system

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving input information of a user; means for collecting healthcare data and GPS data; means for analyzing the collected data and generating a meal menu; means for cooperating with an external service; and means for notifying the user of 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, the time and effort required for preparing and cooking meals is a significant burden. Furthermore, the content and nutritional balance of meals are also important, making effective meal planning necessary. Conventional methods make it difficult to efficiently manage all of these factors. The present invention aims to reduce the burden of meal planning on users by providing a system that proposes meal menus tailored to the user and provides consistent support from meal preparation to cooking by linking with various services related to eating out, delivery, and the use of home appliances. [Means for solving the problem]

[0005] The present invention is a system that includes a means for receiving information entered by a user, a means for collecting healthcare data and GPS data, a means for analyzing the collected data and generating a meal menu, a means for linking with external services, and a means for notifying the user of the generated menu. Specifically, the system generates an optimal meal menu taking into consideration the user's dietary preferences, allergies, health status, current location, etc. In addition, the system searches for and recommends restaurant information for dining out options, and in the case of delivery options, orders ingredients and dishes and arranges delivery. Furthermore, the system sends cooking instructions via a means for linking with smart home appliances, automating cooking. This allows users to efficiently manage their meals from planning to preparation and cooking in a consistent manner.

[0006] "User" refers to an individual who uses this system.

[0007] "Input information" refers to information about a user's preferences, allergies, diet goals, current mood, and the like.

[0008] "Terminal" refers to a device operated by a user, such as a smartphone, tablet, or computer.

[0009] "Server" refers to the computer system that serves as the core of the system, analyzing collected data, generating meal menus, and linking with external services.

[0010] "Healthcare data" refers to health information such as the user's number of steps, heart rate, and amount of exercise.

[0011] "GPS Data" refers to information about a user's current location.

[0012] "Analysis" refers to data processing that combines user input information, healthcare data, GPS data, etc. to generate meal menus.

[0013] "Meal menu" refers to the eating and drinking plan and specific meal contents recommended to the user.

[0014] "External Services" refers to third-party services outside the system, such as restaurant search services and food and meal delivery services.

[0015] "Means of collaboration" refers to the method or mechanism by which a server exchanges data with external services and smart home appliances and collaborates on their operations.

[0016] "Notification" refers to the act of informing the user of the generated meal menu and recommended information.

[0017] "Smart home appliances" refer to home appliances that have internet connectivity and automatic operation functions.

[0018] "Cooking instructions" refer to commands sent to smart home appliances, such as to start cooking or change settings. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0027] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0040] This invention is a system that allows users to easily input information about meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and the use of home appliances, and provides consistent support from preparing ingredients to cooking, thereby reducing the invisible burden of housework for users.

[0041] Input and collection of user information

[0042] Users input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I'd like to avoid nuts because I have an allergy." This information is sent from the device to the server.

[0043] Linking healthcare data and GPS data

[0044] The device collects the user's health data from health apps installed on the smartphone (such as Google Fit or Apple Health). This data includes the number of steps taken, heart rate, and recent exercise volume. It also obtains the user's current location using GPS. This data is also sent to the server.

[0045] Analyzing data and generating meal menus

[0046] The server's AI analyzes the information entered by the user, health care data, and GPS data. For example, if a user requests a "healthy lunch," has recently exercised 10,000 steps, and is currently located in Shinjuku, the AI ​​will generate an optimal meal menu based on this data. An example menu might include grilled chicken salad, yogurt, and a green smoothie.

[0047] Linking menus with external services

[0048] When a user requests to eat out, the server connects with the API of a gourmet website (such as Tabelog) to search for restaurants that serve healthy lunches near the user's current location. For example, an initiative could recommend multiple restaurants in Shinjuku that serve grilled chicken salad. When the user requests delivery, the server connects with the API of a delivery service (such as Uber Eats) to order ingredients and dishes that match the suggested menu. It then arranges for delivery to the user's home.

[0049] Integration with smart home appliances

[0050] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[0051] User Notification and Confirmation

[0052] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the information and make changes or corrections as necessary. The confirmed details are then sent back to the server, and processing continues.

[0053] To give a specific example, suppose a user enters "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps) and current location (Shinjuku) to the server. The server's AI analyzes this and recommends several restaurants in the Shinjuku area that offer healthy menus. This recommendation information is notified to the user via the device, allowing the user to select the desired restaurant and complete the reservation on the spot.

[0054] This system allows users to plan and prepare their daily meals more efficiently, leading to healthier and more fulfilling eating habits.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] The user opens a dedicated app or chats on LINE and enters information about their diet, such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy."

[0058] Step 2:

[0059] The device receives user input, including information about food preferences, allergies, and diet goals.

[0060] Step 3:

[0061] The terminal transmits the user's input data to the server.

[0062] Step 4:

[0063] The device obtains the user's recent health data (e.g., number of steps, heart rate, and amount of exercise) from a health app (e.g., Google Fit, Apple Health) installed on the smartphone.

[0064] Step 5:

[0065] The device acquires GPS data and determines the user's current location.

[0066] Step 6:

[0067] The healthcare data and GPS data acquired by the device are sent to the server.

[0068] Step 7:

[0069] The server receives user input data, health care data, and GPS data and manages them centrally.

[0070] Step 8:

[0071] The server's AI analyzes this data and makes a comprehensive assessment of the user's health, location, and food preferences.

[0072] Step 9:

[0073] Based on the analysis results, the server's AI generates the optimal meal menu for the user, suggesting items such as "grilled chicken salad," "yogurt," and "green smoothie."

[0074] Step 10:

[0075] The device will present the user with the options of "eating out," "delivery," and "cooking at home."

[0076] Step 11:

[0077] The user chooses from the options "eating out," "delivery," or "cooking at home."

[0078] Step 12 (If dining out):

[0079] The server connects to the gourmet site's API and searches for restaurants that offer the suggested menu near the user's current location.

[0080] Step 13 (If dining out):

[0081] The server transmits the recommendation results to the terminal, and the terminal notifies the user of the recommended restaurant information.

[0082] Step 14 (For Delivery):

[0083] The server connects to the delivery service's API and orders ingredients and dishes based on the suggested menu.

[0084] Step 15 (For Delivery):

[0085] The server checks the order and arranges for the ingredients and food to be delivered to the user's home.

[0086] Step 16 (Home Cooking):

[0087] The server obtains information about the user's smart appliances (e.g., smart oven, rice cooker).

[0088] Step 17 (Home Cooking):

[0089] The server sends appropriate cooking instructions to smart appliances, such as "heat at 200 degrees for 20 minutes" to an oven.

[0090] Step 18:

[0091] The device notifies the user of the generated menu, recommended restaurants, and ordered delivery details.

[0092] Step 19:

[0093] The user checks the notification content and makes changes or corrections as necessary.

[0094] Step 20:

[0095] The terminal transmits the final confirmation made by the user to the server, and the process is completed.

[0096] Example 1

[0097] 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."

[0098] Conventional meal planning systems require users to input detailed information themselves, which is often time-consuming. Furthermore, it is difficult to suggest optimal meal plans based on the user's health status and current location, and they lack sufficient integration with external services. This increases the invisible burden of housework for users, making it difficult to achieve an efficient diet.

[0099] 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.

[0100] In this invention, the server includes a means for receiving user input information, a means for collecting health data and location data, a means for analyzing the collected data and generating a meal plan, a means for linking with external services, and a means for notifying the user of the generated plan. This allows the user to obtain an optimal meal plan based on the input information and health and location data, thereby reducing the burden of invisible housework. In addition, by linking with external services, the system can meet a variety of needs by providing options such as eating out, delivery, and cooking at home.

[0101] "Means for receiving user input information" refers to an interface through which a user inputs their dietary requests and preferences, and to a device or software that transmits this input to a server.

[0102] "Health Data" refers to physiological data collected from health apps, such as a user's steps, heart rate, and recent exercise volume.

[0103] "Location data" refers to information indicating the user's current location obtained using the GPS function of the device.

[0104] "Means of collection" refers to devices or software that acquire health care data and location data and transmit them to a server.

[0105] "Means for analyzing and generating meal plans" refers to devices and software that perform a series of data processing and calculations that allow the server's AI to derive the optimal meal menu based on the user's input information, health data, and location data.

[0106] "Means for linking with external services" refers to the function that enables the server to call the API of an external service provider (e.g., a restaurant search site or delivery service) and use an external service that is appropriate for the user.

[0107] "Means for notifying the user of the generated plan" refers to a device or software that sends the meal plan and recommendation information generated by the server to the user's terminal and allows the user to check it.

[0108] MODE FOR CARRYING OUT THE INVENTION

[0109] This invention is a system in which AI proposes appropriate meal menus based on meal information entered by a user and provides meal options in cooperation with external services. The following describes in detail the embodiments of the invention.

[0110] Input and collection of user information

[0111] Users input information about their meals using a dedicated app or messaging service. Specific examples include requests such as "I want to eat a healthy lunch today" or "I'm allergic to nuts, so I want to avoid them." The device uses devices and software to collect this information and send it to a server.

[0112] Linking healthcare data and GPS data

[0113] The device automatically collects the user's health data using a health app (e.g., Google Fit, Apple Health) installed on the smartphone. This data includes the number of steps taken, heart rate, and recent exercise volume. The device also uses GPS to obtain the user's current location. This data is sent from the device to a server.

[0114] Analyzing data and generating meal menus

[0115] The server implements a generative AI model that integrates and analyzes user input, health data, and location data. This AI generates optimal meal plans based on information obtained from multiple data sources. For example, if a user requests a "healthy lunch," has recently taken 10,000 steps, and is currently located in an urban area, the AI ​​might generate a menu such as "grilled chicken salad, yogurt, and a green smoothie."

[0116] Linking menus with external services

[0117] If a user wishes to eat out, the server uses the API of a gourmet site to search for and recommend restaurants that offer healthy lunches near the user's current location. For example, it might recommend multiple restaurants that serve grilled chicken salad near Shinjuku. If the user wishes to have delivery, the server uses the API of a delivery service to order ingredients and dishes that match the suggested menu and arrange for delivery.

[0118] Integration with smart home appliances

[0119] If a user selects home cooking, the server will connect to the smart appliances and send the appropriate cooking instructions. For example, if a smart oven or rice cooker is connected, the server can send specific instructions to these appliances, such as "Start cooking grilled chicken salad at 6:30 PM." The user simply sets the ingredients, and cooking will begin automatically at the specified time.

[0120] User Notification and Confirmation

[0121] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the notification and make changes or corrections as necessary. This confirmed information is then sent back to the server, completing the process.

[0122] To give a specific example, when a user types "I want to eat a healthy lunch outside today" into a dedicated app, the device sends their recent exercise data (10,000 steps) and current location (urban area) to a server. The server's AI analyzes this and recommends several restaurants in the urban area that offer healthy menus. This recommendation information is notified to the user via the device, allowing them to select the restaurant of their choice and complete the reservation on the spot. This system allows users to plan and prepare their daily meals more efficiently, leading to healthier and more fulfilling eating habits.

[0123] Prompt Sentence Examples

[0124] An example of a prompt sentence to input to the generative AI model is as follows:

[0125] "Suggest a healthy lunch menu for today based on your recent activity and current location."

[0126] "Analyze my steps and heart rate data to create a meal plan for this week and recommend the best menu for me."

[0127] "Recommend restaurants for eating out and introduce healthy places to eat nearby."

[0128] This invention helps users to lead an efficient and healthy diet.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1: Enter and collect user information

[0131] Users input their meal preferences and requirements using a dedicated app or messaging service. For example, they can input, "I want to eat a healthy lunch today." The input information is sent to the device, which then sends it to the server.

[0132] Input: Information about the user's preferences and requirements (e.g., "healthy lunch")

[0133] Output: User's wishes and conditions transferred to the server

[0134] Step 2: Linking Healthcare and GPS Data

[0135] The device automatically collects the user's health data (number of steps, heart rate, recent exercise volume, etc.) using the smartphone's health care app (Google Fit or Apple Health). In addition, the device obtains the user's current location using the GPS function. This data is also sent to the server.

[0136] Input: User's health data and current location

[0137] Output: Health data and current location sent to the server

[0138] Step 3: Analyze the data and generate a meal menu

[0139] The server's generative AI model integrates and analyzes the user's preferences, conditions, health data, and current location received from Steps 1 and 2. For example, if the user requests a "healthy lunch," their recent exercise data shows 10,000 steps, and their current location is in an urban area, the AI ​​analyzes this data and generates an optimal menu, such as grilled chicken salad, yogurt, or a green smoothie.

[0140] Input: Integrated user preferences, health data, current location

[0141] Output: Generated meal menu

[0142] Step 4: Integrate the menu with external services

[0143] When a user wants to eat out, the server calls the API of a gourmet site to search for and recommend restaurants that offer healthy lunches near the user's current location. For example, a request such as "Recommend multiple restaurants that serve grilled chicken salad around Shinjuku" is sent to the API. When the user wants delivery, the server calls the API of a delivery service, orders ingredients and dishes that match the suggested menu, and arranges for delivery.

[0144] Input: Generated meal menu, user's dining out or delivery preference

[0145] Output: Recommended restaurant information, delivery order details

[0146] Step 5: Connect to smart appliances

[0147] If a user selects home cooking, the server will connect to smart appliances. For example, it will send specific instructions to a smart oven or rice cooker, such as "Start cooking grilled chicken salad at 6:30 PM." The user simply sets the necessary ingredients, and cooking will begin automatically at the specified time.

[0148] Input: User's cooking selection, generated meal menu

[0149] Output: Cooking instructions sent to smart appliances

[0150] Step 6: User notification and confirmation

[0151] The device notifies the user of the proposed menu, recommended restaurants, and delivery details. The user can then check the information in the app and make any necessary changes or corrections. Once the confirmation is complete, the confirmed information is sent back to the server, completing the process.

[0152] Input: Generated meal menu, recommendation information, delivery details

[0153] Output: Information notified to the user, user confirmation and corrections

[0154] (Application example 1)

[0155] 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."

[0156] In modern life, choosing meals that are appropriate for a user's health condition and the situation of the day is a time-consuming and laborious task. Furthermore, there is still no system that consistently proposes optimal menus that take into account dining out, delivery, and even home cooking options. Additionally, there is a need for a unified solution to reduce the invisible burden of household chores on users.

[0157] 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.

[0158] In this invention, the server includes a means for receiving user input information, a means for collecting health care data and location data, a means for analyzing the collected data and generating a meal menu, a means for linking with external services, a means for notifying the user of the generated menu, and a means for linking with the external service to order dishes corresponding to the proposed menu. This allows the user to receive optimal meal menu suggestions based on their health condition and current location. Furthermore, consistent support is provided for all options, including eating out, delivery, and cooking at home, significantly reducing the user's invisible household chores.

[0159] The "means for receiving user input information" is an interface that allows the user to input information such as dietary preferences and allergies.

[0160] "Healthcare data" is information that indicates the user's health condition, and includes data such as the number of steps taken, heart rate, and amount of exercise.

[0161] "Location data" is information indicating the current location of the user, and is data obtained from a positioning system such as a GPS.

[0162] The "means for analyzing collected data and generating a meal menu" refers to an algorithm or program for generating an appropriate meal menu based on user input information, health care data, and location data.

[0163] "Means of linking with external services" refers to APIs and communication methods for linking with gourmet sites and delivery services to exchange information.

[0164] The "means for notifying the user of the generated menu" refers to an application or communication interface for sending a notification to the user's terminal.

[0165] "Means for ordering food corresponding to the proposed menu in cooperation with an external service" refers to a system for ordering food through an external delivery service based on the generated menu and delivering it to the user.

[0166] The present invention is a system that allows users to easily input information about meals and allows AI to suggest appropriate meal menus. Specific embodiments of the system are described below.

[0167] System Configuration

[0168] This system is composed of a means for receiving user input information, a means for collecting healthcare data and location data, a means for analyzing the collected data and generating a meal menu, a means for linking with external services, and a means for notifying the user of the generated menu. It also includes a means for linking with external services and ordering food corresponding to the suggested menu.

[0169] Explanation of program processing

[0170] 1. A way to receive user input:

[0171] Users use a dedicated application to input information such as dietary requests and allergies, and this information is sent to the system via devices such as smartphones and tablets.

[0172] 2. How we collect health and location data:

[0173] The device collects the user's health data from health apps installed on the smartphone (e.g., Google Fit or Apple Health), and also obtains the user's current location using the device's GPS function.

[0174] 3. How to analyze the collected data and generate a meal menu:

[0175] The server comprehensively analyzes the user's input information, healthcare data, and location data, and uses a generative AI model to generate the optimal meal menu.

[0176] 4. Means of linking with external services:

[0177] The system works in conjunction with the APIs of gourmet sites and delivery services to search for and order restaurants and dishes that correspond to the generated menu.

[0178] 5. How to notify the user of the generated menu:

[0179] The generated menu and recommendation information are sent to the user's device, where the user can review it and select the restaurant or delivery service of their choice.

[0180] 6. Means of ordering food corresponding to the suggested menu in cooperation with external services:

[0181] Based on the dining out and delivery options selected by the user, the system will connect with external delivery services to order and arrange for the appropriate food to be delivered.

[0182] Examples of concrete examples and prompts

[0183] As a concrete example, let's say a user enters "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps) and current location (Shinjuku) to the server. The server's AI analyzes this data and recommends restaurants in the Shinjuku area that offer healthy menus. This recommendation information is notified to the user via the device, allowing the user to select the desired restaurant and complete the reservation on the spot.

[0184] Example prompt sentence:

[0185] "I have a nut allergy. Can you suggest a healthy lunch? I currently walk 10,000 steps. I'm considering eating out in Shinjuku or delivery."

[0186] This system allows users to plan and prepare their daily meals more efficiently, enabling them to lead healthier and more fulfilling diets.

[0187] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0188] Step 1:

[0189] Collecting user input

[0190] How it works: Users enter their dietary preferences (e.g., "I want a healthy lunch"), allergy information, etc. into a dedicated app.

[0191] Input: Text data of user preferences and allergy information.

[0192] Output: The collected data on user preferences and allergy information is sent to the server.

[0193] Step 2:

[0194] Healthcare and location data collection

[0195] How it works: The device obtains data from the smartphone's health app (Google Fit, Apple Health, etc.) and location information from GPS.

[0196] Input: Health data (steps, heart rate, activity, etc.) from the user's Health app and location data from GPS.

[0197] Output: Collected health and location data is sent to a server.

