system

Abstract

We provide the system. [Solution] A means by which users can input and update health information and acquire health measurement data in real time from a measuring device, A data processing means for selecting appropriate food and beverages in nearby restaurants and shops based on the user's entered health information and acquired measurement data, A means of presenting users with information on the nutritional composition and dietary restrictions of selected food and beverage products, In home appliances, an interactive means for suggesting ingredients or dishes based on the user's health condition and providing cooking assistance, A system that includes this.

Description

Technical Field

[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 character of the chatbot, 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] The problem to be solved by the present invention is to provide a system that can assist in making appropriate dietary choices based on the health status of a user. In particular, it aims to solve the problem that it is difficult to provide a means for the user to effectively input and update their health information and, based on that, provide options for healthy food and beverages corresponding to dietary restrictions. Also, it is required to respond to the construction of a food and beverage provision system according to regional characteristics and customer characteristics.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a means for users to input and update health information and acquire health measurement data in real time from a wearable device. Furthermore, based on this information, a means is constructed to select appropriate food and beverages from nearby restaurants and shops using data processing means, and to present users with information corresponding to nutritional composition and dietary restrictions. In addition, the invention proposes a system that supports meal selection tailored to individual health conditions and preferences by providing cooking procedures and alternative ingredient suggestions, and also enables service providers to offer new food and beverages that cater to patients with lifestyle-related diseases by generating and proposing cooking procedures based on regional and customer characteristics.

[0006] A "user" refers to an individual who inputs health information into the system and receives food and beverage recommendations.

[0007] "Health information" refers to data that users input into an application or obtain through wearable devices, and includes diagnostic results and measurements that indicate health status.

[0008] A "wearable device" refers to a device that measures a user's health status in real time and provides that data to an application.

[0009] "Data processing means" refers to a computation method that includes algorithms used by the system to select nearby food and beverages based on the user's health information and location information.

[0010] "Food and beverages" refers to ingredients and menu items that users can consume, and for which options are available to accommodate health restrictions.

[0011] "Selecting" refers to analyzing collected data to choose the food and beverage products that are best suited to the user's health condition.

[0012] "Nutritional composition" refers to the composition of nutrients and calories contained in food and beverages, and is information used for users' health management.

[0013] "Cooking procedures" refer to the steps and methods necessary to prepare a particular food or beverage.

[0014] "Ingredient substitutions" refer to suggestions to change ingredients used in a particular cooking procedure to suit health conditions or personal preferences.

[0015] "Regional characteristics" refer to geographical, cultural, and social factors that influence the provision of food and beverages.

[0016] "Customer characteristics" refer to the characteristics of a user group that consumes food and beverages, such as their health status, preferences, and purchasing habits.

[0017] "Adult disease patients" refer to adult individuals suffering from chronic diseases such as hyperlipidemia or diabetes. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] 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. [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.

Mode for Carrying Out the Invention

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

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

[0021] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

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

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0033] 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.

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

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is a system that accurately manages the user's health status and supports the selection of health-conscious food and beverages. The system is configured as follows:

[0040] First, users input their health information through the application. This information, including diagnostic results and hospital test results, is regularly updated within the app. In addition, users send real-time health data measured using wearable devices to their terminals, and this data is aggregated on a server. The accumulated health information is then analyzed by the system's central data processing system.

[0041] The server executes a process to appropriately select food and beverages from nearby restaurants and shops based on the user's health and location information. This includes evaluating nutritional composition and calorie information, and extracting store information that takes health restrictions into consideration. The selection results are presented to the user via the terminal to support healthy choices.

[0042] Furthermore, the system includes a recipe suggestion function tailored to the user's preferences and the season. The server searches the database for recipes and displays those suitable for the user on the terminal. These recipes include cooking instructions and ingredient substitutions, allowing users to enjoy healthy meals at home.

[0043] To explain in more detail, for example, if a user has diabetes, real-time data on their heart rate and blood sugar levels is entered into the app. Based on this data, the server analyzes information on nearby restaurants, selects restaurants that are likely to offer menus that take carbohydrate restriction into consideration, and displays them on the user's device. As a result, users can make food choices that are more mindful of their own health.

[0044] Furthermore, the server generates and provides new recipes tailored to lifestyle-related disease patients, based on regional and customer characteristics, for food service businesses. This feature allows businesses to develop unique menus and attract customers to specific segments. Overall, the system aims to provide a dining experience optimized for individual health conditions.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] Users launch the application on their devices and input and update their health information. This includes manually entering diagnostic and test results, as well as scanning and registering digital documents from hospitals.

[0048] Step 2:

[0049] The device connects with wearable devices to acquire real-time health measurement data from the user. This measurement data (e.g., heart rate, blood glucose level) is sent from the device to the server.

[0050] Step 3:

[0051] The server stores received health information and measurement data in a database. Security features are used to encrypt the data and ensure its confidentiality.

[0052] Step 4:

[0053] The device uses its GPS function to obtain the user's current location information. This location information is then sent to the server.

[0054] Step 5:

[0055] The server processes data based on the user's health information and location to select appropriate food and beverage items from nearby restaurants and shops. This includes using AI to analyze menus based on nutritional composition and health restrictions.

[0056] Step 6:

[0057] The server generates a list of food and beverages that match the user's health condition as a result of the selection process and sends it to the terminal.

[0058] Step 7:

[0059] The terminal displays a list of food and beverages received from the server to the user. The list includes nutritional information and calorie information, which the user can use to make informed food choices.

[0060] Step 8:

[0061] The server considers the user's health status and seasonal information to search for a suitable recipe from the database. The selected recipe includes cooking instructions and alternative ingredients.

[0062] Step 9:

[0063] The device displays recipes suitable for the user. The user can then prepare healthy meals at home based on the suggested recipes.

[0064] (Example 1)

[0065] 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."

[0066] In modern society, maintaining personal health is a matter of great concern, and managing one's diet is particularly central to this. However, selecting appropriate foods and beverages according to individual health conditions and preferences is not easy, and selection based on regional conditions and information about restaurants and bars becomes even more complex. Furthermore, while there is a need for prompt and accurate responses to changes in health conditions, the systems to achieve this are not adequately developed. In this context, there is a need for a support system that enables individuals to accurately understand their own health conditions and, based on that understanding, to achieve appropriate dietary habits.

[0067] 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.

[0068] This invention includes a server that enables individuals to input and update health information and acquire health measurement data in real time from a portable measuring device; an information processing means for selecting appropriate food and beverages at nearby restaurants and shops based on the individual's input health information and acquired measurement data; a means for presenting the individual with information corresponding to the nutritional composition and dietary restrictions of the selected food and beverages; a means for searching nearby restaurants and shops using location identification technology based on the individual's current location information; a means for identifying changes in the individual's health status and suggesting the next course of action to the individual using a generative artificial intelligence model; and a means for collecting individual feedback and learning to improve the accuracy of future suggestions. This makes it possible to make optimal food and beverage choices according to the individual's health status and to realize an effective diet for maintaining health.

[0069] An "individual" is a user of a system that manages health information using input devices and collects data using measuring devices.

[0070] "Health information" refers to data about a user's physical condition provided using an input device, including diagnostic results and allergy information.

[0071] "A means of acquiring health measurement data in real time" refers to a system that can receive data from wearable devices and other measurement devices without any time delay.

[0072] "Information processing means" refers to a system component that includes technology for analyzing an individual's health information and measurement data, and selecting the most suitable food and beverages based on that analysis.

[0073] "Nutritional composition" refers to information about the nutritional components contained in a particular food or food product, and is composed of components such as energy, protein, fat, carbohydrates, vitamins, and minerals.

[0074] A "dietary restriction" is a list of specific nutrients or foods whose intake is restricted or recommended based on an individual's health condition.

[0075] A "generative artificial intelligence model" is a computer program that learns from past data and predicts or suggests future actions by recognizing patterns and changes.

[0076] "Location-based technology" refers to technologies used to detect a user's current location, such as GPS and Wi-Fi triangulation, to acquire location information.

[0077] "Means for collecting feedback and learning to improve the accuracy of future suggestions" refers to a function that aims to improve the system based on user responses and opinions, and to make more accurate suggestions.

[0078] This invention provides a specific method for implementing a health management system that manages an individual's health status and suggests the most suitable food and beverages accordingly.

[0079] Users first input their health information using a dedicated application. This health information includes diagnostic results, allergy information, and information about their dietary preferences and lifestyle. Users can also use wearable devices to acquire real-time data such as heart rate, steps taken, and blood glucose levels, and send this data to the terminal. The terminal then aggregates this data and sends it to the server.

[0080] The server uses received health information and real-time data to perform analysis using a generative artificial intelligence model. This identifies changes in the user's health status and suggests the next course of action. The server also uses location-based technology to identify nearby restaurants and shops based on the user's current location and selects food and beverages suitable for the individual. Furthermore, it provides detailed information about the selected food and beverages based on nutritional composition and dietary restrictions.

[0081] For example, if a user has diabetes, the application receives real-time blood glucose readings. Based on this data, the server uses a generative AI model to suggest nearby restaurants that offer low-carb menus. For instance, it can generate a prompt such as, "Show me nearby restaurants that offer low-carb menus based on my current blood glucose level," providing the user with appropriate options.

[0082] This system's architecture aims to enable users to more effectively manage their own health by utilizing advanced information processing technology to support healthy eating habits. It also allows for improved suggestion accuracy through a feedback function, providing valuable support to users.

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

[0084] Step 1:

[0085] Users input health information through a dedicated application. This input includes height, weight, allergy information, and information on lifestyle-related diseases. This information is stored in the application's database. The entered data is used as foundational data for subsequent processing.

[0086] Step 2:

[0087] Users use wearable devices to acquire real-time health data such as heart rate, steps taken, and blood glucose levels. The device aggregates this real-time data and sends it to a server. The aggregated data serves as input for monitoring the user's health status in real time.

[0088] Step 3:

[0089] The server inputs received health information and real-time data into a generating AI model. The generating AI model analyzes this data, identifies changes in the user's health status, and predicts the next course of action. As a result of the analysis, health guidelines and precautions are generated.

[0090] Step 4:

[0091] The server obtains the user's current location information and uses location-based technology to search for nearby restaurants and shops. Combining the current location information with the analysis results, it selects food and beverages that are appropriate for the user's health condition. The selected food and beverages take nutritional information and dietary restrictions into consideration.

[0092] Step 5:

[0093] The server generates detailed information about the selected food and beverage, such as nutritional composition and appropriate dietary restrictions, and sends it to the terminal. The terminal then displays this information in an easy-to-understand format to support the user in making healthy food choices.

[0094] Step 6:

[0095] Users select food and beverages based on the provided information and enter feedback into the app after consumption. The device collects this feedback and sends it to the server. The server updates the generative AI model based on the feedback and learns to improve the accuracy of its suggestions.

[0096] This system allows users to monitor their health status in real time and maintain a healthy diet.

[0097] (Application Example 1)

[0098] 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."

[0099] In modern society, choosing foods and beverages that suit individual health conditions is crucial, but it is not easy for users to maintain a diet that aligns with their own health. In particular, there is a lack of support for selecting appropriate ingredients and dishes based on health information, as well as guidance on cooking methods, in daily life. Furthermore, it is difficult to propose dietary recommendations that take into account users' real-time health data and preferences. These problems need to be addressed.

[0100] 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.

[0101] In this invention, the server includes means for users to input and update health information and acquire health measurement data in real time from a measuring device; data processing means for selecting appropriate food and beverages from nearby restaurants and shops based on the health information input by the user and the acquired measurement data; means for presenting the user with information corresponding to the nutritional composition and dietary restrictions of the selected food and beverages; and interactive means for providing cooking support by suggesting ingredients or dishes based on the user's health condition in a home appliance. As a result, users can make appropriate food and beverage choices based on their own health condition, and support a healthy diet becomes possible.

[0102] "Health information" is a general term for data related to physiological indicators and health status that users input or acquire.

[0103] A "measuring device" is a device used to acquire real-time health data from users, measuring their physical condition and providing that data.

[0104] "Data processing means" refers to a process or apparatus for selecting nearby food and beverages based on the user's health information and analyzing their nutritional components and restrictions.

[0105] "Selection of food and beverages" refers to the act of identifying and selecting food and beverages that are suitable for the user based on health information.

[0106] "Interactive means" refers to functions that enable communication between the user and the device, allowing for suggestions of ingredients and dishes.

[0107] The system that implements this application consists of a user, a terminal, and a server. First, the user inputs their health information into the terminal via an input device. This health information includes physiological indicators and diagnostic results. The measuring device detects the user's real-time health data, and this data is transmitted to the server via the terminal.

[0108] After receiving health information and measurement data, the server analyzes this data using data processing tools. This analysis utilizes cloud platforms such as AWS® Lambda and data processing frameworks such as Apache® Hadoop. Based on the analysis results, the server identifies food and beverages suitable for the user's health condition and selects them considering their nutritional content and dietary restrictions.

[0109] The terminal presents the user with selected food and beverages and recommended recipes based on information transmitted from the server. Furthermore, it utilizes interactive means with home appliances and a voice assistant function to communicate with the user and assist with cooking procedures.

