system

Abstract

We provide the system. [Solution] A means of receiving health information obtained from multiple information processing programs, A means for analyzing received health information and calculating the nutritional components required by each individual, A means of generating individually optimized meal plans in conjunction with information provision services based on calculated nutritional components, A means of procuring necessary ingredients through information provision services, A method for suggesting the most suitable food service provider in the surrounding area using location information, A system that includes a means of notifying individuals of information that provides optimal meals based on their individual health information, in conjunction with goods delivery services.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern busy lives, there is a problem that it is difficult for individual users to plan and prepare an optimal healthy diet for themselves. This problem arises because the data of multiple health management applications need to be analyzed individually and appropriate nutrients need to be grasped dynamically. In addition, it takes a lot of time and effort to obtain ingredients based on nutrients and select appropriate meal-providing facilities within the living area.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a means for receiving health data acquired from multiple health management applications and analyzing that data using a generating AI model. Based on the analysis results, it calculates necessary nutrients and generates individually optimized meal plans in cooperation with food provision services. Furthermore, by utilizing location information to suggest the most suitable nearby food provision facilities, it enables users to enjoy healthy meals without hassle. In addition, by re-entering user feedback into the system and reflecting it in subsequent meal plans, continuous optimization is achieved.

[0006] "Health data" refers to information related to health management, such as an individual's weight, diet, and sleep duration.

[0007] A "generative AI model" is a model that utilizes artificial intelligence technology to analyze input data and derive specific results.

[0008] "Nutrients" are various components such as vitamins, minerals, and proteins that are necessary for humans to maintain health.

[0009] A "food supply service" refers to online services or physical stores that can provide ingredients and meals to users.

[0010] A "meal plan" is a schedule of healthy meals suggested based on the user's nutritional needs.

[0011] "Location information" refers to the user's current location and is geographical data obtained using technologies such as GPS.

[0012] A "food service provider" refers to restaurants, delivery services, and other establishments that can provide meals to users.

[0013] "Feedback" refers to information that users re-enter regarding their choices and opinions about the system, which is used to improve future services. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [[ID=4'2]] [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that provides individually optimized meal plans based on the user's health data. The specific embodiments and processing flow are described in detail below.

[0036] The system primarily involves users, terminals, and servers. Users input their health data using a health management application. This includes information such as weight, diet, and sleep duration. Terminals acquire this data and send it to the server.

[0037] The server uses a generative AI model to analyze the received health data. The AI ​​model calculates the necessary nutrients based on the user's health status and generates a personalized meal plan. This plan is designed to contribute to the user's health and specifically identifies which nutrients are needed and in what quantities.

[0038] The server then collaborates with food delivery services to create menus based on necessary nutrients and procures ingredients through those services. Furthermore, the server obtains the user's current location via GPS and searches for the best options from nearby food delivery facilities. This makes it possible to present the healthiest choices within the user's living area.

[0039] The terminal receives instructions from the server and notifies the user with specific meal suggestions. The user can then purchase the recommended ingredients from an online supermarket or obtain a meal from a designated restaurant or delivery service.

[0040] Furthermore, the information selected by the user is fed back into the system. This feedback is reflected in future plan creation and helps suggest more suitable dietary options for the user. This feedback loop is expected to lead to sustainable health improvements.

[0041] For example, suppose a user enters their weight into their device in the morning and sends it to the server. Based on this data, the server identifies a protein deficiency and suggests a menu including chicken through an online supermarket. It also suggests nearby cafes that offer protein-rich menus based on the user's location. In this way, users can easily obtain the best options for consuming the nutrients they need that day.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user enters health data such as weight, diet, and sleep duration into a health management application. The device collects this data in the background.

[0045] Step 2:

[0046] The device collects health data and sends it to a server via the network. The server receives the data and stores it in a database.

[0047] Step 3:

[0048] The server uses a generated AI model to analyze stored health data. This analysis calculates the user's health status and necessary nutrients.

[0049] Step 4:

[0050] Based on the analysis results, the server works with food delivery services to generate the optimal menu for the user. It identifies the necessary ingredients and prepares the order data for the online supermarket.

[0051] Step 5:

[0052] The server receives GPS data from the device to obtain the user's current location. It then uses this location information to search for nearby restaurants and other food establishments.

[0053] Step 6:

[0054] The server selects the optimal meal delivery option and notifies the user's terminal. The terminal then displays this information to the user in a visual format.

[0055] Step 7:

[0056] Users select a meal from the options presented on their device. They can also purchase groceries from online supermarkets or make restaurant reservations.

[0057] Step 8:

[0058] The device feeds back the user's choices and usage data to the server. Based on this information, the server makes adjustments to further optimize future meal plans.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Providing optimal meal plans for individual users has not been efficient with traditional methods. In particular, suggesting meals that take into account the user's location and up-to-date health information has been difficult. Therefore, there is a need for technology that automates the food delivery process and provides customized meal plans for each user in real time.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for receiving health information acquired from multiple health management devices, means for automatically generating a meal plan from the health information using generative AI technology, and means for suggesting the most suitable nearby dining facilities using location information. This makes it possible to provide an efficient and personalized meal plan based on the user's health information and location information.

[0064] A "health management device" is a device used to record and manage a user's health information.

[0065] "Health information" refers to data related to the user's health status, such as weight, diet, and sleep duration.

[0066] "Nutritional components" refer to the types and amounts of nutrients that users should consume based on health information.

[0067] A "food supply system" is a system for procuring and providing ingredients and food products.

[0068] "Location information" refers to information such as GPS data that indicates the user's current location.

[0069] A "food and beverage establishment" refers to a facility that serves meals, such as a restaurant or cafe.

[0070] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and generate new information.

[0071] A "meal plan" is a set of meal plans and schedules proposed to the user based on their health condition.

[0072] This invention is a system that provides personalized meal plans based on the user's health information. The implementation details are described below.

[0073] This system involves users, terminals, and servers. Users input their health information using a health management device. This health information includes, for example, weight, meal records, and sleep duration. Terminals collect this data and send it to the server. Internet connectivity is used for data transmission, and HTTPS is adopted as the communication protocol.

[0074] The server analyzes the received health information using generative AI technology. Specifically, it uses generative AI models that run on frameworks such as TENSORFLOW® and PyTorch. This AI model calculates the nutritional content for each user based on the health information. For example, if it determines that a user is deficient in protein, it will recommend foods that are high in protein.

[0075] The server, based on the calculated nutritional information, interacts with the food delivery system to generate a meal plan. For example, a program implemented in Python uses the results of an AI model to construct a meal menu. The generated meal plan is then notified to the user via their device.

[0076] Furthermore, the server obtains the user's location information and suggests the most suitable dining establishments. GPS is used to obtain location information, and a geocoding API is used to search for facility information. This ensures that the most suitable dining locations near the user are presented.

[0077] The terminal receives notifications from the server and transmits suggestions to the user. The user can then purchase groceries from an online supermarket or use designated dining facilities according to the suggestions. The user's choices are fed back into the system and reflected in future meal planning.

[0078] As a concrete example, suppose a user enters their weight in the morning, and an AI model calculates their protein deficiency. Based on this, the server suggests a menu including chicken, and further recommends protein-rich menu items from nearby cafes based on the user's location.

[0079] The following are specific examples of prompt statements for a generative AI model:

[0080] User data: Weight: 70kg, Recent meals: No breakfast, Lunch: Pasta, Dinner: Salad

[0081] Analysis task: Generate a one-day meal plan for the next day to compensate for the user's protein deficiency.

[0082] In this way, the present invention makes it possible to easily and efficiently provide meal plans optimized for each individual user.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users input their health information using a health management device. This input data includes weight, diet, and sleep duration. This information is stored digitally on the device.

[0086] Step 2:

[0087] The device receives health information entered by the user and transmits it to the server via the internet. This input includes the user ID and health data. The data is securely transmitted to the server via the HTTPS protocol.

[0088] Step 3:

[0089] The server uses a generative AI model to analyze health information received from the terminal. Input data includes the user's health status and location data. The AI ​​model analyzes this information to calculate appropriate nutritional components. Through data processing, conclusions such as protein deficiency can be drawn.

[0090] Step 4:

[0091] The server, in conjunction with the food delivery system, generates an optimal meal plan based on nutritional information calculated by the AI ​​model. It determines which ingredients and menu items are appropriate based on the AI ​​model's output. This process utilizes a Python script and applies the output data from the generated AI model.

[0092] Step 5:

[0093] The server uses the user's location information to search for the most suitable restaurants and bars in the vicinity. Using GPS data as input, it retrieves information about nearby establishments using a geocoding API. The output is a list of restaurants and bars that are suitable for the user's health condition.

[0094] Step 6:

[0095] The device notifies the user of meal plans and restaurant information received from the server. Specifically, push notifications are sent, which the user confirms. The notifications include details about the suggested meal menus and restaurants.

[0096] Step 7:

[0097] Users act based on the suggested meal menu and send feedback information back to the system via their device. Information about the meals and facilities selected by the user is sent to the system and reflected in future meal plans. Specific actions include entering ratings within the app.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] Nutritional management based on individual health conditions requires the aggregation and analysis of diverse health information to create an optimal meal plan. However, current technology has not been able to accurately calculate nutrients based on individual health data and suggest suitable foods and food providers. As a result, it has been difficult for many people to efficiently enjoy a personalized and healthy diet.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for receiving health information obtained from multiple information processing programs, means for analyzing the received health information and calculating the nutritional components required by each individual, and means for notifying information on providing the optimal diet based on the health information of each individual, in cooperation with the goods delivery service. This enables highly accurate nutritional management and optimal meal suggestions tailored to each individual's health condition.

[0103] An "information processing program" is software capable of collecting and processing multiple types of health-related data.

[0104] "Health information" refers to data that indicates the physical condition of an individual, including weight, dietary history, and sleep duration.

[0105] "Nutritional components" refer to the specific nutrients and energy necessary to support an individual's health.

[0106] "Information provision services" refer to a series of service activities aimed at providing the information necessary for meal planning.

[0107] A "meal plan" is a schedule or proposal that outlines the content of meals optimized for an individual's health condition.

[0108] "Goods delivery services" refers to the service of delivering food and ingredients to a designated location.

[0109] "Ingredients" refer to raw materials used for cooking, including meat, vegetables, and seasonings.

[0110] A "food service provider" refers to a facility or business that specializes in providing food or meals.

[0111] This invention provides a system that delivers meal plans optimized for individual users. It primarily involves a server, terminals, and users. The following describes in detail how each component operates.

[0112] The server operates as an API server to receive health information from multiple information processing programs. Health information sent from the terminal includes weight, meal history, and sleep duration. The server then uses a generative AI model based on Python and TensorFlow to analyze this health information. Based on the analysis, the server calculates the necessary nutrients for each individual.

[0113] Users input and update their health information using devices such as smartphones or tablets. The application provided on the device is developed using React Native, through which users can input data and send information to the server.

[0114] The server also works in conjunction with goods delivery and information provision services to notify users of the most suitable ingredients and menus for their individual needs. This integration allows, for example, ingredients to be selected based on the amount of protein the user requires, and the most suitable restaurants and delivery services to be recommended.

[0115] As a concrete example, suppose a user enters their weight into the app in the morning. Based on this information, the server determines that the user is deficient in protein and suggests a food provider nearby that sells protein-rich salads. The user can also order the suggested menu for lunch that day via delivery.

[0116] Examples of prompts to input into a generative AI model include the following:

[0117] "Today's health data: Weight 68kg, 7 hours of sleep, oatmeal for breakfast. Please suggest a good lunch menu."

[0118] In this way, users can easily select and enjoy meals that are appropriate for their own health condition.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] Users input health information (weight, meal history, sleep duration, etc.) into the application using their device. The entered data is sent from the device to the server in JSON format. At this stage, the integrity and format of the input data are primarily checked.

[0122] Step 2:

[0123] The server receives health information from the terminal and launches a generative AI model using Python and TensorFlow. It analyzes the input data and calculates the necessary nutrients for each individual. This process involves data calculations to identify which nutrients are deficient based on past data and health status.

[0124] Step 3:

[0125] Based on the calculated nutritional information, the server connects with product delivery and information provision services via a dedicated API to select ingredients and menus that are suitable for the user's health condition. In this process, a database of available products is searched, and calculations are performed to extract the most suitable items.