[0198] Step 3:

[0199] Data analysis and meal menu generation

[0200] How it works: The server uses a generative AI model to analyze user input, health data, and location data to generate an appropriate meal menu.

[0201] Input: User preferences, allergy information, health data, location data.

[0202] Data processing: Data integration and analysis, using generative AI models to generate meal plans based on health status and location.

[0203] Output: The generated meal menu.

[0204] Step 4:

[0205] Integration with external services

[0206] How it works: The server accesses the APIs of gourmet sites and delivery services to search for and order appropriate restaurants and dishes.

[0207] Input: The generated meal menu.

[0208] Data processing: Data exchange with external services using API. Search for restaurants and dishes based on pre-generated menus.

[0209] Output: Recommended restaurant information and ordered food information.

[0210] Step 5:

[0211] User Notification

[0212] Operation: The generated menu and recommendation information are sent to the user's device.

[0213] Input: Recommended store information and order information.

[0214] Output: A notification message is displayed on the user's terminal.

[0215] Step 6:

[0216] Food ordering and delivery arrangements

[0217] How it works: The user selects a dine-in or delivery option, and the system uses that information to arrange the order and delivery.

[0218] Input: The user's selected store or delivery option.

[0219] Data processing: Generating order information and linking to external delivery services.

[0220] Output: Order confirmed and delivery arrangements completed.

[0221] In this way, the system can integrate the user's input information and collected data at each step, and provide the optimal meal menu after analysis and collaboration.

[0222] 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.

[0223] This invention is a system that allows users to easily input information about their meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and home appliance use, and provides comprehensive support from ingredient preparation to cooking, thereby reducing the invisible burden of housework for users. It also features an emotion engine that recognizes the user's emotional state, suggesting meal menus that match the user's mood.

[0224] Input and collection of user information

[0225] Users input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I'm allergic to nuts, so I'd like to avoid them." This information is sent from the device to the server.

[0226] Linking healthcare data and GPS data

[0227] The device collects the user's health data from health apps installed on the smartphone (such as Google Fit or Apple Health). This data includes the number of steps taken, heart rate, and recent exercise volume. It also obtains the user's current location using GPS. This data is also sent to the server.

[0228] Emotion engine recognizes emotional states

[0229] The emotion engine analyzes the user's facial expressions, voice, and text input to recognize the user's emotional state. For example, if the user inputs "I'm tired" or "I'm irritated," the engine generates emotion data based on that. The emotion data is then sent to the server along with other data.

[0230] Analyzing data and generating meal menus

[0231] The server's AI analyzes the user's input information, health care data, GPS data, and emotional data. For example, if a user requests a "healthy lunch," has recently exercised 10,000 steps, is currently located in Shinjuku, and is emotionally "tired," the AI ​​will generate the optimal meal menu based on this information. For example, it might suggest grilled chicken salad, a spinach smoothie, or fruit yogurt.

[0232] Linking menus with external services

[0233] When a user requests to eat out, the server connects with the API of a gourmet website to search for restaurants that offer the suggested menu near the user's current location. For example, an initiative could recommend multiple restaurants in Shinjuku that serve grilled chicken salad. When the user requests delivery, the server connects with the API of a delivery service to order ingredients and dishes that match the suggested menu. It then arranges for delivery to the user's home.

[0234] Integration with smart home appliances

[0235] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[0236] User Notification and Confirmation

[0237] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the information and make changes or corrections as necessary. The confirmed details are then sent back to the server, and processing continues.

[0238] To give a specific example, a user might enter "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps), current location (Shinjuku), and emotional data (tired) to a server. The server's AI analyzes this data and recommends several restaurants in the Shinjuku area that offer healthy menus. This recommendation information is then sent to the user via the device, allowing them to select the desired restaurant and complete the reservation on the spot.

[0239] This system allows users to efficiently plan and prepare their daily meals and receive appropriate meal suggestions tailored to their emotional and health states, enabling them to live a healthier and more fulfilling diet.

[0240] The processing flow will be explained below.

[0241] Step 1:

[0242] The user opens a dedicated app or chats on LINE and enters information about their diet. For example, they might enter, "I want to eat a healthy lunch today," or "I want to avoid nuts because I have an allergy."

[0243] Step 2:

[0244] The device receives user input information and sends it to the server, including information about dietary preferences, allergies, and diet goals.

[0245] Step 3:

[0246] The device accesses the health app (Google Fit, Apple Health, etc.) installed on the smartphone and obtains the user's health data (e.g., number of steps, heart rate, amount of exercise).

[0247] Step 4:

[0248] The device uses the GPS function to obtain the user's current location.

[0249] Step 5:

[0250] The healthcare data and GPS data acquired by the device are sent to the server.

[0251] Step 6:

[0252] The device uses an emotion engine to analyze the user's facial expressions, voice, text input, etc. to recognize the user's emotional state. For example, it can recognize the emotion "tired."

[0253] Step 7:

[0254] The device transmits the emotion data to the server.

[0255] Step 8:

[0256] The server centrally manages user input information, health care data, GPS data, and emotional data.

[0257] Step 9:

[0258] The server's AI analyzes this data and makes a comprehensive assessment of the user's health, location, food preferences, and emotional state.

[0259] Step 10:

[0260] Based on the analysis results, the server's AI generates the optimal meal menu for the user, suggesting dishes such as "grilled chicken salad," "spinach smoothie," and "fruit yogurt."

[0261] Step 11:

[0262] The device will present the user with the options of "eating out," "delivery," and "cooking at home."

[0263] Step 12:

[0264] The user selects either "eating out," "delivery," or "cooking at home."

[0265] Step 13 (If dining out):

[0266] Based on the selection, the server works with the gourmet site's API to search for restaurants that offer the suggested menu near the user's current location.

[0267] Step 14 (If dining out):

[0268] The server transmits the recommendation results to the terminal, and the terminal notifies the user of the recommended restaurant information.

[0269] Step 15 (For Delivery):

[0270] The server connects to the delivery service's API and orders ingredients and dishes based on the suggested menu.

[0271] Step 16 (For Delivery):

[0272] The server checks the order and arranges for the ingredients and food to be delivered to the user's home.

[0273] Step 17 (Home Cooking):

[0274] The server obtains information about the user's smart appliances (e.g., smart oven, rice cooker).

[0275] Step 18 (Home Cooking):

[0276] The server sends appropriate cooking instructions to smart appliances, such as "heat at 200 degrees for 20 minutes" to an oven.

[0277] Step 19:

[0278] The device notifies the user of the generated menu, recommended restaurants, and ordered delivery details.

[0279] Step 20:

[0280] The user checks the notification content and makes changes or corrections as necessary.

[0281] Step 21:

[0282] The terminal transmits the final confirmation made by the user to the server, and the process is completed.

[0283] Example 2

[0284] 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."

[0285] Conventional meal recommendation systems have not adequately optimized individual meals based on the user's health and emotional state. Furthermore, they lack sufficient integration with user input information and external services, making it difficult to efficiently recommend meal menus suited to the user.

[0286] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving input information about meals from the user, means for collecting health data and location data, means for analyzing the collected data and emotion data and generating a meal menu, means for providing information based on the proposed meal menu in cooperation with external services, and means for notifying the user of the generated menu and related information. This enables highly accurate meal suggestions based on the user's health and emotional state. In addition, by strengthening cooperation with external services, it is possible to provide the user with various options, such as eating out, delivery, and cooking at home.

[0287] "Input information" refers to data such as dietary requests and allergy information provided by users via a dedicated app or chat.

[0288] "Health data" refers to physical data such as number of steps, heart rate, and amount of exercise collected from healthcare apps, etc.

[0289] "Location data" is information about the user's current location obtained using GPS.

[0290] "Emotion data" refers to data relating to the emotional state of a user that is generated by analyzing the user's facial expressions, voice, text input, and the like.

[0291] "Analysis" is the process of integrating the collected data and generating an appropriate meal menu based on the user's condition and needs.

[0292] A "meal menu" is a specific meal suggestion optimized for the user's health and emotional state.

[0293] "External services" are other food-related service providers, such as gourmet sites, delivery services, and smart home appliances.

[0294] "Notification" is a means for informing the user of the generated meal menu and related information.

[0295] "Collaboration" is a mechanism for communicating with external services and providing services related to the proposed meal menu.

[0296] A "user" is an entity that uses the system to receive meal suggestions.

[0297] This invention is a system that allows users to easily input information about their meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and home appliance use, and provides comprehensive support from ingredient preparation to cooking, thereby reducing the invisible burden of housework for users. It also features an emotion engine that recognizes the user's emotional state, suggesting meal menus that match the user's mood.

[0298] Users can input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy." This information is sent from the device to the server.

[0299] The device collects health data such as the number of steps, heart rate, and recent exercise volume from health apps installed on the smartphone (e.g., Google Fit or Apple Health).The device also obtains the user's current location using GPS and sends this data to the server.

[0300] The emotion engine analyzes the user's facial expressions, voice, and text input to recognize the user's emotional state. For example, if the user inputs "I'm tired" or "I'm irritated," emotion data is generated based on that. The emotion data is sent to the server along with other data.

[0301] The server's AI analyzes the user's input information, health care data, GPS data, and emotional data to generate the optimal meal menu. For example, if a user requests a "healthy lunch," their recent exercise data indicates 10,000 steps, their current location is Shinjuku, and their emotional state is "tired," the AI ​​will generate the optimal meal menu based on this information. Specific suggested examples include grilled chicken salad, spinach smoothie, and fruit yogurt.

[0302] If a user wishes to eat out, the server will connect with the API of a gourmet website to search for restaurants near the user's current location that offer the suggested menu. For example, it will recommend multiple restaurants in Shinjuku that serve grilled chicken salad. If the user wishes to order delivery, the server will connect with the API of a delivery service to order ingredients and dishes that match the suggested menu and arrange for delivery to the user's home.

[0303] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[0304] The device notifies the user of the suggested menu, recommended restaurants, and ordered delivery details. The user can review this and make changes or corrections as necessary. The confirmed details are sent back to the server, and processing continues. For example, the user might enter "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps), current location (Shinjuku), and emotional data (tired) to the server. The server's AI analyzes this and recommends several restaurants around Shinjuku that serve healthy lunches. This recommendation information is notified to the user via the device, allowing them to select the desired restaurant and complete the reservation on the spot.

[0305] Below are some examples of prompts for generative AI models:

[0306] User: I'd like to eat a healthy lunch out today. I'm allergic to nuts, so please avoid them.

[0307] Device: Recent exercise data is 10,000 steps, current location is Shinjuku, and emotional state is recognized as "tired."

[0308] Server: Using this information, the server searches for and recommends restaurants that serve healthy lunches in the Shinjuku area.

[0309] This system allows users to efficiently plan and prepare their daily meals and receive appropriate meal suggestions based on their emotional and health state.

[0310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0311] Step 1:

[0312] The user inputs information about their meal using a dedicated app or chat. They input specific requests such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy." This is the input information. The device sends this information to the server. The output is the user's meal request data.

[0313] Step 2:

[0314] The device collects health data from health apps (such as Google Fit or Apple Health) installed on the smartphone. This health data includes the number of steps, heart rate, and recent exercise volume. The device also obtains the user's current location using GPS. The obtained health data and location data are also sent to the server. The output is the health data and location data.

[0315] Step 3:

[0316] The emotion engine analyzes the user's facial expressions, voice, and text input to generate emotion data. For example, if a user inputs "I'm tired today," emotion data is generated based on that. The emotion data is sent to the server. The input is the user's text and voice information, and the output is emotion data.

[0317] Step 4:

[0318] The server's AI integrates and analyzes the user's input information, health data, location data, and emotional data. For example, if a user requests a "healthy lunch," and health data indicates 10,000 steps, their current location is Shinjuku, and their emotional state is "tired," the AI ​​will generate an optimal meal menu based on this information. The output is a suggested meal menu.

[0319] Step 5:

[0320] The server connects to the API of a gourmet site and searches for restaurants that offer the suggested menu near the user's current location. For example, the server recommends restaurants in Shinjuku that serve grilled chicken salad. The input is the meal menu and location information, and the output is a list of recommended restaurants.

[0321] Step 6:

[0322] The server connects to the delivery service's API, and if the user requests delivery, it orders ingredients and dishes that match the suggested menu. The order is then arranged for delivery to the user's home. The input is the meal menu and delivery address information, and the output is the delivery order information.

[0323] Step 7:

[0324] When a user selects home cooking, the server works with smart home appliances to support the cooking. The server sends appropriate cooking instructions to a smart oven or rice cooker. For example, the server can set the smart oven to start cooking at a specific time. The input is a meal recipe and time information, and the output is cooking instructions.

[0325] Step 8:

[0326] The terminal notifies the user of the proposed menu, recommended restaurants, and delivery order. The user can review the information and make any necessary changes or corrections. The confirmed information is sent back to the server, and processing continues. The input is the food menu and selection options, and the output is a notification via the user interface.

[0327] (Application example 2)

[0328] 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."

[0329] In modern society, personal health management and dietary choices are important issues. However, in our busy daily lives, it is not easy to choose appropriate meals that match our individual health and emotional state. Furthermore, when dining out or using delivery services, there are only a limited number of systems that automatically suggest meal menus tailored to the user and efficiently order and deliver them. Therefore, there is a need for support that makes it easier for users to choose meals that take their health and emotional state into consideration.

[0330] 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.

[0331] In this invention, the server includes means for receiving user input information, means for collecting health care data and location data, means for analyzing the collected data and generating a meal menu, means for analyzing the user's emotional state and generating emotion data, means for coordinating with external services to order ingredients and dishes based on the meal menu and arrange for delivery, and means for notifying the user of the generated menu. This allows the user to receive suggestions for an optimal meal menu based on their health and emotional state, enabling them to efficiently use eating out and delivery services.

[0332] "User input" refers to data that a user provides to the system to express a dietary request or state, such as text input such as "I want to eat a healthy lunch" or "I'm tired today."

[0333] "Healthcare data" refers to data that indicates the user's health condition, and is mainly information such as the number of steps, heart rate, and amount of exercise obtained from health care apps.

[0334] "Location data" is information indicating the user's current location, and is latitude and longitude data obtained using the GPS function.

[0335] "Emotion data" is data generated by analyzing the user's emotional state, and is information analyzed by the emotion engine from the user's facial expressions, voice, text input, etc.

[0336] A "meal menu" is a specific combination of dishes and ingredients suggested based on the user's health, emotional state, and requests.

[0337] "External services" are services provided by third parties that provide and deliver meal menus in response to user requests by linking with the system, such as restaurants and delivery companies.

[0338] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates optimal meal menus.

[0339] A "prompt sentence" is a text sentence in input format used to pass data to a generative AI model, which indicates the user's detailed request or status.

[0340] The present invention provides a system that allows a user to easily input information about their meals and allows AI to suggest optimal meal menus. Hereinafter, embodiments of the present invention will be described in detail.

[0341] First, the user inputs their meal request using a dedicated app. They can easily input information such as "I want to eat a healthy lunch today." This application is installed on a smartphone and collects the user's input information.

[0342] Next, the user's smartphone automatically collects health data such as the number of steps, heart rate, and recent exercise volume from health apps (Google Fit, Apple Health, etc.), and obtains the user's current location using the GPS function. This allows data on the user's health condition and current location to be collected.

[0343] Furthermore, a server equipped with an emotion engine is provided that analyzes the user's facial expressions, voice, and text input to recognize their emotional state. For example, if the user inputs "tired," it generates emotion data such as "tired."

[0344] The server analyzes this input information, health data, and emotional data, and uses a generative AI model to generate an optimal meal menu. During the generation process, specific dishes and ingredient combinations are suggested based on the user's health condition, current location, and emotional state. For example, if a user inputs "I feel tired today," has walked 10,000 steps, has a heart rate of 70, and is currently located in Shinjuku, a suggestion would be a grilled chicken salad made with healthy ingredients in the Shinjuku area.

[0345] The server then connects to external services. If the user requests delivery, the server communicates with the delivery service's API, orders ingredients and dishes based on the suggested menu, and arranges for delivery. For example, if the user selects the suggested grilled chicken salad, the delivery service delivers the dish to the user's current location. This allows the user to receive the optimal meal without having to make any special arrangements.

[0346] Finally, the server sends a notification to the user through the application to inform them of the generated meal menu and delivery arrangement status, allowing them to review it and make any necessary changes or corrections.

[0347] For example, a user might say, "I'm tired today, so I'd like a light dinner," and the system might analyze their recent exercise data, current location, and emotional state to suggest things like smoothies or grilled vegetables.

[0348] An example of a prompt for a generative AI model might look something like this:

[0349] I'm tired today, so I'd like to have a light dinner.

[0350] "Current health data: steps 8000, heart rate 72"

[0351] "Current location: Latitude 35.6895, Longitude 139.6917"

[0352] "Emotional state: Tired"

[0353] In this way, a support system can be realized that helps users lead healthy and fulfilling eating habits.

[0354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0355] Step 1:

[0356] A user inputs a meal request using a dedicated smartphone app. For example, they input, "I want to eat a healthy lunch today." This input information is stored on the smartphone device and used for later processing.

[0357] Step 2:

[0358] The device collects health data from health apps, such as Google Fit and Apple Health, automatically acquiring the user's steps, heart rate, and recent exercise volume, and stores the health data on the device.

[0359] Step 3:

[0360] The device uses the GPS function to obtain the user's current location (latitude and longitude). For example, if you are in Shinjuku, the GPS data obtained will be latitude 35.6895 and longitude 139.6917. This data will also be saved on the device.

[0361] Step 4:

[0362] When a user types "tired" into the device, the device passes the text to the emotion engine and analyzes the emotional state. This generates emotion data for "tired." The emotion data is then stored on the device.

[0363] Step 5:

[0364] The device transmits the user's input information, health data, GPS data, and emotion data to the server, where it is stored for use in subsequent processing steps.

[0365] Step 6:

[0366] The server uses the received data to generate an optimal meal plan using a generative AI model. For example, if a user inputs, "I'm tired today, so I'd like to have a light dinner," the generative AI model will suggest specific menu items, such as smoothies and grilled vegetables, based on health data (step count 8,000, heart rate 72), GPS data (Shinjuku), and emotional data (tired).

[0367] Step 7:

[0368] The server connects to external delivery services via API to order ingredients and dishes based on the proposed menu. For example, if a customer selects a smoothie and grilled vegetables, the server places an order with the delivery company that provides those dishes.

[0369] Step 8:

[0370] The delivery service takes the order, prepares the meal, and delivers it to the user's current location. The server monitors the delivery progress and notifies the user of progress as needed.