[0110] For example, if a user has diabetes, real-time data on heart rate and blood glucose levels is sent from the measuring device to the server. The server analyzes this information and recommends restaurants and ingredients that offer carbohydrate-restricted meals. The terminal visualizes and presents this information to the user, assisting them in purchasing appropriate ingredients and preparing meals.

[0111] An example of a prompt to be input into the generating AI model might be: "Based on the user's health data, please suggest a low-calorie, nutritionally balanced meal menu. The user's current health status is as follows: heart rate 80, blood glucose 100, BMI 25."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] Users input health information into the terminal via an input device. This information includes physiological indicators and diagnostic results, which are converted into a standardized format by the terminal. This converted data is then sent to the server.

[0115] Step 2:

[0116] The terminal acquires the user's real-time health data via a measuring device. This measurement data includes heart rate and blood glucose levels, and is acquired at regular intervals and sent to the server. Upon receiving this data, the terminal immediately forwards it to the server using a communication module.

[0117] Step 3:

[0118] The server stores received health information and real-time health data in a database. Based on the stored data, data analysis is performed using data processing tools such as Apache Hadoop and AWS Lambda. In the analysis, the data is clustered to identify the most suitable food and beverage candidates for the user's health condition. This identification result is then passed to the next step.

[0119] Step 4:

[0120] The server uses the analysis results to match the user's location information with a database of nearby restaurants and bars. This allows it to select restaurants offering food and beverages suitable for the user and create a final recommendation list. This list, which includes nutritional information and dietary restrictions, is returned to the user's device.

[0121] Step 5:

[0122] The terminal visually and audibly presents the user with a list of suggestions received from the server. Interacting with the user is conducted using interactive means, such as a voice assistant function, as a home device. Based on the suggested food and drink selections, guidance is provided regarding the purchase of necessary ingredients and the preparation of meals.

[0123] Step 6:

[0124] If the user requests additional information or different options, they send a request to the server via their device. The server uses a generative AI model to create a new prompt and performs a re-analysis. Based on the results of this analysis, an updated suggestion is provided to the user. Specifically, a prompt such as "Please suggest a better nutritionally balanced meal menu based on the user's health data" is generated and analyzed by the model.

[0125] 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.

[0126] This invention is a system for supporting appropriate meal choices based on the user's health and emotional state, and aims to provide a more sophisticated service by utilizing an emotional engine. This system is implemented in the manner described below.

[0127] The user first launches the application and enters their health information. This includes diagnostic results and real-time health measurement data obtained from wearable devices. This health information is stored on a server via the terminal and used for subsequent data processing.

[0128] A distinctive feature of this system is the recognition of the user's emotional state by an emotion engine. The terminal is equipped with a function to analyze the user's face and voice tone, thereby measuring the user's emotions and acquiring that data. The emotional state is transmitted to the server as data associated with the user's health information.

[0129] The server integrates emotional states and health information provided by the emotion engine to select appropriate food and beverages for the user from nearby restaurants and shops. The selection process also considers the influence of the user's emotional state (e.g., stress, happiness) on their food choices. The selection results are sent to the terminal, and the user uses this information to make healthy food choices.

[0130] Furthermore, based on this information, the server provides cooking procedures and ingredient alternatives that are best suited to the user's emotional state. For example, if the user is experiencing high stress, options will be provided to recommend ingredients and cooking methods that have a relaxing effect. In this way, meal suggestions are tailored to each individual's emotional state.

[0131] For example, if a user is feeling stressed, the emotion engine recognizes this, and the server selects food and beverages that can help reduce stress based on the user's health and emotional state. For instance, it might display information on nearby stores offering relaxing herbal teas or nutritious salads on the user's terminal.

[0132] Furthermore, the server generates new cooking procedures for customers with lifestyle-related diseases based on regional and customer characteristics. This information is used in menu development at restaurants, enabling them to provide food and beverages tailored to specific customer needs.

[0133] Based on this model, users can receive comprehensive health support that takes into account their emotional and physical state, enabling them to make more personalized food choices.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user launches the application using their device and enters health information. This health information includes not only hospital test results but also real-time health measurement data obtained from wearable devices.

[0137] Step 2:

[0138] The device uses built-in sensors and cameras to detect the user's emotional state from their voice tone and facial expressions. An emotion engine analyzes this data to identify stress levels and types of emotions.

[0139] Step 3:

[0140] Health information and emotional state data are sent from the device to the server. The server securely receives this information and stores it in a database.

[0141] Step 4:

[0142] The server uses the user's health information, emotional state, and current location information obtained from the device to refer to a database of nearby restaurants and shops and select appropriate food and beverages. Using AI, it identifies menus that provide nutritional balance and address dietary restrictions based on the user's emotional state.

[0143] Step 5:

[0144] The server generates the selection results and sends them to the user via the terminal. The user can view a list of recommended foods and beverages on the terminal and see detailed nutritional information and dietary restrictions for each item.

[0145] Step 6:

[0146] The server creates cooking procedures and ingredient substitutions based on the user's emotional state. This information is designed to stabilize the user's emotions and contribute to improved health.

[0147] Step 7:

[0148] The device presents the user with this customized recipe information. The user can then use these recipes to prepare healthy meals at home that suit their emotional state.

[0149] Step 8:

[0150] The server considers regional and customer characteristics and proposes new cooking procedures for restaurants targeting patients with lifestyle-related diseases. Restaurants can then use this as a basis for menu development and improve service for specific customer segments.

[0151] (Example 2)

[0152] 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 will be referred to as the "terminal."

[0153] In modern society, it is important to make appropriate dietary choices based on individual health and emotional states. However, in many cases, emotional states are not considered when choosing food, making it difficult to select appropriate foods, beverages, and cooking methods. Furthermore, there is a lack of well-established systems that present meals tailored to individual users, resulting in a current shortage of personalized meal suggestions.

[0154] 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.

[0155] In this invention, the server includes means for users to input and update health information and acquire health indicator data in real time from wearable devices; information processing means for selecting appropriate food and beverages from nearby food and beverage establishments and stores based on the health information input by the user and the acquired indicator data; and means for analyzing the user's emotional state using an emotion determination mechanism and integrating it with health information to reflect in the selection of food and beverages. This enables personalized meal selection support based on the user's health and emotional state.

[0156] A "user" refers to an individual who uses the system to input health information and emotional states and make appropriate dietary choices.

[0157] A "wearable device" refers to a device worn on the body to acquire and record the user's health indicator data in real time.

[0158] "Health indicator data" refers to data that quantifies the user's health status, including heart rate and blood pressure.

[0159] "Information processing means" refers to the function of a server that integrates and analyzes a user's health information and emotional state to select appropriate food and beverages.

[0160] "Food and beverage service" refers to businesses or establishments that sell or provide food and beverages.

[0161] An "emotion determination mechanism" refers to a technological element used to analyze and quantify a user's emotional state.

[0162] "Cooking procedures" refer to the methods and processes involved in preparing food and beverages.

[0163] "Ingredient alternatives" refer to options for suggesting substitute ingredients when a particular ingredient cannot be used.

[0164] "Personalized meal selection" refers to individual meal suggestions optimized to take into account each user's health and emotional state.

[0165] This system supports users in making optimal dietary choices by allowing them to input their health information and emotional state. First, the user launches the application using their device and inputs their health information. This includes diagnostic results and health indicator data obtained in real time from wearable devices. Suitable wearable devices include smartwatches and fitness trackers.

[0166] After the user enters their health information, the device sends that data to a server. The server receives it and stores it in a database. Furthermore, the device is equipped with an emotion detection mechanism that identifies the user's emotional state by analyzing their voice tone and facial expressions. This emotion data is also sent to the server and integrated with the health information.

[0167] The server uses this data to leverage a generative AI model to select the most suitable food and beverages based on the user's health and emotional state. Specifically, it searches a database for suitable food and beverages from nearby food and beverage providers and stores, and makes selections that match the user's condition.

[0168] For example, if the emotion assessment mechanism detects that a user is feeling stressed, the server can suggest relaxing foods and drinks. This could include information on stores that offer herbal teas or nutritious salads.

[0169] This result is sent to the terminal and presented to the user. Based on the presented information, the user can choose a store and select healthy meals. Furthermore, if the user enters a prompt such as "Tell me about a relaxing drink," the system will provide information that meets that request.

[0170] Furthermore, the server provides cooking procedures and ingredient substitutions tailored to each user's emotional state. For example, it suggests recipes using herbs with calming effects for users experiencing high stress levels. In this way, the system supports users' health management and enables personalized meal choices.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user launches the application using a terminal and enters health information. This includes diagnostic results and real-time health indicator data obtained from wearable devices. The terminal receives this input and sends the data to the server as health information. The output is the saved user health information data.

[0174] Step 2:

[0175] The device is equipped with an emotion detection mechanism. When the user faces the device's camera, it analyzes their emotional state from their facial expressions. It also recognizes emotions from the tone of the user's voice, thereby acquiring emotional data. The output obtained from this process is the analyzed emotional state data, which is also sent to the server.

[0176] Step 3:

[0177] The server receives health information and emotional data sent by the user and stores it in a database. Next, it uses a generative AI model to analyze the data and select food and beverages appropriate for the user's state. The input is the stored health information and emotional data, and the output is a list of selected food and beverages.

[0178] Step 4:

[0179] Based on the analysis results, the server selects appropriate food and beverages from nearby food and beverage providers and stores. In this process, the information processing system searches a database and selects food and beverages corresponding to the user's health and emotional state. The output is specific store information related to the selected food and beverages.

[0180] Step 5:

[0181] The server transmits the selected meal information to the terminal. The terminal displays the received information to the user. The user can then make healthy meal choices based on the presented store information and food / drink options. The output is detailed meal suggestion information displayed to the user.

[0182] Step 6:

[0183] The server utilizes a generative AI model to generate cooking procedures and ingredient alternatives tailored to the user's emotional state. This analysis considers the input emotional state and proposes the most suitable cooking method for the user. The output consists of cooking procedures and ingredient alternatives that match the user's state.

[0184] (Application Example 2)

[0185] 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".

[0186] There is a need for a system that can select food and beverages that meet individual needs, taking into account a comprehensive understanding of health and emotional states. However, current systems do not adequately consider the user's emotional state, resulting in the challenge of making personalized and healthy food choices.

[0187] 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.

[0188] In this invention, the server includes means for users to input and update health information and acquire health measurement data in real time from wearable devices, means for analyzing the user's emotional state and acquiring data thereof, and means for selecting appropriate food and beverages offered at nearby restaurants and shops based on the user's health information and emotional state using data processing means. This enables precise meal selection tailored to the individual's health and emotional state.

[0189] "Health information" refers to data about the user's physical condition, including diagnostic results and real-time measurement data obtained from wearable devices.

[0190] "Emotional state" refers to the state of mind recognized by analyzing the user's facial features, voice tone, etc., and includes psychological factors such as stress and happiness.

[0191] A "wearable device" is an electronic device that a user can wear, and it is a device that acquires health status and activity data in real time.

[0192] "Data processing means" refers to technical means for analyzing collected health information and emotional state data, and for selecting the most suitable food and beverages for the user based on this analysis.

[0193] "Food and beverages" refers to drinks and food, and includes items tailored to the user's nutritional status and dietary restrictions.

[0194] "Processing procedures" refer to the steps involved in selecting appropriate cooking methods and ingredients based on the user's health and emotional state.

[0195] "Regional characteristics" refer to the food culture and consumption trends unique to a particular geographical area, and are factors that should be considered when providing food and beverages.

[0196] "Customer characteristics" refer to the preferences and health needs common to a specific group of users, and are used to provide personalized recommendations.

[0197] This invention is a system that enables personalized meal selection based on the user's health and emotional state. The system uses a smartphone, server, and wearable device as hardware. The software includes an application that runs on the user's terminal, a Python script for data analysis, and an emotion recognition AI model (e.g., Amazon Rekognition).

[0198] First, the user enters health information through a smartphone app. This information, along with data acquired in real time from a wearable device, is sent to a server. At that time, the app uses emotion recognition technology to analyze the user's face and voice and recognize their emotional state.

[0199] The server integrates the received health information and emotional state and processes the data. This allows it to select the most suitable food and beverages offered by nearby restaurants and shops. During the selection process, the user's emotional state (e.g., stress) is taken into consideration, and foods with relaxing effects are prioritized.

[0200] For example, if the server detects that a user's stress level is high during a specific time period, it will select a menu containing relaxing herbal tea and nutritious foods and notify the user's device. Furthermore, the AI ​​model can process prompts such as, "What foods would you recommend when a user's emotional state requires relaxation?"

[0201] This allows users to make optimal dietary choices tailored to their individual health and emotional states, supporting a healthy lifestyle.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user launches a smartphone app and enters health information. This data includes weight, height, and previous health checkup results. This data is sent directly from the application to a server and stored as baseline data for health management.

[0205] Step 2:

[0206] Heart rate and stress level data acquired in real time from wearable devices are sent to the user's terminal, and then further sent to a server. This data is used to monitor health status and as an indicator to evaluate the current physical condition.

[0207] Step 3:

[0208] On the user's device, emotion recognition technology is used to analyze the user's face and voice and determine their emotional state. In this process, features obtained from facial expressions and voice tone are provided as input to an AI model to recognize emotions such as stress and happiness.

