system

Abstract

We provide the system. [Solution] An input method for recording the contents of meals, An analytical means for analyzing recorded meal data to evaluate nutritional status, A proposal generation method for generating food ingredients and nutritional supplements that should be proposed based on nutritional evaluation, A notification mechanism to inform the user of the generated suggestions, 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, the method including 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】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In modern society, there is a problem that dietary habits tend to be unbalanced due to a busy life. Specifically, depending on eating out or processed foods, there may be a lack or excess of nutritional balance, making it difficult to maintain health. Therefore, there is a need for a system that effectively manages the nutritional status based on individual meal contents and supports appropriate nutrient intake. 【Means for Solving the Problems】 【0005】 This invention provides a system that records a user's diet, analyzes the data, and evaluates their nutritional status. Based on the analysis results, the system suggests foods and nutritional supplements to compensate for any nutrient deficiencies and notifies the user of this information. This allows users to easily adjust the nutritional balance of their diet and maintain a healthy lifestyle. 【0006】 "Meal content" refers to information about the ingredients and dishes consumed by the user, and is treated as data based on calculations of nutrients and calories. 【0007】 An "input method" is an interface for users to record the contents of their meals, and has the function of registering information in an application or device. 【0008】 "Analysis means" refers to technical means that have the function of analyzing recorded dietary data and evaluating the balance and imbalance of nutrients. 【0009】 The "proposal generation means" is a function that generates suggestions for food ingredients and nutritional supplements to supplement the nutrients that the user is lacking, based on the analysis results. 【0010】 A "notification method" is an interface for informing users of generated suggestions, and a technology that enables users to review the suggestions and take action. [Brief explanation of the drawing] 【0011】 [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 the data processing device and 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0012】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0013】 First, let's explain the terminology used in the following explanation. 【0014】 In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0015】 In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0016】 In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0017】 In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0018】 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." 【0019】 [First Embodiment] 【0020】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0021】 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. 【0022】 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). 【0023】 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. 【0024】 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. 【0025】 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. 【0026】 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. 【0027】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0028】 As shown in Figure 2, in the data processing device 12, 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. 【0029】 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. 【0030】 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. 【0031】 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". 【0032】 This invention is a system for effectively managing nutrition and is designed to support users' daily eating habits. By integrating functions such as meal recording, analysis, suggestions, and notifications, this system helps users maintain a healthy nutritional balance. 【0033】 Users first register their meal details in the application on their device. This input can be done via text, photos, or voice input, and it is recommended to include the names of the ingredients and their quantities. 【0034】 The terminal sends the data entered by the user to the server. The data sent includes detailed information about the meal and the date and time of registration. 【0035】 The server uses AI to analyze the received data. This analysis references the nutrients in each food item from a database and calculates the total amount of nutrients the user has consumed. Based on the analysis results, the server evaluates the user's current nutritional balance and identifies any nutrient deficiencies or excesses. 【0036】 Once the evaluation is complete, the server will identify recommended foods and dishes for the next meal. This suggestion will provide effective options to improve the individual's nutritional status and may include information on nutritional supplements if necessary. 【0037】 The device notifies the user of these suggestions. These notifications are delivered via push messages or in-app notification tabs, allowing users to easily access and review the content. This enables users to obtain information to make appropriate food choices. 【0038】 For example, if a user registers bread and coffee for breakfast, the server analyzes this and determines that the user is deficient in vitamins. The server then lists recommended fruits and vegetables for lunch, and if protein is lacking, it suggests foods like tofu or chicken. This information is notified to the user via their device, allowing them to make informed meal choices and maintain overall health. 【0039】 Thus, the system of the present invention supports individual nutritional management through AI analysis and is an effective means of achieving long-term health maintenance. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 Users enter their meal details into the application. They can record their meals by writing detailed text descriptions or by uploading photos. 【0043】 Step 2: 【0044】 The terminal formats the entered meal data and sends it to the server. This data includes ingredient names, quantities, user ID, and date and time information. 【0045】 Step 3: 【0046】 The server inputs the received meal data into an AI module for analysis. The AI ​​module retrieves nutritional information for each food item from the database and calculates the total amount of each nutrient consumed by the user. 【0047】 Step 4: 【0048】 The server uses the calculated nutritional data to assess the user's nutritional status. It identifies nutrient deficiencies or excesses and lists nutrients that require particular attention. 【0049】 Step 5: 【0050】 The server calculates recommended ingredients and dishes for the next meal based on the nutritional assessment. It also takes into account seasonal ingredients and potential nutritional supplements. 【0051】 Step 6: 【0052】 The server organizes recommended ingredients and recipe information and generates a notification message. This message may also include information about necessary nutritional supplements. 【0053】 Step 7: 【0054】 The device notifies the user of the generated notification message. The user can then use this information to plan their next meal. 【0055】 (Example 1) 【0056】 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." 【0057】 Managing a healthy nutritional balance efficiently while addressing the diversity and variability of individual users' dietary habits is challenging. Conventional methods involve the cumbersome task of recording meal contents, and furthermore, they fail to adequately assess nutritional status or provide suggestions for improvement in subsequent meals. 【0058】 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. 【0059】 In this invention, the server includes recording means, analysis means, suggestion generation means using a generative AI model, and notification means. This allows users to easily record their meal contents and analyze their nutritional status using the AI ​​model, enabling personalized nutritional management. By receiving suggestions quickly, improvements to eating habits can be made efficiently. 【0060】 "Information input means" refers to methods and devices that allow users to record the contents of their meals. Specifically, this includes text input, image recognition, and voice input. 【0061】 A "processing unit" refers to a computing device or program used for data analysis and evaluation. It primarily functions as a server or computer system. 【0062】 "Analysis methods" refer to methods and devices that evaluate nutrients based on recorded dietary information. This is done using AI models and databases. 【0063】 "Suggestion generation means" refers to a method or apparatus that generates food and nutritional supplement options suitable for the next meal based on nutritional assessment. It utilizes a generation AI model. 【0064】 A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to analyze data and make optimal suggestions for a specific purpose. 【0065】 "Notification means" refers to methods or devices used to inform users of generated suggestions. This is primarily done through push notifications and in-app notifications. 【0066】 "Communication means" refers to methods and devices for exchanging information with other devices or systems. This is done via networks or the internet. 【0067】 The system of this invention efficiently manages the user's daily eating habits, integrating the functions of information input, data analysis, suggestion generation, and notification. The implementation method will be described below, specifying the hardware and software. 【0068】 Users install and use a dedicated application on their smartphones, tablets, or other devices. Through this application, users record their meals. For example, users can enter the names and quantities of ingredients using the text input function, or take photos of their meals using the camera function and identify the ingredients through image recognition. It is also possible to register meal details using the voice input function. 【0069】 The terminal sends the meal information entered by the user to the server. This communication uses data transfer technology via an internet connection. The information sent includes detailed meal information and the date and time of registration. 【0070】 The server includes a processing unit for analyzing received data and a database containing nutritional information for each food item. The received data is analyzed by an AI model to calculate the total amount of nutrients consumed by the user. In this process, the analysis also considers the user's past dietary information. 