system

Abstract

Provide a system. A method for collecting food price data from a plurality of stores; a means for storing and updating the collected price data in a database; means for generating a list of required foods based on a user's request; A way to match the collected price data with the list of food items you need and find the cheapest combination; and means for presenting the cheapest combination found to the user.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional food purchasing workflows, users must first decide on a menu, create a list of the necessary foods, and then visit multiple stores to compare prices while shopping. This time-consuming process requires time and effort, placing a significant burden on consumers. Furthermore, food price information fluctuates daily, making it difficult to find the cheapest food at the right time. Furthermore, creating a menu that takes into account a user's food preferences and allergy information is not easy, and managing everything at once is difficult. Therefore, the present invention aims to solve these problems and provide a system that enables efficient and hassle-free food purchasing and menu suggestions. [Means for solving the problem]

[0005] The present invention relates to a system that includes a means for collecting food price data from multiple stores, a means for saving and updating the collected price data in a database, a means for generating a required food list based on a user's request, a means for comparing the collected price data with the required food list to find the cheapest combination, and a means for presenting the found cheapest combination to the user. It also includes a means for searching for recipes from the presented food list, a means for generating a menu plan optimal for the user from the searched recipes, a means for presenting the generated menu plan and the optimal food list to the user, a means for accepting user modifications and finalizing the final purchase list, and a means for customizing the required food list taking into account the user's ingredient preferences and allergy information, thereby enabling users to purchase foods efficiently and at the lowest possible prices and create menus suited to their individual needs.

[0006] "Price data" refers to sales price information for food and other products at multiple stores.

[0007] "Database" means a centralized information management system for storing collected price data and updating and retrieving it as needed.

[0008] "User Requests" refers to the user's requests regarding menus and food requirements for a specific period or situation.

[0009] A "needed food list" refers to a list of foods that need to be purchased based on the user's specified menu or meal plan.

[0010] "Matching" refers to the process of comparing collected price data with the list of food needs and selecting the most economical options.

[0011] The "optimal combination" refers to the result of comparing price data from multiple stores and selecting the cheapest and most rational purchasing pattern.

[0012] A "cooking recipe" refers to a document or data that describes cooking methods and steps using specific ingredients.

[0013] "Menu Plan" refers to suggested food combinations and meal plans based on the user's preferences and conditions.

[0014] "Modification" means any change or adjustment a user makes to a suggested food list or meal plan.

[0015] "Final Shopping List" refers to the final list of foods to be purchased after being reviewed and modified by the User.

[0016] "Food preferences" refers to a user's individual preferences regarding foods that they particularly like or dislike in meals or cooking.

[0017] "Allergy Information" refers to information about any allergies a User has to certain foods. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

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

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention is a system that allows users to determine optimal food purchases and menus based on price data collected from multiple stores. This system is implemented by the specific program processing shown below.

[0040] System configuration

[0041] This system consists of three parties: a server, a terminal, and a user.

[0042] 1. Server

[0043] Responsible for collecting, storing and updating price data.

[0044] Generate optimal purchasing patterns and menu suggestions based on user requirements.

[0045] 2. Terminal

[0046] It interfaces with the user, taking requests, displaying results, and accepting modifications.

[0047] Generate a list of food items you need and search for recipes.

[0048] 3. Users

[0049] Enter food purchasing and menu planning requests and make final decisions based on the information provided.

[0050] Program processing overview

[0051] Server collects and updates price data

[0052] The server retrieves price data from multiple partner stores via API and stores it in a database. This process is executed periodically to update the price information.

[0053] Examples:

[0054] The server sends a request to the APIs of "Store A" and "Store B" to retrieve the latest food price data. The retrieved data is stored in a database as "chicken 500 yen" and "cabbage 150 yen," for example.

[0055] Get user requests and generate a food list

[0056] A user inputs requests such as "weekly menu" or "dinner menu" through a terminal. This request may also include information about food preferences and allergies.

[0057] The device receives this request and generates a list of food items based on the user's needs, such as "chicken," "cabbage," and "soy sauce."

[0058] Examples:

[0059] The user inputs a request such as "dinner for a family of four," and the device generates a list of required foods (e.g., "chicken," "cabbage," and "soy sauce").

[0060] Searching for and suggesting optimal purchasing patterns

[0061] The server compares the collected price data with the list of necessary foods generated by the terminal and searches for the cheapest combination.

[0062] The device presents the user with optimal purchasing patterns sent from the server, including specific advice on which stores to buy which foods.

[0063] Examples:

[0064] The server calculates the cheapest combination as "chicken for 500 yen at store A, cabbage for 150 yen at store A, and soy sauce for 200 yen at store B." The terminal displays a suggestion to the user to "purchase chicken and cabbage from store A and soy sauce from store B."

[0065] Recipe search and meal plan suggestions

[0066] The device searches the internet for cooking recipes based on a list of optimal foods, taking into account the user's preferences and allergies.

[0067] The server analyzes the recipe information sent from the device and generates the optimal menu plan for the user, which is then sent to the device and presented to the user.

[0068] Examples:

[0069] The device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[0070] Final purchase list confirmation

[0071] The user can check the proposed menu and food list through the terminal and make any necessary changes. Once the user has finished making changes, the final shopping list is confirmed.

[0072] Examples:

[0073] The user makes modifications, such as "increase the amount of cabbage." After the modifications are complete, the final shopping list is confirmed.

[0074] In this way, the system provides users with efficient and economical food purchasing and menu suggestions. The above is a specific embodiment for carrying out the present invention.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] The terminal displays a list of affiliated stores to the user and prompts the user to select which store they would like to collect price information from.

[0078] Step 2:

[0079] The user selects the store of interest and presses the "Next" button.

[0080] Step 3:

[0081] The server sends a request to the API of the store selected by the user to retrieve the latest food price data.

[0082] Step 4:

[0083] The server saves the acquired price data in the database and updates existing data, such as "chicken 500 yen" and "cabbage 150 yen."

[0084] Step 5:

[0085] Users can use the terminal to input requests such as "weekly menu" or "dinner menu." They can also input food preferences and allergy information.

[0086] Step 6:

[0087] The device receives the user's request and generates a list of the required foods, such as "chicken," "cabbage," and "soy sauce."

[0088] Step 7:

[0089] The server matches the price data collected in step 4 with the list of required foods generated in step 6 to find the cheapest combination.

[0090] Step 8:

[0091] The server identifies the cheapest purchase pattern and transmits the corresponding combination to the terminal.

[0092] Step 9:

[0093] The device presents the optimal purchasing pattern sent from the server to the user, for example, displaying advice such as "Buy chicken and cabbage at supermarket A, and buy cooking oil at supermarket B."

[0094] Step 10:

[0095] The device searches for recipe information on the Internet based on the suggested food list, for example, "recipes using chicken and cabbage."

[0096] Step 11:

[0097] The server analyzes the recipe information received from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[0098] Step 12:

[0099] The device then presents the generated menu plan and optimal food list to the user as a final proposal, along with a food shopping list and recipes.

[0100] Step 13:

[0101] The user checks the presented menu and food list and makes any necessary changes, such as "increase the amount of cabbage" or "switch to a different brand of soy sauce."

[0102] Step 14:

[0103] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the shopping list.

[0104] In this way, the system provides users with efficient and economical food purchasing and meal planning suggestions.

[0105] Example 1

[0106] 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."

[0107] Conventional food purchasing and menu planning systems have difficulty collecting price data from multiple stores and providing optimal purchasing patterns and menu plans based on that data. Furthermore, customizing lists that take into account the user's food preferences and allergy information, and finalizing the purchase list, are cumbersome, making them difficult to use for users.

[0108] 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.

[0109] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for acquiring user requests and generating a food list, means for comparing the collected price data with the necessary food list to search for the cheapest combination, means for presenting the searched cheapest combination to the user, means for displaying the presented cheapest purchasing pattern, means for searching the Internet for cooking recipes based on the generated ingredient list, means for generating an optimal menu plan for the user, means for the user to finalize the shopping list, and means for customizing the necessary food list taking into account the user's ingredient preferences and allergy information, thereby enabling the user to purchase foods efficiently and determine the optimal menu.

[0110] "Price data" is price information for food products offered by multiple stores.

[0111] "Database" means an information storage system for storing and updating collected pricing data.

[0112] "User requirements" are requests or conditions entered by a user for food purchases or menu decisions.

[0113] A "food list" is a list of foods that need to be purchased, generated based on a user's request.

[0114] The "cheapest purchasing pattern" is information that shows the cheapest combination by comparing the collected price data with the list of necessary foods.

[0115] A "cooking recipe" is a set of instructions that describes how to cook a dish using specific ingredients.

[0116] A "menu plan" is a combination of dishes suggested for multiple days or meals.

[0117] The "final purchase list" is a list of foods that should be finally purchased, confirmed after the user has confirmed and corrected them.

[0118] "User preference and allergy information" refers to information about whether a user likes or avoids certain ingredients.

[0119] This invention is a system that allows users to determine optimal food purchases and menus based on price data collected from multiple stores. This system is composed of three entities: a server, a terminal, and a user.

[0120] server

[0121] The server obtains price data from multiple affiliated stores via API, and stores and updates it in a database. This process is performed periodically to keep price information up to date. For example, the server sends a request to the APIs of "Store A" and "Store B" to obtain the latest food price data. The obtained data is stored in the database as "chicken 500 yen" and "cabbage 150 yen." The server then generates a list of necessary foods based on the user's requests, and generates optimal purchasing patterns and menu suggestions.

[0122] Terminal

[0123] The terminal is responsible for interfacing with the user. It receives requests from the user and presents the generated list of necessary foods to the user. It also accepts corrections as necessary and finalizes the final shopping list. For example, if a user inputs a request for "dinner for a family of four," the terminal will generate and display a list of necessary foods (e.g., "chicken," "cabbage," and "soy sauce") based on this request.

[0124] User

[0125] The user inputs requests for food purchases and menu decisions, and makes a final decision based on the information presented. The user inputs requests such as "weekly menu" or "dinner menu" through the terminal. This request may also include information on food preferences and allergies. The user checks the proposed menu and food list, and makes any necessary corrections to finalize the shopping list. For example, the user makes a correction on the terminal, such as "increase the amount of cabbage," and then finalizes the revised shopping list.

[0126] Specific system configuration and operation example

[0127] 1. The server sends a request to the APIs of "Store A" and "Store B" to obtain the latest food price data. The obtained data is stored in the database as "chicken 500 yen" and "cabbage 150 yen," for example.

[0128] 2. The user inputs requests such as "weekly menu" or "dinner menu" through the terminal.

[0129] 3. The device receives the user's request and generates a list of required foods, such as "chicken," "cabbage," and "soy sauce."

[0130] 4. The price data collected by the server is compared with the list of necessary foods generated by the terminal to find the cheapest combination.

[0131] 5. The device presents the user with the optimal purchasing pattern sent from the server, including specific advice on which stores to purchase which foods.

[0132] 6. The device searches for cooking recipes on the Internet based on the optimal food list and suggests a menu plan that takes into account the user's preferences and allergy information.

[0133] 7. The user reviews the proposed menu and food list and makes any necessary modifications. After modifications, the final shopping list is confirmed.

[0134] Prompt Sentence Examples

[0135] "Create a weekly menu for a family of four. Get ingredient price data from this API. Generate the optimal menu plan and shopping list, taking into account the lowest price for each ingredient."

[0136] In this way, the system provides users with efficient and economical food purchasing and menu suggestions. The above is a specific embodiment for carrying out the present invention.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1:

[0139] Price data collection and updates

[0140] Input: Store API endpoint

[0141] Processing: The server retrieves price data from multiple partner stores via API. This is done periodically to update the price data.

[0142] Specific operation: The server sends requests to the API endpoints of "Store A" and "Store B" to obtain new price data. The obtained data is saved in the database as "chicken 500 yen" and "cabbage 150 yen," etc.

[0143] Output: A database containing updated price data

[0144] Step 2:

[0145] Get the user request

[0146] Input: A request entered by the user through the device (e.g., "weekly menu" or "dinner menu")

[0147] Processing: The device receives the user's request and analyzes the request content.

[0148] How it works: A user enters a request for "dinner for a family of four" into a smartphone app. The device receives the request and analyzes the details.

[0149] Output: Parsed user request data

[0150] Step 3:

[0151] Generate a list of food needs

[0152] Input: Parsed user request data

[0153] Processing: The device generates a list of required foods based on the content of the request.

[0154] Specific operation: The device analyzes the request content and generates a list of foods needed for "dinner for a family of four" (e.g., "chicken," "cabbage," and "soy sauce") and saves the list of needed foods in local storage.

[0155] Output: Generated food needs list

[0156] Step 4:

[0157] Finding optimal purchasing patterns

[0158] Input: Collected price data and generated food needs list

[0159] Processing: The server compares the collected price data with the list of required foods and searches for the cheapest combination.

[0160] How it works: The server compares the list of food items needed with the price data in the database and runs an algorithm to find the cheapest combination. For example, it calculates the cheapest combination as "chicken at store A for 500 yen, cabbage at store A for 150 yen, and soy sauce at store B for 200 yen."

[0161] Output: lowest price purchase pattern data

[0162] Step 5:

[0163] Showing the cheapest purchase pattern

[0164] Input: lowest price purchase pattern data

[0165] Processing: The terminal presents the lowest price purchase pattern sent from the server to the user.

[0166] Specific operation: The terminal receives the lowest price purchasing pattern data and displays specific instructions to the user, such as "purchase chicken and cabbage from store A, and purchase oil from store B."

[0167] Output: The cheapest purchase pattern presented to the user

[0168] Step 6:

[0169] Find recipes and generate meal plans

[0170] Input: Generated food requirements list, user preferences, and allergy information

[0171] Processing: The device searches for cooking recipes on the Internet based on the optimal food list. The server analyzes the recipe information and generates the optimal menu plan.

[0172] Specific operation: The device searches the internet for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[0173] Output: A suggested meal plan for the user

[0174] Step 7:

[0175] Final purchase list confirmation

[0176] Input: Suggested menu and food list, user modifications

[0177] Processing: The user reviews the proposed menu and food list and makes any necessary adjustments. The final shopping list is confirmed.

[0178] Specific operation: The user makes modifications on the device, such as "increase the amount of cabbage." After the modifications are complete, the final shopping list is confirmed and displayed to the user.

[0179] Output: Final purchase list confirmed

[0180] (Application example 1)

[0181] 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."

[0182] Conventional food purchasing and menu suggestion systems require users to spend a lot of time and effort to purchase the cheapest foods. Furthermore, they do not provide optimal suggestions for food lists and recipes that meet the user's needs, making it difficult to make efficient purchases and decide on menus. Another problem is that they do not adequately customize the system to accommodate user requests, food preferences, or allergies.

[0183] 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.

[0184] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a list of necessary foods based on a user's request, means for comparing the collected price data with the list of necessary foods to search for the cheapest combination, means for presenting the searched cheapest combination to the user, means for acquiring the user's request and displaying it to the user via a smartphone or smart glasses, and means for accepting user corrections and presenting a final purchasing route, thereby enabling the user to purchase food efficiently and economically and quickly decide on an appropriate menu.

[0185] A "means for collecting food price data from multiple stores" is a method or device for collecting food price information from different points of sale.

[0186] "Means for storing and updating collected price data in a database" refers to a storage device or system for storing the obtained price information, and has the function of updating the information to the latest version at any time.

[0187] The "means for generating a list of required foods based on a user's request" refers to a method or device for creating a list of ingredients to be purchased based on a user's request.

[0188] "Means for matching collected price data with a list of required foods to find the cheapest combination" refers to a method or device that compares collected price information with a generated list of ingredients to find the most affordable combination.

[0189] "Means for presenting the cheapest combination found to the user" refers to a method or device for informing the user of the cheapest combination found.

[0190] "Means for acquiring user requests and displaying them to the user through a smartphone or smart glasses" refers to a method or apparatus for collecting purchase requests from users and displaying the information via a smart device.

[0191] "Means for accepting user corrections and presenting a final shopping route" refers to a method or device for accepting corrections from a user and presenting a final shopping route or plan after incorporating the corrections.

[0192] A "means for retrieving recipes from a presented food list" refers to a method or device for finding cooking methods based on a proposed list of ingredients.

[0193] "Means for generating a menu plan optimal for the user from the searched recipes" refers to a method or device for creating a menu plan optimal for the user using the cooking methods found.

[0194] "Means for guiding the optimal shopping route in a store" refers to a method or device for guiding the route to buy the necessary ingredients most efficiently in the store.

[0195] "Means for customizing the required food list taking into account the user's food preferences and allergy information" refers to a method or device for individually adjusting the food list based on the user's preferences and allergy information.

[0196] "Means for optimizing recipe candidates using a generative AI model" refers to a method or device that uses artificial intelligence to suggest the best cooking method and efficiently select those candidates.

[0197] "Means for modifying and optimizing generated recipe candidates based on user request prompts" refers to a method or device for modifying and optimizing recipes suggested by AI in accordance with the specific requests of users.

[0198] This invention is a system that allows users to purchase food efficiently and economically and determine optimal menus based on price data collected from multiple stores. This system is composed of a server, terminals, and users.

[0199] Server Features

[0200] Price data collection, storage and updating

[0201] The server periodically collects food price data through APIs from multiple stores and stores it in a database. This stored data is always updated. This function on the server is essential for efficiently managing price information and providing it to users. A Python program is implemented on the server to retrieve store price data through RESTful APIs.

[0202] Finding optimal purchasing patterns

[0203] The server compares the collected price data with the food list generated by the device to find the cheapest combination, which allows the user to create an economical shopping plan. This search algorithm compares the price data with the food list to find the lowest-cost combination.

[0204] Analyzing recipe data and generating menu plans

[0205] The server analyzes the recipe information sent from the device and generates the optimal menu plan for the user. This function selects the most suitable recipes from multiple recipe databases and proposes menus that take into account the user's food preferences and allergies.

[0206] Device Features

[0207] Providing a user interface and receiving requests

[0208] The device, such as a smartphone or smart glasses, directly interfaces with the user, acquiring the user's request and generating a list of food items based on the request. The user can generate a list of food items by inputting a request such as "dinner tonight."

[0209] Providing optimal purchasing routes

[0210] The device presents the user with optimal purchasing patterns sent from the server and guides them to an efficient shopping route within the store based on this. This shopping route is visually displayed on the user's smartphone or smart glasses, helping to ensure a stress-free shopping experience.

[0211] Menu and recipe suggestions

[0212] The device searches the internet for recipes based on the optimal food list and suggests them to the user. For example, a search for "recipes using chicken and cabbage" will result in recipes such as "stir-fried chicken and cabbage." A generative AI model is also used to filter recipes based on the user's preferences and allergies.

[0213] User Roles

[0214] Entering and Modifying Requests

[0215] Users input their requests through their smartphones or smart glasses, review the food list and meal plan provided, and can modify the list or plan as needed to finalize the shopping list. This process allows users to efficiently decide on the food purchases and meal plans that best suit their needs.

[0216] Examples of concrete examples and prompts

[0217] A concrete example is a system where a user requests the best recipes for a "dinner for a family of four." Based on this request, the server collects price data and the device displays the best food list and purchasing patterns.

[0218] Example prompt sentence:

[0219] "Suggest a recipe for a perfect dinner for a family of four. The ingredients are chicken, cabbage, and soy sauce."

[0220] In this way, the invention realizes a highly functional system that supports efficient food purchasing and menu planning decisions.

[0221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0222] Step 1: The server collects price data from multiple stores and saves and updates it in a database.

[0223] Input: Store API URL list

[0224] Processing: The server accesses each store's API to obtain the latest food price data. It parses the obtained data in JSON format and saves it in the database. This process is performed periodically to keep the data up to date.

[0225] Output: A database containing the latest food price data

[0226] Step 2: The user enters a request through the terminal.

[0227] Input: User request (e.g., "Dinner tonight" or "Dinner for a family of four")

[0228] Processing: The terminal receives the user's request through the interface. This request may include food preferences and allergy information. The terminal then sends the input request to the server.

[0229] Output: User request data

[0230] Step 3: The server generates a list of required foods based on the user's request.

[0231] Input: User request data

[0232] Processing: The server analyzes the input request data and generates a food list based on it. The generated food list includes the names of the necessary ingredients, such as "chicken," "cabbage," and "soy sauce."

[0233] Output: Generated food list

[0234] Step 4: The server compares the collected price data with the food list to find the cheapest combination.

[0235] Input: Generated food list, latest price data

[0236] Processing: The server searches the price database to find the cheapest store and price for each ingredient, and presents this to the user as the cheapest combination.

[0237] Output: Data for the cheapest combination

[0238] Step 5: The device presents the cheapest combination to the user.

[0239] Input: Data for the cheapest combination

[0240] Processing: The device receives the data sent from the server and presents it visually to the user, such as on the screen of a smartphone or smart glasses.

[0241] Output: The cheapest combination presented to the user

[0242] Step 6: The device guides the user to the optimal shopping route within the store

[0243] Input: Data on the cheapest combination, data on in-store placement

[0244] Processing: Based on the information on the cheapest combination displayed on the device, the device calculates a route that will allow the user to purchase the necessary ingredients by going around the store efficiently. The device then visually guides the user along this route.

[0245] Output: The purchasing route presented to the user

[0246] Step 7: The device searches for recipes based on the food list and suggests them to the user.

[0247] Input: Generated food list

[0248] Processing: The device searches the internet for recipes that match the food list. It uses a generative AI model to select the best recipes and suggest them to the user. An example might be "Stir-fried chicken and cabbage."