[0209] Step 4:

[0210] The server integrates the received health information and emotional state to perform data analysis. Based on the health and emotional data sent to the server as input, it uses a Python script to execute an algorithm to select the most suitable food and beverage from the database.

[0211] Step 5:

[0212] Based on the analysis results, the server selects the most suitable meals and drinks for the user and notifies the user of this information on their smartphone. This information includes a list of foods suitable for the user's health and emotional state, as well as their nutritional value and health benefits.

[0213] Step 6:

[0214] Users receive notifications, review suggested food and beverages, and decide on meal details as needed. Following the optimized meal selection, they can then order from nearby restaurants.

[0215] Step 7:

[0216] A prompt is sent to the AI ​​model, which then suggests recommended food items based on the user's emotional state. The prompt includes questions such as, "What food would you recommend when the user's emotional state requires relaxation?"

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention is a system that accurately manages the user's health status and supports the selection of health-conscious food and beverages. The system is configured as follows:

[0234] First, users input their health information through the application. This information, including diagnostic results and hospital test results, is regularly updated within the app. In addition, users send real-time health data measured using wearable devices to their terminals, and this data is aggregated on a server. The accumulated health information is then analyzed by the system's central data processing system.

[0235] The server executes a process to appropriately select food and beverages from nearby restaurants and shops based on the user's health and location information. This includes evaluating nutritional composition and calorie information, and extracting store information that takes health restrictions into consideration. The selection results are presented to the user via the terminal to support healthy choices.

[0236] Furthermore, the system includes a recipe suggestion function tailored to the user's preferences and the season. The server searches the database for recipes and displays those suitable for the user on the terminal. These recipes include cooking instructions and ingredient substitutions, allowing users to enjoy healthy meals at home.

[0237] To explain in more detail, for example, if a user has diabetes, real-time data on their heart rate and blood sugar levels is entered into the app. Based on this data, the server analyzes information on nearby restaurants, selects restaurants that are likely to offer menus that take carbohydrate restriction into consideration, and displays them on the user's device. As a result, users can make food choices that are more mindful of their own health.

[0238] Furthermore, the server generates and provides new recipes tailored to lifestyle-related disease patients, based on regional and customer characteristics, for food service businesses. This feature allows businesses to develop unique menus and attract customers to specific segments. Overall, the system aims to provide a dining experience optimized for individual health conditions.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] Users launch the application on their devices and input and update their health information. This includes manually entering diagnostic and test results, as well as scanning and registering digital documents from hospitals.

[0242] Step 2:

[0243] The device connects with wearable devices to acquire real-time health measurement data from the user. This measurement data (e.g., heart rate, blood glucose level) is sent from the device to the server.

[0244] Step 3:

[0245] The server stores received health information and measurement data in a database. Security features are used to encrypt the data and ensure its confidentiality.

[0246] Step 4:

[0247] The device uses its GPS function to obtain the user's current location information. This location information is then sent to the server.

[0248] Step 5:

[0249] The server processes data based on the user's health information and location to select appropriate food and beverage items from nearby restaurants and shops. This includes using AI to analyze menus based on nutritional composition and health restrictions.

[0250] Step 6:

[0251] The server generates a list of food and beverages that match the user's health condition as a result of the selection process and sends it to the terminal.

[0252] Step 7:

[0253] The terminal displays a list of food and beverages received from the server to the user. The list includes nutritional information and calorie information, which the user can use to make informed food choices.

[0254] Step 8:

[0255] The server considers the user's health status and seasonal information to search for a suitable recipe from the database. The selected recipe includes cooking instructions and alternative ingredients.

[0256] Step 9:

[0257] The device displays recipes suitable for the user. The user can then prepare healthy meals at home based on the suggested recipes.

[0258] (Example 1)

[0259] 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."

[0260] In modern society, maintaining personal health is a matter of great concern, and managing one's diet is particularly central to this. However, selecting appropriate foods and beverages according to individual health conditions and preferences is not easy, and selection based on regional conditions and information about restaurants and bars becomes even more complex. Furthermore, while there is a need for prompt and accurate responses to changes in health conditions, the systems to achieve this are not adequately developed. In this context, there is a need for a support system that enables individuals to accurately understand their own health conditions and, based on that understanding, to achieve appropriate dietary habits.

[0261] 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.

[0262] This invention includes a server that enables individuals to input and update health information and acquire health measurement data in real time from a portable measuring device; an information processing means for selecting appropriate food and beverages at nearby restaurants and shops based on the individual's input health information and acquired measurement data; a means for presenting the individual with information corresponding to the nutritional composition and dietary restrictions of the selected food and beverages; a means for searching nearby restaurants and shops using location identification technology based on the individual's current location information; a means for identifying changes in the individual's health status and suggesting the next course of action to the individual using a generative artificial intelligence model; and a means for collecting individual feedback and learning to improve the accuracy of future suggestions. This makes it possible to make optimal food and beverage choices according to the individual's health status and to realize an effective diet for maintaining health.

[0263] An "individual" is a user of a system that manages health information using input devices and collects data using measuring devices.

[0264] "Health information" refers to data about a user's physical condition provided using an input device, including diagnostic results and allergy information.

[0265] "A means of acquiring health measurement data in real time" refers to a system that can receive data from wearable devices and other measurement devices without any time delay.

[0266] "Information processing means" refers to a system component that includes technology for analyzing an individual's health information and measurement data, and selecting the most suitable food and beverages based on that analysis.

[0267] "Nutritional composition" refers to information about the nutritional components contained in a particular food or food product, and is composed of components such as energy, protein, fat, carbohydrates, vitamins, and minerals.

[0268] A "dietary restriction" is a list of specific nutrients or foods whose intake is restricted or recommended based on an individual's health condition.

[0269] A "generative artificial intelligence model" is a computer program that learns from past data and predicts or suggests future actions by recognizing patterns and changes.

[0270] "Location-based technology" refers to technologies used to detect a user's current location, such as GPS and Wi-Fi triangulation, to acquire location information.

[0271] "Means for collecting feedback and learning to improve the accuracy of future suggestions" refers to a function that aims to improve the system based on user responses and opinions, and to make more accurate suggestions.

[0272] This invention provides a specific method for implementing a health management system that manages an individual's health status and suggests the most suitable food and beverages accordingly.

[0273] Users first input their health information using a dedicated application. This health information includes diagnostic results, allergy information, and information about their dietary preferences and lifestyle. Users can also use wearable devices to acquire real-time data such as heart rate, steps taken, and blood glucose levels, and send this data to the terminal. The terminal then aggregates this data and sends it to the server.

[0274] The server uses received health information and real-time data to perform analysis using a generative artificial intelligence model. This identifies changes in the user's health status and suggests the next course of action. The server also uses location-based technology to identify nearby restaurants and shops based on the user's current location and selects food and beverages suitable for the individual. Furthermore, it provides detailed information about the selected food and beverages based on nutritional composition and dietary restrictions.

[0275] For example, if a user has diabetes, the application receives real-time blood glucose readings. Based on this data, the server uses a generative AI model to suggest nearby restaurants that offer low-carb menus. For instance, it can generate a prompt such as, "Show me nearby restaurants that offer low-carb menus based on my current blood glucose level," providing the user with appropriate options.

[0276] This system's architecture aims to enable users to more effectively manage their own health by utilizing advanced information processing technology to support healthy eating habits. It also allows for improved suggestion accuracy through a feedback function, providing valuable support to users.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] Users input health information through a dedicated application. This input includes height, weight, allergy information, and information on lifestyle-related diseases. This information is stored in the application's database. The entered data is used as foundational data for subsequent processing.

[0280] Step 2:

[0281] Users use wearable devices to acquire real-time health data such as heart rate, steps taken, and blood glucose levels. The device aggregates this real-time data and sends it to a server. The aggregated data serves as input for monitoring the user's health status in real time.

[0282] Step 3:

[0283] The server inputs the received health information and real-time data into the generative AI model. The generative AI model analyzes these data, identifies changes in the user's health status, and predicts the actions to be taken next. As an analysis result, health guidelines and precautions are generated.

[0284] Step 4:

[0285] The server acquires the user's current location information and uses location identification technology to search for nearby food and beverage establishments and sales outlets. By combining the current location information and the analysis result, food and beverages suitable for the user's health condition are selected. The selected food and beverages take into account nutritional information and dietary restrictions.

[0286] Step 5:

[0287] The server generates detailed information about the selected food and beverages, such as nutritional composition and information about suitable dietary restrictions, and transmits it to the terminal. The terminal supports the user's healthy food choices by presenting this information to the user in an easy-to-understand manner.

[0288] Step 6:

[0289] The user selects food and beverages based on the provided information and enters feedback after consumption into the app. The terminal collects this feedback and transmits it to the server. The server updates the generative AI model based on the feedback and conducts learning to improve the proposal accuracy.

[0290] With this system, the user can grasp their own health status in real time and lead a healthy diet.

[0291] (Application Example 1)

[0292] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0293] In modern society, choosing foods and beverages that suit individual health conditions is crucial, but it is not easy for users to maintain a diet that aligns with their own health. In particular, there is a lack of support for selecting appropriate ingredients and dishes based on health information, as well as guidance on cooking methods, in daily life. Furthermore, it is difficult to propose dietary recommendations that take into account users' real-time health data and preferences. These problems need to be addressed.

[0294] 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.

[0295] In this invention, the server includes means for users to input and update health information and acquire health measurement data in real time from a measuring device; data processing means for selecting appropriate food and beverages from nearby restaurants and shops based on the health information input by the user and the acquired measurement data; means for presenting the user with information corresponding to the nutritional composition and dietary restrictions of the selected food and beverages; and interactive means for providing cooking support by suggesting ingredients or dishes based on the user's health condition in a home appliance. As a result, users can make appropriate food and beverage choices based on their own health condition, and support a healthy diet becomes possible.

[0296] "Health information" is a general term for data related to physiological indicators and health status that users input or acquire.

[0297] A "measuring device" is a device used to acquire real-time health data from users, measuring their physical condition and providing that data.

[0298] "Data processing means" refers to a process or apparatus for selecting nearby food and beverages based on the user's health information and analyzing their nutritional components and restrictions.

[0299] "Selection of food and beverages" refers to the act of identifying and selecting food and beverages that are suitable for the user based on health information.

[0300] "Interactive means" refers to functions that enable communication between the user and the device, allowing for suggestions of ingredients and dishes.

[0301] The system that implements this application consists of a user, a terminal, and a server. First, the user inputs their health information into the terminal via an input device. This health information includes physiological indicators and diagnostic results. The measuring device detects the user's real-time health data, and this data is transmitted to the server via the terminal.

[0302] After receiving health information and measurement data, the server analyzes this data using data processing tools. This analysis utilizes cloud platforms such as AWS Lambda and data processing frameworks such as Apache Hadoop. Based on the analysis results, the server identifies food and beverages suitable for the user's health condition and selects them considering their nutritional content and dietary restrictions.

[0303] The terminal presents the user with selected food and beverages and recommended recipes based on information transmitted from the server. Furthermore, it utilizes interactive means with home appliances and a voice assistant function to communicate with the user and assist with cooking procedures.

[0304] For example, if a user has diabetes, real-time data on heart rate and blood glucose levels is sent from the measuring device to the server. The server analyzes this information and recommends restaurants and ingredients that offer carbohydrate-restricted meals. The terminal visualizes and presents this information to the user, assisting them in purchasing appropriate ingredients and preparing meals.

[0305] An example of a prompt to be input into the generating AI model might be: "Based on the user's health data, please suggest a low-calorie, nutritionally balanced meal menu. The user's current health status is as follows: heart rate 80, blood glucose 100, BMI 25."

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The user inputs health information into the terminal through the input device. The information to be input includes physiological indicators and diagnostic results, which are converted by the terminal into a standardized format. This converted data is then sent to the server.

[0309] Step 2:

[0310] The terminal acquires the user's real-time health data via the measurement device. The measurement data includes items such as heart rate and blood glucose level, which are acquired at regular intervals and sent to the server. As soon as the terminal receives this data, it uses the communication module to transfer it to the server.

[0311] Step 3:

[0312] The server stores the received health information and real-time health data in the database. Based on the stored data, data analysis is performed using data processing means such as Apache Hadoop or AWS Lambda. In the analysis, the data is clustered to identify food and beverage candidates optimal for the user's health condition. This identification result is passed to the next step.

[0313] Step 4:

[0314] The server collates the analysis result with the user's location information and the database of surrounding food and beverage vendors. Thereby, store information providing food and beverages suitable for the user is selected to create a final proposal list. This list includes information regarding nutritional components and dietary restrictions and is returned to the terminal.

[0315] Step 5:

[0316] The terminal visually and audibly presents the user with a list of suggestions received from the server. Interacting with the user is conducted using interactive means, such as a voice assistant function, as a home device. Based on the suggested food and drink selections, guidance is provided regarding the purchase of necessary ingredients and the preparation of meals.

[0317] Step 6:

[0318] If the user requests additional information or different options, they send a request to the server via their device. The server uses a generative AI model to create a new prompt and performs a re-analysis. Based on the results of this analysis, an updated suggestion is provided to the user. Specifically, a prompt such as "Please suggest a better nutritionally balanced meal menu based on the user's health data" is generated and analyzed by the model.