[0126] Step 4:

[0127] The server uses the processed information to identify appropriate food providers, taking into account the user's current location. Here, location-based data processing is performed, and the most suitable restaurants and delivery options are output.

[0128] Step 5:

[0129] The user's smartphone receives notifications from the server, displaying a personalized meal plan and a list of available ingredients. Based on this information, the user can order necessary ingredients or select recommended menus. The selected meal information is then fed back to the server and reflected in future nutritional plans.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention is a system that analyzes the user's emotional state using an emotion engine, in addition to the user's health data, and provides an individually optimized meal plan based on this analysis. Specifically, it achieves more personalized health management by incorporating emotional information into the processes of receiving, analyzing, and suggesting meals based on health data.

[0132] The system requires coordination between the user, terminal, server, and emotion engine. Users input necessary health data, such as weight and diet, into the terminal via a standard health management application. The terminal collects this data and sends it to the server.

[0133] The server inputs the received health data into a generating AI model and begins analysis. Crucially, an emotion engine is integrated into the server. This emotion engine analyzes the user's emotional state and uses that information to interpret the health data. For example, it can recommend a diet containing ingredients with calming effects to a user experiencing high stress levels.

[0134] Based on the results of the emotion engine, the server collaborates with food delivery services to create a menu tailored to the user. This menu includes ingredient selection that matches the user's emotional state and offers specific food options. Furthermore, it utilizes the user's location information to search for and recommend nearby dining establishments.

[0135] The terminal receives meal suggestions from the server and displays a range of options to the user. The user can then intuitively choose a meal that suits their mood and order it online or at a nearby restaurant.

[0136] To give a concrete example, suppose a user senses fatigue and stress before noon. The device sends its emotional state, along with health data at that time, to the server. The server uses an emotion engine to analyze the user's state and recommends a lunch plan that includes herbal tea to help alleviate stress. Based on this recommendation, the user can choose to purchase the necessary ingredients from an online supermarket or order that lunch from a nearby cafe.

[0137] By incorporating an emotion engine in this way, it becomes possible to provide users with healthy food choices that respond to their instantaneous mood and emotions, thereby improving their quality of life.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user inputs health data such as weight, diet, and sleep duration through a health management application. The device collects this data and prepares to send it to the server.

[0141] Step 2:

[0142] The device collects not only health data but also emotional data obtained through sensors on the user's mobile device and other applications, and transmits it to the server.

[0143] Step 3:

[0144] The server inputs the received health and emotional data into a generating AI model. The model analyzes this data and generates a health plan based on the user's needs and emotional state.

[0145] Step 4:

[0146] The emotion engine within the server analyzes the user's emotional data and assesses the need for nutritional supplementation based on stress levels and emotional states. Based on this assessment, recommended amounts of specific nutrients, such as protein and vitamins, are adjusted.

[0147] Step 5:

[0148] The server combines the results of emotion analysis and nutritional analysis, and works with food delivery services to create an optimal menu. This menu includes ingredients that correspond to the user's emotional state.

[0149] Step 6:

[0150] The server requests the user's location information from the terminal and receives the location information from the terminal. Using the location information, the server searches for nearby restaurants and identifies the best option for the user.

[0151] Step 7:

[0152] The server sends meal suggestions tailored to the user's emotions to the device. The device notifies the user of these suggestions and displays them in a visualized format. The user can then review these suggestions and make purchases from online supermarkets or place orders at restaurants.

[0153] Step 8:

[0154] The device then feeds back information about the user's meal choices and emotional changes to the server. The server uses this information to optimize future meal plans.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] Providing individually optimized meal plans based on a user's health and emotional state is difficult with existing technologies. In particular, it is challenging to provide optimal meal choices while considering the daily changes in a user's emotions, resulting in a problem where effective health management of the user is not possible.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes means for receiving health indicators obtained from multiple health management applications, means for analyzing the received health indicators and emotional state to calculate the nutrients required by each individual, and means for generating an individually optimized meal plan using a generative AI model based on the calculated nutrients and emotional state. This makes it possible to provide an appropriate meal plan according to the user's health and emotional state.

[0160] "Health indicators" are elements that quantify or represent a user's physical health status, such as weight, diet, and activity level, using data.

[0161] "Emotional state" refers to information that represents the user's mood and emotional state at a given time, including states such as stress and happiness.

[0162] "Nutrients" are chemical substances necessary for the body's growth and activity, and refer to vitamins, minerals, proteins, lipids, carbohydrates, etc., found in food.

[0163] A "generative AI model" is a model that uses artificial intelligence technology to process and analyze data, and its role is to generate meal plans from health data and emotional states.

[0164] A "food supply service" is a service that procures and provides the food ingredients that users need, and includes supermarkets and online stores.

[0165] "Location information" refers to data used to identify a user's current location, such as geographical coordinates or address information.

[0166] A "food service facility" is a place that provides meals to users, and includes restaurants, cafes, and dining halls.

[0167] This invention is a system that provides individually optimized meal plans based on the user's health and emotional state. Details of how this objective is achieved using specific hardware and software are described below.

[0168] Users input daily health indicators such as weight, diet, activity level, and emotional state using a health management application running on a device such as a smartphone or tablet. The device collects this information and sends it to a server. Because the server requires powerful processing capabilities, it is suitable to use a high-performance cloud computing service.

[0169] The server utilizes a generative AI model to analyze the received health indicators. This model is implemented using Python and deep learning libraries such as TensorFlow or PyTorch. The generative AI model interprets prompt text as input and dynamically generates a meal plan that takes the user's health status into account. During this process, an emotion engine built into the server analyzes the user's input emotional state.

[0170] The emotion engine utilizes natural language processing technology to convert emotional information from users into numerical data. This allows the generative AI model to recommend foods and beverages containing appropriate nutrients based on emotions such as stress and happiness.

[0171] The generated meal plans can be implemented through food supply services partnered with the server. For example, if a user is feeling stressed, the server might suggest a plan that includes relaxing foods, such as herbal tea.

[0172] Furthermore, the server utilizes the user's location information to search for nearby restaurants and present the user with the best options.

[0173] A concrete example of a prompt would be, "Please suggest a lunch menu based on today's emotional state and health indicators."

[0174] Users select meal plans and provide feedback via an application on their device. The feedback information is resent to the server and used to improve the accuracy of future analyses.

[0175] This system allows users to easily make dietary choices that suit their health and emotional state, thereby improving their quality of life.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] Users input health indicators such as weight, diet, daily activity, and emotional state through a health management application. Once this data is entered into the application, the device aggregates the information and prepares to send it directly to the server. The input data includes numerical data (weight, activity level) and text data (emotional state). These are the inputs sent to the server.

[0179] Step 2:

[0180] The device sends health indicator data acquired from the user to the server. The server receives the data and first uses an emotion engine to analyze the emotional state, which is text data, and generates numerical emotion data. Next, the health indicators and emotion data are input into a generating AI model for analysis. Based on these inputs, the model interprets the user's health status and identifies potential nutrients and meal plans.

[0181] Step 3:

[0182] Based on the analysis, the server creates a meal plan using a generative AI model. This model generates meal options using prompts. For example, it can suggest a "relaxing lunch" to a user experiencing high stress levels. The output at this stage is a list of specific meal recommendations containing particular nutrients.

[0183] Step 4:

[0184] The server uses the generated meal plan to send the plan details to partner food supply services to confirm available ingredients. It also uses the user's location information to search for nearby restaurants and create a list of suitable establishments. This information is output in list format and sent back from the server to the terminal.

[0185] Step 5:

[0186] The terminal displays meal plans and a list of facilities received from the server to the user. The user then reviews the plan and selects a meal that suits their preferences. This selection is sent back to the server as feedback. The user can then take action, such as placing an online order or ordering in-store, as appropriate.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] In health management and meal selection, there is a lack of personalized suggestions that take into account the emotional state of individual users, resulting in a challenge in making effective meal choices that respond to users' momentary emotions and health conditions. Furthermore, there is a need for an efficient ordering system that allows users to quickly access the suggested meal options.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] This invention includes a server that integrates health data and emotional state using emotion analysis technology to provide more personalized meal suggestions, and a means that enables users to directly order the suggested meal content from a delivery service via a user terminal. This makes it possible to select the optimal meal according to the user's emotional state and to place orders quickly.

[0192] A "health management application" is software used to collect, analyze, and manage data related to a user's health.

[0193] "Health data" refers to information that indicates the user's physical measurements and health status.

[0194] Nutrients are chemical substances necessary for the growth, development, and maintenance of health in living organisms.

[0195] A "food supply service" is a business entity that provides or supplies food or meals to users.

[0196] "Emotion analysis technology" is a technology that estimates a user's emotional state at a given time based on their input data and behavior.

[0197] A "meal plan" refers to the content and schedule of meals proposed based on an individual's health condition and preferences.

[0198] "Location information" refers to information that indicates the user's current location or a specific place.

[0199] A "user terminal" is a device that a user directly operates or uses to input and retrieve information.

[0200] A "delivery service" is a service that delivers food or goods to a specific location or user.

[0201] "Feedback data" refers to information that shows evaluations and impressions based on user behavior and choices.

[0202] The system implementing this invention operates in conjunction with a user terminal, a server, and a distribution service.

[0203] The server receives health and emotional data from the user's terminal. The received data is analyzed using a generative AI model running on the server. This model integrates diverse data obtained through the health management application and further identifies the user's emotional state using emotion analysis technology. Based on the analysis results, the server generates a personalized meal plan for each user and sends the details to the user's terminal.

[0204] The user terminal displays the received meal plan on its screen and intuitively offers the user options tailored to their emotional state. Using the latest location information, the user terminal can guide users to nearby restaurants and menus from online delivery services. This allows the user to instantly order an optimized meal.

[0205] As a concrete example, suppose a user enters their weight and past eating history into a health management application and also sends emotional data from their smart device. At this point, the server analyzes that the user is stressed and suggests meals containing ingredients that are expected to have a relaxing effect. The user can select a suggested menu on the app and immediately receive their meal, with pre-booking at a nearby restaurant or online ordering. This process particularly requires the effective use of a "generative AI model." An example of a prompt message would be: "Create a recommended meal plan based on the user's current emotional state. The user is stressed. What foods are recommended?"

[0206] In terms of hardware, user terminals will be smartphones or tablets, and the server will utilize a general cloud server environment. The software will employ various APIs, including Microsoft Azure's emotion recognition API and the Yelp API. Firebase will be used for the database, enabling real-time data management.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The user enters their health data into the device.

[0210] The data entered includes weight, dietary history, and allergy information. This data is formatted by the terminal and prepared to be sent to the server.

[0211] Step 2:

[0212] The device collects emotional data.

[0213] The device analyzes the user's emotional state based on sensors in the smart device and the user's self-reported information. This information is also ready to be sent to the server.

[0214] Step 3:

[0215] The server receives health data and emotional data.

[0216] The server stores this data in a database and processes it into the format required for analysis in the next step. This processing includes operations such as data standardization and missing value imputation.

[0217] Step 4:

[0218] The server activates the generated AI model using the data it receives.

[0219] The generative AI model receives user health and emotional data as input and creates individually optimized meal plans based on this data. As output, it generates meal suggestions tailored to each user.

[0220] Step 5:

[0221] The server sends meal suggestions.

[0222] The generated meal plan is sent to the user's device and displayed on the user interface. This display includes specific nutrient suggestions and information on relaxation effects based on emotions.

[0223] Step 6:

[0224] The user checks meal suggestions on their device and enters their choices.

[0225] The user selects from the displayed menu and decides what to order. The user's choices are sent to the server as feedback data.

[0226] Step 7:

[0227] The device uses location information to search for the most suitable restaurant.

[0228] Based on location information, the system uses the Yelp API and other tools to search for and suggest nearby restaurants. Along with restaurant information, it also displays whether online ordering is available.

[0229] Step 8:

[0230] The user places an order with the distribution service based on their selections.

[0231] Users can select meal options suggested within the application and purchase them online or make reservations at nearby establishments. This step provides an interface for direct ordering.

[0232] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0233] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0234] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0235] [Second Embodiment]

[0236] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0237] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0238] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0239] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0240] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0241] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0242] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0243] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0244] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0245] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0246] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0247] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0248] This invention is a system that provides individually optimized meal plans based on the user's health data. The specific embodiments and processing flow are described in detail below.

