System

Abstract

A system is provided.SOLUTION: The system includes a means for registering personal information, a means for acquiring nearest store information, a means for generating a menu in consideration of nutritional balance based on the registered information and the acquired store information, and a means for displaying the generated menu.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's busy lifestyles, many families lack the time to prepare nutritionally balanced meals. In particular, those who are primarily responsible for cooking or those who live alone tend to have limited menu options, leading to nutritionally unbalanced meals. Furthermore, rising prices have created a growing need to reduce household food costs. Existing menu suggestion systems are often impractical, unable to provide suggestions that take into account the specific needs of individual users or information about nearby restaurants. There is a need to address these issues and simultaneously achieve health management for the entire family and efficient management of food costs. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides a system having the following configuration.

[0006] The system includes a means for registering personal information, a means for acquiring information on the nearest store, a means for generating a menu that takes into consideration nutritional balance based on the registered information and the acquired store information, and a means for displaying the generated menu.

[0007] Specifically, users register their personal information, family composition, allergy information, and food preferences in the system. Based on the registered information, the system retrieves advertisement information for the store closest to the user's location and generates a menu that takes nutritional balance into consideration. This menu also includes a list of necessary ingredients and their prices, allowing users to efficiently select and purchase ingredients for their daily meals. This allows users to prepare nutritionally balanced meals without spending a lot of time and also save on food costs.

[0008] "Personal information" refers to personal data such as the user's name, age, height, weight, allergy information, food preferences, and family composition.

[0009] "Nearest store information" refers to advertising information, sale information, price information, etc. of the nearest store based on the user's location.

[0010] "Means for registration" refers to the method by which a user enters their information into the system and stores it in the database.

[0011] "Means for obtaining" refers to a method for obtaining necessary advertising information and price information from the nearest store based on the user's location.

[0012] "Means of generation" refers to the process by which AI creates a menu that takes nutritional balance into consideration based on registered user information and acquired store information.

[0013] "Display means" refers to a method for outputting the generated menu, list of necessary ingredients, price information, etc. to the user's terminal.

[0014] The term "system" refers to a comprehensive tool that includes the above means and supports the user in managing their health and food expenses efficiently. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention relates to a system that improves the efficiency of meal preparation at home in busy daily lives and proposes menus that take nutritional balance into consideration. This system provides user information, information on the nearest store, and menus that take into consideration overall nutritional balance, and is implemented in the following form.

[0037] Registering user information

[0038] The user uses the device to enter personal information and family composition information, such as the user's and family members' names, ages, heights, weights, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database.

[0039] Get information about the nearest store

[0040] The server acquires the nearest store information based on the location of the registered user information, including advertisement information, sale information, and price information acquired from the store corresponding to the user's location.

[0041] Menu generation

[0042] The server uses an AI algorithm to generate a menu that takes into account nutritional balance based on user information and acquired store information. This menu generation process takes into account the user's food preferences, allergies, family composition, etc. to suggest the most suitable menu for each individual's needs.

[0043] Menu display

[0044] The generated menu is then sent back to the user's device, where the user can check the suggested menu, the list of ingredients needed, and the total price. It also displays discounted items based on store sales information, helping users save money on food.

[0045] Specific examples

[0046] For example, consider a situation where a user registers information about themselves and their family, and the nearest store is having a "sale" and offering "beef" at a discounted price. In this case, the server suggests a menu item such as "beef stew." The user can check the details of "beef stew" on their device and find out the list of ingredients needed, such as "beef," "onion," "carrot," and "potato," along with their prices. In addition, store sales information is taken into consideration, allowing the user to purchase ingredients at a discounted price. Also, if the user's family has a nut allergy, the AI ​​will suggest nut-free menu items.

[0047] In this way, the present system is able to provide efficient and healthy meal suggestions based on the user's personal information, information on the nearest store, and nutritional balance.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] The user enters personal and family information into the device, such as name, age, height, weight, allergy information, food preferences, and family composition.

[0051] Step 2:

[0052] The device sends the entered user information to the server, which converts the data into JSON format and sends it as a POST request to the API endpoint.

[0053] Step 3:

[0054] The server stores the received data in a database, which is used for subsequent processing.

[0055] Step 4:

[0056] The device obtains the user's location information, either via GPS or manual user input.

[0057] Step 5:

[0058] The server retrieves the nearest store information based on the user's location information. Specifically, an API request is sent to retrieve data including store advertising and sales information.

[0059] Step 6:

[0060] The server uses an AI algorithm to generate menus based on user information and the nearest store, and suggests nutritionally balanced menus taking into account registered user information (allergies, food preferences, etc.).

[0061] Step 7:

[0062] The server generates a menu and sends it to the device. The menu includes a list of ingredients and pricing information.

[0063] Step 8:

[0064] The device displays the menu information it receives on the screen, allowing users to check the suggested menu, ingredients needed, and total price.

[0065] Step 9:

[0066] The user can check the menu displayed on the device and purchase ingredients as needed, while also taking advantage of sales information to save on food costs.

[0067] Example 1

[0068] 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."

[0069] Meal preparation at home can be a significant burden in busy daily lives, and it can be difficult to come up with a menu that takes nutritional balance into account. For this reason, there is a need for a system that streamlines meal preparation and suggests nutritionally balanced menus that take family health into consideration. It is also necessary to accommodate food allergies and individual preferences. Furthermore, it is important to have a way to purchase ingredients economically by utilizing information on nearby stores and sales.

[0070] 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.

[0071] In this invention, the server includes a means for registering personal information, a means for acquiring information on the nearest store, a means for generating a nutritionally balanced menu based on the registered information and the acquired store information, a means for displaying the generated menu, a means for inputting a prompt sentence into the generation AI model, and a means for suggesting a menu based on the input prompt sentence. This makes it possible to efficiently suggest an appropriate menu based on the user's individual nutritional needs and allergy information. Furthermore, by utilizing the information on the nearest store, economical food ingredient purchasing can be realized, providing a system that comprehensively supports home meal preparation.

[0072] "Personal information" refers to personal data about the user, such as the user's name, age, height, weight, allergy information, food preferences, and family composition information.

[0073] "Nearest store information" is data that includes store advertising information, sale information, and product price information, which is obtained based on the user's location.

[0074] "Menus that take nutritional balance into consideration" refers to the creation of a balanced meal menu based on the user's individual health condition, food preferences, and allergy information.

[0075] A "prompt" is text data that is input to a generative AI model, and is an instruction that allows the model to suggest an appropriate menu based on its content.

[0076] "Generative AI model" is a general term for artificial intelligence algorithms and machine learning models that generate optimal menus based on user and store information.

[0077] The "ingredient list" is a list of ingredients required for the generated menu, and includes the specific ingredient names and quantities.

[0078] "Price information" is data indicating the price of each ingredient in the ingredient list.

[0079] "User" refers to a person or family who uses this system to register personal information and check suggested menus.

[0080] This invention is a system that streamlines home meal preparation and suggests menus that take nutritional balance into consideration. This system is broadly composed of the following elements: user information registration, acquisition of information on the nearest store, menu creation, and menu display. Specific usage methods will be explained below.

[0081] Registering user information

[0082] The user uses a device to enter personal information and family composition information. Specifically, the user and family members' names, ages, heights, weights, allergy information, food preferences, etc. are entered. This information is sent from the device to a server and stored in a database. The device used can be a smartphone, tablet, personal computer, etc. Data is sent via an internet connection.

[0083] Get information about the nearest store

[0084] The server obtains information about the nearest store based on the location of the registered user information. Specifically, this information includes advertising information, sales information, and price information obtained from the store corresponding to the user's location. This information is collected through external APIs and store RSS feeds. For example, the server uses the Google (registered trademark) Maps API to search for stores near the user's location and obtains price and sales information from the target store's API.

[0085] Menu generation

[0086] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. This process takes into account the user's individual preferences, allergy information, family composition, etc. The AI ​​model uses deep learning and neural networks to predict and generate appropriate menus. As specific examples, it uses Tensorflow (registered trademark) and PyTorch, which are Python machine learning frameworks.

[0087] Menu display

[0088] The generated menu is then sent back to the user's device. The user can then check the proposed menu, the list of ingredients needed, and the total price on their device. In addition, discounted items based on store sales information are also displayed, helping to reduce food costs. The user interface is implemented as a mobile or web application and is designed to be intuitive to use.

[0089] Specific examples

[0090] For example, consider a case where a user registers information about themselves and their family, and the nearest store is having a "sale" and offering "beef" at a discounted price. In this case, the server suggests menu items such as "beef stew." The user can check the details of "beef stew" on their device and find out the list of ingredients needed, such as "beef," "onion," "carrot," and "potato," along with their prices. The server also displays store sales information, allowing the user to purchase ingredients at discounted prices. If the user's family has a nut allergy, the AI ​​will suggest nut-free menu items.

[0091] Here are some example prompts for a generative AI model:

[0092] "The user information entered is for a man aged 30, 175cm tall, and weighing 70kg, along with his family (wife and two children). If the nearest store to this user is having a beef sale, what kind of menu would this system suggest?"

[0093] "User has a nut allergy. Please take this into consideration and suggest healthy options based on the location of your nearest store."

[0094] In this way, the present invention realizes efficient and healthy meal suggestions based on the user's personal information, information on the nearest store, and nutritional balance.

[0095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0096] Step 1:

[0097] The user uses the device to enter personal information and family composition information. Specifically, the user and family members' names, ages, heights, weights, allergy information, and food preferences are entered. The entered data is packaged in JSON format and sent to the server via the Internet. The server stores the received information in a database. This records basic user information in the system.

[0098] Input: User's personal information and family information

[0099] Output: Personal information stored in the server database

[0100] Step 2:

[0101] The server obtains information about the nearest store based on the location of the registered user information. Specifically, it uses the Google Maps API and store-specific APIs to collect advertising information, sales information, and price information for stores corresponding to the user's location. The obtained information is analyzed, and the necessary data is extracted and temporarily stored in memory storage.

[0102] Input: User's location information

[0103] Output: Parsed nearest store information

[0104] Step 3:

[0105] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. This process takes into account factors such as the user's food preferences, allergies, and family composition. The generative AI model (e.g., a model using TensorFlow or PyTorch) receives this information as input, analyzes it, and performs calculations to output the optimal menu in JSON format.

[0106] Input: User information, store information

[0107] Output: Generated menu data (JSON format)

[0108] Step 4:

[0109] The generated menu data is then sent back to the user's device. The user can then check the proposed menu, a list of ingredients needed, and the total price on their device. It also displays discounted items based on store sales information, helping users save money on food. The UI is implemented as a mobile or web application, and is intuitive to use.

[0110] Input: Generated menu data

[0111] Output: Menu, ingredients list, and total price displayed on the user's device

[0112] Step 5:

[0113] The user checks the proposed menu and purchases ingredients as necessary. They can select ingredients at discounted prices based on store sales information. After purchasing, they can also provide feedback on their purchase status and menu on the device. This allows the system to learn the user's preferences and usage patterns and further optimize suggestions for future purchases.

[0114] Input: User feedback and purchase status

[0115] Output: Learned user preference data, data to be used for next suggestions

[0116] (Application example 1)

[0117] 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."

[0118] The present invention relates to a system that streamlines meal preparation for busy daily lives and suggests nutritionally balanced menus. However, current systems are insufficient in that users have to purchase ingredients and cook them themselves, which requires time and effort, and they lack functionality that not only suggests meals but also takes into account the use of delivery services. Therefore, a challenge is to enable users to easily enjoy healthy meals.

[0119] 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.

[0120] In this invention, the server includes means for registering personal information, means for acquiring information on the nearest store, means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, means for displaying the generated menu, and means for acquiring information on the nearest delivery service and generating meal suggestions to be provided. This allows the user to easily receive suggestions for nutritionally balanced meals and enjoy meals quickly and efficiently by using the nearest delivery service.

[0121] A "means" refers to a method or apparatus for achieving a particular function or operation.

[0122] "Personal information" refers to information in which the system contains various data about the user, including name, age, height, weight, allergy information, food preferences, etc.

[0123] "Nearest store information" is information about the store closest to the user's location, including the store's name, address, menu items offered, prices, discount information, and the like.

[0124] A "nutritional balanced menu" is a meal plan designed to include the appropriate amount of nutrients needed based on the user's health condition and specific dietary requirements.

[0125] "Delivery service" means a service that delivers meals ordered by a user to their home or designated location through an online platform or application.

[0126] An "ingredient list" is a list of ingredients required to create a specific menu, including the specific name and required amount of each ingredient.

[0127] "Price information" refers to information about the price of a product or service provided, including details such as ingredients and delivery costs.

[0128] The present invention relates to a system that streamlines meal preparation that takes nutritional balance into consideration even in busy daily lives. This system generates nutritionally balanced menus based on user information and information on the nearest store, and also provides meals easily by linking with delivery services.

[0129] System Overview

[0130] The hardware used consists of a user device (such as a smartphone) and a server, while the software includes a smartphone app (iOS / ANDROID (registered trademark)), a cloud database, an AI algorithm, a location information service API, and a food delivery API.

[0131] Program processing explanation

[0132] The server does the following:

[0133] 1. User information registration

[0134] Users use a smartphone app to enter information about themselves and their family members (such as name, age, height, weight, allergy information, and food preferences).

[0135] The entered information is sent from the smartphone to a cloud server and stored in a database.

[0136] 2. Obtaining information about the nearest store

[0137] The server uses the location service API to detect the user's current location.

[0138] Based on the detected location information, information about the nearest stores and delivery services (menus, prices, discount information, etc.) is obtained through the food delivery API.

[0139] 3. Menu generation

[0140] An AI algorithm (generative AI model) is used on the server to generate a menu that takes nutritional balance into consideration based on user information and acquired store information.

[0141] The menu is designed taking into account the user's health status and individual dietary needs.

[0142] 4. Menu display

[0143] The generated menu and its details (dish name, required ingredients, nutritional information, price, discount information) are sent to and displayed on the user's smartphone.

[0144] Users can view the suggested delivery menu and place an order immediately through the app.

[0145] Specific examples

[0146] Users register their information on the app, including any allergies their family members may have. The server detects the user's current location and obtains information on the nearest delivery service. An AI algorithm generates a menu suitable for the user, suggesting, for example, a "healthy salad lunch with plenty of vegetables." Menu details, delivery costs, and discount information are displayed on the user's smartphone. Users can then order the "healthy salad lunch with plenty of vegetables" through the app.

[0147] Prompt Sentence Examples

[0148] Personal information entered by the user:

[0149] Name: User A

[0150] Age: 35

[0151] Family: Spouse, two children

[0152] Allergy information: User A is allergic to nuts

[0153] Food preference: Japanese food, lots of vegetables

[0154] User's current location: Tokyo

[0155] Food delivery information obtained:

[0156] Store 1: Menu and price list, current discounts

[0157] Store 2: Menu and price list, current discounts

[0158] Generated menu:

[0159] Healthy salad lunch with plenty of vegetables (from store 1)

[0160] Total cost: 1500 yen (after discount)

[0161] In this way, the system of the present invention links easy and healthy meal suggestions with delivery services based on the user's personal information, information on the nearest store, and nutritional balance.

[0162] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0163] Step 1:

[0164] The user uses a smartphone app to enter information about themselves and their family members (such as name, age, height, weight, allergy information, and food preferences). The entered information is sent from the smartphone to a cloud server and stored in a database. The input in this step is the user's personal information, and the output is the information stored in the cloud database.

[0165] Step 2:

[0166] The server obtains the user's current location using a location information service API. The current location information becomes the server's input, and based on that, it obtains information about the nearest store and delivery service through the food delivery API. During this process, the location information data is processed and the store information is filtered based on that location. As an output, the server obtains information about the nearest store and delivery service.

[0167] Step 3:

[0168] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. In this step, the AI ​​algorithm selects the optimal menu from a vast recipe database based on the input data (user information and store information). The output is the menu most suitable for the user and its details.

[0169] Step 4:

[0170] The generated menu and its details (dish name, required ingredients, nutritional information, price, discount information) are sent from the server to the user's smartphone. During this process, the server converts the generated menu data into the app's display format and sends it to the user's device. The input is the menu generation result, and the output is the information displayed on the user's smartphone.

