system

Abstract

Provide a system. [Solution] A means for users to input preferences, allergy information, family composition, and budget; a means for obtaining inventory and pricing information for nearby stores; A means for generating a menu using an AI algorithm based on the input information and acquired information; means for displaying the generated menu to a user; A system including:

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] For users who want to cook at home, planning a daily menu is a hassle, and it is difficult to easily enjoy a nutritionally balanced meal. Furthermore, obtaining the necessary ingredients before starting to cook requires the time and effort of checking inventory and price information at nearby stores. Therefore, there is a need for a way to efficiently plan a menu and obtain the appropriate ingredients. [Means for solving the problem]

[0005] The present invention is a system that includes a means for users to input preferences, allergy information, family composition, and budget, a means for acquiring inventory and price information from nearby stores, a means for generating menus using an AI algorithm based on the input and acquired information, and a means for displaying the generated menus to the user. Furthermore, by adding a means for learning from past search data and making optimal suggestions to the user, and a means for adding links to detailed recipe sites, the system provides users with efficient, nutritionally balanced menus and allows them to easily obtain the necessary ingredients.

[0006] A "user" is an individual or group that uses the system to receive menu suggestions.

[0007] "Preferences" refers to the tastes and preferences that a user has for specific ingredients or dishes.

[0008] "Allergy information" is information about specific ingredients or substances that a user wants to avoid consuming.

[0009] "Family composition" is information indicating the number of people in the user's household, their age groups, and the amount of food they need.

[0010] "Budget" refers to the amount a user can spend per meal.

[0011] "Store" refers to a store or supermarket designated by the user to purchase ingredients.

[0012] "Inventory information" is data regarding the current stock status of ingredients offered at a specified store.

[0013] "Price information" is data relating to the selling price of ingredients at the specified store.

[0014] An "AI algorithm" is a type of artificial intelligence technology used to generate optimal menus based on user preferences, budget, and store information.

[0015] A "menu" refers to a proposal of multiple dishes combined based on specific conditions.

[0016] "Display" refers to outputting the generated menu to a terminal in a form that allows the user to visually confirm it.

[0017] "Search data" refers to the user's past search history for dishes and ingredients within the system.

[0018] A "recipe site" refers to a website that provides detailed instructions on how to make a dish and the ingredients needed.

[0019] "Link" means a hypertext link that allows a user to access more information. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] MODE FOR CARRYING OUT THE INVENTION

[0042] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs their mood, preferences, allergy information, family composition, and budget for the day, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate the optimal menu. The generated menu is then displayed on the user's device.

[0043] Program processing and specific examples

[0044] 1. Enter your user information

[0045] The user launches the application and enters their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), budget (e.g., 2,000 yen), etc.

[0046] The entered information is sent to the server.

[0047] 2. Obtaining store information

[0048] The server obtains inventory and price information for nearby stores specified by the user through the API.

[0049] For example, store A's inventory information may be "chicken, spinach, and tofu available," and price information may be "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[0050] 3. Generating optimal menus

[0051] The server uses an AI algorithm to generate the optimal menu based on the user's input information and the acquired store information. The AI ​​algorithm also takes into account the user's past search data and suggests menus tailored to the user.

[0052] Specifically, the system generates dishes such as sauteed chicken and spinach and tofu miso soup.

[0053] 4. Menu display

[0054] The terminal displays the generated menu and its recipes to the user. For example, "Sauteed chicken and spinach" and "Miso soup with tofu" are displayed, and the respective recipes are provided.

[0055] Users who want to see more details about the recipe can click on the link to visit the recipe site.

[0056] This series of processes allows users to easily eliminate the hassle of planning menus and enjoy cooking at home. The system takes into account the user's individual needs and nutritional balance, and utilizes store inventory information to efficiently provide optimal menus. This is expected to have the effect of supporting healthy eating habits.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user launches the application and the home screen appears.

[0060] The user inputs their mood for the day (e.g., Japanese food, Western food), preferred ingredients (e.g., chicken), ingredients to avoid or allergy information (e.g., dairy products), family size (e.g., 2 people), and budget per meal (e.g., 2,000 yen).

[0061] The user presses the "Submit" button and the input data is sent to the server.

[0062] Step 2:

[0063] The server receives the user's input data and sends a request to the API of the specified nearby store to obtain inventory and price information.

[0064] The server receives the response from the store and obtains inventory and price information in JSON format.

[0065] Example: Obtain store A's inventory information "Chicken available, spinach available, tofu available" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen."

[0066] Step 3:

[0067] The server runs an AI algorithm based on the user's input information and the acquired store inventory and price information to generate the optimal menu.

[0068] The server also references past search data to suggest menus suitable for the user, such as sautéed chicken and spinach or tofu miso soup.

[0069] Step 4:

[0070] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[0071] The device receives the response from the server and displays the menu and recipe on the screen. For example, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are provided.

[0072] Step 5:

[0073] The user selects the menu they like from the displayed options and clicks the "View detailed recipe" button.

[0074] The device will open the link, allowing the user to visit the detailed recipe site.

[0075] The recipe site page will be displayed in your browser or in-app browser, where you can check the detailed instructions and necessary ingredients.

[0076] In this way, the system helps users easily create optimal menus and supports healthy and efficient eating habits.

[0077] Example 1

[0078] 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."

[0079] In today's busy lifestyles, users need a system that can quickly select ingredients, purchase them, and plan menus so that they can cook at home easily and efficiently. However, it is extremely tedious for users to manually plan the optimal menu based on their individual preferences, allergies, family composition, and budget, and they must also consider inventory and price information from nearby stores. A system that can solve these problems and provide users with the optimal menu is needed.

[0080] 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.

[0081] In this invention, the server includes a means for inputting preferences, allergy information, family composition, and budget from the user, a means for acquiring inventory and price information from nearby stores, and a means for generating prompt sentences using a generative AI model based on the input and acquired information to generate an optimal menu, thereby enabling users to obtain an optimal menu that meets their individual needs without any hassle.

[0082] "User" refers to an individual who utilizes the system to input their information to generate menus.

[0083] "Terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0084] "Server" refers to a computer system that receives information sent by users and processes and analyzes the data.

[0085] "Preferences" refers to the user's food preferences and tastes, and includes categories such as Japanese food and Western food.

[0086] "Allergy information" refers to information about whether a user has an allergy to a particular food.

[0087] "Family composition" refers to the number of people who eat together and their composition.

[0088] "Budget" refers to the upper limit of the cost that a user sets for a single meal.

[0089] "Nearby stores" refers to stores and supermarkets that provide ingredients within the area specified by the user.

[0090] "Inventory information" refers to the availability and quantity of products in the store.

[0091] "Price information" refers to information regarding the selling price of each product.

[0092] "Means of acquisition" refers to the method or technology by which the server collects store inventory information and price information.

[0093] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate prompts from user input and store information, and then generates the optimal menu.

[0094] A "prompt sentence" refers to a sentence containing user information and store information to be input into the generative AI model.

[0095] "Optimal menu" refers to the most suitable dish suggestions for the user, generated by an AI algorithm based on the user's input information and store information.

[0096] "Display means" refers to the technology or method for displaying the generated menu on the user's terminal.

[0097] MODE FOR CARRYING OUT THE INVENTION

[0098] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs their mood and preferences for the day, allergy information, family composition, and budget, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate the optimal menu based on that information. This invention involves a series of steps: the user inputs information, the server receives and processes the information, and the final generated menu is displayed on the user's device.

[0099] Hardware and Software Used

[0100] User device: Input and display devices such as smartphones, tablets, and computers.

[0101] Server: A central system that receives, processes, and analyzes data. Specifically, it retrieves nearby store information and analyzes the data using generative AI models.

[0102] Generative AI model: An artificial intelligence algorithm that generates optimal menus based on user input and acquired information.

[0103] Explanation of system processing

[0104] 1. Enter your user information

[0105] The user launches the application and enters information such as preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen) into the form that appears on the screen.

[0106] Once the input is complete, the user clicks the "Submit" button.

[0107] The entered information is sent from the device to the server, where the data is structured in text or JSON format.

[0108] 2. Obtaining store information

[0109] The server calls the API to obtain store information for the area specified by the user.

[0110] The server collects inventory and price information from each store, analyzes it, and extracts the necessary information, such as "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[0111] 3. Generating optimal menus

[0112] The server launches a generative AI model based on user information and store information to generate a prompt sentence.

[0113] The generated prompt sentences are input into a generative AI model to generate the optimal menu. Specific examples of prompt sentences are as follows:

[0114] Please suggest a menu for two people who prefer Japanese food and have a dairy allergy, within a budget of 2000 yen. The inventory information for nearby stores is as follows: 200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen.

[0115] The system receives a menu and recipe returned by the generative AI model, such as "sauteed chicken and spinach" and "tofu miso soup."

[0116] 4. Menu display

[0117] The server sends the generated menu and recipes to the terminal.

[0118] The device displays the received information on its screen. Examples of displays include:

[0119] menu:

[0120] 1. Sauteed chicken and spinach

[0121] Recipe:

[0122] 200g chicken, cut into bite-sized pieces

[0123] Wash and chop a bunch of spinach

[0124] Sauté the chicken and spinach in a frying pan, season with salt and pepper.

[0125] 2. Tofu miso soup

[0126] Recipe:

[0127] 1 pack of tofu, cut into bite-sized pieces

[0128] Add water to a pot, add tofu and miso, and bring to a boil.

[0129] Users can view the recipe and start cooking, and are also provided with a link to view the detailed recipe.

[0130] This system allows users to easily find the optimal menu that meets their individual needs. Its unique feature is that it can provide optimal suggestions to users in real time by combining a generative AI model with store information.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1: Enter your user information

[0133] The user starts the application and enters information such as preferences, allergy information, family composition, budget, etc. into the form displayed on the screen. For example, "Japanese food," "dairy allergy," "serves two," and "2,000 yen."

[0134] When the user has completed the input, he / she clicks the "Send" button. The submitted information is sent from the user's terminal to the server.

[0135] Input: Preferences, allergy information, family composition, budget

[0136] Output: User information sent to the server

[0137] Step 2: Get store information

[0138] The server calls the API to obtain store information for the area specified by the user.

[0139] The server sends an API request to obtain inventory and price information for nearby stores. For example, store A's inventory information might be "200g of chicken, 1 bunch of spinach, 1 pack of tofu" and its price information might be "chicken: 500 yen, spinach: 300 yen, tofu: 100 yen."

[0140] Input: User's locale

[0141] Output: Store inventory and pricing information

[0142] Step 3: Generate a prompt statement

[0143] The server generates a prompt sentence for the generative AI model based on user information and store information.

[0144] The server generates a prompt sentence by combining the preferences, allergy information, family structure, budget, and acquired store information input by the user.

[0145] An example of a generated prompt is as follows:

[0146] Please suggest a menu for two people who prefer Japanese food and have a dairy allergy, within a budget of 2000 yen. The inventory information for nearby stores is as follows: 200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen.

[0147] Input: User information (preferences, allergy information, family composition, budget), store information (stock information, price information)

[0148] Output: Prompt sentence for the generative AI model

[0149] Step 4: Generate the optimal menu

[0150] The server inputs the generated prompt sentences into a generative AI model to generate the optimal menu.

[0151] The generative AI model analyzes the prompt and generates the optimal menu for the user, for example, "Sauteed chicken and spinach" and "Tofu miso soup."

[0152] Input: Generated prompt statement

[0153] Output: Optimal menu (e.g., sauteed chicken and spinach, tofu miso soup)

[0154] Step 5: Display the menu

[0155] The server sends the generated menu and its recipes to the user's terminal.

[0156] The device displays the received menu and recipes on the screen.

[0157] Examples of displays include:

[0158] menu:

[0159] 1. Sauteed chicken and spinach

[0160] Recipe:

[0161] 200g chicken, cut into bite-sized pieces

[0162] Wash and chop a bunch of spinach

[0163] Sauté the chicken and spinach in a frying pan, season with salt and pepper.

[0164] 2. Tofu miso soup

[0165] Recipe:

[0166] 1 pack of tofu, cut into bite-sized pieces

[0167] Add water to a pot, add tofu and miso, and bring to a boil.

[0168] The user can start cooking using the displayed recipe, and a link to view the detailed recipe is also provided.

[0169] Input: Best menu and recipes

[0170] Output: Menu and recipe displayed on the user's device

[0171] (Application example 1)

[0172] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0173] In recent years, busy urban lifestyles have made it difficult for consumers to cook at home in order to maintain an efficient and healthy diet. Furthermore, consumers often lack the means to quickly check the availability and price information of ingredients at nearby stores, which can lead to inefficient shopping. Furthermore, consumers often waste time and effort due to lack of knowledge of efficient shopping routes. The purpose of this invention is to solve these problems and provide a system that allows users to shop efficiently and effectively and enjoy cooking at home.

[0174] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0175] In this invention, the server includes means for inputting preferences, allergy information, family composition, and budget from the user, means for acquiring inventory and price information from nearby retail stores, means for generating menus using an AI algorithm based on the input and acquired information, means for displaying the generated menus to the user, and means for displaying efficient routes for shopping in the store. This enables users to streamline their consumer lifestyles, generate optimal shopping lists and menus, and reduce wasted time and effort in the store.

[0176] "User" refers to a consumer or individual who uses the system.

[0177] "Preferences" refers to the user's dietary preferences and tastes.

[0178] "Allergy information" refers to information including foods and ingredients that a user cannot consume.

[0179] "Family structure" refers to information about the number and structure of the user's family.

[0180] "Budget" refers to the upper limit of the amount set by the user for the ingredients they plan to purchase.

[0181] "Retail store" means a store that sells ingredients and food products.

[0182] "Inventory information" refers to information regarding the availability and quantity of a product at a particular store.

[0183] "Price Information" means information regarding the price at which a particular product is sold in a store.

[0184] An "AI algorithm" refers to a calculation procedure that uses artificial intelligence technology to analyze data and derive optimal solutions.

[0185] "Menu" means a collection of meal menus and recipes for a certain period of time.

[0186] A "circulation route" refers to a path that a user takes to efficiently move around the store and find the desired product.

[0187] "Server" means a computer system for processing data and providing services to users.

[0188] This invention is a system that suggests optimal menus for users to efficiently cook at home and also supports shopping in physical stores. The system runs on devices such as smartphones and tablets, and when users input their preferences, allergies, family composition, and budget for the day, it obtains inventory and price information from nearby retailers and uses an AI algorithm to generate optimal menus and shopping lists. It also suggests efficient routes for people to navigate within the store based on this information.

[0189] Hardware and software used

[0190] Hardware: Smartphones, tablets, personal computers

[0191] Software: Mobile applications, cloud servers, RESTful APIs, AI algorithms, map APIs (e.g., Google Maps API)

[0192] Data processing and calculation

[0193] 1. Enter your user information

[0194] Through the application, users input their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen).

[0195] The entered information is sent to a cloud server and stored.

[0196] 2. Obtaining store information

[0197] The server obtains inventory and price information from nearby retailers via API based on the user's location information.

[0198] For example, store A's inventory information may be "chicken, spinach, and tofu available," and price information may be "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[0199] 3. Generate optimal menus and shopping lists

[0200] The server uses an AI algorithm to generate optimal menus and shopping lists based on the user's input information and the acquired store information.

[0201] Specifically, the system generates dishes such as sauteed chicken and spinach and tofu miso soup.

[0202] 4. Displaying efficient routes within the store

[0203] Based on the generated shopping list, a map API is used to display the optimal route within the store.

[0204] The user can shop while moving around the store efficiently based on the proposed route.

[0205] Specific examples

[0206] For example, a user opens a smartphone application and enters their preference as Japanese food, dairy allergy information, family size for two people, and a budget of 2,000 yen. Based on this information, the server retrieves inventory and price information from nearby retailers and uses an AI algorithm to suggest a menu of "sauteed chicken and spinach" and "tofu miso soup." It then compares the price at which each ingredient is sold at each store and displays an efficient route to get around.

[0207] Example prompt sentence:

[0208] Please suggest the optimal menu based on the user's preferences. The preference is Japanese food, the allergy information is dairy allergy, the family size is for two people, and the budget is 2000 yen. Please generate the optimal menu and shopping list based on inventory and price information from nearby physical stores, and display the route to go around. The location is Tokyo.

[0209] This allows users to easily maintain an efficient and healthy diet.

[0210] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0211] Step 1:

[0212] The user launches the application using a device (e.g., a smartphone or tablet). The user inputs their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen). This information is sent to the cloud server via the application. Input: User's preferences, allergy information, family composition, budget. Output: User information stored on the cloud server.

[0213] Step 2:

[0214] The server obtains inventory and price information from nearby retailers based on the user's location information. It communicates with the store system using a RESTful API. The response it receives is inventory status and price information for each retailer. Input: User's location information. Output: Retailer inventory and price information.

[0215] Step 3:

[0216] The server uses an AI algorithm to generate optimal menus and shopping lists based on the user's input information (preferences, allergy information, family composition, budget) and acquired store information (stock information and price information). The AI ​​algorithm used here compares it with past data to improve accuracy. Input: User information, store information. Output: Optimal menus and shopping lists.

[0217] Step 4:

[0218] The terminal displays the optimal menu and shopping list sent from the server, including detailed recipes and a list of products to purchase. The user can view the generated list and check links to detailed recipe sites. Input: Menu and shopping list generated by the server. Output: Menu and shopping list displayed on the terminal.

[0219] Step 5:

[0220] The server generates an efficient route for shopping in the store and sends it to the device. At this time, it uses a map API (e.g., Google Maps API) to visualize the location of each product and calculate the shortest route. Input: Product location information within the store. Output: Shopping route displayed on the device.