[0249] Output: Suggested recipe

[0250] Step 8: User reviews, modifies, and finally confirms the suggested recipe and food list

[0251] Input: Suggested recipes, generated food list

[0252] Processing: The user checks the suggested recipes and food list through the terminal and makes any necessary corrections. Once corrections are complete, the final shopping list is confirmed.

[0253] Output: Final purchase list confirmed

[0254] Example prompt sentence:

[0255] "Suggest a recipe for a perfect dinner for a family of four. The ingredients are chicken, cabbage, and soy sauce."

[0256] 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.

[0257] This invention relates to a system that helps users efficiently and economically purchase food and create optimal menus using that food. The system also incorporates an emotion engine that recognizes the user's emotions and adjusts the food list and menu accordingly.

[0258] System configuration

[0259] This system has the following components:

[0260] 1. Server

[0261] It is the central component for implementing the present invention and is responsible for collecting, storing and updating price data.

[0262] Generate optimal purchasing patterns and menu suggestions based on user requirements.

[0263] It integrates an emotion engine to adjust food lists and menus based on user emotion recognition.

[0264] 2. Terminal

[0265] It is a device for interfacing with the user, taking requests, displaying results, and accepting modifications.

[0266] Generate a list of food items you need and search for recipes.

[0267] 3. Users

[0268] Enter food purchasing and menu planning requests and make final decisions based on the information provided.

[0269] Emotional data is input through the terminal and this information is used by the system.

[0270] Program processing overview

[0271] Server collects and updates price data

[0272] The server periodically retrieves price data from multiple partner stores via API and stores it in a database, ensuring that the latest price information is always available.

[0273] Examples:

[0274] The server sends a request to the APIs of "Store A" and "Store B" to retrieve the latest food price data. The retrieved data is stored in a database as "chicken 500 yen" and "cabbage 150 yen," for example.

[0275] Get user requests and generate a food list

[0276] Users input requests such as "weekly menu" or "dinner menu" through a terminal. These requests can also include food preferences, allergy information, and emotional data.

[0277] The device receives this request and generates a list of food needs based on the user's requirements, such as "chicken," "cabbage," and "soy sauce."

[0278] Examples:

[0279] The user inputs a request such as "dinner for a family of four," and the device generates a list of required foods (e.g., "chicken," "cabbage," and "soy sauce").

[0280] Searching for and suggesting optimal purchasing patterns

[0281] The server compares the collected price data with the necessary food list generated by the terminal to find the cheapest purchasing pattern.

[0282] The terminal presents the optimal purchasing pattern sent from the server to the user. For example, advice such as "Buy chicken and cabbage at supermarket A, and buy sesame oil at supermarket B."

[0283] Examples:

[0284] The server calculates the cheapest combination as "chicken for 500 yen at store A, cabbage for 150 yen at store A, and soy sauce for 200 yen at store B." The terminal displays a suggestion to the user to "purchase chicken and cabbage from store A and soy sauce from store B."

[0285] Recipe search and meal plan suggestions

[0286] The device searches the internet for recipes based on the best food list, e.g., "Recipes using chicken and cabbage."

[0287] The server analyzes the recipe information sent from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[0288] Examples:

[0289] The device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[0290] Final purchase list confirmation

[0291] The user can check the proposed menu and food list through the device and make any necessary changes, such as "increase the amount of cabbage" or "switch to a different brand of soy sauce."

[0292] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the purchase list.

[0293] Use of emotion engine

[0294] The emotion engine analyzes emotional data entered by the user into the device or acquired through sensors, and this data is used to adjust food lists and meal plans.

[0295] Examples:

[0296] The server uses an emotion engine to suggest relaxing recipes such as "chicken and cabbage soup" to reduce the user's stress level.

[0297] The device uses an emotion engine to search for and suggest recipes that better reflect the user's preferences.

[0298] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors the system to the user's emotions and preferences, providing a more personalized experience.

[0299] The processing flow will be explained below.

[0300] Step 1:

[0301] The terminal displays a list of affiliated stores to the user and prompts the user to select which store they would like to collect price information from.

[0302] Step 2:

[0303] The user selects the store of interest and presses the "Next" button.

[0304] Step 3:

[0305] The server sends a request to the API of the store selected by the user to retrieve the latest food price data.

[0306] Step 4:

[0307] The server saves the acquired price data in the database and updates existing data, such as "chicken 500 yen" and "cabbage 150 yen."

[0308] Step 5:

[0309] Users can use the terminal to input requests such as "weekly menu" or "dinner menu." They can also input food preferences, allergy information, and emotional data.

[0310] Step 6:

[0311] The device receives the user's request and generates a list of the required foods, such as "chicken," "cabbage," and "soy sauce."

[0312] Step 7:

[0313] The server matches the price data collected in step 4 with the list of required foods generated in step 6 to find the cheapest combination.

[0314] Step 8:

[0315] The server identifies the cheapest purchase pattern and transmits the corresponding combination to the terminal.

[0316] Step 9:

[0317] The device presents the optimal purchasing pattern sent from the server to the user, for example, displaying advice such as "Buy chicken and cabbage at store A and sesame oil at store B."

[0318] Step 10:

[0319] The device searches for recipe information on the Internet based on the suggested food list, for example, "recipes using chicken and cabbage."

[0320] Step 11:

[0321] The server analyzes the recipe information received from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[0322] Step 12:

[0323] The device then presents the generated menu plan and optimal food list to the user as a final proposal, along with a food shopping list and recipes.

[0324] Step 13:

[0325] The user can review the proposed menu and food list and make any necessary changes, such as adding more cabbage or switching to a different brand of soy sauce.

[0326] Step 14:

[0327] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the purchase list.

[0328] Step 15:

[0329] The user inputs emotional data into the terminal, which includes numerical values ​​and options that indicate the user's emotional state.

[0330] Step 16:

[0331] The terminal acquires the emotion data and transmits it to the server.

[0332] Step 17:

[0333] The server uses an emotion engine to analyze the emotional data and adjust the food list and menu based on the user's emotional state.

[0334] Examples:

[0335] If the user is feeling stressed, the server uses its emotion engine to suggest "chicken and cabbage soup, which has a relaxing effect."

[0336] If a user is feeling low in energy, the app suggests a "high-protein chicken dish" to replenish their energy.

[0337] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors them to their emotions and preferences, providing a more personalized experience.

[0338] Example 2

[0339] 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."

[0340] Conventional food purchasing systems make it difficult for users to purchase food efficiently and economically, and they also have problems with reducing user satisfaction because food lists and menus are not tailored to the user's individual emotional state or preferences.

[0341] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0342] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a required food list based on a user's request, means for comparing the collected price data with the required food list to search for the cheapest combination, means for presenting the searched cheapest combination to the user, and an emotion engine for analyzing emotion data entered by the user. This enables users to make efficient and economical food purchases and also makes it possible to provide food lists and menus that correspond to each user's emotional state.

[0343] "Stores" are multiple retail outlets or commercial facilities that provide food price data.

[0344] "Price data" refers to price information for food products sold at each store.

[0345] "Database" means an information management system for storing collected price data and user information and updating it as necessary.

[0346] "User Requests" refers to the preferences or conditions entered by the user in order to receive food list or menu suggestions.

[0347] A "food list" is a list of food needs generated based on a user's request.

[0348] The "emotion engine" is the part of the system that analyzes the emotional data entered by the user and adjusts the food list and menu accordingly.

[0349] A "cooking recipe" is an instruction manual that describes cooking methods and steps using specific ingredients.

[0350] A "menu plan" is a plan of meal combinations and cooking sequences suggested to the user.

[0351] "Final Shopping List" is the final list for purchasing food items that is confirmed after the user makes any modifications.

[0352] "Preferences" refers to the ingredients and types of dishes that a user particularly likes.

[0353] "Allergy information" refers to information about a user's allergic reaction to food ingredients or substances.

[0354] "Emotional data" is information entered by a user about their current emotional state.

[0355] "Suggestion" refers to the act of the system showing the user optimal food purchasing patterns and menu plans.

[0356] This invention relates to a system that helps users efficiently and economically purchase food and create optimal menus using that food. The system also incorporates an emotion engine that recognizes the user's emotions and adjusts the food list and menu accordingly.

[0357] System configuration

[0358] This system has the following components:

[0359] 1. Server

[0360] The server is the central component for implementing this invention, and is responsible for collecting, storing, updating, and processing price data. It periodically retrieves price data from multiple stores through APIs and stores it in a database. It also compares the collected price data with the user's food list based on their request to find the cheapest combination. It also integrates an emotion engine to adjust the food list and menu based on the user's emotion recognition.

[0361] Examples of hardware and software: For the server hardware, commercial cloud servers (e.g., AWS (registered trademark), Google (registered trademark)) can be used. For API communication and database operations, programming languages ​​such as Python and Java (registered trademark) and SQL databases (e.g., MySQL (registered trademark), PostgreSQL) are used.

[0362] 2. Terminal

[0363] The terminal is a device that interfaces with the user, receiving requests, displaying results, and accepting corrections. Based on the information entered by the user, it generates a list of necessary foods and searches for cooking recipes on the Internet. It also uses an emotion engine to make suggestions based on the user's emotions.

[0364] Examples of hardware and software: Devices can include smartphones, tablets, and PCs. Front-end frameworks such as React and Angular can be used to develop the user interface.

[0365] 3. Users

[0366] The user inputs requests for food purchases and menu decisions through the terminal, and then makes a final decision based on the displayed information. In addition, the user inputs their own emotional data, which is used by the system.

[0367] Program processing overview

[0368] Price data collection and updates

[0369] Server: Periodically sends requests to multiple partner store APIs to retrieve price data and store it in a database.

[0370] Example: The server obtains price data for "chicken 500 yen" and "cabbage 150 yen" from "Supermarket A" and saves it in the database.

[0371] Get user requests and generate a food list

[0372] User: Input requests such as "weekly menu" or "dinner menu" through the device. They can also include food preferences, allergy information, and emotional data.

[0373] Terminal: Receives this request and generates a food list based on the user's requirements.

[0374] Example: A user inputs a request for "dinner for a family of four," and the device generates a food list of "chicken," "cabbage," and "soy sauce."

[0375] Searching for and suggesting optimal purchasing patterns

[0376] Server: Matches the collected price data with the food list generated by the device to find the cheapest purchasing pattern.

[0377] Device: Presents the optimal purchasing pattern sent from the server to the user.

[0378] Example: The server calculates that "chicken is 500 yen at store A, cabbage is 150 yen at store A, and soy sauce is 200 yen at store B," and the terminal suggests, "Purchase chicken and cabbage at store A, and soy sauce at store B."

[0379] Recipe search and meal plan suggestions

[0380] Device: Search the internet for cooking recipes based on a list of the best foods.

[0381] Server: Analyzes the submitted recipe information and generates a menu plan that best suits the user's requirements.

[0382] Example: A device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes this and suggests it to the user as a "weekly dinner menu plan."

[0383] Final purchase list confirmation

[0384] User: Review the suggested menu and food list and modify as needed.

[0385] Device: Once the modifications are confirmed, the information is sent to the server and the final purchase list is confirmed.

[0386] Example: A user adds the modification "increase the amount of cabbage" and finalizes the final list.

[0387] Use of emotion engine

[0388] Emotion engine: Analyzes the emotional data entered by the user into the device and uses it to adjust food lists and menus.

[0389] Server: Uses an emotion engine to suggest menus based on the user's emotional state.

[0390] Example: When a user types "I'm feeling stressed," an emotion engine analyzes that information and the server suggests "chicken and cabbage soup."

[0391] Example prompts for generative AI models

[0392] "Please suggest a weekly menu for a family of four. The user's favorite ingredients are chicken and cabbage, and they have no allergies. Also, the user is currently feeling stressed, so please take that into consideration when creating a menu."

[0393] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors them to the user's emotions and preferences, providing a more personalized experience.

[0394] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0395] Specific processing steps of the system

[0396] Step 1: Collect price data

[0397] The server sends requests to the APIs of multiple partner stores to collect the latest food price data.

[0398] Input: API endpoint of partner store

[0399] Data processing: The server generates an API request and sends it to each store's API.

[0400] Output: Price data returned from each store

[0401] Specific operation: The server sends an API request to "Store A" and obtains price data for "Chicken 500 yen" and "Cabbage 150 yen."

[0402] Step 2: Saving and updating price data

[0403] The server stores the acquired price data in a database and updates existing data.

[0404] Input: Collected price data

[0405] Data Processing: The server analyzes the price data and updates the existing data in the database.

[0406] Output: Updated database

[0407] Specific operation: Save or update the data "Chicken 500 yen" and "Cabbage 150 yen" in the price table in the database.

[0408] Step 3: Get the user request

[0409] The user inputs requests such as "weekly menu" or "dinner menu" through the terminal.

[0410] Input: User request (e.g. "Dinner for a family of four")

[0411] Data processing: The terminal receives the request and displays it on the user interface.

[0412] Output: User input information

[0413] Specific operation: A user inputs a request into a terminal: "Dinner for a family of four."

[0414] Step 4: Generate a food list

[0415] The terminal generates a list of required foods based on the user's request.

[0416] Input: User request

[0417] Data processing: Applying a list generation algorithm based on user requests.

[0418] Output: List of food items needed

[0419] Specific operation: Based on the information about "dinner for a family of four," generate a list of "chicken," "cabbage," and "soy sauce."

[0420] Step 5: Finding optimal purchasing patterns

[0421] The server compares the collected price data with the generated food list to find the cheapest purchasing patterns.

[0422] Input: Price data, generated food list

[0423] Data processing: Compare the price data with the food list and use a matching algorithm to calculate the lowest price.

[0424] Output: Optimal purchasing pattern

[0425] Specific operation: Calculate "chicken is 500 yen at store A, cabbage is 150 yen at store A, and soy sauce is 200 yen at store B."

[0426] Step 6: Show purchasing patterns

[0427] The terminal presents the optimal purchasing pattern sent from the server to the user.

[0428] Input: Optimal purchase pattern

[0429] Data processing: Format purchase patterns so they can be displayed on the device.

[0430] Output: Purchase patterns presented to the user

[0431] Specific operation: The terminal displays "Purchase chicken and cabbage at store A, and purchase cooking oil at store B."

[0432] Step 7: Find a recipe

[0433] The device searches the internet for cooking recipes based on a list of optimal foods.

[0434] Input: Optimal Food List

[0435] Data processing: A recipe search engine is used to search recipe databases on the Internet.

[0436] Output: Related recipes

[0437] Action: Search for "chicken and cabbage recipes" and find "chicken and cabbage stir fry."

[0438] Step 8: Menu plan suggestions

[0439] The server analyzes the recipe information sent from the terminal and generates a menu plan that best suits the user's requirements.

[0440] Input: Cooking recipe

[0441] Data processing: Applying an algorithm to analyze recipe information and generate a menu plan.

[0442] Output: Menu plan

[0443] Specific operation: Propose to the user "weekly dinner menu plan."

[0444] Step 9: Finalize the purchase list

[0445] The user reviews the proposed menu and food list and modifies it as needed.

[0446] Input: Suggested menu and food list

[0447] Data processing: Accept user corrections and recalculate.

[0448] Output: Final purchase list

[0449] Specific operation: The user inputs the correction "increase the amount of cabbage" into the terminal and confirms the final list.

[0450] Step 10: Use the Emotion Engine

[0451] The emotion engine analyzes the emotional data entered by the user into the device.

[0452] Input: Emotion data

[0453] Data processing: Analyze the data using sentiment analysis algorithms to generate results.

[0454] Output: Food list and menu adjustments based on analysis results

[0455] Specific behavior: Based on the information that the user is "feeling stressed," the server suggests "chicken and cabbage soup."

[0456] Example prompts for generative AI models

[0457] "Please suggest a weekly menu for a family of four. The user's favorite ingredients are chicken and cabbage, and they have no allergies. Also, the user is currently feeling stressed, so please take that into consideration when creating a menu."

[0458] (Application example 2)

[0459] 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."

[0460] Conventional food purchasing and menu suggestion systems have the problem of not taking into account the user's emotions and being unable to provide personalized menus based on emotions. This makes it difficult for users to choose meals that suit their current psychological state and emotions, which can result in a poor shopping experience. Additionally, suggestions based solely on ingredient price information are difficult to meet the user's psychological and emotional needs.

[0461] The identification process by the identification 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 food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a required food list based on a user's request, and means for recognizing the user's emotional data and adjusting the food list and menu based on that data. This makes it possible to provide an optimal food list and menu based on the user's emotional and psychological state.

[0462] "Multiple stores" means a collection of multiple different stores, each of which may offer different prices and inventory information.

[0463] "Food Price Data" refers to information about the prices of food products sold at each store, including details such as product name, price, and availability.

[0464] "Database" means a set of systems and structures for storing and managing collected price data, allowing for rapid retrieval and updating of the data.

[0465] "User Requests" refers to the preferences and requirements that a user inputs into the system regarding food lists and menus, including the type of menu, number of people, allergy information, etc.

[0466] "Needed Food List" refers to a list of foods generated based on a user's request, which indicates specific food items that the user should purchase.

[0467] "User emotional data" means data that indicates a user's current mental state or emotion, including stress level, enjoyment, sadness, fatigue, etc.

[0468] "Menus" refers to cooking plans and suggestions based on specific foods that assist users in planning their daily meals.

[0469] "Server" refers to a computer system that is the central component of a system and is responsible for collecting, storing, processing, and providing data.

[0470] This invention is a system that helps users efficiently and economically purchase food and create optimal meals. The system integrates an emotion engine that recognizes the user's emotions and adjusts the food list and meal plan accordingly.

[0471] System configuration

[0472] This system has the following components:

[0473] 1. Server

[0474] This is the central component for implementing the present invention, and collects food price data from multiple stores.

[0475] The collected price data is stored in a database and updated regularly.

[0476] Generate optimal food lists and meal suggestions based on user requirements.

[0477] It uses an emotion engine to adjust food lists and menus based on the user's emotional data.

[0478] 2. Terminal

[0479] It is a device for interfacing with the user, and a smartphone is generally used.

[0480] The system uses the smartphone's camera and microphone to collect user emotional data and transmit it to a server.

[0481] The food list and menu suggestions sent from the server are displayed to the user.

[0482] 3. Users

[0483] Requests for food purchases and menu decisions are entered into the terminal, and emotional data is provided through the terminal.

[0484] Based on the information provided, review the food list and menu and finalize the shopping list.

[0485] Program processing overview

[0486] Server collects and updates price data

[0487] The server periodically retrieves price data from multiple local stores via API, and the retrieved data is stored in a database, always maintaining the latest price information.

[0488] Get user requests and generate a food list

[0489] Users input requests such as "dinner menu" or "weekly menu" through the device, and emotional data is also collected, and a list of the necessary foods is generated based on this information.

[0490] Searching for and suggesting optimal purchasing patterns

[0491] The server compares the collected price data with the necessary food list generated by the device to search for the cheapest purchasing pattern, and the search results are sent to the device and presented to the user.

[0492] Recipe search and meal plan suggestions

[0493] The device searches for cooking recipes on the Internet based on the optimal food list, and the server analyzes the recipe information and generates the optimal menu plan for the user based on the emotion data.

[0494] Final purchase list confirmation

[0495] The user can check the proposed menu and food list through the device and make any necessary changes. After confirming the changes, the device sends the information to the server, and the final shopping list is confirmed.

[0496] Examples of concrete examples and prompts

[0497] The server uses an emotion engine to recognize the user's emotions and adjust the food list and meal plan accordingly: for example, if the user is feeling stressed, it can suggest a meal plan that includes ingredients that have a relaxing effect.

[0498] A specific example is the following prompt:

[0499] "Can you suggest a dinner menu for my family of four? I'm feeling stressed right now, so I'd like a recipe with relaxing ingredients."

[0500] "I've been feeling a bit tired lately, so please tell me some ingredients and recipes that will cheer me up."

[0501] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0502] Step 1:

[0503] The server periodically retrieves food price data from multiple stores via API. In this process, the server sends an API request and receives the latest food price data from each store in response. The price data includes product name, price, and stock information. The retrieved data is stored in a database, and existing data is updated. The input is the API request, and the output is the latest price data.

[0504] Step 2:

[0505] The user inputs a request to generate a food list through the device. The request includes information on the type of meal and the number of people. The device also collects the user's emotional data using the smartphone's camera and microphone. The input is the user's request and emotional data, and the output is sending the request to the server.

[0506] Step 3:

[0507] The server receives the request sent from the terminal and generates a list of necessary foods based on the user's request. At this time, the emotion engine analyzes the user's emotion data and adjusts the food list based on the request. The input is the user's request and emotion data, and the output is the necessary food list.

[0508] Step 4:

[0509] The server compares the collected price data with the generated food list to find the cheapest purchasing pattern. The optimal purchasing pattern is calculated based on the price and inventory information at each store. The input is the price data and the food list, and the output is the cheapest purchasing pattern.

[0510] Step 5:

[0511] The server sends the cheapest shopping pattern it has found to the terminal to present to the user. The terminal displays the optimal shopping pattern to the user and advises them on which store to purchase the necessary food. The input is the cheapest shopping pattern, and the output is the information displayed on the user's terminal.

[0512] Step 6:

[0513] The device searches the internet for cooking recipes based on the optimal food list. The searched recipes are selected based on the user's emotional data and other settings and suggested to the user. The input is the food list, and the output is the suggested recipe.

[0514] Step 7:

[0515] The server analyzes the recipe information sent from the device and generates a menu plan that best suits the user's requirements. Based on the emotional data, the server provides a menu plan that matches the user's psychological state. The input is recipe information, and the output is the optimal menu plan.

[0516] Step 8:

[0517] The user checks the proposed menu and food list through the terminal and makes any necessary changes. Once the user confirms the changes, the information is sent to the server. The input is the user's revised information, and the output is the final shopping list.