【0071】 Furthermore, the server uses a generative AI model to generate suggestions based on the analysis results. These suggestions include recommended foods and dishes for the next meal, tailored to the individual's nutritional status. For example, it might suggest oranges or broccoli to supplement a deficient vitamin C. This information is finally communicated to the user via a notification system through their device. 【0072】 As a concrete example, let's look at a prompt: "When a user has had bread and coffee for breakfast, please suggest recommended nutrients and ingredients for their next meal." Based on this prompt, the AI ​​model generates optimal suggestions. In this way, this system is designed to provide flexible nutritional management tailored to each user's eating habits. 【0073】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0074】 Step 1: 【0075】 Users record their meal details in an application on their device. Input methods include directly entering the names and quantities of ingredients as text, or using the camera function to take photos of the meal and utilizing image recognition technology. It is also possible to register meal details using voice input. This generates detailed meal information as input data. 【0076】 Step 2: 【0077】 The terminal processes the meal information entered by the user and sends it to the server. Specifically, it organizes the information into a data format that includes detailed meal information and the registration date and time, and sends it to the server using the HTTP protocol. In this case, the input is the user's meal information, and the output is the data sent to the server. 【0078】 Step 3: 【0079】 The server analyzes the received data using an AI model. It receives meal data sent to the server as input, references nutritional information for each food item from its internal database, and performs calculations to determine the total nutritional content. The output is the analysis result of the user's nutritional intake. 【0080】 Step 4: 【0081】 The server generates suggestions using a generative AI model based on the analysis results. The input is the user's nutritional status data obtained through the analysis. The generative AI model lists recommended foods and dishes to supplement any deficient nutrients, for example. The output is suggested information to refer to for the next meal. 【0082】 Step 5: 【0083】 The device notifies the user of the suggestion information received from the server. Specifically, this involves sending information to the user's device via push notification and displaying the suggestions in the app's notification tab. The input is the generated suggestion information, and the output shows the specific meal suggestions the user receives. 【0084】 (Application Example 1) 【0085】 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." 【0086】 In modern households, individualized nutritional management is required to maintain a healthy diet. However, it is a significant burden for individuals to meticulously record their daily meals, analyze their nutritional balance, and select appropriate ingredients. Furthermore, it is difficult for ordinary users without specialized knowledge to understand the nutritional analysis results and apply them to their daily lives. Therefore, there is a need for a system that can easily support nutritional management. 【0087】 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. 【0088】 In this invention, the server includes an input structure for recording meal information, an analysis structure for analyzing the recorded meal information and evaluating nutritional status, and a suggestion generation structure for generating suggested ingredients and nutritional supplements based on the nutritional evaluation. This makes it possible to easily collect meal data via a home device and immediately receive suggestions in a format that is easy for the user to understand. 【0089】 The "input structure for recording meal information" provides a function that allows users to record details of their daily meals using voice, text, or images. 【0090】 The "analysis structure" is a system that analyzes the nutrients contained in the food based on the input dietary information and has the function of evaluating the balance of nutrients consumed. 【0091】 The "proposal generation structure" refers to a system that, by referring to the results of the analysis structure, selects and proposes suitable ingredients and nutritional supplements for users to maintain a healthy nutritional balance. 【0092】 A "notification structure" is a system that provides a function to easily communicate generated suggestions to users through notifications, such as displaying them or outputting audio. 【0093】 "Consumer-use devices that collect data through interaction with users" are machines used in the home that collect dietary information while interacting with the user through voice and images. 【0094】 An "analysis device that analyzes input audio and image information" is a device that processes audio and image data acquired by consumer electronics to identify ingredients and determine their quantities. 【0095】 An "output device that displays or reads out analysis results" is a device that provides users with the results of the nutritional analysis in a visual or auditory way to aid their understanding. 【0096】 In the system implementing this invention, consumer electronics, a server, and a user terminal work together. The user inputs information about their daily meals via voice or images into the consumer electronics installed in their home. The consumer electronics uses voice processing software for voice recognition and image processing software for image recognition to analyze this input information. 【0097】 Consumer devices use analysis tools such as Google® Speech-to-Text API and Google Cloud Vision API to convert input data into text and identifiable food information. The converted data is then sent to a server. 【0098】 On the server, the acquired meal data is analyzed using AI analysis software such as TENSORFLOW® to analyze nutrients. The server evaluates the user's nutritional status and generates suggestions for foods and nutritional supplements to provide the necessary nutrients. 【0099】 The generated suggestions are sent via a notification structure to the user's terminal or consumer electronics output device. Users can review the suggestions through visual displays or audio output and use them to improve their daily diet. 【0100】 As a concrete example, suppose a user tells a consumer device that they ate eggs and toast for breakfast. The device sends this information to a server, which analyzes it and then provides the user with a suggestion such as, "You should add some fruit containing vitamin C to your next meal." 【0101】 Examples of prompts when using a generative AI model include: "What algorithm should be used to analyze the ingredients a user ate for breakfast and suggest a healthy lunch?" and "Please tell me an effective approach for users to manage their nutrition properly." 【0102】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0103】 Step 1: 【0104】 Users input meal information via voice or images into consumer devices installed in their homes. The input data includes information about the types and quantities of food the user has eaten. 【0105】 Step 2: 【0106】 Consumer devices convert user-input voice data into text data using the Google Speech-to-Text API. Image data is also analyzed using the Google Cloud Vision API to extract food names. The output of this step is text-based meal information. 【0107】 Step 3: 【0108】 The converted meal information is sent from the terminal to the server. This information includes detailed data such as the names and quantities of ingredients. The transmitted data is received by the server. 【0109】 Step 4: 【0110】 The server uses TensorFlow to analyze nutrients based on the received meal information. The input for the analysis is food ingredient data, and the output is an evaluation of the user's nutritional balance. In this step, nutritional deficiencies or excesses are identified. 【0111】 Step 5: 【0112】 The server uses an AI model based on the analysis results to generate suggestions for ingredients and nutritional supplements to recommend for the next meal. The output is a list of suggested ingredients and recipes. 【0113】 Step 6: 【0114】 The generated suggestions are sent to the user's terminal or consumer device through a notification structure. The output of this step is suggestion information in a format that can be confirmed visually and audibly by the user. 【0115】 Step 7: 【0116】 Based on the suggested information, users select their next meal and adjust the nutritional balance. This helps maintain a healthy diet. 【0117】 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. 【0118】 This invention is a system for recording and analyzing dietary content, as well as providing nutritional management that takes into account the user's emotional state. This makes it possible to harmonize not only nutritional intake, but also emotions and eating habits. 【0119】 This system comprehensively performs a series of operations including meal recording, nutritional analysis, emotion recognition, suggestion generation, and notifications. Users input their meal details into the application. The registered information includes not only the names and quantities of ingredients but also the emotional state. The emotional state includes the user's level of stress and joy. 【0120】 The device formats this data individually and sends it to the server. On the server, AI performs nutritional analysis based on the meal data. The analysis method has the ability to more flexibly adjust the necessary nutrients based on the user's emotional state. 【0121】 Specifically, an emotion engine is used to identify the user's current emotional state. This emotional data is then incorporated into the analysis, and in generating suggestions, nutritional balance is considered, along with recommendations for foods that support emotional improvement. For example, if the user is feeling stressed, foods that have a mood-boosting effect will be suggested. 【0122】 The suggestion generation mechanism generates a list containing information on recommended ingredients and dishes for the next meal, as well as nutritional supplements as needed, based on the analysis results and emotional state. The terminal notifies the user of these suggestions. The notification includes special advice that takes emotions into consideration, helping the user correct unbalanced nutrition and achieve emotional stability. 