[0221] Step 6:

[0222] The terminal displays an efficient shopping route to the user. The user can move around the store by following the suggested route and efficiently purchase the desired products. Input: Shopping route generated by the server. Output: Shopping route displayed to the user.

[0223] 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.

[0224] MODE FOR CARRYING OUT THE INVENTION

[0225] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs the day's mood, preferences, allergy information, family composition, and budget, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate an optimal menu based on this information. The generated menu is displayed on the user's device. Furthermore, this invention incorporates an emotion engine that recognizes the user's emotions, making it possible to generate an optimal menu based on the user's emotions.

[0226] Program processing and specific examples

[0227] 1. Enter your user information

[0228] The user launches the application and the home screen is displayed.

[0229] Users input their mood for the day, their favorite ingredients, ingredients they want to avoid, allergy information, family composition, and budget.

[0230] Additionally, an emotion engine recognizes the user's emotional state (e.g., stress, anxiety, joy).

[0231] The entered and recognized information is sent to the server.

[0232] 2. Obtaining store information

[0233] The server receives the user's input and recognition information and sends a request to the API of the specified nearby store to obtain inventory and price information.

[0234] The server receives the response from the store and obtains inventory and price information in JSON format.

[0235] Example: Obtain store A's inventory information "Chicken available, spinach available, tofu available" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen."

[0236] 3. Generating optimal menus

[0237] The server runs an AI algorithm based on the user's input information, recognition information, and acquired store inventory and price information to generate the optimal menu.

[0238] Additionally, an emotion engine assists in menu selection based on the user's emotions.

[0239] For example, if a user is feeling stressed, the system will suggest a menu that includes foods that have a relaxing effect, such as sauteed chicken and spinach or miso soup with tofu.

[0240] 4. Menu display

[0241] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[0242] The device receives the response from the server and displays the menu and recipe on the screen. For example, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are provided.

[0243] Users who want to see more details about the recipe can click on the link to visit the recipe site.

[0244] 5. Accumulation and learning of emotional data

[0245] The server accumulates the user's emotional data and improves the accuracy of the menu by referring to the user's past emotional history.

[0246] The server coordinates the AI ​​algorithm and emotion engine to help generate future menus.

[0247] In this way, the system helps users easily generate optimal menus and supports healthy and efficient eating habits. In particular, by combining it with an emotion engine, it can suggest menus based on the user's mood and emotional state, enabling more personalized meal choices. This also gives users a sense of mental satisfaction.

[0248] The processing flow will be explained below.

[0249] Step 1:

[0250] The user launches the application and the home screen appears.

[0251] Users input their mood for the day, their favorite ingredients, ingredients they want to avoid, allergy information, family composition, and budget.

[0252] The emotion engine uses facial recognition and voice analysis to recognize the user's emotional state (e.g., stress, anxiety, joy).

[0253] The user presses the "send" button, and the input data and the recognized emotion data are sent to the server.

[0254] Step 2:

[0255] A server receives user input data and emotion data.

[0256] The server sends a request to the API of the nearby store specified by the user to obtain inventory and price information.

[0257] The server receives the response from the store and obtains inventory and price information in JSON format.

[0258] For example, store A's inventory information "chicken available, spinach available, tofu available" and price information "200g chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen" are acquired.

[0259] Step 3:

[0260] The server runs an AI algorithm based on the user's input information and the acquired store inventory and price information.

[0261] The AI ​​algorithm takes into account the user's preferences, allergy information, family composition, and budget to generate the optimal menu.

[0262] The server generates a menu that matches the user's emotional state based on data from the emotion engine.

[0263] For example, if a user is feeling stressed, the app suggests dishes such as sautéed chicken and spinach or miso soup with tofu, which have a relaxing effect.

[0264] Step 4:

[0265] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[0266] The terminal receives the response from the server and displays the menu and recipe on the screen.

[0267] Examples of displayed items include "Sauteed chicken and spinach" and "Tofu miso soup," with detailed recipes for each provided.

[0268] It also displays a link that allows users to view the detailed recipe.

[0269] Step 5:

[0270] The user selects the menu they like from the displayed options and clicks the "View detailed recipe" button.

[0271] The device will open the link, allowing the user to visit the detailed recipe site.

[0272] The recipe site page will be displayed in your browser or in-app browser, where you can check the detailed instructions and necessary ingredients.

[0273] Step 6:

[0274] The server stores the user's emotional data and refers to the user's past emotional history to help generate subsequent menus.

[0275] By coordinating the emotion engine with the AI ​​algorithm, it is possible to provide menus that are more suited to the user's emotional state.

[0276] Through this series of steps, the system proposes optimal menus based on the user's mood and emotional state, allowing the user to enjoy cooking efficiently while gaining a sense of mental satisfaction.

[0277] Example 2

[0278] 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."

[0279] In today's busy lifestyles, it is difficult for users to easily plan healthy and balanced meals. Current systems also struggle to suggest menus that reflect the user's mood and emotional state. Therefore, there is a need for a system that allows users to easily create optimal menus based on their mood and health status on that day, and also has a shopping support function.

[0280] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's mood, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget, means for recognizing the user's emotional state using an emotion engine, means for acquiring inventory information and price information of nearby stores, means for generating a menu using artificial intelligence based on the input information, emotion information, and acquired information, and means for displaying the generated menu to the user. This allows the user to easily generate an optimal menu based on the user's mood and emotional state of the day and efficiently plan the purchase of necessary ingredients.

[0281] "User" refers to an individual or household representative who uses the system to receive menu suggestions.

[0282] "Mood" describes the user's mental or physical state for the day, and indicates an emotional state such as "tired" or "wanting to relax."

[0283] "Favorite ingredients" refers to ingredients that the user particularly likes to consume.

[0284] "Ingredients to avoid" refers to ingredients that a user wants to avoid consuming due to health reasons or specific food preferences.

[0285] "Allergy information" refers to information about specific ingredients to which a user or a member of their family has an allergic reaction.

[0286] "Family composition" refers to information indicating the number of people in the user's household and their relationships.

[0287] "Budget" refers to information indicating the upper limit of the amount of money a user can spend on the menu for that day.

[0288] "Emotion Engine" refers to a hardware and software system for detecting and analyzing a user's emotional state in real time.

[0289] "Nearby stores" refers to commercial facilities that are suppliers of ingredients and are located near the user's residence.

[0290] "Inventory Information" refers to detailed information about ingredients and products that a store has in stock and available for sale.

[0291] "Price information" refers to information indicating the selling price of each product in a store.

[0292] "Artificial intelligence" refers to a general term for computational algorithms and models that generate optimal menus based on user input, emotional information, and store information.

[0293] A "menu" refers to a number of dishes and their combinations that the user is expected to consume as part of their meal for that day.

[0294] A "recipe site" refers to a website that provides detailed instructions on how to make each dish and information on the ingredients used.

[0295] MODE FOR CARRYING OUT THE INVENTION

[0296] The present invention provides a system for suggesting optimal menus to enable users to easily cook at home. Specific embodiments of the system will be described below.

[0297] overview

[0298] The user inputs their mood, favorite ingredients, ingredients to avoid, allergies, family composition, budget, etc., and the system uses an emotion engine to recognize their emotional state. The server then obtains inventory and price information from nearby stores and uses artificial intelligence to generate an optimal menu. The generated menu is displayed on the user's device, and a link to a detailed recipe website is also provided.

[0299] Hardware and software used

[0300] This system uses the following hardware and software:

[0301] Server: processes and stores data, runs artificial intelligence models, and operates the emotion engine

[0302] Terminal: Receives input from users, displays menus, and accesses recipe sites

[0303] Emotion engine: Recognizes the user's emotional state (specifically, facial recognition APIs and emotion analysis models)

[0304] Artificial intelligence algorithms: implemented using TENSORFLOW® and PyTorch

[0305] Data processing and calculation

[0306] 1. User information input and emotion recognition

[0307] Users launch the application on their smartphone or PC and enter their mood, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget.

[0308] The emotion engine recognizes the user's emotional state in real time through camera and voice input, using facial recognition APIs and emotion analysis algorithms.

[0309] 2. Obtaining store information

[0310] The server sends a request to the API of a nearby store to obtain inventory and price information. For example, store A's inventory information "chicken, spinach, tofu" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen" are received in JSON format.

[0311] 3. Generating optimal menus

[0312] The server uses an artificial intelligence algorithm to generate an optimal menu based on user input, emotional information, and store information. For example, if a user is feeling stressed, the server will suggest a menu that includes foods that have a relaxing effect. Specific examples include sauteed chicken and spinach and tofu miso soup.

[0313] 4. Menu display

[0314] The server formats the generated menu information in JSON format and sends it to the user's device. The device receives this information and displays the menu in the application's UI. As specific examples, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are also provided.

[0315] 5. Accumulation and learning of emotional data

[0316] The server accumulates user emotional data and menu history, and uses this data to retrain the AI ​​model, improving the accuracy of future suggestions.

[0317] Examples of concrete examples and prompts

[0318] As a concrete example, consider a scenario in which a user uses a system using the following prompt sentence:

[0319] "I'm feeling tired today, so please suggest a meal that will help me relax. I'd like to use chicken and vegetables."

[0320] "My child is allergic to eggs, so please tell me a family-friendly dinner menu that avoids eggs."

[0321] "What's the best recipe to eat when you're feeling stressed? Especially something that's easy to prepare."

[0322] Based on these prompts, the system analyzes the user's input and emotional information with high accuracy, and generates and suggests appropriate menus.

[0323] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0324] The flow of this system's program processing

[0325] Step 1: Enter your user information

[0326] input:

[0327] User's mood, favorite ingredients, ingredients to avoid, allergy information, family structure, budget

[0328] output:

[0329] User input information

[0330] Specific behavior:

[0331] The user launches the application on their smartphone or PC and the home screen appears.

[0332] Users input their mood for the day, food preferences, ingredients to avoid, allergy information, family composition, and budget.

[0333] This information is sent to the server as an HTTP request.

[0334] Step 2: Emotion Recognition

[0335] input:

[0336] User's emotional state (facial expression, voice input)

[0337] output:

[0338] User emotion information

[0339] Specific behavior:

[0340] It uses the user's camera and microphone to capture facial expressions and voice.

[0341] The emotion engine analyzes this data and recognizes the user's emotional state.

[0342] The recognized emotion information is sent to the server together with the user's input information.

[0343] Step 3: Get store information

[0344] input:

[0345] User location information

[0346] output:

[0347] Nearby store inventory and pricing information

[0348] Specific behavior:

[0349] The server sends a request to the API of nearby stores based on the user's location information.

[0350] The store API returns inventory and price information in JSON format.

[0351] The server analyzes the received information and stores it as usable data.

[0352] Step 4: Generate the optimal menu

[0353] input:

[0354] User input information, emotion information, store inventory information and price information

[0355] output:

[0356] Optimal menu information

[0357] Specific behavior:

[0358] The server runs an artificial intelligence algorithm based on the user's input information, emotional information, and acquired store information.

[0359] Artificial intelligence generates optimal menus using deep learning models such as TensorFlow and PyTorch.

[0360] For example, if a user is feeling stressed, a menu containing foods that have a relaxing effect will be suggested.

[0361] Step 5: Display the menu

[0362] input:

[0363] Optimal menu information

[0364] output:

[0365] View menus and detailed recipes

[0366] Specific behavior:

[0367] The server formats the generated menu information in JSON format.

[0368] This information is sent to the user's terminal.

[0369] The device displays the received information on the screen. For example, detailed recipes for "Sauteed Chicken and Spinach" and "Tofu Miso Soup" are displayed on the screen.

[0370] Step 6: Accumulating and Learning Emotional Data

[0371] input:

[0372] User's emotional information and meal history

[0373] output:

[0374] Improved menu suggestion model

[0375] Specific behavior:

[0376] The server stores the user's emotional data and menu history in a database.

[0377] This data is then used periodically to retrain the artificial intelligence algorithms.

[0378] This will improve the accuracy of menu generation from the next time onwards.

[0379] (Application example 2)

[0380] 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."

[0381] In today's busy lifestyles, it is difficult for users to select the optimal meal based on that day's mood, health condition, preferences, and budget, and have the appropriate meal delivered quickly. In particular, it was difficult for conventional systems to select meals based on the user's emotional state. Another challenge was providing personalized meal suggestions to accommodate a wide variety of food allergies and household configurations.

[0382] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's mood, preferences, allergy information, family composition, and budget; means for acquiring inventory and price information from nearby retailers; means for generating an optimal meal plan using an AI algorithm based on the input and acquired information; means for recognizing the user's emotional state and suggesting a meal plan based on the user's emotions; means for displaying the generated meal plan to the user; and means for providing an interface for ordering the meal plan and for processing delivery procedures. This enables the user to select the optimal meal based on their mood, emotional state, and individual needs for that day, and have it delivered quickly.

[0383] "Mood" refers to the user's emotional and psychological state on that day.

[0384] "Preferences" refers to the types of ingredients and dishes that the user likes.

[0385] "Allergy information" refers to information about food ingredients that a user should avoid consuming.

[0386] "Family structure" refers to information such as the number of family members and age groups of the user.

[0387] The "budget" refers to the range of amounts set by the user to be spent on cooking and ingredients.

[0388] A "server" refers to a device that processes information sent by a user and can acquire, store, and manage data via the Internet.

[0389] "Store" refers to a store or online shop that provides ingredients and dishes to users.

[0390] "Inventory information" refers to detailed information about ingredients and dishes currently available at a retailer.

[0391] "Price information" refers to information about the selling prices of ingredients and dishes at retail stores.

[0392] "AI algorithm" refers to an algorithm that automatically generates optimal cooking plans based on user input and acquired information.

[0393] "Meal plan" refers to the proposal of the optimal menu or meal based on the user's requirements.

[0394] "Emotional state" refers to the mental and emotional state of the user.

[0395] "Interface" refers to the screen display and input means that allow the user to operate the system.

[0396] "Delivery procedure" refers to the procedure for delivering the meal plan selected by the user to the location specified by the user.

[0397] This invention is a system that proposes optimal meal plans by having users input their mood, preferences, allergy information, family composition, and budget for the day, and then works in conjunction with food delivery services to quickly deliver meals.

[0398] The system consists of the following elements:

[0399] User information input method

[0400] A means of recognizing emotional states

[0401] How to obtain retailer inventory and pricing information

[0402] AI algorithm for generating meal plans

[0403] How to display meal plans

[0404] Delivery procedure interface

[0405] When the user launches the smartphone application, the home screen appears. The user inputs their mood for the day, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget. The emotion engine recognizes the user's mood and sends the input data along with that information to the server.

[0406] The server sends an API request to retrieve the user's input and sentiment information, as well as stock and price information from nearby retailers, in JSON format.

[0407] The server then uses AI algorithms (e.g., TensorFlow, PyTorch) to combine the user's input data with the acquired data to generate an optimal meal plan. An emotion engine helps select a meal plan based on the user's mood. For example, if the user wants to relax, the system will suggest dishes using ingredients with relaxing effects (e.g., grilled fish, spinach and tomato salad, and green tea).

[0408] The server sends the generated meal plan and recipe information to the user's device, which receives it and displays the meal plan and detailed information on the screen. The meal plan display also includes a specific ordering link, which the user can click to easily confirm the order.

[0409] Finally, the server sends the order information to the food delivery service's API and arranges for the food to be delivered to the specified address. Past search data and emotion data are stored on the server and used to improve the accuracy of future meal plans.

[0410] As a concrete example, the following prompt sentence is used:

[0411] "The user entered their mood for the day, their favorite ingredients, ingredients they want to avoid or have allergies, their family composition, and their budget. The emotion engine also recognized that the user wanted to relax. Please suggest the best relaxing dish for them."

[0412] As described above, this system provides optimal meal plans based on the user's mood, emotional state, and individual needs, and can deliver them quickly through a food delivery service. Furthermore, by learning from past data and improving the accuracy of future menu suggestions, it is possible to further increase user satisfaction.

[0413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0414] Step 1:

[0415] The user launches the smartphone application and enters their mood for the day, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget on the home screen. The entered information is sent to the server as initial data. The emotion engine recognizes the user's mood, and this information is also sent to the server. The input data includes information registered by the user (mood: relaxation, favorite ingredients: fish, ingredients to avoid: tomatoes, etc.).

[0416] Step 2:

[0417] The server receives the input data sent by the user and sends an API request to get stock and price information from nearby stores. Based on the API request, stock information (e.g., fish, spinach, green tea, etc.) and price information (e.g., fish 500 yen, spinach 100 yen, green tea 150 yen) are obtained from the store in JSON format. This data is stored on the server.

[0418] Step 3:

[0419] The server uses AI algorithms (e.g., TensorFlow, PyTorch) to generate optimal meal plans based on the user's input data and the acquired inventory and price information. The AI ​​algorithm analyzes the input data (mood, preferences, budget, etc.) and inventory information to generate optimal meal plans based on the user's criteria. For example, it may select ingredients with a relaxing effect and create a meal plan (e.g., grilled fish, spinach and tomato salad, and green tea).

[0420] Step 4:

[0421] The server sends the generated meal plan to the device. The device receives this data and displays the meal plan and detailed information on the screen. This display includes a list of specific dishes (e.g., grilled fish, spinach and tomato salad) and an ordering link. The user can click the link to view detailed information about the dish.

[0422] Step 5:

[0423] The user selects the desired dish from the displayed meal plan and confirms the order by clicking the order link. The terminal then sends the user's order information to the server.

[0424] Step 6:

[0425] The server receives the order information from the user and sends it to the food delivery service's API. The food delivery service receives the order and arranges for the food to be delivered to the specified address. The server tracks the order status and notifies the user.