[0518] 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.

[0519] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0520] 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.

[0521] [Second embodiment]

[0522] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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).

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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."

[0534] This invention is a system that allows users to determine optimal food purchases and menus based on price data collected from multiple stores. This system is implemented by the specific program processing shown below.

[0535] System configuration

[0536] This system consists of three parties: a server, a terminal, and a user.

[0537] 1. Server

[0538] Responsible for collecting, storing and updating price data.

[0539] Generate optimal purchasing patterns and menu suggestions based on user requirements.

[0540] 2. Terminal

[0541] It interfaces with the user, taking requests, displaying results, and accepting modifications.

[0542] Generate a list of food items you need and search for recipes.

[0543] 3. Users

[0544] Enter food purchasing and menu planning requests and make final decisions based on the information provided.

[0545] Program processing overview

[0546] Server collects and updates price data

[0547] The server retrieves price data from multiple partner stores via API and stores it in a database. This process is executed periodically to update the price information.

[0548] Examples:

[0549] The server sends a request to the APIs of "Store A" and "Store B" to retrieve the latest food price data. The retrieved data is stored in a database as "chicken 500 yen" and "cabbage 150 yen," for example.

[0550] Get user requests and generate a food list

[0551] A user inputs requests such as "weekly menu" or "dinner menu" through a terminal. This request may also include information about food preferences and allergies.

[0552] The device receives this request and generates a list of food items based on the user's needs, such as "chicken," "cabbage," and "soy sauce."

[0553] Examples:

[0554] The user inputs a request such as "dinner for a family of four," and the device generates a list of required foods (e.g., "chicken," "cabbage," and "soy sauce").

[0555] Searching for and suggesting optimal purchasing patterns

[0556] The server compares the collected price data with the list of necessary foods generated by the terminal and searches for the cheapest combination.

[0557] The device presents the user with optimal purchasing patterns sent from the server, including specific advice on which stores to buy which foods.

[0558] Examples:

[0559] The server calculates the cheapest combination as "chicken for 500 yen at store A, cabbage for 150 yen at store A, and soy sauce for 200 yen at store B." The terminal displays a suggestion to the user to "purchase chicken and cabbage from store A and soy sauce from store B."

[0560] Recipe search and meal plan suggestions

[0561] The device searches the internet for cooking recipes based on a list of optimal foods, taking into account the user's preferences and allergies.

[0562] The server analyzes the recipe information sent from the device and generates the optimal menu plan for the user, which is then sent to the device and presented to the user.

[0563] Examples:

[0564] The device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[0565] Final purchase list confirmation

[0566] The user can check the proposed menu and food list through the terminal and make any necessary changes. Once the user has finished making changes, the final shopping list is confirmed.

[0567] Examples:

[0568] The user makes modifications, such as "increase the amount of cabbage." After the modifications are complete, the final shopping list is confirmed.

[0569] In this way, the system provides users with efficient and economical food purchasing and menu suggestions. The above is a specific embodiment for carrying out the present invention.

[0570] The processing flow will be explained below.

[0571] Step 1:

[0572] The terminal displays a list of affiliated stores to the user and prompts the user to select which store they would like to collect price information from.

[0573] Step 2:

[0574] The user selects the store of interest and presses the "Next" button.

[0575] Step 3:

[0576] The server sends a request to the API of the store selected by the user to retrieve the latest food price data.

[0577] Step 4:

[0578] The server saves the acquired price data in the database and updates existing data, such as "chicken 500 yen" and "cabbage 150 yen."

[0579] Step 5:

[0580] Users can use the terminal to input requests such as "weekly menu" or "dinner menu." They can also input food preferences and allergy information.

[0581] Step 6:

[0582] The device receives the user's request and generates a list of the required foods, such as "chicken," "cabbage," and "soy sauce."

[0583] Step 7:

[0584] The server matches the price data collected in step 4 with the list of required foods generated in step 6 to find the cheapest combination.

[0585] Step 8:

[0586] The server identifies the cheapest purchase pattern and transmits the corresponding combination to the terminal.

[0587] Step 9:

[0588] The device presents the optimal purchasing pattern sent from the server to the user, for example, displaying advice such as "Buy chicken and cabbage at supermarket A, and buy cooking oil at supermarket B."

[0589] Step 10:

[0590] The device searches for recipe information on the Internet based on the suggested food list, for example, "recipes using chicken and cabbage."

[0591] Step 11:

[0592] The server analyzes the recipe information received from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[0593] Step 12:

[0594] The device then presents the generated menu plan and optimal food list to the user as a final proposal, along with a food shopping list and recipes.

[0595] Step 13:

[0596] The user checks the presented menu and food list and makes any necessary changes, such as "increase the amount of cabbage" or "switch to a different brand of soy sauce."

[0597] Step 14:

[0598] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the shopping list.

[0599] In this way, the system provides users with efficient and economical food purchasing and meal planning suggestions.

[0600] Example 1

[0601] 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."

[0602] Conventional food purchasing and menu planning systems have difficulty collecting price data from multiple stores and providing optimal purchasing patterns and menu plans based on that data. Furthermore, customizing lists that take into account the user's food preferences and allergy information, and finalizing the purchase list, are cumbersome, making them difficult to use for users.

[0603] 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.

[0604] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for acquiring user requests and generating a food list, means for comparing the collected price data with the necessary food list to search for the cheapest combination, means for presenting the searched cheapest combination to the user, means for displaying the presented cheapest purchasing pattern, means for searching the Internet for cooking recipes based on the generated ingredient list, means for generating an optimal menu plan for the user, means for the user to finalize the shopping list, and means for customizing the necessary food list taking into account the user's ingredient preferences and allergy information, thereby enabling the user to purchase foods efficiently and determine the optimal menu.

[0605] "Price data" is price information for food products offered by multiple stores.

[0606] "Database" means an information storage system for storing and updating collected pricing data.

[0607] "User requirements" are requests or conditions entered by a user for food purchases or menu decisions.

[0608] A "food list" is a list of foods that need to be purchased, generated based on a user's request.

[0609] The "cheapest purchasing pattern" is information that shows the cheapest combination by comparing the collected price data with the list of necessary foods.

[0610] A "cooking recipe" is a set of instructions that describes how to cook a dish using specific ingredients.

[0611] A "menu plan" is a combination of dishes suggested for multiple days or meals.

[0612] The "final purchase list" is a list of foods that should be finally purchased, confirmed after the user has confirmed and corrected them.

[0613] "User preference and allergy information" refers to information about whether a user likes or avoids certain ingredients.

[0614] This invention is a system that allows users to determine optimal food purchases and menus based on price data collected from multiple stores. This system is composed of three entities: a server, a terminal, and a user.

[0615] server

[0616] The server obtains price data from multiple affiliated stores via API, and stores and updates it in a database. This process is performed periodically to keep price information up to date. For example, the server sends a request to the APIs of "Store A" and "Store B" to obtain the latest food price data. The obtained data is stored in the database as "chicken 500 yen" and "cabbage 150 yen." The server then generates a list of necessary foods based on the user's requests, and generates optimal purchasing patterns and menu suggestions.

[0617] Terminal

[0618] The terminal is responsible for interfacing with the user. It receives requests from the user and presents the generated list of necessary foods to the user. It also accepts corrections as necessary and finalizes the final shopping list. For example, if a user inputs a request for "dinner for a family of four," the terminal will generate and display a list of necessary foods (e.g., "chicken," "cabbage," and "soy sauce") based on this request.

[0619] User

[0620] The user inputs requests for food purchases and menu decisions, and makes a final decision based on the information presented. The user inputs requests such as "weekly menu" or "dinner menu" through the terminal. This request may also include information on food preferences and allergies. The user checks the proposed menu and food list, and makes any necessary corrections to finalize the shopping list. For example, the user makes a correction on the terminal, such as "increase the amount of cabbage," and then finalizes the revised shopping list.

[0621] Specific system configuration and operation example

[0622] 1. The server sends a request to the APIs of "Store A" and "Store B" to obtain the latest food price data. The obtained data is stored in the database as "chicken 500 yen" and "cabbage 150 yen," for example.

[0623] 2. The user inputs requests such as "weekly menu" or "dinner menu" through the terminal.

[0624] 3. The device receives the user's request and generates a list of required foods, such as "chicken," "cabbage," and "soy sauce."

[0625] 4. The price data collected by the server is compared with the list of necessary foods generated by the terminal to find the cheapest combination.

[0626] 5. The device presents the user with the optimal purchasing pattern sent from the server, including specific advice on which stores to purchase which foods.

[0627] 6. The device searches for cooking recipes on the Internet based on the optimal food list and suggests a menu plan that takes into account the user's preferences and allergy information.

[0628] 7. The user reviews the proposed menu and food list and makes any necessary modifications. After modifications, the final shopping list is confirmed.

[0629] Prompt Sentence Examples

[0630] "Create a weekly menu for a family of four. Get ingredient price data from this API. Generate the optimal menu plan and shopping list, taking into account the lowest price for each ingredient."

[0631] In this way, the system provides users with efficient and economical food purchasing and menu suggestions. The above is a specific embodiment for carrying out the present invention.

[0632] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0633] Step 1:

[0634] Price data collection and updates

[0635] Input: Store API endpoint

[0636] Processing: The server retrieves price data from multiple partner stores via API. This is done periodically to update the price data.

[0637] Specific operation: The server sends requests to the API endpoints of "Store A" and "Store B" to obtain new price data. The obtained data is saved in the database as "chicken 500 yen" and "cabbage 150 yen," etc.

[0638] Output: A database containing updated price data

[0639] Step 2:

[0640] Get the user request

[0641] Input: A request entered by the user through the device (e.g., "weekly menu" or "dinner menu")

[0642] Processing: The device receives the user's request and analyzes the request content.

[0643] How it works: A user enters a request for "dinner for a family of four" into a smartphone app. The device receives the request and analyzes the details.

[0644] Output: Parsed user request data

[0645] Step 3:

[0646] Generate a list of food needs

[0647] Input: Parsed user request data

[0648] Processing: The device generates a list of required foods based on the content of the request.

[0649] Specific operation: The device analyzes the request content and generates a list of foods needed for "dinner for a family of four" (e.g., "chicken," "cabbage," and "soy sauce") and saves the list of needed foods in local storage.

[0650] Output: Generated food needs list

[0651] Step 4:

[0652] Finding optimal purchasing patterns

[0653] Input: Collected price data and generated food needs list

[0654] Processing: The server compares the collected price data with the list of required foods and searches for the cheapest combination.

[0655] How it works: The server compares the list of food items needed with the price data in the database and runs an algorithm to find the cheapest combination. For example, it calculates the cheapest combination as "chicken at store A for 500 yen, cabbage at store A for 150 yen, and soy sauce at store B for 200 yen."

[0656] Output: lowest price purchase pattern data

[0657] Step 5:

[0658] Showing the cheapest purchase pattern

[0659] Input: lowest price purchase pattern data

[0660] Processing: The terminal presents the lowest price purchase pattern sent from the server to the user.

[0661] Specific operation: The terminal receives the lowest price purchasing pattern data and displays specific instructions to the user, such as "purchase chicken and cabbage from store A, and purchase oil from store B."

[0662] Output: The cheapest purchase pattern presented to the user

[0663] Step 6:

[0664] Find recipes and generate meal plans

[0665] Input: Generated food requirements list, user preferences, and allergy information

[0666] Processing: The device searches for cooking recipes on the Internet based on the optimal food list. The server analyzes the recipe information and generates the optimal menu plan.

[0667] Specific operation: The device searches the internet for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[0668] Output: A suggested meal plan for the user

[0669] Step 7:

[0670] Final purchase list confirmation

[0671] Input: Suggested menu and food list, user modifications

[0672] Processing: The user reviews the proposed menu and food list and makes any necessary adjustments. The final shopping list is confirmed.

[0673] Specific operation: The user makes modifications on the device, such as "increase the amount of cabbage." After the modifications are complete, the final shopping list is confirmed and displayed to the user.

[0674] Output: Final purchase list confirmed

[0675] (Application example 1)

[0676] 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."

[0677] Conventional food purchasing and menu suggestion systems require users to spend a lot of time and effort to purchase the cheapest foods. Furthermore, they do not provide optimal suggestions for food lists and recipes that meet the user's needs, making it difficult to make efficient purchases and decide on menus. Another problem is that they do not adequately customize the system to accommodate user requests, food preferences, or allergies.

[0678] 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.

[0679] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a list of necessary foods based on a user's request, means for comparing the collected price data with the list of necessary foods to search for the cheapest combination, means for presenting the searched cheapest combination to the user, means for acquiring the user's request and displaying it to the user via a smartphone or smart glasses, and means for accepting user corrections and presenting a final purchasing route, thereby enabling the user to purchase food efficiently and economically and quickly decide on an appropriate menu.

[0680] A "means for collecting food price data from multiple stores" is a method or device for collecting food price information from different points of sale.

[0681] "Means for storing and updating collected price data in a database" refers to a storage device or system for storing the obtained price information, and has the function of updating the information to the latest version at any time.

[0682] The "means for generating a list of required foods based on a user's request" refers to a method or device for creating a list of ingredients to be purchased based on a user's request.

[0683] "Means for matching collected price data with a list of required foods to find the cheapest combination" refers to a method or device that compares collected price information with a generated list of ingredients to find the most affordable combination.

[0684] "Means for presenting the cheapest combination found to the user" refers to a method or device for informing the user of the cheapest combination found.

[0685] "Means for acquiring user requests and displaying them to the user through a smartphone or smart glasses" refers to a method or apparatus for collecting purchase requests from users and displaying the information via a smart device.

[0686] "Means for accepting user corrections and presenting a final shopping route" refers to a method or device for accepting corrections from a user and presenting a final shopping route or plan after incorporating the corrections.

[0687] A "means for retrieving recipes from a presented food list" refers to a method or device for finding cooking methods based on a proposed list of ingredients.

[0688] "Means for generating a menu plan optimal for the user from the searched recipes" refers to a method or device for creating a menu plan optimal for the user using the cooking methods found.

[0689] "Means for guiding the optimal shopping route in a store" refers to a method or device for guiding the route to buy the necessary ingredients most efficiently in the store.

[0690] "Means for customizing the required food list taking into account the user's food preferences and allergy information" refers to a method or device for individually adjusting the food list based on the user's preferences and allergy information.

[0691] "Means for optimizing recipe candidates using a generative AI model" refers to a method or device that uses artificial intelligence to suggest the best cooking method and efficiently select those candidates.

[0692] "Means for modifying and optimizing generated recipe candidates based on user request prompts" refers to a method or device for modifying and optimizing recipes suggested by AI in accordance with the specific requests of users.

[0693] This invention is a system that allows users to purchase food efficiently and economically and determine optimal menus based on price data collected from multiple stores. This system is composed of a server, terminals, and users.

[0694] Server Features

[0695] Price data collection, storage and updating

[0696] The server periodically collects food price data through APIs from multiple stores and stores it in a database. This stored data is always updated. This function on the server is essential for efficiently managing price information and providing it to users. A Python program is implemented on the server to retrieve store price data through RESTful APIs.

[0697] Finding optimal purchasing patterns

[0698] The server compares the collected price data with the food list generated by the device to find the cheapest combination, which allows the user to create an economical shopping plan. This search algorithm compares the price data with the food list to find the lowest-cost combination.

[0699] Analyzing recipe data and generating menu plans

[0700] The server analyzes the recipe information sent from the device and generates the optimal menu plan for the user. This function selects the most suitable recipes from multiple recipe databases and proposes menus that take into account the user's food preferences and allergies.

[0701] Device Features

[0702] Providing a user interface and receiving requests

[0703] The device, such as a smartphone or smart glasses, directly interfaces with the user, acquiring the user's request and generating a list of food items based on the request. The user can generate a list of food items by inputting a request such as "dinner tonight."

[0704] Providing optimal purchasing routes

[0705] The device presents the user with optimal purchasing patterns sent from the server and guides them to an efficient shopping route within the store based on this. This shopping route is visually displayed on the user's smartphone or smart glasses, helping to ensure a stress-free shopping experience.

[0706] Menu and recipe suggestions

[0707] The device searches the internet for recipes based on the optimal food list and suggests them to the user. For example, a search for "recipes using chicken and cabbage" will result in recipes such as "stir-fried chicken and cabbage." A generative AI model is also used to filter recipes based on the user's preferences and allergies.

[0708] User Roles

[0709] Entering and Modifying Requests

[0710] Users input their requests through their smartphones or smart glasses, review the food list and meal plan provided, and can modify the list or plan as needed to finalize the shopping list. This process allows users to efficiently decide on the food purchases and meal plans that best suit their needs.

[0711] Examples of concrete examples and prompts

[0712] A concrete example is a system where a user requests the best recipes for a "dinner for a family of four." Based on this request, the server collects price data and the device displays the best food list and purchasing patterns.

[0713] Example prompt sentence:

[0714] "Suggest a recipe for a perfect dinner for a family of four. The ingredients are chicken, cabbage, and soy sauce."

[0715] In this way, the invention realizes a highly functional system that supports efficient food purchasing and menu planning decisions.

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

[0717] Step 1: The server collects price data from multiple stores and saves and updates it in a database.

[0718] Input: Store API URL list

[0719] Processing: The server accesses each store's API to obtain the latest food price data. It parses the obtained data in JSON format and saves it in the database. This process is performed periodically to keep the data up to date.

[0720] Output: A database containing the latest food price data

[0721] Step 2: The user enters a request through the terminal.

[0722] Input: User request (e.g., "Dinner tonight" or "Dinner for a family of four")

[0723] Processing: The terminal receives the user's request through the interface. This request may include food preferences and allergy information. The terminal then sends the input request to the server.

[0724] Output: User request data

[0725] Step 3: The server generates a list of required foods based on the user's request.

[0726] Input: User request data

[0727] Processing: The server analyzes the input request data and generates a food list based on it. The generated food list includes the names of the necessary ingredients, such as "chicken," "cabbage," and "soy sauce."

[0728] Output: Generated food list

[0729] Step 4: The server compares the collected price data with the food list to find the cheapest combination.

[0730] Input: Generated food list, latest price data

[0731] Processing: The server searches the price database to find the cheapest store and price for each ingredient, and presents this to the user as the cheapest combination.

[0732] Output: Data for the cheapest combination

[0733] Step 5: The device presents the cheapest combination to the user.

[0734] Input: Data for the cheapest combination

[0735] Processing: The device receives the data sent from the server and presents it visually to the user, such as on the screen of a smartphone or smart glasses.

[0736] Output: The cheapest combination presented to the user

[0737] Step 6: The device guides the user to the optimal shopping route within the store

[0738] Input: Data on the cheapest combination, data on in-store placement

[0739] Processing: Based on the information on the cheapest combination displayed on the device, the device calculates a route that will allow the user to purchase the necessary ingredients by going around the store efficiently. The device then visually guides the user along this route.

[0740] Output: The purchasing route presented to the user

[0741] Step 7: The device searches for recipes based on the food list and suggests them to the user.

[0742] Input: Generated food list

[0743] Processing: The device searches the internet for recipes that match the food list. It uses a generative AI model to select the best recipes and suggest them to the user. An example might be "Stir-fried chicken and cabbage."

[0744] Output: Suggested recipe

[0745] Step 8: User reviews, modifies, and finally confirms the suggested recipe and food list

[0746] Input: Suggested recipes, generated food list

[0747] Processing: The user checks the suggested recipes and food list through the terminal and makes any necessary corrections. Once corrections are complete, the final shopping list is confirmed.

[0748] Output: Final purchase list confirmed

[0749] Example prompt sentence:

[0750] "Suggest a recipe for a perfect dinner for a family of four. The ingredients are chicken, cabbage, and soy sauce."

[0751] 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.

[0752] This invention relates to a system that helps users efficiently and economically purchase food and create optimal menus using that food. The system also incorporates an emotion engine that recognizes the user's emotions and adjusts the food list and menu accordingly.

[0753] System configuration

[0754] This system has the following components:

[0755] 1. Server

[0756] It is the central component for implementing the present invention and is responsible for collecting, storing and updating price data.

[0757] Generate optimal purchasing patterns and menu suggestions based on user requirements.

[0758] It integrates an emotion engine to adjust food lists and menus based on user emotion recognition.

[0759] 2. Terminal

[0760] It is a device for interfacing with the user, taking requests, displaying results, and accepting modifications.

[0761] Generate a list of food items you need and search for recipes.

[0762] 3. Users

[0763] Enter food purchasing and menu planning requests and make final decisions based on the information provided.

[0764] Emotional data is input through the terminal and this information is used by the system.

[0765] Program processing overview

[0766] Server collects and updates price data

[0767] The server periodically retrieves price data from multiple partner stores via API and stores it in a database, ensuring that the latest price information is always available.

[0768] Examples:

[0769] The server sends a request to the APIs of "Store A" and "Store B" to retrieve the latest food price data. The retrieved data is stored in a database as "chicken 500 yen" and "cabbage 150 yen," for example.

[0770] Get user requests and generate a food list

[0771] Users input requests such as "weekly menu" or "dinner menu" through a terminal. These requests can also include food preferences, allergy information, and emotional data.

[0772] The device receives this request and generates a list of food needs based on the user's requirements, such as "chicken," "cabbage," and "soy sauce."

[0773] Examples:

[0774] The user inputs a request such as "dinner for a family of four," and the device generates a list of required foods (e.g., "chicken," "cabbage," and "soy sauce").

[0775] Searching for and suggesting optimal purchasing patterns

[0776] The server compares the collected price data with the necessary food list generated by the terminal to find the cheapest purchasing pattern.