[0249] The system primarily involves users, terminals, and servers. Users input their health data using a health management application. This includes information such as weight, diet, and sleep duration. Terminals acquire this data and send it to the server.

[0250] The server uses a generative AI model to analyze the received health data. The AI ​​model calculates the necessary nutrients based on the user's health status and generates a personalized meal plan. This plan is designed to contribute to the user's health and specifically identifies which nutrients are needed and in what quantities.

[0251] The server then collaborates with food delivery services to create menus based on necessary nutrients and procures ingredients through those services. Furthermore, the server obtains the user's current location via GPS and searches for the best options from nearby food delivery facilities. This makes it possible to present the healthiest choices within the user's living area.

[0252] The terminal receives instructions from the server and notifies the user with specific meal suggestions. The user can then purchase the recommended ingredients from an online supermarket or obtain a meal from a designated restaurant or delivery service.

[0253] Furthermore, the information selected by the user is fed back into the system. This feedback is reflected in future plan creation and helps suggest more suitable dietary options for the user. This feedback loop is expected to lead to sustainable health improvements.

[0254] For example, suppose a user enters their weight into their device in the morning and sends it to the server. Based on this data, the server identifies a protein deficiency and suggests a menu including chicken through an online supermarket. It also suggests nearby cafes that offer protein-rich menus based on the user's location. In this way, users can easily obtain the best options for consuming the nutrients they need that day.

[0255] The following describes the processing flow.

[0256] Step 1:

[0257] The user enters health data such as weight, diet, and sleep duration into a health management application. The device collects this data in the background.

[0258] Step 2:

[0259] The device collects health data and sends it to a server via the network. The server receives the data and stores it in a database.

[0260] Step 3:

[0261] The server uses a generated AI model to analyze stored health data. This analysis calculates the user's health status and necessary nutrients.

[0262] Step 4:

[0263] Based on the analysis results, the server works with food delivery services to generate the optimal menu for the user. It identifies the necessary ingredients and prepares the order data for the online supermarket.

[0264] Step 5:

[0265] The server receives GPS data from the device to obtain the user's current location. It then uses this location information to search for nearby restaurants and other food establishments.

[0266] Step 6:

[0267] The server selects the optimal meal delivery option and notifies the user's terminal. The terminal then displays this information to the user in a visual format.

[0268] Step 7:

[0269] Users select a meal from the options presented on their device. They can also purchase groceries from online supermarkets or make restaurant reservations.

[0270] Step 8:

[0271] The device feeds back the user's choices and usage data to the server. Based on this information, the server makes adjustments to further optimize future meal plans.

[0272] (Example 1)

[0273] Next, we will describe Example 1. 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."

[0274] Providing optimal meal plans for individual users has not been efficient with traditional methods. In particular, suggesting meals that take into account the user's location and up-to-date health information has been difficult. Therefore, there is a need for technology that automates the food delivery process and provides customized meal plans for each user in real time.

[0275] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0276] In this invention, the server includes means for receiving health information acquired from multiple health management devices, means for automatically generating a meal plan from the health information using generative AI technology, and means for suggesting the most suitable nearby dining facilities using location information. This makes it possible to provide an efficient and personalized meal plan based on the user's health information and location information.

[0277] A "health management device" is a device used to record and manage a user's health information.

[0278] "Health information" refers to data related to the user's health status, such as weight, diet, and sleep duration.

[0279] "Nutritional components" refer to the types and amounts of nutrients that users should consume based on health information.

[0280] A "food provision system" is a system for procuring and providing food ingredients and food.

[0281] "Location information" is information such as GPS indicating the current location of the user.

[0282] A "food service facility" is a facility such as a restaurant or café that provides meals.

[0283] "Generative AI technology" is a technology that analyzes data using artificial intelligence to generate new information.

[0284] A "meal plan" is the content and schedule of meals proposed to the user based on their health condition.

[0285] The present invention is a system that provides an individualized meal plan based on the health information of the user. Hereinafter, the embodiments for its implementation will be described in detail.

[0286] This system involves a user, a terminal, and a server. The user uses a health management device to input their health information. The health information includes, for example, weight, meal records, and sleep time. The terminal collects these data and transmits them to the server. Internet connection is used for data transmission, and HTTPS is adopted as the communication protocol.

[0287] The server analyzes the received health information using generative AI technology. Specifically, a generative AI model operating on a framework such as TensorFlow or PyTorch is used. This AI model calculates the nutritional components for each user based on the health information. For example, if it is determined that protein is lacking, ingredients rich in protein are recommended.

[0288] The server cooperates with the food provision system based on the calculated nutritional components to generate a meal plan. For example, a program implemented in Python uses the results of the AI model to construct a meal menu. The generated meal plan is notified to the user through the terminal.

[0289] Furthermore, the server obtains the user's location information and suggests the most suitable dining establishments. GPS is used to obtain location information, and a geocoding API is used to search for facility information. This ensures that the most suitable dining locations near the user are presented.

[0290] The terminal receives notifications from the server and transmits suggestions to the user. The user can then purchase groceries from an online supermarket or use designated dining facilities according to the suggestions. The user's choices are fed back into the system and reflected in future meal planning.

[0291] As a concrete example, suppose a user enters their weight in the morning, and an AI model calculates their protein deficiency. Based on this, the server suggests a menu including chicken, and further recommends protein-rich menu items from nearby cafes based on the user's location.

[0292] The following are specific examples of prompt statements for a generative AI model:

[0293] User data: Weight: 70kg, Recent meals: No breakfast, Lunch: Pasta, Dinner: Salad

[0294] Analysis task: Generate a one-day meal plan for the next day to compensate for the user's protein deficiency.

[0295] In this way, the present invention makes it possible to easily and efficiently provide meal plans optimized for each individual user.

[0296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0297] Step 1:

[0298] Users input their health information using a health management device. This input data includes weight, diet, and sleep duration. This information is stored digitally on the device.

[0299] Step 2:

[0300] The terminal receives the health information input by the user and sends it to the server via the Internet. The input includes the user ID and health data. The data is securely sent to the server through the HTTPS protocol.

[0301] Step 3:

[0302] The server uses a generated AI model to analyze the health information received from the terminal. The input data includes the user's health status and location data. The AI model analyzes this information and calculates appropriate nutritional components. Through data processing, conclusions such as protein deficiency are drawn.

[0303] Step 4:

[0304] The server collaborates with the food provision system based on the nutritional components calculated by the AI model to generate an optimal diet plan. Based on the output results of the AI model, it determines which ingredients and menus are appropriate. In this process, a Python script is used and the output data of the generated AI model is applied.

[0305] Step 5:

[0306] The server uses the user's location information to search for the optimal dining facilities in the vicinity. Using the GPS data as input, it utilizes the geocoding API to obtain information on nearby facilities. The output is a list of dining facilities that match the user's health condition.

[0307] Step 6:

[0308] The terminal notifies the user of the diet plan and dining facility information received from the server. Specifically, a push notification is sent and the user confirms it. The notification includes the proposed diet menu and detailed information about the facilities.

[0309] Step 7:

[0310] Users act based on the suggested meal menu and send feedback information back to the system via their device. Information about the meals and facilities selected by the user is sent to the system and reflected in future meal plans. Specific actions include entering ratings within the app.

[0311] (Application Example 1)

[0312] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0313] Nutritional management based on individual health conditions requires the aggregation and analysis of diverse health information to create an optimal meal plan. However, current technology has not been able to accurately calculate nutrients based on individual health data and suggest suitable foods and food providers. As a result, it has been difficult for many people to efficiently enjoy a personalized and healthy diet.

[0314] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0315] In this invention, the server includes means for receiving health information obtained from multiple information processing programs, means for analyzing the received health information and calculating the nutritional components required by each individual, and means for notifying information on providing the optimal diet based on the health information of each individual, in cooperation with the goods delivery service. This enables highly accurate nutritional management and optimal meal suggestions tailored to each individual's health condition.

[0316] An "information processing program" is software capable of collecting and processing multiple types of health-related data.

[0317] "Health information" refers to data that indicates the physical condition of an individual, including weight, dietary history, and sleep duration.

[0318] "Nutritional components" refer to the specific nutrients and energy necessary to support an individual's health.

[0319] "Information provision services" refer to a series of service activities aimed at providing the information necessary for meal planning.

[0320] A "meal plan" is a schedule or proposal that outlines the content of meals optimized for an individual's health condition.

[0321] "Goods delivery services" refers to the service of delivering food and ingredients to a designated location.

[0322] "Ingredients" refer to raw materials used for cooking, including meat, vegetables, and seasonings.

[0323] A "food service provider" refers to a facility or business that specializes in providing food or meals.

[0324] This invention provides a system that delivers meal plans optimized for individual users. It primarily involves a server, terminals, and users. The following describes in detail how each component operates.

[0325] The server operates as an API server to receive health information from multiple information processing programs. Health information sent from the terminal includes weight, meal history, and sleep duration. The server then uses a generative AI model based on Python and TensorFlow to analyze this health information. Based on the analysis, the server calculates the necessary nutrients for each individual.

[0326] Users input and update their health information using devices such as smartphones or tablets. The application provided on the device is developed using React Native, through which users can input data and send information to the server.

[0327] The server also works in conjunction with goods delivery and information provision services to notify users of the most suitable ingredients and menus for their individual needs. This integration allows, for example, ingredients to be selected based on the amount of protein the user requires, and the most suitable restaurants and delivery services to be recommended.

[0328] As a concrete example, suppose a user enters their weight into the app in the morning. Based on this information, the server determines that the user is deficient in protein and suggests a food provider nearby that sells protein-rich salads. The user can also order the suggested menu for lunch that day via delivery.

[0329] Examples of prompts to input into a generative AI model include the following:

[0330] "Today's health data: Weight 68kg, 7 hours of sleep, oatmeal for breakfast. Please suggest a good lunch menu."

[0331] In this way, users can easily select and enjoy meals that are appropriate for their own health condition.

[0332] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0333] Step 1:

[0334] Users input health information (weight, meal history, sleep duration, etc.) into the application using their device. The entered data is sent from the device to the server in JSON format. At this stage, the integrity and format of the input data are primarily checked.

[0335] Step 2:

[0336] The server receives health information from the terminal and launches a generative AI model using Python and TensorFlow. It analyzes the input data and calculates the necessary nutrients for each individual. This process involves data calculations to identify which nutrients are deficient based on past data and health status.

[0337] Step 3:

[0338] Based on the calculated nutritional information, the server connects with product delivery and information provision services via a dedicated API to select ingredients and menus that are suitable for the user's health condition. In this process, a database of available products is searched, and calculations are performed to extract the most suitable items.

[0339] Step 4:

[0340] The server uses the processed information to identify appropriate food providers, taking into account the user's current location. Here, location-based data processing is performed, and the most suitable restaurants and delivery options are output.

[0341] Step 5:

[0342] The user's smartphone receives notifications from the server, displaying a personalized meal plan and a list of available ingredients. Based on this information, the user can order necessary ingredients or select recommended menus. The selected meal information is then fed back to the server and reflected in future nutritional plans.

[0343] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0344] This invention is a system that analyzes the user's emotional state using an emotion engine, in addition to the user's health data, and provides an individually optimized meal plan based on this analysis. Specifically, it achieves more personalized health management by incorporating emotional information into the processes of receiving, analyzing, and suggesting meals based on health data.

[0345] The system requires coordination between the user, terminal, server, and emotion engine. Users input necessary health data, such as weight and diet, into the terminal via a standard health management application. The terminal collects this data and sends it to the server.

[0346] The server inputs the received health data into a generating AI model and begins analysis. Crucially, an emotion engine is integrated into the server. This emotion engine analyzes the user's emotional state and uses that information to interpret the health data. For example, it can recommend a diet containing ingredients with calming effects to a user experiencing high stress levels.

[0347] Based on the results of the emotion engine, the server collaborates with food delivery services to create a menu tailored to the user. This menu includes ingredient selection that matches the user's emotional state and offers specific food options. Furthermore, it utilizes the user's location information to search for and recommend nearby dining establishments.

[0348] The terminal receives meal suggestions from the server and displays a range of options to the user. The user can then intuitively choose a meal that suits their mood and order it online or at a nearby restaurant.

[0349] To give a concrete example, suppose a user senses fatigue and stress before noon. The device sends its emotional state, along with health data at that time, to the server. The server uses an emotion engine to analyze the user's state and recommends a lunch plan that includes herbal tea to help alleviate stress. Based on this recommendation, the user can choose to purchase the necessary ingredients from an online supermarket or order that lunch from a nearby cafe.