【0123】 For example, if a user records a decrease in their motivation to cook, the server will take that emotion into consideration and suggest easy-to-prepare, healthy recipes. This allows users to easily supplement their nutrition while maintaining their motivation. 【0124】 Thus, the system of the present invention aims to improve the user's quality of life by managing health from two aspects: nutrition and emotional well-being. 【0125】 The following describes the processing flow. 【0126】 Step 1: 【0127】 When users enter their meal details into the application, they also record their current emotional state. Emotions can be entered by selecting from a preset list of emotions or by setting the emotional level using a slider. 【0128】 Step 2: 【0129】 The terminal formats the entered meal and emotional data and sends it to the server. The transmitted data includes detailed meal information, emotional state, and associated timestamps. 【0130】 Step 3: 【0131】 The server passes the received data to the AI ​​analysis module. The AI ​​analyzes the meal data to calculate the nutrients consumed and adjusts for any nutritional deficiencies or excesses based on emotional data. 【0132】 Step 4: 【0133】 The server evaluates the user's nutritional status based on the analysis results and, taking into account their emotional state, determines the next recommended foods and dishes. In this process, foods that help alleviate stress and improve mood may also be selected. 【0134】 Step 5: 【0135】 The server analyzes the analysis results and emotional state through a suggestion generation mechanism and creates a list that includes recommended ingredients and dishes for the next meal. This list also includes information on relevant nutritional supplements. 【0136】 Step 6: 【0137】 The device notifies the user of the generated list of suggestions. The notification includes special advice that takes emotions into consideration, guiding the user in self-care. 【0138】 Step 7: 【0139】 Users can plan their next meal based on the notifications. They can use the system's feedback to make choices that improve their nutritional status and emotional balance. 【0140】 (Example 2) 【0141】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0142】 Traditional nutrition management systems simply evaluate and suggest nutritional status based on meal content, without considering the user's emotional state. Therefore, they fail to offer meal suggestions that address the user's emotional state, and issues such as stress and motivation are not taken into account. This results in low user acceptance of meal suggestions and makes it difficult to ensure nutritional balance. 【0143】 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. 【0144】 In this invention, the server includes means for inputting meal content and emotional state, means for formatting and transmitting the input meal data and emotional data, and means for analyzing the transmitted data, evaluating nutritional status, and performing a nutritional evaluation that takes emotional state into consideration. This makes it possible to suggest food ingredients and nutritional supplements that reflect the user's emotional state. 【0145】 "Meal content" refers to all information regarding the food consumed by the user and how it is prepared. 【0146】 "Emotional state" refers to information that indicates the psychological state a user is experiencing at a particular point in time, such as stress or joy. 【0147】 "Means for input" refers to the interface through which users provide information about their meals and emotional state to the system. 【0148】 "Means of formatting and transmitting" refers to the process of converting input data into a standard format and transmitting it in a form that can be processed within the system. 【0149】 "Means for analyzing and evaluating nutritional status" refers to a process or technology for analyzing and evaluating a user's nutritional status using dietary data and emotional data. 【0150】 "Suggestion generation means" refers to a function or process that creates suggestions for improving the user's diet based on analyzed data. 【0151】 "Means of notification" refers to an interface or method for communicating generated suggestions to users. 【0152】 This invention is a system for meal management that provides nutritional management that takes into account the user's emotional state. The user inputs their current emotional state along with their meal details through a digital input device. Suitable devices include smartphones and tablet terminals. This allows the user to utilize the system within their daily life. 【0153】 The terminal converts the input information into a standard format. A dedicated application is used for this process to ensure data format consistency. The formatted data is then sent to the server using a secure communication protocol. 【0154】 The server analyzes the received data. This process utilizes a generative AI model to analyze the collected dietary data and emotional state in detail. The use of an emotion engine enables nutritional analysis that reflects the user's emotional state. For example, if the user is stressed, foods with relaxation effects will be identified. 【0155】 The suggestion generation system constructs nutritional suggestions for the user based on analyzed data. This allows users to receive specific suggestions that take into account not only their current nutritional status but also their emotional state. The device notifies the user of these suggestions to help them make daily meal choices. 【0156】 For example, if a user enters "I'm tired today," the server will generate a plan suggesting foods that help with fatigue recovery. This allows the user to make choices based on their own health condition. An example of a prompt might be something like, "Please explain in detail how to suggest foods with relaxation effects based on the user's emotional state." 【0157】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0158】 Step 1: 【0159】 Users input their meal details and emotional state through the application. Specifically, they record daily ingredients, quantities, cooking methods, and their emotions at the time (e.g., stress, happiness). The entered information is stored on the device as meal record data and emotional data. 【0160】 Step 2: 【0161】 The terminal formats the entered meal record data and emotion data. This process converts the data into a standard format to facilitate subsequent processing. Specifically, it normalizes emotion values ​​and tags food categories. As a result, it outputs formatted data. 【0162】 Step 3: 【0163】 The terminal sends formatted data to the server. The transmission uses a secure communication protocol to maintain data confidentiality and integrity. After transmission, the server receives the data. 【0164】 Step 4: 【0165】 The server analyzes the received formatted data. The analysis uses a generative AI model to assess the user's nutritional status using dietary and emotional data. Specifically, it outputs evaluation results such as "calcium deficiency" or "high stress levels." 【0166】 Step 5: 【0167】 The server generates meal suggestions based on the analysis results. The suggestion generation process also considers emotional data to create a list of meals suitable for the user's current physical and mental state. Output includes a "list of ingredients to choose for your next meal" and "recommended recipes." 【0168】 Step 6: 【0169】 The device notifies the user of meal suggestions provided by the server. These notifications include specific ingredients, cooking methods, and even emotional support advice. Users can receive this information and incorporate it into their daily eating habits. 【0170】 (Application Example 2) 【0171】 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". 【0172】 Traditional nutrition management systems focus on recording and analyzing meals, but fail to consider the user's emotional state, making it difficult to improve emotional satisfaction and mental health. Therefore, there is a need to develop new systems that harmonize users' emotions with their eating habits and contribute to improving their quality of life. 【0173】 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. 【0174】 In this invention, the server includes input means for recording the contents of meals and emotional states, analysis means for analyzing the recorded meal data and emotional data to evaluate nutritional status and emotional state, and suggestion generation means for generating food products and nutritional supplements to be suggested based on the nutritional evaluation and emotional state. This makes it possible to realize a healthy diet that takes into account the user's nutritional and emotional balance. 【0175】 "Input means" refers to a device or method used by a user to record the contents of a meal and their emotional state. 【0176】 "Analysis means" refers to a device or method that evaluates recorded meal data and emotional data to analyze nutritional status and emotional state. 【0177】 A "proposal generation means" is an apparatus or method that generates recommended foods or nutritional supplements based on nutritional evaluations and emotional states obtained through analysis. 【0178】 "Notification means" refers to a device or method for communicating the generated proposal to the user. 【0179】 This system records the user's diet and emotional state, and through analysis, provides health management that considers the balance between nutrition and emotions. The system mainly consists of input means, analysis means, suggestion generation means, and notification means. 【0180】 The input method involves users recording their meal content and emotional state, and utilizes devices such as smartphones and tablets. Users use these devices to input specific meal details and their emotional state (stress, joy, etc.). 【0181】 The input data is formatted on the terminal and then sent to the server. On the server, the analysis system uses AI analysis tools such as TensorFlow to evaluate the nutritional and emotional status based on the recorded meal and emotional data. From this evaluation result, suggestions for maintaining an optimal nutritional balance are generated. The suggestion generation system lists recommended foods and nutritional supplements based on the nutritional evaluation and emotional status. 【0182】 The notification system is used to inform the user of generated suggestions. These notifications are displayed on the device via the display or audio output. For example, if the user is feeling stressed, it may suggest oatmeal for breakfast, as it is known to be effective in reducing stress. 【0183】 Through the operation of this system, users can maintain a healthy lifestyle that takes into account the balance between nutrition and emotional well-being. For example, by using prompts such as, "What foods do you recommend when a user is feeling stressed? Please also provide a specific meal plan," it is possible to obtain more effective suggestions. 