[0426] Step 7:

[0427] The server learns from past search data and emotion data and uses it to improve the accuracy of future cooking plan generation. The learning data is analyzed based on the user's past selections and emotional state, and is reflected in future suggestions.

[0428] Through the above processing steps, the user can be provided with the optimal meal plan that suits their mood and individual needs that day, and the meal can be delivered quickly.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Second embodiment]

[0433] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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."

[0445] MODE FOR CARRYING OUT THE INVENTION

[0446] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs their mood, preferences, allergy information, family composition, and budget for the day, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate the optimal menu. The generated menu is then displayed on the user's device.

[0447] Program processing and specific examples

[0448] 1. Enter your user information

[0449] The user launches the application and enters their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), budget (e.g., 2,000 yen), etc.

[0450] The entered information is sent to the server.

[0451] 2. Obtaining store information

[0452] The server obtains inventory and price information for nearby stores specified by the user through the API.

[0453] For example, store A's inventory information may be "chicken, spinach, and tofu available," and price information may be "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[0454] 3. Generating optimal menus

[0455] The server uses an AI algorithm to generate the optimal menu based on the user's input information and the acquired store information. The AI ​​algorithm also takes into account the user's past search data and suggests menus tailored to the user.

[0456] Specifically, the system generates dishes such as sauteed chicken and spinach and tofu miso soup.

[0457] 4. Menu display

[0458] The terminal displays the generated menu and its recipes to the user. For example, "Sauteed chicken and spinach" and "Miso soup with tofu" are displayed, and the respective recipes are provided.

[0459] Users who want to see more details about the recipe can click on the link to visit the recipe site.

[0460] This series of processes allows users to easily eliminate the hassle of planning menus and enjoy cooking at home. The system takes into account the user's individual needs and nutritional balance, and utilizes store inventory information to efficiently provide optimal menus. This is expected to have the effect of supporting healthy eating habits.

[0461] The processing flow will be explained below.

[0462] Step 1:

[0463] The user launches the application and the home screen appears.

[0464] The user inputs their mood for the day (e.g., Japanese food, Western food), preferred ingredients (e.g., chicken), ingredients to avoid or allergy information (e.g., dairy products), family size (e.g., 2 people), and budget per meal (e.g., 2,000 yen).

[0465] The user presses the "Submit" button and the input data is sent to the server.

[0466] Step 2:

[0467] The server receives the user's input data and sends a request to the API of the specified nearby store to obtain inventory and price information.

[0468] The server receives the response from the store and obtains inventory and price information in JSON format.

[0469] Example: Obtain store A's inventory information "Chicken available, spinach available, tofu available" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen."

[0470] Step 3:

[0471] The server runs an AI algorithm based on the user's input information and the acquired store inventory and price information to generate the optimal menu.

[0472] The server also references past search data to suggest menus suitable for the user, such as sautéed chicken and spinach or tofu miso soup.

[0473] Step 4:

[0474] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[0475] The device receives the response from the server and displays the menu and recipe on the screen. For example, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are provided.

[0476] Step 5:

[0477] The user selects the menu they like from the displayed options and clicks the "View detailed recipe" button.

[0478] The device will open the link, allowing the user to visit the detailed recipe site.

[0479] The recipe site page will be displayed in your browser or in-app browser, where you can check the detailed instructions and necessary ingredients.

[0480] In this way, the system helps users easily create optimal menus and supports healthy and efficient eating habits.

[0481] Example 1

[0482] 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."

[0483] In today's busy lifestyles, users need a system that can quickly select ingredients, purchase them, and plan menus so that they can cook at home easily and efficiently. However, it is extremely tedious for users to manually plan the optimal menu based on their individual preferences, allergies, family composition, and budget, and they must also consider inventory and price information from nearby stores. A system that can solve these problems and provide users with the optimal menu is needed.

[0484] 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.

[0485] In this invention, the server includes a means for inputting preferences, allergy information, family composition, and budget from the user, a means for acquiring inventory and price information from nearby stores, and a means for generating prompt sentences using a generative AI model based on the input and acquired information to generate an optimal menu, thereby enabling users to obtain an optimal menu that meets their individual needs without any hassle.

[0486] "User" refers to an individual who utilizes the system to input their information to generate menus.

[0487] "Terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0488] "Server" refers to a computer system that receives information sent by users and processes and analyzes the data.

[0489] "Preferences" refers to the user's food preferences and tastes, and includes categories such as Japanese food and Western food.

[0490] "Allergy information" refers to information about whether a user has an allergy to a particular food.

[0491] "Family composition" refers to the number of people who eat together and their composition.

[0492] "Budget" refers to the upper limit of the cost that a user sets for a single meal.

[0493] "Nearby stores" refers to stores and supermarkets that provide ingredients within the area specified by the user.

[0494] "Inventory information" refers to the availability and quantity of products in the store.

[0495] "Price information" refers to information regarding the selling price of each product.

[0496] "Means of acquisition" refers to the method or technology by which the server collects store inventory information and price information.

[0497] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate prompts from user input and store information, and then generates the optimal menu.

[0498] A "prompt sentence" refers to a sentence containing user information and store information to be input into the generative AI model.

[0499] "Optimal menu" refers to the most suitable dish suggestions for the user, generated by an AI algorithm based on the user's input information and store information.

[0500] "Display means" refers to the technology or method for displaying the generated menu on the user's terminal.

[0501] MODE FOR CARRYING OUT THE INVENTION

[0502] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs their mood and preferences for the day, allergy information, family composition, and budget, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate the optimal menu based on that information. This invention involves a series of steps: the user inputs information, the server receives and processes the information, and the final generated menu is displayed on the user's device.

[0503] Hardware and Software Used

[0504] User device: Input and display devices such as smartphones, tablets, and computers.

[0505] Server: A central system that receives, processes, and analyzes data. Specifically, it retrieves nearby store information and analyzes the data using generative AI models.

[0506] Generative AI model: An artificial intelligence algorithm that generates optimal menus based on user input and acquired information.

[0507] Explanation of system processing

[0508] 1. Enter your user information

[0509] The user launches the application and enters information such as preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen) into the form that appears on the screen.

[0510] Once the input is complete, the user clicks the "Submit" button.

[0511] The entered information is sent from the device to the server, where the data is structured in text or JSON format.

[0512] 2. Obtaining store information

[0513] The server calls the API to obtain store information for the area specified by the user.

[0514] The server collects inventory and price information from each store, analyzes it, and extracts the necessary information, such as "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[0515] 3. Generating optimal menus

[0516] The server launches a generative AI model based on user information and store information to generate a prompt sentence.

[0517] The generated prompt sentences are input into a generative AI model to generate the optimal menu. Specific examples of prompt sentences are as follows:

[0518] Please suggest a menu for two people who prefer Japanese food and have a dairy allergy, within a budget of 2000 yen. The inventory information for nearby stores is as follows: 200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen.

[0519] The system receives a menu and recipe returned by the generative AI model, such as "sauteed chicken and spinach" and "tofu miso soup."

[0520] 4. Menu display

[0521] The server sends the generated menu and recipes to the terminal.

[0522] The device displays the received information on its screen. Examples of displays include:

[0523] menu:

[0524] 1. Sauteed chicken and spinach

[0525] Recipe:

[0526] 200g chicken, cut into bite-sized pieces

[0527] Wash and chop a bunch of spinach

[0528] Sauté the chicken and spinach in a frying pan, season with salt and pepper.

[0529] 2. Tofu miso soup

[0530] Recipe:

[0531] 1 pack of tofu, cut into bite-sized pieces

[0532] Add water to a pot, add tofu and miso, and bring to a boil.

[0533] Users can view the recipe and start cooking, and are also provided with a link to view the detailed recipe.

[0534] This system allows users to easily find the optimal menu that meets their individual needs. Its unique feature is that it can provide optimal suggestions to users in real time by combining a generative AI model with store information.

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] Step 1: Enter your user information

[0537] The user starts the application and enters information such as preferences, allergy information, family composition, budget, etc. into the form displayed on the screen. For example, "Japanese food," "dairy allergy," "serves two," and "2,000 yen."

[0538] When the user has completed the input, he / she clicks the "Send" button. The submitted information is sent from the user's terminal to the server.

[0539] Input: Preferences, allergy information, family composition, budget

[0540] Output: User information sent to the server

[0541] Step 2: Get store information

[0542] The server calls the API to obtain store information for the area specified by the user.

[0543] The server sends an API request to obtain inventory and price information for nearby stores. For example, store A's inventory information might be "200g of chicken, 1 bunch of spinach, 1 pack of tofu" and its price information might be "chicken: 500 yen, spinach: 300 yen, tofu: 100 yen."

[0544] Input: User's locale

[0545] Output: Store inventory and pricing information

[0546] Step 3: Generate a prompt statement

[0547] The server generates a prompt sentence for the generative AI model based on user information and store information.

[0548] The server generates a prompt sentence by combining the preferences, allergy information, family structure, budget, and acquired store information input by the user.

[0549] An example of a generated prompt is as follows:

[0550] Please suggest a menu for two people who prefer Japanese food and have a dairy allergy, within a budget of 2000 yen. The inventory information for nearby stores is as follows: 200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen.

[0551] Input: User information (preferences, allergy information, family composition, budget), store information (stock information, price information)

[0552] Output: Prompt sentence for the generative AI model

[0553] Step 4: Generate the optimal menu

[0554] The server inputs the generated prompt sentences into a generative AI model to generate the optimal menu.

[0555] The generative AI model analyzes the prompt and generates the optimal menu for the user, for example, "Sauteed chicken and spinach" and "Tofu miso soup."

[0556] Input: Generated prompt statement

[0557] Output: Optimal menu (e.g., sauteed chicken and spinach, tofu miso soup)

[0558] Step 5: Display the menu

[0559] The server sends the generated menu and its recipes to the user's terminal.

[0560] The device displays the received menu and recipes on the screen.

[0561] Examples of displays include:

[0562] menu:

[0563] 1. Sauteed chicken and spinach

[0564] Recipe:

[0565] 200g chicken, cut into bite-sized pieces

[0566] Wash and chop a bunch of spinach

[0567] Sauté the chicken and spinach in a frying pan, season with salt and pepper.

[0568] 2. Tofu miso soup

[0569] Recipe:

[0570] 1 pack of tofu, cut into bite-sized pieces

[0571] Add water to a pot, add tofu and miso, and bring to a boil.

[0572] The user can start cooking using the displayed recipe, and a link to view the detailed recipe is also provided.

[0573] Input: Best menu and recipes

[0574] Output: Menu and recipe displayed on the user's device

[0575] (Application example 1)

[0576] 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."

[0577] In recent years, busy urban lifestyles have made it difficult for consumers to cook at home in order to maintain an efficient and healthy diet. Furthermore, consumers often lack the means to quickly check the availability and price information of ingredients at nearby stores, which can lead to inefficient shopping. Furthermore, consumers often waste time and effort due to lack of knowledge of efficient shopping routes. The purpose of this invention is to solve these problems and provide a system that allows users to shop efficiently and effectively and enjoy cooking at home.

[0578] 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.

[0579] In this invention, the server includes means for inputting preferences, allergy information, family composition, and budget from the user, means for acquiring inventory and price information from nearby retail stores, means for generating menus using an AI algorithm based on the input and acquired information, means for displaying the generated menus to the user, and means for displaying efficient routes for shopping in the store. This enables users to streamline their consumer lifestyles, generate optimal shopping lists and menus, and reduce wasted time and effort in the store.

[0580] "User" refers to a consumer or individual who uses the system.

[0581] "Preferences" refers to the user's dietary preferences and tastes.

[0582] "Allergy information" refers to information including foods and ingredients that a user cannot consume.

[0583] "Family structure" refers to information about the number and structure of the user's family.

[0584] "Budget" refers to the upper limit of the amount set by the user for the ingredients they plan to purchase.

[0585] "Retail store" means a store that sells ingredients and food products.

[0586] "Inventory information" refers to information regarding the availability and quantity of a product at a particular store.

[0587] "Price Information" means information regarding the price at which a particular product is sold in a store.

[0588] An "AI algorithm" refers to a calculation procedure that uses artificial intelligence technology to analyze data and derive optimal solutions.

[0589] "Menu" means a collection of meal menus and recipes for a certain period of time.

[0590] A "circulation route" refers to a path that a user takes to efficiently move around the store and find the desired product.

[0591] "Server" means a computer system for processing data and providing services to users.

[0592] This invention is a system that suggests optimal menus for users to efficiently cook at home and also supports shopping in physical stores. The system runs on devices such as smartphones and tablets, and when users input their preferences, allergies, family composition, and budget for the day, it obtains inventory and price information from nearby retailers and uses an AI algorithm to generate optimal menus and shopping lists. It also suggests efficient routes for people to navigate within the store based on this information.

[0593] Hardware and software used

[0594] Hardware: Smartphones, tablets, personal computers

[0595] Software: Mobile applications, cloud servers, RESTful APIs, AI algorithms, map APIs (e.g., Google Maps API)

[0596] Data processing and calculation

[0597] 1. Enter your user information

[0598] Through the application, users input their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen).

[0599] The entered information is sent to a cloud server and stored.

[0600] 2. Obtaining store information

[0601] The server obtains inventory and price information from nearby retailers via API based on the user's location information.

[0602] For example, store A's inventory information may be "chicken, spinach, and tofu available," and price information may be "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[0603] 3. Generate optimal menus and shopping lists

[0604] The server uses an AI algorithm to generate optimal menus and shopping lists based on the user's input information and the acquired store information.

[0605] Specifically, the system generates dishes such as sauteed chicken and spinach and tofu miso soup.

[0606] 4. Displaying efficient routes within the store

[0607] Based on the generated shopping list, a map API is used to display the optimal route within the store.

[0608] The user can shop while moving around the store efficiently based on the proposed route.

[0609] Specific examples

[0610] For example, a user opens a smartphone application and enters their preference as Japanese food, dairy allergy information, family size for two people, and a budget of 2,000 yen. Based on this information, the server retrieves inventory and price information from nearby retailers and uses an AI algorithm to suggest a menu of "sauteed chicken and spinach" and "tofu miso soup." It then compares the price at which each ingredient is sold at each store and displays an efficient route to get around.

[0611] Example prompt sentence:

[0612] Please suggest the optimal menu based on the user's preferences. The preference is Japanese food, the allergy information is dairy allergy, the family size is for two people, and the budget is 2000 yen. Please generate the optimal menu and shopping list based on inventory and price information from nearby physical stores, and display the route to go around. The location is Tokyo.

[0613] This allows users to easily maintain an efficient and healthy diet.

[0614] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0615] Step 1:

[0616] The user launches the application using a device (e.g., a smartphone or tablet). The user inputs their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen). This information is sent to the cloud server via the application. Input: User's preferences, allergy information, family composition, budget. Output: User information stored on the cloud server.

[0617] Step 2:

[0618] The server obtains inventory and price information from nearby retailers based on the user's location information. It communicates with the store system using a RESTful API. The response it receives is inventory status and price information for each retailer. Input: User's location information. Output: Retailer inventory and price information.

[0619] Step 3:

[0620] The server uses an AI algorithm to generate optimal menus and shopping lists based on the user's input information (preferences, allergy information, family composition, budget) and acquired store information (stock information and price information). The AI ​​algorithm used here compares it with past data to improve accuracy. Input: User information, store information. Output: Optimal menus and shopping lists.

[0621] Step 4:

[0622] The terminal displays the optimal menu and shopping list sent from the server, including detailed recipes and a list of products to purchase. The user can view the generated list and check links to detailed recipe sites. Input: Menu and shopping list generated by the server. Output: Menu and shopping list displayed on the terminal.

[0623] Step 5:

[0624] The server generates an efficient route for shopping in the store and sends it to the device. At this time, it uses a map API (e.g., Google Maps API) to visualize the location of each product and calculate the shortest route. Input: Product location information within the store. Output: Shopping route displayed on the device.

[0625] Step 6:

[0626] The terminal displays an efficient shopping route to the user. The user can move around the store by following the suggested route and efficiently purchase the desired products. Input: Shopping route generated by the server. Output: Shopping route displayed to the user.

[0627] 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.

[0628] MODE FOR CARRYING OUT THE INVENTION

[0629] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs the day's mood, preferences, allergy information, family composition, and budget, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate an optimal menu based on this information. The generated menu is displayed on the user's device. Furthermore, this invention incorporates an emotion engine that recognizes the user's emotions, making it possible to generate an optimal menu based on the user's emotions.

[0630] Program processing and specific examples

[0631] 1. Enter your user information

[0632] The user launches the application and the home screen is displayed.

[0633] Users input their mood for the day, their favorite ingredients, ingredients they want to avoid, allergy information, family composition, and budget.

[0634] Additionally, an emotion engine recognizes the user's emotional state (e.g., stress, anxiety, joy).

[0635] The entered and recognized information is sent to the server.

[0636] 2. Obtaining store information

[0637] The server receives the user's input and recognition information and sends a request to the API of the specified nearby store to obtain inventory and price information.

[0638] The server receives the response from the store and obtains inventory and price information in JSON format.

[0639] Example: Obtain store A's inventory information "Chicken available, spinach available, tofu available" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen."

[0640] 3. Generating optimal menus

[0641] The server runs an AI algorithm based on the user's input information, recognition information, and acquired store inventory and price information to generate the optimal menu.

[0642] Additionally, an emotion engine assists in menu selection based on the user's emotions.

[0643] For example, if a user is feeling stressed, the system will suggest a menu that includes foods that have a relaxing effect, such as sauteed chicken and spinach or miso soup with tofu.