[0371] Step 9:

[0372] The server notifies the user of the created meal menu and the delivery arrangement status on their device. The user can review it and make changes or corrections as necessary. Finally, the user receives the optimal meal.

[0373] 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.

[0374] 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.

[0375] 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.

[0376] [Second embodiment]

[0377] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0378] 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.

[0379] 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).

[0380] 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.

[0381] 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.

[0382] 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).

[0383] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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."

[0389] This invention is a system that allows users to easily input information about meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and the use of home appliances, and provides consistent support from preparing ingredients to cooking, thereby reducing the invisible burden of housework for users.

[0390] Input and collection of user information

[0391] Users input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I'd like to avoid nuts because I have an allergy." This information is sent from the device to the server.

[0392] Linking healthcare data and GPS data

[0393] The device collects the user's health data from health apps installed on the smartphone (such as Google Fit or Apple Health). This data includes the number of steps taken, heart rate, and recent exercise volume. It also obtains the user's current location using GPS. This data is also sent to the server.

[0394] Analyzing data and generating meal menus

[0395] The server's AI analyzes the information entered by the user, health care data, and GPS data. For example, if a user requests a "healthy lunch," has recently exercised 10,000 steps, and is currently located in Shinjuku, the AI ​​will generate an optimal meal menu based on this data. An example menu might include grilled chicken salad, yogurt, and a green smoothie.

[0396] Linking menus with external services

[0397] When a user requests to eat out, the server connects with the API of a gourmet website (such as Tabelog) to search for restaurants that serve healthy lunches near the user's current location. For example, an initiative could recommend multiple restaurants in Shinjuku that serve grilled chicken salad. When the user requests delivery, the server connects with the API of a delivery service (such as Uber Eats) to order ingredients and dishes that match the suggested menu. It then arranges for delivery to the user's home.

[0398] Integration with smart home appliances

[0399] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[0400] User Notification and Confirmation

[0401] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the information and make changes or corrections as necessary. The confirmed details are then sent back to the server, and processing continues.

[0402] To give a specific example, suppose a user enters "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps) and current location (Shinjuku) to the server. The server's AI analyzes this and recommends several restaurants in the Shinjuku area that offer healthy menus. This recommendation information is notified to the user via the device, allowing the user to select the desired restaurant and complete the reservation on the spot.

[0403] This system allows users to plan and prepare their daily meals more efficiently, leading to healthier and more fulfilling eating habits.

[0404] The processing flow will be explained below.

[0405] Step 1:

[0406] The user opens a dedicated app or chats on LINE and enters information about their diet, such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy."

[0407] Step 2:

[0408] The device receives user input, including information about food preferences, allergies, and diet goals.

[0409] Step 3:

[0410] The terminal transmits the user's input data to the server.

[0411] Step 4:

[0412] The device obtains the user's recent health data (e.g., number of steps, heart rate, and amount of exercise) from a health app (e.g., Google Fit, Apple Health) installed on the smartphone.

[0413] Step 5:

[0414] The device acquires GPS data and determines the user's current location.

[0415] Step 6:

[0416] The healthcare data and GPS data acquired by the device are sent to the server.

[0417] Step 7:

[0418] The server receives user input data, health care data, and GPS data and manages them centrally.

[0419] Step 8:

[0420] The server's AI analyzes this data and makes a comprehensive assessment of the user's health, location, and food preferences.

[0421] Step 9:

[0422] Based on the analysis results, the server's AI generates the optimal meal menu for the user, suggesting items such as "grilled chicken salad," "yogurt," and "green smoothie."

[0423] Step 10:

[0424] The device will present the user with the options of "eating out," "delivery," and "cooking at home."

[0425] Step 11:

[0426] The user chooses from the options "eating out," "delivery," or "cooking at home."

[0427] Step 12 (If dining out):

[0428] The server connects to the gourmet site's API and searches for restaurants that offer the suggested menu near the user's current location.

[0429] Step 13 (If dining out):

[0430] The server transmits the recommendation results to the terminal, and the terminal notifies the user of the recommended restaurant information.

[0431] Step 14 (For Delivery):

[0432] The server connects to the delivery service's API and orders ingredients and dishes based on the suggested menu.

[0433] Step 15 (For Delivery):

[0434] The server checks the order and arranges for the ingredients and food to be delivered to the user's home.

[0435] Step 16 (Home Cooking):

[0436] The server obtains information about the user's smart appliances (e.g., smart oven, rice cooker).

[0437] Step 17 (Home Cooking):

[0438] The server sends appropriate cooking instructions to smart appliances, such as "heat at 200 degrees for 20 minutes" to an oven.

[0439] Step 18:

[0440] The device notifies the user of the generated menu, recommended restaurants, and ordered delivery details.

[0441] Step 19:

[0442] The user checks the notification content and makes changes or corrections as necessary.

[0443] Step 20:

[0444] The terminal transmits the final confirmation made by the user to the server, and the process is completed.

[0445] Example 1

[0446] 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."

[0447] Conventional meal planning systems require users to input detailed information themselves, which is often time-consuming. Furthermore, it is difficult to suggest optimal meal plans based on the user's health status and current location, and they lack sufficient integration with external services. This increases the invisible burden of housework for users, making it difficult to achieve an efficient diet.

[0448] 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.

[0449] In this invention, the server includes a means for receiving user input information, a means for collecting health data and location data, a means for analyzing the collected data and generating a meal plan, a means for linking with external services, and a means for notifying the user of the generated plan. This allows the user to obtain an optimal meal plan based on the input information and health and location data, thereby reducing the burden of invisible housework. In addition, by linking with external services, the system can meet a variety of needs by providing options such as eating out, delivery, and cooking at home.

[0450] "Means for receiving user input information" refers to an interface through which a user inputs their dietary requests and preferences, and to a device or software that transmits this input to a server.

[0451] "Health Data" refers to physiological data collected from health apps, such as a user's steps, heart rate, and recent exercise volume.

[0452] "Location data" refers to information indicating the user's current location obtained using the GPS function of the device.

[0453] "Means of collection" refers to devices or software that acquire health care data and location data and transmit them to a server.

[0454] "Means for analyzing and generating meal plans" refers to devices and software that perform a series of data processing and calculations that allow the server's AI to derive the optimal meal menu based on the user's input information, health data, and location data.

[0455] "Means for linking with external services" refers to the function that enables the server to call the API of an external service provider (e.g., a restaurant search site or delivery service) and use an external service that is appropriate for the user.

[0456] "Means for notifying the user of the generated plan" refers to a device or software that sends the meal plan and recommendation information generated by the server to the user's terminal and allows the user to check it.

[0457] MODE FOR CARRYING OUT THE INVENTION

[0458] This invention is a system in which AI proposes appropriate meal menus based on meal information entered by a user and provides meal options in cooperation with external services. The following describes in detail the embodiments of the invention.

[0459] Input and collection of user information

[0460] Users input information about their meals using a dedicated app or messaging service. Specific examples include requests such as "I want to eat a healthy lunch today" or "I'm allergic to nuts, so I want to avoid them." The device uses devices and software to collect this information and send it to a server.

[0461] Linking healthcare data and GPS data

[0462] The device automatically collects the user's health data using a health app (e.g., Google Fit, Apple Health) installed on the smartphone. This data includes the number of steps taken, heart rate, and recent exercise volume. The device also uses GPS to obtain the user's current location. This data is sent from the device to a server.

[0463] Analyzing data and generating meal menus

[0464] The server implements a generative AI model that integrates and analyzes user input, health data, and location data. This AI generates optimal meal plans based on information obtained from multiple data sources. For example, if a user requests a "healthy lunch," has recently taken 10,000 steps, and is currently located in an urban area, the AI ​​might generate a menu such as "grilled chicken salad, yogurt, and a green smoothie."

[0465] Linking menus with external services

[0466] If a user wishes to eat out, the server uses the API of a gourmet site to search for and recommend restaurants that offer healthy lunches near the user's current location. For example, it might recommend multiple restaurants that serve grilled chicken salad near Shinjuku. If the user wishes to have delivery, the server uses the API of a delivery service to order ingredients and dishes that match the suggested menu and arrange for delivery.

[0467] Integration with smart home appliances

[0468] If a user selects home cooking, the server will connect to the smart appliances and send the appropriate cooking instructions. For example, if a smart oven or rice cooker is connected, the server can send specific instructions to these appliances, such as "Start cooking grilled chicken salad at 6:30 PM." The user simply sets the ingredients, and cooking will begin automatically at the specified time.

[0469] User Notification and Confirmation

[0470] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the notification and make changes or corrections as necessary. This confirmed information is then sent back to the server, completing the process.

[0471] To give a specific example, when a user types "I want to eat a healthy lunch outside today" into a dedicated app, the device sends their recent exercise data (10,000 steps) and current location (urban area) to a server. The server's AI analyzes this and recommends several restaurants in the urban area that offer healthy menus. This recommendation information is notified to the user via the device, allowing them to select the restaurant of their choice and complete the reservation on the spot. This system allows users to plan and prepare their daily meals more efficiently, leading to healthier and more fulfilling eating habits.

[0472] Prompt Sentence Examples

[0473] An example of a prompt sentence to input to the generative AI model is as follows:

[0474] "Suggest a healthy lunch menu for today based on your recent activity and current location."

[0475] "Analyze my steps and heart rate data to create a meal plan for this week and recommend the best menu for me."

[0476] "Recommend restaurants for eating out and introduce healthy places to eat nearby."

[0477] This invention helps users to lead an efficient and healthy diet.

[0478] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0479] Step 1: Enter and collect user information

[0480] Users input their meal preferences and requirements using a dedicated app or messaging service. For example, they can input, "I want to eat a healthy lunch today." The input information is sent to the device, which then sends it to the server.

[0481] Input: Information about the user's preferences and requirements (e.g., "healthy lunch")

[0482] Output: User's wishes and conditions transferred to the server

[0483] Step 2: Linking Healthcare and GPS Data

[0484] The device automatically collects the user's health data (number of steps, heart rate, recent exercise volume, etc.) using the smartphone's health care app (Google Fit or Apple Health). In addition, the device obtains the user's current location using the GPS function. This data is also sent to the server.

[0485] Input: User's health data and current location

[0486] Output: Health data and current location sent to the server

[0487] Step 3: Analyze the data and generate a meal menu

[0488] The server's generative AI model integrates and analyzes the user's preferences, conditions, health data, and current location received from Steps 1 and 2. For example, if the user requests a "healthy lunch," their recent exercise data shows 10,000 steps, and their current location is in an urban area, the AI ​​analyzes this data and generates an optimal menu, such as grilled chicken salad, yogurt, or a green smoothie.

[0489] Input: Integrated user preferences, health data, current location

[0490] Output: Generated meal menu

[0491] Step 4: Integrate the menu with external services

[0492] When a user wants to eat out, the server calls the API of a gourmet site to search for and recommend restaurants that offer healthy lunches near the user's current location. For example, a request such as "Recommend multiple restaurants that serve grilled chicken salad around Shinjuku" is sent to the API. When the user wants delivery, the server calls the API of a delivery service, orders ingredients and dishes that match the suggested menu, and arranges for delivery.

[0493] Input: Generated meal menu, user's dining out or delivery preference

[0494] Output: Recommended restaurant information, delivery order details

[0495] Step 5: Connect to smart appliances

[0496] If a user selects home cooking, the server will connect to smart appliances. For example, it will send specific instructions to a smart oven or rice cooker, such as "Start cooking grilled chicken salad at 6:30 PM." The user simply sets the necessary ingredients, and cooking will begin automatically at the specified time.

[0497] Input: User's cooking selection, generated meal menu

[0498] Output: Cooking instructions sent to smart appliances

[0499] Step 6: User notification and confirmation

[0500] The device notifies the user of the proposed menu, recommended restaurants, and delivery details. The user can then check the information in the app and make any necessary changes or corrections. Once the confirmation is complete, the confirmed information is sent back to the server, completing the process.

[0501] Input: Generated meal menu, recommendation information, delivery details

[0502] Output: Information notified to the user, user confirmation and corrections

[0503] (Application example 1)

[0504] 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."

[0505] In modern life, choosing meals that are appropriate for a user's health condition and the situation of the day is a time-consuming and laborious task. Furthermore, there is still no system that consistently proposes optimal menus that take into account dining out, delivery, and even home cooking options. Additionally, there is a need for a unified solution to reduce the invisible burden of household chores on users.

[0506] 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.

[0507] In this invention, the server includes a means for receiving user input information, a means for collecting health care data and location data, a means for analyzing the collected data and generating a meal menu, a means for linking with external services, a means for notifying the user of the generated menu, and a means for linking with the external service to order dishes corresponding to the proposed menu. This allows the user to receive optimal meal menu suggestions based on their health condition and current location. Furthermore, consistent support is provided for all options, including eating out, delivery, and cooking at home, significantly reducing the user's invisible household chores.

[0508] The "means for receiving user input information" is an interface that allows the user to input information such as dietary preferences and allergies.

[0509] "Healthcare data" is information that indicates the user's health condition, and includes data such as the number of steps taken, heart rate, and amount of exercise.

[0510] "Location data" is information indicating the current location of the user, and is data obtained from a positioning system such as a GPS.

[0511] The "means for analyzing collected data and generating a meal menu" refers to an algorithm or program for generating an appropriate meal menu based on user input information, health care data, and location data.

[0512] "Means of linking with external services" refers to APIs and communication methods for linking with gourmet sites and delivery services to exchange information.

[0513] The "means for notifying the user of the generated menu" refers to an application or communication interface for sending a notification to the user's terminal.

[0514] "Means for ordering food corresponding to the proposed menu in cooperation with an external service" refers to a system for ordering food through an external delivery service based on the generated menu and delivering it to the user.

[0515] The present invention is a system that allows users to easily input information about meals and allows AI to suggest appropriate meal menus. Specific embodiments of the system are described below.

[0516] System Configuration

[0517] This system is composed of a means for receiving user input information, a means for collecting healthcare data and location data, a means for analyzing the collected data and generating a meal menu, a means for linking with external services, and a means for notifying the user of the generated menu. It also includes a means for linking with external services and ordering food corresponding to the suggested menu.

[0518] Explanation of program processing

[0519] 1. A way to receive user input:

[0520] Users use a dedicated application to input information such as dietary requests and allergies, and this information is sent to the system via devices such as smartphones and tablets.

[0521] 2. How we collect health and location data:

[0522] The device collects the user's health data from health apps installed on the smartphone (e.g., Google Fit or Apple Health), and also obtains the user's current location using the device's GPS function.

[0523] 3. How to analyze the collected data and generate a meal menu:

[0524] The server comprehensively analyzes the user's input information, healthcare data, and location data, and uses a generative AI model to generate the optimal meal menu.

[0525] 4. Means of linking with external services:

[0526] The system works in conjunction with the APIs of gourmet sites and delivery services to search for and order restaurants and dishes that correspond to the generated menu.

[0527] 5. How to notify the user of the generated menu:

[0528] The generated menu and recommendation information are sent to the user's device, where the user can review it and select the restaurant or delivery service of their choice.

[0529] 6. Means of ordering food corresponding to the suggested menu in cooperation with external services:

[0530] Based on the dining out and delivery options selected by the user, the system will connect with external delivery services to order and arrange for the appropriate food to be delivered.

[0531] Examples of concrete examples and prompts

[0532] As a concrete example, let's say a user enters "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps) and current location (Shinjuku) to the server. The server's AI analyzes this data and recommends restaurants in the Shinjuku area that offer healthy menus. This recommendation information is notified to the user via the device, allowing the user to select the desired restaurant and complete the reservation on the spot.

[0533] Example prompt sentence:

[0534] "I have a nut allergy. Can you suggest a healthy lunch? I currently walk 10,000 steps. I'm considering eating out in Shinjuku or delivery."

[0535] This system allows users to plan and prepare their daily meals more efficiently, enabling them to lead healthier and more fulfilling diets.

[0536] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0537] Step 1:

[0538] Collecting user input

[0539] How it works: Users enter their dietary preferences (e.g., "I want a healthy lunch"), allergy information, etc. into a dedicated app.

[0540] Input: Text data of user preferences and allergy information.

[0541] Output: The collected data on user preferences and allergy information is sent to the server.

[0542] Step 2:

[0543] Healthcare and location data collection

[0544] How it works: The device obtains data from the smartphone's health app (Google Fit, Apple Health, etc.) and location information from GPS.

[0545] Input: Health data (steps, heart rate, activity, etc.) from the user's Health app and location data from GPS.

[0546] Output: Collected health and location data is sent to a server.

[0547] Step 3:

[0548] Data analysis and meal menu generation

[0549] How it works: The server uses a generative AI model to analyze user input, health data, and location data to generate an appropriate meal menu.

[0550] Input: User preferences, allergy information, health data, location data.

[0551] Data processing: Data integration and analysis, using generative AI models to generate meal plans based on health status and location.

[0552] Output: The generated meal menu.

[0553] Step 4:

[0554] Integration with external services

[0555] How it works: The server accesses the APIs of gourmet sites and delivery services to search for and order appropriate restaurants and dishes.

[0556] Input: The generated meal menu.

[0557] Data processing: Data exchange with external services using API. Search for restaurants and dishes based on pre-generated menus.

[0558] Output: Recommended restaurant information and ordered food information.

[0559] Step 5:

[0560] User Notification

[0561] Operation: The generated menu and recommendation information are sent to the user's device.

[0562] Input: Recommended store information and order information.

[0563] Output: A notification message is displayed on the user's terminal.

[0564] Step 6:

[0565] Food ordering and delivery arrangements

[0566] How it works: The user selects a dine-in or delivery option, and the system uses that information to arrange the order and delivery.

[0567] Input: The user's selected store or delivery option.

[0568] Data processing: Generating order information and linking to external delivery services.

[0569] Output: Order confirmed and delivery arrangements completed.

[0570] In this way, the system can integrate the user's input information and collected data at each step, and provide the optimal meal menu after analysis and collaboration.

[0571] 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.

[0572] This invention is a system that allows users to easily input information about their meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and home appliance use, and provides comprehensive support from ingredient preparation to cooking, thereby reducing the invisible burden of housework for users. It also features an emotion engine that recognizes the user's emotional state, suggesting meal menus that match the user's mood.

[0573] Input and collection of user information

[0574] Users input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I'm allergic to nuts, so I'd like to avoid them." This information is sent from the device to the server.

[0575] Linking healthcare data and GPS data

[0576] The device collects the user's health data from health apps installed on the smartphone (such as Google Fit or Apple Health). This data includes the number of steps taken, heart rate, and recent exercise volume. It also obtains the user's current location using GPS. This data is also sent to the server.