[0350] By incorporating an emotion engine in this way, it becomes possible to provide users with healthy food choices that respond to their instantaneous mood and emotions, thereby improving their quality of life.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The user inputs health data such as weight, diet, and sleep duration through a health management application. The device collects this data and prepares to send it to the server.

[0354] Step 2:

[0355] The device collects not only health data but also emotional data obtained through sensors on the user's mobile device and other applications, and transmits it to the server.

[0356] Step 3:

[0357] The server inputs the received health and emotional data into a generating AI model. The model analyzes this data and generates a health plan based on the user's needs and emotional state.

[0358] Step 4:

[0359] The emotion engine within the server analyzes the user's emotional data and assesses the need for nutritional supplementation based on stress levels and emotional states. Based on this assessment, recommended amounts of specific nutrients, such as protein and vitamins, are adjusted.

[0360] Step 5:

[0361] The server combines the results of emotion analysis and nutritional analysis, and works with food delivery services to create an optimal menu. This menu includes ingredients that correspond to the user's emotional state.

[0362] Step 6:

[0363] The server requests the user's location information from the terminal and receives the location information from the terminal. Using the location information, the server searches for nearby restaurants and identifies the best option for the user.

[0364] Step 7:

[0365] The server sends meal suggestions tailored to the user's emotions to the device. The device notifies the user of these suggestions and displays them in a visualized format. The user can then review these suggestions and make purchases from online supermarkets or place orders at restaurants.

[0366] Step 8:

[0367] The device then feeds back information about the user's meal choices and emotional changes to the server. The server uses this information to optimize future meal plans.

[0368] (Example 2)

[0369] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0370] Providing individually optimized meal plans based on a user's health and emotional state is difficult with existing technologies. In particular, it is challenging to provide optimal meal choices while considering the daily changes in a user's emotions, resulting in a problem where effective health management of the user is not possible.

[0371] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0372] In this invention, the server includes means for receiving health indicators obtained from multiple health management applications, means for analyzing the received health indicators and emotional state to calculate the nutrients required by each individual, and means for generating an individually optimized meal plan using a generative AI model based on the calculated nutrients and emotional state. This makes it possible to provide an appropriate meal plan according to the user's health and emotional state.

[0373] "Health indicators" are elements that quantify or represent a user's physical health status, such as weight, diet, and activity level, using data.

[0374] "Emotional state" refers to information that represents the user's mood and emotional state at a given time, including states such as stress and happiness.

[0375] "Nutrients" are chemical substances necessary for the body's growth and activity, and refer to vitamins, minerals, proteins, lipids, carbohydrates, etc., found in food.

[0376] A "generative AI model" is a model that uses artificial intelligence technology to process and analyze data, and its role is to generate meal plans from health data and emotional states.

[0377] A "food supply service" is a service that procures and provides the food ingredients that users need, and includes supermarkets and online stores.

[0378] "Location information" refers to data used to identify a user's current location, such as geographical coordinates or address information.

[0379] A "food service facility" is a place that provides meals to users, and includes restaurants, cafes, and dining halls.

[0380] This invention is a system that provides individually optimized meal plans based on the user's health and emotional state. Details of how this objective is achieved using specific hardware and software are described below.

[0381] Users input daily health indicators such as weight, diet, activity level, and emotional state using a health management application running on a device such as a smartphone or tablet. The device collects this information and sends it to a server. Because the server requires powerful processing capabilities, it is suitable to use a high-performance cloud computing service.

[0382] The server utilizes a generative AI model to analyze the received health indicators. This model is implemented using Python and deep learning libraries such as TensorFlow or PyTorch. The generative AI model interprets prompt text as input and dynamically generates a meal plan that takes the user's health status into account. During this process, an emotion engine built into the server analyzes the user's input emotional state.

[0383] The emotion engine utilizes natural language processing technology to convert emotional information from users into numerical data. This allows the generative AI model to recommend foods and beverages containing appropriate nutrients based on emotions such as stress and happiness.

[0384] The generated meal plans can be implemented through food supply services partnered with the server. For example, if a user is feeling stressed, the server might suggest a plan that includes relaxing foods, such as herbal tea.

[0385] Furthermore, the server utilizes the user's location information to search for nearby restaurants and present the user with the best options.

[0386] A concrete example of a prompt would be, "Please suggest a lunch menu based on today's emotional state and health indicators."

[0387] Users select meal plans and provide feedback via an application on their device. The feedback information is resent to the server and used to improve the accuracy of future analyses.

[0388] This system allows users to easily make dietary choices that suit their health and emotional state, thereby improving their quality of life.

[0389] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0390] Step 1:

[0391] Users input health indicators such as weight, diet, daily activity, and emotional state through a health management application. Once this data is entered into the application, the device aggregates the information and prepares to send it directly to the server. The input data includes numerical data (weight, activity level) and text data (emotional state). These are the inputs sent to the server.

[0392] Step 2:

[0393] The device sends health indicator data acquired from the user to the server. The server receives the data and first uses an emotion engine to analyze the emotional state, which is text data, and generates numerical emotion data. Next, the health indicators and emotion data are input into a generating AI model for analysis. Based on these inputs, the model interprets the user's health status and identifies potential nutrients and meal plans.

[0394] Step 3:

[0395] Based on the analysis, the server creates a meal plan using a generative AI model. This model generates meal options using prompts. For example, it can suggest a "relaxing lunch" to a user experiencing high stress levels. The output at this stage is a list of specific meal recommendations containing particular nutrients.

[0396] Step 4:

[0397] The server uses the generated meal plan to send the plan details to partner food supply services to confirm available ingredients. It also uses the user's location information to search for nearby restaurants and create a list of suitable establishments. This information is output in list format and sent back from the server to the terminal.

[0398] Step 5:

[0399] The terminal displays meal plans and a list of facilities received from the server to the user. The user then reviews the plan and selects a meal that suits their preferences. This selection is sent back to the server as feedback. The user can then take action, such as placing an online order or ordering in-store, as appropriate.

[0400] (Application Example 2)

[0401] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0402] In health management and meal selection, there is a lack of personalized suggestions that take into account the emotional state of individual users, resulting in a challenge in making effective meal choices that respond to users' momentary emotions and health conditions. Furthermore, there is a need for an efficient ordering system that allows users to quickly access the suggested meal options.

[0403] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0404] This invention includes a server that integrates health data and emotional state using emotion analysis technology to provide more personalized meal suggestions, and a means that enables users to directly order the suggested meal content from a delivery service via a user terminal. This makes it possible to select the optimal meal according to the user's emotional state and to place orders quickly.

[0405] A "health management application" is software used to collect, analyze, and manage data related to a user's health.

[0406] "Health data" refers to information that indicates the user's physical measurements and health status.

[0407] Nutrients are chemical substances necessary for the growth, development, and maintenance of health in living organisms.

[0408] A "food supply service" is a business entity that provides or supplies food or meals to users.

[0409] "Emotion analysis technology" is a technology that estimates a user's emotional state at a given time based on their input data and behavior.

[0410] A "meal plan" refers to the content and schedule of meals proposed based on an individual's health condition and preferences.

[0411] "Location information" refers to information that indicates the user's current location or a specific place.

[0412] A "user terminal" is a device that a user directly operates or uses to input and retrieve information.

[0413] A "delivery service" is a service that delivers food or goods to a specific location or user.

[0414] "Feedback data" refers to information that shows evaluations and impressions based on user behavior and choices.

[0415] The system implementing this invention operates in conjunction with a user terminal, a server, and a distribution service.

[0416] The server receives health and emotional data from the user's terminal. The received data is analyzed using a generative AI model running on the server. This model integrates diverse data obtained through the health management application and further identifies the user's emotional state using emotion analysis technology. Based on the analysis results, the server generates a personalized meal plan for each user and sends the details to the user's terminal.

[0417] The user terminal displays the received meal plan on its screen and intuitively offers the user options tailored to their emotional state. Using the latest location information, the user terminal can guide users to nearby restaurants and menus from online delivery services. This allows the user to instantly order an optimized meal.

[0418] As a concrete example, suppose a user enters their weight and past eating history into a health management application and also sends emotional data from their smart device. At this point, the server analyzes that the user is stressed and suggests meals containing ingredients that are expected to have a relaxing effect. The user can select a suggested menu on the app and immediately receive their meal, with pre-booking at a nearby restaurant or online ordering. This process particularly requires the effective use of a "generative AI model." An example of a prompt message would be: "Create a recommended meal plan based on the user's current emotional state. The user is stressed. What foods are recommended?"

[0419] In terms of hardware, user terminals will be smartphones or tablets, and the server will utilize a general cloud server environment. The software will employ various APIs, including Microsoft Azure's Sentiment Recognition API and the Yelp API. Firebase will be used for the database, enabling real-time data management.

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] The user enters their health data into the device.

[0423] The data entered includes weight, dietary history, and allergy information. This data is formatted by the terminal and prepared to be sent to the server.

[0424] Step 2:

[0425] The device collects emotional data.

[0426] The device analyzes the user's emotional state based on sensors in the smart device and the user's self-reported information. This information is also ready to be sent to the server.

[0427] Step 3:

[0428] The server receives health data and emotional data.

[0429] The server stores this data in a database and processes it into the format required for analysis in the next step. This processing includes operations such as data standardization and missing value imputation.

[0430] Step 4:

[0431] The server activates the generated AI model using the data it receives.

[0432] The generative AI model receives user health and emotional data as input and creates individually optimized meal plans based on this data. As output, it generates meal suggestions tailored to each user.

[0433] Step 5:

[0434] The server sends meal suggestions.

[0435] The generated meal plan is sent to the user's device and displayed on the user interface. This display includes specific nutrient suggestions and information on relaxation effects based on emotions.

[0436] Step 6:

[0437] The user checks meal suggestions on their device and enters their choices.

[0438] The user selects from the displayed menu and decides what to order. The user's choices are sent to the server as feedback data.

[0439] Step 7:

[0440] The device uses location information to search for the most suitable restaurant.

[0441] Based on location information, the system uses the Yelp API and other tools to search for and suggest nearby restaurants. Along with restaurant information, it also displays whether online ordering is available.

[0442] Step 8:

[0443] The user places an order with the distribution service based on their selections.

[0444] Users can select meal options suggested within the application and purchase them online or make reservations at nearby establishments. This step provides an interface for direct ordering.

[0445] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0446] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0447] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0451] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0452] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0453] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0454] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0455] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0456] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0457] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0458] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0459] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0460] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0461] This invention is a system that provides individually optimized meal plans based on the user's health data. The specific embodiments and processing flow are described in detail below.

[0462] The system primarily involves users, terminals, and servers. Users input their health data using a health management application. This includes information such as weight, diet, and sleep duration. Terminals acquire this data and send it to the server.

[0463] The server uses a generative AI model to analyze the received health data. The AI ​​model calculates the necessary nutrients based on the user's health status and generates a personalized meal plan. This plan is designed to contribute to the user's health and specifically identifies which nutrients are needed and in what quantities.

[0464] The server then collaborates with food delivery services to create menus based on necessary nutrients and procures ingredients through those services. Furthermore, the server obtains the user's current location via GPS and searches for the best options from nearby food delivery facilities. This makes it possible to present the healthiest choices within the user's living area.

[0465] The terminal receives instructions from the server and notifies the user with specific meal suggestions. The user can then purchase the recommended ingredients from an online supermarket or obtain a meal from a designated restaurant or delivery service.

[0466] Furthermore, the information selected by the user is fed back into the system. This feedback is reflected in future plan creation and helps suggest more suitable dietary options for the user. This feedback loop is expected to lead to sustainable health improvements.

[0467] For example, suppose a user enters their weight into their device in the morning and sends it to the server. Based on this data, the server identifies a protein deficiency and suggests a menu including chicken through an online supermarket. It also suggests nearby cafes that offer protein-rich menus based on the user's location. In this way, users can easily obtain the best options for consuming the nutrients they need that day.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The user enters health data such as weight, diet, and sleep duration into a health management application. The device collects this data in the background.

[0471] Step 2:

[0472] The device collects health data and sends it to a server via the network. The server receives the data and stores it in a database.

[0473] Step 3:

[0474] The server uses a generated AI model to analyze stored health data. This analysis calculates the user's health status and necessary nutrients.