[0171] Step 5:

[0172] The user checks the proposed delivery menu through the smartphone app. If necessary, they refer to the provided information (dish name, required ingredients, nutritional information, price, discount information) and place the final order. In this step, the user manually operates the app and places the order using the nearest delivery service. The output is an order request to the nearest delivery service.

[0173] This will allow users to easily receive nutritionally balanced meal suggestions and quickly place delivery orders.

[0174] 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.

[0175] This invention relates to a system that streamlines home meal preparation and proposes menus that take nutritional balance into consideration. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it realizes more appropriate and personalized menu proposals.

[0176] Registering user information

[0177] The user uses the device to enter personal and family information, including name, age, height, weight, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database.

[0178] Get information about the nearest store

[0179] The server retrieves the nearest store information based on the registered user information. Specifically, it sends an API request to retrieve advertisement information and sales information for stores based on the user's location.

[0180] Menu generation

[0181] The server uses an AI algorithm to generate a nutritionally balanced menu based on user information and the nearest store, taking into account registered allergies and food preferences.

[0182] Using the Emotion Engine

[0183] The system includes an emotion engine that recognizes the user's emotions. Specifically, it uses sensors such as a camera built into the device to analyze the user's facial expressions and recognize the user's emotions. The recognized emotion information is used to adjust the menu. For example, if the user is feeling stressed, it will suggest a menu using nutritious ingredients to alleviate the user's emotions.

[0184] Menu display

[0185] The generated menu is sent to the device and displayed on the screen. The suggested menu includes a list of ingredients and price information, and also displays information about food sales. This allows users to choose ingredients efficiently and save money.

[0186] Specific examples

[0187] For example, consider the case where a user registers information about themselves and their family, and the emotion engine recognizes the user's "fatigue" through the device's camera. At this time, the server will suggest "menus to replenish energy" according to the user's condition. For example, it will suggest a smoothie and display a list of ingredients needed to make it, such as "banana," "spinach," and "yogurt," along with the total price. It will also display information about the nearest store, where these ingredients can be purchased at a discount.

[0188] In this way, the system can provide personalized meal suggestions based on the user's emotions in addition to their personal information, information on the nearest store, and nutritional balance.

[0189] The processing flow will be explained below.

[0190] Step 1:

[0191] The user uses the terminal to input personal and family information, specifically, the name, age, height, weight, allergy information, and food preferences of each user.

[0192] Step 2:

[0193] The device sends the entered user information to the server, which converts the information into JSON format and sends it as a POST request to the API endpoint.

[0194] Step 3:

[0195] The server stores the received data in a database, where the information is kept for subsequent processing.

[0196] Step 4:

[0197] The device obtains the user's location information, either via GPS or manual user input.

[0198] Step 5:

[0199] The server retrieves the nearest store information based on the user's location information. Specifically, it retrieves store advertising information and sales information via API requests.

[0200] Step 6:

[0201] The device uses cameras and other sensors to recognize the user's emotions, and an emotion engine analyzes the user's facial expressions to determine their emotions.

[0202] Step 7:

[0203] The device sends the emotion recognition results to the server, which converts the information into JSON format and sends it as a POST request to the API endpoint.

[0204] Step 8:

[0205] The server generates a menu based on user information, information about the nearest store, and emotion recognition results. An AI algorithm comprehensively analyzes this data and proposes a menu that takes nutritional balance into consideration. At this time, ingredients are selected and the menu is adjusted according to the user's emotional state.

[0206] Step 9:

[0207] The server sends the generated menu to the user's device. The proposed menu includes a list of ingredients and pricing information.

[0208] Step 10:

[0209] The device displays the menu information it receives on the screen, and the user can check the suggested menu, required ingredients, and total price on the device.

[0210] Step 11:

[0211] The user can check the menu displayed on the device and purchase ingredients as needed, while also taking advantage of sales information to save on food costs.

[0212] Step 12:

[0213] Users prepare meals based on menus and support their family's healthy eating habits.

[0214] Example 2

[0215] 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."

[0216] In modern society, daily meal preparation is a burden for many households, and planning efficient and nutritionally balanced menus is a difficult task, especially for working people. Additionally, selecting ingredients, checking prices, and addressing food allergies are time-consuming and labor-intensive issues. Furthermore, personalized menus that reflect the user's emotional state and preferences are necessary to improve the quality of meals at home.

[0217] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0218] In this invention, the server includes a means for registering personal information, a means for acquiring information on the nearest store, a means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, a means for displaying the generated menu, a means for recognizing the user's emotional information, and a means for adjusting the menu based on the recognized emotional information, thereby enabling the server to propose a nutritionally balanced menu that reflects the user's personal information and emotional state.

[0219] "Means for registering personal information" refers to a function that allows users to input personal information such as their own and their family members' names, ages, weights, heights, allergy information, and food preferences, and send this information to the server.

[0220] "Means for obtaining information about the nearest store" is a function that sends an API request to obtain advertising information and sales information for nearby stores based on the user's location information.

[0221] "Means for generating menus that take nutritional balance into consideration" is a function that uses an AI algorithm to generate nutritionally balanced menus based on the user's registration information and acquired store information.

[0222] The "means for displaying the generated menu" is a function that sends the generated menu to the user's terminal and displays it on the screen, including the list of ingredients and price information.

[0223] The "means for recognizing the user's emotional information" is a function that uses a camera or sensor built into the device to analyze the user's facial expressions and recognize their emotions.

[0224] The "means for adjusting the menu based on the recognized emotional information" is a function for adjusting the menu to suggest the most suitable menu based on the emotional state of the user recognized by the emotion engine.

[0225] The present invention is a system that proposes menus that take into consideration the efficiency of meal preparation at home and nutritional balance, and furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system realizes more appropriate and personalized menu proposals.

[0226] Registering user information

[0227] The user uses the device to enter personal information about themselves and their family members, including their name, age, height, weight, allergies, and food preferences. This information is sent from the device to a server and stored in a database.

[0228] Hardware and software: Tablets and smartphones are used as devices, and frameworks such as React and Vue.js are used as input UIs (user interfaces).

[0229] Get information about the nearest store

[0230] The server retrieves the nearest store information based on the user's registration information. Based on the user's location, it uses an external API to retrieve advertising and sales information for nearby stores.

[0231] Hardware and software: The server runs on a cloud service (e.g., AWS (registered trademark), Google Cloud), and Python or Node.js is used as the software for making API requests.

[0232] Menu generation

[0233] The server uses an AI algorithm (machine learning model) to generate nutritionally balanced menus based on user information and information on the nearest store, taking into account allergies and food preferences.

[0234] Hardware and software: The AI ​​model uses TensorFlow and PyTorch, and the menu generation algorithm is implemented in Python and runs on a virtual machine in the cloud.

[0235] Using the Emotion Engine

[0236] The system includes an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions using the device's built-in camera and other sensors. The analysis results are sent to a server and used to adjust the menu.

[0237] Hardware / Software: Use the device camera to analyze emotions using OpenCV or an emotion analysis API (e.g., Microsoft® Azure® Face API).

[0238] Menu display

[0239] The generated menu is sent to the device and displayed on the screen, including a list of ingredients and pricing information, as well as store sales information.

[0240] Hardware and software: HTML, CSS, and JavaScript (registered trademark) are used to display the device screen, and the display application is implemented using cross-platform frameworks such as React Native and Flutter (registered trademark).

[0241] Specific examples

[0242] For example, if a user registers information about themselves and their family, and the emotion engine recognizes the user's fatigue through the device's camera, the server will suggest a menu to replenish energy according to the user's condition. Specifically, it will suggest a smoothie and display a list of ingredients needed to make it, such as bananas, spinach, and yogurt, along with the total price. It will also display information about the nearest store, where these ingredients can be purchased at a discount.

[0243] Prompt Sentence Examples

[0244] "Enter user information, and if you recognize that your current emotion is fatigue, suggest a meal plan to replenish your energy. Also provide a list of ingredients and pricing information."

[0245] As described above, the present invention can realize nutritionally balanced and personalized menu suggestions based on the user's personal information and emotional information.

[0246] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0247] Processing flow

[0248] (Step 1): Register user information

[0249] The user uses the device to input personal information and family information, including name, age, height, weight, allergy information, food preferences, etc. The device then transmits this information to the server.

[0250] Input: Name, age, height, weight, allergy information, food preferences

[0251] Data processing: Packaging input information in JSON format

[0252] Output: User information sent to the server

[0253] Specific behavior:

[0254] A text input field and a submit button will appear on the device screen.

[0255] The user enters information into each field and presses the submit button.

[0256] The device sends a POST request to the server's API with information in JSON format.

[0257] The server stores the received information in a database.

[0258] (Step 2): Get information about the nearest store

[0259] The server retrieves the nearest store information based on the registered user information. Based on the user's location, the server retrieves store information and sale information via API request.

[0260] Input: User's location information

[0261] Data processing: API request generation

[0262] Output: Store information and sale information

[0263] Specific behavior:

[0264] The server retrieves the user's location information from a database.

[0265] Create an API request and send it to the store information service.

[0266] Cache the store information and sale information obtained as a response.

[0267] (Step 3): Generate a menu

[0268] The server uses an AI algorithm to generate a nutritionally balanced menu based on user information and the nearest store, taking into account allergies and food preferences.

[0269] Input: User information (allergy information, food preferences), store information

[0270] Data calculation: Considering nutritional balance using AI models

[0271] Output: Menu with nutritional balance in mind

[0272] Specific behavior:

[0273] The server feeds user information and store information to the AI ​​model.

[0274] The model analyzes the data and generates a menu plan.

[0275] The generated menu plan is saved in an internal data store.

[0276] (Step 4): Use the Emotion Engine

[0277] The device uses sensors such as a built-in camera to analyze the user's facial expressions and recognize their emotions. The recognized emotional information is then sent to the server.

[0278] Input: User's face image

[0279] Data processing: Facial expression analysis

[0280] Output: Emotional information

[0281] Specific behavior:

[0282] The device's camera captures the user's face.

[0283] Image analysis software (such as OpenCV) analyzes facial expressions.

[0284] The analysis results are sent to the server in JSON format.

[0285] (Step 5): Adjust the menu

[0286] The server adjusts the menu to suggest the most suitable menu based on the recognized emotion information.

[0287] Input: Emotional information, initial menu plan

[0288] Data Computing: Emotion-Based Menu Adjustment

[0289] Output: Adjusted menu

[0290] Specific behavior:

[0291] The server receives the emotional information and evaluates the initial menu plan.

[0292] The adjustment algorithm takes emotional information into account to make optimal adjustments.

[0293] The adjusted menu is saved in the database.

[0294] (Step 6): Display the menu

[0295] The generated menu is sent to the device and displayed on the screen, including a list of essential ingredients and pricing information, helping users make efficient food choices and save money.

[0296] Input: Adjusted menu

[0297] Data processing: Converting data into a user-friendly format

[0298] Output: Menu, ingredients list, and price information displayed on the device

[0299] Specific behavior:

[0300] The server sends the adjusted menu information to the terminal.

[0301] The terminal parses the received information for display on the screen.

[0302] The user checks the screen and sees the suggested menu and ingredient list.

[0303] The above is the processing flow of this system.

[0304] (Application example 2)

[0305] 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."

[0306] In modern society, it is difficult to find time to prepare meals at home, and it is also not easy to plan menus that take nutritional balance into consideration. Therefore, there is a need for a system that can efficiently prepare meals and achieve nutritional balance. Furthermore, there is a need for personalized menu suggestions that take the user's emotional state into account, but current technology is limited in the systems that can achieve this. A new system is needed to address these challenges.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0308] In this invention, the server includes means for registering personal information, means for acquiring information on the nearest store, means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, means for displaying the generated menu, means for recognizing the user's emotion using an emotion recognition engine, and means for adjusting the menu based on the recognized emotion information. This makes it possible to effectively provide personalized meal suggestions based on the user's emotional state in addition to the user's personal information, information on the nearest store, and nutritional balance, as well as corresponding ingredient lists and store information.

[0309] "Personal information" includes the user's name, age, height, weight, allergy information, food preferences, and the like.

[0310] "Nearest store information" is information about stores within a user's reach, such as advertisement information and sales information for stores based on the user's location.

[0311] A "nutritional balance menu" is a meal menu that contains the necessary nutrients in an appropriate amount to maintain and improve the user's health.

[0312] A "generated menu" is a meal menu created by the server based on the user's personal information and information about the nearest store.

[0313] An "emotion recognition engine" is a technology that uses a device's built-in camera to analyze a user's facial expressions and identify their current emotional state.

[0314] "Recognized emotion information" is information that indicates the emotional state of the user as determined by the emotion recognition engine.

[0315] The "means for adjusting the menu" is a means for changing and optimizing the menu to suggest the most suitable meal menu for the user's emotional state based on the recognized emotional information.

[0316] The "ingredient list" is a list of specific ingredients required to realize the generated menu.

[0317] "Price information" refers to price information for each ingredient included in the ingredient list, including sale information.

[0318] "Personalized meal suggestions" are meal suggestions that are customized based on an individual user's personal information and emotional state.

[0319] This invention relates to a system that streamlines home meal preparation and proposes menus that take nutritional balance into consideration. Furthermore, by combining it with an emotion recognition engine that recognizes the user's emotions, it realizes more appropriate and personalized menu proposals.

[0320] Registering user information

[0321] The user uses the device to enter personal information and family composition information, including name, age, height, weight, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database on the server.

[0322] Get information about the nearest store

[0323] The server retrieves the nearest store information based on the registered user information. To do this, the server sends an API request to retrieve advertisement information and sales information for stores based on the user's location. This API request uses, for example, the Google Places API or the API of a specific supermarket.

[0324] Menu generation

[0325] The server generates a nutritionally balanced menu based on user information and the nearest store information. This process also takes into account registered allergy information and food preferences. An AI algorithm (MenuGenerator) is used to generate the menu.

[0326] Using the Emotion Engine

[0327] The emotion recognition engine uses sensors such as a camera built into the device to analyze the user's facial expressions and recognize their emotions. The emotional information recognized at this time is sent to the server. The server uses this emotional information to adjust the menu and make personalized suggestions.

[0328] Menu display

[0329] The generated menu is sent from the server to the device and displayed on the device screen. The suggested menu includes a list of ingredients and price information, and also displays information about food sales. This allows users to choose ingredients efficiently and save money.

[0330] Specific examples

[0331] For example, if a user registers information about themselves and their family, and the emotion engine recognizes the user's fatigue through the device's camera, the server will suggest a menu to replenish energy depending on the user's condition. For example, it will suggest a smoothie and display a list of ingredients needed to make it, such as bananas, spinach, and yogurt, along with the total price. It will also display information about the nearest store where these ingredients can be purchased at a discount.

[0332] Example prompt sentences to use

[0333] Based on the user's location and current mood, provide nutritionally balanced meals and sale information on ingredients needed for those meals, along with information on the nearest store.

[0334] In this way, in the embodiment of the invention, personalized meal suggestions can be made by combining personal information, store information, and emotion recognition.

[0335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0336] Step 1: Register user information

[0337] The user uses the terminal to input personal information such as name, age, height, weight, allergy information, food preferences, and family composition information.

[0338] The entered information is sent from the terminal to the server and stored in a database within the server. The input in this step is personal information from the user, and the output is data stored on the server.

[0339] Step 2: Get the nearest store

[0340] The server sends an API request to identify the user's location based on the registered user information.

[0341] The API request returns information about the nearest store, advertisements, sales, etc. to the server. Specifically, the Google Places API or a specific supermarket's API is used. The input in this step is the user's location information stored on the server, and the output is information about the nearest store.

[0342] Step 3: Generate a menu that takes nutritional balance into account

[0343] The server uses an AI algorithm (Menu Generator) to generate a menu that takes nutritional balance into consideration based on user information and information about the nearest store.

[0344] This process also takes into account allergy information and food preferences. The input is user information and information about the nearest store, and the output is a menu that takes nutritional balance into consideration.

[0345] Step 4: Recognizing user emotions

[0346] Using the device's built-in camera, the emotion recognition engine (EmotionRecognition) analyzes the user's facial expressions and recognizes their emotions.

[0347] The recognized emotion information is sent to the server. The input is a facial image of the user captured by the device camera, and the output is the recognized emotion information.

[0348] Step 5: Adjust the menu

[0349] The server uses the emotion information obtained by the emotion recognition engine to adjust the generated menu.