[0319] 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.

[0320] This invention is a system for supporting appropriate meal choices based on the user's health and emotional state, and aims to provide a more sophisticated service by utilizing an emotional engine. This system is implemented in the manner described below.

[0321] The user first launches the application and enters their health information. This includes diagnostic results and real-time health measurement data obtained from wearable devices. This health information is stored on a server via the terminal and used for subsequent data processing.

[0322] A distinctive feature of this system is the recognition of the user's emotional state by an emotion engine. The terminal is equipped with a function to analyze the user's face and voice tone, thereby measuring the user's emotions and acquiring that data. The emotional state is transmitted to the server as data associated with the user's health information.

[0323] The server integrates emotional states and health information provided by the emotion engine to select appropriate food and beverages for the user from nearby restaurants and shops. The selection process also considers the influence of the user's emotional state (e.g., stress, happiness) on their food choices. The selection results are sent to the terminal, and the user uses this information to make healthy food choices.

[0324] Furthermore, based on this information, the server provides cooking procedures and ingredient alternatives that are best suited to the user's emotional state. For example, if the user is experiencing high stress, options will be provided to recommend ingredients and cooking methods that have a relaxing effect. In this way, meal suggestions are tailored to each individual's emotional state.

[0325] For example, if a user is feeling stressed, the emotion engine recognizes this, and the server selects food and beverages that can help reduce stress based on the user's health and emotional state. For instance, it might display information on nearby stores offering relaxing herbal teas or nutritious salads on the user's terminal.

[0326] Furthermore, the server generates new cooking procedures for customers with lifestyle-related diseases based on regional and customer characteristics. This information is used in menu development at restaurants, enabling them to provide food and beverages tailored to specific customer needs.

[0327] Based on this model, users can receive comprehensive health support that takes into account their emotional and physical state, enabling them to make more personalized food choices.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The user launches the application using their device and enters health information. This health information includes not only hospital test results but also real-time health measurement data obtained from wearable devices.

[0331] Step 2:

[0332] The device uses built-in sensors and cameras to detect the user's emotional state from their voice tone and facial expressions. An emotion engine analyzes this data to identify stress levels and types of emotions.

[0333] Step 3:

[0334] Health information and emotional state data are sent from the device to the server. The server securely receives this information and stores it in a database.

[0335] Step 4:

[0336] The server uses the user's health information, emotional state, and current location information obtained from the device to refer to a database of nearby restaurants and shops and select appropriate food and beverages. Using AI, it identifies menus that provide nutritional balance and address dietary restrictions based on the user's emotional state.

[0337] Step 5:

[0338] The server generates the selection results and sends them to the user via the terminal. The user can view a list of recommended foods and beverages on the terminal and see detailed nutritional information and dietary restrictions for each item.

[0339] Step 6:

[0340] The server creates cooking procedures and ingredient substitutions based on the user's emotional state. This information is designed to stabilize the user's emotions and contribute to improved health.

[0341] Step 7:

[0342] The device presents the user with this customized recipe information. The user can then use these recipes to prepare healthy meals at home that suit their emotional state.

[0343] Step 8:

[0344] The server considers regional and customer characteristics and proposes new cooking procedures for restaurants targeting patients with lifestyle-related diseases. Restaurants can then use this as a basis for menu development and improve service for specific customer segments.

[0345] (Example 2)

[0346] 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".

[0347] In modern society, it is important to make appropriate dietary choices based on individual health and emotional states. However, in many cases, emotional states are not considered when choosing food, making it difficult to select appropriate foods, beverages, and cooking methods. Furthermore, there is a lack of well-established systems that present meals tailored to individual users, resulting in a current shortage of personalized meal suggestions.

[0348] 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.

[0349] In this invention, the server includes means for users to input and update health information and acquire health indicator data in real time from wearable devices; information processing means for selecting appropriate food and beverages from nearby food and beverage establishments and stores based on the health information input by the user and the acquired indicator data; and means for analyzing the user's emotional state using an emotion determination mechanism and integrating it with health information to reflect in the selection of food and beverages. This enables personalized meal selection support based on the user's health and emotional state.

[0350] A "user" refers to an individual who uses the system to input health information and emotional states and make appropriate dietary choices.

[0351] A "wearable device" refers to a device worn on the body to acquire and record the user's health indicator data in real time.

[0352] "Health indicator data" refers to data that quantifies the user's health status, including heart rate and blood pressure.

[0353] "Information processing means" refers to the function of a server that integrates and analyzes a user's health information and emotional state to select appropriate food and beverages.

[0354] "Food and beverage service" refers to businesses or establishments that sell or provide food and beverages.

[0355] An "emotion determination mechanism" refers to a technological element used to analyze and quantify a user's emotional state.

[0356] "Cooking procedures" refer to the methods and processes involved in preparing food and beverages.

[0357] "Ingredient alternatives" refer to options for suggesting substitute ingredients when a particular ingredient cannot be used.

[0358] "Personalized meal selection" refers to individual meal suggestions optimized to take into account each user's health and emotional state.

[0359] This system supports users in making optimal dietary choices by allowing them to input their health information and emotional state. First, the user launches the application using their device and inputs their health information. This includes diagnostic results and health indicator data obtained in real time from wearable devices. Suitable wearable devices include smartwatches and fitness trackers.

[0360] After the user enters their health information, the device sends that data to a server. The server receives it and stores it in a database. Furthermore, the device is equipped with an emotion detection mechanism that identifies the user's emotional state by analyzing their voice tone and facial expressions. This emotion data is also sent to the server and integrated with the health information.

[0361] The server uses this data to leverage a generative AI model to select the most suitable food and beverages based on the user's health and emotional state. Specifically, it searches a database for suitable food and beverages from nearby food and beverage providers and stores, and makes selections that match the user's condition.

[0362] For example, if the emotion assessment mechanism detects that a user is feeling stressed, the server can suggest relaxing foods and drinks. This could include information on stores that offer herbal teas or nutritious salads.

[0363] This result is sent to the terminal and presented to the user. Based on the presented information, the user can choose a store and select healthy meals. Furthermore, if the user enters a prompt such as "Tell me about a relaxing drink," the system will provide information that meets that request.

[0364] Furthermore, the server provides cooking procedures and ingredient substitutions tailored to each user's emotional state. For example, it suggests recipes using herbs with calming effects for users experiencing high stress levels. In this way, the system supports users' health management and enables personalized meal choices.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The user launches the application using a terminal and enters health information. This includes diagnostic results and real-time health indicator data obtained from wearable devices. The terminal receives this input and sends the data to the server as health information. The output is the saved user health information data.

[0368] Step 2:

[0369] The device is equipped with an emotion detection mechanism. When the user faces the device's camera, it analyzes their emotional state from their facial expressions. It also recognizes emotions from the tone of the user's voice, thereby acquiring emotional data. The output obtained from this process is the analyzed emotional state data, which is also sent to the server.

[0370] Step 3:

[0371] The server receives health information and emotional data sent by the user and stores it in a database. Next, it uses a generative AI model to analyze the data and select food and beverages appropriate for the user's state. The input is the stored health information and emotional data, and the output is a list of selected food and beverages.

[0372] Step 4:

[0373] Based on the analysis results, the server selects appropriate food and beverages from nearby food and beverage providers and stores. In this process, the information processing system searches a database and selects food and beverages corresponding to the user's health and emotional state. The output is specific store information related to the selected food and beverages.

[0374] Step 5:

[0375] The server transmits the selected meal information to the terminal. The terminal displays the received information to the user. The user can then make healthy meal choices based on the presented store information and food / drink options. The output is detailed meal suggestion information displayed to the user.

[0376] Step 6:

[0377] The server utilizes a generative AI model to generate cooking procedures and ingredient alternatives tailored to the user's emotional state. This analysis considers the input emotional state and proposes the most suitable cooking method for the user. The output consists of cooking procedures and ingredient alternatives that match the user's state.

[0378] (Application Example 2)

[0379] 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."

[0380] There is a need for a system that can select food and beverages that meet individual needs, taking into account a comprehensive understanding of health and emotional states. However, current systems do not adequately consider the user's emotional state, resulting in the challenge of making personalized and healthy food choices.

[0381] 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.

[0382] In this invention, the server includes means for users to input and update health information and acquire health measurement data in real time from wearable devices, means for analyzing the user's emotional state and acquiring data thereof, and means for selecting appropriate food and beverages offered at nearby restaurants and shops based on the user's health information and emotional state using data processing means. This enables precise meal selection tailored to the individual's health and emotional state.

[0383] "Health information" refers to data about the user's physical condition, including diagnostic results and real-time measurement data obtained from wearable devices.

[0384] "Emotional state" refers to the state of mind recognized by analyzing the user's facial features, voice tone, etc., and includes psychological factors such as stress and happiness.

[0385] A "wearable device" is an electronic device that a user can wear, and it is a device that acquires health status and activity data in real time.

[0386] "Data processing means" refers to technical means for analyzing collected health information and emotional state data, and for selecting the most suitable food and beverages for the user based on this analysis.

[0387] "Food and beverages" refers to drinks and food, and includes items tailored to the user's nutritional status and dietary restrictions.

[0388] "Processing procedures" refer to the steps involved in selecting appropriate cooking methods and ingredients based on the user's health and emotional state.

[0389] "Regional characteristics" refer to the food culture and consumption trends unique to a particular geographical area, and are factors that should be considered when providing food and beverages.

[0390] "Customer characteristics" refer to the preferences and health needs common to a specific group of users, and are used to provide personalized recommendations.

[0391] This invention is a system that enables personalized meal selection based on the user's health and emotional state. The system uses a smartphone, server, and wearable device as hardware. The software includes an application that runs on the user's terminal, a Python script for data analysis, and an emotion recognition AI model (e.g., Amazon Rekognition).

[0392] First, the user enters health information through a smartphone app. This information, along with data acquired in real time from a wearable device, is sent to a server. At that time, the app uses emotion recognition technology to analyze the user's face and voice and recognize their emotional state.

[0393] The server integrates the received health information and emotional state and processes the data. This allows it to select the most suitable food and beverages offered by nearby restaurants and shops. During the selection process, the user's emotional state (e.g., stress) is taken into consideration, and foods with relaxing effects are prioritized.

[0394] For example, if the server detects that a user's stress level is high during a specific time period, it will select a menu containing relaxing herbal tea and nutritious foods and notify the user's device. Furthermore, the AI ​​model can process prompts such as, "What foods would you recommend when a user's emotional state requires relaxation?"

[0395] This allows users to make optimal dietary choices tailored to their individual health and emotional states, supporting a healthy lifestyle.

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The user launches a smartphone app and enters health information. This data includes weight, height, and previous health checkup results. This data is sent directly from the application to a server and stored as baseline data for health management.

[0399] Step 2:

[0400] Heart rate and stress level data acquired in real time from wearable devices are sent to the user's terminal, and then further sent to a server. This data is used to monitor health status and as an indicator to evaluate the current physical condition.

[0401] Step 3:

[0402] On the user's device, emotion recognition technology is used to analyze the user's face and voice and determine their emotional state. In this process, features obtained from facial expressions and voice tone are provided as input to an AI model to recognize emotions such as stress and happiness.

[0403] Step 4:

[0404] The server integrates the received health information and emotional state to perform data analysis. Based on the health and emotional data sent to the server as input, it uses a Python script to execute an algorithm to select the most suitable food and beverage from the database.

[0405] Step 5:

[0406] Based on the analysis results, the server selects the most suitable meals and drinks for the user and notifies the user of this information on their smartphone. This information includes a list of foods suitable for the user's health and emotional state, as well as their nutritional value and health benefits.

[0407] Step 6:

[0408] Users receive notifications, review suggested food and beverages, and decide on meal details as needed. Following the optimized meal selection, they can then order from nearby restaurants.

[0409] Step 7:

[0410] A prompt is sent to the AI ​​model, which then suggests recommended food items based on the user's emotional state. The prompt includes questions such as, "What food would you recommend when the user's emotional state requires relaxation?"

[0411] 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.

[0412] 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.

[0413] 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.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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".

[0427] This invention is a system that accurately manages the user's health status and supports the selection of health-conscious food and beverages. The system is configured as follows:

[0428] First, users input their health information through the application. This information, including diagnostic results and hospital test results, is regularly updated within the app. In addition, users send real-time health data measured using wearable devices to their terminals, and this data is aggregated on a server. The accumulated health information is then analyzed by the system's central data processing system.

[0429] The server executes a process to appropriately select food and beverages from nearby restaurants and shops based on the user's health and location information. This includes evaluating nutritional composition and calorie information, and extracting store information that takes health restrictions into consideration. The selection results are presented to the user via the terminal to support healthy choices.

[0430] Furthermore, the system includes a recipe suggestion function tailored to the user's preferences and the season. The server searches the database for recipes and displays those suitable for the user on the terminal. These recipes include cooking instructions and ingredient substitutions, allowing users to enjoy healthy meals at home.

[0431] To explain in more detail, for example, if a user has diabetes, real-time data on their heart rate and blood sugar levels is entered into the app. Based on this data, the server analyzes information on nearby restaurants, selects restaurants that are likely to offer menus that take carbohydrate restriction into consideration, and displays them on the user's device. As a result, users can make food choices that are more mindful of their own health.