【0184】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0185】 Step 1: 【0186】 The user uses a terminal to input details about their meal and emotional state. This input includes specific dish names, ingredients, quantities, and their emotional state for the day (e.g., stress, joy). This data is then sent directly to the next processing step. 【0187】 Step 2: 【0188】 The terminal formats the meal details and emotional state entered by the user. This formatted data includes standardized meal information and emotional scores, which are then converted for transmission to the server. The output is sent to the server as formatted data. 【0189】 Step 3: 【0190】 The server analyzes the received formatted data. The analysis uses a generative AI model to evaluate dietary and emotional data, analyzing the user's nutritional and emotional state. From this analysis, a list of necessary nutrients and foods that can improve emotional well-being is generated. The output is a nutritional assessment and a list of suggested foods based on the analysis results. 【0191】 Step 4: 【0192】 The server generates a list of appropriate foods and nutritional supplements to suggest to the user based on the analysis results. It utilizes a generative AI model to select the ingredients and products that best suit the user's current nutritional and emotional state. The output is a list of suggested foods and nutritional supplements. 【0193】 Step 5: 【0194】 The terminal notifies the user of a list of suggestions received from the server. The notification is displayed on the terminal's screen and can also be communicated via voice output. The user can use this information as a reference when selecting their next meal. The output consists of food and nutritional supplement product suggestions for the user. 【0195】 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. 【0196】 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. 【0197】 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. 【0198】 [Second Embodiment] 【0199】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0200】 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. 【0201】 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). 【0202】 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. 【0203】 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. 【0204】 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). 【0205】 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. 【0206】 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. 【0207】 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. 【0208】 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. 【0209】 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. 【0210】 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". 【0211】 This invention is a system for effectively managing nutrition and is designed to support users' daily eating habits. By integrating functions such as meal recording, analysis, suggestions, and notifications, this system helps users maintain a healthy nutritional balance. 【0212】 Users first register their meal details in the application on their device. This input can be done via text, photos, or voice input, and it is recommended to include the names of the ingredients and their quantities. 【0213】 The terminal sends the data entered by the user to the server. The data sent includes detailed information about the meal and the date and time of registration. 【0214】 The server uses AI to analyze the received data. This analysis references the nutrients in each food item from a database and calculates the total amount of nutrients the user has consumed. Based on the analysis results, the server evaluates the user's current nutritional balance and identifies any nutrient deficiencies or excesses. 【0215】 Once the evaluation is complete, the server will identify recommended foods and dishes for the next meal. This suggestion will provide effective options to improve the individual's nutritional status and may include information on nutritional supplements if necessary. 【0216】 The device notifies the user of these suggestions. These notifications are delivered via push messages or in-app notification tabs, allowing users to easily access and review the content. This enables users to obtain information to make appropriate food choices. 【0217】 For example, if a user registers bread and coffee for breakfast, the server analyzes this and determines that the user is deficient in vitamins. The server then lists recommended fruits and vegetables for lunch, and if protein is lacking, it suggests foods like tofu or chicken. This information is notified to the user via their device, allowing them to make informed meal choices and maintain overall health. 【0218】 Thus, the system of the present invention supports individual nutritional management through AI analysis and is an effective means of achieving long-term health maintenance. 【0219】 The following describes the processing flow. 【0220】 Step 1: 【0221】 Users enter their meal details into the application. They can record their meals by writing detailed text descriptions or by uploading photos. 【0222】 Step 2: 【0223】 The terminal formats the entered meal data and sends it to the server. This data includes ingredient names, quantities, user ID, and date and time information. 【0224】 Step 3: 【0225】 The server inputs the received meal data into an AI module for analysis. The AI ​​module retrieves nutritional information for each food item from the database and calculates the total amount of each nutrient consumed by the user. 【0226】 Step 4: 【0227】 The server uses the calculated nutritional data to assess the user's nutritional status. It identifies nutrient deficiencies or excesses and lists nutrients that require particular attention. 【0228】 Step 5: 【0229】 The server calculates recommended ingredients and dishes for the next meal based on the nutritional assessment. It also takes into account seasonal ingredients and potential nutritional supplements. 【0230】 Step 6: 【0231】 The server organizes recommended ingredients and recipe information and generates a notification message. This message may also include information about necessary nutritional supplements. 【0232】 Step 7: 【0233】 The device notifies the user of the generated notification message. The user can then use this information to plan their next meal. 【0234】 (Example 1) 【0235】 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." 【0236】 Managing a healthy nutritional balance efficiently while addressing the diversity and variability of individual users' dietary habits is challenging. Conventional methods involve the cumbersome task of recording meal contents, and furthermore, they fail to adequately assess nutritional status or provide suggestions for improvement in subsequent meals. 【0237】 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. 【0238】 In this invention, the server includes recording means, analysis means, suggestion generation means using a generative AI model, and notification means. This allows users to easily record their meal contents and analyze their nutritional status using the AI ​​model, enabling personalized nutritional management. By receiving suggestions quickly, improvements to eating habits can be made efficiently. 【0239】 "Information input means" refers to methods and devices that allow users to record the contents of their meals. Specifically, this includes text input, image recognition, and voice input. 【0240】 A "processing unit" refers to a computing device or program used for data analysis and evaluation. It primarily functions as a server or computer system. 【0241】 "Analysis methods" refer to methods and devices that evaluate nutrients based on recorded dietary information. This is done using AI models and databases. 【0242】 "Suggestion generation means" refers to a method or apparatus that generates food and nutritional supplement options suitable for the next meal based on nutritional assessment. It utilizes a generation AI model. 【0243】 A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to analyze data and make optimal suggestions for a specific purpose. 【0244】 "Notification means" refers to methods or devices used to inform users of generated suggestions. This is primarily done through push notifications and in-app notifications. 【0245】 "Communication means" refers to methods and devices for exchanging information with other devices or systems. This is done via networks or the internet. 【0246】 The system of this invention efficiently manages the user's daily eating habits, integrating the functions of information input, data analysis, suggestion generation, and notification. The implementation method will be described below, specifying the hardware and software. 【0247】 Users install and use a dedicated application on their smartphones, tablets, or other devices. Through this application, users record their meals. For example, users can enter the names and quantities of ingredients using the text input function, or take photos of their meals using the camera function and identify the ingredients through image recognition. It is also possible to register meal details using the voice input function. 【0248】 The terminal sends the meal information entered by the user to the server. This communication uses data transfer technology via an internet connection. The information sent includes detailed meal information and the date and time of registration. 【0249】 The server includes a processing unit for analyzing received data and a database containing nutritional information for each food item. The received data is analyzed by an AI model to calculate the total amount of nutrients consumed by the user. In this process, the analysis also considers the user's past dietary information. 【0250】 Furthermore, the server uses a generative AI model to generate suggestions based on the analysis results. These suggestions include recommended foods and dishes for the next meal, tailored to the individual's nutritional status. For example, it might suggest oranges or broccoli to supplement a deficient vitamin C. This information is finally communicated to the user via a notification system through their device. 【0251】 As a concrete example, let's look at a prompt: "When a user has had bread and coffee for breakfast, please suggest recommended nutrients and ingredients for their next meal." Based on this prompt, the AI ​​model generates optimal suggestions. In this way, this system is designed to provide flexible nutritional management tailored to each user's eating habits. 