[0350] For example, if the user is tired, it will suggest a meal plan to replenish their energy. The input is the recognized emotion information and the already generated meal plan, and the output is the adjusted meal plan.

[0351] Step 6: View the menu and related information

[0352] The adjusted menu is sent to the device and displayed on the device screen.

[0353] The suggested menu includes a list of ingredients and pricing information, and food sales information is also displayed. The input is the adjusted menu and related information, and the output is the information displayed on the device.

[0354] 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.

[0355] 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.

[0356] 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.

[0357] [Second embodiment]

[0358] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0359] 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.

[0360] 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).

[0361] 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.

[0362] 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.

[0363] 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).

[0364] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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."

[0370] The present invention relates to a system that improves the efficiency of meal preparation at home in busy daily lives and proposes menus that take nutritional balance into consideration. This system provides user information, information on the nearest store, and menus that take into consideration overall nutritional balance, and is implemented in the following form.

[0371] Registering user information

[0372] The user uses the device to enter personal information and family composition information, such as the user's and family members' names, ages, heights, weights, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database.

[0373] Get information about the nearest store

[0374] The server acquires the nearest store information based on the location of the registered user information, including advertisement information, sale information, and price information acquired from the store corresponding to the user's location.

[0375] Menu generation

[0376] The server uses an AI algorithm to generate a menu that takes into account nutritional balance based on user information and acquired store information. This menu generation process takes into account the user's food preferences, allergies, family composition, etc. to suggest the most suitable menu for each individual's needs.

[0377] Menu display

[0378] The generated menu is then sent back to the user's device, where the user can check the suggested menu, the list of ingredients needed, and the total price. It also displays discounted items based on store sales information, helping users save money on food.

[0379] Specific examples

[0380] For example, consider a situation where a user registers information about themselves and their family, and the nearest store is having a "sale" and offering "beef" at a discounted price. In this case, the server suggests a menu item such as "beef stew." The user can check the details of "beef stew" on their device and find out the list of ingredients needed, such as "beef," "onion," "carrot," and "potato," along with their prices. In addition, store sales information is taken into consideration, allowing the user to purchase ingredients at a discounted price. Also, if the user's family has a nut allergy, the AI ​​will suggest nut-free menu items.

[0381] In this way, the present system is able to provide efficient and healthy meal suggestions based on the user's personal information, information on the nearest store, and nutritional balance.

[0382] The processing flow will be explained below.

[0383] Step 1:

[0384] The user enters personal and family information into the device, such as name, age, height, weight, allergy information, food preferences, and family composition.

[0385] Step 2:

[0386] The device sends the entered user information to the server, which converts the data into JSON format and sends it as a POST request to the API endpoint.

[0387] Step 3:

[0388] The server stores the received data in a database, which is used for subsequent processing.

[0389] Step 4:

[0390] The device obtains the user's location information, either via GPS or manual user input.

[0391] Step 5:

[0392] The server retrieves the nearest store information based on the user's location information. Specifically, an API request is sent to retrieve data including store advertising and sales information.

[0393] Step 6:

[0394] The server uses an AI algorithm to generate menus based on user information and the nearest store, and suggests nutritionally balanced menus taking into account registered user information (allergies, food preferences, etc.).

[0395] Step 7:

[0396] The server generates a menu and sends it to the device. The menu includes a list of ingredients and pricing information.

[0397] Step 8:

[0398] The device displays the menu information it receives on the screen, allowing users to check the suggested menu, ingredients needed, and total price.

[0399] Step 9:

[0400] The user can check the menu displayed on the device and purchase ingredients as needed, while also taking advantage of sales information to save on food costs.

[0401] Example 1

[0402] 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."

[0403] Meal preparation at home can be a significant burden in busy daily lives, and it can be difficult to come up with a menu that takes nutritional balance into account. For this reason, there is a need for a system that streamlines meal preparation and suggests nutritionally balanced menus that take family health into consideration. It is also necessary to accommodate food allergies and individual preferences. Furthermore, it is important to have a way to purchase ingredients economically by utilizing information on nearby stores and sales.

[0404] 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.

[0405] In this invention, the server includes a means for registering personal information, a means for acquiring information on the nearest store, a means for generating a nutritionally balanced menu based on the registered information and the acquired store information, a means for displaying the generated menu, a means for inputting a prompt sentence into the generation AI model, and a means for suggesting a menu based on the input prompt sentence. This makes it possible to efficiently suggest an appropriate menu based on the user's individual nutritional needs and allergy information. Furthermore, by utilizing the information on the nearest store, economical food ingredient purchasing can be realized, providing a system that comprehensively supports home meal preparation.

[0406] "Personal information" refers to personal data about the user, such as the user's name, age, height, weight, allergy information, food preferences, and family composition information.

[0407] "Nearest store information" is data that includes store advertising information, sale information, and product price information, which is obtained based on the user's location.

[0408] "Menus that take nutritional balance into consideration" refers to the creation of a balanced meal menu based on the user's individual health condition, food preferences, and allergy information.

[0409] A "prompt" is text data that is input to a generative AI model, and is an instruction that allows the model to suggest an appropriate menu based on its content.

[0410] "Generative AI model" is a general term for artificial intelligence algorithms and machine learning models that generate optimal menus based on user and store information.

[0411] The "ingredient list" is a list of ingredients required for the generated menu, and includes the specific ingredient names and quantities.

[0412] "Price information" is data indicating the price of each ingredient in the ingredient list.

[0413] "User" refers to a person or family who uses this system to register personal information and check suggested menus.

[0414] This invention is a system that streamlines home meal preparation and suggests menus that take nutritional balance into consideration. This system is broadly composed of the following elements: user information registration, acquisition of information on the nearest store, menu creation, and menu display. Specific usage methods will be explained below.

[0415] Registering user information

[0416] The user uses a device to enter personal information and family composition information. Specifically, the user and family members' names, ages, heights, weights, allergy information, food preferences, etc. are entered. This information is sent from the device to a server and stored in a database. The device used can be a smartphone, tablet, personal computer, etc. Data is sent via an internet connection.

[0417] Get information about the nearest store

[0418] The server retrieves information about the nearest store based on the location of the registered user information. Specifically, this information includes advertising information, sales information, and price information obtained from the store corresponding to the user's location. This information is collected through external APIs and store RSS feeds. For example, the server uses the Google Maps API to search for stores near the user's location and retrieves price and sale information from the target store's API.

[0419] Menu generation

[0420] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. This process takes into account the user's individual preferences, allergy information, family composition, etc. The AI ​​model uses deep learning and neural networks to predict and generate appropriate menus. As specific examples, it uses TensorFlow and PyTorch, which are Python machine learning frameworks.

[0421] Menu display

[0422] The generated menu is then sent back to the user's device. The user can then check the proposed menu, the list of ingredients needed, and the total price on their device. In addition, discounted items based on store sales information are also displayed, helping to reduce food costs. The user interface is implemented as a mobile or web application and is designed to be intuitive to use.

[0423] Specific examples

[0424] For example, consider a case where a user registers information about themselves and their family, and the nearest store is having a "sale" and offering "beef" at a discounted price. In this case, the server suggests menu items such as "beef stew." The user can check the details of "beef stew" on their device and find out the list of ingredients needed, such as "beef," "onion," "carrot," and "potato," along with their prices. The server also displays store sales information, allowing the user to purchase ingredients at discounted prices. If the user's family has a nut allergy, the AI ​​will suggest nut-free menu items.

[0425] Here are some example prompts for a generative AI model:

[0426] "The user information entered is for a man aged 30, 175cm tall, and weighing 70kg, along with his family (wife and two children). If the nearest store to this user is having a beef sale, what kind of menu would this system suggest?"

[0427] "User has a nut allergy. Please take this into consideration and suggest healthy options based on the location of your nearest store."

[0428] In this way, the present invention realizes efficient and healthy meal suggestions based on the user's personal information, information on the nearest store, and nutritional balance.

[0429] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0430] Step 1:

[0431] The user uses the device to enter personal information and family composition information. Specifically, the user and family members' names, ages, heights, weights, allergy information, and food preferences are entered. The entered data is packaged in JSON format and sent to the server via the Internet. The server stores the received information in a database. This records basic user information in the system.

[0432] Input: User's personal information and family information

[0433] Output: Personal information stored in the server database

[0434] Step 2:

[0435] The server obtains information about the nearest store based on the location of the registered user information. Specifically, it uses the Google Maps API and store-specific APIs to collect advertising information, sales information, and price information for stores corresponding to the user's location. The obtained information is analyzed, and the necessary data is extracted and temporarily stored in memory storage.

[0436] Input: User's location information

[0437] Output: Parsed nearest store information

[0438] Step 3:

[0439] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. This process takes into account factors such as the user's food preferences, allergies, and family composition. The generative AI model (e.g., a model using TensorFlow or PyTorch) receives this information as input, analyzes it, and performs calculations to output the optimal menu in JSON format.

[0440] Input: User information, store information

[0441] Output: Generated menu data (JSON format)

[0442] Step 4:

[0443] The generated menu data is then sent back to the user's device. The user can then check the proposed menu, a list of ingredients needed, and the total price on their device. It also displays discounted items based on store sales information, helping users save money on food. The UI is implemented as a mobile or web application, and is intuitive to use.

[0444] Input: Generated menu data

[0445] Output: Menu, ingredients list, and total price displayed on the user's device

[0446] Step 5:

[0447] The user checks the proposed menu and purchases ingredients as necessary. They can select ingredients at discounted prices based on store sales information. After purchasing, they can also provide feedback on their purchase status and menu on the device. This allows the system to learn the user's preferences and usage patterns and further optimize suggestions for future purchases.

[0448] Input: User feedback and purchase status

[0449] Output: Learned user preference data, data to be used for next suggestions

[0450] (Application example 1)

[0451] 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."

[0452] The present invention relates to a system that streamlines meal preparation for busy daily lives and suggests nutritionally balanced menus. However, current systems are insufficient in that users have to purchase ingredients and cook them themselves, which requires time and effort, and they lack functionality that not only suggests meals but also takes into account the use of delivery services. Therefore, a challenge is to enable users to easily enjoy healthy meals.

[0453] 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.

[0454] In this invention, the server includes means for registering personal information, means for acquiring information on the nearest store, means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, means for displaying the generated menu, and means for acquiring information on the nearest delivery service and generating meal suggestions to be provided. This allows the user to easily receive suggestions for nutritionally balanced meals and enjoy meals quickly and efficiently by using the nearest delivery service.

[0455] A "means" refers to a method or apparatus for achieving a particular function or operation.

[0456] "Personal information" refers to information in which the system contains various data about the user, including name, age, height, weight, allergy information, food preferences, etc.

[0457] "Nearest store information" is information about the store closest to the user's location, including the store's name, address, menu items offered, prices, discount information, and the like.

[0458] A "nutritional balanced menu" is a meal plan designed to include the appropriate amount of nutrients needed based on the user's health condition and specific dietary requirements.

[0459] "Delivery service" means a service that delivers meals ordered by a user to their home or designated location through an online platform or application.

[0460] An "ingredient list" is a list of ingredients required to create a specific menu, including the specific name and required amount of each ingredient.

[0461] "Price information" refers to information about the price of a product or service provided, including details such as ingredients and delivery costs.

[0462] The present invention relates to a system that streamlines meal preparation that takes nutritional balance into consideration even in busy daily lives. This system generates nutritionally balanced menus based on user information and information on the nearest store, and also provides meals easily by linking with delivery services.

[0463] System Overview

[0464] The hardware used consists of a user device (such as a smartphone) and a server, while the software includes a smartphone app (iOS / Android), a cloud database, an AI algorithm, a location-based service API, and a food delivery API.

[0465] Program processing explanation

[0466] The server does the following:

[0467] 1. User information registration

[0468] Users use a smartphone app to enter information about themselves and their family members (such as name, age, height, weight, allergy information, and food preferences).

[0469] The entered information is sent from the smartphone to a cloud server and stored in a database.

[0470] 2. Obtaining information about the nearest store

[0471] The server uses the location service API to detect the user's current location.

[0472] Based on the detected location information, information about the nearest stores and delivery services (menus, prices, discount information, etc.) is obtained through the food delivery API.

[0473] 3. Menu generation

[0474] An AI algorithm (generative AI model) is used on the server to generate a menu that takes nutritional balance into consideration based on user information and acquired store information.

[0475] The menu is designed taking into account the user's health status and individual dietary needs.

[0476] 4. Menu display

[0477] The generated menu and its details (dish name, required ingredients, nutritional information, price, discount information) are sent to and displayed on the user's smartphone.

[0478] Users can view the suggested delivery menu and place an order immediately through the app.

[0479] Specific examples

[0480] Users register their information on the app, including any allergies their family members may have. The server detects the user's current location and obtains information on the nearest delivery service. An AI algorithm generates a menu suitable for the user, suggesting, for example, a "healthy salad lunch with plenty of vegetables." Menu details, delivery costs, and discount information are displayed on the user's smartphone. Users can then order the "healthy salad lunch with plenty of vegetables" through the app.

[0481] Prompt Sentence Examples

[0482] Personal information entered by the user:

[0483] Name: User A

[0484] Age: 35

[0485] Family: Spouse, two children

[0486] Allergy information: User A is allergic to nuts

[0487] Food preference: Japanese food, lots of vegetables

[0488] User's current location: Tokyo

[0489] Food delivery information obtained:

[0490] Store 1: Menu and price list, current discounts

[0491] Store 2: Menu and price list, current discounts

[0492] Generated menu:

[0493] Healthy salad lunch with plenty of vegetables (from store 1)

[0494] Total cost: 1500 yen (after discount)

[0495] In this way, the system of the present invention links easy and healthy meal suggestions with delivery services based on the user's personal information, information on the nearest store, and nutritional balance.

[0496] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0497] Step 1:

[0498] The user uses a smartphone app to enter information about themselves and their family members (such as name, age, height, weight, allergy information, and food preferences). The entered information is sent from the smartphone to a cloud server and stored in a database. The input in this step is the user's personal information, and the output is the information stored in the cloud database.

[0499] Step 2:

[0500] The server obtains the user's current location using a location information service API. The current location information becomes the server's input, and based on that, it obtains information about the nearest store and delivery service through the food delivery API. During this process, the location information data is processed and the store information is filtered based on that location. As an output, the server obtains information about the nearest store and delivery service.

[0501] Step 3:

[0502] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. In this step, the AI ​​algorithm selects the optimal menu from a vast recipe database based on the input data (user information and store information). The output is the menu most suitable for the user and its details.

[0503] Step 4:

[0504] The generated menu and its details (dish name, required ingredients, nutritional information, price, discount information) are sent from the server to the user's smartphone. During this process, the server converts the generated menu data into the app's display format and sends it to the user's device. The input is the menu generation result, and the output is the information displayed on the user's smartphone.

[0505] Step 5:

[0506] The user checks the proposed delivery menu through the smartphone app. If necessary, they refer to the provided information (dish name, required ingredients, nutritional information, price, discount information) and place the final order. In this step, the user manually operates the app and places the order using the nearest delivery service. The output is an order request to the nearest delivery service.

[0507] This will allow users to easily receive nutritionally balanced meal suggestions and quickly place delivery orders.

[0508] 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.

[0509] This invention relates to a system that streamlines home meal preparation and proposes menus that take nutritional balance into consideration. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it realizes more appropriate and personalized menu proposals.

[0510] Registering user information

[0511] The user uses the device to enter personal and family information, including name, age, height, weight, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database.

[0512] Get information about the nearest store

[0513] The server retrieves the nearest store information based on the registered user information. Specifically, it sends an API request to retrieve advertisement information and sales information for stores based on the user's location.

[0514] Menu generation

[0515] The server uses an AI algorithm to generate a nutritionally balanced menu based on user information and the nearest store, taking into account registered allergies and food preferences.

[0516] Using the Emotion Engine

[0517] The system includes an emotion engine that recognizes the user's emotions. Specifically, it uses sensors such as a camera built into the device to analyze the user's facial expressions and recognize the user's emotions. The recognized emotion information is used to adjust the menu. For example, if the user is feeling stressed, it will suggest a menu using nutritious ingredients to alleviate the user's emotions.

[0518] Menu display

[0519] The generated menu is sent to the device and displayed on the screen. The suggested menu includes a list of ingredients and price information, and also displays information about food sales. This allows users to choose ingredients efficiently and save money.