[0577] Emotion engine recognizes emotional states

[0578] The emotion engine analyzes the user's facial expressions, voice, and text input to recognize the user's emotional state. For example, if the user inputs "I'm tired" or "I'm irritated," the engine generates emotion data based on that. The emotion data is then sent to the server along with other data.

[0579] Analyzing data and generating meal menus

[0580] The server's AI analyzes the user's input information, health care data, GPS data, and emotional data. For example, if a user requests a "healthy lunch," has recently exercised 10,000 steps, is currently located in Shinjuku, and is emotionally "tired," the AI ​​will generate the optimal meal menu based on this information. For example, it might suggest grilled chicken salad, a spinach smoothie, or fruit yogurt.

[0581] Linking menus with external services

[0582] When a user requests to eat out, the server connects with the API of a gourmet website to search for restaurants that offer the suggested menu near the user's current location. For example, an initiative could recommend multiple restaurants in Shinjuku that serve grilled chicken salad. When the user requests delivery, the server connects with the API of a delivery service to order ingredients and dishes that match the suggested menu. It then arranges for delivery to the user's home.

[0583] Integration with smart home appliances

[0584] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[0585] User Notification and Confirmation

[0586] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the information and make changes or corrections as necessary. The confirmed details are then sent back to the server, and processing continues.

[0587] To give a specific example, a user might enter "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps), current location (Shinjuku), and emotional data (tired) to a server. The server's AI analyzes this data and recommends several restaurants in the Shinjuku area that offer healthy menus. This recommendation information is then sent to the user via the device, allowing them to select the desired restaurant and complete the reservation on the spot.

[0588] This system allows users to efficiently plan and prepare their daily meals and receive appropriate meal suggestions tailored to their emotional and health states, enabling them to live a healthier and more fulfilling diet.

[0589] The processing flow will be explained below.

[0590] Step 1:

[0591] The user opens a dedicated app or chats on LINE and enters information about their diet. For example, they might enter, "I want to eat a healthy lunch today," or "I want to avoid nuts because I have an allergy."

[0592] Step 2:

[0593] The device receives user input information and sends it to the server, including information about dietary preferences, allergies, and diet goals.

[0594] Step 3:

[0595] The device accesses the health app (Google Fit, Apple Health, etc.) installed on the smartphone and obtains the user's health data (e.g., number of steps, heart rate, amount of exercise).

[0596] Step 4:

[0597] The device uses the GPS function to obtain the user's current location.

[0598] Step 5:

[0599] The healthcare data and GPS data acquired by the device are sent to the server.

[0600] Step 6:

[0601] The device uses an emotion engine to analyze the user's facial expressions, voice, text input, etc. to recognize the user's emotional state. For example, it can recognize the emotion "tired."

[0602] Step 7:

[0603] The device transmits the emotion data to the server.

[0604] Step 8:

[0605] The server centrally manages user input information, health care data, GPS data, and emotional data.

[0606] Step 9:

[0607] The server's AI analyzes this data and makes a comprehensive assessment of the user's health, location, food preferences, and emotional state.

[0608] Step 10:

[0609] Based on the analysis results, the server's AI generates the optimal meal menu for the user, suggesting dishes such as "grilled chicken salad," "spinach smoothie," and "fruit yogurt."

[0610] Step 11:

[0611] The device will present the user with the options of "eating out," "delivery," and "cooking at home."

[0612] Step 12:

[0613] The user selects either "eating out," "delivery," or "cooking at home."

[0614] Step 13 (If dining out):

[0615] Based on the selection, the server works with the gourmet site's API to search for restaurants that offer the suggested menu near the user's current location.

[0616] Step 14 (If dining out):

[0617] The server transmits the recommendation results to the terminal, and the terminal notifies the user of the recommended restaurant information.

[0618] Step 15 (For Delivery):

[0619] The server connects to the delivery service's API and orders ingredients and dishes based on the suggested menu.

[0620] Step 16 (For Delivery):

[0621] The server checks the order and arranges for the ingredients and food to be delivered to the user's home.

[0622] Step 17 (Home Cooking):

[0623] The server obtains information about the user's smart appliances (e.g., smart oven, rice cooker).

[0624] Step 18 (Home Cooking):

[0625] The server sends appropriate cooking instructions to smart appliances, such as "heat at 200 degrees for 20 minutes" to an oven.

[0626] Step 19:

[0627] The device notifies the user of the generated menu, recommended restaurants, and ordered delivery details.

[0628] Step 20:

[0629] The user checks the notification content and makes changes or corrections as necessary.

[0630] Step 21:

[0631] The terminal transmits the final confirmation made by the user to the server, and the process is completed.

[0632] Example 2

[0633] 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."

[0634] Conventional meal recommendation systems have not adequately optimized individual meals based on the user's health and emotional state. Furthermore, they lack sufficient integration with user input information and external services, making it difficult to efficiently recommend meal menus suited to the user.

[0635] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving input information about meals from the user, means for collecting health data and location data, means for analyzing the collected data and emotion data and generating a meal menu, means for providing information based on the proposed meal menu in cooperation with external services, and means for notifying the user of the generated menu and related information. This enables highly accurate meal suggestions based on the user's health and emotional state. In addition, by strengthening cooperation with external services, it is possible to provide the user with various options, such as eating out, delivery, and cooking at home.

[0636] "Input information" refers to data such as dietary requests and allergy information provided by users via a dedicated app or chat.

[0637] "Health data" refers to physical data such as number of steps, heart rate, and amount of exercise collected from healthcare apps, etc.

[0638] "Location data" is information about the user's current location obtained using GPS.

[0639] "Emotion data" refers to data relating to the emotional state of a user that is generated by analyzing the user's facial expressions, voice, text input, and the like.

[0640] "Analysis" is the process of integrating the collected data and generating an appropriate meal menu based on the user's condition and needs.

[0641] A "meal menu" is a specific meal suggestion optimized for the user's health and emotional state.

[0642] "External services" are other food-related service providers, such as gourmet sites, delivery services, and smart home appliances.

[0643] "Notification" is a means for informing the user of the generated meal menu and related information.

[0644] "Collaboration" is a mechanism for communicating with external services and providing services related to the proposed meal menu.

[0645] A "user" is an entity that uses the system to receive meal suggestions.

[0646] This invention is a system that allows users to easily input information about their meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and home appliance use, and provides comprehensive support from ingredient preparation to cooking, thereby reducing the invisible burden of housework for users. It also features an emotion engine that recognizes the user's emotional state, suggesting meal menus that match the user's mood.

[0647] Users can input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy." This information is sent from the device to the server.

[0648] The device collects health data such as the number of steps, heart rate, and recent exercise volume from health apps installed on the smartphone (e.g., Google Fit or Apple Health).The device also obtains the user's current location using GPS and sends this data to the server.

[0649] The emotion engine analyzes the user's facial expressions, voice, and text input to recognize the user's emotional state. For example, if the user inputs "I'm tired" or "I'm irritated," emotion data is generated based on that. The emotion data is sent to the server along with other data.

[0650] The server's AI analyzes the user's input information, health care data, GPS data, and emotional data to generate the optimal meal menu. For example, if a user requests a "healthy lunch," their recent exercise data indicates 10,000 steps, their current location is Shinjuku, and their emotional state is "tired," the AI ​​will generate the optimal meal menu based on this information. Specific suggested examples include grilled chicken salad, spinach smoothie, and fruit yogurt.

[0651] If a user wishes to eat out, the server will connect with the API of a gourmet website to search for restaurants near the user's current location that offer the suggested menu. For example, it will recommend multiple restaurants in Shinjuku that serve grilled chicken salad. If the user wishes to order delivery, the server will connect with the API of a delivery service to order ingredients and dishes that match the suggested menu and arrange for delivery to the user's home.

[0652] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[0653] The device notifies the user of the suggested menu, recommended restaurants, and ordered delivery details. The user can review this and make changes or corrections as necessary. The confirmed details are sent back to the server, and processing continues. For example, the user might enter "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps), current location (Shinjuku), and emotional data (tired) to the server. The server's AI analyzes this and recommends several restaurants around Shinjuku that serve healthy lunches. This recommendation information is notified to the user via the device, allowing them to select the desired restaurant and complete the reservation on the spot.

[0654] Below are some examples of prompts for generative AI models:

[0655] User: I'd like to eat a healthy lunch out today. I'm allergic to nuts, so please avoid them.

[0656] Device: Recent exercise data is 10,000 steps, current location is Shinjuku, and emotional state is recognized as "tired."

[0657] Server: Using this information, the server searches for and recommends restaurants that serve healthy lunches in the Shinjuku area.

[0658] This system allows users to efficiently plan and prepare their daily meals and receive appropriate meal suggestions based on their emotional and health state.

[0659] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0660] Step 1:

[0661] The user inputs information about their meal using a dedicated app or chat. They input specific requests such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy." This is the input information. The device sends this information to the server. The output is the user's meal request data.

[0662] Step 2:

[0663] The device collects health data from health apps (such as Google Fit or Apple Health) installed on the smartphone. This health data includes the number of steps, heart rate, and recent exercise volume. The device also obtains the user's current location using GPS. The obtained health data and location data are also sent to the server. The output is the health data and location data.

[0664] Step 3:

[0665] The emotion engine analyzes the user's facial expressions, voice, and text input to generate emotion data. For example, if a user inputs "I'm tired today," emotion data is generated based on that. The emotion data is sent to the server. The input is the user's text and voice information, and the output is emotion data.

[0666] Step 4:

[0667] The server's AI integrates and analyzes the user's input information, health data, location data, and emotional data. For example, if a user requests a "healthy lunch," and health data indicates 10,000 steps, their current location is Shinjuku, and their emotional state is "tired," the AI ​​will generate an optimal meal menu based on this information. The output is a suggested meal menu.

[0668] Step 5:

[0669] The server connects to the API of a gourmet site and searches for restaurants that offer the suggested menu near the user's current location. For example, the server recommends restaurants in Shinjuku that serve grilled chicken salad. The input is the meal menu and location information, and the output is a list of recommended restaurants.

[0670] Step 6:

[0671] The server connects to the delivery service's API, and if the user requests delivery, it orders ingredients and dishes that match the suggested menu. The order is then arranged for delivery to the user's home. The input is the meal menu and delivery address information, and the output is the delivery order information.

[0672] Step 7:

[0673] When a user selects home cooking, the server works with smart home appliances to support the cooking. The server sends appropriate cooking instructions to a smart oven or rice cooker. For example, the server can set the smart oven to start cooking at a specific time. The input is a meal recipe and time information, and the output is cooking instructions.

[0674] Step 8:

[0675] The terminal notifies the user of the proposed menu, recommended restaurants, and delivery order. The user can review the information and make any necessary changes or corrections. The confirmed information is sent back to the server, and processing continues. The input is the food menu and selection options, and the output is a notification via the user interface.

[0676] (Application example 2)

[0677] 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."

[0678] In modern society, personal health management and dietary choices are important issues. However, in our busy daily lives, it is not easy to choose appropriate meals that match our individual health and emotional state. Furthermore, when dining out or using delivery services, there are only a limited number of systems that automatically suggest meal menus tailored to the user and efficiently order and deliver them. Therefore, there is a need for support that makes it easier for users to choose meals that take their health and emotional state into consideration.

[0679] 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.

[0680] In this invention, the server includes means for receiving user input information, means for collecting health care data and location data, means for analyzing the collected data and generating a meal menu, means for analyzing the user's emotional state and generating emotion data, means for coordinating with external services to order ingredients and dishes based on the meal menu and arrange for delivery, and means for notifying the user of the generated menu. This allows the user to receive suggestions for an optimal meal menu based on their health and emotional state, enabling them to efficiently use eating out and delivery services.

[0681] "User input" refers to data that a user provides to the system to express a dietary request or state, such as text input such as "I want to eat a healthy lunch" or "I'm tired today."

[0682] "Healthcare data" refers to data that indicates the user's health condition, and is mainly information such as the number of steps, heart rate, and amount of exercise obtained from health care apps.

[0683] "Location data" is information indicating the user's current location, and is latitude and longitude data obtained using the GPS function.

[0684] "Emotion data" is data generated by analyzing the user's emotional state, and is information analyzed by the emotion engine from the user's facial expressions, voice, text input, etc.

[0685] A "meal menu" is a specific combination of dishes and ingredients suggested based on the user's health, emotional state, and requests.

[0686] "External services" are services provided by third parties that provide and deliver meal menus in response to user requests by linking with the system, such as restaurants and delivery companies.

[0687] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates optimal meal menus.

[0688] A "prompt sentence" is a text sentence in input format used to pass data to a generative AI model, which indicates the user's detailed request or status.

[0689] The present invention provides a system that allows a user to easily input information about their meals and allows AI to suggest optimal meal menus. Hereinafter, embodiments of the present invention will be described in detail.

[0690] First, the user inputs their meal request using a dedicated app. They can easily input information such as "I want to eat a healthy lunch today." This application is installed on a smartphone and collects the user's input information.

[0691] Next, the user's smartphone automatically collects health data such as the number of steps, heart rate, and recent exercise volume from health apps (Google Fit, Apple Health, etc.), and obtains the user's current location using the GPS function. This allows data on the user's health condition and current location to be collected.

[0692] Furthermore, a server equipped with an emotion engine is provided that analyzes the user's facial expressions, voice, and text input to recognize their emotional state. For example, if the user inputs "tired," it generates emotion data such as "tired."

[0693] The server analyzes this input information, health data, and emotional data, and uses a generative AI model to generate an optimal meal menu. During the generation process, specific dishes and ingredient combinations are suggested based on the user's health condition, current location, and emotional state. For example, if a user inputs "I feel tired today," has walked 10,000 steps, has a heart rate of 70, and is currently located in Shinjuku, a suggestion would be a grilled chicken salad made with healthy ingredients in the Shinjuku area.

[0694] The server then connects to external services. If the user requests delivery, the server communicates with the delivery service's API, orders ingredients and dishes based on the suggested menu, and arranges for delivery. For example, if the user selects the suggested grilled chicken salad, the delivery service delivers the dish to the user's current location. This allows the user to receive the optimal meal without having to make any special arrangements.

[0695] Finally, the server sends a notification to the user through the application to inform them of the generated meal menu and delivery arrangement status, allowing them to review it and make any necessary changes or corrections.

[0696] For example, a user might say, "I'm tired today, so I'd like a light dinner," and the system might analyze their recent exercise data, current location, and emotional state to suggest things like smoothies or grilled vegetables.

[0697] An example of a prompt for a generative AI model might look something like this:

[0698] I'm tired today, so I'd like to have a light dinner.

[0699] "Current health data: steps 8000, heart rate 72"

[0700] "Current location: Latitude 35.6895, Longitude 139.6917"

[0701] "Emotional state: Tired"

[0702] In this way, a support system can be realized that helps users lead healthy and fulfilling eating habits.

[0703] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0704] Step 1:

[0705] A user inputs a meal request using a dedicated smartphone app. For example, they input, "I want to eat a healthy lunch today." This input information is stored on the smartphone device and used for later processing.

[0706] Step 2:

[0707] The device collects health data from health apps, such as Google Fit and Apple Health, automatically acquiring the user's steps, heart rate, and recent exercise volume, and stores the health data on the device.

[0708] Step 3:

[0709] The device uses the GPS function to obtain the user's current location (latitude and longitude). For example, if you are in Shinjuku, the GPS data obtained will be latitude 35.6895 and longitude 139.6917. This data will also be saved on the device.

[0710] Step 4:

[0711] When a user types "tired" into the device, the device passes the text to the emotion engine and analyzes the emotional state. This generates emotion data for "tired." The emotion data is then stored on the device.

[0712] Step 5:

[0713] The device transmits the user's input information, health data, GPS data, and emotion data to the server, where it is stored for use in subsequent processing steps.

[0714] Step 6:

[0715] The server uses the received data to generate an optimal meal plan using a generative AI model. For example, if a user inputs, "I'm tired today, so I'd like to have a light dinner," the generative AI model will suggest specific menu items, such as smoothies and grilled vegetables, based on health data (step count 8,000, heart rate 72), GPS data (Shinjuku), and emotional data (tired).

[0716] Step 7:

[0717] The server connects to external delivery services via API to order ingredients and dishes based on the proposed menu. For example, if a customer selects a smoothie and grilled vegetables, the server places an order with the delivery company that provides those dishes.

[0718] Step 8:

[0719] The delivery service takes the order, prepares the meal, and delivers it to the user's current location. The server monitors the delivery progress and notifies the user of progress as needed.

[0720] Step 9:

[0721] The server notifies the user of the created meal menu and the delivery arrangement status on their device. The user can review it and make changes or corrections as necessary. Finally, the user receives the optimal meal.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] [Third embodiment]

[0726] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0727] 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.

[0728] 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).

[0729] 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.

[0730] 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.

[0731] 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).

[0732] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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."

[0738] This invention is a system that allows users to easily input information about meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and the use of home appliances, and provides consistent support from preparing ingredients to cooking, thereby reducing the invisible burden of housework for users.

[0739] Input and collection of user information

[0740] Users input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I'd like to avoid nuts because I have an allergy." This information is sent from the device to the server.

[0741] Linking healthcare data and GPS data

[0742] The device collects the user's health data from health apps installed on the smartphone (such as Google Fit or Apple Health). This data includes the number of steps taken, heart rate, and recent exercise volume. It also obtains the user's current location using GPS. This data is also sent to the server.

[0743] Analyzing data and generating meal menus

[0744] The server's AI analyzes the information entered by the user, health care data, and GPS data. For example, if a user requests a "healthy lunch," has recently exercised 10,000 steps, and is currently located in Shinjuku, the AI ​​will generate an optimal meal menu based on this data. An example menu might include grilled chicken salad, yogurt, and a green smoothie.

[0745] Linking menus with external services

[0746] When a user requests to eat out, the server connects with the API of a gourmet website (such as Tabelog) to search for restaurants that serve healthy lunches near the user's current location. For example, an initiative could recommend multiple restaurants in Shinjuku that serve grilled chicken salad. When the user requests delivery, the server connects with the API of a delivery service (such as Uber Eats) to order ingredients and dishes that match the suggested menu. It then arranges for delivery to the user's home.

[0747] Integration with smart home appliances

[0748] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[0749] User Notification and Confirmation

[0750] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the information and make changes or corrections as necessary. The confirmed details are then sent back to the server, and processing continues.