[0475] Step 4:

[0476] Based on the analysis results, the server works with food delivery services to generate the optimal menu for the user. It identifies the necessary ingredients and prepares the order data for the online supermarket.

[0477] Step 5:

[0478] The server receives GPS data from the device to obtain the user's current location. It then uses this location information to search for nearby restaurants and other food establishments.

[0479] Step 6:

[0480] The server selects the optimal meal delivery option and notifies the user's terminal. The terminal then displays this information to the user in a visual format.

[0481] Step 7:

[0482] Users select a meal from the options presented on their device. They can also purchase groceries from online supermarkets or make restaurant reservations.

[0483] Step 8:

[0484] The device feeds back the user's choices and usage data to the server. Based on this information, the server makes adjustments to further optimize future meal plans.

[0485] (Example 1)

[0486] Next, we will describe Example 1. 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."

[0487] Providing optimal meal plans for individual users has not been efficient with traditional methods. In particular, suggesting meals that take into account the user's location and up-to-date health information has been difficult. Therefore, there is a need for technology that automates the food delivery process and provides customized meal plans for each user in real time.

[0488] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0489] In this invention, the server includes means for receiving health information acquired from multiple health management devices, means for automatically generating a meal plan from the health information using generative AI technology, and means for suggesting the most suitable nearby dining facilities using location information. This makes it possible to provide an efficient and personalized meal plan based on the user's health information and location information.

[0490] A "health management device" is a device used to record and manage a user's health information.

[0491] "Health information" refers to data related to the user's health status, such as weight, diet, and sleep duration.

[0492] "Nutritional components" refer to the types and amounts of nutrients that users should consume based on health information.

[0493] A "food supply system" is a system for procuring and providing ingredients and food products.

[0494] "Location information" refers to information such as GPS data that indicates the user's current location.

[0495] A "food and beverage establishment" refers to a facility that serves meals, such as a restaurant or cafe.

[0496] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and generate new information.

[0497] A "meal plan" is a set of meal plans and schedules proposed to the user based on their health condition.

[0498] This invention is a system that provides personalized meal plans based on the user's health information. The implementation details are described below.

[0499] This system involves users, terminals, and servers. Users input their health information using a health management device. This health information includes, for example, weight, meal records, and sleep duration. Terminals collect this data and send it to the server. Internet connectivity is used for data transmission, and HTTPS is adopted as the communication protocol.

[0500] The server analyzes the received health information using generative AI technology. Specifically, it uses generative AI models that run on frameworks such as TensorFlow and PyTorch. This AI model calculates the nutritional content for each user based on the health information. For example, if it determines that a user is deficient in protein, it will recommend foods that are high in protein.

[0501] The server, based on the calculated nutritional information, interacts with the food delivery system to generate a meal plan. For example, a program implemented in Python uses the results of an AI model to construct a meal menu. The generated meal plan is then notified to the user via their device.

[0502] Furthermore, the server obtains the user's location information and suggests the most suitable dining establishments. GPS is used to obtain location information, and a geocoding API is used to search for facility information. This ensures that the most suitable dining locations near the user are presented.

[0503] The terminal receives notifications from the server and transmits suggestions to the user. The user can then purchase groceries from an online supermarket or use designated dining facilities according to the suggestions. The user's choices are fed back into the system and reflected in future meal planning.

[0504] As a concrete example, suppose a user enters their weight in the morning, and an AI model calculates their protein deficiency. Based on this, the server suggests a menu including chicken, and further recommends protein-rich menu items from nearby cafes based on the user's location.

[0505] The following are specific examples of prompt statements for a generative AI model:

[0506] User data: Weight: 70kg, Recent meals: No breakfast, Lunch: Pasta, Dinner: Salad

[0507] Analysis task: Generate a one-day meal plan for the next day to compensate for the user's protein deficiency.

[0508] In this way, the present invention makes it possible to easily and efficiently provide meal plans optimized for each individual user.

[0509] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0510] Step 1:

[0511] Users input their health information using a health management device. This input data includes weight, diet, and sleep duration. This information is stored digitally on the device.

[0512] Step 2:

[0513] The device receives health information entered by the user and transmits it to the server via the internet. This input includes the user ID and health data. The data is securely transmitted to the server via the HTTPS protocol.

[0514] Step 3:

[0515] The server uses a generative AI model to analyze health information received from the terminal. Input data includes the user's health status and location data. The AI ​​model analyzes this information to calculate appropriate nutritional components. Through data processing, conclusions such as protein deficiency can be drawn.

[0516] Step 4:

[0517] The server, in conjunction with the food delivery system, generates an optimal meal plan based on nutritional information calculated by the AI ​​model. It determines which ingredients and menu items are appropriate based on the AI ​​model's output. This process utilizes a Python script and applies the output data from the generated AI model.

[0518] Step 5:

[0519] The server uses the user's location information to search for the most suitable restaurants and bars in the vicinity. Using GPS data as input, it retrieves information about nearby establishments using a geocoding API. The output is a list of restaurants and bars that are suitable for the user's health condition.

[0520] Step 6:

[0521] The device notifies the user of meal plans and restaurant information received from the server. Specifically, push notifications are sent, which the user confirms. The notifications include details about the suggested meal menus and restaurants.

[0522] Step 7:

[0523] Users act based on the suggested meal menu and send feedback information back to the system via their device. Information about the meals and facilities selected by the user is sent to the system and reflected in future meal plans. Specific actions include entering ratings within the app.

[0524] (Application Example 1)

[0525] Next, we will explain Application Example 1. In the following explanation, 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."

[0526] Nutritional management based on individual health conditions requires the aggregation and analysis of diverse health information to create an optimal meal plan. However, current technology has not been able to accurately calculate nutrients based on individual health data and suggest suitable foods and food providers. As a result, it has been difficult for many people to efficiently enjoy a personalized and healthy diet.

[0527] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0528] In this invention, the server includes means for receiving health information obtained from multiple information processing programs, means for analyzing the received health information and calculating the nutritional components required by each individual, and means for notifying information on providing the optimal diet based on the health information of each individual, in cooperation with the goods delivery service. This enables highly accurate nutritional management and optimal meal suggestions tailored to each individual's health condition.

[0529] An "information processing program" is software capable of collecting and processing multiple types of health-related data.

[0530] "Health information" refers to data that indicates the physical condition of an individual, including weight, dietary history, and sleep duration.

[0531] "Nutritional components" refer to the specific nutrients and energy necessary to support an individual's health.

[0532] "Information provision services" refer to a series of service activities aimed at providing the information necessary for meal planning.

[0533] A "meal plan" is a schedule or proposal that outlines the content of meals optimized for an individual's health condition.

[0534] "Goods delivery services" refers to the service of delivering food and ingredients to a designated location.

[0535] "Ingredients" refer to raw materials used for cooking, including meat, vegetables, and seasonings.

[0536] A "food service provider" refers to a facility or business that specializes in providing food or meals.

[0537] This invention provides a system that delivers meal plans optimized for individual users. It primarily involves a server, terminals, and users. The following describes in detail how each component operates.

[0538] The server operates as an API server to receive health information from multiple information processing programs. Health information sent from the terminal includes weight, meal history, and sleep duration. The server then uses a generative AI model based on Python and TensorFlow to analyze this health information. Based on the analysis, the server calculates the necessary nutrients for each individual.

[0539] Users input and update their health information using devices such as smartphones or tablets. The application provided on the device is developed using React Native, through which users can input data and send information to the server.

[0540] The server also works in conjunction with goods delivery and information provision services to notify users of the most suitable ingredients and menus for their individual needs. This integration allows, for example, ingredients to be selected based on the amount of protein the user requires, and the most suitable restaurants and delivery services to be recommended.

[0541] As a concrete example, suppose a user enters their weight into the app in the morning. Based on this information, the server determines that the user is deficient in protein and suggests a food provider nearby that sells protein-rich salads. The user can also order the suggested menu for lunch that day via delivery.

[0542] Examples of prompts to input into a generative AI model include the following:

[0543] "Today's health data: Weight 68kg, 7 hours of sleep, oatmeal for breakfast. Please suggest a good lunch menu."

[0544] In this way, users can easily select and enjoy meals that are appropriate for their own health condition.

[0545] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0546] Step 1:

[0547] Users input health information (weight, meal history, sleep duration, etc.) into the application using their device. The entered data is sent from the device to the server in JSON format. At this stage, the integrity and format of the input data are primarily checked.

[0548] Step 2:

[0549] The server receives health information from the terminal and launches a generative AI model using Python and TensorFlow. It analyzes the input data and calculates the necessary nutrients for each individual. This process involves data calculations to identify which nutrients are deficient based on past data and health status.

[0550] Step 3:

[0551] Based on the calculated nutritional information, the server connects with product delivery and information provision services via a dedicated API to select ingredients and menus that are suitable for the user's health condition. In this process, a database of available products is searched, and calculations are performed to extract the most suitable items.

[0552] Step 4:

[0553] The server uses the processed information to identify appropriate food providers, taking into account the user's current location. Here, location-based data processing is performed, and the most suitable restaurants and delivery options are output.

[0554] Step 5:

[0555] The user's smartphone receives notifications from the server, displaying a personalized meal plan and a list of available ingredients. Based on this information, the user can order necessary ingredients or select recommended menus. The selected meal information is then fed back to the server and reflected in future nutritional plans.

[0556] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0557] This invention is a system that analyzes the user's emotional state using an emotion engine, in addition to the user's health data, and provides an individually optimized meal plan based on this analysis. Specifically, it achieves more personalized health management by incorporating emotional information into the processes of receiving, analyzing, and suggesting meals based on health data.

[0558] The system requires coordination between the user, terminal, server, and emotion engine. Users input necessary health data, such as weight and diet, into the terminal via a standard health management application. The terminal collects this data and sends it to the server.

[0559] The server inputs the received health data into a generating AI model and begins analysis. Crucially, an emotion engine is integrated into the server. This emotion engine analyzes the user's emotional state and uses that information to interpret the health data. For example, it can recommend a diet containing ingredients with calming effects to a user experiencing high stress levels.

[0560] Based on the results of the emotion engine, the server collaborates with food delivery services to create a menu tailored to the user. This menu includes ingredient selection that matches the user's emotional state and offers specific food options. Furthermore, it utilizes the user's location information to search for and recommend nearby dining establishments.

[0561] The terminal receives meal suggestions from the server and displays a range of options to the user. The user can then intuitively choose a meal that suits their mood and order it online or at a nearby restaurant.

[0562] To give a concrete example, suppose a user senses fatigue and stress before noon. The device sends its emotional state, along with health data at that time, to the server. The server uses an emotion engine to analyze the user's state and recommends a lunch plan that includes herbal tea to help alleviate stress. Based on this recommendation, the user can choose to purchase the necessary ingredients from an online supermarket or order that lunch from a nearby cafe.

[0563] By incorporating an emotion engine in this way, it becomes possible to provide users with healthy food choices that respond to their instantaneous mood and emotions, thereby improving their quality of life.

[0564] The following describes the processing flow.

[0565] Step 1:

[0566] The user inputs health data such as weight, diet, and sleep duration through a health management application. The device collects this data and prepares to send it to the server.

[0567] Step 2:

[0568] The device collects not only health data but also emotional data obtained through sensors on the user's mobile device and other applications, and transmits it to the server.

[0569] Step 3:

[0570] The server inputs the received health and emotional data into a generating AI model. The model analyzes this data and generates a health plan based on the user's needs and emotional state.

[0571] Step 4:

[0572] The emotion engine within the server analyzes the user's emotional data and assesses the need for nutritional supplementation based on stress levels and emotional states. Based on this assessment, recommended amounts of specific nutrients, such as protein and vitamins, are adjusted.

[0573] Step 5:

[0574] The server combines the results of emotion analysis and nutritional analysis, and works with food delivery services to create an optimal menu. This menu includes ingredients that correspond to the user's emotional state.

[0575] Step 6:

[0576] The server requests the user's location information from the terminal and receives the location information from the terminal. Using the location information, the server searches for nearby restaurants and identifies the best option for the user.

[0577] Step 7:

[0578] The server sends meal suggestions tailored to the user's emotions to the device. The device notifies the user of these suggestions and displays them in a visualized format. The user can then review these suggestions and make purchases from online supermarkets or place orders at restaurants.

[0579] Step 8:

[0580] The device then feeds back information about the user's meal choices and emotional changes to the server. The server uses this information to optimize future meal plans.