【0252】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0253】 Step 1: 【0254】 Users record their meal details in an application on their device. Input methods include directly entering the names and quantities of ingredients as text, or using the camera function to take photos of the meal and utilizing image recognition technology. It is also possible to register meal details using voice input. This generates detailed meal information as input data. 【0255】 Step 2: 【0256】 The terminal processes the meal information entered by the user and sends it to the server. Specifically, it organizes the information into a data format that includes detailed meal information and the registration date and time, and sends it to the server using the HTTP protocol. In this case, the input is the user's meal information, and the output is the data sent to the server. 【0257】 Step 3: 【0258】 The server analyzes the received data using an AI model. It receives meal data sent to the server as input, references nutritional information for each food item from its internal database, and performs calculations to determine the total nutritional content. The output is the analysis result of the user's nutritional intake. 【0259】 Step 4: 【0260】 The server generates suggestions using a generative AI model based on the analysis results. The input is the user's nutritional status data obtained through the analysis. The generative AI model lists recommended foods and dishes to supplement any deficient nutrients, for example. The output is suggested information to refer to for the next meal. 【0261】 Step 5: 【0262】 The device notifies the user of the suggestion information received from the server. Specifically, this involves sending information to the user's device via push notification and displaying the suggestions in the app's notification tab. The input is the generated suggestion information, and the output shows the specific meal suggestions the user receives. 【0263】 (Application Example 1) 【0264】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0265】 In modern households, individualized nutritional management is required to maintain a healthy diet. However, it is a significant burden for individuals to meticulously record their daily meals, analyze their nutritional balance, and select appropriate ingredients. Furthermore, it is difficult for ordinary users without specialized knowledge to understand the nutritional analysis results and apply them to their daily lives. Therefore, there is a need for a system that can easily support nutritional management. 【0266】 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. 【0267】 In this invention, the server includes an input structure for recording meal information, an analysis structure for analyzing the recorded meal information and evaluating nutritional status, and a suggestion generation structure for generating suggested ingredients and nutritional supplements based on the nutritional evaluation. This makes it possible to easily collect meal data via a home device and immediately receive suggestions in a format that is easy for the user to understand. 【0268】 The "input structure for recording meal information" provides a function that allows users to record details of their daily meals using voice, text, or images. 【0269】 The "analysis structure" is a system that analyzes the nutrients contained in the food based on the input dietary information and has the function of evaluating the balance of nutrients consumed. 【0270】 The "proposal generation structure" refers to a system that, by referring to the results of the analysis structure, selects and proposes suitable ingredients and nutritional supplements for users to maintain a healthy nutritional balance. 【0271】 A "notification structure" is a system that provides a function to easily communicate generated suggestions to users through notifications, such as displaying them or outputting audio. 【0272】 "Consumer-use devices that collect data through interaction with users" are machines used in the home that collect dietary information while interacting with the user through voice and images. 【0273】 An "analysis device that analyzes input audio and image information" is a device that processes audio and image data acquired by consumer electronics to identify ingredients and determine their quantities. 【0274】 An "output device that displays or reads out analysis results" is a device that provides users with the results of the nutritional analysis in a visual or auditory way to aid their understanding. 【0275】 In the system implementing this invention, consumer electronics, a server, and a user terminal work together. The user inputs information about their daily meals via voice or images into the consumer electronics installed in their home. The consumer electronics uses voice processing software for voice recognition and image processing software for image recognition to analyze this input information. 【0276】 Consumer devices use analytical tools such as the Google Speech-to-Text API and the Google Cloud Vision API to convert input data into text and identifiable food information. The converted data is then sent to a server. 【0277】 On the server, the acquired meal data is analyzed using AI analysis software such as TensorFlow to analyze nutrients. The server evaluates the user's nutritional status and generates suggestions for foods and nutritional supplements to provide the necessary nutrients. 【0278】 The generated suggestions are sent via a notification structure to the user's terminal or consumer electronics output device. Users can review the suggestions through visual displays or audio output and use them to improve their daily diet. 【0279】 As a concrete example, suppose a user tells a consumer device that they ate eggs and toast for breakfast. The device sends this information to a server, which analyzes it and then provides the user with a suggestion such as, "You should add some fruit containing vitamin C to your next meal." 【0280】 Examples of prompts when using a generative AI model include: "What algorithm should be used to analyze the ingredients a user ate for breakfast and suggest a healthy lunch?" and "Please tell me an effective approach for users to manage their nutrition properly." 【0281】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0282】 Step 1: 【0283】 The user inputs meal information in voice or image to the consumer devices installed in the home. The input data is information regarding the types and amounts of ingredients the user ate. 【0284】 Step 2: 【0285】 The consumer device converts the voice data input from the user into text data using the Google Speech-to-Text API. Also, the image data is analyzed using the Google Cloud Vision API to extract the ingredient names. The output of this step is the texturized meal information. 【0286】 Step 3: 【0287】 The converted meal information is sent from the terminal to the server. This information includes detailed data such as the names and amounts of the ingredients. The transmitted data is received by the server. 【0288】 Step 4: 【0289】 The server performs nutrient analysis using TensorFlow based on the received meal information. The input for the analysis is the ingredient data, and the output is the evaluation result of the user's nutritional balance. In this step, the excess or deficiency of nutrients is identified. 【0290】 Step 5: 【0291】 The server utilizes the generated AI model based on the analysis result to generate proposals for the ingredients and nutritional supplements recommended for the next meal. The output is a list of the proposed ingredients and recipes for the dishes. 【0292】 Step 6: 【0293】 The generated suggestions are sent to the user's terminal or consumer device through a notification structure. The output of this step is suggestion information in a format that can be confirmed visually and audibly by the user. 【0294】 Step 7: 【0295】 Based on the suggested information, users select their next meal and adjust the nutritional balance. This helps maintain a healthy diet. 【0296】 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. 【0297】 This invention is a system for recording and analyzing dietary content, as well as providing nutritional management that takes into account the user's emotional state. This makes it possible to harmonize not only nutritional intake, but also emotions and eating habits. 【0298】 This system comprehensively performs a series of operations including meal recording, nutritional analysis, emotion recognition, suggestion generation, and notifications. Users input their meal details into the application. The registered information includes not only the names and quantities of ingredients but also the emotional state. The emotional state includes the user's level of stress and joy. 【0299】 The device formats this data individually and sends it to the server. On the server, AI performs nutritional analysis based on the meal data. The analysis method has the ability to more flexibly adjust the necessary nutrients based on the user's emotional state. 【0300】 Specifically, an emotion engine is used to identify the user's current emotional state. This emotional data is then incorporated into the analysis, and in generating suggestions, nutritional balance is considered, along with recommendations for foods that support emotional improvement. For example, if the user is feeling stressed, foods that have a mood-boosting effect will be suggested. 【0301】 Based on the analysis results and the emotional state, the proposal generation means generates a list that includes information on ingredients, dishes recommended for the next meal, and nutritional supplements as needed. The terminal notifies the user of these proposals. The notification includes special advice considering emotions, which helps the user to balance their nutrition and achieve emotional stability. 【0302】 As a specific example, when it records that the user's motivation for cooking has decreased, the server proposes a healthy recipe that can be easily cooked considering that emotion. This allows the user to maintain motivation while easily supplementing nutrition. 【0303】 In this way, the system of the present invention aims to improve the quality of the user's life by performing health management from two aspects: nutrition and emotion. 【0304】 The following describes the processing flow. 【0305】 Step 1: 【0306】 When the user inputs the meal content into the application, the user also records the current emotional state. The emotion input is done by selecting from a preset emotion list or setting the emotion level with a slider. 【0307】 Step 2: 【0308】 The terminal formats the input meal and emotion data and sends it to the server. The transmitted data includes detailed meal information, the emotional state, and the related timestamp. 