.

[1423] Nutritional Balance Calculation

[1424] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the proposed menu. The results are sent to the device and displayed to the user. For example, information such as "This menu has 500 kcal and 25 g of protein per meal" is displayed.

[1425] Generate a shopping list

[1426] The server runs an algorithm to generate a list of ingredients based on the proposed menu. The results are sent to the device and displayed to the user as a shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" might be generated.

[1427] Proposal of the best shopping route

[1428] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs. The results are sent to the device and displayed to the user as an efficient shopping route. For example, a route such as "Purchase cabbage at supermarket A and chicken breast at supermarket B" may be presented.

[1429] Emotion-based menu suggestions

[1430] Based on the emotional data received from the emotion engine, the server generates an optimal menu according to the user's emotional state. For example, if the user is tired, it will suggest nutritious meals, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[1431] Specific examples

[1432] If a user inputs information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories," and the emotion engine detects that they have been under a lot of stress recently, the server will use this information to suggest menu items that have a relaxing effect, and will also suggest a "healthy salad with cabbage and chicken breast" based on nutritional balance. This allows for a meal plan that not only satisfies the whole family, but also takes into account their emotional state.

[1433] This system dramatically improves home meal planning, making meals more economical, nutritionally balanced, and emotionally conscious.

[1434] The processing flow will be explained below.

[1435] Specific processing flow of the program

[1436] Gathering family preferences and health information

[1437] Step 1:

[1438] The user opens the application's settings screen and enters each family member's food preferences, allergy information, health check results, etc.

[1439] Step 2:

[1440] The terminal transmits the input information to the server.

[1441] Step 3:

[1442] The server stores the received family preferences and health information in a database.

[1443] Collecting Emotional Data

[1444] Step 1:

[1445] When a user uses the application, the emotion engine captures the user's facial expression data using a camera, and also obtains emotion data from voice tones and text input.

[1446] Step 2:

[1447] The device transmits the collected emotion data to the server.

[1448] Step 3:

[1449] The server analyzes the emotional data to determine the user's current emotional state.

[1450] Gathering price information and special offers from nearby supermarkets

[1451] Step 1:

[1452] The server periodically sends data collection requests to the local supermarket's website or API.

[1453] Step 2:

[1454] The server extracts product name, price, and sale information from the response.

[1455] Step 3:

[1456] The server stores the extracted price and sale information in a database.

[1457] Menu generation

[1458] Step 1:

[1459] The server uses an algorithm to generate the optimal menu based on the family's preferences, health information, emotional data, and price information from nearby supermarkets.

[1460] Step 2:

[1461] The server sends the contents of the generated menu to the terminal.

[1462] Step 3:

[1463] The terminal displays the contents of the proposed menu to the user.

[1464] Nutritional Balance Calculation

[1465] Step 1:

[1466] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the menu it proposes.

[1467] Step 2:

[1468] The server formats the results and sends them to the terminal.

[1469] Step 3:

[1470] The device displays information about calories and nutrients per meal to the user.

[1471] Generate a shopping list

[1472] Step 1:

[1473] The server runs an algorithm that lists the ingredients needed based on the proposed menu.

[1474] Step 2:

[1475] The server sends the generated shopping list to the terminal.

[1476] Step 3:

[1477] The terminal displays the shopping list to the user.

[1478] Proposal of the best shopping route

[1479] Step 1:

[1480] The server compares price information from multiple supermarkets and runs an algorithm to calculate a shopping route that minimizes costs.

[1481] Step 2:

[1482] The server sends the calculated optimal shopping route to the terminal.

[1483] Step 3:

[1484] The terminal displays an efficient shopping route to the user.

[1485] Emotion-based menu suggestions

[1486] Step 1:

[1487] Based on the emotion data received from the emotion engine, the server executes an algorithm to generate an optimal menu according to the user's emotional state.

[1488] Step 2:

[1489] The server sends the menu contents based on the emotion data to the terminal.

[1490] Step 3:

[1491] The device will display menu suggestions based on the user's emotions. For example, if the user is feeling stressed, it will suggest a menu with a relaxing effect, or if the user wants to feel energized, it will suggest a menu using their favorite ingredients.

[1492] Through these steps, users can enjoy an economical and nutritionally balanced meal plan tailored to their emotional state.

[1493] Example 2

[1494] 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."

[1495] In modern home cooking, it is difficult to propose economical and nutritionally balanced menus that take into account the preferences and health information of all family members. There is also a demand for meal plans that take into account the user's emotional state, but this is difficult to achieve with conventional methods. Furthermore, there are few systems that suggest efficient shopping routes, which increases the burden of housework.