[0432] Furthermore, the server generates and provides new recipes tailored to lifestyle-related disease patients, based on regional and customer characteristics, for food service businesses. This feature allows businesses to develop unique menus and attract customers to specific segments. Overall, the system aims to provide a dining experience optimized for individual health conditions.

[0433] The following describes the processing flow.

[0434] Step 1:

[0435] Users launch the application on their devices and input and update their health information. This includes manually entering diagnostic and test results, as well as scanning and registering digital documents from hospitals.

[0436] Step 2:

[0437] The device connects with wearable devices to acquire real-time health measurement data from the user. This measurement data (e.g., heart rate, blood glucose level) is sent from the device to the server.

[0438] Step 3:

[0439] The server stores received health information and measurement data in a database. Security features are used to encrypt the data and ensure its confidentiality.

[0440] Step 4:

[0441] The device uses its GPS function to obtain the user's current location information. This location information is then sent to the server.

[0442] Step 5:

[0443] The server processes data based on the user's health information and location to select appropriate food and beverage items from nearby restaurants and shops. This includes using AI to analyze menus based on nutritional composition and health restrictions.

[0444] Step 6:

[0445] The server generates a list of food and beverages that match the user's health condition as a result of the selection process and sends it to the terminal.

[0446] Step 7:

[0447] The terminal displays a list of food and beverages received from the server to the user. The list includes nutritional information and calorie information, which the user can use to make informed food choices.

[0448] Step 8:

[0449] The server considers the user's health status and seasonal information to search for a suitable recipe from the database. The selected recipe includes cooking instructions and alternative ingredients.

[0450] Step 9:

[0451] The device displays recipes suitable for the user. The user can then prepare healthy meals at home based on the suggested recipes.

[0452] (Example 1)

[0453] 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."

[0454] In modern society, maintaining personal health is a matter of great concern, and managing one's diet is particularly central to this. However, selecting appropriate foods and beverages according to individual health conditions and preferences is not easy, and selection based on regional conditions and information about restaurants and bars becomes even more complex. Furthermore, while there is a need for prompt and accurate responses to changes in health conditions, the systems to achieve this are not adequately developed. In this context, there is a need for a support system that enables individuals to accurately understand their own health conditions and, based on that understanding, to achieve appropriate dietary habits.

[0455] 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.

[0456] This invention includes a server that enables individuals to input and update health information and acquire health measurement data in real time from a portable measuring device; an information processing means for selecting appropriate food and beverages at nearby restaurants and shops based on the individual's input health information and acquired measurement data; a means for presenting the individual with information corresponding to the nutritional composition and dietary restrictions of the selected food and beverages; a means for searching nearby restaurants and shops using location identification technology based on the individual's current location information; a means for identifying changes in the individual's health status and suggesting the next course of action to the individual using a generative artificial intelligence model; and a means for collecting individual feedback and learning to improve the accuracy of future suggestions. This makes it possible to make optimal food and beverage choices according to the individual's health status and to realize an effective diet for maintaining health.

[0457] An "individual" is a user of a system that manages health information using input devices and collects data using measuring devices.

[0458] "Health information" refers to data about a user's physical condition provided using an input device, including diagnostic results and allergy information.

[0459] "A means of acquiring health measurement data in real time" refers to a system that can receive data from wearable devices and other measurement devices without any time delay.

[0460] "Information processing means" refers to a system component that includes technology for analyzing an individual's health information and measurement data, and selecting the most suitable food and beverages based on that analysis.

[0461] "Nutritional composition" refers to information about the nutritional components contained in a particular food or food product, and is composed of components such as energy, protein, fat, carbohydrates, vitamins, and minerals.

[0462] A "dietary restriction" is a list of specific nutrients or foods whose intake is restricted or recommended based on an individual's health condition.

[0463] A "generative artificial intelligence model" is a computer program that learns from past data and predicts or suggests future actions by recognizing patterns and changes.

[0464] "Location-based technology" refers to technologies used to detect a user's current location, such as GPS and Wi-Fi triangulation, to acquire location information.

[0465] "Means for collecting feedback and learning to improve the accuracy of future suggestions" refers to a function that aims to improve the system based on user responses and opinions, and to make more accurate suggestions.

[0466] This invention provides a specific method for implementing a health management system that manages an individual's health status and suggests the most suitable food and beverages accordingly.

[0467] Users first input their health information using a dedicated application. This health information includes diagnostic results, allergy information, and information about their dietary preferences and lifestyle. Users can also use wearable devices to acquire real-time data such as heart rate, steps taken, and blood glucose levels, and send this data to the terminal. The terminal then aggregates this data and sends it to the server.

[0468] The server uses received health information and real-time data to perform analysis using a generative artificial intelligence model. This identifies changes in the user's health status and suggests the next course of action. The server also uses location-based technology to identify nearby restaurants and shops based on the user's current location and selects food and beverages suitable for the individual. Furthermore, it provides detailed information about the selected food and beverages based on nutritional composition and dietary restrictions.

[0469] For example, if a user has diabetes, the application receives real-time blood glucose readings. Based on this data, the server uses a generative AI model to suggest nearby restaurants that offer low-carb menus. For instance, it can generate a prompt such as, "Show me nearby restaurants that offer low-carb menus based on my current blood glucose level," providing the user with appropriate options.

[0470] This system's architecture aims to enable users to more effectively manage their own health by utilizing advanced information processing technology to support healthy eating habits. It also allows for improved suggestion accuracy through a feedback function, providing valuable support to users.

[0471] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0472] Step 1:

[0473] Users input health information through a dedicated application. This input includes height, weight, allergy information, and information on lifestyle-related diseases. This information is stored in the application's database. The entered data is used as foundational data for subsequent processing.

[0474] Step 2:

[0475] Users use wearable devices to acquire real-time health data such as heart rate, steps taken, and blood glucose levels. The device aggregates this real-time data and sends it to a server. The aggregated data serves as input for monitoring the user's health status in real time.

[0476] Step 3:

[0477] The server inputs received health information and real-time data into a generating AI model. The generating AI model analyzes this data, identifies changes in the user's health status, and predicts the next course of action. As a result of the analysis, health guidelines and precautions are generated.

[0478] Step 4:

[0479] The server obtains the user's current location information and uses location-based technology to search for nearby restaurants and shops. Combining the current location information with the analysis results, it selects food and beverages that are appropriate for the user's health condition. The selected food and beverages take nutritional information and dietary restrictions into consideration.

[0480] Step 5:

[0481] The server generates detailed information about the selected food and beverage, such as nutritional composition and appropriate dietary restrictions, and sends it to the terminal. The terminal then displays this information in an easy-to-understand format to support the user in making healthy food choices.

[0482] Step 6:

[0483] Users select food and beverages based on the provided information and enter feedback into the app after consumption. The device collects this feedback and sends it to the server. The server updates the generative AI model based on the feedback and learns to improve the accuracy of its suggestions.

[0484] This system allows users to monitor their health status in real time and maintain a healthy diet.

[0485] (Application Example 1)

[0486] 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."

[0487] In modern society, choosing foods and beverages that suit individual health conditions is crucial, but it is not easy for users to maintain a diet that aligns with their own health. In particular, there is a lack of support for selecting appropriate ingredients and dishes based on health information, as well as guidance on cooking methods, in daily life. Furthermore, it is difficult to propose dietary recommendations that take into account users' real-time health data and preferences. These problems need to be addressed.

[0488] 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.

[0489] In this invention, the server includes means for users to input and update health information and acquire health measurement data in real time from a measuring device; data processing means for selecting appropriate food and beverages from nearby restaurants and shops based on the health information input by the user and the acquired measurement data; means for presenting the user with information corresponding to the nutritional composition and dietary restrictions of the selected food and beverages; and interactive means for providing cooking support by suggesting ingredients or dishes based on the user's health condition in a home appliance. As a result, users can make appropriate food and beverage choices based on their own health condition, and support a healthy diet becomes possible.

[0490] "Health information" is a general term for data related to physiological indicators and health status that users input or acquire.

[0491] A "measuring device" is a device used to acquire real-time health data from users, measuring their physical condition and providing that data.

[0492] "Data processing means" refers to a process or apparatus for selecting nearby food and beverages based on the user's health information and analyzing their nutritional components and restrictions.

[0493] "Selection of food and beverages" refers to the act of identifying and selecting food and beverages that are suitable for the user based on health information.

[0494] "Interactive means" refers to functions that enable communication between the user and the device, allowing for suggestions of ingredients and dishes.

[0495] The system that implements this application consists of a user, a terminal, and a server. First, the user inputs their health information into the terminal via an input device. This health information includes physiological indicators and diagnostic results. The measuring device detects the user's real-time health data, and this data is transmitted to the server via the terminal.

[0496] After receiving health information and measurement data, the server analyzes this data using data processing tools. This analysis utilizes cloud platforms such as AWS Lambda and data processing frameworks such as Apache Hadoop. Based on the analysis results, the server identifies food and beverages suitable for the user's health condition and selects them considering their nutritional content and dietary restrictions.

[0497] The terminal presents the user with selected food and beverages and recommended recipes based on information transmitted from the server. Furthermore, it utilizes interactive means with home appliances and a voice assistant function to communicate with the user and assist with cooking procedures.

[0498] For example, if a user has diabetes, real-time data on heart rate and blood glucose levels is sent from the measuring device to the server. The server analyzes this information and recommends restaurants and ingredients that offer carbohydrate-restricted meals. The terminal visualizes and presents this information to the user, assisting them in purchasing appropriate ingredients and preparing meals.

[0499] An example of a prompt to be input into the generating AI model might be: "Based on the user's health data, please suggest a low-calorie, nutritionally balanced meal menu. The user's current health status is as follows: heart rate 80, blood glucose 100, BMI 25."

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] Users input health information into the terminal via an input device. This information includes physiological indicators and diagnostic results, which are converted into a standardized format by the terminal. This converted data is then sent to the server.

[0503] Step 2:

[0504] The terminal acquires the user's real-time health data via a measuring device. This measurement data includes heart rate and blood glucose levels, and is acquired at regular intervals and sent to the server. Upon receiving this data, the terminal immediately forwards it to the server using a communication module.

[0505] Step 3:

[0506] The server stores received health information and real-time health data in a database. Based on the stored data, data analysis is performed using data processing tools such as Apache Hadoop and AWS Lambda. In the analysis, the data is clustered to identify the most suitable food and beverage candidates for the user's health condition. This identification result is then passed to the next step.

[0507] Step 4:

[0508] The server uses the analysis results to match the user's location information with a database of nearby restaurants and bars. This allows it to select restaurants offering food and beverages suitable for the user and create a final recommendation list. This list, which includes nutritional information and dietary restrictions, is returned to the user's device.

[0509] Step 5:

[0510] The terminal visually and audibly presents the user with a list of suggestions received from the server. Interacting with the user is conducted using interactive means, such as a voice assistant function, as a home device. Based on the suggested food and drink selections, guidance is provided regarding the purchase of necessary ingredients and the preparation of meals.

[0511] Step 6:

[0512] If the user requests additional information or different options, they send a request to the server via their device. The server uses a generative AI model to create a new prompt and performs a re-analysis. Based on the results of this analysis, an updated suggestion is provided to the user. Specifically, a prompt such as "Please suggest a better nutritionally balanced meal menu based on the user's health data" is generated and analyzed by the model.

[0513] 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.

[0514] This invention is a system for supporting appropriate meal choices based on the user's health and emotional state, and aims to provide a more sophisticated service by utilizing an emotional engine. This system is implemented in the manner described below.

[0515] The user first launches the application and enters their health information. This includes diagnostic results and real-time health measurement data obtained from wearable devices. This health information is stored on a server via the terminal and used for subsequent data processing.

[0516] A distinctive feature of this system is the recognition of the user's emotional state by an emotion engine. The terminal is equipped with a function to analyze the user's face and voice tone, thereby measuring the user's emotions and acquiring that data. The emotional state is transmitted to the server as data associated with the user's health information.

[0517] The server integrates emotional states and health information provided by the emotion engine to select appropriate food and beverages for the user from nearby restaurants and shops. The selection process also considers the influence of the user's emotional state (e.g., stress, happiness) on their food choices. The selection results are sent to the terminal, and the user uses this information to make healthy food choices.

[0518] Furthermore, based on this information, the server provides cooking procedures and ingredient alternatives that are best suited to the user's emotional state. For example, if the user is experiencing high stress, options will be provided to recommend ingredients and cooking methods that have a relaxing effect. In this way, meal suggestions are tailored to each individual's emotional state.

[0519] For example, if a user is feeling stressed, the emotion engine recognizes this, and the server selects food and beverages that can help reduce stress based on the user's health and emotional state. For instance, it might display information on nearby stores offering relaxing herbal teas or nutritious salads on the user's terminal.

[0520] Furthermore, the server generates new cooking procedures for customers with lifestyle-related diseases based on regional and customer characteristics. This information is used in menu development at restaurants, enabling them to provide food and beverages tailored to specific customer needs.

[0521] Based on this model, users can receive comprehensive health support that takes into account their emotional and physical state, enabling them to make more personalized food choices.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The user launches the application using their device and enters health information. This health information includes not only hospital test results but also real-time health measurement data obtained from wearable devices.