[0751] To give a specific example, suppose a user enters "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps) and current location (Shinjuku) to the server. The server's AI analyzes this and recommends several restaurants in the Shinjuku area that offer healthy menus. This recommendation information is notified to the user via the device, allowing the user to select the desired restaurant and complete the reservation on the spot.

[0752] This system allows users to plan and prepare their daily meals more efficiently, leading to healthier and more fulfilling eating habits.

[0753] The processing flow will be explained below.

[0754] Step 1:

[0755] The user opens a dedicated app or chats on LINE and enters information about their diet, such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy."

[0756] Step 2:

[0757] The device receives user input, including information about food preferences, allergies, and diet goals.

[0758] Step 3:

[0759] The terminal transmits the user's input data to the server.

[0760] Step 4:

[0761] The device obtains the user's recent health data (e.g., number of steps, heart rate, and amount of exercise) from a health app (e.g., Google Fit, Apple Health) installed on the smartphone.

[0762] Step 5:

[0763] The device acquires GPS data and determines the user's current location.

[0764] Step 6:

[0765] The healthcare data and GPS data acquired by the device are sent to the server.

[0766] Step 7:

[0767] The server receives user input data, health care data, and GPS data and manages them centrally.

[0768] Step 8:

[0769] The server's AI analyzes this data and makes a comprehensive assessment of the user's health, location, and food preferences.

[0770] Step 9:

[0771] Based on the analysis results, the server's AI generates the optimal meal menu for the user, suggesting items such as "grilled chicken salad," "yogurt," and "green smoothie."

[0772] Step 10:

[0773] The device will present the user with the options of "eating out," "delivery," and "cooking at home."

[0774] Step 11:

[0775] The user chooses from the options "eating out," "delivery," or "cooking at home."

[0776] Step 12 (If dining out):

[0777] The server connects to the gourmet site's API and searches for restaurants that offer the suggested menu near the user's current location.

[0778] Step 13 (If dining out):

[0779] The server transmits the recommendation results to the terminal, and the terminal notifies the user of the recommended restaurant information.

[0780] Step 14 (For Delivery):

[0781] The server connects to the delivery service's API and orders ingredients and dishes based on the suggested menu.

[0782] Step 15 (For Delivery):

[0783] The server checks the order and arranges for the ingredients and food to be delivered to the user's home.

[0784] Step 16 (Home Cooking):

[0785] The server obtains information about the user's smart appliances (e.g., smart oven, rice cooker).

[0786] Step 17 (Home Cooking):

[0787] The server sends appropriate cooking instructions to smart appliances, such as "heat at 200 degrees for 20 minutes" to an oven.

[0788] Step 18:

[0789] The device notifies the user of the generated menu, recommended restaurants, and ordered delivery details.

[0790] Step 19:

[0791] The user checks the notification content and makes changes or corrections as necessary.

[0792] Step 20:

[0793] The terminal transmits the final confirmation made by the user to the server, and the process is completed.

[0794] Example 1

[0795] 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."

[0796] Conventional meal planning systems require users to input detailed information themselves, which is often time-consuming. Furthermore, it is difficult to suggest optimal meal plans based on the user's health status and current location, and they lack sufficient integration with external services. This increases the invisible burden of housework for users, making it difficult to achieve an efficient diet.

[0797] 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.

[0798] In this invention, the server includes a means for receiving user input information, a means for collecting health data and location data, a means for analyzing the collected data and generating a meal plan, a means for linking with external services, and a means for notifying the user of the generated plan. This allows the user to obtain an optimal meal plan based on the input information and health and location data, thereby reducing the burden of invisible housework. In addition, by linking with external services, the system can meet a variety of needs by providing options such as eating out, delivery, and cooking at home.

[0799] "Means for receiving user input information" refers to an interface through which a user inputs their dietary requests and preferences, and to a device or software that transmits this input to a server.

[0800] "Health Data" refers to physiological data collected from health apps, such as a user's steps, heart rate, and recent exercise volume.

[0801] "Location data" refers to information indicating the user's current location obtained using the GPS function of the device.

[0802] "Means of collection" refers to devices or software that acquire health care data and location data and transmit them to a server.

[0803] "Means for analyzing and generating meal plans" refers to devices and software that perform a series of data processing and calculations that allow the server's AI to derive the optimal meal menu based on the user's input information, health data, and location data.

[0804] "Means for linking with external services" refers to the function that enables the server to call the API of an external service provider (e.g., a restaurant search site or delivery service) and use an external service that is appropriate for the user.

[0805] "Means for notifying the user of the generated plan" refers to a device or software that sends the meal plan and recommendation information generated by the server to the user's terminal and allows the user to check it.

[0806] MODE FOR CARRYING OUT THE INVENTION

[0807] This invention is a system in which AI proposes appropriate meal menus based on meal information entered by a user and provides meal options in cooperation with external services. The following describes in detail the embodiments of the invention.

[0808] Input and collection of user information

[0809] Users input information about their meals using a dedicated app or messaging service. Specific examples include requests such as "I want to eat a healthy lunch today" or "I'm allergic to nuts, so I want to avoid them." The device uses devices and software to collect this information and send it to a server.

[0810] Linking healthcare data and GPS data

[0811] The device automatically collects the user's health data using a health app (e.g., Google Fit, Apple Health) installed on the smartphone. This data includes the number of steps taken, heart rate, and recent exercise volume. The device also uses GPS to obtain the user's current location. This data is sent from the device to a server.

[0812] Analyzing data and generating meal menus

[0813] The server implements a generative AI model that integrates and analyzes user input, health data, and location data. This AI generates optimal meal plans based on information obtained from multiple data sources. For example, if a user requests a "healthy lunch," has recently taken 10,000 steps, and is currently located in an urban area, the AI ​​might generate a menu such as "grilled chicken salad, yogurt, and a green smoothie."

[0814] Linking menus with external services

[0815] If a user wishes to eat out, the server uses the API of a gourmet site to search for and recommend restaurants that offer healthy lunches near the user's current location. For example, it might recommend multiple restaurants that serve grilled chicken salad near Shinjuku. If the user wishes to have delivery, the server uses the API of a delivery service to order ingredients and dishes that match the suggested menu and arrange for delivery.

[0816] Integration with smart home appliances

[0817] If a user selects home cooking, the server will connect to the smart appliances and send the appropriate cooking instructions. For example, if a smart oven or rice cooker is connected, the server can send specific instructions to these appliances, such as "Start cooking grilled chicken salad at 6:30 PM." The user simply sets the ingredients, and cooking will begin automatically at the specified time.

[0818] User Notification and Confirmation

[0819] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the notification and make changes or corrections as necessary. This confirmed information is then sent back to the server, completing the process.

[0820] To give a specific example, when a user types "I want to eat a healthy lunch outside today" into a dedicated app, the device sends their recent exercise data (10,000 steps) and current location (urban area) to a server. The server's AI analyzes this and recommends several restaurants in the urban area that offer healthy menus. This recommendation information is notified to the user via the device, allowing them to select the restaurant of their choice and complete the reservation on the spot. This system allows users to plan and prepare their daily meals more efficiently, leading to healthier and more fulfilling eating habits.

[0821] Prompt Sentence Examples

[0822] An example of a prompt sentence to input to the generative AI model is as follows:

[0823] "Suggest a healthy lunch menu for today based on your recent activity and current location."

[0824] "Analyze my steps and heart rate data to create a meal plan for this week and recommend the best menu for me."

[0825] "Recommend restaurants for eating out and introduce healthy places to eat nearby."

[0826] This invention helps users to lead an efficient and healthy diet.

[0827] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0828] Step 1: Enter and collect user information

[0829] Users input their meal preferences and requirements using a dedicated app or messaging service. For example, they can input, "I want to eat a healthy lunch today." The input information is sent to the device, which then sends it to the server.

[0830] Input: Information about the user's preferences and requirements (e.g., "healthy lunch")

[0831] Output: User's wishes and conditions transferred to the server

[0832] Step 2: Linking Healthcare and GPS Data

[0833] The device automatically collects the user's health data (number of steps, heart rate, recent exercise volume, etc.) using the smartphone's health care app (Google Fit or Apple Health). In addition, the device obtains the user's current location using the GPS function. This data is also sent to the server.

[0834] Input: User's health data and current location

[0835] Output: Health data and current location sent to the server

[0836] Step 3: Analyze the data and generate a meal menu

[0837] The server's generative AI model integrates and analyzes the user's preferences, conditions, health data, and current location received from Steps 1 and 2. For example, if the user requests a "healthy lunch," their recent exercise data shows 10,000 steps, and their current location is in an urban area, the AI ​​analyzes this data and generates an optimal menu, such as grilled chicken salad, yogurt, or a green smoothie.

[0838] Input: Integrated user preferences, health data, current location

[0839] Output: Generated meal menu

[0840] Step 4: Integrate the menu with external services

[0841] When a user wants to eat out, the server calls the API of a gourmet site to search for and recommend restaurants that offer healthy lunches near the user's current location. For example, a request such as "Recommend multiple restaurants that serve grilled chicken salad around Shinjuku" is sent to the API. When the user wants delivery, the server calls the API of a delivery service, orders ingredients and dishes that match the suggested menu, and arranges for delivery.

[0842] Input: Generated meal menu, user's dining out or delivery preference

[0843] Output: Recommended restaurant information, delivery order details

[0844] Step 5: Connect to smart appliances

[0845] If a user selects home cooking, the server will connect to smart appliances. For example, it will send specific instructions to a smart oven or rice cooker, such as "Start cooking grilled chicken salad at 6:30 PM." The user simply sets the necessary ingredients, and cooking will begin automatically at the specified time.

[0846] Input: User's cooking selection, generated meal menu

[0847] Output: Cooking instructions sent to smart appliances

[0848] Step 6: User notification and confirmation

[0849] The device notifies the user of the proposed menu, recommended restaurants, and delivery details. The user can then check the information in the app and make any necessary changes or corrections. Once the confirmation is complete, the confirmed information is sent back to the server, completing the process.

[0850] Input: Generated meal menu, recommendation information, delivery details

[0851] Output: Information notified to the user, user confirmation and corrections

[0852] (Application example 1)

[0853] 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."

[0854] In modern life, choosing meals that are appropriate for a user's health condition and the situation of the day is a time-consuming and laborious task. Furthermore, there is still no system that consistently proposes optimal menus that take into account dining out, delivery, and even home cooking options. Additionally, there is a need for a unified solution to reduce the invisible burden of household chores on users.

[0855] 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.

[0856] In this invention, the server includes a means for receiving user input information, a means for collecting health care data and location data, a means for analyzing the collected data and generating a meal menu, a means for linking with external services, a means for notifying the user of the generated menu, and a means for linking with the external service to order dishes corresponding to the proposed menu. This allows the user to receive optimal meal menu suggestions based on their health condition and current location. Furthermore, consistent support is provided for all options, including eating out, delivery, and cooking at home, significantly reducing the user's invisible household chores.

[0857] The "means for receiving user input information" is an interface that allows the user to input information such as dietary preferences and allergies.

[0858] "Healthcare data" is information that indicates the user's health condition, and includes data such as the number of steps taken, heart rate, and amount of exercise.

[0859] "Location data" is information indicating the current location of the user, and is data obtained from a positioning system such as a GPS.

[0860] The "means for analyzing collected data and generating a meal menu" refers to an algorithm or program for generating an appropriate meal menu based on user input information, health care data, and location data.

[0861] "Means of linking with external services" refers to APIs and communication methods for linking with gourmet sites and delivery services to exchange information.

[0862] The "means for notifying the user of the generated menu" refers to an application or communication interface for sending a notification to the user's terminal.

[0863] "Means for ordering food corresponding to the proposed menu in cooperation with an external service" refers to a system for ordering food through an external delivery service based on the generated menu and delivering it to the user.

[0864] The present invention is a system that allows users to easily input information about meals and allows AI to suggest appropriate meal menus. Specific embodiments of the system are described below.

[0865] System Configuration

[0866] This system is composed of a means for receiving user input information, a means for collecting healthcare data and location data, a means for analyzing the collected data and generating a meal menu, a means for linking with external services, and a means for notifying the user of the generated menu. It also includes a means for linking with external services and ordering food corresponding to the suggested menu.

[0867] Explanation of program processing

[0868] 1. A way to receive user input:

[0869] Users use a dedicated application to input information such as dietary requests and allergies, and this information is sent to the system via devices such as smartphones and tablets.

[0870] 2. How we collect health and location data:

[0871] The device collects the user's health data from health apps installed on the smartphone (e.g., Google Fit or Apple Health), and also obtains the user's current location using the device's GPS function.

[0872] 3. How to analyze the collected data and generate a meal menu:

[0873] The server comprehensively analyzes the user's input information, healthcare data, and location data, and uses a generative AI model to generate the optimal meal menu.

[0874] 4. Means of linking with external services:

[0875] The system works in conjunction with the APIs of gourmet sites and delivery services to search for and order restaurants and dishes that correspond to the generated menu.

[0876] 5. How to notify the user of the generated menu:

[0877] The generated menu and recommendation information are sent to the user's device, where the user can review it and select the restaurant or delivery service of their choice.

[0878] 6. Means of ordering food corresponding to the suggested menu in cooperation with external services:

[0879] Based on the dining out and delivery options selected by the user, the system will connect with external delivery services to order and arrange for the appropriate food to be delivered.

[0880] Examples of concrete examples and prompts

[0881] As a concrete example, let's say a user enters "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps) and current location (Shinjuku) to the server. The server's AI analyzes this data and recommends restaurants in the Shinjuku area that offer healthy menus. This recommendation information is notified to the user via the device, allowing the user to select the desired restaurant and complete the reservation on the spot.

[0882] Example prompt sentence:

[0883] "I have a nut allergy. Can you suggest a healthy lunch? I currently walk 10,000 steps. I'm considering eating out in Shinjuku or delivery."

[0884] This system allows users to plan and prepare their daily meals more efficiently, enabling them to lead healthier and more fulfilling diets.

[0885] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0886] Step 1:

[0887] Collecting user input

[0888] How it works: Users enter their dietary preferences (e.g., "I want a healthy lunch"), allergy information, etc. into a dedicated app.

[0889] Input: Text data of user preferences and allergy information.

[0890] Output: The collected data on user preferences and allergy information is sent to the server.

[0891] Step 2:

[0892] Healthcare and location data collection

[0893] How it works: The device obtains data from the smartphone's health app (Google Fit, Apple Health, etc.) and location information from GPS.

[0894] Input: Health data (steps, heart rate, activity, etc.) from the user's Health app and location data from GPS.

[0895] Output: Collected health and location data is sent to a server.

[0896] Step 3:

[0897] Data analysis and meal menu generation

[0898] How it works: The server uses a generative AI model to analyze user input, health data, and location data to generate an appropriate meal menu.

[0899] Input: User preferences, allergy information, health data, location data.

[0900] Data processing: Data integration and analysis, using generative AI models to generate meal plans based on health status and location.

[0901] Output: The generated meal menu.

[0902] Step 4:

[0903] Integration with external services

[0904] How it works: The server accesses the APIs of gourmet sites and delivery services to search for and order appropriate restaurants and dishes.

[0905] Input: The generated meal menu.

[0906] Data processing: Data exchange with external services using API. Search for restaurants and dishes based on pre-generated menus.

[0907] Output: Recommended restaurant information and ordered food information.

[0908] Step 5:

[0909] User Notification

[0910] Operation: The generated menu and recommendation information are sent to the user's device.

[0911] Input: Recommended store information and order information.

[0912] Output: A notification message is displayed on the user's terminal.

[0913] Step 6:

[0914] Food ordering and delivery arrangements

[0915] How it works: The user selects a dine-in or delivery option, and the system uses that information to arrange the order and delivery.

[0916] Input: The user's selected store or delivery option.

[0917] Data processing: Generating order information and linking to external delivery services.

[0918] Output: Order confirmed and delivery arrangements completed.

[0919] In this way, the system can integrate the user's input information and collected data at each step, and provide the optimal meal menu after analysis and collaboration.

[0920] 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.

[0921] This invention is a system that allows users to easily input information about their meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and home appliance use, and provides comprehensive support from ingredient preparation to cooking, thereby reducing the invisible burden of housework for users. It also features an emotion engine that recognizes the user's emotional state, suggesting meal menus that match the user's mood.

[0922] Input and collection of user information

[0923] Users input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I'm allergic to nuts, so I'd like to avoid them." This information is sent from the device to the server.

[0924] Linking healthcare data and GPS data

[0925] The device collects the user's health data from health apps installed on the smartphone (such as Google Fit or Apple Health). This data includes the number of steps taken, heart rate, and recent exercise volume. It also obtains the user's current location using GPS. This data is also sent to the server.

[0926] Emotion engine recognizes emotional states

[0927] The emotion engine analyzes the user's facial expressions, voice, and text input to recognize the user's emotional state. For example, if the user inputs "I'm tired" or "I'm irritated," the engine generates emotion data based on that. The emotion data is then sent to the server along with other data.

[0928] Analyzing data and generating meal menus

[0929] The server's AI analyzes the user's input information, health care data, GPS data, and emotional data. For example, if a user requests a "healthy lunch," has recently exercised 10,000 steps, is currently located in Shinjuku, and is emotionally "tired," the AI ​​will generate the optimal meal menu based on this information. For example, it might suggest grilled chicken salad, a spinach smoothie, or fruit yogurt.

[0930] Linking menus with external services

[0931] When a user requests to eat out, the server connects with the API of a gourmet website to search for restaurants that offer the suggested menu near the user's current location. For example, an initiative could recommend multiple restaurants in Shinjuku that serve grilled chicken salad. When the user requests delivery, the server connects with the API of a delivery service to order ingredients and dishes that match the suggested menu. It then arranges for delivery to the user's home.

[0932] Integration with smart home appliances

[0933] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[0934] User Notification and Confirmation

[0935] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the information and make changes or corrections as necessary. The confirmed details are then sent back to the server, and processing continues.

[0936] To give a specific example, a user might enter "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps), current location (Shinjuku), and emotional data (tired) to a server. The server's AI analyzes this data and recommends several restaurants in the Shinjuku area that offer healthy menus. This recommendation information is then sent to the user via the device, allowing them to select the desired restaurant and complete the reservation on the spot.

[0937] This system allows users to efficiently plan and prepare their daily meals and receive appropriate meal suggestions tailored to their emotional and health states, enabling them to live a healthier and more fulfilling diet.

[0938] The processing flow will be explained below.

[0939] Step 1:

[0940] The user opens a dedicated app or chats on LINE and enters information about their diet. For example, they might enter, "I want to eat a healthy lunch today," or "I want to avoid nuts because I have an allergy."