[0520] Specific examples

[0521] For example, consider the case where a user registers information about themselves and their family, and the emotion engine recognizes the user's "fatigue" through the device's camera. At this time, the server will suggest "menus to replenish energy" according to the user's condition. For example, it will suggest a smoothie and display a list of ingredients needed to make it, such as "banana," "spinach," and "yogurt," along with the total price. It will also display information about the nearest store, where these ingredients can be purchased at a discount.

[0522] In this way, the system can provide personalized meal suggestions based on the user's emotions in addition to their personal information, information on the nearest store, and nutritional balance.

[0523] The processing flow will be explained below.

[0524] Step 1:

[0525] The user uses the terminal to input personal and family information, specifically, the name, age, height, weight, allergy information, and food preferences of each user.

[0526] Step 2:

[0527] The device sends the entered user information to the server, which converts the information into JSON format and sends it as a POST request to the API endpoint.

[0528] Step 3:

[0529] The server stores the received data in a database, where the information is kept for subsequent processing.

[0530] Step 4:

[0531] The device obtains the user's location information, either via GPS or manual user input.

[0532] Step 5:

[0533] The server retrieves the nearest store information based on the user's location information. Specifically, it retrieves store advertising information and sales information via API requests.

[0534] Step 6:

[0535] The device uses cameras and other sensors to recognize the user's emotions, and an emotion engine analyzes the user's facial expressions to determine their emotions.

[0536] Step 7:

[0537] The device sends the emotion recognition results to the server, which converts the information into JSON format and sends it as a POST request to the API endpoint.

[0538] Step 8:

[0539] The server generates a menu based on user information, information about the nearest store, and emotion recognition results. An AI algorithm comprehensively analyzes this data and proposes a menu that takes nutritional balance into consideration. At this time, ingredients are selected and the menu is adjusted according to the user's emotional state.

[0540] Step 9:

[0541] The server sends the generated menu to the user's device. The proposed menu includes a list of ingredients and pricing information.

[0542] Step 10:

[0543] The device displays the menu information it receives on the screen, and the user can check the suggested menu, required ingredients, and total price on the device.

[0544] Step 11:

[0545] The user can check the menu displayed on the device and purchase ingredients as needed, while also taking advantage of sales information to save on food costs.

[0546] Step 12:

[0547] Users prepare meals based on menus and support their family's healthy eating habits.

[0548] Example 2

[0549] 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."

[0550] In modern society, daily meal preparation is a burden for many households, and planning efficient and nutritionally balanced menus is a difficult task, especially for working people. Additionally, selecting ingredients, checking prices, and addressing food allergies are time-consuming and labor-intensive issues. Furthermore, personalized menus that reflect the user's emotional state and preferences are necessary to improve the quality of meals at home.

[0551] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0552] In this invention, the server includes a means for registering personal information, a means for acquiring information on the nearest store, a means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, a means for displaying the generated menu, a means for recognizing the user's emotional information, and a means for adjusting the menu based on the recognized emotional information, thereby enabling the server to propose a nutritionally balanced menu that reflects the user's personal information and emotional state.

[0553] "Means for registering personal information" refers to a function that allows users to input personal information such as their own and their family members' names, ages, weights, heights, allergy information, and food preferences, and send this information to the server.

[0554] "Means for obtaining information about the nearest store" is a function that sends an API request to obtain advertising information and sales information for nearby stores based on the user's location information.

[0555] "Means for generating menus that take nutritional balance into consideration" is a function that uses an AI algorithm to generate nutritionally balanced menus based on the user's registration information and acquired store information.

[0556] The "means for displaying the generated menu" is a function that sends the generated menu to the user's terminal and displays it on the screen, including the list of ingredients and price information.

[0557] The "means for recognizing the user's emotional information" is a function that uses a camera or sensor built into the device to analyze the user's facial expressions and recognize their emotions.

[0558] The "means for adjusting the menu based on the recognized emotional information" is a function for adjusting the menu to suggest the most suitable menu based on the emotional state of the user recognized by the emotion engine.

[0559] The present invention is a system that proposes menus that take into consideration the efficiency of meal preparation at home and nutritional balance, and furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system realizes more appropriate and personalized menu proposals.

[0560] Registering user information

[0561] The user uses the device to enter personal information about themselves and their family members, including their name, age, height, weight, allergies, and food preferences. This information is sent from the device to a server and stored in a database.

[0562] Hardware and software: Tablets and smartphones are used as devices, and frameworks such as React and Vue.js are used as input UIs (user interfaces).

[0563] Get information about the nearest store

[0564] The server retrieves the nearest store information based on the user's registration information. Based on the user's location, it uses an external API to retrieve advertising and sales information for nearby stores.

[0565] Hardware and software: The server runs on a cloud service (e.g., AWS, Google Cloud), and Python or Node.js is used as the software for making API requests.

[0566] Menu generation

[0567] The server uses an AI algorithm (machine learning model) to generate nutritionally balanced menus based on user information and information on the nearest store, taking into account allergies and food preferences.

[0568] Hardware and software: The AI ​​model uses TensorFlow and PyTorch, and the menu generation algorithm is implemented in Python and runs on a virtual machine in the cloud.

[0569] Using the Emotion Engine

[0570] The system includes an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions using the device's built-in camera and other sensors. The analysis results are sent to a server and used to adjust the menu.

[0571] Hardware / Software: Uses the device's camera to analyze emotions using OpenCV and emotion analysis APIs (e.g., Microsoft Azure Face API).

[0572] Menu display

[0573] The generated menu is sent to the device and displayed on the screen, including a list of ingredients and pricing information, as well as store sales information.

[0574] Hardware and software: HTML, CSS, and JavaScript are used to display the device screen, and the display application is implemented using cross-platform frameworks such as React Native and Flutter.

[0575] Specific examples

[0576] For example, if a user registers information about themselves and their family, and the emotion engine recognizes the user's fatigue through the device's camera, the server will suggest a menu to replenish energy according to the user's condition. Specifically, it will suggest a smoothie and display a list of ingredients needed to make it, such as bananas, spinach, and yogurt, along with the total price. It will also display information about the nearest store, where these ingredients can be purchased at a discount.

[0577] Prompt Sentence Examples

[0578] "Enter user information, and if you recognize that your current emotion is fatigue, suggest a meal plan to replenish your energy. Also provide a list of ingredients and pricing information."

[0579] As described above, the present invention can realize nutritionally balanced and personalized menu suggestions based on the user's personal information and emotional information.

[0580] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0581] Processing flow

[0582] (Step 1): Register user information

[0583] The user uses the device to input personal information and family information, including name, age, height, weight, allergy information, food preferences, etc. The device then transmits this information to the server.

[0584] Input: Name, age, height, weight, allergy information, food preferences

[0585] Data processing: Packaging input information in JSON format

[0586] Output: User information sent to the server

[0587] Specific behavior:

[0588] A text input field and a submit button will appear on the device screen.

[0589] The user enters information into each field and presses the submit button.

[0590] The device sends a POST request to the server's API with information in JSON format.

[0591] The server stores the received information in a database.

[0592] (Step 2): Get information about the nearest store

[0593] The server retrieves the nearest store information based on the registered user information. Based on the user's location, the server retrieves store information and sale information via API request.

[0594] Input: User's location information

[0595] Data processing: API request generation

[0596] Output: Store information and sale information

[0597] Specific behavior:

[0598] The server retrieves the user's location information from a database.

[0599] Create an API request and send it to the store information service.

[0600] Cache the store information and sale information obtained as a response.

[0601] (Step 3): Generate a menu

[0602] The server uses an AI algorithm to generate a nutritionally balanced menu based on user information and the nearest store, taking into account allergies and food preferences.

[0603] Input: User information (allergy information, food preferences), store information

[0604] Data calculation: Considering nutritional balance using AI models

[0605] Output: Menu with nutritional balance in mind

[0606] Specific behavior:

[0607] The server feeds user information and store information to the AI ​​model.

[0608] The model analyzes the data and generates a menu plan.

[0609] The generated menu plan is saved in an internal data store.

[0610] (Step 4): Use the Emotion Engine

[0611] The device uses sensors such as a built-in camera to analyze the user's facial expressions and recognize their emotions. The recognized emotional information is then sent to the server.

[0612] Input: User's face image

[0613] Data processing: Facial expression analysis

[0614] Output: Emotional information

[0615] Specific behavior:

[0616] The device's camera captures the user's face.

[0617] Image analysis software (such as OpenCV) analyzes facial expressions.

[0618] The analysis results are sent to the server in JSON format.

[0619] (Step 5): Adjust the menu

[0620] The server adjusts the menu to suggest the most suitable menu based on the recognized emotion information.

[0621] Input: Emotional information, initial menu plan

[0622] Data Computing: Emotion-Based Menu Adjustment

[0623] Output: Adjusted menu

[0624] Specific behavior:

[0625] The server receives the emotional information and evaluates the initial menu plan.

[0626] The adjustment algorithm takes emotional information into account to make optimal adjustments.

[0627] The adjusted menu is saved in the database.

[0628] (Step 6): Display the menu

[0629] The generated menu is sent to the device and displayed on the screen, including a list of essential ingredients and pricing information, helping users make efficient food choices and save money.

[0630] Input: Adjusted menu

[0631] Data processing: Converting data into a user-friendly format

[0632] Output: Menu, ingredients list, and price information displayed on the device

[0633] Specific behavior:

[0634] The server sends the adjusted menu information to the terminal.

[0635] The terminal parses the received information for display on the screen.

[0636] The user checks the screen and sees the suggested menu and ingredient list.

[0637] The above is the processing flow of this system.

[0638] (Application example 2)

[0639] 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."

[0640] In modern society, it is difficult to find time to prepare meals at home, and it is also not easy to plan menus that take nutritional balance into consideration. Therefore, there is a need for a system that can efficiently prepare meals and achieve nutritional balance. Furthermore, there is a need for personalized menu suggestions that take the user's emotional state into account, but current technology is limited in the systems that can achieve this. A new system is needed to address these challenges.

[0641] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0642] In this invention, the server includes means for registering personal information, means for acquiring information on the nearest store, means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, means for displaying the generated menu, means for recognizing the user's emotion using an emotion recognition engine, and means for adjusting the menu based on the recognized emotion information. This makes it possible to effectively provide personalized meal suggestions based on the user's emotional state in addition to the user's personal information, information on the nearest store, and nutritional balance, as well as corresponding ingredient lists and store information.

[0643] "Personal information" includes the user's name, age, height, weight, allergy information, food preferences, and the like.

[0644] "Nearest store information" is information about stores within a user's reach, such as advertisement information and sales information for stores based on the user's location.

[0645] A "nutritional balance menu" is a meal menu that contains the necessary nutrients in an appropriate amount to maintain and improve the user's health.

[0646] A "generated menu" is a meal menu created by the server based on the user's personal information and information about the nearest store.

[0647] An "emotion recognition engine" is a technology that uses a device's built-in camera to analyze a user's facial expressions and identify their current emotional state.

[0648] "Recognized emotion information" is information that indicates the emotional state of the user as determined by the emotion recognition engine.

[0649] The "means for adjusting the menu" is a means for changing and optimizing the menu to suggest the most suitable meal menu for the user's emotional state based on the recognized emotional information.

[0650] The "ingredient list" is a list of specific ingredients required to realize the generated menu.

[0651] "Price information" refers to price information for each ingredient included in the ingredient list, including sale information.

[0652] "Personalized meal suggestions" are meal suggestions that are customized based on an individual user's personal information and emotional state.

[0653] This invention relates to a system that streamlines home meal preparation and proposes menus that take nutritional balance into consideration. Furthermore, by combining it with an emotion recognition engine that recognizes the user's emotions, it realizes more appropriate and personalized menu proposals.

[0654] Registering user information

[0655] The user uses the device to enter personal information and family composition information, including name, age, height, weight, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database on the server.

[0656] Get information about the nearest store

[0657] The server retrieves the nearest store information based on the registered user information. To do this, the server sends an API request to retrieve advertisement information and sales information for stores based on the user's location. This API request uses, for example, the Google Places API or the API of a specific supermarket.

[0658] Menu generation

[0659] The server generates a nutritionally balanced menu based on user information and the nearest store information. This process also takes into account registered allergy information and food preferences. An AI algorithm (MenuGenerator) is used to generate the menu.

[0660] Using the Emotion Engine

[0661] The emotion recognition engine uses sensors such as a camera built into the device to analyze the user's facial expressions and recognize their emotions. The emotional information recognized at this time is sent to the server. The server uses this emotional information to adjust the menu and make personalized suggestions.

[0662] Menu display

[0663] The generated menu is sent from the server to the device and displayed on the device screen. The suggested menu includes a list of ingredients and price information, and also displays information about food sales. This allows users to choose ingredients efficiently and save money.

[0664] Specific examples

[0665] For example, if a user registers information about themselves and their family, and the emotion engine recognizes the user's fatigue through the device's camera, the server will suggest a menu to replenish energy depending on the user's condition. For example, it will suggest a smoothie and display a list of ingredients needed to make it, such as bananas, spinach, and yogurt, along with the total price. It will also display information about the nearest store where these ingredients can be purchased at a discount.

[0666] Example prompt sentences to use

[0667] Based on the user's location and current mood, provide nutritionally balanced meals and sale information on ingredients needed for those meals, along with information on the nearest store.

[0668] In this way, in the embodiment of the invention, personalized meal suggestions can be made by combining personal information, store information, and emotion recognition.

[0669] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0670] Step 1: Register user information

[0671] The user uses the terminal to input personal information such as name, age, height, weight, allergy information, food preferences, and family composition information.

[0672] The entered information is sent from the terminal to the server and stored in a database within the server. The input in this step is personal information from the user, and the output is data stored on the server.

[0673] Step 2: Get the nearest store

[0674] The server sends an API request to identify the user's location based on the registered user information.

[0675] The API request returns information about the nearest store, advertisements, sales, etc. to the server. Specifically, the Google Places API or a specific supermarket's API is used. The input in this step is the user's location information stored on the server, and the output is information about the nearest store.

[0676] Step 3: Generate a menu that takes nutritional balance into account

[0677] The server uses an AI algorithm (Menu Generator) to generate a menu that takes nutritional balance into consideration based on user information and information about the nearest store.

[0678] This process also takes into account allergy information and food preferences. The input is user information and information about the nearest store, and the output is a menu that takes nutritional balance into consideration.

[0679] Step 4: Recognizing user emotions

[0680] Using the device's built-in camera, the emotion recognition engine (EmotionRecognition) analyzes the user's facial expressions and recognizes their emotions.

[0681] The recognized emotion information is sent to the server. The input is a facial image of the user captured by the device camera, and the output is the recognized emotion information.

[0682] Step 5: Adjust the menu

[0683] The server uses the emotion information obtained by the emotion recognition engine to adjust the generated menu.

[0684] For example, if the user is tired, it will suggest a meal plan to replenish their energy. The input is the recognized emotion information and the already generated meal plan, and the output is the adjusted meal plan.

[0685] Step 6: View the menu and related information

[0686] The adjusted menu is sent to the device and displayed on the device screen.

[0687] The suggested menu includes a list of ingredients and pricing information, and food sales information is also displayed. The input is the adjusted menu and related information, and the output is the information displayed on the device.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] [Third embodiment]

[0692] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0693] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0694] 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).

[0695] 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.

[0696] 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.

[0697] 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).

[0698] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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."

[0704] The present invention relates to a system that improves the efficiency of meal preparation at home in busy daily lives and proposes menus that take nutritional balance into consideration. This system provides user information, information on the nearest store, and menus that take into consideration overall nutritional balance, and is implemented in the following form.

[0705] Registering user information

[0706] The user uses the device to enter personal information and family composition information, such as the user's and family members' names, ages, heights, weights, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database.

[0707] Get information about the nearest store

[0708] The server acquires the nearest store information based on the location of the registered user information, including advertisement information, sale information, and price information acquired from the store corresponding to the user's location.

[0709] Menu generation

[0710] The server uses an AI algorithm to generate a menu that takes into account nutritional balance based on user information and acquired store information. This menu generation process takes into account the user's food preferences, allergies, family composition, etc. to suggest the most suitable menu for each individual's needs.

[0711] Menu display

[0712] The generated menu is then sent back to the user's device, where the user can check the suggested menu, the list of ingredients needed, and the total price. It also displays discounted items based on store sales information, helping users save money on food.