[0525] Step 2:

[0526] The device uses built-in sensors and cameras to detect the user's emotional state from their voice tone and facial expressions. An emotion engine analyzes this data to identify stress levels and types of emotions.

[0527] Step 3:

[0528] Health information and emotional state data are sent from the device to the server. The server securely receives this information and stores it in a database.

[0529] Step 4:

[0530] The server uses the user's health information, emotional state, and current location information obtained from the device to refer to a database of nearby restaurants and shops and select appropriate food and beverages. Using AI, it identifies menus that provide nutritional balance and address dietary restrictions based on the user's emotional state.

[0531] Step 5:

[0532] The server generates the selection results and sends them to the user via the terminal. The user can view a list of recommended foods and beverages on the terminal and see detailed nutritional information and dietary restrictions for each item.

[0533] Step 6:

[0534] The server creates cooking procedures and ingredient substitutions based on the user's emotional state. This information is designed to stabilize the user's emotions and contribute to improved health.

[0535] Step 7:

[0536] The device presents the user with this customized recipe information. The user can then use these recipes to prepare healthy meals at home that suit their emotional state.

[0537] Step 8:

[0538] The server considers regional and customer characteristics and proposes new cooking procedures for restaurants targeting patients with lifestyle-related diseases. Restaurants can then use this as a basis for menu development and improve service for specific customer segments.

[0539] (Example 2)

[0540] 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."

[0541] In modern society, it is important to make appropriate dietary choices based on individual health and emotional states. However, in many cases, emotional states are not considered when choosing food, making it difficult to select appropriate foods, beverages, and cooking methods. Furthermore, there is a lack of well-established systems that present meals tailored to individual users, resulting in a current shortage of personalized meal suggestions.

[0542] 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.

[0543] In this invention, the server includes means for users to input and update health information and acquire health indicator data in real time from wearable devices; information processing means for selecting appropriate food and beverages from nearby food and beverage establishments and stores based on the health information input by the user and the acquired indicator data; and means for analyzing the user's emotional state using an emotion determination mechanism and integrating it with health information to reflect in the selection of food and beverages. This enables personalized meal selection support based on the user's health and emotional state.

[0544] A "user" refers to an individual who uses the system to input health information and emotional states and make appropriate dietary choices.

[0545] A "wearable device" refers to a device worn on the body to acquire and record the user's health indicator data in real time.

[0546] "Health indicator data" refers to data that quantifies the user's health status, including heart rate and blood pressure.

[0547] "Information processing means" refers to the function of a server that integrates and analyzes a user's health information and emotional state to select appropriate food and beverages.

[0548] "Food and beverage service" refers to businesses or establishments that sell or provide food and beverages.

[0549] An "emotion determination mechanism" refers to a technological element used to analyze and quantify a user's emotional state.

[0550] "Cooking procedures" refer to the methods and processes involved in preparing food and beverages.

[0551] "Ingredient alternatives" refer to options for suggesting substitute ingredients when a particular ingredient cannot be used.

[0552] "Personalized meal selection" refers to individual meal suggestions optimized to take into account each user's health and emotional state.

[0553] This system supports users in making optimal dietary choices by allowing them to input their health information and emotional state. First, the user launches the application using their device and inputs their health information. This includes diagnostic results and health indicator data obtained in real time from wearable devices. Suitable wearable devices include smartwatches and fitness trackers.

[0554] After the user enters their health information, the device sends that data to a server. The server receives it and stores it in a database. Furthermore, the device is equipped with an emotion detection mechanism that identifies the user's emotional state by analyzing their voice tone and facial expressions. This emotion data is also sent to the server and integrated with the health information.

[0555] The server uses this data to leverage a generative AI model to select the most suitable food and beverages based on the user's health and emotional state. Specifically, it searches a database for suitable food and beverages from nearby food and beverage providers and stores, and makes selections that match the user's condition.

[0556] For example, if the emotion assessment mechanism detects that a user is feeling stressed, the server can suggest relaxing foods and drinks. This could include information on stores that offer herbal teas or nutritious salads.

[0557] This result is sent to the terminal and presented to the user. Based on the presented information, the user can choose a store and select healthy meals. Furthermore, if the user enters a prompt such as "Tell me about a relaxing drink," the system will provide information that meets that request.

[0558] Furthermore, the server provides cooking procedures and ingredient substitutions tailored to each user's emotional state. For example, it suggests recipes using herbs with calming effects for users experiencing high stress levels. In this way, the system supports users' health management and enables personalized meal choices.

[0559] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0560] Step 1:

[0561] The user launches the application using a terminal and enters health information. This includes diagnostic results and real-time health indicator data obtained from wearable devices. The terminal receives this input and sends the data to the server as health information. The output is the saved user health information data.

[0562] Step 2:

[0563] The device is equipped with an emotion detection mechanism. When the user faces the device's camera, it analyzes their emotional state from their facial expressions. It also recognizes emotions from the tone of the user's voice, thereby acquiring emotional data. The output obtained from this process is the analyzed emotional state data, which is also sent to the server.

[0564] Step 3:

[0565] The server receives health information and emotional data sent by the user and stores it in a database. Next, it uses a generative AI model to analyze the data and select food and beverages appropriate for the user's state. The input is the stored health information and emotional data, and the output is a list of selected food and beverages.

[0566] Step 4:

[0567] Based on the analysis results, the server selects appropriate food and beverages from nearby food and beverage providers and stores. In this process, the information processing system searches a database and selects food and beverages corresponding to the user's health and emotional state. The output is specific store information related to the selected food and beverages.

[0568] Step 5:

[0569] The server transmits the selected meal information to the terminal. The terminal displays the received information to the user. The user can then make healthy meal choices based on the presented store information and food / drink options. The output is detailed meal suggestion information displayed to the user.

[0570] Step 6:

[0571] The server utilizes a generative AI model to generate cooking procedures and ingredient alternatives tailored to the user's emotional state. This analysis considers the input emotional state and proposes the most suitable cooking method for the user. The output consists of cooking procedures and ingredient alternatives that match the user's state.

[0572] (Application Example 2)

[0573] 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."

[0574] There is a need for a system that can select food and beverages that meet individual needs, taking into account a comprehensive understanding of health and emotional states. However, current systems do not adequately consider the user's emotional state, resulting in the challenge of making personalized and healthy food choices.

[0575] 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.

[0576] In this invention, the server includes means for users to input and update health information and acquire health measurement data in real time from wearable devices, means for analyzing the user's emotional state and acquiring data thereof, and means for selecting appropriate food and beverages offered at nearby restaurants and shops based on the user's health information and emotional state using data processing means. This enables precise meal selection tailored to the individual's health and emotional state.

[0577] "Health information" refers to data about the user's physical condition, including diagnostic results and real-time measurement data obtained from wearable devices.

[0578] "Emotional state" refers to the state of mind recognized by analyzing the user's facial features, voice tone, etc., and includes psychological factors such as stress and happiness.

[0579] A "wearable device" is an electronic device that a user can wear, and it is a device that acquires health status and activity data in real time.

[0580] "Data processing means" refers to technical means for analyzing collected health information and emotional state data, and for selecting the most suitable food and beverages for the user based on this analysis.

[0581] "Food and beverages" refers to drinks and food, and includes items tailored to the user's nutritional status and dietary restrictions.

[0582] "Processing procedures" refer to the steps involved in selecting appropriate cooking methods and ingredients based on the user's health and emotional state.

[0583] "Regional characteristics" refer to the food culture and consumption trends unique to a particular geographical area, and are factors that should be considered when providing food and beverages.

[0584] "Customer characteristics" refer to the preferences and health needs common to a specific group of users, and are used to provide personalized recommendations.

[0585] This invention is a system that enables personalized meal selection based on the user's health and emotional state. The system uses a smartphone, server, and wearable device as hardware. The software includes an application that runs on the user's terminal, a Python script for data analysis, and an emotion recognition AI model (e.g., Amazon Rekognition).

[0586] First, the user enters health information through a smartphone app. This information, along with data acquired in real time from a wearable device, is sent to a server. At that time, the app uses emotion recognition technology to analyze the user's face and voice and recognize their emotional state.

[0587] The server integrates the received health information and emotional state and processes the data. This allows it to select the most suitable food and beverages offered by nearby restaurants and shops. During the selection process, the user's emotional state (e.g., stress) is taken into consideration, and foods with relaxing effects are prioritized.

[0588] For example, if the server detects that a user's stress level is high during a specific time period, it will select a menu containing relaxing herbal tea and nutritious foods and notify the user's device. Furthermore, the AI ​​model can process prompts such as, "What foods would you recommend when a user's emotional state requires relaxation?"

[0589] This allows users to make optimal dietary choices tailored to their individual health and emotional states, supporting a healthy lifestyle.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The user launches a smartphone app and enters health information. This data includes weight, height, and previous health checkup results. This data is sent directly from the application to a server and stored as baseline data for health management.

[0593] Step 2:

[0594] Heart rate and stress level data acquired in real time from wearable devices are sent to the user's terminal, and then further sent to a server. This data is used to monitor health status and as an indicator to evaluate the current physical condition.

[0595] Step 3:

[0596] On the user's device, emotion recognition technology is used to analyze the user's face and voice and determine their emotional state. In this process, features obtained from facial expressions and voice tone are provided as input to an AI model to recognize emotions such as stress and happiness.

[0597] Step 4:

[0598] The server integrates the received health information and emotional state to perform data analysis. Based on the health and emotional data sent to the server as input, it uses a Python script to execute an algorithm to select the most suitable food and beverage from the database.

[0599] Step 5:

[0600] Based on the analysis results, the server selects the most suitable meals and drinks for the user and notifies the user of this information on their smartphone. This information includes a list of foods suitable for the user's health and emotional state, as well as their nutritional value and health benefits.

[0601] Step 6:

[0602] Users receive notifications, review suggested food and beverages, and decide on meal details as needed. Following the optimized meal selection, they can then order from nearby restaurants.

[0603] Step 7:

[0604] A prompt is sent to the AI ​​model, which then suggests recommended food items based on the user's emotional state. The prompt includes questions such as, "What food would you recommend when the user's emotional state requires relaxation?"

[0605] 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.

[0606] 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.

[0607] 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.

[0608] [Fourth Embodiment]

[0609] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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".

[0622] This invention is a system that accurately manages the user's health status and supports the selection of health-conscious food and beverages. The system is configured as follows:

[0623] First, users input their health information through the application. This information, including diagnostic results and hospital test results, is regularly updated within the app. In addition, users send real-time health data measured using wearable devices to their terminals, and this data is aggregated on a server. The accumulated health information is then analyzed by the system's central data processing system.

[0624] The server executes a process to appropriately select food and beverages from nearby restaurants and shops based on the user's health and location information. This includes evaluating nutritional composition and calorie information, and extracting store information that takes health restrictions into consideration. The selection results are presented to the user via the terminal to support healthy choices.

[0625] Furthermore, the system includes a recipe suggestion function tailored to the user's preferences and the season. The server searches the database for recipes and displays those suitable for the user on the terminal. These recipes include cooking instructions and ingredient substitutions, allowing users to enjoy healthy meals at home.

[0626] To explain in more detail, for example, if a user has diabetes, real-time data on their heart rate and blood sugar levels is entered into the app. Based on this data, the server analyzes information on nearby restaurants, selects restaurants that are likely to offer menus that take carbohydrate restriction into consideration, and displays them on the user's device. As a result, users can make food choices that are more mindful of their own health.

[0627] Furthermore, the server generates and provides new recipes tailored to lifestyle-related disease patients, based on regional and customer characteristics, for food service businesses. This feature allows businesses to develop unique menus and attract customers to specific segments. Overall, the system aims to provide a dining experience optimized for individual health conditions.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] Users launch the application on their devices and input and update their health information. This includes manually entering diagnostic and test results, as well as scanning and registering digital documents from hospitals.

[0631] Step 2:

[0632] The device connects with wearable devices to acquire real-time health measurement data from the user. This measurement data (e.g., heart rate, blood glucose level) is sent from the device to the server.

[0633] Step 3:

[0634] The server stores received health information and measurement data in a database. Security features are used to encrypt the data and ensure its confidentiality.

[0635] Step 4:

[0636] The device uses its GPS function to obtain the user's current location information. This location information is then sent to the server.

[0637] Step 5:

[0638] The server processes data based on the user's health information and location to select appropriate food and beverage items from nearby restaurants and shops. This includes using AI to analyze menus based on nutritional composition and health restrictions.

[0639] Step 6:

[0640] The server generates a list of food and beverages that match the user's health condition as a result of the selection process and sends it to the terminal.

[0641] Step 7:

[0642] The terminal displays a list of food and beverages received from the server to the user. The list includes nutritional information and calorie information, which the user can use to make informed food choices.

[0643] Step 8:

[0644] The server considers the user's health status and seasonal information to search for a suitable recipe from the database. The selected recipe includes cooking instructions and alternative ingredients.

[0645] Step 9:

[0646] The device displays recipes suitable for the user. The user can then prepare healthy meals at home based on the suggested recipes.

[0647] (Example 1)

[0648] 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".