[0644] 4. Menu display

[0645] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[0646] The device receives the response from the server and displays the menu and recipe on the screen. For example, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are provided.

[0647] Users who want to see more details about the recipe can click on the link to visit the recipe site.

[0648] 5. Accumulation and learning of emotional data

[0649] The server accumulates the user's emotional data and improves the accuracy of the menu by referring to the user's past emotional history.

[0650] The server coordinates the AI ​​algorithm and emotion engine to help generate future menus.

[0651] In this way, the system helps users easily generate optimal menus and supports healthy and efficient eating habits. In particular, by combining it with an emotion engine, it can suggest menus based on the user's mood and emotional state, enabling more personalized meal choices. This also gives users a sense of mental satisfaction.

[0652] The processing flow will be explained below.

[0653] Step 1:

[0654] The user launches the application and the home screen appears.

[0655] Users input their mood for the day, their favorite ingredients, ingredients they want to avoid, allergy information, family composition, and budget.

[0656] The emotion engine uses facial recognition and voice analysis to recognize the user's emotional state (e.g., stress, anxiety, joy).

[0657] The user presses the "send" button, and the input data and the recognized emotion data are sent to the server.

[0658] Step 2:

[0659] A server receives user input data and emotion data.

[0660] The server sends a request to the API of the nearby store specified by the user to obtain inventory and price information.

[0661] The server receives the response from the store and obtains inventory and price information in JSON format.

[0662] For example, store A's inventory information "chicken available, spinach available, tofu available" and price information "200g chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen" are acquired.

[0663] Step 3:

[0664] The server runs an AI algorithm based on the user's input information and the acquired store inventory and price information.

[0665] The AI ​​algorithm takes into account the user's preferences, allergy information, family composition, and budget to generate the optimal menu.

[0666] The server generates a menu that matches the user's emotional state based on data from the emotion engine.

[0667] For example, if a user is feeling stressed, the app suggests dishes such as sautéed chicken and spinach or miso soup with tofu, which have a relaxing effect.

[0668] Step 4:

[0669] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[0670] The terminal receives the response from the server and displays the menu and recipe on the screen.

[0671] Examples of displayed items include "Sauteed chicken and spinach" and "Tofu miso soup," with detailed recipes for each provided.

[0672] It also displays a link that allows users to view the detailed recipe.

[0673] Step 5:

[0674] The user selects the menu they like from the displayed options and clicks the "View detailed recipe" button.

[0675] The device will open the link, allowing the user to visit the detailed recipe site.

[0676] The recipe site page will be displayed in your browser or in-app browser, where you can check the detailed instructions and necessary ingredients.

[0677] Step 6:

[0678] The server stores the user's emotional data and refers to the user's past emotional history to help generate subsequent menus.

[0679] By coordinating the emotion engine with the AI ​​algorithm, it is possible to provide menus that are more suited to the user's emotional state.

[0680] Through this series of steps, the system proposes optimal menus based on the user's mood and emotional state, allowing the user to enjoy cooking efficiently while gaining a sense of mental satisfaction.

[0681] Example 2

[0682] 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."

[0683] In today's busy lifestyles, it is difficult for users to easily plan healthy and balanced meals. Current systems also struggle to suggest menus that reflect the user's mood and emotional state. Therefore, there is a need for a system that allows users to easily create optimal menus based on their mood and health status on that day, and also has a shopping support function.

[0684] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's mood, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget, means for recognizing the user's emotional state using an emotion engine, means for acquiring inventory information and price information of nearby stores, means for generating a menu using artificial intelligence based on the input information, emotion information, and acquired information, and means for displaying the generated menu to the user. This allows the user to easily generate an optimal menu based on the user's mood and emotional state of the day and efficiently plan the purchase of necessary ingredients.

[0685] "User" refers to an individual or household representative who uses the system to receive menu suggestions.

[0686] "Mood" describes the user's mental or physical state for the day, and indicates an emotional state such as "tired" or "wanting to relax."

[0687] "Favorite ingredients" refers to ingredients that the user particularly likes to consume.

[0688] "Ingredients to avoid" refers to ingredients that a user wants to avoid consuming due to health reasons or specific food preferences.

[0689] "Allergy information" refers to information about specific ingredients to which a user or a member of their family has an allergic reaction.

[0690] "Family composition" refers to information indicating the number of people in the user's household and their relationships.

[0691] "Budget" refers to information indicating the upper limit of the amount of money a user can spend on the menu for that day.

[0692] "Emotion Engine" refers to a hardware and software system for detecting and analyzing a user's emotional state in real time.

[0693] "Nearby stores" refers to commercial facilities that are suppliers of ingredients and are located near the user's residence.

[0694] "Inventory Information" refers to detailed information about ingredients and products that a store has in stock and available for sale.

[0695] "Price information" refers to information indicating the selling price of each product in a store.

[0696] "Artificial intelligence" refers to a general term for computational algorithms and models that generate optimal menus based on user input, emotional information, and store information.

[0697] A "menu" refers to a number of dishes and their combinations that the user is expected to consume as part of their meal for that day.

[0698] A "recipe site" refers to a website that provides detailed instructions on how to make each dish and information on the ingredients used.

[0699] MODE FOR CARRYING OUT THE INVENTION

[0700] The present invention provides a system for suggesting optimal menus to enable users to easily cook at home. Specific embodiments of the system will be described below.

[0701] overview

[0702] The user inputs their mood, favorite ingredients, ingredients to avoid, allergies, family composition, budget, etc., and the system uses an emotion engine to recognize their emotional state. The server then obtains inventory and price information from nearby stores and uses artificial intelligence to generate an optimal menu. The generated menu is displayed on the user's device, and a link to a detailed recipe website is also provided.

[0703] Hardware and software used

[0704] This system uses the following hardware and software:

[0705] Server: processes and stores data, runs artificial intelligence models, and operates the emotion engine

[0706] Terminal: Receives input from users, displays menus, and accesses recipe sites

[0707] Emotion engine: Recognizes the user's emotional state (specifically, facial recognition APIs and emotion analysis models)

[0708] Artificial intelligence algorithms: implemented using TensorFlow and PyTorch

[0709] Data processing and calculation

[0710] 1. User information input and emotion recognition

[0711] Users launch the application on their smartphone or PC and enter their mood, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget.

[0712] The emotion engine recognizes the user's emotional state in real time through camera and voice input, using facial recognition APIs and emotion analysis algorithms.

[0713] 2. Obtaining store information

[0714] The server sends a request to the API of a nearby store to obtain inventory and price information. For example, store A's inventory information "chicken, spinach, tofu" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen" are received in JSON format.

[0715] 3. Generating optimal menus

[0716] The server uses an artificial intelligence algorithm to generate an optimal menu based on user input, emotional information, and store information. For example, if a user is feeling stressed, the server will suggest a menu that includes foods that have a relaxing effect. Specific examples include sauteed chicken and spinach and tofu miso soup.

[0717] 4. Menu display

[0718] The server formats the generated menu information in JSON format and sends it to the user's device. The device receives this information and displays the menu in the application's UI. As specific examples, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are also provided.

[0719] 5. Accumulation and learning of emotional data

[0720] The server accumulates user emotional data and menu history, and uses this data to retrain the AI ​​model, improving the accuracy of future suggestions.

[0721] Examples of concrete examples and prompts

[0722] As a concrete example, consider a scenario in which a user uses a system using the following prompt sentence:

[0723] "I'm feeling tired today, so please suggest a meal that will help me relax. I'd like to use chicken and vegetables."

[0724] "My child is allergic to eggs, so please tell me a family-friendly dinner menu that avoids eggs."

[0725] "What's the best recipe to eat when you're feeling stressed? Especially something that's easy to prepare."

[0726] Based on these prompts, the system analyzes the user's input and emotional information with high accuracy, and generates and suggests appropriate menus.

[0727] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0728] The flow of this system's program processing

[0729] Step 1: Enter your user information

[0730] input:

[0731] User's mood, favorite ingredients, ingredients to avoid, allergy information, family structure, budget

[0732] output:

[0733] User input information

[0734] Specific behavior:

[0735] The user launches the application on their smartphone or PC and the home screen appears.

[0736] Users input their mood for the day, food preferences, ingredients to avoid, allergy information, family composition, and budget.

[0737] This information is sent to the server as an HTTP request.

[0738] Step 2: Emotion Recognition

[0739] input:

[0740] User's emotional state (facial expression, voice input)

[0741] output:

[0742] User emotion information

[0743] Specific behavior:

[0744] It uses the user's camera and microphone to capture facial expressions and voice.

[0745] The emotion engine analyzes this data and recognizes the user's emotional state.

[0746] The recognized emotion information is sent to the server together with the user's input information.

[0747] Step 3: Get store information

[0748] input:

[0749] User location information

[0750] output:

[0751] Nearby store inventory and pricing information

[0752] Specific behavior:

[0753] The server sends a request to the API of nearby stores based on the user's location information.

[0754] The store API returns inventory and price information in JSON format.

[0755] The server analyzes the received information and stores it as usable data.

[0756] Step 4: Generate the optimal menu

[0757] input:

[0758] User input information, emotion information, store inventory information and price information

[0759] output:

[0760] Optimal menu information

[0761] Specific behavior:

[0762] The server runs an artificial intelligence algorithm based on the user's input information, emotional information, and acquired store information.

[0763] Artificial intelligence generates optimal menus using deep learning models such as TensorFlow and PyTorch.

[0764] For example, if a user is feeling stressed, a menu containing foods that have a relaxing effect will be suggested.

[0765] Step 5: Display the menu

[0766] input:

[0767] Optimal menu information

[0768] output:

[0769] View menus and detailed recipes

[0770] Specific behavior:

[0771] The server formats the generated menu information in JSON format.

[0772] This information is sent to the user's terminal.

[0773] The device displays the received information on the screen. For example, detailed recipes for "Sauteed Chicken and Spinach" and "Tofu Miso Soup" are displayed on the screen.

[0774] Step 6: Accumulating and Learning Emotional Data

[0775] input:

[0776] User's emotional information and meal history

[0777] output:

[0778] Improved menu suggestion model

[0779] Specific behavior:

[0780] The server stores the user's emotional data and menu history in a database.

[0781] This data is then used periodically to retrain the artificial intelligence algorithms.

[0782] This will improve the accuracy of menu generation from the next time onwards.

[0783] (Application example 2)

[0784] 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."

[0785] In today's busy lifestyles, it is difficult for users to select the optimal meal based on that day's mood, health condition, preferences, and budget, and have the appropriate meal delivered quickly. In particular, it was difficult for conventional systems to select meals based on the user's emotional state. Another challenge was providing personalized meal suggestions to accommodate a wide variety of food allergies and household configurations.

[0786] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's mood, preferences, allergy information, family composition, and budget; means for acquiring inventory and price information from nearby retailers; means for generating an optimal meal plan using an AI algorithm based on the input and acquired information; means for recognizing the user's emotional state and suggesting a meal plan based on the user's emotions; means for displaying the generated meal plan to the user; and means for providing an interface for ordering the meal plan and for processing delivery procedures. This enables the user to select the optimal meal based on their mood, emotional state, and individual needs for that day, and have it delivered quickly.

[0787] "Mood" refers to the user's emotional and psychological state on that day.

[0788] "Preferences" refers to the types of ingredients and dishes that the user likes.

[0789] "Allergy information" refers to information about food ingredients that a user should avoid consuming.

[0790] "Family structure" refers to information such as the number of family members and age groups of the user.

[0791] The "budget" refers to the range of amounts set by the user to be spent on cooking and ingredients.

[0792] A "server" refers to a device that processes information sent by a user and can acquire, store, and manage data via the Internet.

[0793] "Store" refers to a store or online shop that provides ingredients and dishes to users.

[0794] "Inventory information" refers to detailed information about ingredients and dishes currently available at a retailer.

[0795] "Price information" refers to information about the selling prices of ingredients and dishes at retail stores.

[0796] "AI algorithm" refers to an algorithm that automatically generates optimal cooking plans based on user input and acquired information.

[0797] "Meal plan" refers to the proposal of the optimal menu or meal based on the user's requirements.

[0798] "Emotional state" refers to the mental and emotional state of the user.

[0799] "Interface" refers to the screen display and input means that allow the user to operate the system.

[0800] "Delivery procedure" refers to the procedure for delivering the meal plan selected by the user to the location specified by the user.

[0801] This invention is a system that proposes optimal meal plans by having users input their mood, preferences, allergy information, family composition, and budget for the day, and then works in conjunction with food delivery services to quickly deliver meals.

[0802] The system consists of the following elements:

[0803] User information input method

[0804] A means of recognizing emotional states

[0805] How to obtain retailer inventory and pricing information

[0806] AI algorithm for generating meal plans

[0807] How to display meal plans

[0808] Delivery procedure interface

[0809] When the user launches the smartphone application, the home screen appears. The user inputs their mood for the day, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget. The emotion engine recognizes the user's mood and sends the input data along with that information to the server.

[0810] The server sends an API request to retrieve the user's input and sentiment information, as well as stock and price information from nearby retailers, in JSON format.

[0811] The server then uses AI algorithms (e.g., TensorFlow, PyTorch) to combine the user's input data with the acquired data to generate an optimal meal plan. An emotion engine helps select a meal plan based on the user's mood. For example, if the user wants to relax, the system will suggest dishes using ingredients with relaxing effects (e.g., grilled fish, spinach and tomato salad, and green tea).

[0812] The server sends the generated meal plan and recipe information to the user's device, which receives it and displays the meal plan and detailed information on the screen. The meal plan display also includes a specific ordering link, which the user can click to easily confirm the order.

[0813] Finally, the server sends the order information to the food delivery service's API and arranges for the food to be delivered to the specified address. Past search data and emotion data are stored on the server and used to improve the accuracy of future meal plans.

[0814] As a concrete example, the following prompt sentence is used:

[0815] "The user entered their mood for the day, their favorite ingredients, ingredients they want to avoid or have allergies, their family composition, and their budget. The emotion engine also recognized that the user wanted to relax. Please suggest the best relaxing dish for them."

[0816] As described above, this system provides optimal meal plans based on the user's mood, emotional state, and individual needs, and can deliver them quickly through a food delivery service. Furthermore, by learning from past data and improving the accuracy of future menu suggestions, it is possible to further increase user satisfaction.

[0817] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0818] Step 1:

[0819] The user launches the smartphone application and enters their mood for the day, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget on the home screen. The entered information is sent to the server as initial data. The emotion engine recognizes the user's mood, and this information is also sent to the server. The input data includes information registered by the user (mood: relaxation, favorite ingredients: fish, ingredients to avoid: tomatoes, etc.).

[0820] Step 2:

[0821] The server receives the input data sent by the user and sends an API request to get stock and price information from nearby stores. Based on the API request, stock information (e.g., fish, spinach, green tea, etc.) and price information (e.g., fish 500 yen, spinach 100 yen, green tea 150 yen) are obtained from the store in JSON format. This data is stored on the server.

[0822] Step 3:

[0823] The server uses AI algorithms (e.g., TensorFlow, PyTorch) to generate optimal meal plans based on the user's input data and the acquired inventory and price information. The AI ​​algorithm analyzes the input data (mood, preferences, budget, etc.) and inventory information to generate optimal meal plans based on the user's criteria. For example, it may select ingredients with a relaxing effect and create a meal plan (e.g., grilled fish, spinach and tomato salad, and green tea).

[0824] Step 4:

[0825] The server sends the generated meal plan to the device. The device receives this data and displays the meal plan and detailed information on the screen. This display includes a list of specific dishes (e.g., grilled fish, spinach and tomato salad) and an ordering link. The user can click the link to view detailed information about the dish.

[0826] Step 5:

[0827] The user selects the desired dish from the displayed meal plan and confirms the order by clicking the order link. The terminal then sends the user's order information to the server.

[0828] Step 6:

[0829] The server receives the order information from the user and sends it to the food delivery service's API. The food delivery service receives the order and arranges for the food to be delivered to the specified address. The server tracks the order status and notifies the user.

[0830] Step 7:

[0831] The server learns from past search data and emotion data and uses it to improve the accuracy of future cooking plan generation. The learning data is analyzed based on the user's past selections and emotional state, and is reflected in future suggestions.

[0832] Through the above processing steps, the user can be provided with the optimal meal plan that suits their mood and individual needs that day, and the meal can be delivered quickly.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] [Third embodiment]

[0837] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0838] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0839] 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).

[0840] 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.

[0841] 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.

[0842] 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).

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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."

[0849] MODE FOR CARRYING OUT THE INVENTION

[0850] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs their mood, preferences, allergy information, family composition, and budget for the day, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate the optimal menu. The generated menu is then displayed on the user's device.

[0851] Program processing and specific examples

[0852] 1. Enter your user information

[0853] The user launches the application and enters their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), budget (e.g., 2,000 yen), etc.

[0854] The entered information is sent to the server.

[0855] 2. Obtaining store information

[0856] The server obtains inventory and price information for nearby stores specified by the user through the API.

[0857] For example, store A's inventory information may be "chicken, spinach, and tofu available," and price information may be "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[0858] 3. Generating optimal menus

[0859] The server uses an AI algorithm to generate the optimal menu based on the user's input information and the acquired store information. The AI ​​algorithm also takes into account the user's past search data and suggests menus tailored to the user.

[0860] Specifically, the system generates dishes such as sauteed chicken and spinach and tofu miso soup.

[0861] 4. Menu display

[0862] The terminal displays the generated menu and its recipes to the user. For example, "Sauteed chicken and spinach" and "Miso soup with tofu" are displayed, and the respective recipes are provided.

[0863] Users who want to see more details about the recipe can click on the link to visit the recipe site.