[0777] The terminal presents the optimal purchasing pattern sent from the server to the user. For example, advice such as "Buy chicken and cabbage at supermarket A, and buy sesame oil at supermarket B."

[0778] Examples:

[0779] The server calculates the cheapest combination as "chicken for 500 yen at store A, cabbage for 150 yen at store A, and soy sauce for 200 yen at store B." The terminal displays a suggestion to the user to "purchase chicken and cabbage from store A and soy sauce from store B."

[0780] Recipe search and meal plan suggestions

[0781] The device searches the internet for recipes based on the best food list, e.g., "Recipes using chicken and cabbage."

[0782] The server analyzes the recipe information sent from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[0783] Examples:

[0784] The device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[0785] Final purchase list confirmation

[0786] The user can check the proposed menu and food list through the device and make any necessary changes, such as "increase the amount of cabbage" or "switch to a different brand of soy sauce."

[0787] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the purchase list.

[0788] Use of emotion engine

[0789] The emotion engine analyzes emotional data entered by the user into the device or acquired through sensors, and this data is used to adjust food lists and meal plans.

[0790] Examples:

[0791] The server uses an emotion engine to suggest relaxing recipes such as "chicken and cabbage soup" to reduce the user's stress level.

[0792] The device uses an emotion engine to search for and suggest recipes that better reflect the user's preferences.

[0793] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors the system to the user's emotions and preferences, providing a more personalized experience.

[0794] The processing flow will be explained below.

[0795] Step 1:

[0796] The terminal displays a list of affiliated stores to the user and prompts the user to select which store they would like to collect price information from.

[0797] Step 2:

[0798] The user selects the store of interest and presses the "Next" button.

[0799] Step 3:

[0800] The server sends a request to the API of the store selected by the user to retrieve the latest food price data.

[0801] Step 4:

[0802] The server saves the acquired price data in the database and updates existing data, such as "chicken 500 yen" and "cabbage 150 yen."

[0803] Step 5:

[0804] Users can use the terminal to input requests such as "weekly menu" or "dinner menu." They can also input food preferences, allergy information, and emotional data.

[0805] Step 6:

[0806] The device receives the user's request and generates a list of the required foods, such as "chicken," "cabbage," and "soy sauce."

[0807] Step 7:

[0808] The server matches the price data collected in step 4 with the list of required foods generated in step 6 to find the cheapest combination.

[0809] Step 8:

[0810] The server identifies the cheapest purchase pattern and transmits the corresponding combination to the terminal.

[0811] Step 9:

[0812] The device presents the optimal purchasing pattern sent from the server to the user, for example, displaying advice such as "Buy chicken and cabbage at store A and sesame oil at store B."

[0813] Step 10:

[0814] The device searches for recipe information on the Internet based on the suggested food list, for example, "recipes using chicken and cabbage."

[0815] Step 11:

[0816] The server analyzes the recipe information received from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[0817] Step 12:

[0818] The device then presents the generated menu plan and optimal food list to the user as a final proposal, along with a food shopping list and recipes.

[0819] Step 13:

[0820] The user can review the proposed menu and food list and make any necessary changes, such as adding more cabbage or switching to a different brand of soy sauce.

[0821] Step 14:

[0822] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the purchase list.

[0823] Step 15:

[0824] The user inputs emotional data into the terminal, which includes numerical values ​​and options that indicate the user's emotional state.

[0825] Step 16:

[0826] The terminal acquires the emotion data and transmits it to the server.

[0827] Step 17:

[0828] The server uses an emotion engine to analyze the emotional data and adjust the food list and menu based on the user's emotional state.

[0829] Examples:

[0830] If the user is feeling stressed, the server uses its emotion engine to suggest "chicken and cabbage soup, which has a relaxing effect."

[0831] If a user is feeling low in energy, the app suggests a "high-protein chicken dish" to replenish their energy.

[0832] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors them to their emotions and preferences, providing a more personalized experience.

[0833] Example 2

[0834] 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."

[0835] Conventional food purchasing systems make it difficult for users to purchase food efficiently and economically, and they also have problems with reducing user satisfaction because food lists and menus are not tailored to the user's individual emotional state or preferences.

[0836] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0837] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a required food list based on a user's request, means for comparing the collected price data with the required food list to search for the cheapest combination, means for presenting the searched cheapest combination to the user, and an emotion engine for analyzing emotion data entered by the user. This enables users to make efficient and economical food purchases and also makes it possible to provide food lists and menus that correspond to each user's emotional state.

[0838] "Stores" are multiple retail outlets or commercial facilities that provide food price data.

[0839] "Price data" refers to price information for food products sold at each store.

[0840] "Database" means an information management system for storing collected price data and user information and updating it as necessary.

[0841] "User Requests" refers to the preferences or conditions entered by the user in order to receive food list or menu suggestions.

[0842] A "food list" is a list of food needs generated based on a user's request.

[0843] The "emotion engine" is the part of the system that analyzes the emotional data entered by the user and adjusts the food list and menu accordingly.

[0844] A "cooking recipe" is an instruction manual that describes cooking methods and steps using specific ingredients.

[0845] A "menu plan" is a plan of meal combinations and cooking sequences suggested to the user.

[0846] "Final Shopping List" is the final list for purchasing food items that is confirmed after the user makes any modifications.

[0847] "Preferences" refers to the ingredients and types of dishes that a user particularly likes.

[0848] "Allergy information" refers to information about a user's allergic reaction to food ingredients or substances.

[0849] "Emotional data" is information entered by a user about their current emotional state.

[0850] "Suggestion" refers to the act of the system showing the user optimal food purchasing patterns and menu plans.

[0851] This invention relates to a system that helps users efficiently and economically purchase food and create optimal menus using that food. The system also incorporates an emotion engine that recognizes the user's emotions and adjusts the food list and menu accordingly.

[0852] System configuration

[0853] This system has the following components:

[0854] 1. Server

[0855] The server is the central component for implementing this invention, and is responsible for collecting, storing, updating, and processing price data. It periodically retrieves price data from multiple stores through APIs and stores it in a database. It also compares the collected price data with the user's food list based on their request to find the cheapest combination. It also integrates an emotion engine to adjust the food list and menu based on the user's emotion recognition.

[0856] Examples of hardware and software: For the server hardware, commercial cloud servers (e.g., AWS, Google Cloud) can be used. For API communication and database operations, programming languages ​​such as Python or Java and SQL databases (e.g., MySQL, PostgreSQL) are used.

[0857] 2. Terminal

[0858] The terminal is a device that interfaces with the user, receiving requests, displaying results, and accepting corrections. Based on the information entered by the user, it generates a list of necessary foods and searches for cooking recipes on the Internet. It also uses an emotion engine to make suggestions based on the user's emotions.

[0859] Examples of hardware and software: Devices can include smartphones, tablets, and PCs. Front-end frameworks such as React and Angular can be used to develop the user interface.

[0860] 3. Users

[0861] The user inputs requests for food purchases and menu decisions through the terminal, and then makes a final decision based on the displayed information. In addition, the user inputs their own emotional data, which is used by the system.

[0862] Program processing overview

[0863] Price data collection and updates

[0864] Server: Periodically sends requests to multiple partner store APIs to retrieve price data and store it in a database.

[0865] Example: The server obtains price data for "chicken 500 yen" and "cabbage 150 yen" from "Supermarket A" and saves it in the database.

[0866] Get user requests and generate a food list

[0867] User: Input requests such as "weekly menu" or "dinner menu" through the device. They can also include food preferences, allergy information, and emotional data.

[0868] Terminal: Receives this request and generates a food list based on the user's requirements.

[0869] Example: A user inputs a request for "dinner for a family of four," and the device generates a food list of "chicken," "cabbage," and "soy sauce."

[0870] Searching for and suggesting optimal purchasing patterns

[0871] Server: Matches the collected price data with the food list generated by the device to find the cheapest purchasing pattern.

[0872] Device: Presents the optimal purchasing pattern sent from the server to the user.

[0873] Example: The server calculates that "chicken is 500 yen at store A, cabbage is 150 yen at store A, and soy sauce is 200 yen at store B," and the terminal suggests, "Purchase chicken and cabbage at store A, and soy sauce at store B."

[0874] Recipe search and meal plan suggestions

[0875] Device: Search the internet for cooking recipes based on a list of the best foods.

[0876] Server: Analyzes the submitted recipe information and generates a menu plan that best suits the user's requirements.

[0877] Example: A device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes this and suggests it to the user as a "weekly dinner menu plan."

[0878] Final purchase list confirmation

[0879] User: Review the suggested menu and food list and modify as needed.

[0880] Device: Once the modifications are confirmed, the information is sent to the server and the final purchase list is confirmed.

[0881] Example: A user adds the modification "increase the amount of cabbage" and finalizes the final list.

[0882] Use of emotion engine

[0883] Emotion engine: Analyzes the emotional data entered by the user into the device and uses it to adjust food lists and menus.

[0884] Server: Uses an emotion engine to suggest menus based on the user's emotional state.

[0885] Example: When a user types "I'm feeling stressed," an emotion engine analyzes that information and the server suggests "chicken and cabbage soup."

[0886] Example prompts for generative AI models

[0887] "Please suggest a weekly menu for a family of four. The user's favorite ingredients are chicken and cabbage, and they have no allergies. Also, the user is currently feeling stressed, so please take that into consideration when creating a menu."

[0888] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors them to the user's emotions and preferences, providing a more personalized experience.

[0889] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0890] Specific processing steps of the system

[0891] Step 1: Collect price data

[0892] The server sends requests to the APIs of multiple partner stores to collect the latest food price data.

[0893] Input: API endpoint of partner store

[0894] Data processing: The server generates an API request and sends it to each store's API.

[0895] Output: Price data returned from each store

[0896] Specific operation: The server sends an API request to "Store A" and obtains price data for "Chicken 500 yen" and "Cabbage 150 yen."

[0897] Step 2: Saving and updating price data

[0898] The server stores the acquired price data in a database and updates existing data.

[0899] Input: Collected price data

[0900] Data Processing: The server analyzes the price data and updates the existing data in the database.

[0901] Output: Updated database

[0902] Specific operation: Save or update the data "Chicken 500 yen" and "Cabbage 150 yen" in the price table in the database.

[0903] Step 3: Get the user request

[0904] The user inputs requests such as "weekly menu" or "dinner menu" through the terminal.

[0905] Input: User request (e.g. "Dinner for a family of four")

[0906] Data processing: The terminal receives the request and displays it on the user interface.

[0907] Output: User input information

[0908] Specific operation: A user inputs a request into a terminal: "Dinner for a family of four."

[0909] Step 4: Generate a food list

[0910] The terminal generates a list of required foods based on the user's request.

[0911] Input: User request

[0912] Data processing: Applying a list generation algorithm based on user requests.

[0913] Output: List of food items needed

[0914] Specific operation: Based on the information about "dinner for a family of four," generate a list of "chicken," "cabbage," and "soy sauce."

[0915] Step 5: Finding optimal purchasing patterns

[0916] The server compares the collected price data with the generated food list to find the cheapest purchasing patterns.

[0917] Input: Price data, generated food list

[0918] Data processing: Compare the price data with the food list and use a matching algorithm to calculate the lowest price.

[0919] Output: Optimal purchasing pattern

[0920] Specific operation: Calculate "chicken is 500 yen at store A, cabbage is 150 yen at store A, and soy sauce is 200 yen at store B."

[0921] Step 6: Show purchasing patterns

[0922] The terminal presents the optimal purchasing pattern sent from the server to the user.

[0923] Input: Optimal purchase pattern

[0924] Data processing: Format purchase patterns so they can be displayed on the device.

[0925] Output: Purchase patterns presented to the user

[0926] Specific operation: The terminal displays "Purchase chicken and cabbage at store A, and purchase cooking oil at store B."

[0927] Step 7: Find a recipe

[0928] The device searches the internet for cooking recipes based on a list of optimal foods.

[0929] Input: Optimal Food List

[0930] Data processing: A recipe search engine is used to search recipe databases on the Internet.

[0931] Output: Related recipes

[0932] Action: Search for "chicken and cabbage recipes" and find "chicken and cabbage stir fry."

[0933] Step 8: Menu plan suggestions

[0934] The server analyzes the recipe information sent from the terminal and generates a menu plan that best suits the user's requirements.

[0935] Input: Cooking recipe

[0936] Data processing: Applying an algorithm to analyze recipe information and generate a menu plan.

[0937] Output: Menu plan

[0938] Specific operation: Propose to the user "weekly dinner menu plan."

[0939] Step 9: Finalize the purchase list

[0940] The user reviews the proposed menu and food list and modifies it as needed.

[0941] Input: Suggested menu and food list

[0942] Data processing: Accept user corrections and recalculate.

[0943] Output: Final purchase list

[0944] Specific operation: The user inputs the correction "increase the amount of cabbage" into the terminal and confirms the final list.

[0945] Step 10: Use the Emotion Engine

[0946] The emotion engine analyzes the emotional data entered by the user into the device.

[0947] Input: Emotion data

[0948] Data processing: Analyze the data using sentiment analysis algorithms to generate results.

[0949] Output: Food list and menu adjustments based on analysis results

[0950] Specific behavior: Based on the information that the user is "feeling stressed," the server suggests "chicken and cabbage soup."

[0951] Example prompts for generative AI models

[0952] "Please suggest a weekly menu for a family of four. The user's favorite ingredients are chicken and cabbage, and they have no allergies. Also, the user is currently feeling stressed, so please take that into consideration when creating a menu."

[0953] (Application example 2)

[0954] 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."

[0955] Conventional food purchasing and menu suggestion systems have the problem of not taking into account the user's emotions and being unable to provide personalized menus based on emotions. This makes it difficult for users to choose meals that suit their current psychological state and emotions, which can result in a poor shopping experience. Additionally, suggestions based solely on ingredient price information are difficult to meet the user's psychological and emotional needs.

[0956] The identification process by the identification 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 food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a required food list based on a user's request, and means for recognizing the user's emotional data and adjusting the food list and menu based on that data. This makes it possible to provide an optimal food list and menu based on the user's emotional and psychological state.

[0957] "Multiple stores" means a collection of multiple different stores, each of which may offer different prices and inventory information.

[0958] "Food Price Data" refers to information about the prices of food products sold at each store, including details such as product name, price, and availability.

[0959] "Database" means a set of systems and structures for storing and managing collected price data, allowing for rapid retrieval and updating of the data.

[0960] "User Requests" refers to the preferences and requirements that a user inputs into the system regarding food lists and menus, including the type of menu, number of people, allergy information, etc.

[0961] "Needed Food List" refers to a list of foods generated based on a user's request, which indicates specific food items that the user should purchase.

[0962] "User emotional data" means data that indicates a user's current mental state or emotion, including stress level, enjoyment, sadness, fatigue, etc.

[0963] "Menus" refers to cooking plans and suggestions based on specific foods that assist users in planning their daily meals.

[0964] "Server" refers to a computer system that is the central component of a system and is responsible for collecting, storing, processing, and providing data.

[0965] This invention is a system that helps users efficiently and economically purchase food and create optimal meals. The system integrates an emotion engine that recognizes the user's emotions and adjusts the food list and meal plan accordingly.

[0966] System configuration

[0967] This system has the following components:

[0968] 1. Server

[0969] This is the central component for implementing the present invention, and collects food price data from multiple stores.

[0970] The collected price data is stored in a database and updated regularly.

[0971] Generate optimal food lists and meal suggestions based on user requirements.

[0972] It uses an emotion engine to adjust food lists and menus based on the user's emotional data.

[0973] 2. Terminal

[0974] It is a device for interfacing with the user, and a smartphone is generally used.

[0975] The system uses the smartphone's camera and microphone to collect user emotional data and transmit it to a server.

[0976] The food list and menu suggestions sent from the server are displayed to the user.

[0977] 3. Users

[0978] Requests for food purchases and menu decisions are entered into the terminal, and emotional data is provided through the terminal.

[0979] Based on the information provided, review the food list and menu and finalize the shopping list.

[0980] Program processing overview

[0981] Server collects and updates price data

[0982] The server periodically retrieves price data from multiple local stores via API, and the retrieved data is stored in a database, always maintaining the latest price information.

[0983] Get user requests and generate a food list

[0984] Users input requests such as "dinner menu" or "weekly menu" through the device, and emotional data is also collected, and a list of the necessary foods is generated based on this information.

[0985] Searching for and suggesting optimal purchasing patterns

[0986] The server compares the collected price data with the necessary food list generated by the device to search for the cheapest purchasing pattern, and the search results are sent to the device and presented to the user.

[0987] Recipe search and meal plan suggestions

[0988] The device searches for cooking recipes on the Internet based on the optimal food list, and the server analyzes the recipe information and generates the optimal menu plan for the user based on the emotion data.

[0989] Final purchase list confirmation

[0990] The user can check the proposed menu and food list through the device and make any necessary changes. After confirming the changes, the device sends the information to the server, and the final shopping list is confirmed.

[0991] Examples of concrete examples and prompts

[0992] The server uses an emotion engine to recognize the user's emotions and adjust the food list and meal plan accordingly: for example, if the user is feeling stressed, it can suggest a meal plan that includes ingredients that have a relaxing effect.

[0993] A specific example is the following prompt:

[0994] "Can you suggest a dinner menu for my family of four? I'm feeling stressed right now, so I'd like a recipe with relaxing ingredients."

[0995] "I've been feeling a bit tired lately, so please tell me some ingredients and recipes that will cheer me up."

[0996] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0997] Step 1:

[0998] The server periodically retrieves food price data from multiple stores via API. In this process, the server sends an API request and receives the latest food price data from each store in response. The price data includes product name, price, and stock information. The retrieved data is stored in a database, and existing data is updated. The input is the API request, and the output is the latest price data.

[0999] Step 2:

[1000] The user inputs a request to generate a food list through the device. The request includes information on the type of meal and the number of people. The device also collects the user's emotional data using the smartphone's camera and microphone. The input is the user's request and emotional data, and the output is sending the request to the server.

[1001] Step 3:

[1002] The server receives the request sent from the terminal and generates a list of necessary foods based on the user's request. At this time, the emotion engine analyzes the user's emotion data and adjusts the food list based on the request. The input is the user's request and emotion data, and the output is the necessary food list.

[1003] Step 4:

[1004] The server compares the collected price data with the generated food list to find the cheapest purchasing pattern. The optimal purchasing pattern is calculated based on the price and inventory information at each store. The input is the price data and the food list, and the output is the cheapest purchasing pattern.

[1005] Step 5:

[1006] The server sends the cheapest shopping pattern it has found to the terminal to present to the user. The terminal displays the optimal shopping pattern to the user and advises them on which store to purchase the necessary food. The input is the cheapest shopping pattern, and the output is the information displayed on the user's terminal.

[1007] Step 6:

[1008] The device searches the internet for cooking recipes based on the optimal food list. The searched recipes are selected based on the user's emotional data and other settings and suggested to the user. The input is the food list, and the output is the suggested recipe.

[1009] Step 7:

[1010] The server analyzes the recipe information sent from the device and generates a menu plan that best suits the user's requirements. Based on the emotional data, the server provides a menu plan that matches the user's psychological state. The input is recipe information, and the output is the optimal menu plan.

[1011] Step 8:

[1012] The user checks the proposed menu and food list through the terminal and makes any necessary changes. Once the user confirms the changes, the information is sent to the server. The input is the user's revised information, and the output is the final shopping list.

[1013] 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.

[1014] 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.

[1015] 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.

[1016] [Third embodiment]

[1017] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1018] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1019] 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).

[1020] 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.

[1021] 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.

[1022] 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).

[1023] 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.

[1024] 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.

[1025] 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.

[1026] 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.

[1027] 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.

[1028] 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."

[1029] This invention is a system that allows users to determine optimal food purchases and menus based on price data collected from multiple stores. This system is implemented by the specific program processing shown below.

[1030] System configuration

[1031] This system consists of three parties: a server, a terminal, and a user.

[1032] 1. Server

[1033] Responsible for collecting, storing and updating price data.

[1034] Generate optimal purchasing patterns and menu suggestions based on user requirements.

[1035] 2. Terminal

[1036] It interfaces with the user, taking requests, displaying results, and accepting modifications.

[1037] Generate a list of food items you need and search for recipes.

[1038] 3. Users

[1039] Enter food purchasing and menu planning requests and make final decisions based on the information provided.

[1040] Program processing overview

[1041] Server collects and updates price data

[1042] The server retrieves price data from multiple partner stores via API and stores it in a database. This process is executed periodically to update the price information.

[1043] Examples:

[1044] The server sends a request to the APIs of "Store A" and "Store B" to retrieve the latest food price data. The retrieved data is stored in a database as "chicken 500 yen" and "cabbage 150 yen," for example.

[1045] Get user requests and generate a food list

[1046] A user inputs requests such as "weekly menu" or "dinner menu" through a terminal. This request may also include information about food preferences and allergies.

[1047] The device receives this request and generates a list of food items based on the user's needs, such as "chicken," "cabbage," and "soy sauce."

[1048] Examples:

[1049] The user inputs a request such as "dinner for a family of four," and the device generates a list of required foods (e.g., "chicken," "cabbage," and "soy sauce").

[1050] Searching for and suggesting optimal purchasing patterns

[1051] The server compares the collected price data with the list of necessary foods generated by the terminal and searches for the cheapest combination.

[1052] The device presents the user with optimal purchasing patterns sent from the server, including specific advice on which stores to buy which foods.

[1053] Examples:

[1054] The server calculates the cheapest combination as "chicken for 500 yen at store A, cabbage for 150 yen at store A, and soy sauce for 200 yen at store B." The terminal displays a suggestion to the user to "purchase chicken and cabbage from store A and soy sauce from store B."