[1496] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting family member preference and health information, means for collecting user emotion data, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information, means for generating a shopping list based on the generated menu, means for suggesting an optimal shopping route, means for calculating the nutritional balance of the generated menu and providing it to the user, and means for suggesting a menu based on the user's emotional state. This provides an economical and nutritionally balanced meal plan that takes into account the preferences and health information of all family members, as well as the user's emotional state, and also suggests an efficient shopping route, thereby reducing the burden of housework.

[1497] "Means for collecting family preferences and health information" refers to a means by which users input their family's food preferences, allergy information, health checkup results, etc. through an application, which then converts this information into a data format and collects it.

[1498] The "means for collecting user emotional data" refers to a means for acquiring user emotional data from facial expressions, tone of voice, input text, etc. when the user uses an application.

[1499] "Means for collecting price information and sale information from nearby stores" refers to a means in which the server sends requests to the websites or APIs of nearby stores, periodically obtains product names, prices, sale information, etc., and stores them in a database.

[1500] "Means for generating optimal menus based on collected information" refers to means for creating optimal menus using algorithms based on family preferences, health information, user emotional data, store price information, etc.

[1501] The "means for generating a shopping list based on the generated menu" is a means for listing necessary ingredients based on the generated menu and presenting it as a shopping list.

[1502] The "means for proposing the optimal shopping route" is a means for calculating and proposing a shopping route that minimizes costs based on collected price information from multiple stores.

[1503] The "means for calculating the nutritional balance of the generated menu and providing it to the user" is a means for calculating the nutritional components and calories of each ingredient included in the proposed menu and providing the results to the user.

[1504] The "means for proposing a menu according to the emotional state of the user" is a means for creating and proposing an optimal menu according to the emotional state of the user based on the collected emotional data of the user.

[1505] This system proposes economical and nutritionally balanced meals and provides efficient shopping routes based on family preferences, health information, price information from nearby stores, and user emotion data. This system is composed of a server, terminals, users, and an emotion engine.

[1506] First, the user enters information about their family's food preferences, allergies, and health checkup results through the application. For example, they may enter information such as "my second daughter doesn't like fish" or "my mother needs to limit her calories." The device collects this data, converts it into a data format, and sends it to the server. Standard data formats such as JSON are used for this purpose.

[1507] Next, while the user is using the application, the emotion engine collects emotion data from facial expressions, voice tones, input text, etc. For example, if the user inputs text or voice tones that indicate they are feeling stressed, the emotion engine detects this and the device sends the detected emotion data to the server.

[1508] In addition, the server periodically sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. For example, information that cabbage costs 98 yen and chicken breast costs 200 yen at Supermarket A is collected and stored in a database. This process uses web scraping technology and API integration.

[1509] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and store price information. For example, based on information such as "the second daughter doesn't like fish," "the mother needs to limit her calories," and "the user is currently under stress," the server might suggest a "healthy salad with cabbage and chicken breast." This algorithm takes into account nutritional balance and emotional state, so machine learning models and rule-based systems may be used.

[1510] Based on the generated menu, the server runs an algorithm to calculate the nutritional content and calories of each ingredient and sends information such as "This menu contains 500 kcal and 25 g of protein per meal" to the device. This data is important for providing users with a nutritionally balanced diet.

[1511] The server then creates a list of ingredients needed based on the generated menu, generating a shopping list such as "1 cabbage, 300g chicken breast, 200g broccoli," and sends it to the terminal.The server then compares price information from multiple stores, calculates the lowest-cost shopping route, and suggests the optimal route to the user, such as "buy the cabbage at supermarket A and the chicken breast at supermarket B."

[1512] Finally, the server generates and suggests a menu based on the user's emotional state based on the emotional data received from the emotion engine. For example, if the user is tired, it will suggest a menu with a relaxing effect, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[1513] As a specific example of a prompt, the above-mentioned process is performed by inputting sentences such as "The second daughter does not like fish, and the mother needs to limit her calories. She has been feeling stressed recently" into the application.

[1514] As described above, by providing meal plans that take into account the preferences and health information of all family members, as well as the user's emotional state, and also suggesting efficient shopping routes, household meal planning and shopping can be dramatically improved.

[1515] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1516] Step 1:

[1517] The user launches the application and inputs information such as the family's food preferences, allergy information, and health checkup results. The input data is information such as "The second daughter doesn't like fish" and "The mother needs to limit her calories." The device collects this information, converts it to JSON format, and sends it to the server. The data entered here is in text format, which is then processed and sent to the server as the body of the API request.