[0864] This series of processes allows users to easily eliminate the hassle of planning menus and enjoy cooking at home. The system takes into account the user's individual needs and nutritional balance, and utilizes store inventory information to efficiently provide optimal menus. This is expected to have the effect of supporting healthy eating habits.

[0865] The processing flow will be explained below.

[0866] Step 1:

[0867] The user launches the application and the home screen appears.

[0868] The user inputs their mood for the day (e.g., Japanese food, Western food), preferred ingredients (e.g., chicken), ingredients to avoid or allergy information (e.g., dairy products), family size (e.g., 2 people), and budget per meal (e.g., 2,000 yen).

[0869] The user presses the "Submit" button and the input data is sent to the server.

[0870] Step 2:

[0871] The server receives the user's input data and sends a request to the API of the specified nearby store to obtain inventory and price information.

[0872] The server receives the response from the store and obtains inventory and price information in JSON format.

[0873] Example: Obtain store A's inventory information "Chicken available, spinach available, tofu available" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen."

[0874] Step 3:

[0875] The server runs an AI algorithm based on the user's input information and the acquired store inventory and price information to generate the optimal menu.

[0876] The server also references past search data to suggest menus suitable for the user, such as sautéed chicken and spinach or tofu miso soup.

[0877] Step 4:

[0878] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[0879] The device receives the response from the server and displays the menu and recipe on the screen. For example, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are provided.

[0880] Step 5:

[0881] The user selects the menu they like from the displayed options and clicks the "View detailed recipe" button.

[0882] The device will open the link, allowing the user to visit the detailed recipe site.

[0883] The recipe site page will be displayed in your browser or in-app browser, where you can check the detailed instructions and necessary ingredients.

[0884] In this way, the system helps users easily create optimal menus and supports healthy and efficient eating habits.

[0885] Example 1

[0886] 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."

[0887] In today's busy lifestyles, users need a system that can quickly select ingredients, purchase them, and plan menus so that they can cook at home easily and efficiently. However, it is extremely tedious for users to manually plan the optimal menu based on their individual preferences, allergies, family composition, and budget, and they must also consider inventory and price information from nearby stores. A system that can solve these problems and provide users with the optimal menu is needed.

[0888] 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.

[0889] In this invention, the server includes a means for inputting preferences, allergy information, family composition, and budget from the user, a means for acquiring inventory and price information from nearby stores, and a means for generating prompt sentences using a generative AI model based on the input and acquired information to generate an optimal menu, thereby enabling users to obtain an optimal menu that meets their individual needs without any hassle.

[0890] "User" refers to an individual who utilizes the system to input their information to generate menus.

[0891] "Terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0892] "Server" refers to a computer system that receives information sent by users and processes and analyzes the data.

[0893] "Preferences" refers to the user's food preferences and tastes, and includes categories such as Japanese food and Western food.

[0894] "Allergy information" refers to information about whether a user has an allergy to a particular food.

[0895] "Family composition" refers to the number of people who eat together and their composition.

[0896] "Budget" refers to the upper limit of the cost that a user sets for a single meal.

[0897] "Nearby stores" refers to stores and supermarkets that provide ingredients within the area specified by the user.

[0898] "Inventory information" refers to the availability and quantity of products in the store.

[0899] "Price information" refers to information regarding the selling price of each product.

[0900] "Means of acquisition" refers to the method or technology by which the server collects store inventory information and price information.

[0901] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate prompts from user input and store information, and then generates the optimal menu.

[0902] A "prompt sentence" refers to a sentence containing user information and store information to be input into the generative AI model.

[0903] "Optimal menu" refers to the most suitable dish suggestions for the user, generated by an AI algorithm based on the user's input information and store information.

[0904] "Display means" refers to the technology or method for displaying the generated menu on the user's terminal.

[0905] MODE FOR CARRYING OUT THE INVENTION

[0906] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs their mood and preferences for the day, allergy information, family composition, and budget, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate the optimal menu based on that information. This invention involves a series of steps: the user inputs information, the server receives and processes the information, and the final generated menu is displayed on the user's device.

[0907] Hardware and Software Used

[0908] User device: Input and display devices such as smartphones, tablets, and computers.

[0909] Server: A central system that receives, processes, and analyzes data. Specifically, it retrieves nearby store information and analyzes the data using generative AI models.

[0910] Generative AI model: An artificial intelligence algorithm that generates optimal menus based on user input and acquired information.

[0911] Explanation of system processing

[0912] 1. Enter your user information

[0913] The user launches the application and enters information such as preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen) into the form that appears on the screen.

[0914] Once the input is complete, the user clicks the "Submit" button.

[0915] The entered information is sent from the device to the server, where the data is structured in text or JSON format.

[0916] 2. Obtaining store information

[0917] The server calls the API to obtain store information for the area specified by the user.

[0918] The server collects inventory and price information from each store, analyzes it, and extracts the necessary information, such as "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[0919] 3. Generating optimal menus

[0920] The server launches a generative AI model based on user information and store information to generate a prompt sentence.

[0921] The generated prompt sentences are input into a generative AI model to generate the optimal menu. Specific examples of prompt sentences are as follows:

[0922] Please suggest a menu for two people who prefer Japanese food and have a dairy allergy, within a budget of 2000 yen. The inventory information for nearby stores is as follows: 200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen.

[0923] The system receives a menu and recipe returned by the generative AI model, such as "sauteed chicken and spinach" and "tofu miso soup."

[0924] 4. Menu display

[0925] The server sends the generated menu and recipes to the terminal.

[0926] The device displays the received information on its screen. Examples of displays include:

[0927] menu:

[0928] 1. Sauteed chicken and spinach

[0929] Recipe:

[0930] 200g chicken, cut into bite-sized pieces

[0931] Wash and chop a bunch of spinach

[0932] Sauté the chicken and spinach in a frying pan, season with salt and pepper.

[0933] 2. Tofu miso soup

[0934] Recipe:

[0935] 1 pack of tofu, cut into bite-sized pieces

[0936] Add water to a pot, add tofu and miso, and bring to a boil.

[0937] Users can view the recipe and start cooking, and are also provided with a link to view the detailed recipe.

[0938] This system allows users to easily find the optimal menu that meets their individual needs. Its unique feature is that it can provide optimal suggestions to users in real time by combining a generative AI model with store information.

[0939] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0940] Step 1: Enter your user information

[0941] The user starts the application and enters information such as preferences, allergy information, family composition, budget, etc. into the form displayed on the screen. For example, "Japanese food," "dairy allergy," "serves two," and "2,000 yen."

[0942] When the user has completed the input, he / she clicks the "Send" button. The submitted information is sent from the user's terminal to the server.

[0943] Input: Preferences, allergy information, family composition, budget

[0944] Output: User information sent to the server

[0945] Step 2: Get store information

[0946] The server calls the API to obtain store information for the area specified by the user.

[0947] The server sends an API request to obtain inventory and price information for nearby stores. For example, store A's inventory information might be "200g of chicken, 1 bunch of spinach, 1 pack of tofu" and its price information might be "chicken: 500 yen, spinach: 300 yen, tofu: 100 yen."

[0948] Input: User's locale

[0949] Output: Store inventory and pricing information

[0950] Step 3: Generate a prompt statement

[0951] The server generates a prompt sentence for the generative AI model based on user information and store information.

[0952] The server generates a prompt sentence by combining the preferences, allergy information, family structure, budget, and acquired store information input by the user.

[0953] An example of a generated prompt is as follows:

[0954] Please suggest a menu for two people who prefer Japanese food and have a dairy allergy, within a budget of 2000 yen. The inventory information for nearby stores is as follows: 200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen.

[0955] Input: User information (preferences, allergy information, family composition, budget), store information (stock information, price information)

[0956] Output: Prompt sentence for the generative AI model

[0957] Step 4: Generate the optimal menu

[0958] The server inputs the generated prompt sentences into a generative AI model to generate the optimal menu.

[0959] The generative AI model analyzes the prompt and generates the optimal menu for the user, for example, "Sauteed chicken and spinach" and "Tofu miso soup."

[0960] Input: Generated prompt statement

[0961] Output: Optimal menu (e.g., sauteed chicken and spinach, tofu miso soup)

[0962] Step 5: Display the menu

[0963] The server sends the generated menu and its recipes to the user's terminal.

[0964] The device displays the received menu and recipes on the screen.

[0965] Examples of displays include:

[0966] menu:

[0967] 1. Sauteed chicken and spinach

[0968] Recipe:

[0969] 200g chicken, cut into bite-sized pieces

[0970] Wash and chop a bunch of spinach

[0971] Sauté the chicken and spinach in a frying pan, season with salt and pepper.

[0972] 2. Tofu miso soup

[0973] Recipe:

[0974] 1 pack of tofu, cut into bite-sized pieces

[0975] Add water to a pot, add tofu and miso, and bring to a boil.

[0976] The user can start cooking using the displayed recipe, and a link to view the detailed recipe is also provided.

[0977] Input: Best menu and recipes

[0978] Output: Menu and recipe displayed on the user's device

[0979] (Application example 1)

[0980] 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."

[0981] In recent years, busy urban lifestyles have made it difficult for consumers to cook at home in order to maintain an efficient and healthy diet. Furthermore, consumers often lack the means to quickly check the availability and price information of ingredients at nearby stores, which can lead to inefficient shopping. Furthermore, consumers often waste time and effort due to lack of knowledge of efficient shopping routes. The purpose of this invention is to solve these problems and provide a system that allows users to shop efficiently and effectively and enjoy cooking at home.

[0982] 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.

[0983] In this invention, the server includes means for inputting preferences, allergy information, family composition, and budget from the user, means for acquiring inventory and price information from nearby retail stores, means for generating menus using an AI algorithm based on the input and acquired information, means for displaying the generated menus to the user, and means for displaying efficient routes for shopping in the store. This enables users to streamline their consumer lifestyles, generate optimal shopping lists and menus, and reduce wasted time and effort in the store.

[0984] "User" refers to a consumer or individual who uses the system.

[0985] "Preferences" refers to the user's dietary preferences and tastes.

[0986] "Allergy information" refers to information including foods and ingredients that a user cannot consume.

[0987] "Family structure" refers to information about the number and structure of the user's family.

[0988] "Budget" refers to the upper limit of the amount set by the user for the ingredients they plan to purchase.

[0989] "Retail store" means a store that sells ingredients and food products.

[0990] "Inventory information" refers to information regarding the availability and quantity of a product at a particular store.

[0991] "Price Information" means information regarding the price at which a particular product is sold in a store.

[0992] An "AI algorithm" refers to a calculation procedure that uses artificial intelligence technology to analyze data and derive optimal solutions.

[0993] "Menu" means a collection of meal menus and recipes for a certain period of time.

[0994] A "circulation route" refers to a path that a user takes to efficiently move around the store and find the desired product.

[0995] "Server" means a computer system for processing data and providing services to users.

[0996] This invention is a system that suggests optimal menus for users to efficiently cook at home and also supports shopping in physical stores. The system runs on devices such as smartphones and tablets, and when users input their preferences, allergies, family composition, and budget for the day, it obtains inventory and price information from nearby retailers and uses an AI algorithm to generate optimal menus and shopping lists. It also suggests efficient routes for people to navigate within the store based on this information.

[0997] Hardware and software used

[0998] Hardware: Smartphones, tablets, personal computers

[0999] Software: Mobile applications, cloud servers, RESTful APIs, AI algorithms, map APIs (e.g., Google Maps API)

[1000] Data processing and calculation

[1001] 1. Enter your user information

[1002] Through the application, users input their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen).

[1003] The entered information is sent to a cloud server and stored.

[1004] 2. Obtaining store information

[1005] The server obtains inventory and price information from nearby retailers via API based on the user's location information.

[1006] For example, store A's inventory information may be "chicken, spinach, and tofu available," and price information may be "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[1007] 3. Generate optimal menus and shopping lists

[1008] The server uses an AI algorithm to generate optimal menus and shopping lists based on the user's input information and the acquired store information.

[1009] Specifically, the system generates dishes such as sauteed chicken and spinach and tofu miso soup.

[1010] 4. Displaying efficient routes within the store

[1011] Based on the generated shopping list, a map API is used to display the optimal route within the store.

[1012] The user can shop while moving around the store efficiently based on the proposed route.

[1013] Specific examples

[1014] For example, a user opens a smartphone application and enters their preference as Japanese food, dairy allergy information, family size for two people, and a budget of 2,000 yen. Based on this information, the server retrieves inventory and price information from nearby retailers and uses an AI algorithm to suggest a menu of "sauteed chicken and spinach" and "tofu miso soup." It then compares the price at which each ingredient is sold at each store and displays an efficient route to get around.

[1015] Example prompt sentence:

[1016] Please suggest the optimal menu based on the user's preferences. The preference is Japanese food, the allergy information is dairy allergy, the family size is for two people, and the budget is 2000 yen. Please generate the optimal menu and shopping list based on inventory and price information from nearby physical stores, and display the route to go around. The location is Tokyo.

[1017] This allows users to easily maintain an efficient and healthy diet.

[1018] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1019] Step 1:

[1020] The user launches the application using a device (e.g., a smartphone or tablet). The user inputs their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen). This information is sent to the cloud server via the application. Input: User's preferences, allergy information, family composition, budget. Output: User information stored on the cloud server.

[1021] Step 2:

[1022] The server obtains inventory and price information from nearby retailers based on the user's location information. It communicates with the store system using a RESTful API. The response it receives is inventory status and price information for each retailer. Input: User's location information. Output: Retailer inventory and price information.

[1023] Step 3:

[1024] The server uses an AI algorithm to generate optimal menus and shopping lists based on the user's input information (preferences, allergy information, family composition, budget) and acquired store information (stock information and price information). The AI ​​algorithm used here compares it with past data to improve accuracy. Input: User information, store information. Output: Optimal menus and shopping lists.

[1025] Step 4:

[1026] The terminal displays the optimal menu and shopping list sent from the server, including detailed recipes and a list of products to purchase. The user can view the generated list and check links to detailed recipe sites. Input: Menu and shopping list generated by the server. Output: Menu and shopping list displayed on the terminal.

[1027] Step 5:

[1028] The server generates an efficient route for shopping in the store and sends it to the device. At this time, it uses a map API (e.g., Google Maps API) to visualize the location of each product and calculate the shortest route. Input: Product location information within the store. Output: Shopping route displayed on the device.

[1029] Step 6:

[1030] The terminal displays an efficient shopping route to the user. The user can move around the store by following the suggested route and efficiently purchase the desired products. Input: Shopping route generated by the server. Output: Shopping route displayed to the user.

[1031] 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.

[1032] MODE FOR CARRYING OUT THE INVENTION

[1033] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs the day's mood, preferences, allergy information, family composition, and budget, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate an optimal menu based on this information. The generated menu is displayed on the user's device. Furthermore, this invention incorporates an emotion engine that recognizes the user's emotions, making it possible to generate an optimal menu based on the user's emotions.

[1034] Program processing and specific examples

[1035] 1. Enter your user information

[1036] The user launches the application and the home screen is displayed.

[1037] Users input their mood for the day, their favorite ingredients, ingredients they want to avoid, allergy information, family composition, and budget.

[1038] Additionally, an emotion engine recognizes the user's emotional state (e.g., stress, anxiety, joy).

[1039] The entered and recognized information is sent to the server.

[1040] 2. Obtaining store information

[1041] The server receives the user's input and recognition information and sends a request to the API of the specified nearby store to obtain inventory and price information.

[1042] The server receives the response from the store and obtains inventory and price information in JSON format.

[1043] Example: Obtain store A's inventory information "Chicken available, spinach available, tofu available" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen."

[1044] 3. Generating optimal menus

[1045] The server runs an AI algorithm based on the user's input information, recognition information, and acquired store inventory and price information to generate the optimal menu.

[1046] Additionally, an emotion engine assists in menu selection based on the user's emotions.

[1047] For example, if a user is feeling stressed, the system will suggest a menu that includes foods that have a relaxing effect, such as sauteed chicken and spinach or miso soup with tofu.

[1048] 4. Menu display

[1049] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[1050] The device receives the response from the server and displays the menu and recipe on the screen. For example, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are provided.

[1051] Users who want to see more details about the recipe can click on the link to visit the recipe site.

[1052] 5. Accumulation and learning of emotional data

[1053] The server accumulates the user's emotional data and improves the accuracy of the menu by referring to the user's past emotional history.

[1054] The server coordinates the AI ​​algorithm and emotion engine to help generate future menus.

[1055] In this way, the system helps users easily generate optimal menus and supports healthy and efficient eating habits. In particular, by combining it with an emotion engine, it can suggest menus based on the user's mood and emotional state, enabling more personalized meal choices. This also gives users a sense of mental satisfaction.

[1056] The processing flow will be explained below.

[1057] Step 1:

[1058] The user launches the application and the home screen appears.

[1059] Users input their mood for the day, their favorite ingredients, ingredients they want to avoid, allergy information, family composition, and budget.

[1060] The emotion engine uses facial recognition and voice analysis to recognize the user's emotional state (e.g., stress, anxiety, joy).

[1061] The user presses the "send" button, and the input data and the recognized emotion data are sent to the server.

[1062] Step 2:

[1063] A server receives user input data and emotion data.

[1064] The server sends a request to the API of the nearby store specified by the user to obtain inventory and price information.

[1065] The server receives the response from the store and obtains inventory and price information in JSON format.

[1066] For example, store A's inventory information "chicken available, spinach available, tofu available" and price information "200g chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen" are acquired.

[1067] Step 3:

[1068] The server runs an AI algorithm based on the user's input information and the acquired store inventory and price information.

[1069] The AI ​​algorithm takes into account the user's preferences, allergy information, family composition, and budget to generate the optimal menu.