[0649] In modern society, maintaining personal health is a matter of great concern, and managing one's diet is particularly central to this. However, selecting appropriate foods and beverages according to individual health conditions and preferences is not easy, and selection based on regional conditions and information about restaurants and bars becomes even more complex. Furthermore, while there is a need for prompt and accurate responses to changes in health conditions, the systems to achieve this are not adequately developed. In this context, there is a need for a support system that enables individuals to accurately understand their own health conditions and, based on that understanding, to achieve appropriate dietary habits.

[0650] 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.

[0651] This invention includes a server that enables individuals to input and update health information and acquire health measurement data in real time from a portable measuring device; an information processing means for selecting appropriate food and beverages at nearby restaurants and shops based on the individual's input health information and acquired measurement data; a means for presenting the individual with information corresponding to the nutritional composition and dietary restrictions of the selected food and beverages; a means for searching nearby restaurants and shops using location identification technology based on the individual's current location information; a means for identifying changes in the individual's health status and suggesting the next course of action to the individual using a generative artificial intelligence model; and a means for collecting individual feedback and learning to improve the accuracy of future suggestions. This makes it possible to make optimal food and beverage choices according to the individual's health status and to realize an effective diet for maintaining health.

[0652] An "individual" is a user of a system that manages health information using input devices and collects data using measuring devices.

[0653] "Health information" refers to data about a user's physical condition provided using an input device, including diagnostic results and allergy information.

[0654] "A means of acquiring health measurement data in real time" refers to a system that can receive data from wearable devices and other measurement devices without any time delay.

[0655] "Information processing means" refers to a system component that includes technology for analyzing an individual's health information and measurement data, and selecting the most suitable food and beverages based on that analysis.

[0656] "Nutritional composition" refers to information about the nutritional components contained in a particular food or food product, and is composed of components such as energy, protein, fat, carbohydrates, vitamins, and minerals.

[0657] A "dietary restriction" is a list of specific nutrients or foods whose intake is restricted or recommended based on an individual's health condition.

[0658] A "generative artificial intelligence model" is a computer program that learns from past data and predicts or suggests future actions by recognizing patterns and changes.

[0659] "Location-based technology" refers to technologies used to detect a user's current location, such as GPS and Wi-Fi triangulation, to acquire location information.

[0660] "Means for collecting feedback and learning to improve the accuracy of future suggestions" refers to a function that aims to improve the system based on user responses and opinions, and to make more accurate suggestions.

[0661] This invention provides a specific method for implementing a health management system that manages an individual's health status and suggests the most suitable food and beverages accordingly.

[0662] Users first input their health information using a dedicated application. This health information includes diagnostic results, allergy information, and information about their dietary preferences and lifestyle. Users can also use wearable devices to acquire real-time data such as heart rate, steps taken, and blood glucose levels, and send this data to the terminal. The terminal then aggregates this data and sends it to the server.

[0663] The server uses received health information and real-time data to perform analysis using a generative artificial intelligence model. This identifies changes in the user's health status and suggests the next course of action. The server also uses location-based technology to identify nearby restaurants and shops based on the user's current location and selects food and beverages suitable for the individual. Furthermore, it provides detailed information about the selected food and beverages based on nutritional composition and dietary restrictions.

[0664] For example, if a user has diabetes, the application receives real-time blood glucose readings. Based on this data, the server uses a generative AI model to suggest nearby restaurants that offer low-carb menus. For instance, it can generate a prompt such as, "Show me nearby restaurants that offer low-carb menus based on my current blood glucose level," providing the user with appropriate options.

[0665] This system's architecture aims to enable users to more effectively manage their own health by utilizing advanced information processing technology to support healthy eating habits. It also allows for improved suggestion accuracy through a feedback function, providing valuable support to users.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] Users input health information through a dedicated application. This input includes height, weight, allergy information, and information on lifestyle-related diseases. This information is stored in the application's database. The entered data is used as foundational data for subsequent processing.

[0669] Step 2:

[0670] Users use wearable devices to acquire real-time health data such as heart rate, steps taken, and blood glucose levels. The device aggregates this real-time data and sends it to a server. The aggregated data serves as input for monitoring the user's health status in real time.

[0671] Step 3:

[0672] The server inputs received health information and real-time data into a generating AI model. The generating AI model analyzes this data, identifies changes in the user's health status, and predicts the next course of action. As a result of the analysis, health guidelines and precautions are generated.

[0673] Step 4:

[0674] The server obtains the user's current location information and uses location-based technology to search for nearby restaurants and shops. Combining the current location information with the analysis results, it selects food and beverages that are appropriate for the user's health condition. The selected food and beverages take nutritional information and dietary restrictions into consideration.

[0675] Step 5:

[0676] The server generates detailed information about the selected food and beverage, such as nutritional composition and appropriate dietary restrictions, and sends it to the terminal. The terminal then displays this information in an easy-to-understand format to support the user in making healthy food choices.

[0677] Step 6:

[0678] Users select food and beverages based on the provided information and enter feedback into the app after consumption. The device collects this feedback and sends it to the server. The server updates the generative AI model based on the feedback and learns to improve the accuracy of its suggestions.

[0679] This system allows users to monitor their health status in real time and maintain a healthy diet.

[0680] (Application Example 1)

[0681] 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".

[0682] In modern society, choosing foods and beverages that suit individual health conditions is crucial, but it is not easy for users to maintain a diet that aligns with their own health. In particular, there is a lack of support for selecting appropriate ingredients and dishes based on health information, as well as guidance on cooking methods, in daily life. Furthermore, it is difficult to propose dietary recommendations that take into account users' real-time health data and preferences. These problems need to be addressed.

[0683] 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.

[0684] In this invention, the server includes means for users to input and update health information and acquire health measurement data in real time from a measuring device; data processing means for selecting appropriate food and beverages from nearby restaurants and shops based on the health information input by the user and the acquired measurement data; means for presenting the user with information corresponding to the nutritional composition and dietary restrictions of the selected food and beverages; and interactive means for providing cooking support by suggesting ingredients or dishes based on the user's health condition in a home appliance. As a result, users can make appropriate food and beverage choices based on their own health condition, and support a healthy diet becomes possible.

[0685] "Health information" is a general term for data related to physiological indicators and health status that users input or acquire.

[0686] A "measuring device" is a device used to acquire real-time health data from users, measuring their physical condition and providing that data.

[0687] "Data processing means" refers to a process or apparatus for selecting nearby food and beverages based on the user's health information and analyzing their nutritional components and restrictions.

[0688] "Selection of food and beverages" refers to the act of identifying and selecting food and beverages that are suitable for the user based on health information.

[0689] "Interactive means" refers to functions that enable communication between the user and the device, allowing for suggestions of ingredients and dishes.

[0690] The system that implements this application consists of a user, a terminal, and a server. First, the user inputs their health information into the terminal via an input device. This health information includes physiological indicators and diagnostic results. The measuring device detects the user's real-time health data, and this data is transmitted to the server via the terminal.

[0691] After receiving health information and measurement data, the server analyzes this data using data processing tools. This analysis utilizes cloud platforms such as AWS Lambda and data processing frameworks such as Apache Hadoop. Based on the analysis results, the server identifies food and beverages suitable for the user's health condition and selects them considering their nutritional content and dietary restrictions.

[0692] The terminal presents the user with selected food and beverages and recommended recipes based on information transmitted from the server. Furthermore, it utilizes interactive means with home appliances and a voice assistant function to communicate with the user and assist with cooking procedures.

[0693] For example, if a user has diabetes, real-time data on heart rate and blood glucose levels is sent from the measuring device to the server. The server analyzes this information and recommends restaurants and ingredients that offer carbohydrate-restricted meals. The terminal visualizes and presents this information to the user, assisting them in purchasing appropriate ingredients and preparing meals.

[0694] An example of a prompt to be input into the generating AI model might be: "Based on the user's health data, please suggest a low-calorie, nutritionally balanced meal menu. The user's current health status is as follows: heart rate 80, blood glucose 100, BMI 25."

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] Users input health information into the terminal via an input device. This information includes physiological indicators and diagnostic results, which are converted into a standardized format by the terminal. This converted data is then sent to the server.

[0698] Step 2:

[0699] The terminal acquires the user's real-time health data via a measuring device. This measurement data includes heart rate and blood glucose levels, and is acquired at regular intervals and sent to the server. Upon receiving this data, the terminal immediately forwards it to the server using a communication module.

[0700] Step 3:

[0701] The server stores received health information and real-time health data in a database. Based on the stored data, data analysis is performed using data processing tools such as Apache Hadoop and AWS Lambda. In the analysis, the data is clustered to identify the most suitable food and beverage candidates for the user's health condition. This identification result is then passed to the next step.

[0702] Step 4:

[0703] The server uses the analysis results to match the user's location information with a database of nearby restaurants and bars. This allows it to select restaurants offering food and beverages suitable for the user and create a final recommendation list. This list, which includes nutritional information and dietary restrictions, is returned to the user's device.

[0704] Step 5:

[0705] The terminal visually and audibly presents the user with a list of suggestions received from the server. Interacting with the user is conducted using interactive means, such as a voice assistant function, as a home device. Based on the suggested food and drink selections, guidance is provided regarding the purchase of necessary ingredients and the preparation of meals.

[0706] Step 6:

[0707] If the user requests additional information or different options, they send a request to the server via their device. The server uses a generative AI model to create a new prompt and performs a re-analysis. Based on the results of this analysis, an updated suggestion is provided to the user. Specifically, a prompt such as "Please suggest a better nutritionally balanced meal menu based on the user's health data" is generated and analyzed by the model.

[0708] 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.

[0709] This invention is a system for supporting appropriate meal choices based on the user's health and emotional state, and aims to provide a more sophisticated service by utilizing an emotional engine. This system is implemented in the manner described below.

[0710] The user first launches the application and enters their health information. This includes diagnostic results and real-time health measurement data obtained from wearable devices. This health information is stored on a server via the terminal and used for subsequent data processing.

[0711] A distinctive feature of this system is the recognition of the user's emotional state by an emotion engine. The terminal is equipped with a function to analyze the user's face and voice tone, thereby measuring the user's emotions and acquiring that data. The emotional state is transmitted to the server as data associated with the user's health information.

[0712] The server integrates emotional states and health information provided by the emotion engine to select appropriate food and beverages for the user from nearby restaurants and shops. The selection process also considers the influence of the user's emotional state (e.g., stress, happiness) on their food choices. The selection results are sent to the terminal, and the user uses this information to make healthy food choices.

[0713] Furthermore, based on this information, the server provides cooking procedures and ingredient alternatives that are best suited to the user's emotional state. For example, if the user is experiencing high stress, options will be provided to recommend ingredients and cooking methods that have a relaxing effect. In this way, meal suggestions are tailored to each individual's emotional state.

[0714] For example, if a user is feeling stressed, the emotion engine recognizes this, and the server selects food and beverages that can help reduce stress based on the user's health and emotional state. For instance, it might display information on nearby stores offering relaxing herbal teas or nutritious salads on the user's terminal.

[0715] Furthermore, the server generates new cooking procedures for customers with lifestyle-related diseases based on regional and customer characteristics. This information is used in menu development at restaurants, enabling them to provide food and beverages tailored to specific customer needs.

[0716] Based on this model, users can receive comprehensive health support that takes into account their emotional and physical state, enabling them to make more personalized food choices.

[0717] The following describes the processing flow.

[0718] Step 1:

[0719] The user launches the application using their device and enters health information. This health information includes not only hospital test results but also real-time health measurement data obtained from wearable devices.

[0720] Step 2:

[0721] The device uses built-in sensors and cameras to detect the user's emotional state from their voice tone and facial expressions. An emotion engine analyzes this data to identify stress levels and types of emotions.

[0722] Step 3:

[0723] Health information and emotional state data are sent from the device to the server. The server securely receives this information and stores it in a database.

[0724] Step 4:

[0725] The server uses the user's health information, emotional state, and current location information obtained from the device to refer to a database of nearby restaurants and shops and select appropriate food and beverages. Using AI, it identifies menus that provide nutritional balance and address dietary restrictions based on the user's emotional state.

[0726] Step 5:

[0727] The server generates the selection results and sends them to the user via the terminal. The user can view a list of recommended foods and beverages on the terminal and see detailed nutritional information and dietary restrictions for each item.

[0728] Step 6:

[0729] The server creates cooking procedures and ingredient substitutions based on the user's emotional state. This information is designed to stabilize the user's emotions and contribute to improved health.

[0730] Step 7:

[0731] The device presents the user with this customized recipe information. The user can then use these recipes to prepare healthy meals at home that suit their emotional state.

[0732] Step 8:

[0733] The server considers regional and customer characteristics and proposes new cooking procedures for restaurants targeting patients with lifestyle-related diseases. Restaurants can then use this as a basis for menu development and improve service for specific customer segments.

[0734] (Example 2)

[0735] 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".

[0736] In modern society, it is important to make appropriate dietary choices based on individual health and emotional states. However, in many cases, emotional states are not considered when choosing food, making it difficult to select appropriate foods, beverages, and cooking methods. Furthermore, there is a lack of well-established systems that present meals tailored to individual users, resulting in a current shortage of personalized meal suggestions.

[0737] 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.