[1055] Recipe search and meal plan suggestions

[1056] The device searches the internet for cooking recipes based on a list of optimal foods, taking into account the user's preferences and allergies.

[1057] The server analyzes the recipe information sent from the device and generates the optimal menu plan for the user, which is then sent to the device and presented to the user.

[1058] Examples:

[1059] The device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[1060] Final purchase list confirmation

[1061] The user can check the proposed menu and food list through the terminal and make any necessary changes. Once the user has finished making changes, the final shopping list is confirmed.

[1062] Examples:

[1063] The user makes modifications, such as "increase the amount of cabbage." After the modifications are complete, the final shopping list is confirmed.

[1064] In this way, the system provides users with efficient and economical food purchasing and menu suggestions. The above is a specific embodiment for carrying out the present invention.

[1065] The processing flow will be explained below.

[1066] Step 1:

[1067] The terminal displays a list of affiliated stores to the user and prompts the user to select which store they would like to collect price information from.

[1068] Step 2:

[1069] The user selects the store of interest and presses the "Next" button.

[1070] Step 3:

[1071] The server sends a request to the API of the store selected by the user to retrieve the latest food price data.

[1072] Step 4:

[1073] The server saves the acquired price data in the database and updates existing data, such as "chicken 500 yen" and "cabbage 150 yen."

[1074] Step 5:

[1075] Users can use the terminal to input requests such as "weekly menu" or "dinner menu." They can also input food preferences and allergy information.

[1076] Step 6:

[1077] The device receives the user's request and generates a list of the required foods, such as "chicken," "cabbage," and "soy sauce."

[1078] Step 7:

[1079] The server matches the price data collected in step 4 with the list of required foods generated in step 6 to find the cheapest combination.

[1080] Step 8:

[1081] The server identifies the cheapest purchase pattern and transmits the corresponding combination to the terminal.

[1082] Step 9:

[1083] The device presents the optimal purchasing pattern sent from the server to the user, for example, displaying advice such as "Buy chicken and cabbage at supermarket A, and buy cooking oil at supermarket B."

[1084] Step 10:

[1085] The device searches for recipe information on the Internet based on the suggested food list, for example, "recipes using chicken and cabbage."

[1086] Step 11:

[1087] The server analyzes the recipe information received from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[1088] Step 12:

[1089] The device then presents the generated menu plan and optimal food list to the user as a final proposal, along with a food shopping list and recipes.

[1090] Step 13:

[1091] The user checks the presented menu and food list and makes any necessary changes, such as "increase the amount of cabbage" or "switch to a different brand of soy sauce."

[1092] Step 14:

[1093] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the shopping list.

[1094] In this way, the system provides users with efficient and economical food purchasing and meal planning suggestions.

[1095] Example 1

[1096] 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."

[1097] Conventional food purchasing and menu planning systems have difficulty collecting price data from multiple stores and providing optimal purchasing patterns and menu plans based on that data. Furthermore, customizing lists that take into account the user's food preferences and allergy information, and finalizing the purchase list, are cumbersome, making them difficult to use for users.

[1098] 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.

[1099] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for acquiring user requests and generating a food list, means for comparing the collected price data with the necessary food list to search for the cheapest combination, means for presenting the searched cheapest combination to the user, means for displaying the presented cheapest purchasing pattern, means for searching the Internet for cooking recipes based on the generated ingredient list, means for generating an optimal menu plan for the user, means for the user to finalize the shopping list, and means for customizing the necessary food list taking into account the user's ingredient preferences and allergy information, thereby enabling the user to purchase foods efficiently and determine the optimal menu.

[1100] "Price data" is price information for food products offered by multiple stores.

[1101] "Database" means an information storage system for storing and updating collected pricing data.

[1102] "User requirements" are requests or conditions entered by a user for food purchases or menu decisions.

[1103] A "food list" is a list of foods that need to be purchased, generated based on a user's request.

[1104] The "cheapest purchasing pattern" is information that shows the cheapest combination by comparing the collected price data with the list of necessary foods.

[1105] A "cooking recipe" is a set of instructions that describes how to cook a dish using specific ingredients.

[1106] A "menu plan" is a combination of dishes suggested for multiple days or meals.

[1107] The "final purchase list" is a list of foods that should be finally purchased, confirmed after the user has confirmed and corrected them.

[1108] "User preference and allergy information" refers to information about whether a user likes or avoids certain ingredients.

[1109] This invention is a system that allows users to determine optimal food purchases and menus based on price data collected from multiple stores. This system is composed of three entities: a server, a terminal, and a user.

[1110] server

[1111] The server obtains price data from multiple affiliated stores via API, and stores and updates it in a database. This process is performed periodically to keep price information up to date. For example, the server sends a request to the APIs of "Store A" and "Store B" to obtain the latest food price data. The obtained data is stored in the database as "chicken 500 yen" and "cabbage 150 yen." The server then generates a list of necessary foods based on the user's requests, and generates optimal purchasing patterns and menu suggestions.

[1112] Terminal

[1113] The terminal is responsible for interfacing with the user. It receives requests from the user and presents the generated list of necessary foods to the user. It also accepts corrections as necessary and finalizes the final shopping list. For example, if a user inputs a request for "dinner for a family of four," the terminal will generate and display a list of necessary foods (e.g., "chicken," "cabbage," and "soy sauce") based on this request.

[1114] User

[1115] The user inputs requests for food purchases and menu decisions, and makes a final decision based on the information presented. The user inputs requests such as "weekly menu" or "dinner menu" through the terminal. This request may also include information on food preferences and allergies. The user checks the proposed menu and food list, and makes any necessary corrections to finalize the shopping list. For example, the user makes a correction on the terminal, such as "increase the amount of cabbage," and then finalizes the revised shopping list.

[1116] Specific system configuration and operation example

[1117] 1. The server sends a request to the APIs of "Store A" and "Store B" to obtain the latest food price data. The obtained data is stored in the database as "chicken 500 yen" and "cabbage 150 yen," for example.

[1118] 2. The user inputs requests such as "weekly menu" or "dinner menu" through the terminal.

[1119] 3. The device receives the user's request and generates a list of required foods, such as "chicken," "cabbage," and "soy sauce."

[1120] 4. The price data collected by the server is compared with the list of necessary foods generated by the terminal to find the cheapest combination.

[1121] 5. The device presents the user with the optimal purchasing pattern sent from the server, including specific advice on which stores to purchase which foods.

[1122] 6. The device searches for cooking recipes on the Internet based on the optimal food list and suggests a menu plan that takes into account the user's preferences and allergy information.

[1123] 7. The user reviews the proposed menu and food list and makes any necessary modifications. After modifications, the final shopping list is confirmed.

[1124] Prompt Sentence Examples

[1125] "Create a weekly menu for a family of four. Get ingredient price data from this API. Generate the optimal menu plan and shopping list, taking into account the lowest price for each ingredient."

[1126] In this way, the system provides users with efficient and economical food purchasing and menu suggestions. The above is a specific embodiment for carrying out the present invention.

[1127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1128] Step 1:

[1129] Price data collection and updates

[1130] Input: Store API endpoint

[1131] Processing: The server retrieves price data from multiple partner stores via API. This is done periodically to update the price data.

[1132] Specific operation: The server sends requests to the API endpoints of "Store A" and "Store B" to obtain new price data. The obtained data is saved in the database as "chicken 500 yen" and "cabbage 150 yen," etc.

[1133] Output: A database containing updated price data

[1134] Step 2:

[1135] Get the user request

[1136] Input: A request entered by the user through the device (e.g., "weekly menu" or "dinner menu")

[1137] Processing: The device receives the user's request and analyzes the request content.

[1138] How it works: A user enters a request for "dinner for a family of four" into a smartphone app. The device receives the request and analyzes the details.

[1139] Output: Parsed user request data

[1140] Step 3:

[1141] Generate a list of food needs

[1142] Input: Parsed user request data

[1143] Processing: The device generates a list of required foods based on the content of the request.

[1144] Specific operation: The device analyzes the request content and generates a list of foods needed for "dinner for a family of four" (e.g., "chicken," "cabbage," and "soy sauce") and saves the list of needed foods in local storage.

[1145] Output: Generated food needs list

[1146] Step 4:

[1147] Finding optimal purchasing patterns

[1148] Input: Collected price data and generated food needs list

[1149] Processing: The server compares the collected price data with the list of required foods and searches for the cheapest combination.

[1150] How it works: The server compares the list of food items needed with the price data in the database and runs an algorithm to find the cheapest combination. For example, it calculates the cheapest combination as "chicken at store A for 500 yen, cabbage at store A for 150 yen, and soy sauce at store B for 200 yen."

[1151] Output: lowest price purchase pattern data

[1152] Step 5:

[1153] Showing the cheapest purchase pattern

[1154] Input: lowest price purchase pattern data

[1155] Processing: The terminal presents the lowest price purchase pattern sent from the server to the user.

[1156] Specific operation: The terminal receives the lowest price purchasing pattern data and displays specific instructions to the user, such as "purchase chicken and cabbage from store A, and purchase oil from store B."

[1157] Output: The cheapest purchase pattern presented to the user

[1158] Step 6:

[1159] Find recipes and generate meal plans

[1160] Input: Generated food requirements list, user preferences, and allergy information

[1161] Processing: The device searches for cooking recipes on the Internet based on the optimal food list. The server analyzes the recipe information and generates the optimal menu plan.

[1162] Specific operation: The device searches the internet for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[1163] Output: A suggested meal plan for the user

[1164] Step 7:

[1165] Final purchase list confirmation

[1166] Input: Suggested menu and food list, user modifications

[1167] Processing: The user reviews the proposed menu and food list and makes any necessary adjustments. The final shopping list is confirmed.

[1168] Specific operation: The user makes modifications on the device, such as "increase the amount of cabbage." After the modifications are complete, the final shopping list is confirmed and displayed to the user.

[1169] Output: Final purchase list confirmed

[1170] (Application example 1)

[1171] 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."

[1172] Conventional food purchasing and menu suggestion systems require users to spend a lot of time and effort to purchase the cheapest foods. Furthermore, they do not provide optimal suggestions for food lists and recipes that meet the user's needs, making it difficult to make efficient purchases and decide on menus. Another problem is that they do not adequately customize the system to accommodate user requests, food preferences, or allergies.

[1173] 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.

[1174] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a list of necessary foods based on a user's request, means for comparing the collected price data with the list of necessary foods to search for the cheapest combination, means for presenting the searched cheapest combination to the user, means for acquiring the user's request and displaying it to the user via a smartphone or smart glasses, and means for accepting user corrections and presenting a final purchasing route, thereby enabling the user to purchase food efficiently and economically and quickly decide on an appropriate menu.

[1175] A "means for collecting food price data from multiple stores" is a method or device for collecting food price information from different points of sale.

[1176] "Means for storing and updating collected price data in a database" refers to a storage device or system for storing the obtained price information, and has the function of updating the information to the latest version at any time.

[1177] The "means for generating a list of required foods based on a user's request" refers to a method or device for creating a list of ingredients to be purchased based on a user's request.

[1178] "Means for matching collected price data with a list of required foods to find the cheapest combination" refers to a method or device that compares collected price information with a generated list of ingredients to find the most affordable combination.

[1179] "Means for presenting the cheapest combination found to the user" refers to a method or device for informing the user of the cheapest combination found.

[1180] "Means for acquiring user requests and displaying them to the user through a smartphone or smart glasses" refers to a method or apparatus for collecting purchase requests from users and displaying the information via a smart device.

[1181] "Means for accepting user corrections and presenting a final shopping route" refers to a method or device for accepting corrections from a user and presenting a final shopping route or plan after incorporating the corrections.

[1182] A "means for retrieving recipes from a presented food list" refers to a method or device for finding cooking methods based on a proposed list of ingredients.

[1183] "Means for generating a menu plan optimal for the user from the searched recipes" refers to a method or device for creating a menu plan optimal for the user using the cooking methods found.

[1184] "Means for guiding the optimal shopping route in a store" refers to a method or device for guiding the route to buy the necessary ingredients most efficiently in the store.

[1185] "Means for customizing the required food list taking into account the user's food preferences and allergy information" refers to a method or device for individually adjusting the food list based on the user's preferences and allergy information.

[1186] "Means for optimizing recipe candidates using a generative AI model" refers to a method or device that uses artificial intelligence to suggest the best cooking method and efficiently select those candidates.

[1187] "Means for modifying and optimizing generated recipe candidates based on user request prompts" refers to a method or device for modifying and optimizing recipes suggested by AI in accordance with the specific requests of users.

[1188] This invention is a system that allows users to purchase food efficiently and economically and determine optimal menus based on price data collected from multiple stores. This system is composed of a server, terminals, and users.

[1189] Server Features

[1190] Price data collection, storage and updating

[1191] The server periodically collects food price data through APIs from multiple stores and stores it in a database. This stored data is always updated. This function on the server is essential for efficiently managing price information and providing it to users. A Python program is implemented on the server to retrieve store price data through RESTful APIs.

[1192] Finding optimal purchasing patterns

[1193] The server compares the collected price data with the food list generated by the device to find the cheapest combination, which allows the user to create an economical shopping plan. This search algorithm compares the price data with the food list to find the lowest-cost combination.

[1194] Analyzing recipe data and generating menu plans

[1195] The server analyzes the recipe information sent from the device and generates the optimal menu plan for the user. This function selects the most suitable recipes from multiple recipe databases and proposes menus that take into account the user's food preferences and allergies.

[1196] Device Features

[1197] Providing a user interface and receiving requests

[1198] The device, such as a smartphone or smart glasses, directly interfaces with the user, acquiring the user's request and generating a list of food items based on the request. The user can generate a list of food items by inputting a request such as "dinner tonight."

[1199] Providing optimal purchasing routes

[1200] The device presents the user with optimal purchasing patterns sent from the server and guides them to an efficient shopping route within the store based on this. This shopping route is visually displayed on the user's smartphone or smart glasses, helping to ensure a stress-free shopping experience.

[1201] Menu and recipe suggestions

[1202] The device searches the internet for recipes based on the optimal food list and suggests them to the user. For example, a search for "recipes using chicken and cabbage" will result in recipes such as "stir-fried chicken and cabbage." A generative AI model is also used to filter recipes based on the user's preferences and allergies.

[1203] User Roles

[1204] Entering and Modifying Requests

[1205] Users input their requests through their smartphones or smart glasses, review the food list and meal plan provided, and can modify the list or plan as needed to finalize the shopping list. This process allows users to efficiently decide on the food purchases and meal plans that best suit their needs.

[1206] Examples of concrete examples and prompts

[1207] A concrete example is a system where a user requests the best recipes for a "dinner for a family of four." Based on this request, the server collects price data and the device displays the best food list and purchasing patterns.

[1208] Example prompt sentence:

[1209] "Suggest a recipe for a perfect dinner for a family of four. The ingredients are chicken, cabbage, and soy sauce."

[1210] In this way, the invention realizes a highly functional system that supports efficient food purchasing and menu planning decisions.

[1211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1212] Step 1: The server collects price data from multiple stores and saves and updates it in a database.

[1213] Input: Store API URL list

[1214] Processing: The server accesses each store's API to obtain the latest food price data. It parses the obtained data in JSON format and saves it in the database. This process is performed periodically to keep the data up to date.

[1215] Output: A database containing the latest food price data

[1216] Step 2: The user enters a request through the terminal.

[1217] Input: User request (e.g., "Dinner tonight" or "Dinner for a family of four")

[1218] Processing: The terminal receives the user's request through the interface. This request may include food preferences and allergy information. The terminal then sends the input request to the server.

[1219] Output: User request data

[1220] Step 3: The server generates a list of required foods based on the user's request.

[1221] Input: User request data

[1222] Processing: The server analyzes the input request data and generates a food list based on it. The generated food list includes the names of the necessary ingredients, such as "chicken," "cabbage," and "soy sauce."

[1223] Output: Generated food list

[1224] Step 4: The server compares the collected price data with the food list to find the cheapest combination.

[1225] Input: Generated food list, latest price data

[1226] Processing: The server searches the price database to find the cheapest store and price for each ingredient, and presents this to the user as the cheapest combination.

[1227] Output: Data for the cheapest combination

[1228] Step 5: The device presents the cheapest combination to the user.

[1229] Input: Data for the cheapest combination

[1230] Processing: The device receives the data sent from the server and presents it visually to the user, such as on the screen of a smartphone or smart glasses.

[1231] Output: The cheapest combination presented to the user

[1232] Step 6: The device guides the user to the optimal shopping route within the store

[1233] Input: Data on the cheapest combination, data on in-store placement

[1234] Processing: Based on the information on the cheapest combination displayed on the device, the device calculates a route that will allow the user to purchase the necessary ingredients by going around the store efficiently. The device then visually guides the user along this route.

[1235] Output: The purchasing route presented to the user

[1236] Step 7: The device searches for recipes based on the food list and suggests them to the user.

[1237] Input: Generated food list

[1238] Processing: The device searches the internet for recipes that match the food list. It uses a generative AI model to select the best recipes and suggest them to the user. An example might be "Stir-fried chicken and cabbage."

[1239] Output: Suggested recipe

[1240] Step 8: User reviews, modifies, and finally confirms the suggested recipe and food list

[1241] Input: Suggested recipes, generated food list

[1242] Processing: The user checks the suggested recipes and food list through the terminal and makes any necessary corrections. Once corrections are complete, the final shopping list is confirmed.

[1243] Output: Final purchase list confirmed

[1244] Example prompt sentence:

[1245] "Suggest a recipe for a perfect dinner for a family of four. The ingredients are chicken, cabbage, and soy sauce."

[1246] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1247] This invention relates to a system that helps users efficiently and economically purchase food and create optimal menus using that food. The system also incorporates an emotion engine that recognizes the user's emotions and adjusts the food list and menu accordingly.

[1248] System configuration

[1249] This system has the following components:

[1250] 1. Server

[1251] It is the central component for implementing the present invention and is responsible for collecting, storing and updating price data.

[1252] Generate optimal purchasing patterns and menu suggestions based on user requirements.

[1253] It integrates an emotion engine to adjust food lists and menus based on user emotion recognition.

[1254] 2. Terminal

[1255] It is a device for interfacing with the user, taking requests, displaying results, and accepting modifications.

[1256] Generate a list of food items you need and search for recipes.

[1257] 3. Users

[1258] Enter food purchasing and menu planning requests and make final decisions based on the information provided.

[1259] Emotional data is input through the terminal and this information is used by the system.

[1260] Program processing overview

[1261] Server collects and updates price data

[1262] The server periodically retrieves price data from multiple partner stores via API and stores it in a database, ensuring that the latest price information is always available.

[1263] Examples:

[1264] The server sends a request to the APIs of "Store A" and "Store B" to retrieve the latest food price data. The retrieved data is stored in a database as "chicken 500 yen" and "cabbage 150 yen," for example.

[1265] Get user requests and generate a food list

[1266] Users input requests such as "weekly menu" or "dinner menu" through a terminal. These requests can also include food preferences, allergy information, and emotional data.

[1267] The device receives this request and generates a list of food needs based on the user's requirements, such as "chicken," "cabbage," and "soy sauce."

[1268] Examples:

[1269] The user inputs a request such as "dinner for a family of four," and the device generates a list of required foods (e.g., "chicken," "cabbage," and "soy sauce").

[1270] Searching for and suggesting optimal purchasing patterns

[1271] The server compares the collected price data with the necessary food list generated by the terminal to find the cheapest purchasing pattern.

[1272] The terminal presents the optimal purchasing pattern sent from the server to the user. For example, advice such as "Buy chicken and cabbage at supermarket A, and buy sesame oil at supermarket B."

[1273] Examples:

[1274] The server calculates the cheapest combination as "chicken for 500 yen at store A, cabbage for 150 yen at store A, and soy sauce for 200 yen at store B." The terminal displays a suggestion to the user to "purchase chicken and cabbage from store A and soy sauce from store B."

[1275] Recipe search and meal plan suggestions

[1276] The device searches the internet for recipes based on the best food list, e.g., "Recipes using chicken and cabbage."

[1277] The server analyzes the recipe information sent from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[1278] Examples:

[1279] The device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[1280] Final purchase list confirmation

[1281] The user can check the proposed menu and food list through the device and make any necessary changes, such as "increase the amount of cabbage" or "switch to a different brand of soy sauce."

[1282] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the purchase list.

[1283] Use of emotion engine

[1284] The emotion engine analyzes emotional data entered by the user into the device or acquired through sensors, and this data is used to adjust food lists and meal plans.

[1285] Examples:

[1286] The server uses an emotion engine to suggest relaxing recipes such as "chicken and cabbage soup" to reduce the user's stress level.

[1287] The device uses an emotion engine to search for and suggest recipes that better reflect the user's preferences.

[1288] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors the system to the user's emotions and preferences, providing a more personalized experience.

[1289] The processing flow will be explained below.

[1290] Step 1:

[1291] The terminal displays a list of affiliated stores to the user and prompts the user to select which store they would like to collect price information from.

[1292] Step 2:

[1293] The user selects the store of interest and presses the "Next" button.

[1294] Step 3:

[1295] The server sends a request to the API of the store selected by the user to retrieve the latest food price data.

[1296] Step 4:

[1297] The server saves the acquired price data in the database and updates existing data, such as "chicken 500 yen" and "cabbage 150 yen."

[1298] Step 5:

[1299] Users can use the terminal to input requests such as "weekly menu" or "dinner menu." They can also input food preferences, allergy information, and emotional data.

[1300] Step 6:

[1301] The device receives the user's request and generates a list of the required foods, such as "chicken," "cabbage," and "soy sauce."

[1302] Step 7:

[1303] The server matches the price data collected in step 4 with the list of required foods generated in step 6 to find the cheapest combination.