[1070] The server generates a menu that matches the user's emotional state based on data from the emotion engine.

[1071] For example, if a user is feeling stressed, the app suggests dishes such as sautéed chicken and spinach or miso soup with tofu, which have a relaxing effect.

[1072] Step 4:

[1073] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[1074] The terminal receives the response from the server and displays the menu and recipe on the screen.

[1075] Examples of displayed items include "Sauteed chicken and spinach" and "Tofu miso soup," with detailed recipes for each provided.

[1076] It also displays a link that allows users to view the detailed recipe.

[1077] Step 5:

[1078] The user selects the menu they like from the displayed options and clicks the "View detailed recipe" button.

[1079] The device will open the link, allowing the user to visit the detailed recipe site.

[1080] The recipe site page will be displayed in your browser or in-app browser, where you can check the detailed instructions and necessary ingredients.

[1081] Step 6:

[1082] The server stores the user's emotional data and refers to the user's past emotional history to help generate subsequent menus.

[1083] By coordinating the emotion engine with the AI ​​algorithm, it is possible to provide menus that are more suited to the user's emotional state.

[1084] Through this series of steps, the system proposes optimal menus based on the user's mood and emotional state, allowing the user to enjoy cooking efficiently while gaining a sense of mental satisfaction.

[1085] Example 2

[1086] 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."

[1087] In today's busy lifestyles, it is difficult for users to easily plan healthy and balanced meals. Current systems also struggle to suggest menus that reflect the user's mood and emotional state. Therefore, there is a need for a system that allows users to easily create optimal menus based on their mood and health status on that day, and also has a shopping support function.

[1088] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's mood, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget, means for recognizing the user's emotional state using an emotion engine, means for acquiring inventory information and price information of nearby stores, means for generating a menu using artificial intelligence based on the input information, emotion information, and acquired information, and means for displaying the generated menu to the user. This allows the user to easily generate an optimal menu based on the user's mood and emotional state of the day and efficiently plan the purchase of necessary ingredients.

[1089] "User" refers to an individual or household representative who uses the system to receive menu suggestions.

[1090] "Mood" describes the user's mental or physical state for the day, and indicates an emotional state such as "tired" or "wanting to relax."

[1091] "Favorite ingredients" refers to ingredients that the user particularly likes to consume.

[1092] "Ingredients to avoid" refers to ingredients that a user wants to avoid consuming due to health reasons or specific food preferences.

[1093] "Allergy information" refers to information about specific ingredients to which a user or a member of their family has an allergic reaction.

[1094] "Family composition" refers to information indicating the number of people in the user's household and their relationships.

[1095] "Budget" refers to information indicating the upper limit of the amount of money a user can spend on the menu for that day.

[1096] "Emotion Engine" refers to a hardware and software system for detecting and analyzing a user's emotional state in real time.

[1097] "Nearby stores" refers to commercial facilities that are suppliers of ingredients and are located near the user's residence.

[1098] "Inventory Information" refers to detailed information about ingredients and products that a store has in stock and available for sale.

[1099] "Price information" refers to information indicating the selling price of each product in a store.

[1100] "Artificial intelligence" refers to a general term for computational algorithms and models that generate optimal menus based on user input, emotional information, and store information.

[1101] A "menu" refers to a number of dishes and their combinations that the user is expected to consume as part of their meal for that day.

[1102] A "recipe site" refers to a website that provides detailed instructions on how to make each dish and information on the ingredients used.

[1103] MODE FOR CARRYING OUT THE INVENTION

[1104] The present invention provides a system for suggesting optimal menus to enable users to easily cook at home. Specific embodiments of the system will be described below.

[1105] overview

[1106] The user inputs their mood, favorite ingredients, ingredients to avoid, allergies, family composition, budget, etc., and the system uses an emotion engine to recognize their emotional state. The server then obtains inventory and price information from nearby stores and uses artificial intelligence to generate an optimal menu. The generated menu is displayed on the user's device, and a link to a detailed recipe website is also provided.

[1107] Hardware and software used

[1108] This system uses the following hardware and software:

[1109] Server: processes and stores data, runs artificial intelligence models, and operates the emotion engine

[1110] Terminal: Receives input from users, displays menus, and accesses recipe sites

[1111] Emotion engine: Recognizes the user's emotional state (specifically, facial recognition APIs and emotion analysis models)

[1112] Artificial intelligence algorithms: implemented using TensorFlow and PyTorch

[1113] Data processing and calculation

[1114] 1. User information input and emotion recognition

[1115] Users launch the application on their smartphone or PC and enter their mood, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget.

[1116] The emotion engine recognizes the user's emotional state in real time through camera and voice input, using facial recognition APIs and emotion analysis algorithms.

[1117] 2. Obtaining store information

[1118] The server sends a request to the API of a nearby store to obtain inventory and price information. For example, store A's inventory information "chicken, spinach, tofu" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen" are received in JSON format.

[1119] 3. Generating optimal menus

[1120] The server uses an artificial intelligence algorithm to generate an optimal menu based on user input, emotional information, and store information. For example, if a user is feeling stressed, the server will suggest a menu that includes foods that have a relaxing effect. Specific examples include sauteed chicken and spinach and tofu miso soup.

[1121] 4. Menu display

[1122] The server formats the generated menu information in JSON format and sends it to the user's device. The device receives this information and displays the menu in the application's UI. As specific examples, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are also provided.

[1123] 5. Accumulation and learning of emotional data

[1124] The server accumulates user emotional data and menu history, and uses this data to retrain the AI ​​model, improving the accuracy of future suggestions.

[1125] Examples of concrete examples and prompts

[1126] As a concrete example, consider a scenario in which a user uses a system using the following prompt sentence:

[1127] "I'm feeling tired today, so please suggest a meal that will help me relax. I'd like to use chicken and vegetables."

[1128] "My child is allergic to eggs, so please tell me a family-friendly dinner menu that avoids eggs."

[1129] "What's the best recipe to eat when you're feeling stressed? Especially something that's easy to prepare."

[1130] Based on these prompts, the system analyzes the user's input and emotional information with high accuracy, and generates and suggests appropriate menus.

[1131] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1132] The flow of this system's program processing

[1133] Step 1: Enter your user information

[1134] input:

[1135] User's mood, favorite ingredients, ingredients to avoid, allergy information, family structure, budget

[1136] output:

[1137] User input information

[1138] Specific behavior:

[1139] The user launches the application on their smartphone or PC and the home screen appears.

[1140] Users input their mood for the day, food preferences, ingredients to avoid, allergy information, family composition, and budget.

[1141] This information is sent to the server as an HTTP request.

[1142] Step 2: Emotion Recognition

[1143] input:

[1144] User's emotional state (facial expression, voice input)

[1145] output:

[1146] User emotion information

[1147] Specific behavior:

[1148] It uses the user's camera and microphone to capture facial expressions and voice.

[1149] The emotion engine analyzes this data and recognizes the user's emotional state.

[1150] The recognized emotion information is sent to the server together with the user's input information.

[1151] Step 3: Get store information

[1152] input:

[1153] User location information

[1154] output:

[1155] Nearby store inventory and pricing information

[1156] Specific behavior:

[1157] The server sends a request to the API of nearby stores based on the user's location information.

[1158] The store API returns inventory and price information in JSON format.

[1159] The server analyzes the received information and stores it as usable data.

[1160] Step 4: Generate the optimal menu

[1161] input:

[1162] User input information, emotion information, store inventory information and price information

[1163] output:

[1164] Optimal menu information

[1165] Specific behavior:

[1166] The server runs an artificial intelligence algorithm based on the user's input information, emotional information, and acquired store information.

[1167] Artificial intelligence generates optimal menus using deep learning models such as TensorFlow and PyTorch.

[1168] For example, if a user is feeling stressed, a menu containing foods that have a relaxing effect will be suggested.

[1169] Step 5: Display the menu

[1170] input:

[1171] Optimal menu information

[1172] output:

[1173] View menus and detailed recipes

[1174] Specific behavior:

[1175] The server formats the generated menu information in JSON format.

[1176] This information is sent to the user's terminal.

[1177] The device displays the received information on the screen. For example, detailed recipes for "Sauteed Chicken and Spinach" and "Tofu Miso Soup" are displayed on the screen.

[1178] Step 6: Accumulating and Learning Emotional Data

[1179] input:

[1180] User's emotional information and meal history

[1181] output:

[1182] Improved menu suggestion model

[1183] Specific behavior:

[1184] The server stores the user's emotional data and menu history in a database.

[1185] This data is then used periodically to retrain the artificial intelligence algorithms.

[1186] This will improve the accuracy of menu generation from the next time onwards.

[1187] (Application example 2)

[1188] 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."

[1189] In today's busy lifestyles, it is difficult for users to select the optimal meal based on that day's mood, health condition, preferences, and budget, and have the appropriate meal delivered quickly. In particular, it was difficult for conventional systems to select meals based on the user's emotional state. Another challenge was providing personalized meal suggestions to accommodate a wide variety of food allergies and household configurations.

[1190] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's mood, preferences, allergy information, family composition, and budget; means for acquiring inventory and price information from nearby retailers; means for generating an optimal meal plan using an AI algorithm based on the input and acquired information; means for recognizing the user's emotional state and suggesting a meal plan based on the user's emotions; means for displaying the generated meal plan to the user; and means for providing an interface for ordering the meal plan and for processing delivery procedures. This enables the user to select the optimal meal based on their mood, emotional state, and individual needs for that day, and have it delivered quickly.

[1191] "Mood" refers to the user's emotional and psychological state on that day.

[1192] "Preferences" refers to the types of ingredients and dishes that the user likes.

[1193] "Allergy information" refers to information about food ingredients that a user should avoid consuming.

[1194] "Family structure" refers to information such as the number of family members and age groups of the user.

[1195] The "budget" refers to the range of amounts set by the user to be spent on cooking and ingredients.

[1196] A "server" refers to a device that processes information sent by a user and can acquire, store, and manage data via the Internet.

[1197] "Store" refers to a store or online shop that provides ingredients and dishes to users.

[1198] "Inventory information" refers to detailed information about ingredients and dishes currently available at a retailer.

[1199] "Price information" refers to information about the selling prices of ingredients and dishes at retail stores.

[1200] "AI algorithm" refers to an algorithm that automatically generates optimal cooking plans based on user input and acquired information.

[1201] "Meal plan" refers to the proposal of the optimal menu or meal based on the user's requirements.

[1202] "Emotional state" refers to the mental and emotional state of the user.

[1203] "Interface" refers to the screen display and input means that allow the user to operate the system.

[1204] "Delivery procedure" refers to the procedure for delivering the meal plan selected by the user to the location specified by the user.

[1205] This invention is a system that proposes optimal meal plans by having users input their mood, preferences, allergy information, family composition, and budget for the day, and then works in conjunction with food delivery services to quickly deliver meals.

[1206] The system consists of the following elements:

[1207] User information input method

[1208] A means of recognizing emotional states

[1209] How to obtain retailer inventory and pricing information

[1210] AI algorithm for generating meal plans

[1211] How to display meal plans

[1212] Delivery procedure interface

[1213] When the user launches the smartphone application, the home screen appears. The user inputs their mood for the day, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget. The emotion engine recognizes the user's mood and sends the input data along with that information to the server.

[1214] The server sends an API request to retrieve the user's input and sentiment information, as well as stock and price information from nearby retailers, in JSON format.

[1215] The server then uses AI algorithms (e.g., TensorFlow, PyTorch) to combine the user's input data with the acquired data to generate an optimal meal plan. An emotion engine helps select a meal plan based on the user's mood. For example, if the user wants to relax, the system will suggest dishes using ingredients with relaxing effects (e.g., grilled fish, spinach and tomato salad, and green tea).

[1216] The server sends the generated meal plan and recipe information to the user's device, which receives it and displays the meal plan and detailed information on the screen. The meal plan display also includes a specific ordering link, which the user can click to easily confirm the order.

[1217] Finally, the server sends the order information to the food delivery service's API and arranges for the food to be delivered to the specified address. Past search data and emotion data are stored on the server and used to improve the accuracy of future meal plans.

[1218] As a concrete example, the following prompt sentence is used:

[1219] "The user entered their mood for the day, their favorite ingredients, ingredients they want to avoid or have allergies, their family composition, and their budget. The emotion engine also recognized that the user wanted to relax. Please suggest the best relaxing dish for them."

[1220] As described above, this system provides optimal meal plans based on the user's mood, emotional state, and individual needs, and can deliver them quickly through a food delivery service. Furthermore, by learning from past data and improving the accuracy of future menu suggestions, it is possible to further increase user satisfaction.

[1221] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1222] Step 1:

[1223] The user launches the smartphone application and enters their mood for the day, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget on the home screen. The entered information is sent to the server as initial data. The emotion engine recognizes the user's mood, and this information is also sent to the server. The input data includes information registered by the user (mood: relaxation, favorite ingredients: fish, ingredients to avoid: tomatoes, etc.).

[1224] Step 2:

[1225] The server receives the input data sent by the user and sends an API request to get stock and price information from nearby stores. Based on the API request, stock information (e.g., fish, spinach, green tea, etc.) and price information (e.g., fish 500 yen, spinach 100 yen, green tea 150 yen) are obtained from the store in JSON format. This data is stored on the server.

[1226] Step 3:

[1227] The server uses AI algorithms (e.g., TensorFlow, PyTorch) to generate optimal meal plans based on the user's input data and the acquired inventory and price information. The AI ​​algorithm analyzes the input data (mood, preferences, budget, etc.) and inventory information to generate optimal meal plans based on the user's criteria. For example, it may select ingredients with a relaxing effect and create a meal plan (e.g., grilled fish, spinach and tomato salad, and green tea).

[1228] Step 4:

[1229] The server sends the generated meal plan to the device. The device receives this data and displays the meal plan and detailed information on the screen. This display includes a list of specific dishes (e.g., grilled fish, spinach and tomato salad) and an ordering link. The user can click the link to view detailed information about the dish.

[1230] Step 5:

[1231] The user selects the desired dish from the displayed meal plan and confirms the order by clicking the order link. The terminal then sends the user's order information to the server.

[1232] Step 6:

[1233] The server receives the order information from the user and sends it to the food delivery service's API. The food delivery service receives the order and arranges for the food to be delivered to the specified address. The server tracks the order status and notifies the user.

[1234] Step 7:

[1235] The server learns from past search data and emotion data and uses it to improve the accuracy of future cooking plan generation. The learning data is analyzed based on the user's past selections and emotional state, and is reflected in future suggestions.

[1236] Through the above processing steps, the user can be provided with the optimal meal plan that suits their mood and individual needs that day, and the meal can be delivered quickly.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] [Fourth embodiment]

[1241] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1242] 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.

[1243] 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).

[1244] 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.

[1245] 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.

[1246] 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).

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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."

[1254] MODE FOR CARRYING OUT THE INVENTION

[1255] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs their mood, preferences, allergy information, family composition, and budget for the day, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate the optimal menu. The generated menu is then displayed on the user's device.

[1256] Program processing and specific examples

[1257] 1. Enter your user information

[1258] The user launches the application and enters their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), budget (e.g., 2,000 yen), etc.

[1259] The entered information is sent to the server.

[1260] 2. Obtaining store information

[1261] The server obtains inventory and price information for nearby stores specified by the user through the API.

[1262] For example, store A's inventory information may be "chicken, spinach, and tofu available," and price information may be "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[1263] 3. Generating optimal menus

[1264] The server uses an AI algorithm to generate the optimal menu based on the user's input information and the acquired store information. The AI ​​algorithm also takes into account the user's past search data and suggests menus tailored to the user.

[1265] Specifically, the system generates dishes such as sauteed chicken and spinach and tofu miso soup.

[1266] 4. Menu display

[1267] The terminal displays the generated menu and its recipes to the user. For example, "Sauteed chicken and spinach" and "Miso soup with tofu" are displayed, and the respective recipes are provided.

[1268] Users who want to see more details about the recipe can click on the link to visit the recipe site.

[1269] This series of processes allows users to easily eliminate the hassle of planning menus and enjoy cooking at home. The system takes into account the user's individual needs and nutritional balance, and utilizes store inventory information to efficiently provide optimal menus. This is expected to have the effect of supporting healthy eating habits.

[1270] The processing flow will be explained below.

[1271] Step 1:

[1272] The user launches the application and the home screen appears.

[1273] The user inputs their mood for the day (e.g., Japanese food, Western food), preferred ingredients (e.g., chicken), ingredients to avoid or allergy information (e.g., dairy products), family size (e.g., 2 people), and budget per meal (e.g., 2,000 yen).

[1274] The user presses the "Submit" button and the input data is sent to the server.

[1275] Step 2:

[1276] The server receives the user's input data and sends a request to the API of the specified nearby store to obtain inventory and price information.

[1277] The server receives the response from the store and obtains inventory and price information in JSON format.

[1278] Example: Obtain store A's inventory information "Chicken available, spinach available, tofu available" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen."

[1279] Step 3:

[1280] The server runs an AI algorithm based on the user's input information and the acquired store inventory and price information to generate the optimal menu.

[1281] The server also references past search data to suggest menus suitable for the user, such as sautéed chicken and spinach or tofu miso soup.

[1282] Step 4:

[1283] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[1284] The device receives the response from the server and displays the menu and recipe on the screen. For example, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are provided.

[1285] Step 5:

[1286] The user selects the menu they like from the displayed options and clicks the "View detailed recipe" button.

[1287] The device will open the link, allowing the user to visit the detailed recipe site.

[1288] The recipe site page will be displayed in your browser or in-app browser, where you can check the detailed instructions and necessary ingredients.

[1289] In this way, the system helps users easily create optimal menus and supports healthy and efficient eating habits.