[1518] Step 2:

[1519] While the user is using the application, the emotion engine collects emotion data from the user's facial expressions, voice tone, input text, etc. Specifically, the user inputs "I've been feeling stressed lately." This data is collected by the device and sent as a request to the server. The input emotion data is used to determine the user's emotional state through text and voice analysis.

[1520] Step 3:

[1521] The server periodically sends requests to the websites and APIs of nearby stores to obtain product names, prices, and sale information. For example, the server obtains price information from Supermarket A, such as "cabbage is 98 yen, chicken breast is 200 yen," and stores this information in a database. The data obtained is updated periodically, so it is necessary to obtain the latest price information.

[1522] Step 4:

[1523] The server uses AI models and algorithms to generate optimal menus based on family preferences, health information, emotional data, and store price information. The input data are family preferences, health information, emotional data, and store price information, and the output is specific menu suggestions. For example, an algorithm might suggest a "healthy salad with cabbage and chicken breast." By taking emotional data into account, a menu suited to the user's mood is generated.

[1524] Step 5:

[1525] The server runs an algorithm that calculates the nutritional content and calories of each ingredient in the proposed menu. The input data is the generated menu, and the output is a detailed description of its nutritional balance. For example, the server calculates information such as "This menu contains 500 kcal and 25 g of protein per serving" and sends it to the device.

[1526] Step 6:

[1527] The server creates a shopping list by listing the ingredients needed based on the menu. The input data is the menu and information about each ingredient, and the output is a specific shopping list. For example, a list such as "1 cabbage, 300g chicken breast, 200g broccoli" is created and sent to the terminal.

[1528] Step 7:

[1529] The server compares price information from multiple stores and runs an algorithm to calculate a shopping route that minimizes costs. The input data is the price information from each store, and the output is the optimal shopping route. For example, it suggests a route such as "buy cabbage at supermarket A and chicken breast at supermarket B" and sends it to the terminal.

[1530] Step 8:

[1531] Based on the emotional data received by the server from the emotion engine, the server generates and suggests a menu that matches the user's emotional state. The input data is emotional data, preferences, and health information, and the output is a menu that is appropriate for the user's emotional state at the time. For example, if the user is tired, the system suggests "relaxing chamomile tea and avocado salad."

[1532] Through these steps, the system provides meal plans that take into account family preferences, health information, and the user's emotional state, and also suggests efficient shopping routes, dramatically improving household meal planning and shopping.

[1533] (Application example 2)

[1534] 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."

[1535] Conventional menu suggestion systems often generate menus that only take into account family preferences and health information, and are unable to provide meal plans that reflect the user's emotional state. As a result, they are unable to provide appropriate meal suggestions that reflect the user's emotional state or temporary changes in physical condition, making it difficult to provide a satisfying and nutritionally balanced diet. Furthermore, there has been a lack of systems that effectively utilize price information from nearby stores to suggest shopping routes that increase economic efficiency.

[1536] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting family preference and health information, means for analyzing the user's emotional state, means for collecting price information and sale information from nearby stores, means for generating an optimal menu based on the collected information and the analyzed emotional state, means for generating a shopping list based on the generated menu, and means for suggesting an optimal shopping route. This makes it possible to suggest a nutritionally balanced menu that takes the user's emotional state into consideration, and an optimal shopping route that takes economic efficiency into account.

[1537] "Family preferences" refers to individual preference information regarding favorite and disliked foods and ingredients of each family member.

[1538] "Health Information" means data about the health status of family members, including allergy information, medical test results, and specific nutrient needs or restrictions.

[1539] "Emotional state" refers to the user's current mood or emotional state, including feelings such as stress, relaxation, satisfaction, fatigue, etc.

[1540] "Price information" is data about the prices of products offered at nearby stores and supermarkets.

[1541] "Special sale information" is information about sales and promotions in which specific products are discounted for a limited time.

[1542] "Analyzing" is a process of processing collected data or information to arrive at a specific conclusion or result.

[1543] "Menu" refers to the meal plan and menu composition proposed based on set conditions.

[1544] A "shopping list" is a list of ingredients and products a user needs, including items necessary to create a specific meal plan.

[1545] The "optimal shopping route" refers to the shortest or most efficient route that a user should take to purchase the products they need, taking into consideration cost and efficiency.

[1546] A "system" is a collection of hardware and software that interrelate to accomplish a particular purpose.

[1547] This system proposes optimal menus, generates shopping lists, and suggests optimal shopping routes based on the user's family preferences, health information, price information from nearby stores, and the user's emotional state. This system is composed of a server, terminals, users, and an emotion engine.