[0738] In this invention, the server includes means for users to input and update health information and acquire health indicator data in real time from wearable devices; information processing means for selecting appropriate food and beverages from nearby food and beverage establishments and stores based on the health information input by the user and the acquired indicator data; and means for analyzing the user's emotional state using an emotion determination mechanism and integrating it with health information to reflect in the selection of food and beverages. This enables personalized meal selection support based on the user's health and emotional state.

[0739] A "user" refers to an individual who uses the system to input health information and emotional states and make appropriate dietary choices.

[0740] A "wearable device" refers to a device worn on the body to acquire and record the user's health indicator data in real time.

[0741] "Health indicator data" refers to data that quantifies the user's health status, including heart rate and blood pressure.

[0742] "Information processing means" refers to the function of a server that integrates and analyzes a user's health information and emotional state to select appropriate food and beverages.

[0743] "Food and beverage service" refers to businesses or establishments that sell or provide food and beverages.

[0744] An "emotion determination mechanism" refers to a technological element used to analyze and quantify a user's emotional state.

[0745] "Cooking procedures" refer to the methods and processes involved in preparing food and beverages.

[0746] "Ingredient alternatives" refer to options for suggesting substitute ingredients when a particular ingredient cannot be used.

[0747] "Personalized meal selection" refers to individual meal suggestions optimized to take into account each user's health and emotional state.

[0748] This system supports users in making optimal dietary choices by allowing them to input their health information and emotional state. First, the user launches the application using their device and inputs their health information. This includes diagnostic results and health indicator data obtained in real time from wearable devices. Suitable wearable devices include smartwatches and fitness trackers.

[0749] After the user enters their health information, the device sends that data to a server. The server receives it and stores it in a database. Furthermore, the device is equipped with an emotion detection mechanism that identifies the user's emotional state by analyzing their voice tone and facial expressions. This emotion data is also sent to the server and integrated with the health information.

[0750] The server uses this data to leverage a generative AI model to select the most suitable food and beverages based on the user's health and emotional state. Specifically, it searches a database for suitable food and beverages from nearby food and beverage providers and stores, and makes selections that match the user's condition.

[0751] For example, if the emotion assessment mechanism detects that a user is feeling stressed, the server can suggest relaxing foods and drinks. This could include information on stores that offer herbal teas or nutritious salads.

[0752] This result is sent to the terminal and presented to the user. Based on the presented information, the user can choose a store and select healthy meals. Furthermore, if the user enters a prompt such as "Tell me about a relaxing drink," the system will provide information that meets that request.

[0753] Furthermore, the server provides cooking procedures and ingredient substitutions tailored to each user's emotional state. For example, it suggests recipes using herbs with calming effects for users experiencing high stress levels. In this way, the system supports users' health management and enables personalized meal choices.

[0754] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0755] Step 1:

[0756] The user launches the application using a terminal and enters health information. This includes diagnostic results and real-time health indicator data obtained from wearable devices. The terminal receives this input and sends the data to the server as health information. The output is the saved user health information data.

[0757] Step 2:

[0758] The device is equipped with an emotion detection mechanism. When the user faces the device's camera, it analyzes their emotional state from their facial expressions. It also recognizes emotions from the tone of the user's voice, thereby acquiring emotional data. The output obtained from this process is the analyzed emotional state data, which is also sent to the server.

[0759] Step 3:

[0760] The server receives health information and emotional data sent by the user and stores it in a database. Next, it uses a generative AI model to analyze the data and select food and beverages appropriate for the user's state. The input is the stored health information and emotional data, and the output is a list of selected food and beverages.

[0761] Step 4:

[0762] Based on the analysis results, the server selects appropriate food and beverages from nearby food and beverage providers and stores. In this process, the information processing system searches a database and selects food and beverages corresponding to the user's health and emotional state. The output is specific store information related to the selected food and beverages.

[0763] Step 5:

[0764] The server transmits the selected meal information to the terminal. The terminal displays the received information to the user. The user can then make healthy meal choices based on the presented store information and food / drink options. The output is detailed meal suggestion information displayed to the user.

[0765] Step 6:

[0766] The server utilizes a generative AI model to generate cooking procedures and ingredient alternatives tailored to the user's emotional state. This analysis considers the input emotional state and proposes the most suitable cooking method for the user. The output consists of cooking procedures and ingredient alternatives that match the user's state.

[0767] (Application Example 2)

[0768] 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".

[0769] There is a need for a system that can select food and beverages that meet individual needs, taking into account a comprehensive understanding of health and emotional states. However, current systems do not adequately consider the user's emotional state, resulting in the challenge of making personalized and healthy food choices.

[0770] 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.

[0771] In this invention, the server includes means for users to input and update health information and acquire health measurement data in real time from wearable devices, means for analyzing the user's emotional state and acquiring data thereof, and means for selecting appropriate food and beverages offered at nearby restaurants and shops based on the user's health information and emotional state using data processing means. This enables precise meal selection tailored to the individual's health and emotional state.

[0772] "Health information" refers to data about the user's physical condition, including diagnostic results and real-time measurement data obtained from wearable devices.

[0773] "Emotional state" refers to the state of mind recognized by analyzing the user's facial features, voice tone, etc., and includes psychological factors such as stress and happiness.

[0774] A "wearable device" is an electronic device that a user can wear, and it is a device that acquires health status and activity data in real time.

[0775] "Data processing means" refers to technical means for analyzing collected health information and emotional state data, and for selecting the most suitable food and beverages for the user based on this analysis.

[0776] "Food and beverages" refers to drinks and food, and includes items tailored to the user's nutritional status and dietary restrictions.

[0777] "Processing procedures" refer to the steps involved in selecting appropriate cooking methods and ingredients based on the user's health and emotional state.

[0778] "Regional characteristics" refer to the food culture and consumption trends unique to a particular geographical area, and are factors that should be considered when providing food and beverages.

[0779] "Customer characteristics" refer to the preferences and health needs common to a specific group of users, and are used to provide personalized recommendations.

[0780] This invention is a system that enables personalized meal selection based on the user's health and emotional state. The system uses a smartphone, server, and wearable device as hardware. The software includes an application that runs on the user's terminal, a Python script for data analysis, and an emotion recognition AI model (e.g., Amazon Rekognition).

[0781] First, the user enters health information through a smartphone app. This information, along with data acquired in real time from a wearable device, is sent to a server. At that time, the app uses emotion recognition technology to analyze the user's face and voice and recognize their emotional state.

[0782] The server integrates the received health information and emotional state and processes the data. This allows it to select the most suitable food and beverages offered by nearby restaurants and shops. During the selection process, the user's emotional state (e.g., stress) is taken into consideration, and foods with relaxing effects are prioritized.

[0783] For example, if the server detects that a user's stress level is high during a specific time period, it will select a menu containing relaxing herbal tea and nutritious foods and notify the user's device. Furthermore, the AI ​​model can process prompts such as, "What foods would you recommend when a user's emotional state requires relaxation?"

[0784] This allows users to make optimal dietary choices tailored to their individual health and emotional states, supporting a healthy lifestyle.

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The user launches a smartphone app and enters health information. This data includes weight, height, and previous health checkup results. This data is sent directly from the application to a server and stored as baseline data for health management.

[0788] Step 2:

[0789] Heart rate and stress level data acquired in real time from wearable devices are sent to the user's terminal, and then further sent to a server. This data is used to monitor health status and as an indicator to evaluate the current physical condition.

[0790] Step 3:

[0791] On the user's device, emotion recognition technology is used to analyze the user's face and voice and determine their emotional state. In this process, features obtained from facial expressions and voice tone are provided as input to an AI model to recognize emotions such as stress and happiness.

[0792] Step 4:

[0793] The server integrates the received health information and emotional state to perform data analysis. Based on the health and emotional data sent to the server as input, it uses a Python script to execute an algorithm to select the most suitable food and beverage from the database.

[0794] Step 5:

[0795] Based on the analysis results, the server selects the most suitable meals and drinks for the user and notifies the user of this information on their smartphone. This information includes a list of foods suitable for the user's health and emotional state, as well as their nutritional value and health benefits.

[0796] Step 6:

[0797] Users receive notifications, review suggested food and beverages, and decide on meal details as needed. Following the optimized meal selection, they can then order from nearby restaurants.

[0798] Step 7:

[0799] A prompt is sent to the AI ​​model, which then suggests recommended food items based on the user's emotional state. The prompt includes questions such as, "What food would you recommend when the user's emotional state requires relaxation?"

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0807] 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.

[0808] 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."

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] The following is further disclosed regarding the embodiments described above.

[0822] (Claim 1)

[0823] A means by which users can input and update health information and obtain health measurement data in real time from wearable devices,

[0824] A data processing means for selecting appropriate food and beverages in nearby restaurants and shops based on the user's entered health information and acquired measurement data,

[0825] A means of presenting users with information on the nutritional composition and dietary restrictions of selected food and beverage products,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, further comprising means for providing the user with cooking procedures and ingredient alternatives that take into account the user's health condition and preferences.

[0829] (Claim 3)

[0830] The system according to claim 1, further comprising means for generating and proposing new cooking procedures for the food service industry and retail stores that are suitable for patients with lifestyle-related diseases, based on regional characteristics and customer characteristics.

[0831] "Example 1"

[0832] (Claim 1)

[0833] A means by which individuals can input and update their health information and acquire health measurement data in real time from a portable measuring device,

[0834] An information processing means for selecting appropriate food and beverages at nearby restaurants and shops based on an individual's entered health information and acquired measurement data,

[0835] A means of providing individuals with information on the nutritional composition and dietary restrictions of selected food and beverages,

[0836] A method for searching for nearby restaurants and bars using location-based technology that utilizes an individual's current location information,

[0837] Generative artificial intelligence models are used to identify changes in an individual's health status and to suggest the next course of action to that individual.

[0838] A means of collecting individual feedback and using it to learn in order to improve the accuracy of future suggestions,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, further comprising means for taking into account an individual's health condition and preferences and providing suitable cooking procedures and ingredient alternatives.

[0842] (Claim 3)

[0843] The system according to claim 1, further comprising means for generating and proposing new cooking procedures for adult disease patients to food service establishments and retail outlets based on regional characteristics and customer characteristics.

[0844] "Application Example 1"

[0845] (Claim 1)

[0846] A means by which users can input and update health information and acquire health measurement data in real time from a measuring device,

[0847] A data processing means for selecting appropriate food and beverages in nearby restaurants and shops based on the user's entered health information and acquired measurement data,

[0848] A means of presenting users with information on the nutritional composition and dietary restrictions of selected food and beverage products,

[0849] In home appliances, an interactive means for suggesting ingredients or dishes based on the user's health condition and providing cooking assistance,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, further comprising means for providing the user with cooking procedures and ingredient alternatives that take into account the user's health condition and preferences.

[0853] (Claim 3)

[0854] The system according to claim 1, further comprising means for generating and proposing new cooking procedures for the food service industry and retail stores that are tailored to individuals with specific diseases, based on regional characteristics and customer characteristics.

[0855] "Example 2 of combining an emotion engine"

[0856] (Claim 1)

[0857] A means by which users can input and update health information and obtain health indicator data in real time from wearable devices,

[0858] An information processing means for selecting appropriate food and beverages from nearby food and beverage establishments and stores based on user-entered health information and acquired indicator data,

[0859] A means of presenting users with information on nutritional elements and dietary restrictions for selected food and beverages,

[0860] A means of analyzing the user's emotional state using an emotion determination mechanism, integrating it with health information, and reflecting it in the selection of food and beverages,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising means for providing the user with cooking procedures and ingredient alternatives that are appropriate to the user's health condition, preferences, and emotional state.

[0864] (Claim 3)

[0865] The system according to claim 1, further comprising means for generating and proposing new cooking procedures for food service providers and stores that accommodate patients with chronic diseases, based on regional characteristics and customer characteristics.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] A means by which users can input and update health information and obtain health measurement data in real time from wearable devices,

[0869] A means of analyzing the emotional state of users and acquiring that data,

[0870] A data processing means for selecting appropriate food and beverages offered at nearby restaurants and shops based on the user's health information and emotional state,

[0871] A means of presenting users with information on nutritional content and dietary restrictions for selected food and beverages,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, further comprising means for taking into account the user's health and emotional state and providing cooking procedures and ingredient alternatives that are appropriate thereto.

[0875] (Claim 3)

[0876] The system according to claim 1, further comprising means for generating and proposing new cooking procedures tailored to individuals requiring health management for the food service and retail industries, based on regional and customer characteristics. [Explanation of Symbols]

[0877] 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 by which users can input and update health information and acquire health measurement data in real time from a measuring device, A data processing means for selecting appropriate food and beverages in nearby restaurants and shops based on the user's entered health information and acquired measurement data, A means of presenting users with information on the nutritional composition and dietary restrictions of selected food and beverage products, In home appliances, an interactive means for suggesting ingredients or dishes based on the user's health condition and providing cooking assistance, A system that includes this.

2. The system according to claim 1, further comprising means for providing the user with cooking procedures and ingredient alternatives that take into account the user's health condition and preferences.

3. The system according to claim 1, further comprising means for generating and proposing new cooking procedures for the food service industry and retail stores that are tailored to individuals with specific diseases, based on regional characteristics and customer characteristics.

Images