[0941] Step 2:

[0942] The device receives user input information and sends it to the server, including information about dietary preferences, allergies, and diet goals.

[0943] Step 3:

[0944] The device accesses the health app (Google Fit, Apple Health, etc.) installed on the smartphone and obtains the user's health data (e.g., number of steps, heart rate, amount of exercise).

[0945] Step 4:

[0946] The device uses the GPS function to obtain the user's current location.

[0947] Step 5:

[0948] The healthcare data and GPS data acquired by the device are sent to the server.

[0949] Step 6:

[0950] The device uses an emotion engine to analyze the user's facial expressions, voice, text input, etc. to recognize the user's emotional state. For example, it can recognize the emotion "tired."

[0951] Step 7:

[0952] The device transmits the emotion data to the server.

[0953] Step 8:

[0954] The server centrally manages user input information, health care data, GPS data, and emotional data.

[0955] Step 9:

[0956] The server's AI analyzes this data and makes a comprehensive assessment of the user's health, location, food preferences, and emotional state.

[0957] Step 10:

[0958] Based on the analysis results, the server's AI generates the optimal meal menu for the user, suggesting dishes such as "grilled chicken salad," "spinach smoothie," and "fruit yogurt."

[0959] Step 11:

[0960] The device will present the user with the options of "eating out," "delivery," and "cooking at home."

[0961] Step 12:

[0962] The user selects either "eating out," "delivery," or "cooking at home."

[0963] Step 13 (If dining out):

[0964] Based on the selection, the server works with the gourmet site's API to search for restaurants that offer the suggested menu near the user's current location.

[0965] Step 14 (If dining out):

[0966] The server transmits the recommendation results to the terminal, and the terminal notifies the user of the recommended restaurant information.

[0967] Step 15 (For Delivery):

[0968] The server connects to the delivery service's API and orders ingredients and dishes based on the suggested menu.

[0969] Step 16 (For Delivery):

[0970] The server checks the order and arranges for the ingredients and food to be delivered to the user's home.

[0971] Step 17 (Home Cooking):

[0972] The server obtains information about the user's smart appliances (e.g., smart oven, rice cooker).

[0973] Step 18 (Home Cooking):

[0974] The server sends appropriate cooking instructions to smart appliances, such as "heat at 200 degrees for 20 minutes" to an oven.

[0975] Step 19:

[0976] The device notifies the user of the generated menu, recommended restaurants, and ordered delivery details.

[0977] Step 20:

[0978] The user checks the notification content and makes changes or corrections as necessary.

[0979] Step 21:

[0980] The terminal transmits the final confirmation made by the user to the server, and the process is completed.

[0981] Example 2

[0982] 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."

[0983] Conventional meal recommendation systems have not adequately optimized individual meals based on the user's health and emotional state. Furthermore, they lack sufficient integration with user input information and external services, making it difficult to efficiently recommend meal menus suited to the user.

[0984] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving input information about meals from the user, means for collecting health data and location data, means for analyzing the collected data and emotion data and generating a meal menu, means for providing information based on the proposed meal menu in cooperation with external services, and means for notifying the user of the generated menu and related information. This enables highly accurate meal suggestions based on the user's health and emotional state. In addition, by strengthening cooperation with external services, it is possible to provide the user with various options, such as eating out, delivery, and cooking at home.

[0985] "Input information" refers to data such as dietary requests and allergy information provided by users via a dedicated app or chat.

[0986] "Health data" refers to physical data such as number of steps, heart rate, and amount of exercise collected from healthcare apps, etc.

[0987] "Location data" is information about the user's current location obtained using GPS.

[0988] "Emotion data" refers to data relating to the emotional state of a user that is generated by analyzing the user's facial expressions, voice, text input, and the like.

[0989] "Analysis" is the process of integrating the collected data and generating an appropriate meal menu based on the user's condition and needs.

[0990] A "meal menu" is a specific meal suggestion optimized for the user's health and emotional state.

[0991] "External services" are other food-related service providers, such as gourmet sites, delivery services, and smart home appliances.

[0992] "Notification" is a means for informing the user of the generated meal menu and related information.

[0993] "Collaboration" is a mechanism for communicating with external services and providing services related to the proposed meal menu.

[0994] A "user" is an entity that uses the system to receive meal suggestions.

[0995] This invention is a system that allows users to easily input information about their meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and home appliance use, and provides comprehensive support from ingredient preparation to cooking, thereby reducing the invisible burden of housework for users. It also features an emotion engine that recognizes the user's emotional state, suggesting meal menus that match the user's mood.

[0996] Users can input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy." This information is sent from the device to the server.

[0997] The device collects health data such as the number of steps, heart rate, and recent exercise volume from health apps installed on the smartphone (e.g., Google Fit or Apple Health).The device also obtains the user's current location using GPS and sends this data to the server.

[0998] The emotion engine analyzes the user's facial expressions, voice, and text input to recognize the user's emotional state. For example, if the user inputs "I'm tired" or "I'm irritated," emotion data is generated based on that. The emotion data is sent to the server along with other data.

[0999] The server's AI analyzes the user's input information, health care data, GPS data, and emotional data to generate the optimal meal menu. For example, if a user requests a "healthy lunch," their recent exercise data indicates 10,000 steps, their current location is Shinjuku, and their emotional state is "tired," the AI ​​will generate the optimal meal menu based on this information. Specific suggested examples include grilled chicken salad, spinach smoothie, and fruit yogurt.

[1000] If a user wishes to eat out, the server will connect with the API of a gourmet website to search for restaurants near the user's current location that offer the suggested menu. For example, it will recommend multiple restaurants in Shinjuku that serve grilled chicken salad. If the user wishes to order delivery, the server will connect with the API of a delivery service to order ingredients and dishes that match the suggested menu and arrange for delivery to the user's home.

[1001] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[1002] The device notifies the user of the suggested menu, recommended restaurants, and ordered delivery details. The user can review this and make changes or corrections as necessary. The confirmed details are sent back to the server, and processing continues. For example, the user might enter "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps), current location (Shinjuku), and emotional data (tired) to the server. The server's AI analyzes this and recommends several restaurants around Shinjuku that serve healthy lunches. This recommendation information is notified to the user via the device, allowing them to select the desired restaurant and complete the reservation on the spot.

[1003] Below are some examples of prompts for generative AI models:

[1004] User: I'd like to eat a healthy lunch out today. I'm allergic to nuts, so please avoid them.

[1005] Device: Recent exercise data is 10,000 steps, current location is Shinjuku, and emotional state is recognized as "tired."

[1006] Server: Using this information, the server searches for and recommends restaurants that serve healthy lunches in the Shinjuku area.

[1007] This system allows users to efficiently plan and prepare their daily meals and receive appropriate meal suggestions based on their emotional and health state.

[1008] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1009] Step 1:

[1010] The user inputs information about their meal using a dedicated app or chat. They input specific requests such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy." This is the input information. The device sends this information to the server. The output is the user's meal request data.

[1011] Step 2:

[1012] The device collects health data from health apps (such as Google Fit or Apple Health) installed on the smartphone. This health data includes the number of steps, heart rate, and recent exercise volume. The device also obtains the user's current location using GPS. The obtained health data and location data are also sent to the server. The output is the health data and location data.

[1013] Step 3:

[1014] The emotion engine analyzes the user's facial expressions, voice, and text input to generate emotion data. For example, if a user inputs "I'm tired today," emotion data is generated based on that. The emotion data is sent to the server. The input is the user's text and voice information, and the output is emotion data.

[1015] Step 4:

[1016] The server's AI integrates and analyzes the user's input information, health data, location data, and emotional data. For example, if a user requests a "healthy lunch," and health data indicates 10,000 steps, their current location is Shinjuku, and their emotional state is "tired," the AI ​​will generate an optimal meal menu based on this information. The output is a suggested meal menu.

[1017] Step 5:

[1018] The server connects to the API of a gourmet site and searches for restaurants that offer the suggested menu near the user's current location. For example, the server recommends restaurants in Shinjuku that serve grilled chicken salad. The input is the meal menu and location information, and the output is a list of recommended restaurants.

[1019] Step 6:

[1020] The server connects to the delivery service's API, and if the user requests delivery, it orders ingredients and dishes that match the suggested menu. The order is then arranged for delivery to the user's home. The input is the meal menu and delivery address information, and the output is the delivery order information.

[1021] Step 7:

[1022] When a user selects home cooking, the server works with smart home appliances to support the cooking. The server sends appropriate cooking instructions to a smart oven or rice cooker. For example, the server can set the smart oven to start cooking at a specific time. The input is a meal recipe and time information, and the output is cooking instructions.

[1023] Step 8:

[1024] The terminal notifies the user of the proposed menu, recommended restaurants, and delivery order. The user can review the information and make any necessary changes or corrections. The confirmed information is sent back to the server, and processing continues. The input is the food menu and selection options, and the output is a notification via the user interface.

[1025] (Application example 2)

[1026] 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."

[1027] In modern society, personal health management and dietary choices are important issues. However, in our busy daily lives, it is not easy to choose appropriate meals that match our individual health and emotional state. Furthermore, when dining out or using delivery services, there are only a limited number of systems that automatically suggest meal menus tailored to the user and efficiently order and deliver them. Therefore, there is a need for support that makes it easier for users to choose meals that take their health and emotional state into consideration.

[1028] 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.

[1029] In this invention, the server includes means for receiving user input information, means for collecting health care data and location data, means for analyzing the collected data and generating a meal menu, means for analyzing the user's emotional state and generating emotion data, means for coordinating with external services to order ingredients and dishes based on the meal menu and arrange for delivery, and means for notifying the user of the generated menu. This allows the user to receive suggestions for an optimal meal menu based on their health and emotional state, enabling them to efficiently use eating out and delivery services.

[1030] "User input" refers to data that a user provides to the system to express a dietary request or state, such as text input such as "I want to eat a healthy lunch" or "I'm tired today."

[1031] "Healthcare data" refers to data that indicates the user's health condition, and is mainly information such as the number of steps, heart rate, and amount of exercise obtained from health care apps.

[1032] "Location data" is information indicating the user's current location, and is latitude and longitude data obtained using the GPS function.

[1033] "Emotion data" is data generated by analyzing the user's emotional state, and is information analyzed by the emotion engine from the user's facial expressions, voice, text input, etc.

[1034] A "meal menu" is a specific combination of dishes and ingredients suggested based on the user's health, emotional state, and requests.

[1035] "External services" are services provided by third parties that provide and deliver meal menus in response to user requests by linking with the system, such as restaurants and delivery companies.

[1036] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates optimal meal menus.

[1037] A "prompt sentence" is a text sentence in input format used to pass data to a generative AI model, which indicates the user's detailed request or status.

[1038] The present invention provides a system that allows a user to easily input information about their meals and allows AI to suggest optimal meal menus. Hereinafter, embodiments of the present invention will be described in detail.

[1039] First, the user inputs their meal request using a dedicated app. They can easily input information such as "I want to eat a healthy lunch today." This application is installed on a smartphone and collects the user's input information.

[1040] Next, the user's smartphone automatically collects health data such as the number of steps, heart rate, and recent exercise volume from health apps (Google Fit, Apple Health, etc.), and obtains the user's current location using the GPS function. This allows data on the user's health condition and current location to be collected.

[1041] Furthermore, a server equipped with an emotion engine is provided that analyzes the user's facial expressions, voice, and text input to recognize their emotional state. For example, if the user inputs "tired," it generates emotion data such as "tired."

[1042] The server analyzes this input information, health data, and emotional data, and uses a generative AI model to generate an optimal meal menu. During the generation process, specific dishes and ingredient combinations are suggested based on the user's health condition, current location, and emotional state. For example, if a user inputs "I feel tired today," has walked 10,000 steps, has a heart rate of 70, and is currently located in Shinjuku, a suggestion would be a grilled chicken salad made with healthy ingredients in the Shinjuku area.

[1043] The server then connects to external services. If the user requests delivery, the server communicates with the delivery service's API, orders ingredients and dishes based on the suggested menu, and arranges for delivery. For example, if the user selects the suggested grilled chicken salad, the delivery service delivers the dish to the user's current location. This allows the user to receive the optimal meal without having to make any special arrangements.

[1044] Finally, the server sends a notification to the user through the application to inform them of the generated meal menu and delivery arrangement status, allowing them to review it and make any necessary changes or corrections.

[1045] For example, a user might say, "I'm tired today, so I'd like a light dinner," and the system might analyze their recent exercise data, current location, and emotional state to suggest things like smoothies or grilled vegetables.

[1046] An example of a prompt for a generative AI model might look something like this:

[1047] I'm tired today, so I'd like to have a light dinner.

[1048] "Current health data: steps 8000, heart rate 72"

[1049] "Current location: Latitude 35.6895, Longitude 139.6917"

[1050] "Emotional state: Tired"

[1051] In this way, a support system can be realized that helps users lead healthy and fulfilling eating habits.

[1052] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1053] Step 1:

[1054] A user inputs a meal request using a dedicated smartphone app. For example, they input, "I want to eat a healthy lunch today." This input information is stored on the smartphone device and used for later processing.

[1055] Step 2:

[1056] The device collects health data from health apps, such as Google Fit and Apple Health, automatically acquiring the user's steps, heart rate, and recent exercise volume, and stores the health data on the device.

[1057] Step 3:

[1058] The device uses the GPS function to obtain the user's current location (latitude and longitude). For example, if you are in Shinjuku, the GPS data obtained will be latitude 35.6895 and longitude 139.6917. This data will also be saved on the device.

[1059] Step 4:

[1060] When a user types "tired" into the device, the device passes the text to the emotion engine and analyzes the emotional state. This generates emotion data for "tired." The emotion data is then stored on the device.

[1061] Step 5:

[1062] The device transmits the user's input information, health data, GPS data, and emotion data to the server, where it is stored for use in subsequent processing steps.

[1063] Step 6:

[1064] The server uses the received data to generate an optimal meal plan using a generative AI model. For example, if a user inputs, "I'm tired today, so I'd like to have a light dinner," the generative AI model will suggest specific menu items, such as smoothies and grilled vegetables, based on health data (step count 8,000, heart rate 72), GPS data (Shinjuku), and emotional data (tired).

[1065] Step 7:

[1066] The server connects to external delivery services via API to order ingredients and dishes based on the proposed menu. For example, if a customer selects a smoothie and grilled vegetables, the server places an order with the delivery company that provides those dishes.

[1067] Step 8:

[1068] The delivery service takes the order, prepares the meal, and delivers it to the user's current location. The server monitors the delivery progress and notifies the user of progress as needed.

[1069] Step 9:

[1070] The server notifies the user of the created meal menu and the delivery arrangement status on their device. The user can review it and make changes or corrections as necessary. Finally, the user receives the optimal meal.

[1071] 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.

[1072] 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.

[1073] 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.

[1074] [Fourth embodiment]

[1075] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1076] 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.

[1077] 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).

[1078] 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.

[1079] 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.

[1080] 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).

[1081] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1082] 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.

[1083] 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.

[1084] 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.

[1085] 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.

[1086] 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.

[1087] 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."

[1088] This invention is a system that allows users to easily input information about meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and the use of home appliances, and provides consistent support from preparing ingredients to cooking, thereby reducing the invisible burden of housework for users.

[1089] Input and collection of user information

[1090] Users input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I'd like to avoid nuts because I have an allergy." This information is sent from the device to the server.

[1091] Linking healthcare data and GPS data

[1092] The device collects the user's health data from health apps installed on the smartphone (such as Google Fit or Apple Health). This data includes the number of steps taken, heart rate, and recent exercise volume. It also obtains the user's current location using GPS. This data is also sent to the server.

[1093] Analyzing data and generating meal menus

[1094] The server's AI analyzes the information entered by the user, health care data, and GPS data. For example, if a user requests a "healthy lunch," has recently exercised 10,000 steps, and is currently located in Shinjuku, the AI ​​will generate an optimal meal menu based on this data. An example menu might include grilled chicken salad, yogurt, and a green smoothie.

[1095] Linking menus with external services

[1096] When a user requests to eat out, the server connects with the API of a gourmet website (such as Tabelog) to search for restaurants that serve healthy lunches near the user's current location. For example, an initiative could recommend multiple restaurants in Shinjuku that serve grilled chicken salad. When the user requests delivery, the server connects with the API of a delivery service (such as Uber Eats) to order ingredients and dishes that match the suggested menu. It then arranges for delivery to the user's home.

[1097] Integration with smart home appliances

[1098] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[1099] User Notification and Confirmation

[1100] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the information and make changes or corrections as necessary. The confirmed details are then sent back to the server, and processing continues.

[1101] To give a specific example, suppose a user enters "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps) and current location (Shinjuku) to the server. The server's AI analyzes this and recommends several restaurants in the Shinjuku area that offer healthy menus. This recommendation information is notified to the user via the device, allowing the user to select the desired restaurant and complete the reservation on the spot.

[1102] This system allows users to plan and prepare their daily meals more efficiently, leading to healthier and more fulfilling eating habits.

[1103] The processing flow will be explained below.

[1104] Step 1:

[1105] The user opens a dedicated app or chats on LINE and enters information about their diet, such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy."

[1106] Step 2:

[1107] The device receives user input, including information about food preferences, allergies, and diet goals.

[1108] Step 3:

[1109] The terminal transmits the user's input data to the server.

[1110] Step 4:

[1111] The device obtains the user's recent health data (e.g., number of steps, heart rate, and amount of exercise) from a health app (e.g., Google Fit, Apple Health) installed on the smartphone.

[1112] Step 5:

[1113] The device acquires GPS data and determines the user's current location.

[1114] Step 6:

[1115] The healthcare data and GPS data acquired by the device are sent to the server.

[1116] Step 7:

[1117] The server receives user input data, health care data, and GPS data and manages them centrally.

[1118] Step 8:

[1119] The server's AI analyzes this data and makes a comprehensive assessment of the user's health, location, and food preferences.

[1120] Step 9:

[1121] Based on the analysis results, the server's AI generates the optimal meal menu for the user, suggesting items such as "grilled chicken salad," "yogurt," and "green smoothie."

[1122] Step 10:

[1123] The device will present the user with the options of "eating out," "delivery," and "cooking at home."

[1124] Step 11:

[1125] The user chooses from the options "eating out," "delivery," or "cooking at home."

[1126] Step 12 (If dining out):

[1127] The server connects to the gourmet site's API and searches for restaurants that offer the suggested menu near the user's current location.

[1128] Step 13 (If dining out):

[1129] The server transmits the recommendation results to the terminal, and the terminal notifies the user of the recommended restaurant information.