[1548] Gathering family preferences and health information

[1549] The user inputs information about their family's food preferences, allergy information, health checkup results, etc. through the device. Specifically, data about each family member's likes and dislikes and health status is entered, and the device collects this information and sends it to the server.

[1550] Collecting Emotional Data

[1551] When a user uses a device (smart glasses or smartphone), the emotion engine collects emotion data from the user's facial expressions, voice tone, input text, etc. For example, data is collected depending on whether the user is stressed or relaxed.

[1552] Gathering price and sale information from nearby stores

[1553] The server sends requests to local stores' websites and APIs to get product names, prices, and sale information. The server periodically updates the information and stores the latest prices and sale information in a database.

[1554] Menu generation

[1555] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information from nearby stores. By taking emotional data into account, the server suggests menus that suit the user's mood and health condition.

[1556] Nutritional Balance Calculation

[1557] The server runs an algorithm to calculate the nutritional content and calories of each ingredient in the proposed menu, and the results are sent to the device and displayed to the user.

[1558] Generate a shopping list

[1559] The server creates a list of ingredients based on the proposed menu, and the generated shopping list is sent to the terminal and displayed to the user.

[1560] Proposal of the best shopping route

[1561] The server compares prices from multiple stores and runs an algorithm to calculate the shopping route that minimizes costs. The results are sent to the terminal, and the user is shown an efficient shopping route.

[1562] Emotion-based menu suggestions

[1563] Based on the emotional data received from the emotion engine, the server generates an optimal menu according to the user's emotional state. For example, if the user is tired, it will suggest nutritious meals, and if the user is feeling down, it will suggest a menu that includes their favorite foods.

[1564] Specific examples

[1565] If a user inputs "my second daughter doesn't like fish" or "my mother needs to limit her calories," and the emotion engine detects that they have been under a lot of stress recently, the server will use this information to suggest relaxing menu items, and will also consider nutritional balance and suggest a "healthy salad with cabbage and chicken breast." This allows for a meal plan that not only satisfies the whole family, but also takes into account their emotional state.

[1566] Prompts for generative AI models

[1567] For example, use the following prompt:

[1568] "Please suggest a relaxing meal plan that takes into account the user's current emotional state (stress) and the family's preferences (the second daughter doesn't like fish, and the mother needs to limit her calories)."

[1569] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1570] Step 1:

[1571] The user inputs their family's food preferences, allergy information, and health checkup results through the device. This input includes detailed data on each family member's likes and dislikes and health conditions. The device collects this information and sends it to the server. The input information is stored in a database and used for subsequent analysis and menu generation.

[1572] Step 2:

[1573] Through the user's device (smart glasses or smartphone), the emotion engine collects emotional data from the user's facial expressions, voice tone, and input text. The emotion engine uses facial recognition technology to analyze the image data captured by the camera and determine the user's emotional state. The analysis results are sent to the server and used for subsequent processes.

[1574] Step 3:

[1575] The server obtains product name, price, and sale information from nearby stores via their websites and APIs. This information collection process uses a script that accesses the store's database at a specified frequency to continuously obtain the latest price and sale information. The collected data is stored in the server's database and used for subsequent menu generation.

[1576] Step 4:

[1577] The server uses an algorithm to generate optimal menus based on family preferences, health information, emotional data, and price information. The algorithm uses various data to calculate menus that will satisfy the entire family, and the resulting menus are sent to the user's device. The menu generation takes into account multiple parameters, including pre-set nutritional balance, the user's emotional state, and economic efficiency.

[1578] Step 5:

[1579] Based on the generated menu, the server lists the necessary ingredients and generates a shopping list. The shopping list includes detailed information such as the required amount of each ingredient and recommended purchasing locations. The shopping list is sent to the user's device and can be viewed by the user.

[1580] Step 6:

[1581] The server calculates the optimal shopping route that minimizes costs based on price information collected from multiple stores. This process uses a price comparison algorithm to derive the most economical shopping route based on the price information of each store. The calculation results are sent to the terminal and displayed to the user.

[1582] Step 7:

[1583] Based on the suggested shopping route and emotion data, the server makes a final, emotion-based menu suggestion. A nutritionally balanced menu is displayed according to the user's mood and health condition, and the user can shop and cook accordingly.

[1584] This allows users to easily and efficiently plan optimal meals based on each family member's preferences, health status, and real-time emotional state.

[1585] 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.

[1586] 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.

[1587] 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.

[1588] 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.

[1589] FIG. 9 illustrates 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 behaviors 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.

[1590] 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.

[1591] 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).

[1592] 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.

[1593] 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."

[1594] 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.

[1595] 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).

[1596] 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.

[1597] 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.

[1598] 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.