[0713] Specific examples

[0714] For example, consider a situation where a user registers information about themselves and their family, and the nearest store is having a "sale" and offering "beef" at a discounted price. In this case, the server suggests a menu item such as "beef stew." The user can check the details of "beef stew" on their device and find out the list of ingredients needed, such as "beef," "onion," "carrot," and "potato," along with their prices. In addition, store sales information is taken into consideration, allowing the user to purchase ingredients at a discounted price. Also, if the user's family has a nut allergy, the AI ​​will suggest nut-free menu items.

[0715] In this way, the present system is able to provide efficient and healthy meal suggestions based on the user's personal information, information on the nearest store, and nutritional balance.

[0716] The processing flow will be explained below.

[0717] Step 1:

[0718] The user enters personal and family information into the device, such as name, age, height, weight, allergy information, food preferences, and family composition.

[0719] Step 2:

[0720] The device sends the entered user information to the server, which converts the data into JSON format and sends it as a POST request to the API endpoint.

[0721] Step 3:

[0722] The server stores the received data in a database, which is used for subsequent processing.

[0723] Step 4:

[0724] The device obtains the user's location information, either via GPS or manual user input.

[0725] Step 5:

[0726] The server retrieves the nearest store information based on the user's location information. Specifically, an API request is sent to retrieve data including store advertising and sales information.

[0727] Step 6:

[0728] The server uses an AI algorithm to generate menus based on user information and the nearest store, and suggests nutritionally balanced menus taking into account registered user information (allergies, food preferences, etc.).

[0729] Step 7:

[0730] The server generates a menu and sends it to the device. The menu includes a list of ingredients and pricing information.

[0731] Step 8:

[0732] The device displays the menu information it receives on the screen, allowing users to check the suggested menu, ingredients needed, and total price.

[0733] Step 9:

[0734] The user can check the menu displayed on the device and purchase ingredients as needed, while also taking advantage of sales information to save on food costs.

[0735] Example 1

[0736] 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."

[0737] Meal preparation at home can be a significant burden in busy daily lives, and it can be difficult to come up with a menu that takes nutritional balance into account. For this reason, there is a need for a system that streamlines meal preparation and suggests nutritionally balanced menus that take family health into consideration. It is also necessary to accommodate food allergies and individual preferences. Furthermore, it is important to have a way to purchase ingredients economically by utilizing information on nearby stores and sales.

[0738] 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.

[0739] In this invention, the server includes a means for registering personal information, a means for acquiring information on the nearest store, a means for generating a nutritionally balanced menu based on the registered information and the acquired store information, a means for displaying the generated menu, a means for inputting a prompt sentence into the generation AI model, and a means for suggesting a menu based on the input prompt sentence. This makes it possible to efficiently suggest an appropriate menu based on the user's individual nutritional needs and allergy information. Furthermore, by utilizing the information on the nearest store, economical food ingredient purchasing can be realized, providing a system that comprehensively supports home meal preparation.

[0740] "Personal information" refers to personal data about the user, such as the user's name, age, height, weight, allergy information, food preferences, and family composition information.

[0741] "Nearest store information" is data that includes store advertising information, sale information, and product price information, which is obtained based on the user's location.

[0742] "Menus that take nutritional balance into consideration" refers to the creation of a balanced meal menu based on the user's individual health condition, food preferences, and allergy information.

[0743] A "prompt" is text data that is input to a generative AI model, and is an instruction that allows the model to suggest an appropriate menu based on its content.

[0744] "Generative AI model" is a general term for artificial intelligence algorithms and machine learning models that generate optimal menus based on user and store information.

[0745] The "ingredient list" is a list of ingredients required for the generated menu, and includes the specific ingredient names and quantities.

[0746] "Price information" is data indicating the price of each ingredient in the ingredient list.

[0747] "User" refers to a person or family who uses this system to register personal information and check suggested menus.

[0748] This invention is a system that streamlines home meal preparation and suggests menus that take nutritional balance into consideration. This system is broadly composed of the following elements: user information registration, acquisition of information on the nearest store, menu creation, and menu display. Specific usage methods will be explained below.

[0749] Registering user information

[0750] The user uses a device to enter personal information and family composition information. Specifically, the user and family members' names, ages, heights, weights, allergy information, food preferences, etc. are entered. This information is sent from the device to a server and stored in a database. The device used can be a smartphone, tablet, personal computer, etc. Data is sent via an internet connection.

[0751] Get information about the nearest store

[0752] The server retrieves information about the nearest store based on the location of the registered user information. Specifically, this information includes advertising information, sales information, and price information obtained from the store corresponding to the user's location. This information is collected through external APIs and store RSS feeds. For example, the server uses the Google Maps API to search for stores near the user's location and retrieves price and sale information from the target store's API.

[0753] Menu generation

[0754] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. This process takes into account the user's individual preferences, allergy information, family composition, etc. The AI ​​model uses deep learning and neural networks to predict and generate appropriate menus. As specific examples, it uses TensorFlow and PyTorch, which are Python machine learning frameworks.

[0755] Menu display

[0756] The generated menu is then sent back to the user's device. The user can then check the proposed menu, the list of ingredients needed, and the total price on their device. In addition, discounted items based on store sales information are also displayed, helping to reduce food costs. The user interface is implemented as a mobile or web application and is designed to be intuitive to use.

[0757] Specific examples

[0758] For example, consider a case where a user registers information about themselves and their family, and the nearest store is having a "sale" and offering "beef" at a discounted price. In this case, the server suggests menu items such as "beef stew." The user can check the details of "beef stew" on their device and find out the list of ingredients needed, such as "beef," "onion," "carrot," and "potato," along with their prices. The server also displays store sales information, allowing the user to purchase ingredients at discounted prices. If the user's family has a nut allergy, the AI ​​will suggest nut-free menu items.

[0759] Here are some example prompts for a generative AI model:

[0760] "The user information entered is for a man aged 30, 175cm tall, and weighing 70kg, along with his family (wife and two children). If the nearest store to this user is having a beef sale, what kind of menu would this system suggest?"

[0761] "User has a nut allergy. Please take this into consideration and suggest healthy options based on the location of your nearest store."

[0762] In this way, the present invention realizes efficient and healthy meal suggestions based on the user's personal information, information on the nearest store, and nutritional balance.

[0763] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0764] Step 1:

[0765] The user uses the device to enter personal information and family composition information. Specifically, the user and family members' names, ages, heights, weights, allergy information, and food preferences are entered. The entered data is packaged in JSON format and sent to the server via the Internet. The server stores the received information in a database. This records basic user information in the system.

[0766] Input: User's personal information and family information

[0767] Output: Personal information stored in the server database

[0768] Step 2:

[0769] The server obtains information about the nearest store based on the location of the registered user information. Specifically, it uses the Google Maps API and store-specific APIs to collect advertising information, sales information, and price information for stores corresponding to the user's location. The obtained information is analyzed, and the necessary data is extracted and temporarily stored in memory storage.

[0770] Input: User's location information

[0771] Output: Parsed nearest store information

[0772] Step 3:

[0773] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. This process takes into account factors such as the user's food preferences, allergies, and family composition. The generative AI model (e.g., a model using TensorFlow or PyTorch) receives this information as input, analyzes it, and performs calculations to output the optimal menu in JSON format.

[0774] Input: User information, store information

[0775] Output: Generated menu data (JSON format)

[0776] Step 4:

[0777] The generated menu data is then sent back to the user's device. The user can then check the proposed menu, a list of ingredients needed, and the total price on their device. It also displays discounted items based on store sales information, helping users save money on food. The UI is implemented as a mobile or web application, and is intuitive to use.

[0778] Input: Generated menu data

[0779] Output: Menu, ingredients list, and total price displayed on the user's device

[0780] Step 5:

[0781] The user checks the proposed menu and purchases ingredients as necessary. They can select ingredients at discounted prices based on store sales information. After purchasing, they can also provide feedback on their purchase status and menu on the device. This allows the system to learn the user's preferences and usage patterns and further optimize suggestions for future purchases.

[0782] Input: User feedback and purchase status

[0783] Output: Learned user preference data, data to be used for next suggestions

[0784] (Application example 1)

[0785] 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."

[0786] The present invention relates to a system that streamlines meal preparation for busy daily lives and suggests nutritionally balanced menus. However, current systems are insufficient in that users have to purchase ingredients and cook them themselves, which requires time and effort, and they lack functionality that not only suggests meals but also takes into account the use of delivery services. Therefore, a challenge is to enable users to easily enjoy healthy meals.

[0787] 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.

[0788] In this invention, the server includes means for registering personal information, means for acquiring information on the nearest store, means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, means for displaying the generated menu, and means for acquiring information on the nearest delivery service and generating meal suggestions to be provided. This allows the user to easily receive suggestions for nutritionally balanced meals and enjoy meals quickly and efficiently by using the nearest delivery service.

[0789] A "means" refers to a method or apparatus for achieving a particular function or operation.

[0790] "Personal information" refers to information in which the system contains various data about the user, including name, age, height, weight, allergy information, food preferences, etc.

[0791] "Nearest store information" is information about the store closest to the user's location, including the store's name, address, menu items offered, prices, discount information, and the like.

[0792] A "nutritional balanced menu" is a meal plan designed to include the appropriate amount of nutrients needed based on the user's health condition and specific dietary requirements.

[0793] "Delivery service" means a service that delivers meals ordered by a user to their home or designated location through an online platform or application.

[0794] An "ingredient list" is a list of ingredients required to create a specific menu, including the specific name and required amount of each ingredient.

[0795] "Price information" refers to information about the price of a product or service provided, including details such as ingredients and delivery costs.

[0796] The present invention relates to a system that streamlines meal preparation that takes nutritional balance into consideration even in busy daily lives. This system generates nutritionally balanced menus based on user information and information on the nearest store, and also provides meals easily by linking with delivery services.

[0797] System Overview

[0798] The hardware used consists of a user device (such as a smartphone) and a server, while the software includes a smartphone app (iOS / Android), a cloud database, an AI algorithm, a location-based service API, and a food delivery API.

[0799] Program processing explanation

[0800] The server does the following:

[0801] 1. User information registration

[0802] Users use a smartphone app to enter information about themselves and their family members (such as name, age, height, weight, allergy information, and food preferences).

[0803] The entered information is sent from the smartphone to a cloud server and stored in a database.

[0804] 2. Obtaining information about the nearest store

[0805] The server uses the location service API to detect the user's current location.

[0806] Based on the detected location information, information about the nearest stores and delivery services (menus, prices, discount information, etc.) is obtained through the food delivery API.

[0807] 3. Menu generation

[0808] An AI algorithm (generative AI model) is used on the server to generate a menu that takes nutritional balance into consideration based on user information and acquired store information.

[0809] The menu is designed taking into account the user's health status and individual dietary needs.

[0810] 4. Menu display

[0811] The generated menu and its details (dish name, required ingredients, nutritional information, price, discount information) are sent to and displayed on the user's smartphone.

[0812] Users can view the suggested delivery menu and place an order immediately through the app.

[0813] Specific examples

[0814] Users register their information on the app, including any allergies their family members may have. The server detects the user's current location and obtains information on the nearest delivery service. An AI algorithm generates a menu suitable for the user, suggesting, for example, a "healthy salad lunch with plenty of vegetables." Menu details, delivery costs, and discount information are displayed on the user's smartphone. Users can then order the "healthy salad lunch with plenty of vegetables" through the app.

[0815] Prompt Sentence Examples

[0816] Personal information entered by the user:

[0817] Name: User A

[0818] Age: 35

[0819] Family: Spouse, two children

[0820] Allergy information: User A is allergic to nuts

[0821] Food preference: Japanese food, lots of vegetables

[0822] User's current location: Tokyo

[0823] Food delivery information obtained:

[0824] Store 1: Menu and price list, current discounts

[0825] Store 2: Menu and price list, current discounts

[0826] Generated menu:

[0827] Healthy salad lunch with plenty of vegetables (from store 1)

[0828] Total cost: 1500 yen (after discount)

[0829] In this way, the system of the present invention links easy and healthy meal suggestions with delivery services based on the user's personal information, information on the nearest store, and nutritional balance.

[0830] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0831] Step 1:

[0832] The user uses a smartphone app to enter information about themselves and their family members (such as name, age, height, weight, allergy information, and food preferences). The entered information is sent from the smartphone to a cloud server and stored in a database. The input in this step is the user's personal information, and the output is the information stored in the cloud database.

[0833] Step 2:

[0834] The server obtains the user's current location using a location information service API. The current location information becomes the server's input, and based on that, it obtains information about the nearest store and delivery service through the food delivery API. During this process, the location information data is processed and the store information is filtered based on that location. As an output, the server obtains information about the nearest store and delivery service.

[0835] Step 3:

[0836] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. In this step, the AI ​​algorithm selects the optimal menu from a vast recipe database based on the input data (user information and store information). The output is the menu most suitable for the user and its details.

[0837] Step 4:

[0838] The generated menu and its details (dish name, required ingredients, nutritional information, price, discount information) are sent from the server to the user's smartphone. During this process, the server converts the generated menu data into the app's display format and sends it to the user's device. The input is the menu generation result, and the output is the information displayed on the user's smartphone.

[0839] Step 5:

[0840] The user checks the proposed delivery menu through the smartphone app. If necessary, they refer to the provided information (dish name, required ingredients, nutritional information, price, discount information) and place the final order. In this step, the user manually operates the app and places the order using the nearest delivery service. The output is an order request to the nearest delivery service.

[0841] This will allow users to easily receive nutritionally balanced meal suggestions and quickly place delivery orders.

[0842] 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.

[0843] This invention relates to a system that streamlines home meal preparation and proposes menus that take nutritional balance into consideration. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it realizes more appropriate and personalized menu proposals.

[0844] Registering user information

[0845] The user uses the device to enter personal and family information, including name, age, height, weight, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database.

[0846] Get information about the nearest store

[0847] The server retrieves the nearest store information based on the registered user information. Specifically, it sends an API request to retrieve advertisement information and sales information for stores based on the user's location.

[0848] Menu generation

[0849] The server uses an AI algorithm to generate a nutritionally balanced menu based on user information and the nearest store, taking into account registered allergies and food preferences.

[0850] Using the Emotion Engine

[0851] The system includes an emotion engine that recognizes the user's emotions. Specifically, it uses sensors such as a camera built into the device to analyze the user's facial expressions and recognize the user's emotions. The recognized emotion information is used to adjust the menu. For example, if the user is feeling stressed, it will suggest a menu using nutritious ingredients to alleviate the user's emotions.

[0852] Menu display

[0853] The generated menu is sent to the device and displayed on the screen. The suggested menu includes a list of ingredients and price information, and also displays information about food sales. This allows users to choose ingredients efficiently and save money.

[0854] Specific examples

[0855] For example, consider the case where a user registers information about themselves and their family, and the emotion engine recognizes the user's "fatigue" through the device's camera. At this time, the server will suggest "menus to replenish energy" according to the user's condition. For example, it will suggest a smoothie and display a list of ingredients needed to make it, such as "banana," "spinach," and "yogurt," along with the total price. It will also display information about the nearest store, where these ingredients can be purchased at a discount.

[0856] In this way, the system can provide personalized meal suggestions based on the user's emotions in addition to their personal information, information on the nearest store, and nutritional balance.

[0857] The processing flow will be explained below.

[0858] Step 1:

[0859] The user uses the terminal to input personal and family information, specifically, the name, age, height, weight, allergy information, and food preferences of each user.

[0860] Step 2:

[0861] The device sends the entered user information to the server, which converts the information into JSON format and sends it as a POST request to the API endpoint.

[0862] Step 3:

[0863] The server stores the received data in a database, where the information is kept for subsequent processing.

[0864] Step 4:

[0865] The device obtains the user's location information, either via GPS or manual user input.

[0866] Step 5:

[0867] The server retrieves the nearest store information based on the user's location information. Specifically, it retrieves store advertising information and sales information via API requests.

[0868] Step 6:

[0869] The device uses cameras and other sensors to recognize the user's emotions, and an emotion engine analyzes the user's facial expressions to determine their emotions.

[0870] Step 7:

[0871] The device sends the emotion recognition results to the server, which converts the information into JSON format and sends it as a POST request to the API endpoint.

[0872] Step 8:

[0873] The server generates a menu based on user information, information about the nearest store, and emotion recognition results. An AI algorithm comprehensively analyzes this data and proposes a menu that takes nutritional balance into consideration. At this time, ingredients are selected and the menu is adjusted according to the user's emotional state.