[1304] Step 8:

[1305] The server identifies the cheapest purchase pattern and transmits the corresponding combination to the terminal.

[1306] Step 9:

[1307] The device presents the optimal purchasing pattern sent from the server to the user, for example, displaying advice such as "Buy chicken and cabbage at store A and sesame oil at store B."

[1308] Step 10:

[1309] The device searches for recipe information on the Internet based on the suggested food list, for example, "recipes using chicken and cabbage."

[1310] Step 11:

[1311] The server analyzes the recipe information received from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[1312] Step 12:

[1313] The device then presents the generated menu plan and optimal food list to the user as a final proposal, along with a food shopping list and recipes.

[1314] Step 13:

[1315] The user can review the proposed menu and food list and make any necessary changes, such as adding more cabbage or switching to a different brand of soy sauce.

[1316] Step 14:

[1317] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the purchase list.

[1318] Step 15:

[1319] The user inputs emotional data into the terminal, which includes numerical values ​​and options that indicate the user's emotional state.

[1320] Step 16:

[1321] The terminal acquires the emotion data and transmits it to the server.

[1322] Step 17:

[1323] The server uses an emotion engine to analyze the emotional data and adjust the food list and menu based on the user's emotional state.

[1324] Examples:

[1325] If the user is feeling stressed, the server uses its emotion engine to suggest "chicken and cabbage soup, which has a relaxing effect."

[1326] If a user is feeling low in energy, the app suggests a "high-protein chicken dish" to replenish their energy.

[1327] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors them to their emotions and preferences, providing a more personalized experience.

[1328] Example 2

[1329] 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."

[1330] Conventional food purchasing systems make it difficult for users to purchase food efficiently and economically, and they also have problems with reducing user satisfaction because food lists and menus are not tailored to the user's individual emotional state or preferences.

[1331] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1332] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a required food list based on a user's request, means for comparing the collected price data with the required food list to search for the cheapest combination, means for presenting the searched cheapest combination to the user, and an emotion engine for analyzing emotion data entered by the user. This enables users to make efficient and economical food purchases and also makes it possible to provide food lists and menus that correspond to each user's emotional state.

[1333] "Stores" are multiple retail outlets or commercial facilities that provide food price data.

[1334] "Price data" refers to price information for food products sold at each store.

[1335] "Database" means an information management system for storing collected price data and user information and updating it as necessary.

[1336] "User Requests" refers to the preferences or conditions entered by the user in order to receive food list or menu suggestions.

[1337] A "food list" is a list of food needs generated based on a user's request.

[1338] The "emotion engine" is the part of the system that analyzes the emotional data entered by the user and adjusts the food list and menu accordingly.

[1339] A "cooking recipe" is an instruction manual that describes cooking methods and steps using specific ingredients.

[1340] A "menu plan" is a plan of meal combinations and cooking sequences suggested to the user.

[1341] "Final Shopping List" is the final list for purchasing food items that is confirmed after the user makes any modifications.

[1342] "Preferences" refers to the ingredients and types of dishes that a user particularly likes.

[1343] "Allergy information" refers to information about a user's allergic reaction to food ingredients or substances.

[1344] "Emotional data" is information entered by a user about their current emotional state.

[1345] "Suggestion" refers to the act of the system showing the user optimal food purchasing patterns and menu plans.

[1346] This invention relates to a system that helps users efficiently and economically purchase food and create optimal menus using that food. The system also incorporates an emotion engine that recognizes the user's emotions and adjusts the food list and menu accordingly.

[1347] System configuration

[1348] This system has the following components:

[1349] 1. Server

[1350] The server is the central component for implementing this invention, and is responsible for collecting, storing, updating, and processing price data. It periodically retrieves price data from multiple stores through APIs and stores it in a database. It also compares the collected price data with the user's food list based on their request to find the cheapest combination. It also integrates an emotion engine to adjust the food list and menu based on the user's emotion recognition.

[1351] Examples of hardware and software: For the server hardware, commercial cloud servers (e.g., AWS, Google Cloud) can be used. For API communication and database operations, programming languages ​​such as Python or Java and SQL databases (e.g., MySQL, PostgreSQL) are used.

[1352] 2. Terminal

[1353] The terminal is a device that interfaces with the user, receiving requests, displaying results, and accepting corrections. Based on the information entered by the user, it generates a list of necessary foods and searches for cooking recipes on the Internet. It also uses an emotion engine to make suggestions based on the user's emotions.

[1354] Examples of hardware and software: Devices can include smartphones, tablets, and PCs. Front-end frameworks such as React and Angular can be used to develop the user interface.

[1355] 3. Users

[1356] The user inputs requests for food purchases and menu decisions through the terminal, and then makes a final decision based on the displayed information. In addition, the user inputs their own emotional data, which is used by the system.

[1357] Program processing overview

[1358] Price data collection and updates

[1359] Server: Periodically sends requests to multiple partner store APIs to retrieve price data and store it in a database.

[1360] Example: The server obtains price data for "chicken 500 yen" and "cabbage 150 yen" from "Supermarket A" and saves it in the database.

[1361] Get user requests and generate a food list

[1362] User: Input requests such as "weekly menu" or "dinner menu" through the device. They can also include food preferences, allergy information, and emotional data.

[1363] Terminal: Receives this request and generates a food list based on the user's requirements.

[1364] Example: A user inputs a request for "dinner for a family of four," and the device generates a food list of "chicken," "cabbage," and "soy sauce."

[1365] Searching for and suggesting optimal purchasing patterns

[1366] Server: Matches the collected price data with the food list generated by the device to find the cheapest purchasing pattern.

[1367] Device: Presents the optimal purchasing pattern sent from the server to the user.

[1368] Example: The server calculates that "chicken is 500 yen at store A, cabbage is 150 yen at store A, and soy sauce is 200 yen at store B," and the terminal suggests, "Purchase chicken and cabbage at store A, and soy sauce at store B."

[1369] Recipe search and meal plan suggestions

[1370] Device: Search the internet for cooking recipes based on a list of the best foods.

[1371] Server: Analyzes the submitted recipe information and generates a menu plan that best suits the user's requirements.

[1372] Example: A device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes this and suggests it to the user as a "weekly dinner menu plan."

[1373] Final purchase list confirmation

[1374] User: Review the suggested menu and food list and modify as needed.

[1375] Device: Once the modifications are confirmed, the information is sent to the server and the final purchase list is confirmed.

[1376] Example: A user adds the modification "increase the amount of cabbage" and finalizes the final list.

[1377] Use of emotion engine

[1378] Emotion engine: Analyzes the emotional data entered by the user into the device and uses it to adjust food lists and menus.

[1379] Server: Uses an emotion engine to suggest menus based on the user's emotional state.

[1380] Example: When a user types "I'm feeling stressed," an emotion engine analyzes that information and the server suggests "chicken and cabbage soup."

[1381] Example prompts for generative AI models

[1382] "Please suggest a weekly menu for a family of four. The user's favorite ingredients are chicken and cabbage, and they have no allergies. Also, the user is currently feeling stressed, so please take that into consideration when creating a menu."

[1383] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors them to the user's emotions and preferences, providing a more personalized experience.

[1384] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1385] Specific processing steps of the system

[1386] Step 1: Collect price data

[1387] The server sends requests to the APIs of multiple partner stores to collect the latest food price data.

[1388] Input: API endpoint of partner store

[1389] Data processing: The server generates an API request and sends it to each store's API.

[1390] Output: Price data returned from each store

[1391] Specific operation: The server sends an API request to "Store A" and obtains price data for "Chicken 500 yen" and "Cabbage 150 yen."

[1392] Step 2: Saving and updating price data

[1393] The server stores the acquired price data in a database and updates existing data.

[1394] Input: Collected price data

[1395] Data Processing: The server analyzes the price data and updates the existing data in the database.

[1396] Output: Updated database

[1397] Specific operation: Save or update the data "Chicken 500 yen" and "Cabbage 150 yen" in the price table in the database.

[1398] Step 3: Get the user request

[1399] The user inputs requests such as "weekly menu" or "dinner menu" through the terminal.

[1400] Input: User request (e.g. "Dinner for a family of four")

[1401] Data processing: The terminal receives the request and displays it on the user interface.

[1402] Output: User input information

[1403] Specific operation: A user inputs a request into a terminal: "Dinner for a family of four."

[1404] Step 4: Generate a food list

[1405] The terminal generates a list of required foods based on the user's request.

[1406] Input: User request

[1407] Data processing: Applying a list generation algorithm based on user requests.

[1408] Output: List of food items needed

[1409] Specific operation: Based on the information about "dinner for a family of four," generate a list of "chicken," "cabbage," and "soy sauce."

[1410] Step 5: Finding optimal purchasing patterns

[1411] The server compares the collected price data with the generated food list to find the cheapest purchasing patterns.

[1412] Input: Price data, generated food list

[1413] Data processing: Compare the price data with the food list and use a matching algorithm to calculate the lowest price.

[1414] Output: Optimal purchasing pattern

[1415] Specific operation: Calculate "chicken is 500 yen at store A, cabbage is 150 yen at store A, and soy sauce is 200 yen at store B."

[1416] Step 6: Show purchasing patterns

[1417] The terminal presents the optimal purchasing pattern sent from the server to the user.

[1418] Input: Optimal purchase pattern

[1419] Data processing: Format purchase patterns so they can be displayed on the device.

[1420] Output: Purchase patterns presented to the user

[1421] Specific operation: The terminal displays "Purchase chicken and cabbage at store A, and purchase cooking oil at store B."

[1422] Step 7: Find a recipe

[1423] The device searches the internet for cooking recipes based on a list of optimal foods.

[1424] Input: Optimal Food List

[1425] Data processing: A recipe search engine is used to search recipe databases on the Internet.

[1426] Output: Related recipes

[1427] Action: Search for "chicken and cabbage recipes" and find "chicken and cabbage stir fry."

[1428] Step 8: Menu plan suggestions

[1429] The server analyzes the recipe information sent from the terminal and generates a menu plan that best suits the user's requirements.

[1430] Input: Cooking recipe

[1431] Data processing: Applying an algorithm to analyze recipe information and generate a menu plan.

[1432] Output: Menu plan

[1433] Specific operation: Propose to the user "weekly dinner menu plan."

[1434] Step 9: Finalize the purchase list

[1435] The user reviews the proposed menu and food list and modifies it as needed.

[1436] Input: Suggested menu and food list

[1437] Data processing: Accept user corrections and recalculate.

[1438] Output: Final purchase list

[1439] Specific operation: The user inputs the correction "increase the amount of cabbage" into the terminal and confirms the final list.

[1440] Step 10: Use the Emotion Engine

[1441] The emotion engine analyzes the emotional data entered by the user into the device.

[1442] Input: Emotion data

[1443] Data processing: Analyze the data using sentiment analysis algorithms to generate results.

[1444] Output: Food list and menu adjustments based on analysis results

[1445] Specific behavior: Based on the information that the user is "feeling stressed," the server suggests "chicken and cabbage soup."

[1446] Example prompts for generative AI models

[1447] "Please suggest a weekly menu for a family of four. The user's favorite ingredients are chicken and cabbage, and they have no allergies. Also, the user is currently feeling stressed, so please take that into consideration when creating a menu."

[1448] (Application example 2)

[1449] 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."

[1450] Conventional food purchasing and menu suggestion systems have the problem of not taking into account the user's emotions and being unable to provide personalized menus based on emotions. This makes it difficult for users to choose meals that suit their current psychological state and emotions, which can result in a poor shopping experience. Additionally, suggestions based solely on ingredient price information are difficult to meet the user's psychological and emotional needs.

[1451] The identification process by the identification 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 food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a required food list based on a user's request, and means for recognizing the user's emotional data and adjusting the food list and menu based on that data. This makes it possible to provide an optimal food list and menu based on the user's emotional and psychological state.

[1452] "Multiple stores" means a collection of multiple different stores, each of which may offer different prices and inventory information.

[1453] "Food Price Data" refers to information about the prices of food products sold at each store, including details such as product name, price, and availability.

[1454] "Database" means a set of systems and structures for storing and managing collected price data, allowing for rapid retrieval and updating of the data.

[1455] "User Requests" refers to the preferences and requirements that a user inputs into the system regarding food lists and menus, including the type of menu, number of people, allergy information, etc.

[1456] "Needed Food List" refers to a list of foods generated based on a user's request, which indicates specific food items that the user should purchase.

[1457] "User emotional data" means data that indicates a user's current mental state or emotion, including stress level, enjoyment, sadness, fatigue, etc.

[1458] "Menus" refers to cooking plans and suggestions based on specific foods that assist users in planning their daily meals.

[1459] "Server" refers to a computer system that is the central component of a system and is responsible for collecting, storing, processing, and providing data.

[1460] This invention is a system that helps users efficiently and economically purchase food and create optimal meals. The system integrates an emotion engine that recognizes the user's emotions and adjusts the food list and meal plan accordingly.

[1461] System configuration

[1462] This system has the following components:

[1463] 1. Server

[1464] This is the central component for implementing the present invention, and collects food price data from multiple stores.

[1465] The collected price data is stored in a database and updated regularly.

[1466] Generate optimal food lists and meal suggestions based on user requirements.

[1467] It uses an emotion engine to adjust food lists and menus based on the user's emotional data.

[1468] 2. Terminal

[1469] It is a device for interfacing with the user, and a smartphone is generally used.

[1470] The system uses the smartphone's camera and microphone to collect user emotional data and transmit it to a server.

[1471] The food list and menu suggestions sent from the server are displayed to the user.

[1472] 3. Users

[1473] Requests for food purchases and menu decisions are entered into the terminal, and emotional data is provided through the terminal.

[1474] Based on the information provided, review the food list and menu and finalize the shopping list.

[1475] Program processing overview

[1476] Server collects and updates price data

[1477] The server periodically retrieves price data from multiple local stores via API, and the retrieved data is stored in a database, always maintaining the latest price information.

[1478] Get user requests and generate a food list

[1479] Users input requests such as "dinner menu" or "weekly menu" through the device, and emotional data is also collected, and a list of the necessary foods is generated based on this information.

[1480] Searching for and suggesting optimal purchasing patterns

[1481] The server compares the collected price data with the necessary food list generated by the device to search for the cheapest purchasing pattern, and the search results are sent to the device and presented to the user.

[1482] Recipe search and meal plan suggestions

[1483] The device searches for cooking recipes on the Internet based on the optimal food list, and the server analyzes the recipe information and generates the optimal menu plan for the user based on the emotion data.

[1484] Final purchase list confirmation

[1485] The user can check the proposed menu and food list through the device and make any necessary changes. After confirming the changes, the device sends the information to the server, and the final shopping list is confirmed.

[1486] Examples of concrete examples and prompts

[1487] The server uses an emotion engine to recognize the user's emotions and adjust the food list and meal plan accordingly: for example, if the user is feeling stressed, it can suggest a meal plan that includes ingredients that have a relaxing effect.

[1488] A specific example is the following prompt:

[1489] "Can you suggest a dinner menu for my family of four? I'm feeling stressed right now, so I'd like a recipe with relaxing ingredients."

[1490] "I've been feeling a bit tired lately, so please tell me some ingredients and recipes that will cheer me up."

[1491] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1492] Step 1:

[1493] The server periodically retrieves food price data from multiple stores via API. In this process, the server sends an API request and receives the latest food price data from each store in response. The price data includes product name, price, and stock information. The retrieved data is stored in a database, and existing data is updated. The input is the API request, and the output is the latest price data.

[1494] Step 2:

[1495] The user inputs a request to generate a food list through the device. The request includes information on the type of meal and the number of people. The device also collects the user's emotional data using the smartphone's camera and microphone. The input is the user's request and emotional data, and the output is sending the request to the server.

[1496] Step 3:

[1497] The server receives the request sent from the terminal and generates a list of necessary foods based on the user's request. At this time, the emotion engine analyzes the user's emotion data and adjusts the food list based on the request. The input is the user's request and emotion data, and the output is the necessary food list.

[1498] Step 4:

[1499] The server compares the collected price data with the generated food list to find the cheapest purchasing pattern. The optimal purchasing pattern is calculated based on the price and inventory information at each store. The input is the price data and the food list, and the output is the cheapest purchasing pattern.

[1500] Step 5:

[1501] The server sends the cheapest shopping pattern it has found to the terminal to present to the user. The terminal displays the optimal shopping pattern to the user and advises them on which store to purchase the necessary food. The input is the cheapest shopping pattern, and the output is the information displayed on the user's terminal.

[1502] Step 6:

[1503] The device searches the internet for cooking recipes based on the optimal food list. The searched recipes are selected based on the user's emotional data and other settings and suggested to the user. The input is the food list, and the output is the suggested recipe.

[1504] Step 7:

[1505] The server analyzes the recipe information sent from the device and generates a menu plan that best suits the user's requirements. Based on the emotional data, the server provides a menu plan that matches the user's psychological state. The input is recipe information, and the output is the optimal menu plan.

[1506] Step 8:

[1507] The user checks the proposed menu and food list through the terminal and makes any necessary changes. Once the user confirms the changes, the information is sent to the server. The input is the user's revised information, and the output is the final shopping list.

[1508] 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.

[1509] 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.

[1510] 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.

[1511] [Fourth embodiment]

[1512] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1513] 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.

[1514] 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).

[1515] 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.

[1516] 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.

[1517] 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).

[1518] 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.

[1519] 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.

[1520] 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.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] 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."

[1525] This invention is a system that allows users to determine optimal food purchases and menus based on price data collected from multiple stores. This system is implemented by the specific program processing shown below.

[1526] System configuration

[1527] This system consists of three parties: a server, a terminal, and a user.

[1528] 1. Server

[1529] Responsible for collecting, storing and updating price data.

[1530] Generate optimal purchasing patterns and menu suggestions based on user requirements.

[1531] 2. Terminal

[1532] It interfaces with the user, taking requests, displaying results, and accepting modifications.

[1533] Generate a list of food items you need and search for recipes.

[1534] 3. Users

[1535] Enter food purchasing and menu planning requests and make final decisions based on the information provided.

[1536] Program processing overview

[1537] Server collects and updates price data

[1538] The server retrieves price data from multiple partner stores via API and stores it in a database. This process is executed periodically to update the price information.

[1539] Examples:

[1540] The server sends a request to the APIs of "Store A" and "Store B" to retrieve the latest food price data. The retrieved data is stored in a database as "chicken 500 yen" and "cabbage 150 yen," for example.

[1541] Get user requests and generate a food list

[1542] A user inputs requests such as "weekly menu" or "dinner menu" through a terminal. This request may also include information about food preferences and allergies.

[1543] The device receives this request and generates a list of food items based on the user's needs, such as "chicken," "cabbage," and "soy sauce."

[1544] Examples:

[1545] The user inputs a request such as "dinner for a family of four," and the device generates a list of required foods (e.g., "chicken," "cabbage," and "soy sauce").

[1546] Searching for and suggesting optimal purchasing patterns

[1547] The server compares the collected price data with the list of necessary foods generated by the terminal and searches for the cheapest combination.

[1548] The device presents the user with optimal purchasing patterns sent from the server, including specific advice on which stores to buy which foods.

[1549] Examples:

[1550] The server calculates the cheapest combination as "chicken for 500 yen at store A, cabbage for 150 yen at store A, and soy sauce for 200 yen at store B." The terminal displays a suggestion to the user to "purchase chicken and cabbage from store A and soy sauce from store B."

[1551] Recipe search and meal plan suggestions

[1552] The device searches the internet for cooking recipes based on a list of optimal foods, taking into account the user's preferences and allergies.

[1553] The server analyzes the recipe information sent from the device and generates the optimal menu plan for the user, which is then sent to the device and presented to the user.

[1554] Examples:

[1555] The device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[1556] Final purchase list confirmation

[1557] The user can check the proposed menu and food list through the terminal and make any necessary changes. Once the user has finished making changes, the final shopping list is confirmed.

[1558] Examples:

[1559] The user makes modifications, such as "increase the amount of cabbage." After the modifications are complete, the final shopping list is confirmed.

[1560] In this way, the system provides users with efficient and economical food purchasing and menu suggestions. The above is a specific embodiment for carrying out the present invention.

[1561] The processing flow will be explained below.

[1562] Step 1:

[1563] The terminal displays a list of affiliated stores to the user and prompts the user to select which store they would like to collect price information from.

[1564] Step 2:

[1565] The user selects the store of interest and presses the "Next" button.

[1566] Step 3:

[1567] The server sends a request to the API of the store selected by the user to retrieve the latest food price data.

[1568] Step 4:

[1569] The server saves the acquired price data in the database and updates existing data, such as "chicken 500 yen" and "cabbage 150 yen."

[1570] Step 5:

[1571] Users can use the terminal to input requests such as "weekly menu" or "dinner menu." They can also input food preferences and allergy information.

[1572] Step 6:

[1573] The device receives the user's request and generates a list of the required foods, such as "chicken," "cabbage," and "soy sauce."

[1574] Step 7:

[1575] The server matches the price data collected in step 4 with the list of required foods generated in step 6 to find the cheapest combination.

[1576] Step 8:

[1577] The server identifies the cheapest purchase pattern and transmits the corresponding combination to the terminal.

[1578] Step 9:

[1579] The device presents the optimal purchasing pattern sent from the server to the user, for example, displaying advice such as "Buy chicken and cabbage at supermarket A, and buy cooking oil at supermarket B."

[1580] Step 10:

[1581] The device searches for recipe information on the Internet based on the suggested food list, for example, "recipes using chicken and cabbage."

[1582] Step 11:

[1583] The server analyzes the recipe information received from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[1584] Step 12:

[1585] The device then presents the generated menu plan and optimal food list to the user as a final proposal, along with a food shopping list and recipes.