[1290] Example 1

[1291] 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."

[1292] In today's busy lifestyles, users need a system that can quickly select ingredients, purchase them, and plan menus so that they can cook at home easily and efficiently. However, it is extremely tedious for users to manually plan the optimal menu based on their individual preferences, allergies, family composition, and budget, and they must also consider inventory and price information from nearby stores. A system that can solve these problems and provide users with the optimal menu is needed.

[1293] 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.

[1294] In this invention, the server includes a means for inputting preferences, allergy information, family composition, and budget from the user, a means for acquiring inventory and price information from nearby stores, and a means for generating prompt sentences using a generative AI model based on the input and acquired information to generate an optimal menu, thereby enabling users to obtain an optimal menu that meets their individual needs without any hassle.

[1295] "User" refers to an individual who utilizes the system to input their information to generate menus.

[1296] "Terminal" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[1297] "Server" refers to a computer system that receives information sent by users and processes and analyzes the data.

[1298] "Preferences" refers to the user's food preferences and tastes, and includes categories such as Japanese food and Western food.

[1299] "Allergy information" refers to information about whether a user has an allergy to a particular food.

[1300] "Family composition" refers to the number of people who eat together and their composition.

[1301] "Budget" refers to the upper limit of the cost that a user sets for a single meal.

[1302] "Nearby stores" refers to stores and supermarkets that provide ingredients within the area specified by the user.

[1303] "Inventory information" refers to the availability and quantity of products in the store.

[1304] "Price information" refers to information regarding the selling price of each product.

[1305] "Means of acquisition" refers to the method or technology by which the server collects store inventory information and price information.

[1306] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate prompts from user input and store information, and then generates the optimal menu.

[1307] A "prompt sentence" refers to a sentence containing user information and store information to be input into the generative AI model.

[1308] "Optimal menu" refers to the most suitable dish suggestions for the user, generated by an AI algorithm based on the user's input information and store information.

[1309] "Display means" refers to the technology or method for displaying the generated menu on the user's terminal.

[1310] MODE FOR CARRYING OUT THE INVENTION

[1311] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs their mood and preferences for the day, allergy information, family composition, and budget, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate the optimal menu based on that information. This invention involves a series of steps: the user inputs information, the server receives and processes the information, and the final generated menu is displayed on the user's device.

[1312] Hardware and Software Used

[1313] User device: Input and display devices such as smartphones, tablets, and computers.

[1314] Server: A central system that receives, processes, and analyzes data. Specifically, it retrieves nearby store information and analyzes the data using generative AI models.

[1315] Generative AI model: An artificial intelligence algorithm that generates optimal menus based on user input and acquired information.

[1316] Explanation of system processing

[1317] 1. Enter your user information

[1318] The user launches the application and enters information such as preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen) into the form that appears on the screen.

[1319] Once the input is complete, the user clicks the "Submit" button.

[1320] The entered information is sent from the device to the server, where the data is structured in text or JSON format.

[1321] 2. Obtaining store information

[1322] The server calls the API to obtain store information for the area specified by the user.

[1323] The server collects inventory and price information from each store, analyzes it, and extracts the necessary information, such as "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[1324] 3. Generating optimal menus

[1325] The server launches a generative AI model based on user information and store information to generate a prompt sentence.

[1326] The generated prompt sentences are input into a generative AI model to generate the optimal menu. Specific examples of prompt sentences are as follows:

[1327] Please suggest a menu for two people who prefer Japanese food and have a dairy allergy, within a budget of 2000 yen. The inventory information for nearby stores is as follows: 200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen.

[1328] The system receives a menu and recipe returned by the generative AI model, such as "sauteed chicken and spinach" and "tofu miso soup."

[1329] 4. Menu display

[1330] The server sends the generated menu and recipes to the terminal.

[1331] The device displays the received information on its screen. Examples of displays include:

[1332] menu:

[1333] 1. Sauteed chicken and spinach

[1334] Recipe:

[1335] 200g chicken, cut into bite-sized pieces

[1336] Wash and chop a bunch of spinach

[1337] Sauté the chicken and spinach in a frying pan, season with salt and pepper.

[1338] 2. Tofu miso soup

[1339] Recipe:

[1340] 1 pack of tofu, cut into bite-sized pieces

[1341] Add water to a pot, add tofu and miso, and bring to a boil.

[1342] Users can view the recipe and start cooking, and are also provided with a link to view the detailed recipe.

[1343] This system allows users to easily find the optimal menu that meets their individual needs. Its unique feature is that it can provide optimal suggestions to users in real time by combining a generative AI model with store information.

[1344] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1345] Step 1: Enter your user information

[1346] The user starts the application and enters information such as preferences, allergy information, family composition, budget, etc. into the form displayed on the screen. For example, "Japanese food," "dairy allergy," "serves two," and "2,000 yen."

[1347] When the user has completed the input, he / she clicks the "Send" button. The submitted information is sent from the user's terminal to the server.

[1348] Input: Preferences, allergy information, family composition, budget

[1349] Output: User information sent to the server

[1350] Step 2: Get store information

[1351] The server calls the API to obtain store information for the area specified by the user.

[1352] The server sends an API request to obtain inventory and price information for nearby stores. For example, store A's inventory information might be "200g of chicken, 1 bunch of spinach, 1 pack of tofu" and its price information might be "chicken: 500 yen, spinach: 300 yen, tofu: 100 yen."

[1353] Input: User's locale

[1354] Output: Store inventory and pricing information

[1355] Step 3: Generate a prompt statement

[1356] The server generates a prompt sentence for the generative AI model based on user information and store information.

[1357] The server generates a prompt sentence by combining the preferences, allergy information, family structure, budget, and acquired store information input by the user.

[1358] An example of a generated prompt is as follows:

[1359] Please suggest a menu for two people who prefer Japanese food and have a dairy allergy, within a budget of 2000 yen. The inventory information for nearby stores is as follows: 200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen.

[1360] Input: User information (preferences, allergy information, family composition, budget), store information (stock information, price information)

[1361] Output: Prompt sentence for the generative AI model

[1362] Step 4: Generate the optimal menu

[1363] The server inputs the generated prompt sentences into a generative AI model to generate the optimal menu.

[1364] The generative AI model analyzes the prompt and generates the optimal menu for the user, for example, "Sauteed chicken and spinach" and "Tofu miso soup."

[1365] Input: Generated prompt statement

[1366] Output: Optimal menu (e.g., sauteed chicken and spinach, tofu miso soup)

[1367] Step 5: Display the menu

[1368] The server sends the generated menu and its recipes to the user's terminal.

[1369] The device displays the received menu and recipes on the screen.

[1370] Examples of displays include:

[1371] menu:

[1372] 1. Sauteed chicken and spinach

[1373] Recipe:

[1374] 200g chicken, cut into bite-sized pieces

[1375] Wash and chop a bunch of spinach

[1376] Sauté the chicken and spinach in a frying pan, season with salt and pepper.

[1377] 2. Tofu miso soup

[1378] Recipe:

[1379] 1 pack of tofu, cut into bite-sized pieces

[1380] Add water to a pot, add tofu and miso, and bring to a boil.

[1381] The user can start cooking using the displayed recipe, and a link to view the detailed recipe is also provided.

[1382] Input: Best menu and recipes

[1383] Output: Menu and recipe displayed on the user's device

[1384] (Application example 1)

[1385] 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."

[1386] In recent years, busy urban lifestyles have made it difficult for consumers to cook at home in order to maintain an efficient and healthy diet. Furthermore, consumers often lack the means to quickly check the availability and price information of ingredients at nearby stores, which can lead to inefficient shopping. Furthermore, consumers often waste time and effort due to lack of knowledge of efficient shopping routes. The purpose of this invention is to solve these problems and provide a system that allows users to shop efficiently and effectively and enjoy cooking at home.

[1387] 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.

[1388] In this invention, the server includes means for inputting preferences, allergy information, family composition, and budget from the user, means for acquiring inventory and price information from nearby retail stores, means for generating menus using an AI algorithm based on the input and acquired information, means for displaying the generated menus to the user, and means for displaying efficient routes for shopping in the store. This enables users to streamline their consumer lifestyles, generate optimal shopping lists and menus, and reduce wasted time and effort in the store.

[1389] "User" refers to a consumer or individual who uses the system.

[1390] "Preferences" refers to the user's dietary preferences and tastes.

[1391] "Allergy information" refers to information including foods and ingredients that a user cannot consume.

[1392] "Family structure" refers to information about the number and structure of the user's family.

[1393] "Budget" refers to the upper limit of the amount set by the user for the ingredients they plan to purchase.

[1394] "Retail store" means a store that sells ingredients and food products.

[1395] "Inventory information" refers to information regarding the availability and quantity of a product at a particular store.

[1396] "Price Information" means information regarding the price at which a particular product is sold in a store.

[1397] An "AI algorithm" refers to a calculation procedure that uses artificial intelligence technology to analyze data and derive optimal solutions.

[1398] "Menu" means a collection of meal menus and recipes for a certain period of time.

[1399] A "circulation route" refers to a path that a user takes to efficiently move around the store and find the desired product.

[1400] "Server" means a computer system for processing data and providing services to users.

[1401] This invention is a system that suggests optimal menus for users to efficiently cook at home and also supports shopping in physical stores. The system runs on devices such as smartphones and tablets, and when users input their preferences, allergies, family composition, and budget for the day, it obtains inventory and price information from nearby retailers and uses an AI algorithm to generate optimal menus and shopping lists. It also suggests efficient routes for people to navigate within the store based on this information.

[1402] Hardware and software used

[1403] Hardware: Smartphones, tablets, personal computers

[1404] Software: Mobile applications, cloud servers, RESTful APIs, AI algorithms, map APIs (e.g., Google Maps API)

[1405] Data processing and calculation

[1406] 1. Enter your user information

[1407] Through the application, users input their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen).

[1408] The entered information is sent to a cloud server and stored.

[1409] 2. Obtaining store information

[1410] The server obtains inventory and price information from nearby retailers via API based on the user's location information.

[1411] For example, store A's inventory information may be "chicken, spinach, and tofu available," and price information may be "200g of chicken for 500 yen, a bunch of spinach for 300 yen, and a pack of tofu for 100 yen."

[1412] 3. Generate optimal menus and shopping lists

[1413] The server uses an AI algorithm to generate optimal menus and shopping lists based on the user's input information and the acquired store information.

[1414] Specifically, the system generates dishes such as sauteed chicken and spinach and tofu miso soup.

[1415] 4. Displaying efficient routes within the store

[1416] Based on the generated shopping list, a map API is used to display the optimal route within the store.

[1417] The user can shop while moving around the store efficiently based on the proposed route.

[1418] Specific examples

[1419] For example, a user opens a smartphone application and enters their preference as Japanese food, dairy allergy information, family size for two people, and a budget of 2,000 yen. Based on this information, the server retrieves inventory and price information from nearby retailers and uses an AI algorithm to suggest a menu of "sauteed chicken and spinach" and "tofu miso soup." It then compares the price at which each ingredient is sold at each store and displays an efficient route to get around.

[1420] Example prompt sentence:

[1421] Please suggest the optimal menu based on the user's preferences. The preference is Japanese food, the allergy information is dairy allergy, the family size is for two people, and the budget is 2000 yen. Please generate the optimal menu and shopping list based on inventory and price information from nearby physical stores, and display the route to go around. The location is Tokyo.

[1422] This allows users to easily maintain an efficient and healthy diet.

[1423] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1424] Step 1:

[1425] The user launches the application using a device (e.g., a smartphone or tablet). The user inputs their preferences (e.g., Japanese food, Western food), allergy information (e.g., dairy allergy), family composition (e.g., for two people), and budget (e.g., 2,000 yen). This information is sent to the cloud server via the application. Input: User's preferences, allergy information, family composition, budget. Output: User information stored on the cloud server.

[1426] Step 2:

[1427] The server obtains inventory and price information from nearby retailers based on the user's location information. It communicates with the store system using a RESTful API. The response it receives is inventory status and price information for each retailer. Input: User's location information. Output: Retailer inventory and price information.

[1428] Step 3:

[1429] The server uses an AI algorithm to generate optimal menus and shopping lists based on the user's input information (preferences, allergy information, family composition, budget) and acquired store information (stock information and price information). The AI ​​algorithm used here compares it with past data to improve accuracy. Input: User information, store information. Output: Optimal menus and shopping lists.

[1430] Step 4:

[1431] The terminal displays the optimal menu and shopping list sent from the server, including detailed recipes and a list of products to purchase. The user can view the generated list and check links to detailed recipe sites. Input: Menu and shopping list generated by the server. Output: Menu and shopping list displayed on the terminal.

[1432] Step 5:

[1433] The server generates an efficient route for shopping in the store and sends it to the device. At this time, it uses a map API (e.g., Google Maps API) to visualize the location of each product and calculate the shortest route. Input: Product location information within the store. Output: Shopping route displayed on the device.

[1434] Step 6:

[1435] The terminal displays an efficient shopping route to the user. The user can move around the store by following the suggested route and efficiently purchase the desired products. Input: Shopping route generated by the server. Output: Shopping route displayed to the user.

[1436] 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.

[1437] MODE FOR CARRYING OUT THE INVENTION

[1438] This invention is a system that suggests optimal menus for users to easily cook at home. When a user inputs the day's mood, preferences, allergy information, family composition, and budget, the system obtains inventory and price information from nearby stores and uses an AI algorithm to generate an optimal menu based on this information. The generated menu is displayed on the user's device. Furthermore, this invention incorporates an emotion engine that recognizes the user's emotions, making it possible to generate an optimal menu based on the user's emotions.

[1439] Program processing and specific examples

[1440] 1. Enter your user information

[1441] The user launches the application and the home screen is displayed.

[1442] Users input their mood for the day, their favorite ingredients, ingredients they want to avoid, allergy information, family composition, and budget.

[1443] Additionally, an emotion engine recognizes the user's emotional state (e.g., stress, anxiety, joy).

[1444] The entered and recognized information is sent to the server.

[1445] 2. Obtaining store information

[1446] The server receives the user's input and recognition information and sends a request to the API of the specified nearby store to obtain inventory and price information.

[1447] The server receives the response from the store and obtains inventory and price information in JSON format.

[1448] Example: Obtain store A's inventory information "Chicken available, spinach available, tofu available" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen."

[1449] 3. Generating optimal menus

[1450] The server runs an AI algorithm based on the user's input information, recognition information, and acquired store inventory and price information to generate the optimal menu.

[1451] Additionally, an emotion engine assists in menu selection based on the user's emotions.

[1452] For example, if a user is feeling stressed, the system will suggest a menu that includes foods that have a relaxing effect, such as sauteed chicken and spinach or miso soup with tofu.

[1453] 4. Menu display

[1454] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[1455] The device receives the response from the server and displays the menu and recipe on the screen. For example, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are provided.

[1456] Users who want to see more details about the recipe can click on the link to visit the recipe site.

[1457] 5. Accumulation and learning of emotional data

[1458] The server accumulates the user's emotional data and improves the accuracy of the menu by referring to the user's past emotional history.

[1459] The server coordinates the AI ​​algorithm and emotion engine to help generate future menus.

[1460] In this way, the system helps users easily generate optimal menus and supports healthy and efficient eating habits. In particular, by combining it with an emotion engine, it can suggest menus based on the user's mood and emotional state, enabling more personalized meal choices. This also gives users a sense of mental satisfaction.

[1461] The processing flow will be explained below.

[1462] Step 1:

[1463] The user launches the application and the home screen appears.

[1464] Users input their mood for the day, their favorite ingredients, ingredients they want to avoid, allergy information, family composition, and budget.

[1465] The emotion engine uses facial recognition and voice analysis to recognize the user's emotional state (e.g., stress, anxiety, joy).

[1466] The user presses the "send" button, and the input data and the recognized emotion data are sent to the server.

[1467] Step 2:

[1468] A server receives user input data and emotion data.

[1469] The server sends a request to the API of the nearby store specified by the user to obtain inventory and price information.

[1470] The server receives the response from the store and obtains inventory and price information in JSON format.

[1471] For example, store A's inventory information "chicken available, spinach available, tofu available" and price information "200g chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen" are acquired.

[1472] Step 3:

[1473] The server runs an AI algorithm based on the user's input information and the acquired store inventory and price information.

[1474] The AI ​​algorithm takes into account the user's preferences, allergy information, family composition, and budget to generate the optimal menu.

[1475] The server generates a menu that matches the user's emotional state based on data from the emotion engine.

[1476] For example, if a user is feeling stressed, the app suggests dishes such as sautéed chicken and spinach or miso soup with tofu, which have a relaxing effect.

[1477] Step 4:

[1478] The server formats the generated menu and its recipe information in JSON format and sends it to the user's device.

[1479] The terminal receives the response from the server and displays the menu and recipe on the screen.

[1480] Examples of displayed items include "Sauteed chicken and spinach" and "Tofu miso soup," with detailed recipes for each provided.

[1481] It also displays a link that allows users to view the detailed recipe.

[1482] Step 5:

[1483] The user selects the menu they like from the displayed options and clicks the "View detailed recipe" button.

[1484] The device will open the link, allowing the user to visit the detailed recipe site.

[1485] The recipe site page will be displayed in your browser or in-app browser, where you can check the detailed instructions and necessary ingredients.

[1486] Step 6:

[1487] The server stores the user's emotional data and refers to the user's past emotional history to help generate subsequent menus.

[1488] By coordinating the emotion engine with the AI ​​algorithm, it is possible to provide menus that are more suited to the user's emotional state.

[1489] Through this series of steps, the system proposes optimal menus based on the user's mood and emotional state, allowing the user to enjoy cooking efficiently while gaining a sense of mental satisfaction.