[1599] 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.

[1600] 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.

[1601] 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.

[1602] 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.

[1603] 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.

[1604] 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.

[1605] 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.

[1606] The following is further disclosed regarding the above embodiment.

[1607] (Claim 1)

[1608] a means of collecting family preferences and health information;

[1609] A means of gathering price and sale information from nearby supermarkets,

[1610] A means for generating an optimal menu based on the collected information;

[1611] means for generating a shopping list based on the generated menu;

[1612] A system including a means for suggesting optimal shopping routes.

[1613] (Claim 2)

[1614] 2. The system according to claim 1, further comprising a means for customizing a menu taking into consideration the likes and dislikes of each family member and the results of health checkups.

[1615] (Claim 3)

[1616] 2. The system according to claim 1, further comprising means for calculating a nutritional balance based on the proposed menu and providing it to the user.

[1617] "Example 1"

[1618] (Claim 1)

[1619] a means of collecting family preferences and health information;

[1620] A means of gathering price and sale information from nearby stores;

[1621] A means for generating an optimal menu based on the collected information;

[1622] A means for calculating nutritional content and calories for the generated menu;

[1623] means for generating a shopping list based on the generated menu;

[1624] A system including a means for suggesting optimal shopping routes.

[1625] (Claim 2)

[1626] 2. The system according to claim 1, further comprising a means for customizing a menu taking into consideration the likes and dislikes of each family member and the results of health checkups.

[1627] (Claim 3)

[1628] 2. The system according to claim 1, further comprising means for calculating a nutritional balance based on the proposed menu and providing it to the user.

[1629] "Application Example 1"

[1630] (Claim 1)

[1631] a means of collecting family preferences and health information;

[1632] A means of gathering price and sale information from nearby stores;

[1633] A means for generating an optimal menu based on the collected information;

[1634] means for generating a shopping list based on the generated menu;

[1635] A means of suggesting the best shopping route,

[1636] A means of collecting price information for food delivery services and proposing menus that can be delivered;

[1637] means for generating a menu using the generative AI model;

[1638] A means for inputting a prompt sentence and generating a menu;

[1639] A system including:

[1640] (Claim 2)

[1641] 2. The system according to claim 1, further comprising a means for customizing a menu taking into consideration the likes and dislikes of each family member and the results of health checkups.

[1642] (Claim 3)

[1643] 2. The system according to claim 1, further comprising means for calculating a nutritional balance based on the proposed menu and providing it to the user.

[1644] "Example 2: Combining Emotion Engines"

[1645] (Claim 1)

[1646] a means of collecting family preferences and health information;

[1647] means for collecting user emotion data;

[1648] A means of gathering price and sale information from nearby stores;

[1649] A means for generating an optimal menu based on the collected information;

[1650] means for generating a shopping list based on the generated menu;

[1651] A means of suggesting the best shopping route,

[1652] A means for calculating the nutritional balance of the generated menu and providing it to a user;

[1653] A method for suggesting menus according to the user's emotional state

[1654] A system including:

[1655] (Claim 2)

[1656] 2. The system according to claim 1, further comprising a means for customizing a menu taking into consideration the likes and dislikes of each family member and the results of health checkups.

[1657] (Claim 3)

[1658] 2. The system according to claim 1, further comprising means for generating a menu according to the emotional state of the user based on the collected emotional data of the user.

[1659] "Application example 2 when combining emotion engines"

[1660] (Claim 1)

[1661] a means of collecting family preferences and health information;

[1662] means for analyzing the emotional state of a user;

[1663] a means of collecting price and sale information from nearby stores;

[1664] A means for generating an optimal menu based on the collected information and the analyzed emotional state;

[1665] means for generating a shopping list based on the generated menu;

[1666] A system including a means for suggesting optimal shopping routes.

[1667] (Claim 2)

[1668] 10. The system of claim 1, further comprising means for customizing the menu taking into account each family member's likes and dislikes, health checkup results, and emotional state.

[1669] (Claim 3)

[1670] 2. The system according to claim 1, further comprising means for calculating a nutritional balance based on the proposed menu and providing it to the user. [Explanation of symbols]

[1671] 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 of collecting family preferences and health information; A means of gathering price and sale information from nearby supermarkets, A means for generating an optimal menu based on the collected information; means for generating a shopping list based on the generated menu; A system including a means for suggesting optimal shopping routes.

2. 2. The system according to claim 1, further comprising means for customizing a menu taking into consideration the likes and dislikes of each family member and the results of a health check.

3. 2. The system according to claim 1, further comprising means for calculating a nutritional balance based on the proposed menu and providing it to the user.

Images