[1586] Step 13:

[1587] The user checks the presented menu and food list and makes any necessary changes, such as "increase the amount of cabbage" or "switch to a different brand of soy sauce."

[1588] Step 14:

[1589] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the shopping list.

[1590] In this way, the system provides users with efficient and economical food purchasing and meal planning suggestions.

[1591] Example 1

[1592] 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."

[1593] Conventional food purchasing and menu planning systems have difficulty collecting price data from multiple stores and providing optimal purchasing patterns and menu plans based on that data. Furthermore, customizing lists that take into account the user's food preferences and allergy information, and finalizing the purchase list, are cumbersome, making them difficult to use for users.

[1594] 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.

[1595] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for acquiring user requests and generating a food list, means for comparing the collected price data with the necessary food list to search for the cheapest combination, means for presenting the searched cheapest combination to the user, means for displaying the presented cheapest purchasing pattern, means for searching the Internet for cooking recipes based on the generated ingredient list, means for generating an optimal menu plan for the user, means for the user to finalize the shopping list, and means for customizing the necessary food list taking into account the user's ingredient preferences and allergy information, thereby enabling the user to purchase foods efficiently and determine the optimal menu.

[1596] "Price data" is price information for food products offered by multiple stores.

[1597] "Database" means an information storage system for storing and updating collected pricing data.

[1598] "User requirements" are requests or conditions entered by a user for food purchases or menu decisions.

[1599] A "food list" is a list of foods that need to be purchased, generated based on a user's request.

[1600] The "cheapest purchasing pattern" is information that shows the cheapest combination by comparing the collected price data with the list of necessary foods.

[1601] A "cooking recipe" is a set of instructions that describes how to cook a dish using specific ingredients.

[1602] A "menu plan" is a combination of dishes suggested for multiple days or meals.

[1603] The "final purchase list" is a list of foods that should be finally purchased, confirmed after the user has confirmed and corrected them.

[1604] "User preference and allergy information" refers to information about whether a user likes or avoids certain ingredients.

[1605] This invention is a system that allows users to determine optimal food purchases and menus based on price data collected from multiple stores. This system is composed of three entities: a server, a terminal, and a user.

[1606] server

[1607] The server obtains price data from multiple affiliated stores via API, and stores and updates it in a database. This process is performed periodically to keep price information up to date. For example, the server sends a request to the APIs of "Store A" and "Store B" to obtain the latest food price data. The obtained data is stored in the database as "chicken 500 yen" and "cabbage 150 yen." The server then generates a list of necessary foods based on the user's requests, and generates optimal purchasing patterns and menu suggestions.

[1608] Terminal

[1609] The terminal is responsible for interfacing with the user. It receives requests from the user and presents the generated list of necessary foods to the user. It also accepts corrections as necessary and finalizes the final shopping list. For example, if a user inputs a request for "dinner for a family of four," the terminal will generate and display a list of necessary foods (e.g., "chicken," "cabbage," and "soy sauce") based on this request.

[1610] User

[1611] The user inputs requests for food purchases and menu decisions, and makes a final decision based on the information presented. The user inputs requests such as "weekly menu" or "dinner menu" through the terminal. This request may also include information on food preferences and allergies. The user checks the proposed menu and food list, and makes any necessary corrections to finalize the shopping list. For example, the user makes a correction on the terminal, such as "increase the amount of cabbage," and then finalizes the revised shopping list.

[1612] Specific system configuration and operation example

[1613] 1. The server sends a request to the APIs of "Store A" and "Store B" to obtain the latest food price data. The obtained data is stored in the database as "chicken 500 yen" and "cabbage 150 yen," for example.

[1614] 2. The user inputs requests such as "weekly menu" or "dinner menu" through the terminal.

[1615] 3. The device receives the user's request and generates a list of required foods, such as "chicken," "cabbage," and "soy sauce."

[1616] 4. The price data collected by the server is compared with the list of necessary foods generated by the terminal to find the cheapest combination.

[1617] 5. The device presents the user with the optimal purchasing pattern sent from the server, including specific advice on which stores to purchase which foods.

[1618] 6. The device searches for cooking recipes on the Internet based on the optimal food list and suggests a menu plan that takes into account the user's preferences and allergy information.

[1619] 7. The user reviews the proposed menu and food list and makes any necessary modifications. After modifications, the final shopping list is confirmed.

[1620] Prompt Sentence Examples

[1621] "Create a weekly menu for a family of four. Get ingredient price data from this API. Generate the optimal menu plan and shopping list, taking into account the lowest price for each ingredient."

[1622] In this way, the system provides users with efficient and economical food purchasing and menu suggestions. The above is a specific embodiment for carrying out the present invention.

[1623] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1624] Step 1:

[1625] Price data collection and updates

[1626] Input: Store API endpoint

[1627] Processing: The server retrieves price data from multiple partner stores via API. This is done periodically to update the price data.

[1628] Specific operation: The server sends requests to the API endpoints of "Store A" and "Store B" to obtain new price data. The obtained data is saved in the database as "chicken 500 yen" and "cabbage 150 yen," etc.

[1629] Output: A database containing updated price data

[1630] Step 2:

[1631] Get the user request

[1632] Input: A request entered by the user through the device (e.g., "weekly menu" or "dinner menu")

[1633] Processing: The device receives the user's request and analyzes the request content.

[1634] How it works: A user enters a request for "dinner for a family of four" into a smartphone app. The device receives the request and analyzes the details.

[1635] Output: Parsed user request data

[1636] Step 3:

[1637] Generate a list of food needs

[1638] Input: Parsed user request data

[1639] Processing: The device generates a list of required foods based on the content of the request.

[1640] Specific operation: The device analyzes the request content and generates a list of foods needed for "dinner for a family of four" (e.g., "chicken," "cabbage," and "soy sauce") and saves the list of needed foods in local storage.

[1641] Output: Generated food needs list

[1642] Step 4:

[1643] Finding optimal purchasing patterns

[1644] Input: Collected price data and generated food needs list

[1645] Processing: The server compares the collected price data with the list of required foods and searches for the cheapest combination.

[1646] How it works: The server compares the list of food items needed with the price data in the database and runs an algorithm to find the cheapest combination. For example, it calculates the cheapest combination as "chicken at store A for 500 yen, cabbage at store A for 150 yen, and soy sauce at store B for 200 yen."

[1647] Output: lowest price purchase pattern data

[1648] Step 5:

[1649] Showing the cheapest purchase pattern

[1650] Input: lowest price purchase pattern data

[1651] Processing: The terminal presents the lowest price purchase pattern sent from the server to the user.

[1652] Specific operation: The terminal receives the lowest price purchasing pattern data and displays specific instructions to the user, such as "purchase chicken and cabbage from store A, and purchase oil from store B."

[1653] Output: The cheapest purchase pattern presented to the user

[1654] Step 6:

[1655] Find recipes and generate meal plans

[1656] Input: Generated food requirements list, user preferences, and allergy information

[1657] Processing: The device searches for cooking recipes on the Internet based on the optimal food list. The server analyzes the recipe information and generates the optimal menu plan.

[1658] Specific operation: The device searches the internet for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[1659] Output: A suggested meal plan for the user

[1660] Step 7:

[1661] Final purchase list confirmation

[1662] Input: Suggested menu and food list, user modifications

[1663] Processing: The user reviews the proposed menu and food list and makes any necessary adjustments. The final shopping list is confirmed.

[1664] Specific operation: The user makes modifications on the device, such as "increase the amount of cabbage." After the modifications are complete, the final shopping list is confirmed and displayed to the user.

[1665] Output: Final purchase list confirmed

[1666] (Application example 1)

[1667] 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."

[1668] Conventional food purchasing and menu suggestion systems require users to spend a lot of time and effort to purchase the cheapest foods. Furthermore, they do not provide optimal suggestions for food lists and recipes that meet the user's needs, making it difficult to make efficient purchases and decide on menus. Another problem is that they do not adequately customize the system to accommodate user requests, food preferences, or allergies.

[1669] 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.

[1670] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a list of necessary foods based on a user's request, means for comparing the collected price data with the list of necessary foods to search for the cheapest combination, means for presenting the searched cheapest combination to the user, means for acquiring the user's request and displaying it to the user via a smartphone or smart glasses, and means for accepting user corrections and presenting a final purchasing route, thereby enabling the user to purchase food efficiently and economically and quickly decide on an appropriate menu.

[1671] A "means for collecting food price data from multiple stores" is a method or device for collecting food price information from different points of sale.

[1672] "Means for storing and updating collected price data in a database" refers to a storage device or system for storing the obtained price information, and has the function of updating the information to the latest version at any time.

[1673] The "means for generating a list of required foods based on a user's request" refers to a method or device for creating a list of ingredients to be purchased based on a user's request.

[1674] "Means for matching collected price data with a list of required foods to find the cheapest combination" refers to a method or device that compares collected price information with a generated list of ingredients to find the most affordable combination.

[1675] "Means for presenting the cheapest combination found to the user" refers to a method or device for informing the user of the cheapest combination found.

[1676] "Means for acquiring user requests and displaying them to the user through a smartphone or smart glasses" refers to a method or apparatus for collecting purchase requests from users and displaying the information via a smart device.

[1677] "Means for accepting user corrections and presenting a final shopping route" refers to a method or device for accepting corrections from a user and presenting a final shopping route or plan after incorporating the corrections.

[1678] A "means for retrieving recipes from a presented food list" refers to a method or device for finding cooking methods based on a proposed list of ingredients.

[1679] "Means for generating a menu plan optimal for the user from the searched recipes" refers to a method or device for creating a menu plan optimal for the user using the cooking methods found.

[1680] "Means for guiding the optimal shopping route in a store" refers to a method or device for guiding the route to buy the necessary ingredients most efficiently in the store.

[1681] "Means for customizing the required food list taking into account the user's food preferences and allergy information" refers to a method or device for individually adjusting the food list based on the user's preferences and allergy information.

[1682] "Means for optimizing recipe candidates using a generative AI model" refers to a method or device that uses artificial intelligence to suggest the best cooking method and efficiently select those candidates.

[1683] "Means for modifying and optimizing generated recipe candidates based on user request prompts" refers to a method or device for modifying and optimizing recipes suggested by AI in accordance with the specific requests of users.

[1684] This invention is a system that allows users to purchase food efficiently and economically and determine optimal menus based on price data collected from multiple stores. This system is composed of a server, terminals, and users.

[1685] Server Features

[1686] Price data collection, storage and updating

[1687] The server periodically collects food price data through APIs from multiple stores and stores it in a database. This stored data is always updated. This function on the server is essential for efficiently managing price information and providing it to users. A Python program is implemented on the server to retrieve store price data through RESTful APIs.

[1688] Finding optimal purchasing patterns

[1689] The server compares the collected price data with the food list generated by the device to find the cheapest combination, which allows the user to create an economical shopping plan. This search algorithm compares the price data with the food list to find the lowest-cost combination.

[1690] Analyzing recipe data and generating menu plans

[1691] The server analyzes the recipe information sent from the device and generates the optimal menu plan for the user. This function selects the most suitable recipes from multiple recipe databases and proposes menus that take into account the user's food preferences and allergies.

[1692] Device Features

[1693] Providing a user interface and receiving requests

[1694] The device, such as a smartphone or smart glasses, directly interfaces with the user, acquiring the user's request and generating a list of food items based on the request. The user can generate a list of food items by inputting a request such as "dinner tonight."

[1695] Providing optimal purchasing routes

[1696] The device presents the user with optimal purchasing patterns sent from the server and guides them to an efficient shopping route within the store based on this. This shopping route is visually displayed on the user's smartphone or smart glasses, helping to ensure a stress-free shopping experience.

[1697] Menu and recipe suggestions

[1698] The device searches the internet for recipes based on the optimal food list and suggests them to the user. For example, a search for "recipes using chicken and cabbage" will result in recipes such as "stir-fried chicken and cabbage." A generative AI model is also used to filter recipes based on the user's preferences and allergies.

[1699] User Roles

[1700] Entering and Modifying Requests

[1701] Users input their requests through their smartphones or smart glasses, review the food list and meal plan provided, and can modify the list or plan as needed to finalize the shopping list. This process allows users to efficiently decide on the food purchases and meal plans that best suit their needs.

[1702] Examples of concrete examples and prompts

[1703] A concrete example is a system where a user requests the best recipes for a "dinner for a family of four." Based on this request, the server collects price data and the device displays the best food list and purchasing patterns.

[1704] Example prompt sentence:

[1705] "Suggest a recipe for a perfect dinner for a family of four. The ingredients are chicken, cabbage, and soy sauce."

[1706] In this way, the invention realizes a highly functional system that supports efficient food purchasing and menu planning decisions.

[1707] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1708] Step 1: The server collects price data from multiple stores and saves and updates it in a database.

[1709] Input: Store API URL list

[1710] Processing: The server accesses each store's API to obtain the latest food price data. It parses the obtained data in JSON format and saves it in the database. This process is performed periodically to keep the data up to date.

[1711] Output: A database containing the latest food price data

[1712] Step 2: The user enters a request through the terminal.

[1713] Input: User request (e.g., "Dinner tonight" or "Dinner for a family of four")

[1714] Processing: The terminal receives the user's request through the interface. This request may include food preferences and allergy information. The terminal then sends the input request to the server.

[1715] Output: User request data

[1716] Step 3: The server generates a list of required foods based on the user's request.

[1717] Input: User request data

[1718] Processing: The server analyzes the input request data and generates a food list based on it. The generated food list includes the names of the necessary ingredients, such as "chicken," "cabbage," and "soy sauce."

[1719] Output: Generated food list

[1720] Step 4: The server compares the collected price data with the food list to find the cheapest combination.

[1721] Input: Generated food list, latest price data

[1722] Processing: The server searches the price database to find the cheapest store and price for each ingredient, and presents this to the user as the cheapest combination.

[1723] Output: Data for the cheapest combination

[1724] Step 5: The device presents the cheapest combination to the user.

[1725] Input: Data for the cheapest combination

[1726] Processing: The device receives the data sent from the server and presents it visually to the user, such as on the screen of a smartphone or smart glasses.

[1727] Output: The cheapest combination presented to the user

[1728] Step 6: The device guides the user to the optimal shopping route within the store

[1729] Input: Data on the cheapest combination, data on in-store placement

[1730] Processing: Based on the information on the cheapest combination displayed on the device, the device calculates a route that will allow the user to purchase the necessary ingredients by going around the store efficiently. The device then visually guides the user along this route.

[1731] Output: The purchasing route presented to the user

[1732] Step 7: The device searches for recipes based on the food list and suggests them to the user.

[1733] Input: Generated food list

[1734] Processing: The device searches the internet for recipes that match the food list. It uses a generative AI model to select the best recipes and suggest them to the user. An example might be "Stir-fried chicken and cabbage."

[1735] Output: Suggested recipe

[1736] Step 8: User reviews, modifies, and finally confirms the suggested recipe and food list

[1737] Input: Suggested recipes, generated food list

[1738] Processing: The user checks the suggested recipes and food list through the terminal and makes any necessary corrections. Once corrections are complete, the final shopping list is confirmed.

[1739] Output: Final purchase list confirmed

[1740] Example prompt sentence:

[1741] "Suggest a recipe for a perfect dinner for a family of four. The ingredients are chicken, cabbage, and soy sauce."

[1742] 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.

[1743] This invention relates to a system that helps users efficiently and economically purchase food and create optimal menus using that food. The system also incorporates an emotion engine that recognizes the user's emotions and adjusts the food list and menu accordingly.

[1744] System configuration

[1745] This system has the following components:

[1746] 1. Server

[1747] It is the central component for implementing the present invention and is responsible for collecting, storing and updating price data.

[1748] Generate optimal purchasing patterns and menu suggestions based on user requirements.

[1749] It integrates an emotion engine to adjust food lists and menus based on user emotion recognition.

[1750] 2. Terminal

[1751] It is a device for interfacing with the user, taking requests, displaying results, and accepting modifications.

[1752] Generate a list of food items you need and search for recipes.

[1753] 3. Users

[1754] Enter food purchasing and menu planning requests and make final decisions based on the information provided.

[1755] Emotional data is input through the terminal and this information is used by the system.

[1756] Program processing overview

[1757] Server collects and updates price data

[1758] The server periodically retrieves price data from multiple partner stores via API and stores it in a database, ensuring that the latest price information is always available.

[1759] Examples:

[1760] The server sends a request to the APIs of "Store A" and "Store B" to retrieve the latest food price data. The retrieved data is stored in a database as "chicken 500 yen" and "cabbage 150 yen," for example.

[1761] Get user requests and generate a food list

[1762] Users input requests such as "weekly menu" or "dinner menu" through a terminal. These requests can also include food preferences, allergy information, and emotional data.

[1763] The device receives this request and generates a list of food needs based on the user's requirements, such as "chicken," "cabbage," and "soy sauce."

[1764] Examples:

[1765] The user inputs a request such as "dinner for a family of four," and the device generates a list of required foods (e.g., "chicken," "cabbage," and "soy sauce").

[1766] Searching for and suggesting optimal purchasing patterns

[1767] The server compares the collected price data with the necessary food list generated by the terminal to find the cheapest purchasing pattern.

[1768] The terminal presents the optimal purchasing pattern sent from the server to the user. For example, advice such as "Buy chicken and cabbage at supermarket A, and buy sesame oil at supermarket B."

[1769] Examples:

[1770] The server calculates the cheapest combination as "chicken for 500 yen at store A, cabbage for 150 yen at store A, and soy sauce for 200 yen at store B." The terminal displays a suggestion to the user to "purchase chicken and cabbage from store A and soy sauce from store B."

[1771] Recipe search and meal plan suggestions

[1772] The device searches the internet for recipes based on the best food list, e.g., "Recipes using chicken and cabbage."

[1773] The server analyzes the recipe information sent from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[1774] Examples:

[1775] The device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes these and suggests them to the user as "weekly dinner menu suggestions."

[1776] Final purchase list confirmation

[1777] The user can check the proposed menu and food list through the device and make any necessary changes, such as "increase the amount of cabbage" or "switch to a different brand of soy sauce."

[1778] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the purchase list.

[1779] Use of emotion engine

[1780] The emotion engine analyzes emotional data entered by the user into the device or acquired through sensors, and this data is used to adjust food lists and meal plans.

[1781] Examples:

[1782] The server uses an emotion engine to suggest relaxing recipes such as "chicken and cabbage soup" to reduce the user's stress level.

[1783] The device uses an emotion engine to search for and suggest recipes that better reflect the user's preferences.

[1784] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors the system to the user's emotions and preferences, providing a more personalized experience.

[1785] The processing flow will be explained below.

[1786] Step 1:

[1787] The terminal displays a list of affiliated stores to the user and prompts the user to select which store they would like to collect price information from.

[1788] Step 2:

[1789] The user selects the store of interest and presses the "Next" button.

[1790] Step 3:

[1791] The server sends a request to the API of the store selected by the user to retrieve the latest food price data.

[1792] Step 4:

[1793] The server saves the acquired price data in the database and updates existing data, such as "chicken 500 yen" and "cabbage 150 yen."

[1794] Step 5:

[1795] Users can use the terminal to input requests such as "weekly menu" or "dinner menu." They can also input food preferences, allergy information, and emotional data.

[1796] Step 6:

[1797] The device receives the user's request and generates a list of the required foods, such as "chicken," "cabbage," and "soy sauce."

[1798] Step 7:

[1799] The server matches the price data collected in step 4 with the list of required foods generated in step 6 to find the cheapest combination.

[1800] Step 8:

[1801] The server identifies the cheapest purchase pattern and transmits the corresponding combination to the terminal.

[1802] Step 9:

[1803] The device presents the optimal purchasing pattern sent from the server to the user, for example, displaying advice such as "Buy chicken and cabbage at store A and sesame oil at store B."

[1804] Step 10:

[1805] The device searches for recipe information on the Internet based on the suggested food list, for example, "recipes using chicken and cabbage."

[1806] Step 11:

[1807] The server analyzes the recipe information received from the device and generates a menu plan that best suits the user's requirements, such as "stir-fried chicken and cabbage," "simmered chicken," or "cabbage and chicken salad."

[1808] Step 12:

[1809] The device then presents the generated menu plan and optimal food list to the user as a final proposal, along with a food shopping list and recipes.

[1810] Step 13:

[1811] The user can review the proposed menu and food list and make any necessary changes, such as adding more cabbage or switching to a different brand of soy sauce.

[1812] Step 14:

[1813] Once the user confirms the modifications, the terminal transmits the information to the server and finalizes the purchase list.

[1814] Step 15:

[1815] The user inputs emotional data into the terminal, which includes numerical values ​​and options that indicate the user's emotional state.

[1816] Step 16:

[1817] The terminal acquires the emotion data and transmits it to the server.

[1818] Step 17:

[1819] The server uses an emotion engine to analyze the emotional data and adjust the food list and menu based on the user's emotional state.

[1820] Examples:

[1821] If the user is feeling stressed, the server uses its emotion engine to suggest "chicken and cabbage soup, which has a relaxing effect."

[1822] If a user is feeling low in energy, the app suggests a "high-protein chicken dish" to replenish their energy.

[1823] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors them to their emotions and preferences, providing a more personalized experience.

[1824] Example 2

[1825] 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."

[1826] Conventional food purchasing systems make it difficult for users to purchase food efficiently and economically, and they also have problems with reducing user satisfaction because food lists and menus are not tailored to the user's individual emotional state or preferences.

[1827] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1828] In this invention, the server includes means for collecting food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a required food list based on a user's request, means for comparing the collected price data with the required food list to search for the cheapest combination, means for presenting the searched cheapest combination to the user, and an emotion engine for analyzing emotion data entered by the user. This enables users to make efficient and economical food purchases and also makes it possible to provide food lists and menus that correspond to each user's emotional state.