[0874] Step 9:

[0875] The server sends the generated menu to the user's device. The proposed menu includes a list of ingredients and pricing information.

[0876] Step 10:

[0877] The device displays the menu information it receives on the screen, and the user can check the suggested menu, required ingredients, and total price on the device.

[0878] Step 11:

[0879] The user can check the menu displayed on the device and purchase ingredients as needed, while also taking advantage of sales information to save on food costs.

[0880] Step 12:

[0881] Users prepare meals based on menus and support their family's healthy eating habits.

[0882] Example 2

[0883] 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."

[0884] In modern society, daily meal preparation is a burden for many households, and planning efficient and nutritionally balanced menus is a difficult task, especially for working people. Additionally, selecting ingredients, checking prices, and addressing food allergies are time-consuming and labor-intensive issues. Furthermore, personalized menus that reflect the user's emotional state and preferences are necessary to improve the quality of meals at home.

[0885] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0886] In this invention, the server includes a means for registering personal information, a means for acquiring information on the nearest store, a means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, a means for displaying the generated menu, a means for recognizing the user's emotional information, and a means for adjusting the menu based on the recognized emotional information, thereby enabling the server to propose a nutritionally balanced menu that reflects the user's personal information and emotional state.

[0887] "Means for registering personal information" refers to a function that allows users to input personal information such as their own and their family members' names, ages, weights, heights, allergy information, and food preferences, and send this information to the server.

[0888] "Means for obtaining information about the nearest store" is a function that sends an API request to obtain advertising information and sales information for nearby stores based on the user's location information.

[0889] "Means for generating menus that take nutritional balance into consideration" is a function that uses an AI algorithm to generate nutritionally balanced menus based on the user's registration information and acquired store information.

[0890] The "means for displaying the generated menu" is a function that sends the generated menu to the user's terminal and displays it on the screen, including the list of ingredients and price information.

[0891] The "means for recognizing the user's emotional information" is a function that uses a camera or sensor built into the device to analyze the user's facial expressions and recognize their emotions.

[0892] The "means for adjusting the menu based on the recognized emotional information" is a function for adjusting the menu to suggest the most suitable menu based on the emotional state of the user recognized by the emotion engine.

[0893] The present invention is a system that proposes menus that take into consideration the efficiency of meal preparation at home and nutritional balance, and furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system realizes more appropriate and personalized menu proposals.

[0894] Registering user information

[0895] The user uses the device to enter personal information about themselves and their family members, including their name, age, height, weight, allergies, and food preferences. This information is sent from the device to a server and stored in a database.

[0896] Hardware and software: Tablets and smartphones are used as devices, and frameworks such as React and Vue.js are used as input UIs (user interfaces).

[0897] Get information about the nearest store

[0898] The server retrieves the nearest store information based on the user's registration information. Based on the user's location, it uses an external API to retrieve advertising and sales information for nearby stores.

[0899] Hardware and software: The server runs on a cloud service (e.g., AWS, Google Cloud), and Python or Node.js is used as the software for making API requests.

[0900] Menu generation

[0901] The server uses an AI algorithm (machine learning model) to generate nutritionally balanced menus based on user information and information on the nearest store, taking into account allergies and food preferences.

[0902] Hardware and software: The AI ​​model uses TensorFlow and PyTorch, and the menu generation algorithm is implemented in Python and runs on a virtual machine in the cloud.

[0903] Using the Emotion Engine

[0904] The system includes an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions using the device's built-in camera and other sensors. The analysis results are sent to a server and used to adjust the menu.

[0905] Hardware / Software: Uses the device's camera to analyze emotions using OpenCV and emotion analysis APIs (e.g., Microsoft Azure Face API).

[0906] Menu display

[0907] The generated menu is sent to the device and displayed on the screen, including a list of ingredients and pricing information, as well as store sales information.

[0908] Hardware and software: HTML, CSS, and JavaScript are used to display the device screen, and the display application is implemented using cross-platform frameworks such as React Native and Flutter.

[0909] Specific examples

[0910] For example, if a user registers information about themselves and their family, and the emotion engine recognizes the user's fatigue through the device's camera, the server will suggest a menu to replenish energy according to the user's condition. Specifically, it will suggest a smoothie and display a list of ingredients needed to make it, such as bananas, spinach, and yogurt, along with the total price. It will also display information about the nearest store, where these ingredients can be purchased at a discount.

[0911] Prompt Sentence Examples

[0912] "Enter user information, and if you recognize that your current emotion is fatigue, suggest a meal plan to replenish your energy. Also provide a list of ingredients and pricing information."

[0913] As described above, the present invention can realize nutritionally balanced and personalized menu suggestions based on the user's personal information and emotional information.

[0914] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0915] Processing flow

[0916] (Step 1): Register user information

[0917] The user uses the device to input personal information and family information, including name, age, height, weight, allergy information, food preferences, etc. The device then transmits this information to the server.

[0918] Input: Name, age, height, weight, allergy information, food preferences

[0919] Data processing: Packaging input information in JSON format

[0920] Output: User information sent to the server

[0921] Specific behavior:

[0922] A text input field and a submit button will appear on the device screen.

[0923] The user enters information into each field and presses the submit button.

[0924] The device sends a POST request to the server's API with information in JSON format.

[0925] The server stores the received information in a database.

[0926] (Step 2): Get information about the nearest store

[0927] The server retrieves the nearest store information based on the registered user information. Based on the user's location, the server retrieves store information and sale information via API request.

[0928] Input: User's location information

[0929] Data processing: API request generation

[0930] Output: Store information and sale information

[0931] Specific behavior:

[0932] The server retrieves the user's location information from a database.

[0933] Create an API request and send it to the store information service.

[0934] Cache the store information and sale information obtained as a response.

[0935] (Step 3): Generate a menu

[0936] The server uses an AI algorithm to generate a nutritionally balanced menu based on user information and the nearest store, taking into account allergies and food preferences.

[0937] Input: User information (allergy information, food preferences), store information

[0938] Data calculation: Considering nutritional balance using AI models

[0939] Output: Menu with nutritional balance in mind

[0940] Specific behavior:

[0941] The server feeds user information and store information to the AI ​​model.

[0942] The model analyzes the data and generates a menu plan.

[0943] The generated menu plan is saved in an internal data store.

[0944] (Step 4): Use the Emotion Engine

[0945] The device uses sensors such as a built-in camera to analyze the user's facial expressions and recognize their emotions. The recognized emotional information is then sent to the server.

[0946] Input: User's face image

[0947] Data processing: Facial expression analysis

[0948] Output: Emotional information

[0949] Specific behavior:

[0950] The device's camera captures the user's face.

[0951] Image analysis software (such as OpenCV) analyzes facial expressions.

[0952] The analysis results are sent to the server in JSON format.

[0953] (Step 5): Adjust the menu

[0954] The server adjusts the menu to suggest the most suitable menu based on the recognized emotion information.

[0955] Input: Emotional information, initial menu plan

[0956] Data Computing: Emotion-Based Menu Adjustment

[0957] Output: Adjusted menu

[0958] Specific behavior:

[0959] The server receives the emotional information and evaluates the initial menu plan.

[0960] The adjustment algorithm takes emotional information into account to make optimal adjustments.

[0961] The adjusted menu is saved in the database.

[0962] (Step 6): Display the menu

[0963] The generated menu is sent to the device and displayed on the screen, including a list of essential ingredients and pricing information, helping users make efficient food choices and save money.

[0964] Input: Adjusted menu

[0965] Data processing: Converting data into a user-friendly format

[0966] Output: Menu, ingredients list, and price information displayed on the device

[0967] Specific behavior:

[0968] The server sends the adjusted menu information to the terminal.

[0969] The terminal parses the received information for display on the screen.

[0970] The user checks the screen and sees the suggested menu and ingredient list.

[0971] The above is the processing flow of this system.

[0972] (Application example 2)

[0973] 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."

[0974] In modern society, it is difficult to find time to prepare meals at home, and it is also not easy to plan menus that take nutritional balance into consideration. Therefore, there is a need for a system that can efficiently prepare meals and achieve nutritional balance. Furthermore, there is a need for personalized menu suggestions that take the user's emotional state into account, but current technology is limited in the systems that can achieve this. A new system is needed to address these challenges.

[0975] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0976] In this invention, the server includes means for registering personal information, means for acquiring information on the nearest store, means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, means for displaying the generated menu, means for recognizing the user's emotion using an emotion recognition engine, and means for adjusting the menu based on the recognized emotion information. This makes it possible to effectively provide personalized meal suggestions based on the user's emotional state in addition to the user's personal information, information on the nearest store, and nutritional balance, as well as corresponding ingredient lists and store information.

[0977] "Personal information" includes the user's name, age, height, weight, allergy information, food preferences, and the like.

[0978] "Nearest store information" is information about stores within a user's reach, such as advertisement information and sales information for stores based on the user's location.

[0979] A "nutritional balance menu" is a meal menu that contains the necessary nutrients in an appropriate amount to maintain and improve the user's health.

[0980] A "generated menu" is a meal menu created by the server based on the user's personal information and information about the nearest store.

[0981] An "emotion recognition engine" is a technology that uses a device's built-in camera to analyze a user's facial expressions and identify their current emotional state.

[0982] "Recognized emotion information" is information that indicates the emotional state of the user as determined by the emotion recognition engine.

[0983] The "means for adjusting the menu" is a means for changing and optimizing the menu to suggest the most suitable meal menu for the user's emotional state based on the recognized emotional information.

[0984] The "ingredient list" is a list of specific ingredients required to realize the generated menu.

[0985] "Price information" refers to price information for each ingredient included in the ingredient list, including sale information.

[0986] "Personalized meal suggestions" are meal suggestions that are customized based on an individual user's personal information and emotional state.

[0987] This invention relates to a system that streamlines home meal preparation and proposes menus that take nutritional balance into consideration. Furthermore, by combining it with an emotion recognition engine that recognizes the user's emotions, it realizes more appropriate and personalized menu proposals.

[0988] Registering user information

[0989] The user uses the device to enter personal information and family composition information, including name, age, height, weight, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database on the server.

[0990] Get information about the nearest store

[0991] The server retrieves the nearest store information based on the registered user information. To do this, the server sends an API request to retrieve advertisement information and sales information for stores based on the user's location. This API request uses, for example, the Google Places API or the API of a specific supermarket.

[0992] Menu generation

[0993] The server generates a nutritionally balanced menu based on user information and the nearest store information. This process also takes into account registered allergy information and food preferences. An AI algorithm (MenuGenerator) is used to generate the menu.

[0994] Using the Emotion Engine

[0995] The emotion recognition engine uses sensors such as a camera built into the device to analyze the user's facial expressions and recognize their emotions. The emotional information recognized at this time is sent to the server. The server uses this emotional information to adjust the menu and make personalized suggestions.

[0996] Menu display

[0997] The generated menu is sent from the server to the device and displayed on the device screen. The suggested menu includes a list of ingredients and price information, and also displays information about food sales. This allows users to choose ingredients efficiently and save money.

[0998] Specific examples

[0999] For example, if a user registers information about themselves and their family, and the emotion engine recognizes the user's fatigue through the device's camera, the server will suggest a menu to replenish energy depending on the user's condition. For example, it will suggest a smoothie and display a list of ingredients needed to make it, such as bananas, spinach, and yogurt, along with the total price. It will also display information about the nearest store where these ingredients can be purchased at a discount.

[1000] Example prompt sentences to use

[1001] Based on the user's location and current mood, provide nutritionally balanced meals and sale information on ingredients needed for those meals, along with information on the nearest store.

[1002] In this way, in the embodiment of the invention, personalized meal suggestions can be made by combining personal information, store information, and emotion recognition.

[1003] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1004] Step 1: Register user information

[1005] The user uses the terminal to input personal information such as name, age, height, weight, allergy information, food preferences, and family composition information.

[1006] The entered information is sent from the terminal to the server and stored in a database within the server. The input in this step is personal information from the user, and the output is data stored on the server.

[1007] Step 2: Get the nearest store

[1008] The server sends an API request to identify the user's location based on the registered user information.

[1009] The API request returns information about the nearest store, advertisements, sales, etc. to the server. Specifically, the Google Places API or a specific supermarket's API is used. The input in this step is the user's location information stored on the server, and the output is information about the nearest store.

[1010] Step 3: Generate a menu that takes nutritional balance into account

[1011] The server uses an AI algorithm (Menu Generator) to generate a menu that takes nutritional balance into consideration based on user information and information about the nearest store.

[1012] This process also takes into account allergy information and food preferences. The input is user information and information about the nearest store, and the output is a menu that takes nutritional balance into consideration.

[1013] Step 4: Recognizing user emotions

[1014] Using the device's built-in camera, the emotion recognition engine (EmotionRecognition) analyzes the user's facial expressions and recognizes their emotions.

[1015] The recognized emotion information is sent to the server. The input is a facial image of the user captured by the device camera, and the output is the recognized emotion information.

[1016] Step 5: Adjust the menu

[1017] The server uses the emotion information obtained by the emotion recognition engine to adjust the generated menu.

[1018] For example, if the user is tired, it will suggest a meal plan to replenish their energy. The input is the recognized emotion information and the already generated meal plan, and the output is the adjusted meal plan.

[1019] Step 6: View the menu and related information

[1020] The adjusted menu is sent to the device and displayed on the device screen.

[1021] The suggested menu includes a list of ingredients and pricing information, and food sales information is also displayed. The input is the adjusted menu and related information, and the output is the information displayed on the device.

[1022] 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.

[1023] 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.

[1024] 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.

[1025] [Fourth embodiment]

[1026] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1027] 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.

[1028] 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).

[1029] 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.

[1030] 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.

[1031] 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).

[1032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] 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.

[1037] 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.

[1038] 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."

[1039] The present invention relates to a system that improves the efficiency of meal preparation at home in busy daily lives and proposes menus that take nutritional balance into consideration. This system provides user information, information on the nearest store, and menus that take into consideration overall nutritional balance, and is implemented in the following form.

[1040] Registering user information

[1041] The user uses the device to enter personal information and family composition information, such as the user's and family members' names, ages, heights, weights, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database.

[1042] Get information about the nearest store

[1043] The server acquires the nearest store information based on the location of the registered user information, including advertisement information, sale information, and price information acquired from the store corresponding to the user's location.

[1044] Menu generation

[1045] The server uses an AI algorithm to generate a menu that takes into account nutritional balance based on user information and acquired store information. This menu generation process takes into account the user's food preferences, allergies, family composition, etc. to suggest the most suitable menu for each individual's needs.

[1046] Menu display

[1047] The generated menu is then sent back to the user's device, where the user can check the suggested menu, the list of ingredients needed, and the total price. It also displays discounted items based on store sales information, helping users save money on food.

[1048] Specific examples

[1049] For example, consider a situation where a user registers information about themselves and their family, and the nearest store is having a "sale" and offering "beef" at a discounted price. In this case, the server suggests a menu item such as "beef stew." The user can check the details of "beef stew" on their device and find out the list of ingredients needed, such as "beef," "onion," "carrot," and "potato," along with their prices. In addition, store sales information is taken into consideration, allowing the user to purchase ingredients at a discounted price. Also, if the user's family has a nut allergy, the AI ​​will suggest nut-free menu items.

[1050] In this way, the present system is able to provide efficient and healthy meal suggestions based on the user's personal information, information on the nearest store, and nutritional balance.

[1051] The processing flow will be explained below.

[1052] Step 1:

[1053] The user enters personal and family information into the device, such as name, age, height, weight, allergy information, food preferences, and family composition.

[1054] Step 2:

[1055] The device sends the entered user information to the server, which converts the data into JSON format and sends it as a POST request to the API endpoint.

[1056] Step 3:

[1057] The server stores the received data in a database, which is used for subsequent processing.

[1058] Step 4:

[1059] The device obtains the user's location information, either via GPS or manual user input.

[1060] Step 5:

[1061] The server retrieves the nearest store information based on the user's location information. Specifically, an API request is sent to retrieve data including store advertising and sales information.

[1062] Step 6:

[1063] The server uses an AI algorithm to generate menus based on user information and the nearest store, and suggests nutritionally balanced menus taking into account registered user information (allergies, food preferences, etc.).