[0581] (Example 2)

[0582] Next, we will describe Example 2. 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."

[0583] Providing individually optimized meal plans based on a user's health and emotional state is difficult with existing technologies. In particular, it is challenging to provide optimal meal choices while considering the daily changes in a user's emotions, resulting in a problem where effective health management of the user is not possible.

[0584] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0585] In this invention, the server includes means for receiving health indicators obtained from multiple health management applications, means for analyzing the received health indicators and emotional state to calculate the nutrients required by each individual, and means for generating an individually optimized meal plan using a generative AI model based on the calculated nutrients and emotional state. This makes it possible to provide an appropriate meal plan according to the user's health and emotional state.

[0586] "Health indicators" are elements that quantify or represent a user's physical health status, such as weight, diet, and activity level, using data.

[0587] "Emotional state" refers to information that represents the user's mood and emotional state at a given time, including states such as stress and happiness.

[0588] "Nutrients" are chemical substances necessary for the body's growth and activity, and refer to vitamins, minerals, proteins, lipids, carbohydrates, etc., found in food.

[0589] A "generative AI model" is a model that uses artificial intelligence technology to process and analyze data, and its role is to generate meal plans from health data and emotional states.

[0590] A "food supply service" is a service that procures and provides the food ingredients that users need, and includes supermarkets and online stores.

[0591] "Location information" refers to data used to identify a user's current location, such as geographical coordinates or address information.

[0592] A "food service facility" is a place that provides meals to users, and includes restaurants, cafes, and dining halls.

[0593] This invention is a system that provides individually optimized meal plans based on the user's health and emotional state. Details of how this objective is achieved using specific hardware and software are described below.

[0594] Users input daily health indicators such as weight, diet, activity level, and emotional state using a health management application running on a device such as a smartphone or tablet. The device collects this information and sends it to a server. Because the server requires powerful processing capabilities, it is suitable to use a high-performance cloud computing service.

[0595] The server utilizes a generative AI model to analyze the received health indicators. This model is implemented using Python and deep learning libraries such as TensorFlow or PyTorch. The generative AI model interprets prompt text as input and dynamically generates a meal plan that takes the user's health status into account. During this process, an emotion engine built into the server analyzes the user's input emotional state.

[0596] The emotion engine utilizes natural language processing technology to convert emotional information from users into numerical data. This allows the generative AI model to recommend foods and beverages containing appropriate nutrients based on emotions such as stress and happiness.

[0597] The generated meal plans can be implemented through food supply services partnered with the server. For example, if a user is feeling stressed, the server might suggest a plan that includes relaxing foods, such as herbal tea.

[0598] Furthermore, the server utilizes the user's location information to search for nearby restaurants and present the user with the best options.

[0599] A concrete example of a prompt would be, "Please suggest a lunch menu based on today's emotional state and health indicators."

[0600] Users select meal plans and provide feedback via an application on their device. The feedback information is resent to the server and used to improve the accuracy of future analyses.

[0601] This system allows users to easily make dietary choices that suit their health and emotional state, thereby improving their quality of life.

[0602] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0603] Step 1:

[0604] Users input health indicators such as weight, diet, daily activity, and emotional state through a health management application. Once this data is entered into the application, the device aggregates the information and prepares to send it directly to the server. The input data includes numerical data (weight, activity level) and text data (emotional state). These are the inputs sent to the server.

[0605] Step 2:

[0606] The device sends health indicator data acquired from the user to the server. The server receives the data and first uses an emotion engine to analyze the emotional state, which is text data, and generates numerical emotion data. Next, the health indicators and emotion data are input into a generating AI model for analysis. Based on these inputs, the model interprets the user's health status and identifies potential nutrients and meal plans.

[0607] Step 3:

[0608] Based on the analysis, the server creates a meal plan using a generative AI model. This model generates meal options using prompts. For example, it can suggest a "relaxing lunch" to a user experiencing high stress levels. The output at this stage is a list of specific meal recommendations containing particular nutrients.

[0609] Step 4:

[0610] The server uses the generated meal plan to send the plan details to partner food supply services to confirm available ingredients. It also uses the user's location information to search for nearby restaurants and create a list of suitable establishments. This information is output in list format and sent back from the server to the terminal.

[0611] Step 5:

[0612] The terminal displays meal plans and a list of facilities received from the server to the user. The user then reviews the plan and selects a meal that suits their preferences. This selection is sent back to the server as feedback. The user can then take action, such as placing an online order or ordering in-store, as appropriate.

[0613] (Application Example 2)

[0614] Next, we will explain application example 2. In the following explanation, 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."

[0615] In health management and meal selection, there is a lack of personalized suggestions that take into account the emotional state of individual users, resulting in a challenge in making effective meal choices that respond to users' momentary emotions and health conditions. Furthermore, there is a need for an efficient ordering system that allows users to quickly access the suggested meal options.

[0616] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0617] This invention includes a server that integrates health data and emotional state using emotion analysis technology to provide more personalized meal suggestions, and a means that enables users to directly order the suggested meal content from a delivery service via a user terminal. This makes it possible to select the optimal meal according to the user's emotional state and to place orders quickly.

[0618] A "health management application" is software used to collect, analyze, and manage data related to a user's health.

[0619] "Health data" refers to information that indicates the user's physical measurements and health status.

[0620] Nutrients are chemical substances necessary for the growth, development, and maintenance of health in living organisms.

[0621] A "food supply service" is a business entity that provides or supplies food or meals to users.

[0622] "Emotion analysis technology" is a technology that estimates a user's emotional state at a given time based on their input data and behavior.

[0623] A "meal plan" refers to the content and schedule of meals proposed based on an individual's health condition and preferences.

[0624] "Location information" refers to information that indicates the user's current location or a specific place.

[0625] A "user terminal" is a device that a user directly operates or uses to input and retrieve information.

[0626] A "delivery service" is a service that delivers food or goods to a specific location or user.

[0627] "Feedback data" refers to information that shows evaluations and impressions based on user behavior and choices.

[0628] The system implementing this invention operates in conjunction with a user terminal, a server, and a distribution service.

[0629] The server receives health and emotional data from the user's terminal. The received data is analyzed using a generative AI model running on the server. This model integrates diverse data obtained through the health management application and further identifies the user's emotional state using emotion analysis technology. Based on the analysis results, the server generates a personalized meal plan for each user and sends the details to the user's terminal.

[0630] The user terminal displays the received meal plan on its screen and intuitively offers the user options tailored to their emotional state. Using the latest location information, the user terminal can guide users to nearby restaurants and menus from online delivery services. This allows the user to instantly order an optimized meal.

[0631] As a concrete example, suppose a user enters their weight and past eating history into a health management application and also sends emotional data from their smart device. At this point, the server analyzes that the user is stressed and suggests meals containing ingredients that are expected to have a relaxing effect. The user can select a suggested menu on the app and immediately receive their meal, with pre-booking at a nearby restaurant or online ordering. This process particularly requires the effective use of a "generative AI model." An example of a prompt message would be: "Create a recommended meal plan based on the user's current emotional state. The user is stressed. What foods are recommended?"

[0632] In terms of hardware, user terminals will be smartphones or tablets, and the server will utilize a general cloud server environment. The software will employ various APIs, including Microsoft Azure's Sentiment Recognition API and the Yelp API. Firebase will be used for the database, enabling real-time data management.

[0633] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0634] Step 1:

[0635] The user enters their health data into the device.

[0636] The data entered includes weight, dietary history, and allergy information. This data is formatted by the terminal and prepared to be sent to the server.

[0637] Step 2:

[0638] The device collects emotional data.

[0639] The device analyzes the user's emotional state based on sensors in the smart device and the user's self-reported information. This information is also ready to be sent to the server.

[0640] Step 3:

[0641] The server receives health data and emotional data.

[0642] The server stores this data in a database and processes it into the format required for analysis in the next step. This processing includes operations such as data standardization and missing value imputation.

[0643] Step 4:

[0644] The server activates the generated AI model using the data it receives.

[0645] The generative AI model receives user health and emotional data as input and creates individually optimized meal plans based on this data. As output, it generates meal suggestions tailored to each user.

[0646] Step 5:

[0647] The server sends meal suggestions.

[0648] The generated meal plan is sent to the user's device and displayed on the user interface. This display includes specific nutrient suggestions and information on relaxation effects based on emotions.

[0649] Step 6:

[0650] The user checks meal suggestions on their device and enters their choices.

[0651] The user selects from the displayed menu and decides what to order. The user's choices are sent to the server as feedback data.

[0652] Step 7:

[0653] The device uses location information to search for the most suitable restaurant.

[0654] Based on location information, the system uses the Yelp API and other tools to search for and suggest nearby restaurants. Along with restaurant information, it also displays whether online ordering is available.

[0655] Step 8:

[0656] The user places an order with the distribution service based on their selections.

[0657] Users can select meal options suggested within the application and purchase them online or make reservations at nearby establishments. This step provides an interface for direct ordering.

[0658] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0659] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0660] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0661] [Fourth Embodiment]

[0662] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0663] As shown in Figure 7, the 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.

[0664] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0665] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0666] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0667] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0668] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0669] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0670] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0671] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0672] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0673] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0674] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0675] This invention is a system that provides individually optimized meal plans based on the user's health data. The specific embodiments and processing flow are described in detail below.

[0676] The system primarily involves users, terminals, and servers. Users input their health data using a health management application. This includes information such as weight, diet, and sleep duration. Terminals acquire this data and send it to the server.

[0677] The server uses a generative AI model to analyze the received health data. The AI ​​model calculates the necessary nutrients based on the user's health status and generates a personalized meal plan. This plan is designed to contribute to the user's health and specifically identifies which nutrients are needed and in what quantities.

[0678] The server then collaborates with food delivery services to create menus based on necessary nutrients and procures ingredients through those services. Furthermore, the server obtains the user's current location via GPS and searches for the best options from nearby food delivery facilities. This makes it possible to present the healthiest choices within the user's living area.

[0679] The terminal receives instructions from the server and notifies the user with specific meal suggestions. The user can then purchase the recommended ingredients from an online supermarket or obtain a meal from a designated restaurant or delivery service.

[0680] Furthermore, the information selected by the user is fed back into the system. This feedback is reflected in future plan creation and helps suggest more suitable dietary options for the user. This feedback loop is expected to lead to sustainable health improvements.

[0681] For example, suppose a user enters their weight into their device in the morning and sends it to the server. Based on this data, the server identifies a protein deficiency and suggests a menu including chicken through an online supermarket. It also suggests nearby cafes that offer protein-rich menus based on the user's location. In this way, users can easily obtain the best options for consuming the nutrients they need that day.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] The user enters health data such as weight, diet, and sleep duration into a health management application. The device collects this data in the background.

[0685] Step 2:

[0686] The device collects health data and sends it to a server via the network. The server receives the data and stores it in a database.

[0687] Step 3:

[0688] The server uses a generated AI model to analyze stored health data. This analysis calculates the user's health status and necessary nutrients.

[0689] Step 4:

[0690] Based on the analysis results, the server works with food delivery services to generate the optimal menu for the user. It identifies the necessary ingredients and prepares the order data for the online supermarket.

[0691] Step 5:

[0692] The server receives GPS data from the device to obtain the user's current location. It then uses this location information to search for nearby restaurants and other food establishments.

[0693] Step 6:

[0694] The server selects the optimal meal delivery option and notifies the user's terminal. The terminal then displays this information to the user in a visual format.

[0695] Step 7:

[0696] Users select a meal from the options presented on their device. They can also purchase groceries from online supermarkets or make restaurant reservations.

[0697] Step 8:

[0698] The device feeds back the user's choices and usage data to the server. Based on this information, the server makes adjustments to further optimize future meal plans.

[0699] (Example 1)

[0700] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] Providing optimal meal plans for individual users has not been efficient with traditional methods. In particular, suggesting meals that take into account the user's location and up-to-date health information has been difficult. Therefore, there is a need for technology that automates the food delivery process and provides customized meal plans for each user in real time.

[0702] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0703] In this invention, the server includes means for receiving health information acquired from multiple health management devices, means for automatically generating a meal plan from the health information using generative AI technology, and means for suggesting the most suitable nearby dining facilities using location information. This makes it possible to provide an efficient and personalized meal plan based on the user's health information and location information.

[0704] A "health management device" is a device used to record and manage a user's health information.