【0309】 Step 3: 【0310】 The server passes the received data to the AI analysis module. The AI analyzes the meal data to calculate the nutrients ingested and adjusts the nutritional balance based on the emotion data. 【0311】 Step 4: 【0312】 The server evaluates the user's nutritional status based on the analysis results and, taking into account their emotional state, determines the next recommended foods and dishes. In this process, foods that help alleviate stress and improve mood may also be selected. 【0313】 Step 5: 【0314】 The server analyzes the analysis results and emotional state through a suggestion generation mechanism and creates a list that includes recommended ingredients and dishes for the next meal. This list also includes information on relevant nutritional supplements. 【0315】 Step 6: 【0316】 The device notifies the user of the generated list of suggestions. The notification includes special advice that takes emotions into consideration, guiding the user in self-care. 【0317】 Step 7: 【0318】 Users can plan their next meal based on the notifications. They can use the system's feedback to make choices that improve their nutritional status and emotional balance. 【0319】 (Example 2) 【0320】 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". 【0321】 Traditional nutrition management systems simply evaluate and suggest nutritional status based on meal content, without considering the user's emotional state. Therefore, they fail to offer meal suggestions that address the user's emotional state, and issues such as stress and motivation are not taken into account. This results in low user acceptance of meal suggestions and makes it difficult to ensure nutritional balance. 【0322】 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. 【0323】 In this invention, the server includes means for inputting meal content and emotional state, means for formatting and transmitting the input meal data and emotional data, and means for analyzing the transmitted data, evaluating nutritional status, and performing a nutritional evaluation that takes emotional state into consideration. This makes it possible to suggest food ingredients and nutritional supplements that reflect the user's emotional state. 【0324】 "Meal content" refers to all information regarding the food consumed by the user and how it is prepared. 【0325】 "Emotional state" refers to information that indicates the psychological state, such as stress or joy, that a user is experiencing at a particular point in time. 【0326】 "Means for input" refers to the interface through which users provide information about their meals and emotional state to the system. 【0327】 "Means of formatting and transmitting" refers to the process of converting input data into a standard format and transmitting it in a form that can be processed within the system. 【0328】 "Means for analyzing and evaluating nutritional status" refers to a process or technology for analyzing and evaluating a user's nutritional status using dietary data and emotional data. 【0329】 "Suggestion generation means" refers to a function or process that creates suggestions for improving the user's diet based on analyzed data. 【0330】 "Means of notification" refers to an interface or method for communicating generated suggestions to users. 【0331】 This invention is a system for meal management that provides nutritional management that takes into account the user's emotional state. The user inputs their current emotional state along with their meal details through a digital input device. Suitable devices include smartphones and tablet terminals. This allows the user to utilize the system within their daily life. 【0332】 The terminal converts the input information into a standard format. A dedicated application is used for this process to ensure data format consistency. The formatted data is then sent to the server using a secure communication protocol. 【0333】 The server analyzes the received data. This process utilizes a generative AI model to analyze the collected dietary data and emotional state in detail. The use of an emotion engine enables nutritional analysis that reflects the user's emotional state. For example, if the user is stressed, foods with relaxation effects will be identified. 【0334】 The suggestion generation system constructs nutritional suggestions for the user based on analyzed data. This allows users to receive specific suggestions that take into account not only their current nutritional status but also their emotional state. The device notifies the user of these suggestions to help them make daily meal choices. 【0335】 For example, if a user enters "I'm tired today," the server will generate a plan suggesting foods that help with fatigue recovery. This allows the user to make choices based on their own health condition. An example of a prompt might be something like, "Please explain in detail how to suggest foods with relaxation effects based on the user's emotional state." 【0336】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0337】 Step 1: 【0338】 Users input their meal details and emotional state through the application. Specifically, they record daily ingredients, quantities, cooking methods, and their emotions at the time (e.g., stress, happiness). The entered information is stored on the device as meal record data and emotional data. 【0339】 Step 2: 【0340】 The terminal formats the entered meal record data and emotion data. This process converts the data into a standard format to facilitate subsequent processing. Specifically, it normalizes emotion values ​​and tags food categories. As a result, it outputs formatted data. 【0341】 Step 3: 【0342】 The terminal sends formatted data to the server. The transmission uses a secure communication protocol to maintain data confidentiality and integrity. After transmission, the server receives the data. 【0343】 Step 4: 【0344】 The server analyzes the received formatted data. The analysis uses a generative AI model to assess the user's nutritional status using dietary and emotional data. Specifically, it outputs evaluation results such as "calcium deficiency" or "high stress levels." 【0345】 Step 5: 【0346】 The server generates meal suggestions based on the analysis results. The suggestion generation process also considers emotional data to create a list of meals suitable for the user's current physical and mental state. Output includes a "list of ingredients to choose for your next meal" and "recommended recipes." 【0347】 Step 6: 【0348】 The device notifies the user of meal suggestions provided by the server. These notifications include specific ingredients, cooking methods, and even emotional support advice. Users can receive this information and incorporate it into their daily eating habits. 【0349】 (Application Example 2) 【0350】 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." 【0351】 Traditional nutrition management systems focus on recording and analyzing meals, but fail to consider the user's emotional state, making it difficult to improve emotional satisfaction and mental health. Therefore, there is a need to develop new systems that harmonize users' emotions with their eating habits and contribute to improving their quality of life. 【0352】 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. 【0353】 In this invention, the server includes input means for recording the contents of meals and emotional states, analysis means for analyzing the recorded meal data and emotional data to evaluate nutritional status and emotional state, and suggestion generation means for generating food products and nutritional supplements to be suggested based on the nutritional evaluation and emotional state. This makes it possible to realize a healthy diet that takes into account the user's nutritional and emotional balance. 【0354】 "Input means" refers to a device or method used by a user to record the contents of a meal and their emotional state. 【0355】 "Analysis means" refers to a device or method that evaluates recorded meal data and emotional data to analyze nutritional status and emotional state. 【0356】 A "proposal generation means" is an apparatus or method that generates recommended foods or nutritional supplements based on nutritional evaluations and emotional states obtained through analysis. 【0357】 "Notification means" refers to a device or method for communicating the generated proposal to the user. 【0358】 This system records the user's diet and emotional state, and through analysis, provides health management that considers the balance between nutrition and emotions. The system mainly consists of input means, analysis means, suggestion generation means, and notification means. 【0359】 The input method involves users recording their meal content and emotional state, and utilizes devices such as smartphones and tablets. Users use these devices to input specific meal details and their emotional state (stress, joy, etc.). 【0360】 The input data is formatted on the terminal and then sent to the server. On the server, the analysis system uses AI analysis tools such as TensorFlow to evaluate the nutritional and emotional status based on the recorded meal and emotional data. From this evaluation result, suggestions for maintaining an optimal nutritional balance are generated. The suggestion generation system lists recommended foods and nutritional supplements based on the nutritional evaluation and emotional status. 【0361】 The notification system is used to inform the user of generated suggestions. These notifications are displayed on the device via the display or audio output. For example, if the user is feeling stressed, it may suggest oatmeal for breakfast, as it is known to be effective in reducing stress. 【0362】 Through the operation of this system, users can maintain a healthy lifestyle that takes into account the balance between nutrition and emotional well-being. For example, by using prompts such as, "What foods do you recommend when a user is feeling stressed? Please also provide a specific meal plan," it is possible to obtain more effective suggestions. 【0363】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0364】 Step 1: 【0365】 The user uses a terminal to input details about their meal and emotional state. This input includes specific dish names, ingredients, quantities, and their emotional state for the day (e.g., stress, joy). This data is then sent directly to the next processing step. 【0366】 Step 2: 【0367】 The terminal formats the meal details and emotional state entered by the user. This formatted data includes standardized meal information and emotional scores, which are then converted for transmission to the server. The output is sent to the server as formatted data. 