[1130] Step 14 (For Delivery):

[1131] The server connects to the delivery service's API and orders ingredients and dishes based on the suggested menu.

[1132] Step 15 (For Delivery):

[1133] The server checks the order and arranges for the ingredients and food to be delivered to the user's home.

[1134] Step 16 (Home Cooking):

[1135] The server obtains information about the user's smart appliances (e.g., smart oven, rice cooker).

[1136] Step 17 (Home Cooking):

[1137] The server sends appropriate cooking instructions to smart appliances, such as "heat at 200 degrees for 20 minutes" to an oven.

[1138] Step 18:

[1139] The device notifies the user of the generated menu, recommended restaurants, and ordered delivery details.

[1140] Step 19:

[1141] The user checks the notification content and makes changes or corrections as necessary.

[1142] Step 20:

[1143] The terminal transmits the final confirmation made by the user to the server, and the process is completed.

[1144] Example 1

[1145] 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."

[1146] Conventional meal planning systems require users to input detailed information themselves, which is often time-consuming. Furthermore, it is difficult to suggest optimal meal plans based on the user's health status and current location, and they lack sufficient integration with external services. This increases the invisible burden of housework for users, making it difficult to achieve an efficient diet.

[1147] 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.

[1148] In this invention, the server includes a means for receiving user input information, a means for collecting health data and location data, a means for analyzing the collected data and generating a meal plan, a means for linking with external services, and a means for notifying the user of the generated plan. This allows the user to obtain an optimal meal plan based on the input information and health and location data, thereby reducing the burden of invisible housework. In addition, by linking with external services, the system can meet a variety of needs by providing options such as eating out, delivery, and cooking at home.

[1149] "Means for receiving user input information" refers to an interface through which a user inputs their dietary requests and preferences, and to a device or software that transmits this input to a server.

[1150] "Health Data" refers to physiological data collected from health apps, such as a user's steps, heart rate, and recent exercise volume.

[1151] "Location data" refers to information indicating the user's current location obtained using the GPS function of the device.

[1152] "Means of collection" refers to devices or software that acquire health care data and location data and transmit them to a server.

[1153] "Means for analyzing and generating meal plans" refers to devices and software that perform a series of data processing and calculations that allow the server's AI to derive the optimal meal menu based on the user's input information, health data, and location data.

[1154] "Means for linking with external services" refers to the function that enables the server to call the API of an external service provider (e.g., a restaurant search site or delivery service) and use an external service that is appropriate for the user.

[1155] "Means for notifying the user of the generated plan" refers to a device or software that sends the meal plan and recommendation information generated by the server to the user's terminal and allows the user to check it.

[1156] MODE FOR CARRYING OUT THE INVENTION

[1157] This invention is a system in which AI proposes appropriate meal menus based on meal information entered by a user and provides meal options in cooperation with external services. The following describes in detail the embodiments of the invention.

[1158] Input and collection of user information

[1159] Users input information about their meals using a dedicated app or messaging service. Specific examples include requests such as "I want to eat a healthy lunch today" or "I'm allergic to nuts, so I want to avoid them." The device uses devices and software to collect this information and send it to a server.

[1160] Linking healthcare data and GPS data

[1161] The device automatically collects the user's health data using a health app (e.g., Google Fit, Apple Health) installed on the smartphone. This data includes the number of steps taken, heart rate, and recent exercise volume. The device also uses GPS to obtain the user's current location. This data is sent from the device to a server.

[1162] Analyzing data and generating meal menus

[1163] The server implements a generative AI model that integrates and analyzes user input, health data, and location data. This AI generates optimal meal plans based on information obtained from multiple data sources. For example, if a user requests a "healthy lunch," has recently taken 10,000 steps, and is currently located in an urban area, the AI ​​might generate a menu such as "grilled chicken salad, yogurt, and a green smoothie."

[1164] Linking menus with external services

[1165] If a user wishes to eat out, the server uses the API of a gourmet site to search for and recommend restaurants that offer healthy lunches near the user's current location. For example, it might recommend multiple restaurants that serve grilled chicken salad near Shinjuku. If the user wishes to have delivery, the server uses the API of a delivery service to order ingredients and dishes that match the suggested menu and arrange for delivery.

[1166] Integration with smart home appliances

[1167] If a user selects home cooking, the server will connect to the smart appliances and send the appropriate cooking instructions. For example, if a smart oven or rice cooker is connected, the server can send specific instructions to these appliances, such as "Start cooking grilled chicken salad at 6:30 PM." The user simply sets the ingredients, and cooking will begin automatically at the specified time.

[1168] User Notification and Confirmation

[1169] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the notification and make changes or corrections as necessary. This confirmed information is then sent back to the server, completing the process.

[1170] To give a specific example, when a user types "I want to eat a healthy lunch outside today" into a dedicated app, the device sends their recent exercise data (10,000 steps) and current location (urban area) to a server. The server's AI analyzes this and recommends several restaurants in the urban area that offer healthy menus. This recommendation information is notified to the user via the device, allowing them to select the restaurant of their choice and complete the reservation on the spot. This system allows users to plan and prepare their daily meals more efficiently, leading to healthier and more fulfilling eating habits.

[1171] Prompt Sentence Examples

[1172] An example of a prompt sentence to input to the generative AI model is as follows:

[1173] "Suggest a healthy lunch menu for today based on your recent activity and current location."

[1174] "Analyze my steps and heart rate data to create a meal plan for this week and recommend the best menu for me."

[1175] "Recommend restaurants for eating out and introduce healthy places to eat nearby."

[1176] This invention helps users to lead an efficient and healthy diet.

[1177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1178] Step 1: Enter and collect user information

[1179] Users input their meal preferences and requirements using a dedicated app or messaging service. For example, they can input, "I want to eat a healthy lunch today." The input information is sent to the device, which then sends it to the server.

[1180] Input: Information about the user's preferences and requirements (e.g., "healthy lunch")

[1181] Output: User's wishes and conditions transferred to the server

[1182] Step 2: Linking Healthcare and GPS Data

[1183] The device automatically collects the user's health data (number of steps, heart rate, recent exercise volume, etc.) using the smartphone's health care app (Google Fit or Apple Health). In addition, the device obtains the user's current location using the GPS function. This data is also sent to the server.

[1184] Input: User's health data and current location

[1185] Output: Health data and current location sent to the server

[1186] Step 3: Analyze the data and generate a meal menu

[1187] The server's generative AI model integrates and analyzes the user's preferences, conditions, health data, and current location received from Steps 1 and 2. For example, if the user requests a "healthy lunch," their recent exercise data shows 10,000 steps, and their current location is in an urban area, the AI ​​analyzes this data and generates an optimal menu, such as grilled chicken salad, yogurt, or a green smoothie.

[1188] Input: Integrated user preferences, health data, current location

[1189] Output: Generated meal menu

[1190] Step 4: Integrate the menu with external services

[1191] When a user wants to eat out, the server calls the API of a gourmet site to search for and recommend restaurants that offer healthy lunches near the user's current location. For example, a request such as "Recommend multiple restaurants that serve grilled chicken salad around Shinjuku" is sent to the API. When the user wants delivery, the server calls the API of a delivery service, orders ingredients and dishes that match the suggested menu, and arranges for delivery.

[1192] Input: Generated meal menu, user's dining out or delivery preference

[1193] Output: Recommended restaurant information, delivery order details

[1194] Step 5: Connect to smart appliances

[1195] If a user selects home cooking, the server will connect to smart appliances. For example, it will send specific instructions to a smart oven or rice cooker, such as "Start cooking grilled chicken salad at 6:30 PM." The user simply sets the necessary ingredients, and cooking will begin automatically at the specified time.

[1196] Input: User's cooking selection, generated meal menu

[1197] Output: Cooking instructions sent to smart appliances

[1198] Step 6: User notification and confirmation

[1199] The device notifies the user of the proposed menu, recommended restaurants, and delivery details. The user can then check the information in the app and make any necessary changes or corrections. Once the confirmation is complete, the confirmed information is sent back to the server, completing the process.

[1200] Input: Generated meal menu, recommendation information, delivery details

[1201] Output: Information notified to the user, user confirmation and corrections

[1202] (Application example 1)

[1203] 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."

[1204] In modern life, choosing meals that are appropriate for a user's health condition and the situation of the day is a time-consuming and laborious task. Furthermore, there is still no system that consistently proposes optimal menus that take into account dining out, delivery, and even home cooking options. Additionally, there is a need for a unified solution to reduce the invisible burden of household chores on users.

[1205] 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.

[1206] In this invention, the server includes a means for receiving user input information, a means for collecting health care data and location data, a means for analyzing the collected data and generating a meal menu, a means for linking with external services, a means for notifying the user of the generated menu, and a means for linking with the external service to order dishes corresponding to the proposed menu. This allows the user to receive optimal meal menu suggestions based on their health condition and current location. Furthermore, consistent support is provided for all options, including eating out, delivery, and cooking at home, significantly reducing the user's invisible household chores.

[1207] The "means for receiving user input information" is an interface that allows the user to input information such as dietary preferences and allergies.

[1208] "Healthcare data" is information that indicates the user's health condition, and includes data such as the number of steps taken, heart rate, and amount of exercise.

[1209] "Location data" is information indicating the current location of the user, and is data obtained from a positioning system such as a GPS.

[1210] The "means for analyzing collected data and generating a meal menu" refers to an algorithm or program for generating an appropriate meal menu based on user input information, health care data, and location data.

[1211] "Means of linking with external services" refers to APIs and communication methods for linking with gourmet sites and delivery services to exchange information.

[1212] The "means for notifying the user of the generated menu" refers to an application or communication interface for sending a notification to the user's terminal.

[1213] "Means for ordering food corresponding to the proposed menu in cooperation with an external service" refers to a system for ordering food through an external delivery service based on the generated menu and delivering it to the user.

[1214] The present invention is a system that allows users to easily input information about meals and allows AI to suggest appropriate meal menus. Specific embodiments of the system are described below.

[1215] System Configuration

[1216] This system is composed of a means for receiving user input information, a means for collecting healthcare data and location data, a means for analyzing the collected data and generating a meal menu, a means for linking with external services, and a means for notifying the user of the generated menu. It also includes a means for linking with external services and ordering food corresponding to the suggested menu.

[1217] Explanation of program processing

[1218] 1. A way to receive user input:

[1219] Users use a dedicated application to input information such as dietary requests and allergies, and this information is sent to the system via devices such as smartphones and tablets.

[1220] 2. How we collect health and location data:

[1221] The device collects the user's health data from health apps installed on the smartphone (e.g., Google Fit or Apple Health), and also obtains the user's current location using the device's GPS function.

[1222] 3. How to analyze the collected data and generate a meal menu:

[1223] The server comprehensively analyzes the user's input information, healthcare data, and location data, and uses a generative AI model to generate the optimal meal menu.

[1224] 4. Means of linking with external services:

[1225] The system works in conjunction with the APIs of gourmet sites and delivery services to search for and order restaurants and dishes that correspond to the generated menu.

[1226] 5. How to notify the user of the generated menu:

[1227] The generated menu and recommendation information are sent to the user's device, where the user can review it and select the restaurant or delivery service of their choice.

[1228] 6. Means of ordering food corresponding to the suggested menu in cooperation with external services:

[1229] Based on the dining out and delivery options selected by the user, the system will connect with external delivery services to order and arrange for the appropriate food to be delivered.

[1230] Examples of concrete examples and prompts

[1231] As a concrete example, let's say a user enters "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps) and current location (Shinjuku) to the server. The server's AI analyzes this data and recommends restaurants in the Shinjuku area that offer healthy menus. This recommendation information is notified to the user via the device, allowing the user to select the desired restaurant and complete the reservation on the spot.

[1232] Example prompt sentence:

[1233] "I have a nut allergy. Can you suggest a healthy lunch? I currently walk 10,000 steps. I'm considering eating out in Shinjuku or delivery."

[1234] This system allows users to plan and prepare their daily meals more efficiently, enabling them to lead healthier and more fulfilling diets.

[1235] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1236] Step 1:

[1237] Collecting user input

[1238] How it works: Users enter their dietary preferences (e.g., "I want a healthy lunch"), allergy information, etc. into a dedicated app.

[1239] Input: Text data of user preferences and allergy information.

[1240] Output: The collected data on user preferences and allergy information is sent to the server.

[1241] Step 2:

[1242] Healthcare and location data collection

[1243] How it works: The device obtains data from the smartphone's health app (Google Fit, Apple Health, etc.) and location information from GPS.

[1244] Input: Health data (steps, heart rate, activity, etc.) from the user's Health app and location data from GPS.

[1245] Output: Collected health and location data is sent to a server.

[1246] Step 3:

[1247] Data analysis and meal menu generation

[1248] How it works: The server uses a generative AI model to analyze user input, health data, and location data to generate an appropriate meal menu.

[1249] Input: User preferences, allergy information, health data, location data.

[1250] Data processing: Data integration and analysis, using generative AI models to generate meal plans based on health status and location.

[1251] Output: The generated meal menu.

[1252] Step 4:

[1253] Integration with external services

[1254] How it works: The server accesses the APIs of gourmet sites and delivery services to search for and order appropriate restaurants and dishes.

[1255] Input: The generated meal menu.

[1256] Data processing: Data exchange with external services using API. Search for restaurants and dishes based on pre-generated menus.

[1257] Output: Recommended restaurant information and ordered food information.

[1258] Step 5:

[1259] User Notification

[1260] Operation: The generated menu and recommendation information are sent to the user's device.

[1261] Input: Recommended store information and order information.

[1262] Output: A notification message is displayed on the user's terminal.

[1263] Step 6:

[1264] Food ordering and delivery arrangements

[1265] How it works: The user selects a dine-in or delivery option, and the system uses that information to arrange the order and delivery.

[1266] Input: The user's selected store or delivery option.

[1267] Data processing: Generating order information and linking to external delivery services.

[1268] Output: Order confirmed and delivery arrangements completed.

[1269] In this way, the system can integrate the user's input information and collected data at each step, and provide the optimal meal menu after analysis and collaboration.

[1270] 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.

[1271] This invention is a system that allows users to easily input information about their meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and home appliance use, and provides comprehensive support from ingredient preparation to cooking, thereby reducing the invisible burden of housework for users. It also features an emotion engine that recognizes the user's emotional state, suggesting meal menus that match the user's mood.

[1272] Input and collection of user information

[1273] Users input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I'm allergic to nuts, so I'd like to avoid them." This information is sent from the device to the server.

[1274] Linking healthcare data and GPS data

[1275] The device collects the user's health data from health apps installed on the smartphone (such as Google Fit or Apple Health). This data includes the number of steps taken, heart rate, and recent exercise volume. It also obtains the user's current location using GPS. This data is also sent to the server.

[1276] Emotion engine recognizes emotional states

[1277] The emotion engine analyzes the user's facial expressions, voice, and text input to recognize the user's emotional state. For example, if the user inputs "I'm tired" or "I'm irritated," the engine generates emotion data based on that. The emotion data is then sent to the server along with other data.

[1278] Analyzing data and generating meal menus

[1279] The server's AI analyzes the user's input information, health care data, GPS data, and emotional data. For example, if a user requests a "healthy lunch," has recently exercised 10,000 steps, is currently located in Shinjuku, and is emotionally "tired," the AI ​​will generate the optimal meal menu based on this information. For example, it might suggest grilled chicken salad, a spinach smoothie, or fruit yogurt.

[1280] Linking menus with external services

[1281] When a user requests to eat out, the server connects with the API of a gourmet website to search for restaurants that offer the suggested menu near the user's current location. For example, an initiative could recommend multiple restaurants in Shinjuku that serve grilled chicken salad. When the user requests delivery, the server connects with the API of a delivery service to order ingredients and dishes that match the suggested menu. It then arranges for delivery to the user's home.

[1282] Integration with smart home appliances

[1283] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[1284] User Notification and Confirmation

[1285] The device notifies the user of the proposed menu, recommended restaurants, and delivery order details. The user can review the information and make changes or corrections as necessary. The confirmed details are then sent back to the server, and processing continues.

[1286] To give a specific example, a user might enter "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps), current location (Shinjuku), and emotional data (tired) to a server. The server's AI analyzes this data and recommends several restaurants in the Shinjuku area that offer healthy menus. This recommendation information is then sent to the user via the device, allowing them to select the desired restaurant and complete the reservation on the spot.

[1287] This system allows users to efficiently plan and prepare their daily meals and receive appropriate meal suggestions tailored to their emotional and health states, enabling them to live a healthier and more fulfilling diet.

[1288] The processing flow will be explained below.

[1289] Step 1:

[1290] The user opens a dedicated app or chats on LINE and enters information about their diet. For example, they might enter, "I want to eat a healthy lunch today," or "I want to avoid nuts because I have an allergy."

[1291] Step 2:

[1292] The device receives user input information and sends it to the server, including information about dietary preferences, allergies, and diet goals.

[1293] Step 3:

[1294] The device accesses the health app (Google Fit, Apple Health, etc.) installed on the smartphone and obtains the user's health data (e.g., number of steps, heart rate, amount of exercise).

[1295] Step 4:

[1296] The device uses the GPS function to obtain the user's current location.

[1297] Step 5:

[1298] The healthcare data and GPS data acquired by the device are sent to the server.

[1299] Step 6:

[1300] The device uses an emotion engine to analyze the user's facial expressions, voice, text input, etc. to recognize the user's emotional state. For example, it can recognize the emotion "tired."

[1301] Step 7:

[1302] The device transmits the emotion data to the server.

[1303] Step 8:

[1304] The server centrally manages user input information, health care data, GPS data, and emotional data.

[1305] Step 9:

[1306] The server's AI analyzes this data and makes a comprehensive assessment of the user's health, location, food preferences, and emotional state.

[1307] Step 10:

[1308] Based on the analysis results, the server's AI generates the optimal meal menu for the user, suggesting dishes such as "grilled chicken salad," "spinach smoothie," and "fruit yogurt."

[1309] Step 11:

[1310] The device will present the user with the options of "eating out," "delivery," and "cooking at home."

[1311] Step 12:

[1312] The user selects either "eating out," "delivery," or "cooking at home."

[1313] Step 13 (If dining out):

[1314] Based on the selection, the server works with the gourmet site's API to search for restaurants that offer the suggested menu near the user's current location.

[1315] Step 14 (If dining out):

[1316] The server transmits the recommendation results to the terminal, and the terminal notifies the user of the recommended restaurant information.