[0705] "Health information" refers to data related to the user's health status, such as weight, diet, and sleep duration.

[0706] "Nutritional components" refer to the types and amounts of nutrients that users should consume based on health information.

[0707] A "food supply system" is a system for procuring and providing ingredients and food products.

[0708] "Location information" refers to information such as GPS data that indicates the user's current location.

[0709] A "food and beverage establishment" refers to a facility that serves meals, such as a restaurant or cafe.

[0710] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and generate new information.

[0711] A "meal plan" is a set of meal plans and schedules proposed to the user based on their health condition.

[0712] This invention is a system that provides personalized meal plans based on the user's health information. The implementation details are described below.

[0713] This system involves users, terminals, and servers. Users input their health information using a health management device. This health information includes, for example, weight, meal records, and sleep duration. Terminals collect this data and send it to the server. Internet connectivity is used for data transmission, and HTTPS is adopted as the communication protocol.

[0714] The server analyzes the received health information using generative AI technology. Specifically, it uses generative AI models that run on frameworks such as TensorFlow and PyTorch. This AI model calculates the nutritional content for each user based on the health information. For example, if it determines that a user is deficient in protein, it will recommend foods that are high in protein.

[0715] The server, based on the calculated nutritional information, interacts with the food delivery system to generate a meal plan. For example, a program implemented in Python uses the results of an AI model to construct a meal menu. The generated meal plan is then notified to the user via their device.

[0716] Furthermore, the server obtains the user's location information and suggests the most suitable dining establishments. GPS is used to obtain location information, and a geocoding API is used to search for facility information. This ensures that the most suitable dining locations near the user are presented.

[0717] The terminal receives notifications from the server and transmits suggestions to the user. The user can then purchase groceries from an online supermarket or use designated dining facilities according to the suggestions. The user's choices are fed back into the system and reflected in future meal planning.

[0718] As a concrete example, suppose a user enters their weight in the morning, and an AI model calculates their protein deficiency. Based on this, the server suggests a menu including chicken, and further recommends protein-rich menu items from nearby cafes based on the user's location.

[0719] The following are specific examples of prompt statements for a generative AI model:

[0720] User data: Weight: 70kg, Recent meals: No breakfast, Lunch: Pasta, Dinner: Salad

[0721] Analysis task: Generate a one-day meal plan for the next day to compensate for the user's protein deficiency.

[0722] In this way, the present invention makes it possible to easily and efficiently provide meal plans optimized for each individual user.

[0723] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0724] Step 1:

[0725] Users input their health information using a health management device. This input data includes weight, diet, and sleep duration. This information is stored digitally on the device.

[0726] Step 2:

[0727] The device receives health information entered by the user and transmits it to the server via the internet. This input includes the user ID and health data. The data is securely transmitted to the server via the HTTPS protocol.

[0728] Step 3:

[0729] The server uses a generative AI model to analyze health information received from the terminal. Input data includes the user's health status and location data. The AI ​​model analyzes this information to calculate appropriate nutritional components. Through data processing, conclusions such as protein deficiency can be drawn.

[0730] Step 4:

[0731] The server, in conjunction with the food delivery system, generates an optimal meal plan based on nutritional information calculated by the AI ​​model. It determines which ingredients and menu items are appropriate based on the AI ​​model's output. This process utilizes a Python script and applies the output data from the generated AI model.

[0732] Step 5:

[0733] The server uses the user's location information to search for the most suitable restaurants and bars in the vicinity. Using GPS data as input, it retrieves information about nearby establishments using a geocoding API. The output is a list of restaurants and bars that are suitable for the user's health condition.

[0734] Step 6:

[0735] The device notifies the user of meal plans and restaurant information received from the server. Specifically, push notifications are sent, which the user confirms. The notifications include details about the suggested meal menus and restaurants.

[0736] Step 7:

[0737] Users act based on the suggested meal menu and send feedback information back to the system via their device. Information about the meals and facilities selected by the user is sent to the system and reflected in future meal plans. Specific actions include entering ratings within the app.

[0738] (Application Example 1)

[0739] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0740] Nutritional management based on individual health conditions requires the aggregation and analysis of diverse health information to create an optimal meal plan. However, current technology has not been able to accurately calculate nutrients based on individual health data and suggest suitable foods and food providers. As a result, it has been difficult for many people to efficiently enjoy a personalized and healthy diet.

[0741] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0742] In this invention, the server includes means for receiving health information obtained from multiple information processing programs, means for analyzing the received health information and calculating the nutritional components required by each individual, and means for notifying information on providing the optimal diet based on the health information of each individual, in cooperation with the goods delivery service. This enables highly accurate nutritional management and optimal meal suggestions tailored to each individual's health condition.

[0743] An "information processing program" is software capable of collecting and processing multiple types of health-related data.

[0744] "Health information" refers to data that indicates the physical condition of an individual, including weight, dietary history, and sleep duration.

[0745] "Nutritional components" refer to the specific nutrients and energy necessary to support an individual's health.

[0746] "Information provision services" refer to a series of service activities aimed at providing the information necessary for meal planning.

[0747] A "meal plan" is a schedule or proposal that outlines the content of meals optimized for an individual's health condition.

[0748] "Goods delivery services" refers to the service of delivering food and ingredients to a designated location.

[0749] "Ingredients" refer to raw materials used for cooking, including meat, vegetables, and seasonings.

[0750] A "food service provider" refers to a facility or business that specializes in providing food or meals.

[0751] This invention provides a system that delivers meal plans optimized for individual users. It primarily involves a server, terminals, and users. The following describes in detail how each component operates.

[0752] The server operates as an API server to receive health information from multiple information processing programs. Health information sent from the terminal includes weight, meal history, and sleep duration. The server then uses a generative AI model based on Python and TensorFlow to analyze this health information. Based on the analysis, the server calculates the necessary nutrients for each individual.

[0753] Users input and update their health information using devices such as smartphones or tablets. The application provided on the device is developed using React Native, through which users can input data and send information to the server.

[0754] The server also works in conjunction with goods delivery and information provision services to notify users of the most suitable ingredients and menus for their individual needs. This integration allows, for example, ingredients to be selected based on the amount of protein the user requires, and the most suitable restaurants and delivery services to be recommended.

[0755] As a concrete example, suppose a user enters their weight into the app in the morning. Based on this information, the server determines that the user is deficient in protein and suggests a food provider nearby that sells protein-rich salads. The user can also order the suggested menu for lunch that day via delivery.

[0756] Examples of prompts to input into a generative AI model include the following:

[0757] "Today's health data: Weight 68kg, 7 hours of sleep, oatmeal for breakfast. Please suggest a good lunch menu."

[0758] In this way, users can easily select and enjoy meals that are appropriate for their own health condition.

[0759] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0760] Step 1:

[0761] Users input health information (weight, meal history, sleep duration, etc.) into the application using their device. The entered data is sent from the device to the server in JSON format. At this stage, the integrity and format of the input data are primarily checked.

[0762] Step 2:

[0763] The server receives health information from the terminal and launches a generative AI model using Python and TensorFlow. It analyzes the input data and calculates the necessary nutrients for each individual. This process involves data calculations to identify which nutrients are deficient based on past data and health status.

[0764] Step 3:

[0765] Based on the calculated nutritional information, the server connects with product delivery and information provision services via a dedicated API to select ingredients and menus that are suitable for the user's health condition. In this process, a database of available products is searched, and calculations are performed to extract the most suitable items.

[0766] Step 4:

[0767] The server uses the processed information to identify appropriate food providers, taking into account the user's current location. Here, location-based data processing is performed, and the most suitable restaurants and delivery options are output.

[0768] Step 5:

[0769] The user's smartphone receives notifications from the server, displaying a personalized meal plan and a list of available ingredients. Based on this information, the user can order necessary ingredients or select recommended menus. The selected meal information is then fed back to the server and reflected in future nutritional plans.

[0770] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0771] This invention is a system that analyzes the user's emotional state using an emotion engine, in addition to the user's health data, and provides an individually optimized meal plan based on this analysis. Specifically, it achieves more personalized health management by incorporating emotional information into the processes of receiving, analyzing, and suggesting meals based on health data.

[0772] The system requires coordination between the user, terminal, server, and emotion engine. Users input necessary health data, such as weight and diet, into the terminal via a standard health management application. The terminal collects this data and sends it to the server.

[0773] The server inputs the received health data into a generating AI model and begins analysis. Crucially, an emotion engine is integrated into the server. This emotion engine analyzes the user's emotional state and uses that information to interpret the health data. For example, it can recommend a diet containing ingredients with calming effects to a user experiencing high stress levels.

[0774] Based on the results of the emotion engine, the server collaborates with food delivery services to create a menu tailored to the user. This menu includes ingredient selection that matches the user's emotional state and offers specific food options. Furthermore, it utilizes the user's location information to search for and recommend nearby dining establishments.

[0775] The terminal receives meal suggestions from the server and displays a range of options to the user. The user can then intuitively choose a meal that suits their mood and order it online or at a nearby restaurant.

[0776] To give a concrete example, suppose a user senses fatigue and stress before noon. The device sends its emotional state, along with health data at that time, to the server. The server uses an emotion engine to analyze the user's state and recommends a lunch plan that includes herbal tea to help alleviate stress. Based on this recommendation, the user can choose to purchase the necessary ingredients from an online supermarket or order that lunch from a nearby cafe.

[0777] By incorporating an emotion engine in this way, it becomes possible to provide users with healthy food choices that respond to their instantaneous mood and emotions, thereby improving their quality of life.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] The user inputs health data such as weight, diet, and sleep duration through a health management application. The device collects this data and prepares to send it to the server.

[0781] Step 2:

[0782] The device collects not only health data but also emotional data obtained through sensors on the user's mobile device and other applications, and transmits it to the server.

[0783] Step 3:

[0784] The server inputs the received health and emotional data into a generating AI model. The model analyzes this data and generates a health plan based on the user's needs and emotional state.

[0785] Step 4:

[0786] The emotion engine within the server analyzes the user's emotional data and assesses the need for nutritional supplementation based on stress levels and emotional states. Based on this assessment, recommended amounts of specific nutrients, such as protein and vitamins, are adjusted.

[0787] Step 5:

[0788] The server combines the results of emotion analysis and nutritional analysis, and works with food delivery services to create an optimal menu. This menu includes ingredients that correspond to the user's emotional state.

[0789] Step 6:

[0790] The server requests the user's location information from the terminal and receives the location information from the terminal. Using the location information, the server searches for nearby restaurants and identifies the best option for the user.

[0791] Step 7:

[0792] The server sends meal suggestions tailored to the user's emotions to the device. The device notifies the user of these suggestions and displays them in a visualized format. The user can then review these suggestions and make purchases from online supermarkets or place orders at restaurants.

[0793] Step 8:

[0794] The device then feeds back information about the user's meal choices and emotional changes to the server. The server uses this information to optimize future meal plans.

[0795] (Example 2)

[0796] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0797] Providing individually optimized meal plans based on a user's health and emotional state is difficult with existing technologies. In particular, it is challenging to provide optimal meal choices while considering the daily changes in a user's emotions, resulting in a problem where effective health management of the user is not possible.

[0798] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0799] In this invention, the server includes means for receiving health indicators obtained from multiple health management applications, means for analyzing the received health indicators and emotional state to calculate the nutrients required by each individual, and means for generating an individually optimized meal plan using a generative AI model based on the calculated nutrients and emotional state. This makes it possible to provide an appropriate meal plan according to the user's health and emotional state.

[0800] "Health indicators" are elements that quantify or represent a user's physical health status, such as weight, diet, and activity level, using data.

[0801] "Emotional state" refers to information that represents the user's mood and emotional state at a given time, including states such as stress and happiness.

[0802] "Nutrients" are chemical substances necessary for the body's growth and activity, and refer to vitamins, minerals, proteins, lipids, carbohydrates, etc., found in food.

[0803] A "generative AI model" is a model that uses artificial intelligence technology to process and analyze data, and its role is to generate meal plans from health data and emotional states.

[0804] A "food supply service" is a service that procures and provides the food ingredients that users need, and includes supermarkets and online stores.

[0805] "Location information" refers to data used to identify a user's current location, such as geographical coordinates or address information.

[0806] A "food service facility" is a place that provides meals to users, and includes restaurants, cafes, and dining halls.