[1064] Step 7:

[1065] The server generates a menu and sends it to the device. The menu includes a list of ingredients and pricing information.

[1066] Step 8:

[1067] The device displays the menu information it receives on the screen, allowing users to check the suggested menu, ingredients needed, and total price.

[1068] Step 9:

[1069] The user can check the menu displayed on the device and purchase ingredients as needed, while also taking advantage of sales information to save on food costs.

[1070] Example 1

[1071] 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."

[1072] Meal preparation at home can be a significant burden in busy daily lives, and it can be difficult to come up with a menu that takes nutritional balance into account. For this reason, there is a need for a system that streamlines meal preparation and suggests nutritionally balanced menus that take family health into consideration. It is also necessary to accommodate food allergies and individual preferences. Furthermore, it is important to have a way to purchase ingredients economically by utilizing information on nearby stores and sales.

[1073] 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.

[1074] In this invention, the server includes a means for registering personal information, a means for acquiring information on the nearest store, a means for generating a nutritionally balanced menu based on the registered information and the acquired store information, a means for displaying the generated menu, a means for inputting a prompt sentence into the generation AI model, and a means for suggesting a menu based on the input prompt sentence. This makes it possible to efficiently suggest an appropriate menu based on the user's individual nutritional needs and allergy information. Furthermore, by utilizing the information on the nearest store, economical food ingredient purchasing can be realized, providing a system that comprehensively supports home meal preparation.

[1075] "Personal information" refers to personal data about the user, such as the user's name, age, height, weight, allergy information, food preferences, and family composition information.

[1076] "Nearest store information" is data that includes store advertising information, sale information, and product price information, which is obtained based on the user's location.

[1077] "Menus that take nutritional balance into consideration" refers to the creation of a balanced meal menu based on the user's individual health condition, food preferences, and allergy information.

[1078] A "prompt" is text data that is input to a generative AI model, and is an instruction that allows the model to suggest an appropriate menu based on its content.

[1079] "Generative AI model" is a general term for artificial intelligence algorithms and machine learning models that generate optimal menus based on user and store information.

[1080] The "ingredient list" is a list of ingredients required for the generated menu, and includes the specific ingredient names and quantities.

[1081] "Price information" is data indicating the price of each ingredient in the ingredient list.

[1082] "User" refers to a person or family who uses this system to register personal information and check suggested menus.

[1083] This invention is a system that streamlines home meal preparation and suggests menus that take nutritional balance into consideration. This system is broadly composed of the following elements: user information registration, acquisition of information on the nearest store, menu creation, and menu display. Specific usage methods will be explained below.

[1084] Registering user information

[1085] The user uses a device to enter personal information and family composition information. Specifically, the user and family members' names, ages, heights, weights, allergy information, food preferences, etc. are entered. This information is sent from the device to a server and stored in a database. The device used can be a smartphone, tablet, personal computer, etc. Data is sent via an internet connection.

[1086] Get information about the nearest store

[1087] The server retrieves information about the nearest store based on the location of the registered user information. Specifically, this information includes advertising information, sales information, and price information obtained from the store corresponding to the user's location. This information is collected through external APIs and store RSS feeds. For example, the server uses the Google Maps API to search for stores near the user's location and retrieves price and sale information from the target store's API.

[1088] Menu generation

[1089] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. This process takes into account the user's individual preferences, allergy information, family composition, etc. The AI ​​model uses deep learning and neural networks to predict and generate appropriate menus. As specific examples, it uses TensorFlow and PyTorch, which are Python machine learning frameworks.

[1090] Menu display

[1091] The generated menu is then sent back to the user's device. The user can then check the proposed menu, the list of ingredients needed, and the total price on their device. In addition, discounted items based on store sales information are also displayed, helping to reduce food costs. The user interface is implemented as a mobile or web application and is designed to be intuitive to use.

[1092] Specific examples

[1093] For example, consider a case where a user registers information about themselves and their family, and the nearest store is having a "sale" and offering "beef" at a discounted price. In this case, the server suggests menu items such as "beef stew." The user can check the details of "beef stew" on their device and find out the list of ingredients needed, such as "beef," "onion," "carrot," and "potato," along with their prices. The server also displays store sales information, allowing the user to purchase ingredients at discounted prices. If the user's family has a nut allergy, the AI ​​will suggest nut-free menu items.

[1094] Here are some example prompts for a generative AI model:

[1095] "The user information entered is for a man aged 30, 175cm tall, and weighing 70kg, along with his family (wife and two children). If the nearest store to this user is having a beef sale, what kind of menu would this system suggest?"

[1096] "User has a nut allergy. Please take this into consideration and suggest healthy options based on the location of your nearest store."

[1097] In this way, the present invention realizes efficient and healthy meal suggestions based on the user's personal information, information on the nearest store, and nutritional balance.

[1098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1099] Step 1:

[1100] The user uses the device to enter personal information and family composition information. Specifically, the user and family members' names, ages, heights, weights, allergy information, and food preferences are entered. The entered data is packaged in JSON format and sent to the server via the Internet. The server stores the received information in a database. This records basic user information in the system.

[1101] Input: User's personal information and family information

[1102] Output: Personal information stored in the server database

[1103] Step 2:

[1104] The server obtains information about the nearest store based on the location of the registered user information. Specifically, it uses the Google Maps API and store-specific APIs to collect advertising information, sales information, and price information for stores corresponding to the user's location. The obtained information is analyzed, and the necessary data is extracted and temporarily stored in memory storage.

[1105] Input: User's location information

[1106] Output: Parsed nearest store information

[1107] Step 3:

[1108] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. This process takes into account factors such as the user's food preferences, allergies, and family composition. The generative AI model (e.g., a model using TensorFlow or PyTorch) receives this information as input, analyzes it, and performs calculations to output the optimal menu in JSON format.

[1109] Input: User information, store information

[1110] Output: Generated menu data (JSON format)

[1111] Step 4:

[1112] The generated menu data is then sent back to the user's device. The user can then check the proposed menu, a list of ingredients needed, and the total price on their device. It also displays discounted items based on store sales information, helping users save money on food. The UI is implemented as a mobile or web application, and is intuitive to use.

[1113] Input: Generated menu data

[1114] Output: Menu, ingredients list, and total price displayed on the user's device

[1115] Step 5:

[1116] The user checks the proposed menu and purchases ingredients as necessary. They can select ingredients at discounted prices based on store sales information. After purchasing, they can also provide feedback on their purchase status and menu on the device. This allows the system to learn the user's preferences and usage patterns and further optimize suggestions for future purchases.

[1117] Input: User feedback and purchase status

[1118] Output: Learned user preference data, data to be used for next suggestions

[1119] (Application example 1)

[1120] 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."

[1121] The present invention relates to a system that streamlines meal preparation for busy daily lives and suggests nutritionally balanced menus. However, current systems are insufficient in that users have to purchase ingredients and cook them themselves, which requires time and effort, and they lack functionality that not only suggests meals but also takes into account the use of delivery services. Therefore, a challenge is to enable users to easily enjoy healthy meals.

[1122] 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.

[1123] In this invention, the server includes means for registering personal information, means for acquiring information on the nearest store, means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, means for displaying the generated menu, and means for acquiring information on the nearest delivery service and generating meal suggestions to be provided. This allows the user to easily receive suggestions for nutritionally balanced meals and enjoy meals quickly and efficiently by using the nearest delivery service.

[1124] A "means" refers to a method or apparatus for achieving a particular function or operation.

[1125] "Personal information" refers to information in which the system contains various data about the user, including name, age, height, weight, allergy information, food preferences, etc.

[1126] "Nearest store information" is information about the store closest to the user's location, including the store's name, address, menu items offered, prices, discount information, and the like.

[1127] A "nutritional balanced menu" is a meal plan designed to include the appropriate amount of nutrients needed based on the user's health condition and specific dietary requirements.

[1128] "Delivery service" means a service that delivers meals ordered by a user to their home or designated location through an online platform or application.

[1129] An "ingredient list" is a list of ingredients required to create a specific menu, including the specific name and required amount of each ingredient.

[1130] "Price information" refers to information about the price of a product or service provided, including details such as ingredients and delivery costs.

[1131] The present invention relates to a system that streamlines meal preparation that takes nutritional balance into consideration even in busy daily lives. This system generates nutritionally balanced menus based on user information and information on the nearest store, and also provides meals easily by linking with delivery services.

[1132] System Overview

[1133] The hardware used consists of a user device (such as a smartphone) and a server, while the software includes a smartphone app (iOS / Android), a cloud database, an AI algorithm, a location-based service API, and a food delivery API.

[1134] Program processing explanation

[1135] The server does the following:

[1136] 1. User information registration

[1137] Users use a smartphone app to enter information about themselves and their family members (such as name, age, height, weight, allergy information, and food preferences).

[1138] The entered information is sent from the smartphone to a cloud server and stored in a database.

[1139] 2. Obtaining information about the nearest store

[1140] The server uses the location service API to detect the user's current location.

[1141] Based on the detected location information, information about the nearest stores and delivery services (menus, prices, discount information, etc.) is obtained through the food delivery API.

[1142] 3. Menu generation

[1143] An AI algorithm (generative AI model) is used on the server to generate a menu that takes nutritional balance into consideration based on user information and acquired store information.

[1144] The menu is designed taking into account the user's health status and individual dietary needs.

[1145] 4. Menu display

[1146] The generated menu and its details (dish name, required ingredients, nutritional information, price, discount information) are sent to and displayed on the user's smartphone.

[1147] Users can view the suggested delivery menu and place an order immediately through the app.

[1148] Specific examples

[1149] Users register their information on the app, including any allergies their family members may have. The server detects the user's current location and obtains information on the nearest delivery service. An AI algorithm generates a menu suitable for the user, suggesting, for example, a "healthy salad lunch with plenty of vegetables." Menu details, delivery costs, and discount information are displayed on the user's smartphone. Users can then order the "healthy salad lunch with plenty of vegetables" through the app.

[1150] Prompt Sentence Examples

[1151] Personal information entered by the user:

[1152] Name: User A

[1153] Age: 35

[1154] Family: Spouse, two children

[1155] Allergy information: User A is allergic to nuts

[1156] Food preference: Japanese food, lots of vegetables

[1157] User's current location: Tokyo

[1158] Food delivery information obtained:

[1159] Store 1: Menu and price list, current discounts

[1160] Store 2: Menu and price list, current discounts

[1161] Generated menu:

[1162] Healthy salad lunch with plenty of vegetables (from store 1)

[1163] Total cost: 1500 yen (after discount)

[1164] In this way, the system of the present invention links easy and healthy meal suggestions with delivery services based on the user's personal information, information on the nearest store, and nutritional balance.

[1165] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1166] Step 1:

[1167] The user uses a smartphone app to enter information about themselves and their family members (such as name, age, height, weight, allergy information, and food preferences). The entered information is sent from the smartphone to a cloud server and stored in a database. The input in this step is the user's personal information, and the output is the information stored in the cloud database.

[1168] Step 2:

[1169] The server obtains the user's current location using a location information service API. The current location information becomes the server's input, and based on that, it obtains information about the nearest store and delivery service through the food delivery API. During this process, the location information data is processed and the store information is filtered based on that location. As an output, the server obtains information about the nearest store and delivery service.

[1170] Step 3:

[1171] The server uses a generative AI model to generate a menu that takes nutritional balance into consideration based on user information and acquired store information. In this step, the AI ​​algorithm selects the optimal menu from a vast recipe database based on the input data (user information and store information). The output is the menu most suitable for the user and its details.

[1172] Step 4:

[1173] The generated menu and its details (dish name, required ingredients, nutritional information, price, discount information) are sent from the server to the user's smartphone. During this process, the server converts the generated menu data into the app's display format and sends it to the user's device. The input is the menu generation result, and the output is the information displayed on the user's smartphone.

[1174] Step 5:

[1175] The user checks the proposed delivery menu through the smartphone app. If necessary, they refer to the provided information (dish name, required ingredients, nutritional information, price, discount information) and place the final order. In this step, the user manually operates the app and places the order using the nearest delivery service. The output is an order request to the nearest delivery service.

[1176] This will allow users to easily receive nutritionally balanced meal suggestions and quickly place delivery orders.

[1177] 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.

[1178] This invention relates to a system that streamlines home meal preparation and proposes menus that take nutritional balance into consideration. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it realizes more appropriate and personalized menu proposals.

[1179] Registering user information

[1180] The user uses the device to enter personal and family information, including name, age, height, weight, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database.

[1181] Get information about the nearest store

[1182] The server retrieves the nearest store information based on the registered user information. Specifically, it sends an API request to retrieve advertisement information and sales information for stores based on the user's location.

[1183] Menu generation

[1184] The server uses an AI algorithm to generate a nutritionally balanced menu based on user information and the nearest store, taking into account registered allergies and food preferences.

[1185] Using the Emotion Engine

[1186] The system includes an emotion engine that recognizes the user's emotions. Specifically, it uses sensors such as a camera built into the device to analyze the user's facial expressions and recognize the user's emotions. The recognized emotion information is used to adjust the menu. For example, if the user is feeling stressed, it will suggest a menu using nutritious ingredients to alleviate the user's emotions.

[1187] Menu display

[1188] The generated menu is sent to the device and displayed on the screen. The suggested menu includes a list of ingredients and price information, and also displays information about food sales. This allows users to choose ingredients efficiently and save money.

[1189] Specific examples

[1190] For example, consider the case where a user registers information about themselves and their family, and the emotion engine recognizes the user's "fatigue" through the device's camera. At this time, the server will suggest "menus to replenish energy" according to the user's condition. For example, it will suggest a smoothie and display a list of ingredients needed to make it, such as "banana," "spinach," and "yogurt," along with the total price. It will also display information about the nearest store, where these ingredients can be purchased at a discount.

[1191] In this way, the system can provide personalized meal suggestions based on the user's emotions in addition to their personal information, information on the nearest store, and nutritional balance.

[1192] The processing flow will be explained below.

[1193] Step 1:

[1194] The user uses the terminal to input personal and family information, specifically, the name, age, height, weight, allergy information, and food preferences of each user.

[1195] Step 2:

[1196] The device sends the entered user information to the server, which converts the information into JSON format and sends it as a POST request to the API endpoint.

[1197] Step 3:

[1198] The server stores the received data in a database, where the information is kept for subsequent processing.

[1199] Step 4:

[1200] The device obtains the user's location information, either via GPS or manual user input.

[1201] Step 5:

[1202] The server retrieves the nearest store information based on the user's location information. Specifically, it retrieves store advertising information and sales information via API requests.

[1203] Step 6:

[1204] The device uses cameras and other sensors to recognize the user's emotions, and an emotion engine analyzes the user's facial expressions to determine their emotions.

[1205] Step 7:

[1206] The device sends the emotion recognition results to the server, which converts the information into JSON format and sends it as a POST request to the API endpoint.

[1207] Step 8:

[1208] The server generates a menu based on user information, information about the nearest store, and emotion recognition results. An AI algorithm comprehensively analyzes this data and proposes a menu that takes nutritional balance into consideration. At this time, ingredients are selected and the menu is adjusted according to the user's emotional state.

[1209] Step 9:

[1210] The server sends the generated menu to the user's device. The proposed menu includes a list of ingredients and pricing information.

[1211] Step 10:

[1212] The device displays the menu information it receives on the screen, and the user can check the suggested menu, required ingredients, and total price on the device.

[1213] Step 11:

[1214] The user can check the menu displayed on the device and purchase ingredients as needed, while also taking advantage of sales information to save on food costs.

[1215] Step 12:

[1216] Users prepare meals based on menus and support their family's healthy eating habits.

[1217] Example 2

[1218] 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."

[1219] In modern society, daily meal preparation is a burden for many households, and planning efficient and nutritionally balanced menus is a difficult task, especially for working people. Additionally, selecting ingredients, checking prices, and addressing food allergies are time-consuming and labor-intensive issues. Furthermore, personalized menus that reflect the user's emotional state and preferences are necessary to improve the quality of meals at home.

[1220] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1221] In this invention, the server includes a means for registering personal information, a means for acquiring information on the nearest store, a means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, a means for displaying the generated menu, a means for recognizing the user's emotional information, and a means for adjusting the menu based on the recognized emotional information, thereby enabling the server to propose a nutritionally balanced menu that reflects the user's personal information and emotional state.