【0368】 Step 3: 【0369】 The server analyzes the received formatted data. The analysis uses a generative AI model to evaluate dietary and emotional data, analyzing the user's nutritional and emotional state. From this analysis, a list of necessary nutrients and foods that can improve emotional well-being is generated. The output is a nutritional assessment and a list of suggested foods based on the analysis results. 【0370】 Step 4: 【0371】 The server generates a list of appropriate foods and nutritional supplements to suggest to the user based on the analysis results. It utilizes a generative AI model to select the ingredients and products that best suit the user's current nutritional and emotional state. The output is a list of suggested foods and nutritional supplements. 【0372】 Step 5: 【0373】 The terminal notifies the user of a list of suggestions received from the server. The notification is displayed on the terminal's screen and can also be communicated via voice output. The user can use this information as a reference when selecting their next meal. The output consists of suggestions for food and nutritional supplements for the user. 【0374】 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. 【0375】 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. 【0376】 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. 【0377】 [Third Embodiment] 【0378】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0379】 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. 【0380】 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). 【0381】 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. 【0382】 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. 【0383】 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). 【0384】 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. 【0385】 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. 【0386】 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. 【0387】 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. 【0388】 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. 【0389】 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". 【0390】 This invention is a system for effectively managing nutrition and is designed to support users' daily eating habits. By integrating functions such as meal recording, analysis, suggestions, and notifications, this system helps users maintain a healthy nutritional balance. 【0391】 Users first register their meal details in the application on their device. This input can be done via text, photos, or voice input, and it is recommended to include the names of the ingredients and their quantities. 【0392】 The terminal sends the data entered by the user to the server. The data sent includes detailed information about the meal and the date and time of registration. 【0393】 The server uses AI to analyze the received data. This analysis references the nutrients in each food item from a database and calculates the total amount of nutrients the user has consumed. Based on the analysis results, the server evaluates the user's current nutritional balance and identifies any nutrient deficiencies or excesses. 【0394】 Once the evaluation is complete, the server will identify recommended foods and dishes for the next meal. This suggestion will provide effective options to improve the individual's nutritional status and may include information on nutritional supplements if necessary. 【0395】 The device notifies the user of these suggestions. These notifications are delivered via push messages or in-app notification tabs, allowing users to easily access and review the content. This enables users to obtain information to make appropriate food choices. 【0396】 For example, if a user registers bread and coffee for breakfast, the server analyzes this and determines that the user is deficient in vitamins. The server then lists recommended fruits and vegetables for lunch, and if protein is lacking, it suggests foods like tofu or chicken. This information is notified to the user via their device, allowing them to make informed meal choices and maintain overall health. 【0397】 Thus, the system of the present invention supports individual nutritional management through AI analysis and is an effective means of achieving long-term health maintenance. 【0398】 The following describes the processing flow. 【0399】 Step 1: 【0400】 Users enter their meal details into the application. They can record their meals by writing detailed text descriptions or by uploading photos. 【0401】 Step 2: 【0402】 The terminal formats the entered meal data and sends it to the server. This data includes ingredient names, quantities, user ID, and date and time information. 【0403】 Step 3: 【0404】 The server inputs the received meal data into an AI module for analysis. The AI ​​module retrieves nutritional information for each food item from the database and calculates the total amount of each nutrient consumed by the user. 【0405】 Step 4: 【0406】 The server uses the calculated nutritional data to assess the user's nutritional status. It identifies nutrient deficiencies or excesses and lists nutrients that require particular attention. 【0407】 Step 5: 【0408】 The server calculates recommended ingredients and dishes for the next meal based on the nutritional assessment. It also takes into account seasonal ingredients and potential nutritional supplements. 【0409】 Step 6: 【0410】 The server organizes recommended ingredients and recipe information and generates a notification message. This message may also include information about necessary nutritional supplements. 【0411】 Step 7: 【0412】 The device notifies the user of the generated notification message. The user can then use this information to plan their next meal. 【0413】 (Example 1) 【0414】 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." 【0415】 Managing a healthy nutritional balance efficiently while addressing the diversity and variability of individual users' dietary habits is challenging. Conventional methods involve the cumbersome task of recording meal contents, and furthermore, they fail to adequately assess nutritional status or provide suggestions for improvement in subsequent meals. 【0416】 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. 【0417】 In this invention, the server includes recording means, analysis means, suggestion generation means using a generative AI model, and notification means. This allows users to easily record their meal contents and analyze their nutritional status using the AI ​​model, enabling personalized nutritional management. By receiving suggestions quickly, improvements to eating habits can be made efficiently. 【0418】 "Information input means" refers to methods and devices that allow users to record the contents of their meals. Specifically, this includes text input, image recognition, and voice input. 【0419】 A "processing unit" refers to a computing device or program used for data analysis and evaluation. It primarily functions as a server or computer system. 【0420】 "Analysis methods" refer to methods and devices that evaluate nutrients based on recorded dietary information. This is done using AI models and databases. 【0421】 "Suggestion generation means" refers to a method or apparatus that generates food and nutritional supplement options suitable for the next meal based on nutritional assessment. It utilizes a generation AI model. 【0422】 A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to analyze data and make optimal suggestions for a specific purpose. 【0423】 "Notification means" refers to methods or devices used to inform users of generated suggestions. This is primarily done through push notifications and in-app notifications. 【0424】 "Communication means" refers to methods and devices for exchanging information with other devices or systems. This is done via networks or the internet. 【0425】 The system of this invention efficiently manages the user's daily eating habits, integrating the functions of information input, data analysis, suggestion generation, and notification. The implementation method will be described below, specifying the hardware and software. 【0426】 Users install and use a dedicated application on their smartphones, tablets, or other devices. Through this application, users record their meals. For example, users can enter the names and quantities of ingredients using the text input function, or take photos of their meals using the camera function and identify the ingredients through image recognition. It is also possible to register meal details using the voice input function. 【0427】 The terminal sends the meal information entered by the user to the server. This communication uses data transfer technology via an internet connection. The information sent includes detailed meal information and the date and time of registration. 【0428】 The server includes a processing unit for analyzing received data and a database containing nutritional information for each food item. The received data is analyzed by an AI model to calculate the total amount of nutrients consumed by the user. In this process, the analysis also considers the user's past dietary information. 【0429】 Furthermore, the server uses a generative AI model to generate suggestions based on the analysis results. These suggestions include recommended foods and dishes for the next meal, tailored to the individual's nutritional status. For example, it might suggest oranges or broccoli to supplement a deficient vitamin C. This information is finally communicated to the user via a notification system through their device. 【0430】 As a concrete example, let's look at a prompt: "When a user has had bread and coffee for breakfast, please suggest recommended nutrients and ingredients for their next meal." Based on this prompt, the AI ​​model generates optimal suggestions. In this way, this system is designed to provide flexible nutritional management tailored to each user's eating habits. 【0431】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0432】 Step 1: 【0433】 Users record their meal details in an application on their device. Input methods include directly entering the names and quantities of ingredients as text, or using the camera function to take photos of the meal and utilizing image recognition technology. It is also possible to register meal details using voice input. This generates detailed meal information as input data. 【0434】 Step 2: 【0435】 The terminal processes the meal information entered by the user and sends it to the server. Specifically, it organizes the information into a data format that includes detailed meal information and the registration date and time, and sends it to the server using the HTTP protocol. In this case, the input is the user's meal information, and the output is the data sent to the server. 