[1317] Step 15 (For Delivery):

[1318] The server connects to the delivery service's API and orders ingredients and dishes based on the suggested menu.

[1319] Step 16 (For Delivery):

[1320] The server checks the order and arranges for the ingredients and food to be delivered to the user's home.

[1321] Step 17 (Home Cooking):

[1322] The server obtains information about the user's smart appliances (e.g., smart oven, rice cooker).

[1323] Step 18 (Home Cooking):

[1324] The server sends appropriate cooking instructions to smart appliances, such as "heat at 200 degrees for 20 minutes" to an oven.

[1325] Step 19:

[1326] The device notifies the user of the generated menu, recommended restaurants, and ordered delivery details.

[1327] Step 20:

[1328] The user checks the notification content and makes changes or corrections as necessary.

[1329] Step 21:

[1330] The terminal transmits the final confirmation made by the user to the server, and the process is completed.

[1331] Example 2

[1332] 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."

[1333] Conventional meal recommendation systems have not adequately optimized individual meals based on the user's health and emotional state. Furthermore, they lack sufficient integration with user input information and external services, making it difficult to efficiently recommend meal menus suited to the user.

[1334] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving input information about meals from the user, means for collecting health data and location data, means for analyzing the collected data and emotion data and generating a meal menu, means for providing information based on the proposed meal menu in cooperation with external services, and means for notifying the user of the generated menu and related information. This enables highly accurate meal suggestions based on the user's health and emotional state. In addition, by strengthening cooperation with external services, it is possible to provide the user with various options, such as eating out, delivery, and cooking at home.

[1335] "Input information" refers to data such as dietary requests and allergy information provided by users via a dedicated app or chat.

[1336] "Health data" refers to physical data such as number of steps, heart rate, and amount of exercise collected from healthcare apps, etc.

[1337] "Location data" is information about the user's current location obtained using GPS.

[1338] "Emotion data" refers to data relating to the emotional state of a user that is generated by analyzing the user's facial expressions, voice, text input, and the like.

[1339] "Analysis" is the process of integrating the collected data and generating an appropriate meal menu based on the user's condition and needs.

[1340] A "meal menu" is a specific meal suggestion optimized for the user's health and emotional state.

[1341] "External services" are other food-related service providers, such as gourmet sites, delivery services, and smart home appliances.

[1342] "Notification" is a means for informing the user of the generated meal menu and related information.

[1343] "Collaboration" is a mechanism for communicating with external services and providing services related to the proposed meal menu.

[1344] A "user" is an entity that uses the system to receive meal suggestions.

[1345] This invention is a system that allows users to easily input information about their meals and AI suggests appropriate meal menus. Furthermore, this system works in conjunction with various services related to eating out, delivery, and home appliance use, and provides comprehensive support from ingredient preparation to cooking, thereby reducing the invisible burden of housework for users. It also features an emotion engine that recognizes the user's emotional state, suggesting meal menus that match the user's mood.

[1346] Users can input information about their meals using a dedicated app or LINE chat. For example, they can input specific requests such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy." This information is sent from the device to the server.

[1347] The device collects health data such as the number of steps, heart rate, and recent exercise volume from health apps installed on the smartphone (e.g., Google Fit or Apple Health).The device also obtains the user's current location using GPS and sends this data to the server.

[1348] The emotion engine analyzes the user's facial expressions, voice, and text input to recognize the user's emotional state. For example, if the user inputs "I'm tired" or "I'm irritated," emotion data is generated based on that. The emotion data is sent to the server along with other data.

[1349] The server's AI analyzes the user's input information, health care data, GPS data, and emotional data to generate the optimal meal menu. For example, if a user requests a "healthy lunch," their recent exercise data indicates 10,000 steps, their current location is Shinjuku, and their emotional state is "tired," the AI ​​will generate the optimal meal menu based on this information. Specific suggested examples include grilled chicken salad, spinach smoothie, and fruit yogurt.

[1350] If a user wishes to eat out, the server will connect with the API of a gourmet website to search for restaurants near the user's current location that offer the suggested menu. For example, it will recommend multiple restaurants in Shinjuku that serve grilled chicken salad. If the user wishes to order delivery, the server will connect with the API of a delivery service to order ingredients and dishes that match the suggested menu and arrange for delivery to the user's home.

[1351] When a user selects home cooking, the server connects to smart appliances. For example, if a smart oven or rice cooker is connected, the server sends the appropriate cooking instructions to these appliances. The user simply sets the ingredients in advance, and cooking begins automatically at the specified time.

[1352] The device notifies the user of the suggested menu, recommended restaurants, and ordered delivery details. The user can review this and make changes or corrections as necessary. The confirmed details are sent back to the server, and processing continues. For example, the user might enter "I want to eat a healthy lunch outside today" into a dedicated app. The device then sends recent exercise data (10,000 steps), current location (Shinjuku), and emotional data (tired) to the server. The server's AI analyzes this and recommends several restaurants around Shinjuku that serve healthy lunches. This recommendation information is notified to the user via the device, allowing them to select the desired restaurant and complete the reservation on the spot.

[1353] Below are some examples of prompts for generative AI models:

[1354] User: I'd like to eat a healthy lunch out today. I'm allergic to nuts, so please avoid them.

[1355] Device: Recent exercise data is 10,000 steps, current location is Shinjuku, and emotional state is recognized as "tired."

[1356] Server: Using this information, the server searches for and recommends restaurants that serve healthy lunches in the Shinjuku area.

[1357] This system allows users to efficiently plan and prepare their daily meals and receive appropriate meal suggestions based on their emotional and health state.

[1358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1359] Step 1:

[1360] The user inputs information about their meal using a dedicated app or chat. They input specific requests such as "I want to eat a healthy lunch today" or "I want to avoid nuts because I have an allergy." This is the input information. The device sends this information to the server. The output is the user's meal request data.

[1361] Step 2:

[1362] The device collects health data from health apps (such as Google Fit or Apple Health) installed on the smartphone. This health data includes the number of steps, heart rate, and recent exercise volume. The device also obtains the user's current location using GPS. The obtained health data and location data are also sent to the server. The output is the health data and location data.

[1363] Step 3:

[1364] The emotion engine analyzes the user's facial expressions, voice, and text input to generate emotion data. For example, if a user inputs "I'm tired today," emotion data is generated based on that. The emotion data is sent to the server. The input is the user's text and voice information, and the output is emotion data.

[1365] Step 4:

[1366] The server's AI integrates and analyzes the user's input information, health data, location data, and emotional data. For example, if a user requests a "healthy lunch," and health data indicates 10,000 steps, their current location is Shinjuku, and their emotional state is "tired," the AI ​​will generate an optimal meal menu based on this information. The output is a suggested meal menu.

[1367] Step 5:

[1368] The server connects to the API of a gourmet site and searches for restaurants that offer the suggested menu near the user's current location. For example, the server recommends restaurants in Shinjuku that serve grilled chicken salad. The input is the meal menu and location information, and the output is a list of recommended restaurants.

[1369] Step 6:

[1370] The server connects to the delivery service's API, and if the user requests delivery, it orders ingredients and dishes that match the suggested menu. The order is then arranged for delivery to the user's home. The input is the meal menu and delivery address information, and the output is the delivery order information.

[1371] Step 7:

[1372] When a user selects home cooking, the server works with smart home appliances to support the cooking. The server sends appropriate cooking instructions to a smart oven or rice cooker. For example, the server can set the smart oven to start cooking at a specific time. The input is a meal recipe and time information, and the output is cooking instructions.

[1373] Step 8:

[1374] The terminal notifies the user of the proposed menu, recommended restaurants, and delivery order. The user can review the information and make any necessary changes or corrections. The confirmed information is sent back to the server, and processing continues. The input is the food menu and selection options, and the output is a notification via the user interface.

[1375] (Application example 2)

[1376] 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."

[1377] In modern society, personal health management and dietary choices are important issues. However, in our busy daily lives, it is not easy to choose appropriate meals that match our individual health and emotional state. Furthermore, when dining out or using delivery services, there are only a limited number of systems that automatically suggest meal menus tailored to the user and efficiently order and deliver them. Therefore, there is a need for support that makes it easier for users to choose meals that take their health and emotional state into consideration.

[1378] 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.

[1379] In this invention, the server includes means for receiving user input information, means for collecting health care data and location data, means for analyzing the collected data and generating a meal menu, means for analyzing the user's emotional state and generating emotion data, means for coordinating with external services to order ingredients and dishes based on the meal menu and arrange for delivery, and means for notifying the user of the generated menu. This allows the user to receive suggestions for an optimal meal menu based on their health and emotional state, enabling them to efficiently use eating out and delivery services.

[1380] "User input" refers to data that a user provides to the system to express a dietary request or state, such as text input such as "I want to eat a healthy lunch" or "I'm tired today."

[1381] "Healthcare data" refers to data that indicates the user's health condition, and is mainly information such as the number of steps, heart rate, and amount of exercise obtained from health care apps.

[1382] "Location data" is information indicating the user's current location, and is latitude and longitude data obtained using the GPS function.

[1383] "Emotion data" is data generated by analyzing the user's emotional state, and is information analyzed by the emotion engine from the user's facial expressions, voice, text input, etc.

[1384] A "meal menu" is a specific combination of dishes and ingredients suggested based on the user's health, emotional state, and requests.

[1385] "External services" are services provided by third parties that provide and deliver meal menus in response to user requests by linking with the system, such as restaurants and delivery companies.

[1386] A "generative AI model" is an artificial intelligence model that analyzes collected data and generates optimal meal menus.

[1387] A "prompt sentence" is a text sentence in input format used to pass data to a generative AI model, which indicates the user's detailed request or status.

[1388] The present invention provides a system that allows a user to easily input information about their meals and allows AI to suggest optimal meal menus. Hereinafter, embodiments of the present invention will be described in detail.

[1389] First, the user inputs their meal request using a dedicated app. They can easily input information such as "I want to eat a healthy lunch today." This application is installed on a smartphone and collects the user's input information.

[1390] Next, the user's smartphone automatically collects health data such as the number of steps, heart rate, and recent exercise volume from health apps (Google Fit, Apple Health, etc.), and obtains the user's current location using the GPS function. This allows data on the user's health condition and current location to be collected.

[1391] Furthermore, a server equipped with an emotion engine is provided that analyzes the user's facial expressions, voice, and text input to recognize their emotional state. For example, if the user inputs "tired," it generates emotion data such as "tired."

[1392] The server analyzes this input information, health data, and emotional data, and uses a generative AI model to generate an optimal meal menu. During the generation process, specific dishes and ingredient combinations are suggested based on the user's health condition, current location, and emotional state. For example, if a user inputs "I feel tired today," has walked 10,000 steps, has a heart rate of 70, and is currently located in Shinjuku, a suggestion would be a grilled chicken salad made with healthy ingredients in the Shinjuku area.

[1393] The server then connects to external services. If the user requests delivery, the server communicates with the delivery service's API, orders ingredients and dishes based on the suggested menu, and arranges for delivery. For example, if the user selects the suggested grilled chicken salad, the delivery service delivers the dish to the user's current location. This allows the user to receive the optimal meal without having to make any special arrangements.

[1394] Finally, the server sends a notification to the user through the application to inform them of the generated meal menu and delivery arrangement status, allowing them to review it and make any necessary changes or corrections.

[1395] For example, a user might say, "I'm tired today, so I'd like a light dinner," and the system might analyze their recent exercise data, current location, and emotional state to suggest things like smoothies or grilled vegetables.

[1396] An example of a prompt for a generative AI model might look something like this:

[1397] I'm tired today, so I'd like to have a light dinner.

[1398] "Current health data: steps 8000, heart rate 72"

[1399] "Current location: Latitude 35.6895, Longitude 139.6917"

[1400] "Emotional state: Tired"

[1401] In this way, a support system can be realized that helps users lead healthy and fulfilling eating habits.

[1402] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1403] Step 1:

[1404] A user inputs a meal request using a dedicated smartphone app. For example, they input, "I want to eat a healthy lunch today." This input information is stored on the smartphone device and used for later processing.

[1405] Step 2:

[1406] The device collects health data from health apps, such as Google Fit and Apple Health, automatically acquiring the user's steps, heart rate, and recent exercise volume, and stores the health data on the device.

[1407] Step 3:

[1408] The device uses the GPS function to obtain the user's current location (latitude and longitude). For example, if you are in Shinjuku, the GPS data obtained will be latitude 35.6895 and longitude 139.6917. This data will also be saved on the device.

[1409] Step 4:

[1410] When a user types "tired" into the device, the device passes the text to the emotion engine and analyzes the emotional state. This generates emotion data for "tired." The emotion data is then stored on the device.

[1411] Step 5:

[1412] The device transmits the user's input information, health data, GPS data, and emotion data to the server, where it is stored for use in subsequent processing steps.

[1413] Step 6:

[1414] The server uses the received data to generate an optimal meal plan using a generative AI model. For example, if a user inputs, "I'm tired today, so I'd like to have a light dinner," the generative AI model will suggest specific menu items, such as smoothies and grilled vegetables, based on health data (step count 8,000, heart rate 72), GPS data (Shinjuku), and emotional data (tired).

[1415] Step 7:

[1416] The server connects to external delivery services via API to order ingredients and dishes based on the proposed menu. For example, if a customer selects a smoothie and grilled vegetables, the server places an order with the delivery company that provides those dishes.

[1417] Step 8:

[1418] The delivery service takes the order, prepares the meal, and delivers it to the user's current location. The server monitors the delivery progress and notifies the user of progress as needed.

[1419] Step 9:

[1420] The server notifies the user of the created meal menu and the delivery arrangement status on their device. The user can review it and make changes or corrections as necessary. Finally, the user receives the optimal meal.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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).

[1428] 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.

[1429] 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."

[1430] 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.

[1431] 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).

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] 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.

[1437] 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.

[1438] 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.

[1439] 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.

[1440] 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.

[1441] 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.

[1442] The following is further disclosed regarding the above embodiment.

[1443] (Claim 1)

[1444] means for receiving user input information;

[1445] a means for collecting healthcare data and GPS data;

[1446] A means for analyzing the collected data and generating a meal menu;

[1447] Means of linking with external services,

[1448] means for notifying a user of the generated menu;

[1449] A system including:

[1450] (Claim 2)

[1451] 2. The system according to claim 1, wherein the means for linking with external services is to search for and recommend restaurant information when eating out is an option.

[1452] (Claim 3)

[1453] The system according to claim 1, wherein the means for linking with an external service is to order ingredients and food and arrange for delivery if a delivery option is selected.

[1454] (Claim 4)

[1455] 2. The system of claim 1, wherein the healthcare data includes the number of steps taken, heart rate, and amount of recent exercise.

[1456] (Claim 5)

[1457] 2. The system according to claim 1, wherein the meal menu generating means generates the menu taking into consideration the user's preferences, allergies, health conditions, and current location.

[1458] (Claim 6)

[1459] The system according to claim 1, further comprising means for linking with smart home appliances and sending cooking instructions to the smart home appliances.

[1460] "Example 1"

[1461] (Claim 1)

[1462] means for receiving user input information;

[1463] a means for collecting health data and location data;

[1464] means for analyzing the collected data and generating a meal plan;

[1465] Means of linking with external services,

[1466] means for notifying a user of the generated plan;

[1467] A system including:

[1468] (Claim 2)

[1469] The system according to claim 1, wherein the means for linking with an external service searches for and recommends restaurant information when a dining out option is selected.

[1470] (Claim 3)

[1471] The system according to claim 1, wherein the means for linking with an external service is to order ingredients and food and arrange for delivery if a delivery option is available.

[1472] "Application Example 1"

[1473] (Claim 1)

[1474] means for receiving user input information;

[1475] a means for collecting healthcare data and location data;

[1476] A means for analyzing the collected data and generating a meal menu;

[1477] Means of linking with external services,

[1478] means for notifying a user of the generated menu;

[1479] A means to order dishes corresponding to the suggested menu by linking with external services;

[1480] A system including:

[1481] (Claim 2)

[1482] 2. The system according to claim 1, wherein the means for linking with external services is to search for and recommend store information when eating out is an option.

[1483] (Claim 3)

[1484] The system according to claim 1, wherein the means for linking with an external service is to order ingredients and food and arrange for delivery if a delivery option is selected.

[1485] "Example 2: Combining Emotion Engines"

[1486] (Claim 1)

[1487] a means for Ewia to receive dietary input; and

[1488] a means for collecting health data and location data;

[1489] A means for analyzing the collected data and emotion data and generating a meal menu;

[1490] A means of providing information based on suggested meal menus in cooperation with external services;

[1491] means for notifying a user of the generated menu and related information;

[1492] A system including:

[1493] (Claim 2)

[1494] 2. The system according to claim 1, wherein the means for linking with external services has a function for searching and recommending restaurant information when eating out is an option.

[1495] (Claim 3)

[1496] The system according to claim 1, wherein the means for linking with external services has a function for ordering ingredients and food and arranging delivery in the case of a delivery option.

[1497] "Application example 2 when combining emotion engines"

[1498] (Claim 1)

[1499] means for receiving user input information;

[1500] a means for collecting healthcare data and location data;

[1501] A means for analyzing the collected data and generating a meal menu;

[1502] means for analyzing a user's emotional state and generating emotional data;

[1503] Linking with external services to order ingredients and dishes based on meal menus and arranging delivery;

[1504] means for notifying a user of the generated menu;

[1505] A system including:

[1506] (Claim 2)

[1507] The system according to claim 1, which cooperates with external services to generate an optimal meal menu based on the user's health data and emotional state, and places an order with a delivery service.

[1508] (Claim 3)

[1509] The system of claim 1, wherein the system uses a generative AI model to analyze a user's input information, health data, location data, and emotional data to generate optimal prompt sentences. [Explanation of symbols]

[1510] 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. means for receiving user input information; a means for collecting healthcare data and GPS data; A means for analyzing the collected data and generating a meal menu; Means of linking with external services, means for notifying a user of the generated menu; A system including:

2. 2. The system according to claim 1, wherein the means for linking with an external service searches for restaurant information and makes recommendations when a dining out option is selected.

3. The system according to claim 1, wherein the means for linking with an external service is for ordering ingredients and food and arranging delivery when a delivery option is selected.

4. The system of claim 1 , wherein the healthcare data includes the number of steps taken, the heart rate, and the amount of recent exercise.

5. 2. The system according to claim 1, wherein the meal menu generating means generates the menu taking into consideration the user's preferences, allergies, health conditions, and current location.

6. The system according to claim 1, further comprising a means for linking with a smart home appliance and sending cooking instructions to the smart home appliance.

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