[1829] "Stores" are multiple retail outlets or commercial facilities that provide food price data.

[1830] "Price data" refers to price information for food products sold at each store.

[1831] "Database" means an information management system for storing collected price data and user information and updating it as necessary.

[1832] "User Requests" refers to the preferences or conditions entered by the user in order to receive food list or menu suggestions.

[1833] A "food list" is a list of food needs generated based on a user's request.

[1834] The "emotion engine" is the part of the system that analyzes the emotional data entered by the user and adjusts the food list and menu accordingly.

[1835] A "cooking recipe" is an instruction manual that describes cooking methods and steps using specific ingredients.

[1836] A "menu plan" is a plan of meal combinations and cooking sequences suggested to the user.

[1837] "Final Shopping List" is the final list for purchasing food items that is confirmed after the user makes any modifications.

[1838] "Preferences" refers to the ingredients and types of dishes that a user particularly likes.

[1839] "Allergy information" refers to information about a user's allergic reaction to food ingredients or substances.

[1840] "Emotional data" is information entered by a user about their current emotional state.

[1841] "Suggestion" refers to the act of the system showing the user optimal food purchasing patterns and menu plans.

[1842] This invention relates to a system that helps users efficiently and economically purchase food and create optimal menus using that food. The system also incorporates an emotion engine that recognizes the user's emotions and adjusts the food list and menu accordingly.

[1843] System configuration

[1844] This system has the following components:

[1845] 1. Server

[1846] The server is the central component for implementing this invention, and is responsible for collecting, storing, updating, and processing price data. It periodically retrieves price data from multiple stores through APIs and stores it in a database. It also compares the collected price data with the user's food list based on their request to find the cheapest combination. It also integrates an emotion engine to adjust the food list and menu based on the user's emotion recognition.

[1847] Examples of hardware and software: For the server hardware, commercial cloud servers (e.g., AWS, Google Cloud) can be used. For API communication and database operations, programming languages ​​such as Python or Java and SQL databases (e.g., MySQL, PostgreSQL) are used.

[1848] 2. Terminal

[1849] The terminal is a device that interfaces with the user, receiving requests, displaying results, and accepting corrections. Based on the information entered by the user, it generates a list of necessary foods and searches for cooking recipes on the Internet. It also uses an emotion engine to make suggestions based on the user's emotions.

[1850] Examples of hardware and software: Devices can include smartphones, tablets, and PCs. Front-end frameworks such as React and Angular can be used to develop the user interface.

[1851] 3. Users

[1852] The user inputs requests for food purchases and menu decisions through the terminal, and then makes a final decision based on the displayed information. In addition, the user inputs their own emotional data, which is used by the system.

[1853] Program processing overview

[1854] Price data collection and updates

[1855] Server: Periodically sends requests to multiple partner store APIs to retrieve price data and store it in a database.

[1856] Example: The server obtains price data for "chicken 500 yen" and "cabbage 150 yen" from "Supermarket A" and saves it in the database.

[1857] Get user requests and generate a food list

[1858] User: Input requests such as "weekly menu" or "dinner menu" through the device. They can also include food preferences, allergy information, and emotional data.

[1859] Terminal: Receives this request and generates a food list based on the user's requirements.

[1860] Example: A user inputs a request for "dinner for a family of four," and the device generates a food list of "chicken," "cabbage," and "soy sauce."

[1861] Searching for and suggesting optimal purchasing patterns

[1862] Server: Matches the collected price data with the food list generated by the device to find the cheapest purchasing pattern.

[1863] Device: Presents the optimal purchasing pattern sent from the server to the user.

[1864] Example: The server calculates that "chicken is 500 yen at store A, cabbage is 150 yen at store A, and soy sauce is 200 yen at store B," and the terminal suggests, "Purchase chicken and cabbage at store A, and soy sauce at store B."

[1865] Recipe search and meal plan suggestions

[1866] Device: Search the internet for cooking recipes based on a list of the best foods.

[1867] Server: Analyzes the submitted recipe information and generates a menu plan that best suits the user's requirements.

[1868] Example: A device searches for "recipe using chicken and cabbage" and finds recipes such as "stir-fried chicken and cabbage." The server analyzes this and suggests it to the user as a "weekly dinner menu plan."

[1869] Final purchase list confirmation

[1870] User: Review the suggested menu and food list and modify as needed.

[1871] Device: Once the modifications are confirmed, the information is sent to the server and the final purchase list is confirmed.

[1872] Example: A user adds the modification "increase the amount of cabbage" and finalizes the final list.

[1873] Use of emotion engine

[1874] Emotion engine: Analyzes the emotional data entered by the user into the device and uses it to adjust food lists and menus.

[1875] Server: Uses an emotion engine to suggest menus based on the user's emotional state.

[1876] Example: When a user types "I'm feeling stressed," an emotion engine analyzes that information and the server suggests "chicken and cabbage soup."

[1877] Example prompts for generative AI models

[1878] "Please suggest a weekly menu for a family of four. The user's favorite ingredients are chicken and cabbage, and they have no allergies. Also, the user is currently feeling stressed, so please take that into consideration when creating a menu."

[1879] In this way, the system not only provides users with efficient and economical food shopping and meal suggestions, but also tailors them to the user's emotions and preferences, providing a more personalized experience.

[1880] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1881] Specific processing steps of the system

[1882] Step 1: Collect price data

[1883] The server sends requests to the APIs of multiple partner stores to collect the latest food price data.

[1884] Input: API endpoint of partner store

[1885] Data processing: The server generates an API request and sends it to each store's API.

[1886] Output: Price data returned from each store

[1887] Specific operation: The server sends an API request to "Store A" and obtains price data for "Chicken 500 yen" and "Cabbage 150 yen."

[1888] Step 2: Saving and updating price data

[1889] The server stores the acquired price data in a database and updates existing data.

[1890] Input: Collected price data

[1891] Data Processing: The server analyzes the price data and updates the existing data in the database.

[1892] Output: Updated database

[1893] Specific operation: Save or update the data "Chicken 500 yen" and "Cabbage 150 yen" in the price table in the database.

[1894] Step 3: Get the user request

[1895] The user inputs requests such as "weekly menu" or "dinner menu" through the terminal.

[1896] Input: User request (e.g. "Dinner for a family of four")

[1897] Data processing: The terminal receives the request and displays it on the user interface.

[1898] Output: User input information

[1899] Specific operation: A user inputs a request into a terminal: "Dinner for a family of four."

[1900] Step 4: Generate a food list

[1901] The terminal generates a list of required foods based on the user's request.

[1902] Input: User request

[1903] Data processing: Applying a list generation algorithm based on user requests.

[1904] Output: List of food items needed

[1905] Specific operation: Based on the information about "dinner for a family of four," generate a list of "chicken," "cabbage," and "soy sauce."

[1906] Step 5: Finding optimal purchasing patterns

[1907] The server compares the collected price data with the generated food list to find the cheapest purchasing patterns.

[1908] Input: Price data, generated food list

[1909] Data processing: Compare the price data with the food list and use a matching algorithm to calculate the lowest price.

[1910] Output: Optimal purchasing pattern

[1911] Specific operation: Calculate "chicken is 500 yen at store A, cabbage is 150 yen at store A, and soy sauce is 200 yen at store B."

[1912] Step 6: Show purchasing patterns

[1913] The terminal presents the optimal purchasing pattern sent from the server to the user.

[1914] Input: Optimal purchase pattern

[1915] Data processing: Format purchase patterns so they can be displayed on the device.

[1916] Output: Purchase patterns presented to the user

[1917] Specific operation: The terminal displays "Purchase chicken and cabbage at store A, and purchase cooking oil at store B."

[1918] Step 7: Find a recipe

[1919] The device searches the internet for cooking recipes based on a list of optimal foods.

[1920] Input: Optimal Food List

[1921] Data processing: A recipe search engine is used to search recipe databases on the Internet.

[1922] Output: Related recipes

[1923] Action: Search for "chicken and cabbage recipes" and find "chicken and cabbage stir fry."

[1924] Step 8: Menu plan suggestions

[1925] The server analyzes the recipe information sent from the terminal and generates a menu plan that best suits the user's requirements.

[1926] Input: Cooking recipe

[1927] Data processing: Applying an algorithm to analyze recipe information and generate a menu plan.

[1928] Output: Menu plan

[1929] Specific operation: Propose to the user "weekly dinner menu plan."

[1930] Step 9: Finalize the purchase list

[1931] The user reviews the proposed menu and food list and modifies it as needed.

[1932] Input: Suggested menu and food list

[1933] Data processing: Accept user corrections and recalculate.

[1934] Output: Final purchase list

[1935] Specific operation: The user inputs the correction "increase the amount of cabbage" into the terminal and confirms the final list.

[1936] Step 10: Use the Emotion Engine

[1937] The emotion engine analyzes the emotional data entered by the user into the device.

[1938] Input: Emotion data

[1939] Data processing: Analyze the data using sentiment analysis algorithms to generate results.

[1940] Output: Food list and menu adjustments based on analysis results

[1941] Specific behavior: Based on the information that the user is "feeling stressed," the server suggests "chicken and cabbage soup."

[1942] Example prompts for generative AI models

[1943] "Please suggest a weekly menu for a family of four. The user's favorite ingredients are chicken and cabbage, and they have no allergies. Also, the user is currently feeling stressed, so please take that into consideration when creating a menu."

[1944] (Application example 2)

[1945] 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."

[1946] Conventional food purchasing and menu suggestion systems have the problem of not taking into account the user's emotions and being unable to provide personalized menus based on emotions. This makes it difficult for users to choose meals that suit their current psychological state and emotions, which can result in a poor shopping experience. Additionally, suggestions based solely on ingredient price information are difficult to meet the user's psychological and emotional needs.

[1947] The identification process by the identification 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 food price data from multiple stores, means for saving and updating the collected price data in a database, means for generating a required food list based on a user's request, and means for recognizing the user's emotional data and adjusting the food list and menu based on that data. This makes it possible to provide an optimal food list and menu based on the user's emotional and psychological state.

[1948] "Multiple stores" means a collection of multiple different stores, each of which may offer different prices and inventory information.

[1949] "Food Price Data" refers to information about the prices of food products sold at each store, including details such as product name, price, and availability.

[1950] "Database" means a set of systems and structures for storing and managing collected price data, allowing for rapid retrieval and updating of the data.

[1951] "User Requests" refers to the preferences and requirements that a user inputs into the system regarding food lists and menus, including the type of menu, number of people, allergy information, etc.

[1952] "Needed Food List" refers to a list of foods generated based on a user's request, which indicates specific food items that the user should purchase.

[1953] "User emotional data" means data that indicates a user's current mental state or emotion, including stress level, enjoyment, sadness, fatigue, etc.

[1954] "Menus" refers to cooking plans and suggestions based on specific foods that assist users in planning their daily meals.

[1955] "Server" refers to a computer system that is the central component of a system and is responsible for collecting, storing, processing, and providing data.

[1956] This invention is a system that helps users efficiently and economically purchase food and create optimal meals. The system integrates an emotion engine that recognizes the user's emotions and adjusts the food list and meal plan accordingly.

[1957] System configuration

[1958] This system has the following components:

[1959] 1. Server

[1960] This is the central component for implementing the present invention, and collects food price data from multiple stores.

[1961] The collected price data is stored in a database and updated regularly.

[1962] Generate optimal food lists and meal suggestions based on user requirements.

[1963] It uses an emotion engine to adjust food lists and menus based on the user's emotional data.

[1964] 2. Terminal

[1965] It is a device for interfacing with the user, and a smartphone is generally used.

[1966] The system uses the smartphone's camera and microphone to collect user emotional data and transmit it to a server.

[1967] The food list and menu suggestions sent from the server are displayed to the user.

[1968] 3. Users

[1969] Requests for food purchases and menu decisions are entered into the terminal, and emotional data is provided through the terminal.

[1970] Based on the information provided, review the food list and menu and finalize the shopping list.

[1971] Program processing overview

[1972] Server collects and updates price data

[1973] The server periodically retrieves price data from multiple local stores via API, and the retrieved data is stored in a database, always maintaining the latest price information.

[1974] Get user requests and generate a food list

[1975] Users input requests such as "dinner menu" or "weekly menu" through the device, and emotional data is also collected, and a list of the necessary foods is generated based on this information.

[1976] Searching for and suggesting optimal purchasing patterns

[1977] The server compares the collected price data with the necessary food list generated by the device to search for the cheapest purchasing pattern, and the search results are sent to the device and presented to the user.

[1978] Recipe search and meal plan suggestions

[1979] The device searches for cooking recipes on the Internet based on the optimal food list, and the server analyzes the recipe information and generates the optimal menu plan for the user based on the emotion data.

[1980] Final purchase list confirmation

[1981] The user can check the proposed menu and food list through the device and make any necessary changes. After confirming the changes, the device sends the information to the server, and the final shopping list is confirmed.

[1982] Examples of concrete examples and prompts

[1983] The server uses an emotion engine to recognize the user's emotions and adjust the food list and meal plan accordingly: for example, if the user is feeling stressed, it can suggest a meal plan that includes ingredients that have a relaxing effect.

[1984] A specific example is the following prompt:

[1985] "Can you suggest a dinner menu for my family of four? I'm feeling stressed right now, so I'd like a recipe with relaxing ingredients."

[1986] "I've been feeling a bit tired lately, so please tell me some ingredients and recipes that will cheer me up."

[1987] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1988] Step 1:

[1989] The server periodically retrieves food price data from multiple stores via API. In this process, the server sends an API request and receives the latest food price data from each store in response. The price data includes product name, price, and stock information. The retrieved data is stored in a database, and existing data is updated. The input is the API request, and the output is the latest price data.

[1990] Step 2:

[1991] The user inputs a request to generate a food list through the device. The request includes information on the type of meal and the number of people. The device also collects the user's emotional data using the smartphone's camera and microphone. The input is the user's request and emotional data, and the output is sending the request to the server.

[1992] Step 3:

[1993] The server receives the request sent from the terminal and generates a list of necessary foods based on the user's request. At this time, the emotion engine analyzes the user's emotion data and adjusts the food list based on the request. The input is the user's request and emotion data, and the output is the necessary food list.

[1994] Step 4:

[1995] The server compares the collected price data with the generated food list to find the cheapest purchasing pattern. The optimal purchasing pattern is calculated based on the price and inventory information at each store. The input is the price data and the food list, and the output is the cheapest purchasing pattern.

[1996] Step 5:

[1997] The server sends the cheapest shopping pattern it has found to the terminal to present to the user. The terminal displays the optimal shopping pattern to the user and advises them on which store to purchase the necessary food. The input is the cheapest shopping pattern, and the output is the information displayed on the user's terminal.

[1998] Step 6:

[1999] The device searches the internet for cooking recipes based on the optimal food list. The searched recipes are selected based on the user's emotional data and other settings and suggested to the user. The input is the food list, and the output is the suggested recipe.

[2000] Step 7:

[2001] The server analyzes the recipe information sent from the device and generates a menu plan that best suits the user's requirements. Based on the emotional data, the server provides a menu plan that matches the user's psychological state. The input is recipe information, and the output is the optimal menu plan.

[2002] Step 8:

[2003] The user checks the proposed menu and food list through the terminal and makes any necessary changes. Once the user confirms the changes, the information is sent to the server. The input is the user's revised information, and the output is the final shopping list.

[2004] 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.

[2005] 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.

[2006] 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.

[2007] 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.

[2008] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2009] 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.

[2010] 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).

[2011] 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.

[2012] 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."

[2013] 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.

[2014] 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).

[2015] 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.

[2016] 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.

[2017] 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.

[2018] 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.

[2019] 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.

[2020] 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.

[2021] 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.

[2022] 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.

[2023] 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.

[2024] 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.

[2025] The following is further disclosed regarding the above embodiment.

[2026] (Claim 1)

[2027] a means for collecting food price data from multiple stores;

[2028] a means for storing and updating the collected price data in a database;

[2029] means for generating a list of required foods based on a user's request;

[2030] A way to match the collected price data with the list of food items you need and find the cheapest combination;

[2031] The system includes a means for presenting the cheapest combination found to the user.

[2032] (Claim 2)

[2033] A means for searching for cooking recipes from a presented food list;

[2034] A means for generating a meal plan that is optimal for the user from the searched recipes;

[2035] A means for presenting the generated menu plan and optimal food list to the user;

[2036] 10. The system of claim 1, further comprising means for accepting user modifications and finalizing the final purchase list.

[2037] (Claim 3)

[2038] 10. The system of claim 1, further comprising means for customizing the required food list taking into account the user's food preferences and allergy information.

[2039] "Example 1"

[2040] (Claim 1)

[2041] a means for collecting food price data from multiple stores;

[2042] a means for storing and updating the collected price data in a database;

[2043] means for generating a list of required foods based on a user's request;

[2044] a means for obtaining a user's request and generating a food list;

[2045] A way to match the collected price data with the list of food items you need and find the cheapest combination;

[2046] A means for presenting the cheapest combination found to the user;

[2047] a means for displaying the lowest offered purchase pattern;

[2048] A means for searching for cooking recipes on the Internet based on the generated ingredient list;

[2049] A means to generate a meal plan that is optimal for the user;

[2050] The system includes a means for the user to finalize the final shopping list.

[2051] (Claim 2)

[2052] A means for searching for cooking recipes from a presented food list;

[2053] A means for generating a meal plan that is optimal for the user from the searched recipes;

[2054] A means for presenting the generated menu plan and optimal food list to the user;

[2055] 10. The system of claim 1, further comprising means for accepting user modifications and finalizing the final purchase list.

[2056] (Claim 3)

[2057] 10. The system of claim 1, further comprising means for customizing the required food list taking into account the user's food preferences and allergy information.

[2058] "Application Example 1"

[2059] (Claim 1)

[2060] a means for collecting food price data from multiple stores;

[2061] a means for storing and updating the collected price data in a database;

[2062] means for generating a list of required foods based on a user's request;

[2063] A way to match the collected price data with the list of food items you need and find the cheapest combination;

[2064] A means for presenting the cheapest combination found to the user;

[2065] A means for obtaining user requests and displaying them to the user through a smartphone or smart glasses;

[2066] The system includes a means for accepting user corrections and presenting a final purchase route.

[2067] (Claim 2)

[2068] A means for searching for cooking recipes from a presented food list;

[2069] A means for generating a meal plan that is optimal for the user from the searched recipes;

[2070] A means for presenting the generated menu plan and optimal food list to the user;

[2071] A means to accept user modifications and finalize the final purchase list;

[2072] 2. The system according to claim 1, further comprising means for guiding the customer through an optimal shopping route within the store.

[2073] (Claim 3)

[2074] A way to customize the list of foods required, taking into account the user's food preferences and allergy information;

[2075] A means to optimize recipe candidates using generative AI models;

[2076] The system according to claim 1, further comprising means for modifying and optimizing the generated recipe candidates based on a user request prompt.

[2077] "Example 2: Combining Emotion Engines"

[2078] (Claim 1)

[2079] a means for collecting food price data from multiple stores;

[2080] a means for storing and updating the collected price data in a database;

[2081] means for generating a list of required foods based on a user's request;

[2082] A way to match the collected price data with the list of food items you need and find the cheapest combination;

[2083] A means for presenting the cheapest combination found to the user;

[2084] A system that includes an emotion engine that analyzes emotion data entered by a user.

[2085] (Claim 2)

[2086] A means for searching for cooking recipes from a presented food list;

[2087] A means for generating a meal plan that is optimal for the user from the searched recipes;

[2088] A means for presenting the generated menu plan and optimal food list to the user;

[2089] Includes a means to accept user modifications and finalize the final purchase list

[2090] 10. The system of claim 1.

[2091] (Claim 3)

[2092] A way to customize the list of foods required, taking into account the user's food preferences and allergy information;

[2093] Includes a means to adjust food lists and menus based on analyzed emotional data

[2094] 10. The system of claim 1.

[2095] "Application example 2 when combining emotion engines"

[2096] (Claim 1)

[2097] a means for collecting food price data from multiple stores;

[2098] a means for storing and updating the collected price data in a database;

[2099] means for generating a list of required foods based on a user's request;

[2100] A way to match the collected price data with the list of food items you need and find the cheapest combination;

[2101] A means of recognizing a user's emotional data and adjusting food lists and meals based on that data;

[2102] The system includes a means for presenting the cheapest combination found to the user.

[2103] (Claim 2)

[2104] A means for searching for cooking recipes from a presented food list;

[2105] A means for generating a meal plan that is optimal for the user from the searched recipes;

[2106] A means for presenting the generated menu plan and optimal food list to the user;

[2107] A means to accept user modifications and finalize the final purchase list;

[2108] 10. The system of claim 1, further comprising means for adjusting the recipe based on the user's emotional data.

[2109] (Claim 3)

[2110] A way to customize the list of foods required, taking into account the user's food preferences and allergy information;

[2111] 10. The system of claim 1, further comprising means for customizing food lists and menus based on user emotional data. [Explanation of symbols]

[2112] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting food price data from multiple stores; a means for storing and updating the collected price data in a database; means for generating a list of required foods based on a user's request; A way to match the collected price data with the list of food items you need and find the cheapest combination; and means for presenting the cheapest combination found to the user.

2. A means for searching for cooking recipes from a presented food list; A means for generating a meal plan that is optimal for the user from the searched recipes; A means for presenting the generated menu plan and optimal food list to the user; and means for accepting user modifications and finalizing the final purchase list.

3. 10. The system of claim 1, further comprising means for customizing the required food list taking into account the user's food preferences and allergy information.

Images