[1222] "Means for registering personal information" refers to a function that allows users to input personal information such as their own and their family members' names, ages, weights, heights, allergy information, and food preferences, and send this information to the server.

[1223] "Means for obtaining information about the nearest store" is a function that sends an API request to obtain advertising information and sales information for nearby stores based on the user's location information.

[1224] "Means for generating menus that take nutritional balance into consideration" is a function that uses an AI algorithm to generate nutritionally balanced menus based on the user's registration information and acquired store information.

[1225] The "means for displaying the generated menu" is a function that sends the generated menu to the user's terminal and displays it on the screen, including the list of ingredients and price information.

[1226] The "means for recognizing the user's emotional information" is a function that uses a camera or sensor built into the device to analyze the user's facial expressions and recognize their emotions.

[1227] The "means for adjusting the menu based on the recognized emotional information" is a function for adjusting the menu to suggest the most suitable menu based on the emotional state of the user recognized by the emotion engine.

[1228] The present invention is a system that proposes menus that take into consideration the efficiency of meal preparation at home and nutritional balance, and furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system realizes more appropriate and personalized menu proposals.

[1229] Registering user information

[1230] The user uses the device to enter personal information about themselves and their family members, including their name, age, height, weight, allergies, and food preferences. This information is sent from the device to a server and stored in a database.

[1231] Hardware and software: Tablets and smartphones are used as devices, and frameworks such as React and Vue.js are used as input UIs (user interfaces).

[1232] Get information about the nearest store

[1233] The server retrieves the nearest store information based on the user's registration information. Based on the user's location, it uses an external API to retrieve advertising and sales information for nearby stores.

[1234] Hardware and software: The server runs on a cloud service (e.g., AWS, Google Cloud), and Python or Node.js is used as the software for making API requests.

[1235] Menu generation

[1236] The server uses an AI algorithm (machine learning model) to generate nutritionally balanced menus based on user information and information on the nearest store, taking into account allergies and food preferences.

[1237] Hardware and software: The AI ​​model uses TensorFlow and PyTorch, and the menu generation algorithm is implemented in Python and runs on a virtual machine in the cloud.

[1238] Using the Emotion Engine

[1239] The system includes an emotion engine that recognizes the user's emotions and analyzes the user's facial expressions using the device's built-in camera and other sensors. The analysis results are sent to a server and used to adjust the menu.

[1240] Hardware / Software: Uses the device's camera to analyze emotions using OpenCV and emotion analysis APIs (e.g., Microsoft Azure Face API).

[1241] Menu display

[1242] The generated menu is sent to the device and displayed on the screen, including a list of ingredients and pricing information, as well as store sales information.

[1243] Hardware and software: HTML, CSS, and JavaScript are used to display the device screen, and the display application is implemented using cross-platform frameworks such as React Native and Flutter.

[1244] Specific examples

[1245] For example, if a user registers information about themselves and their family, and the emotion engine recognizes the user's fatigue through the device's camera, the server will suggest a menu to replenish energy according to the user's condition. Specifically, it will suggest a smoothie and display a list of ingredients needed to make it, such as bananas, spinach, and yogurt, along with the total price. It will also display information about the nearest store, where these ingredients can be purchased at a discount.

[1246] Prompt Sentence Examples

[1247] "Enter user information, and if you recognize that your current emotion is fatigue, suggest a meal plan to replenish your energy. Also provide a list of ingredients and pricing information."

[1248] As described above, the present invention can realize nutritionally balanced and personalized menu suggestions based on the user's personal information and emotional information.

[1249] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1250] Processing flow

[1251] (Step 1): Register user information

[1252] The user uses the device to input personal information and family information, including name, age, height, weight, allergy information, food preferences, etc. The device then transmits this information to the server.

[1253] Input: Name, age, height, weight, allergy information, food preferences

[1254] Data processing: Packaging input information in JSON format

[1255] Output: User information sent to the server

[1256] Specific behavior:

[1257] A text input field and a submit button will appear on the device screen.

[1258] The user enters information into each field and presses the submit button.

[1259] The device sends a POST request to the server's API with information in JSON format.

[1260] The server stores the received information in a database.

[1261] (Step 2): Get information about the nearest store

[1262] The server retrieves the nearest store information based on the registered user information. Based on the user's location, the server retrieves store information and sale information via API request.

[1263] Input: User's location information

[1264] Data processing: API request generation

[1265] Output: Store information and sale information

[1266] Specific behavior:

[1267] The server retrieves the user's location information from a database.

[1268] Create an API request and send it to the store information service.

[1269] Cache the store information and sale information obtained as a response.

[1270] (Step 3): Generate a menu

[1271] The server uses an AI algorithm to generate a nutritionally balanced menu based on user information and the nearest store, taking into account allergies and food preferences.

[1272] Input: User information (allergy information, food preferences), store information

[1273] Data calculation: Considering nutritional balance using AI models

[1274] Output: Menu with nutritional balance in mind

[1275] Specific behavior:

[1276] The server feeds user information and store information to the AI ​​model.

[1277] The model analyzes the data and generates a menu plan.

[1278] The generated menu plan is saved in an internal data store.

[1279] (Step 4): Use the Emotion Engine

[1280] The device uses sensors such as a built-in camera to analyze the user's facial expressions and recognize their emotions. The recognized emotional information is then sent to the server.

[1281] Input: User's face image

[1282] Data processing: Facial expression analysis

[1283] Output: Emotional information

[1284] Specific behavior:

[1285] The device's camera captures the user's face.

[1286] Image analysis software (such as OpenCV) analyzes facial expressions.

[1287] The analysis results are sent to the server in JSON format.

[1288] (Step 5): Adjust the menu

[1289] The server adjusts the menu to suggest the most suitable menu based on the recognized emotion information.

[1290] Input: Emotional information, initial menu plan

[1291] Data Computing: Emotion-Based Menu Adjustment

[1292] Output: Adjusted menu

[1293] Specific behavior:

[1294] The server receives the emotional information and evaluates the initial menu plan.

[1295] The adjustment algorithm takes emotional information into account to make optimal adjustments.

[1296] The adjusted menu is saved in the database.

[1297] (Step 6): Display the menu

[1298] The generated menu is sent to the device and displayed on the screen, including a list of essential ingredients and pricing information, helping users make efficient food choices and save money.

[1299] Input: Adjusted menu

[1300] Data processing: Converting data into a user-friendly format

[1301] Output: Menu, ingredients list, and price information displayed on the device

[1302] Specific behavior:

[1303] The server sends the adjusted menu information to the terminal.

[1304] The terminal parses the received information for display on the screen.

[1305] The user checks the screen and sees the suggested menu and ingredient list.

[1306] The above is the processing flow of this system.

[1307] (Application example 2)

[1308] 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."

[1309] In modern society, it is difficult to find time to prepare meals at home, and it is also not easy to plan menus that take nutritional balance into consideration. Therefore, there is a need for a system that can efficiently prepare meals and achieve nutritional balance. Furthermore, there is a need for personalized menu suggestions that take the user's emotional state into account, but current technology is limited in the systems that can achieve this. A new system is needed to address these challenges.

[1310] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1311] In this invention, the server includes means for registering personal information, means for acquiring information on the nearest store, means for generating a menu that takes nutritional balance into consideration based on the registered information and the acquired store information, means for displaying the generated menu, means for recognizing the user's emotion using an emotion recognition engine, and means for adjusting the menu based on the recognized emotion information. This makes it possible to effectively provide personalized meal suggestions based on the user's emotional state in addition to the user's personal information, information on the nearest store, and nutritional balance, as well as corresponding ingredient lists and store information.

[1312] "Personal information" includes the user's name, age, height, weight, allergy information, food preferences, and the like.

[1313] "Nearest store information" is information about stores within a user's reach, such as advertisement information and sales information for stores based on the user's location.

[1314] A "nutritional balance menu" is a meal menu that contains the necessary nutrients in an appropriate amount to maintain and improve the user's health.

[1315] A "generated menu" is a meal menu created by the server based on the user's personal information and information about the nearest store.

[1316] An "emotion recognition engine" is a technology that uses a device's built-in camera to analyze a user's facial expressions and identify their current emotional state.

[1317] "Recognized emotion information" is information that indicates the emotional state of the user as determined by the emotion recognition engine.

[1318] The "means for adjusting the menu" is a means for changing and optimizing the menu to suggest the most suitable meal menu for the user's emotional state based on the recognized emotional information.

[1319] The "ingredient list" is a list of specific ingredients required to realize the generated menu.

[1320] "Price information" refers to price information for each ingredient included in the ingredient list, including sale information.

[1321] "Personalized meal suggestions" are meal suggestions that are customized based on an individual user's personal information and emotional state.

[1322] This invention relates to a system that streamlines home meal preparation and proposes menus that take nutritional balance into consideration. Furthermore, by combining it with an emotion recognition engine that recognizes the user's emotions, it realizes more appropriate and personalized menu proposals.

[1323] Registering user information

[1324] The user uses the device to enter personal information and family composition information, including name, age, height, weight, allergy information, food preferences, etc. This information is sent from the device to the server and stored in a database on the server.

[1325] Get information about the nearest store

[1326] The server retrieves the nearest store information based on the registered user information. To do this, the server sends an API request to retrieve advertisement information and sales information for stores based on the user's location. This API request uses, for example, the Google Places API or the API of a specific supermarket.

[1327] Menu generation

[1328] The server generates a nutritionally balanced menu based on user information and the nearest store information. This process also takes into account registered allergy information and food preferences. An AI algorithm (MenuGenerator) is used to generate the menu.

[1329] Using the Emotion Engine

[1330] The emotion recognition engine uses sensors such as a camera built into the device to analyze the user's facial expressions and recognize their emotions. The emotional information recognized at this time is sent to the server. The server uses this emotional information to adjust the menu and make personalized suggestions.

[1331] Menu display

[1332] The generated menu is sent from the server to the device and displayed on the device screen. The suggested menu includes a list of ingredients and price information, and also displays information about food sales. This allows users to choose ingredients efficiently and save money.

[1333] Specific examples

[1334] For example, if a user registers information about themselves and their family, and the emotion engine recognizes the user's fatigue through the device's camera, the server will suggest a menu to replenish energy depending on the user's condition. For example, it will suggest a smoothie and display a list of ingredients needed to make it, such as bananas, spinach, and yogurt, along with the total price. It will also display information about the nearest store where these ingredients can be purchased at a discount.

[1335] Example prompt sentences to use

[1336] Based on the user's location and current mood, provide nutritionally balanced meals and sale information on ingredients needed for those meals, along with information on the nearest store.

[1337] In this way, in the embodiment of the invention, personalized meal suggestions can be made by combining personal information, store information, and emotion recognition.

[1338] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1339] Step 1: Register user information

[1340] The user uses the terminal to input personal information such as name, age, height, weight, allergy information, food preferences, and family composition information.

[1341] The entered information is sent from the terminal to the server and stored in a database within the server. The input in this step is personal information from the user, and the output is data stored on the server.

[1342] Step 2: Get the nearest store

[1343] The server sends an API request to identify the user's location based on the registered user information.

[1344] The API request returns information about the nearest store, advertisements, sales, etc. to the server. Specifically, the Google Places API or a specific supermarket's API is used. The input in this step is the user's location information stored on the server, and the output is information about the nearest store.

[1345] Step 3: Generate a menu that takes nutritional balance into account

[1346] The server uses an AI algorithm (Menu Generator) to generate a menu that takes nutritional balance into consideration based on user information and information about the nearest store.

[1347] This process also takes into account allergy information and food preferences. The input is user information and information about the nearest store, and the output is a menu that takes nutritional balance into consideration.

[1348] Step 4: Recognizing user emotions

[1349] Using the device's built-in camera, the emotion recognition engine (EmotionRecognition) analyzes the user's facial expressions and recognizes their emotions.

[1350] The recognized emotion information is sent to the server. The input is a facial image of the user captured by the device camera, and the output is the recognized emotion information.

[1351] Step 5: Adjust the menu

[1352] The server uses the emotion information obtained by the emotion recognition engine to adjust the generated menu.

[1353] For example, if the user is tired, it will suggest a meal plan to replenish their energy. The input is the recognized emotion information and the already generated meal plan, and the output is the adjusted meal plan.

[1354] Step 6: View the menu and related information

[1355] The adjusted menu is sent to the device and displayed on the device screen.

[1356] The suggested menu includes a list of ingredients and pricing information, and food sales information is also displayed. The input is the adjusted menu and related information, and the output is the information displayed on the device.

[1357] 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.

[1358] 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.

[1359] 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.

[1360] 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.

[1361] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1362] 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.

[1363] 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).

[1364] 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.

[1365] 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."

[1366] 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.

[1367] 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).

[1368] 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.

[1369] 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.

[1370] 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.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] The following is further disclosed regarding the above embodiment.

[1379] (Claim 1)

[1380] A means for registering personal information;

[1381] A means of obtaining information about the nearest store,

[1382] A means for generating a menu that takes into consideration nutritional balance based on the registered information and the acquired store information;

[1383] A means for displaying the generated menu;

[1384] A system including:

[1385] (Claim 2)

[1386] 10. The system of claim 1, further comprising means for including allergy information in the registered information.

[1387] (Claim 3)

[1388] 10. The system of claim 1, further comprising means for displaying an ingredient list and price information corresponding to the generated menu.

[1389] "Example 1"

[1390] (Claim 1)

[1391] A means for registering personal information;

[1392] A means of obtaining information about the nearest store,

[1393] A means for generating a menu that takes into consideration nutritional balance based on the registered information and the acquired store information;

[1394] A means for displaying the generated menu;

[1395] a means for inputting a prompt sentence into a generative AI model;

[1396] a means for suggesting a menu based on an input prompt;

[1397] A system including:

[1398] (Claim 2)

[1399] 10. The system of claim 1, further comprising means for including allergy information in the registered information.

[1400] (Claim 3)

[1401] 10. The system of claim 1, further comprising means for displaying an ingredient list and price information corresponding to the generated menu.

[1402] "Application Example 1"

[1403] (Claim 1)

[1404] A means for registering personal information;

[1405] A means of obtaining information about the nearest store,

[1406] A means for generating a menu that takes into consideration nutritional balance based on the registered information and the acquired store information;

[1407] A means for displaying the generated menu;

[1408] a means for obtaining information about nearby delivery services and generating meal suggestions;

[1409] A system including:

[1410] (Claim 2)

[1411] 10. The system of claim 1, further comprising means for including allergy information in the registered information.

[1412] (Claim 3)

[1413] 10. The system of claim 1, further comprising means for displaying an ingredient list and pricing information corresponding to the generated menu, as well as the cost of delivery service.

[1414] "Example 2: Combining Emotion Engines"

[1415] (Claim 1)

[1416] A means for registering personal information;

[1417] A means of obtaining information about the nearest store,

[1418] A means for generating a menu that takes into consideration nutritional balance based on the registered information and the acquired store information;

[1419] A means for displaying the generated menu;

[1420] means for recognizing user emotion information;

[1421] means for adjusting a menu based on the recognized emotional information;

[1422] A system including:

[1423] (Claim 2)

[1424] 10. The system of claim 1, further comprising means for including allergy information in the registered information.

[1425] (Claim 3)

[1426] 10. The system of claim 1, further comprising means for displaying an ingredient list and price information corresponding to the generated menu.

[1427] "Application example 2 when combining emotion engines"

[1428] (Claim 1)

[1429] A means for registering personal information;

[1430] A means of obtaining information about the nearest store,

[1431] A means for generating a menu that takes into consideration nutritional balance based on the registered information and the acquired store information;

[1432] A means for displaying the generated menu;

[1433] means for recognizing a user's emotion using an emotion recognition engine;

[1434] a means for adjusting a menu based on the recognized emotional information;

[1435] A system including:

[1436] (Claim 2)

[1437] 10. The system of claim 1, further comprising means for including allergy information in the registered information.

[1438] (Claim 3)

[1439] 10. The system of claim 1, further comprising means for displaying an ingredient list and price information corresponding to the generated menu. [Explanation of symbols]

[1440] 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 registering personal information; A means of obtaining information about the nearest store, A means for generating a menu that takes into consideration nutritional balance based on the registered information and the acquired store information; A means for displaying the generated menu; A system including:

2. 2. The system of claim 1, further comprising means for including allergy information in the registered information.

3. 10. The system of claim 1, further comprising means for displaying an ingredient list and pricing information corresponding to the generated menu.

Images