[0807] This invention is a system that provides individually optimized meal plans based on the user's health and emotional state. Details of how this objective is achieved using specific hardware and software are described below.

[0808] Users input daily health indicators such as weight, diet, activity level, and emotional state using a health management application running on a device such as a smartphone or tablet. The device collects this information and sends it to a server. Because the server requires powerful processing capabilities, it is suitable to use a high-performance cloud computing service.

[0809] The server utilizes a generative AI model to analyze the received health indicators. This model is implemented using Python and deep learning libraries such as TensorFlow or PyTorch. The generative AI model interprets prompt text as input and dynamically generates a meal plan that takes the user's health status into account. During this process, an emotion engine built into the server analyzes the user's input emotional state.

[0810] The emotion engine utilizes natural language processing technology to convert emotional information from users into numerical data. This allows the generative AI model to recommend foods and beverages containing appropriate nutrients based on emotions such as stress and happiness.

[0811] The generated meal plans can be implemented through food supply services partnered with the server. For example, if a user is feeling stressed, the server might suggest a plan that includes relaxing foods, such as herbal tea.

[0812] Furthermore, the server utilizes the user's location information to search for nearby restaurants and present the user with the best options.

[0813] A concrete example of a prompt would be, "Please suggest a lunch menu based on today's emotional state and health indicators."

[0814] Users select meal plans and provide feedback via an application on their device. The feedback information is resent to the server and used to improve the accuracy of future analyses.

[0815] This system allows users to easily make dietary choices that suit their health and emotional state, thereby improving their quality of life.

[0816] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0817] Step 1:

[0818] Users input health indicators such as weight, diet, daily activity, and emotional state through a health management application. Once this data is entered into the application, the device aggregates the information and prepares to send it directly to the server. The input data includes numerical data (weight, activity level) and text data (emotional state). These are the inputs sent to the server.

[0819] Step 2:

[0820] The device sends health indicator data acquired from the user to the server. The server receives the data and first uses an emotion engine to analyze the emotional state, which is text data, and generates numerical emotion data. Next, the health indicators and emotion data are input into a generating AI model for analysis. Based on these inputs, the model interprets the user's health status and identifies potential nutrients and meal plans.

[0821] Step 3:

[0822] Based on the analysis, the server creates a meal plan using a generative AI model. This model generates meal options using prompts. For example, it can suggest a "relaxing lunch" to a user experiencing high stress levels. The output at this stage is a list of specific meal recommendations containing particular nutrients.

[0823] Step 4:

[0824] The server uses the generated meal plan to send the plan details to partner food supply services to confirm available ingredients. It also uses the user's location information to search for nearby restaurants and create a list of suitable establishments. This information is output in list format and sent back from the server to the terminal.

[0825] Step 5:

[0826] The terminal displays meal plans and a list of facilities received from the server to the user. The user then reviews the plan and selects a meal that suits their preferences. This selection is sent back to the server as feedback. The user can then take action, such as placing an online order or ordering in-store, as appropriate.

[0827] (Application Example 2)

[0828] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0829] In health management and meal selection, there is a lack of personalized suggestions that take into account the emotional state of individual users, resulting in a challenge in making effective meal choices that respond to users' momentary emotions and health conditions. Furthermore, there is a need for an efficient ordering system that allows users to quickly access the suggested meal options.

[0830] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0831] This invention includes a server that integrates health data and emotional state using emotion analysis technology to provide more personalized meal suggestions, and a means that enables users to directly order the suggested meal content from a delivery service via a user terminal. This makes it possible to select the optimal meal according to the user's emotional state and to place orders quickly.

[0832] A "health management application" is software used to collect, analyze, and manage data related to a user's health.

[0833] "Health data" refers to information that indicates the user's physical measurements and health status.

[0834] Nutrients are chemical substances necessary for the growth, development, and maintenance of health in living organisms.

[0835] A "food supply service" is a business entity that provides or supplies food or meals to users.

[0836] "Emotion analysis technology" is a technology that estimates a user's emotional state at a given time based on their input data and behavior.

[0837] A "meal plan" refers to the content and schedule of meals proposed based on an individual's health condition and preferences.

[0838] "Location information" refers to information that indicates the user's current location or a specific place.

[0839] A "user terminal" is a device that a user directly operates or uses to input and retrieve information.

[0840] A "delivery service" is a service that delivers food or goods to a specific location or user.

[0841] "Feedback data" refers to information that shows evaluations and impressions based on user behavior and choices.

[0842] The system implementing this invention operates in conjunction with a user terminal, a server, and a distribution service.

[0843] The server receives health and emotional data from the user's terminal. The received data is analyzed using a generative AI model running on the server. This model integrates diverse data obtained through the health management application and further identifies the user's emotional state using emotion analysis technology. Based on the analysis results, the server generates a personalized meal plan for each user and sends the details to the user's terminal.

[0844] The user terminal displays the received meal plan on its screen and intuitively offers the user options tailored to their emotional state. Using the latest location information, the user terminal can guide users to nearby restaurants and menus from online delivery services. This allows the user to instantly order an optimized meal.

[0845] As a concrete example, suppose a user enters their weight and past eating history into a health management application and also sends emotional data from their smart device. At this point, the server analyzes that the user is stressed and suggests meals containing ingredients that are expected to have a relaxing effect. The user can select a suggested menu on the app and immediately receive their meal, with pre-booking at a nearby restaurant or online ordering. This process particularly requires the effective use of a "generative AI model." An example of a prompt message would be: "Create a recommended meal plan based on the user's current emotional state. The user is stressed. What foods are recommended?"

[0846] In terms of hardware, user terminals will be smartphones or tablets, and the server will utilize a general cloud server environment. The software will employ various APIs, including Microsoft Azure's Sentiment Recognition API and the Yelp API. Firebase will be used for the database, enabling real-time data management.

[0847] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0848] Step 1:

[0849] The user enters their health data into the device.

[0850] The data entered includes weight, dietary history, and allergy information. This data is formatted by the terminal and prepared to be sent to the server.

[0851] Step 2:

[0852] The device collects emotional data.

[0853] The device analyzes the user's emotional state based on sensors in the smart device and the user's self-reported information. This information is also ready to be sent to the server.

[0854] Step 3:

[0855] The server receives health data and emotional data.

[0856] The server stores this data in a database and processes it into the format required for analysis in the next step. This processing includes operations such as data standardization and missing value imputation.

[0857] Step 4:

[0858] The server activates the generated AI model using the data it receives.

[0859] The generative AI model receives user health and emotional data as input and creates individually optimized meal plans based on this data. As output, it generates meal suggestions tailored to each user.

[0860] Step 5:

[0861] The server sends meal suggestions.

[0862] The generated meal plan is sent to the user's device and displayed on the user interface. This display includes specific nutrient suggestions and information on relaxation effects based on emotions.

[0863] Step 6:

[0864] The user checks meal suggestions on their device and enters their choices.

[0865] The user selects from the displayed menu and decides what to order. The user's choices are sent to the server as feedback data.

[0866] Step 7:

[0867] The device uses location information to search for the most suitable restaurant.

[0868] Based on location information, the system uses the Yelp API and other tools to search for and suggest nearby restaurants. Along with restaurant information, it also displays whether online ordering is available.

[0869] Step 8:

[0870] The user places an order with the distribution service based on their selections.

[0871] Users can select meal options suggested within the application and purchase them online or make reservations at nearby establishments. This step provides an interface for direct ordering.

[0872] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0873] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0874] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0875] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0876] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0877] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0878] The inside of the Emotion Map 400 represents what's in your mind, while the outside represents what you're doing. Therefore, the further you go out the 400-coordinate scale, the more visible your emotions become (the more they manifest in your actions).

[0879] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0880] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0881] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0882] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0883] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0884] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0885] 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.

[0886] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0887] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0888] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0889] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0890] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0891] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0892] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0893] The following is further disclosed regarding the embodiments described above.

[0894] (Claim 1)

[0895] A means of receiving health data acquired from multiple health management applications,

[0896] A means of analyzing received health data and calculating the nutrients each individual needs,

[0897] A means of generating an individually optimized meal plan in cooperation with a food delivery service based on the calculated nutrients,

[0898] A means of procuring necessary ingredients through food provision services,

[0899] A system that includes a means of suggesting the most suitable nearby dining facilities using location information.

[0900] (Claim 2)

[0901] The system according to claim 1, which notifies a user terminal of food options based on information from a food delivery service.

[0902] (Claim 3)

[0903] The system according to claim 1, which feeds the user's selection back into the system and reflects it in future meal planning.

[0904] "Example 1"

[0905] (Claim 1)

[0906] A means for receiving health information acquired from multiple health management devices,

[0907] A means of analyzing received health information and calculating the nutritional components necessary for each individual user,

[0908] A means of generating individually optimized meal plans in conjunction with a food delivery system based on calculated nutritional components,

[0909] Means of procuring necessary ingredients through a food supply system,

[0910] A method for suggesting the most suitable restaurants and bars in the surrounding area using location information,

[0911] A system that includes a means of automatically generating meal plans from health information using generative AI technology.

[0912] (Claim 2)

[0913] The system according to claim 1, which notifies a user device of food options based on information from a food supply system.

[0914] (Claim 3)

[0915] The system according to claim 1, which feeds the user's choices back into the system and reflects them in future meal plans.

[0916] "Application Example 1"

[0917] (Claim 1)

[0918] A means of receiving health information obtained from multiple information processing programs,

[0919] A means for analyzing received health information and calculating the nutritional components required by each individual,

[0920] A means of generating individually optimized meal plans in conjunction with information provision services based on calculated nutritional components,

[0921] A means of procuring necessary ingredients through information provision services,

[0922] A method for suggesting the most suitable food service provider in the surrounding area using location information,

[0923] A system that includes a means of notifying individuals of information that provides optimal meals based on their individual health information, in conjunction with goods delivery services.

[0924] (Claim 2)

[0925] The system according to claim 1, which notifies a user terminal of food options based on information from goods delivery operations.

[0926] (Claim 3)

[0927] The system according to claim 1, which feeds back the individual selection to the system and reflects it in the meal plan for the next time.

[0928] "Example 2 of combining an emotion engine"

[0929] (Claim 1)

[0930] A means of receiving health indicators obtained from multiple health management applications,

[0931] A method for analyzing received health indicators and emotional states to calculate the nutrients each individual needs,

[0932] A means of generating an individually optimized meal plan using a generative AI model based on calculated nutrients and emotional state,

[0933] A means of procuring necessary ingredients through ingredient supply services,

[0934] A system that includes a means of suggesting the most suitable nearby dining facilities using location information.

[0935] (Claim 2)

[0936] The system according to claim 1, which notifies a user terminal of food options based on information from a food supply service.

[0937] (Claim 3)

[0938] The system according to claim 1, which feeds the user's selection back into the system and reflects it in future meal planning.

[0939] "Application example 2 when combining with an emotional engine"

[0940] (Claim 1)

[0941] A means of receiving health data acquired from multiple health management applications,

[0942] A means of analyzing received health data and calculating the nutrients each individual needs,

[0943] A means of generating an individually optimized meal plan in cooperation with a food delivery service based on the calculated nutrients,

[0944] A means of procuring necessary ingredients through food provision services,

[0945] A method for suggesting the most suitable nearby dining facilities using location information,

[0946] A means of integrating health data and emotional state using emotion analysis technology to provide more personalized dietary recommendations,

[0947] A system that includes means for allowing users to directly order meals suggested via their terminal from a delivery service.

[0948] (Claim 2)

[0949] The system according to claim 1, which, based on information from a food service, notifies the user terminal of food options and presents selection options according to the user's emotional state.

[0950] (Claim 3)

[0951] The system according to claim 1, which analyzes feedback data including user choices and reflects improvements that take into account the user's emotional state in future meal plans. [Explanation of Symbols]

[0952] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving health information obtained from multiple information processing programs, A means for analyzing received health information and calculating the nutritional components required by each individual, A means of generating individually optimized meal plans in conjunction with information provision services based on calculated nutritional components, A means of procuring necessary ingredients through information provision services, A method for suggesting the most suitable food service provider in the surrounding area using location information, A system that includes a means of notifying individuals of information that provides optimal meals based on their individual health information, in conjunction with goods delivery services.

2. The system according to claim 1, which notifies a user terminal of food options based on information from goods delivery operations.

3. The system according to claim 1, which feeds back the individual selection to the system and reflects it in the meal plan for the next time.

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