[1490] Example 2

[1491] 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."

[1492] In today's busy lifestyles, it is difficult for users to easily plan healthy and balanced meals. Current systems also struggle to suggest menus that reflect the user's mood and emotional state. Therefore, there is a need for a system that allows users to easily create optimal menus based on their mood and health status on that day, and also has a shopping support function.

[1493] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's mood, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget, means for recognizing the user's emotional state using an emotion engine, means for acquiring inventory information and price information of nearby stores, means for generating a menu using artificial intelligence based on the input information, emotion information, and acquired information, and means for displaying the generated menu to the user. This allows the user to easily generate an optimal menu based on the user's mood and emotional state of the day and efficiently plan the purchase of necessary ingredients.

[1494] "User" refers to an individual or household representative who uses the system to receive menu suggestions.

[1495] "Mood" describes the user's mental or physical state for the day, and indicates an emotional state such as "tired" or "wanting to relax."

[1496] "Favorite ingredients" refers to ingredients that the user particularly likes to consume.

[1497] "Ingredients to avoid" refers to ingredients that a user wants to avoid consuming due to health reasons or specific food preferences.

[1498] "Allergy information" refers to information about specific ingredients to which a user or a member of their family has an allergic reaction.

[1499] "Family composition" refers to information indicating the number of people in the user's household and their relationships.

[1500] "Budget" refers to information indicating the upper limit of the amount of money a user can spend on the menu for that day.

[1501] "Emotion Engine" refers to a hardware and software system for detecting and analyzing a user's emotional state in real time.

[1502] "Nearby stores" refers to commercial facilities that are suppliers of ingredients and are located near the user's residence.

[1503] "Inventory Information" refers to detailed information about ingredients and products that a store has in stock and available for sale.

[1504] "Price information" refers to information indicating the selling price of each product in a store.

[1505] "Artificial intelligence" refers to a general term for computational algorithms and models that generate optimal menus based on user input, emotional information, and store information.

[1506] A "menu" refers to a number of dishes and their combinations that the user is expected to consume as part of their meal for that day.

[1507] A "recipe site" refers to a website that provides detailed instructions on how to make each dish and information on the ingredients used.

[1508] MODE FOR CARRYING OUT THE INVENTION

[1509] The present invention provides a system for suggesting optimal menus to enable users to easily cook at home. Specific embodiments of the system will be described below.

[1510] overview

[1511] The user inputs their mood, favorite ingredients, ingredients to avoid, allergies, family composition, budget, etc., and the system uses an emotion engine to recognize their emotional state. The server then obtains inventory and price information from nearby stores and uses artificial intelligence to generate an optimal menu. The generated menu is displayed on the user's device, and a link to a detailed recipe website is also provided.

[1512] Hardware and software used

[1513] This system uses the following hardware and software:

[1514] Server: processes and stores data, runs artificial intelligence models, and operates the emotion engine

[1515] Terminal: Receives input from users, displays menus, and accesses recipe sites

[1516] Emotion engine: Recognizes the user's emotional state (specifically, facial recognition APIs and emotion analysis models)

[1517] Artificial intelligence algorithms: implemented using TensorFlow and PyTorch

[1518] Data processing and calculation

[1519] 1. User information input and emotion recognition

[1520] Users launch the application on their smartphone or PC and enter their mood, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget.

[1521] The emotion engine recognizes the user's emotional state in real time through camera and voice input, using facial recognition APIs and emotion analysis algorithms.

[1522] 2. Obtaining store information

[1523] The server sends a request to the API of a nearby store to obtain inventory and price information. For example, store A's inventory information "chicken, spinach, tofu" and price information "200g of chicken for 500 yen, 1 bunch of spinach for 300 yen, 1 pack of tofu for 100 yen" are received in JSON format.

[1524] 3. Generating optimal menus

[1525] The server uses an artificial intelligence algorithm to generate an optimal menu based on user input, emotional information, and store information. For example, if a user is feeling stressed, the server will suggest a menu that includes foods that have a relaxing effect. Specific examples include sauteed chicken and spinach and tofu miso soup.

[1526] 4. Menu display

[1527] The server formats the generated menu information in JSON format and sends it to the user's device. The device receives this information and displays the menu in the application's UI. As specific examples, "Sauteed chicken and spinach" and "Tofu miso soup" are displayed, and detailed recipes for each are also provided.

[1528] 5. Accumulation and learning of emotional data

[1529] The server accumulates user emotional data and menu history, and uses this data to retrain the AI ​​model, improving the accuracy of future suggestions.

[1530] Examples of concrete examples and prompts

[1531] As a concrete example, consider a scenario in which a user uses a system using the following prompt sentence:

[1532] "I'm feeling tired today, so please suggest a meal that will help me relax. I'd like to use chicken and vegetables."

[1533] "My child is allergic to eggs, so please tell me a family-friendly dinner menu that avoids eggs."

[1534] "What's the best recipe to eat when you're feeling stressed? Especially something that's easy to prepare."

[1535] Based on these prompts, the system analyzes the user's input and emotional information with high accuracy, and generates and suggests appropriate menus.

[1536] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1537] The flow of this system's program processing

[1538] Step 1: Enter your user information

[1539] input:

[1540] User's mood, favorite ingredients, ingredients to avoid, allergy information, family structure, budget

[1541] output:

[1542] User input information

[1543] Specific behavior:

[1544] The user launches the application on their smartphone or PC and the home screen appears.

[1545] Users input their mood for the day, food preferences, ingredients to avoid, allergy information, family composition, and budget.

[1546] This information is sent to the server as an HTTP request.

[1547] Step 2: Emotion Recognition

[1548] input:

[1549] User's emotional state (facial expression, voice input)

[1550] output:

[1551] User emotion information

[1552] Specific behavior:

[1553] It uses the user's camera and microphone to capture facial expressions and voice.

[1554] The emotion engine analyzes this data and recognizes the user's emotional state.

[1555] The recognized emotion information is sent to the server together with the user's input information.

[1556] Step 3: Get store information

[1557] input:

[1558] User location information

[1559] output:

[1560] Nearby store inventory and pricing information

[1561] Specific behavior:

[1562] The server sends a request to the API of nearby stores based on the user's location information.

[1563] The store API returns inventory and price information in JSON format.

[1564] The server analyzes the received information and stores it as usable data.

[1565] Step 4: Generate the optimal menu

[1566] input:

[1567] User input information, emotion information, store inventory information and price information

[1568] output:

[1569] Optimal menu information

[1570] Specific behavior:

[1571] The server runs an artificial intelligence algorithm based on the user's input information, emotional information, and acquired store information.

[1572] Artificial intelligence generates optimal menus using deep learning models such as TensorFlow and PyTorch.

[1573] For example, if a user is feeling stressed, a menu containing foods that have a relaxing effect will be suggested.

[1574] Step 5: Display the menu

[1575] input:

[1576] Optimal menu information

[1577] output:

[1578] View menus and detailed recipes

[1579] Specific behavior:

[1580] The server formats the generated menu information in JSON format.

[1581] This information is sent to the user's terminal.

[1582] The device displays the received information on the screen. For example, detailed recipes for "Sauteed Chicken and Spinach" and "Tofu Miso Soup" are displayed on the screen.

[1583] Step 6: Accumulating and Learning Emotional Data

[1584] input:

[1585] User's emotional information and meal history

[1586] output:

[1587] Improved menu suggestion model

[1588] Specific behavior:

[1589] The server stores the user's emotional data and menu history in a database.

[1590] This data is then used periodically to retrain the artificial intelligence algorithms.

[1591] This will improve the accuracy of menu generation from the next time onwards.

[1592] (Application example 2)

[1593] 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."

[1594] In today's busy lifestyles, it is difficult for users to select the optimal meal based on that day's mood, health condition, preferences, and budget, and have the appropriate meal delivered quickly. In particular, it was difficult for conventional systems to select meals based on the user's emotional state. Another challenge was providing personalized meal suggestions to accommodate a wide variety of food allergies and household configurations.

[1595] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's mood, preferences, allergy information, family composition, and budget; means for acquiring inventory and price information from nearby retailers; means for generating an optimal meal plan using an AI algorithm based on the input and acquired information; means for recognizing the user's emotional state and suggesting a meal plan based on the user's emotions; means for displaying the generated meal plan to the user; and means for providing an interface for ordering the meal plan and for processing delivery procedures. This enables the user to select the optimal meal based on their mood, emotional state, and individual needs for that day, and have it delivered quickly.

[1596] "Mood" refers to the user's emotional and psychological state on that day.

[1597] "Preferences" refers to the types of ingredients and dishes that the user likes.

[1598] "Allergy information" refers to information about food ingredients that a user should avoid consuming.

[1599] "Family structure" refers to information such as the number of family members and age groups of the user.

[1600] The "budget" refers to the range of amounts set by the user to be spent on cooking and ingredients.

[1601] A "server" refers to a device that processes information sent by a user and can acquire, store, and manage data via the Internet.

[1602] "Store" refers to a store or online shop that provides ingredients and dishes to users.

[1603] "Inventory information" refers to detailed information about ingredients and dishes currently available at a retailer.

[1604] "Price information" refers to information about the selling prices of ingredients and dishes at retail stores.

[1605] "AI algorithm" refers to an algorithm that automatically generates optimal cooking plans based on user input and acquired information.

[1606] "Meal plan" refers to the proposal of the optimal menu or meal based on the user's requirements.

[1607] "Emotional state" refers to the mental and emotional state of the user.

[1608] "Interface" refers to the screen display and input means that allow the user to operate the system.

[1609] "Delivery procedure" refers to the procedure for delivering the meal plan selected by the user to the location specified by the user.

[1610] This invention is a system that proposes optimal meal plans by having users input their mood, preferences, allergy information, family composition, and budget for the day, and then works in conjunction with food delivery services to quickly deliver meals.

[1611] The system consists of the following elements:

[1612] User information input method

[1613] A means of recognizing emotional states

[1614] How to obtain retailer inventory and pricing information

[1615] AI algorithm for generating meal plans

[1616] How to display meal plans

[1617] Delivery procedure interface

[1618] When the user launches the smartphone application, the home screen appears. The user inputs their mood for the day, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget. The emotion engine recognizes the user's mood and sends the input data along with that information to the server.

[1619] The server sends an API request to retrieve the user's input and sentiment information, as well as stock and price information from nearby retailers, in JSON format.

[1620] The server then uses AI algorithms (e.g., TensorFlow, PyTorch) to combine the user's input data with the acquired data to generate an optimal meal plan. An emotion engine helps select a meal plan based on the user's mood. For example, if the user wants to relax, the system will suggest dishes using ingredients with relaxing effects (e.g., grilled fish, spinach and tomato salad, and green tea).

[1621] The server sends the generated meal plan and recipe information to the user's device, which receives it and displays the meal plan and detailed information on the screen. The meal plan display also includes a specific ordering link, which the user can click to easily confirm the order.

[1622] Finally, the server sends the order information to the food delivery service's API and arranges for the food to be delivered to the specified address. Past search data and emotion data are stored on the server and used to improve the accuracy of future meal plans.

[1623] As a concrete example, the following prompt sentence is used:

[1624] "The user entered their mood for the day, their favorite ingredients, ingredients they want to avoid or have allergies, their family composition, and their budget. The emotion engine also recognized that the user wanted to relax. Please suggest the best relaxing dish for them."

[1625] As described above, this system provides optimal meal plans based on the user's mood, emotional state, and individual needs, and can deliver them quickly through a food delivery service. Furthermore, by learning from past data and improving the accuracy of future menu suggestions, it is possible to further increase user satisfaction.

[1626] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1627] Step 1:

[1628] The user launches the smartphone application and enters their mood for the day, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget on the home screen. The entered information is sent to the server as initial data. The emotion engine recognizes the user's mood, and this information is also sent to the server. The input data includes information registered by the user (mood: relaxation, favorite ingredients: fish, ingredients to avoid: tomatoes, etc.).

[1629] Step 2:

[1630] The server receives the input data sent by the user and sends an API request to get stock and price information from nearby stores. Based on the API request, stock information (e.g., fish, spinach, green tea, etc.) and price information (e.g., fish 500 yen, spinach 100 yen, green tea 150 yen) are obtained from the store in JSON format. This data is stored on the server.

[1631] Step 3:

[1632] The server uses AI algorithms (e.g., TensorFlow, PyTorch) to generate optimal meal plans based on the user's input data and the acquired inventory and price information. The AI ​​algorithm analyzes the input data (mood, preferences, budget, etc.) and inventory information to generate optimal meal plans based on the user's criteria. For example, it may select ingredients with a relaxing effect and create a meal plan (e.g., grilled fish, spinach and tomato salad, and green tea).

[1633] Step 4:

[1634] The server sends the generated meal plan to the device. The device receives this data and displays the meal plan and detailed information on the screen. This display includes a list of specific dishes (e.g., grilled fish, spinach and tomato salad) and an ordering link. The user can click the link to view detailed information about the dish.

[1635] Step 5:

[1636] The user selects the desired dish from the displayed meal plan and confirms the order by clicking the order link. The terminal then sends the user's order information to the server.

[1637] Step 6:

[1638] The server receives the order information from the user and sends it to the food delivery service's API. The food delivery service receives the order and arranges for the food to be delivered to the specified address. The server tracks the order status and notifies the user.

[1639] Step 7:

[1640] The server learns from past search data and emotion data and uses it to improve the accuracy of future cooking plan generation. The learning data is analyzed based on the user's past selections and emotional state, and is reflected in future suggestions.

[1641] Through the above processing steps, the user can be provided with the optimal meal plan that suits their mood and individual needs that day, and the meal can be delivered quickly.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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).

[1649] 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.

[1650] 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."

[1651] 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.

[1652] 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).

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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.

[1663] The following is further disclosed regarding the above embodiment.

[1664] (Claim 1)

[1665] A means for users to input preferences, allergy information, family composition, and budget;

[1666] a means for obtaining inventory and pricing information for nearby stores;

[1667] A means for generating a menu using an AI algorithm based on the input information and acquired information;

[1668] means for displaying the generated menu to a user;

[1669] A system including:

[1670] (Claim 2)

[1671] 10. The system of claim 1, further comprising means for learning from past search data and providing optimal suggestions to the user.

[1672] (Claim 3)

[1673] 10. The system of claim 1, further comprising: means for adding a link to a detailed recipe site.

[1674] "Example 1"

[1675] (Claim 1)

[1676] A means for users to input preferences, allergy information, family composition, and budget;

[1677] a means for obtaining inventory and pricing information for nearby stores;

[1678] A means for generating a prompt sentence using a generative AI model based on the input information and the acquired information, and generating an optimal menu;

[1679] A means for displaying the generated menu on a user's terminal;

[1680] A system including:

[1681] (Claim 2)

[1682] 10. The system of claim 1, further comprising means for learning from past search data and providing optimal suggestions to the user.

[1683] (Claim 3)

[1684] 10. The system of claim 1, further comprising: means for adding a link to a detailed recipe site.

[1685] "Application Example 1"

[1686] (Claim 1)

[1687] A means for users to input preferences, allergy information, family composition, and budget;

[1688] a means for obtaining inventory and pricing information from nearby retailers;

[1689] A means for generating a menu using an AI algorithm based on the input information and acquired information;

[1690] means for displaying the generated menu to a user;

[1691] A means of displaying efficient routes within the store,

[1692] A system including:

[1693] (Claim 2)

[1694] 10. The system of claim 1, further comprising means for learning from past search data and providing optimal suggestions to the user.

[1695] (Claim 3)

[1696] 10. The system of claim 1, further comprising: means for adding a link to a detailed recipe site.

[1697] "Example 2: Combining Emotion Engines"

[1698] (Claim 1)

[1699] A means for users to input their mood, favorite ingredients, ingredients to avoid, allergy information, family composition, and budget.

[1700] means for recognizing an emotional state of a user using an emotion engine;

[1701] a means for obtaining inventory and pricing information for nearby stores;

[1702] A means for generating a menu using artificial intelligence based on the input information, emotion information, and acquired information;

[1703] means for displaying the generated menu to a user;

[1704] A system including:

[1705] (Claim 2)

[1706] 2. The system according to claim 1, further comprising means for accumulating past emotion data and menu history, and having an artificial intelligence model learn from the data to provide optimal suggestions to the user.

[1707] (Claim 3)

[1708] 10. The system of claim 1, further comprising: means for adding a link to a detailed recipe site.

[1709] "Application example 2 when combining emotion engines"

[1710] (Claim 1)

[1711] A means for users to input their mood, preferences, allergy information, family composition, and budget;

[1712] a means for obtaining inventory and pricing information from nearby retailers;

[1713] A means for generating an optimal cooking plan using an AI algorithm based on the input information and acquired information;

[1714] means for recognizing the emotional state of a user and providing emotion-based meal plan suggestions;

[1715] means for displaying the generated cooking plan to a user;

[1716] A system that provides an interface for ordering meal plans and includes a means for arranging delivery.

[1717] (Claim 2)

[1718] 10. The system of claim 1, further comprising means for learning past search data and sentiment data to provide optimal suggestions to the user.

[1719] (Claim 3)

[1720] 10. The system of claim 1, further comprising: means for adding a link to a detailed recipe site. [Explanation of symbols]

[1721] 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 users to input preferences, allergy information, family composition, and budget; a means for obtaining inventory and pricing information for nearby stores; A means for generating a menu using an AI algorithm based on the input information and acquired information; means for displaying the generated menu to a user; A system including:

2. The system of claim 1 further comprising means for learning past search data and providing optimal suggestions to the user.

3. The system of claim 1 further comprising: means for adding a link to a detailed recipe site.

Images