【0436】 Step 3: 【0437】 The server analyzes the received data using an AI model. It receives meal data sent to the server as input, references nutritional information for each food item from its internal database, and performs calculations to determine the total nutritional content. The output is the analysis result of the user's nutritional intake. 【0438】 Step 4: 【0439】 The server generates suggestions using a generative AI model based on the analysis results. The input is the user's nutritional status data obtained through the analysis. The generative AI model lists recommended foods and dishes to supplement any deficient nutrients, for example. The output is suggested information to refer to for the next meal. 【0440】 Step 5: 【0441】 The device notifies the user of the suggestion information received from the server. Specifically, this involves sending information to the user's device via push notification and displaying the suggestions in the app's notification tab. The input is the generated suggestion information, and the output shows the specific meal suggestions the user receives. 【0442】 (Application Example 1) 【0443】 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." 【0444】 In modern households, individualized nutritional management is required to maintain a healthy diet. However, it is a significant burden for individuals to meticulously record their daily meals, analyze their nutritional balance, and select appropriate ingredients. Furthermore, it is difficult for ordinary users without specialized knowledge to understand the nutritional analysis results and apply them to their daily lives. Therefore, there is a need for a system that can easily support nutritional management. 【0445】 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. 【0446】 In this invention, the server includes an input structure for recording meal information, an analysis structure for analyzing the recorded meal information and evaluating nutritional status, and a suggestion generation structure for generating suggested ingredients and nutritional supplements based on the nutritional evaluation. This makes it possible to easily collect meal data via a home device and immediately receive suggestions in a format that is easy for the user to understand. 【0447】 The "input structure for recording meal information" provides a function that allows users to record details of their daily meals using voice, text, or images. 【0448】 The "analysis structure" is a system that analyzes the nutrients contained in the food based on the input dietary information and has the function of evaluating the balance of nutrients consumed. 【0449】 The "proposal generation structure" refers to a system that, by referring to the results of the analysis structure, selects and proposes suitable ingredients and nutritional supplements for users to maintain a healthy nutritional balance. 【0450】 A "notification structure" is a system that provides a function to easily communicate generated suggestions to users through notifications, such as displaying them or outputting audio. 【0451】 "Consumer-use devices that collect data through interaction with users" are machines used in the home that collect dietary information while interacting with the user through voice and images. 【0452】 An "analysis device that analyzes input audio and image information" is a device that processes audio and image data acquired by consumer electronics to identify ingredients and determine their quantities. 【0453】 An "output device that displays or reads out analysis results" is a device that provides users with the results of the nutritional analysis in a visual or auditory way to aid their understanding. 【0454】 In the system implementing this invention, consumer electronics, a server, and a user terminal work together. The user inputs information about their daily meals via voice or images into the consumer electronics installed in their home. The consumer electronics uses voice processing software for voice recognition and image processing software for image recognition to analyze this input information. 【0455】 Consumer devices use analytical tools such as the Google Speech-to-Text API and the Google Cloud Vision API to convert input data into text and identifiable food information. The converted data is then sent to a server. 【0456】 On the server, the acquired meal data is analyzed using AI analysis software such as TensorFlow to analyze nutrients. The server evaluates the user's nutritional status and generates suggestions for foods and nutritional supplements to provide the necessary nutrients. 【0457】 The generated suggestions are sent via a notification structure to the user's terminal or consumer electronics output device. Users can review the suggestions through visual displays or audio output and use them to improve their daily diet. 【0458】 As a concrete example, suppose a user tells a consumer device that they ate eggs and toast for breakfast. The device sends this information to a server, which analyzes it and then provides the user with a suggestion such as, "You should add some fruit containing vitamin C to your next meal." 【0459】 Examples of prompts when using a generative AI model include: "What algorithm should be used to analyze the ingredients a user ate for breakfast and suggest a healthy lunch?" and "Please tell me an effective approach for users to manage their nutrition properly." 【0460】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0461】 Step 1: 【0462】 Users input meal information via voice or images into consumer devices installed in their homes. The input data includes information about the types and quantities of food the user has eaten. 【0463】 Step 2: 【0464】 Consumer devices convert user-input voice data into text data using the Google Speech-to-Text API. Image data is also analyzed using the Google Cloud Vision API to extract food names. The output of this step is text-based meal information. 【0465】 Step 3: 【0466】 The converted meal information is sent from the terminal to the server. This information includes detailed data such as the names and quantities of ingredients. The transmitted data is received by the server. 【0467】 Step 4: 【0468】 The server uses TensorFlow to analyze nutrients based on the received meal information. The input for the analysis is food ingredient data, and the output is an evaluation of the user's nutritional balance. In this step, nutritional deficiencies or excesses are identified. 【0469】 Step 5: 【0470】 The server uses an AI model based on the analysis results to generate suggestions for ingredients and nutritional supplements to recommend for the next meal. The output is a list of suggested ingredients and recipes. 【0471】 Step 6: 【0472】 The generated suggestions are sent to the user's terminal or consumer device through a notification structure. The output of this step is suggestion information in a format that can be confirmed visually and audibly by the user. 【0473】 Step 7: 【0474】 Based on the suggested information, users select their next meal and adjust the nutritional balance. This helps maintain a healthy diet. 【0475】 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. 【0476】 This invention is a system for recording and analyzing dietary content, as well as providing nutritional management that takes into account the user's emotional state. This makes it possible to harmonize not only nutritional intake, but also emotions and eating habits. 【0477】 This system comprehensively performs a series of operations including meal recording, nutritional analysis, emotion recognition, suggestion generation, and notifications. Users input their meal details into the application. The registered information includes not only the names and quantities of ingredients but also the emotional state. The emotional state includes the user's level of stress and joy. 【0478】 The device formats this data individually and sends it to the server. On the server, AI performs nutritional analysis based on the meal data. The analysis method has the ability to more flexibly adjust the necessary nutrients based on the user's emotional state. 【0479】 Specifically, an emotion engine is used to identify the user's current emotional state. This emotional data is then incorporated into the analysis, and in generating suggestions, nutritional balance is considered, along with recommendations for foods that support emotional improvement. For example, if the user is feeling stressed, foods that have a mood-boosting effect will be suggested. 【0480】 The suggestion generation mechanism generates a list containing information on recommended ingredients and dishes for the next meal, as well as nutritional supplements as needed, based on the analysis results and emotional state. The terminal notifies the user of these suggestions. The notification includes special advice that takes emotions into consideration, helping the user correct unbalanced nutrition and achieve emotional stability. 【0481】 For example, if a user records a decrease in their motivation to cook, the server will take that emotion into consideration and suggest easy-to-prepare, healthy recipes. This allows users to easily supplement their nutrition while maintaining their motivation. 【0482】 Thus, the system of the present invention aims to improve the user's quality of life by managing health from two aspects: nutrition and emotional well-being. 【0483】 The following describes the processing flow. 【0484】 Step 1: 【0485】 When users enter their meal details into the application, they also record their current emotional state. Emotions can be entered by selecting from a preset list of emotions or by setting the emotional level using a slider. 【0486】 Step 2: 【0487】 The terminal formats the entered meal and emotional data and sends it to the server. The transmitted data includes detailed meal information, emotional state, and associated timestamps. 【0488】 Step 3: 【0489】 The server passes the received data to the AI ​​analysis module. The AI ​​analyzes the meal data to calculate the nutrients consumed and adjusts for any nutritional deficiencies or excesses based on emotional data. 【0490】 Step 4: 【0491】 The server evaluates the user's nutritional status based on the analysis results and, taking into account their emotional state, determines the next recommended foods and dishes. In this process, foods that help alleviate stress and improve mood may also be selected. 【0492】 Step 5: 【0493】 The server analyzes the analysis results and emotional state through a suggestion generation mechanism and creates a list that includes recommended ingredients and dishes for the next meal. This list also includes information on relevant nutritional supplements. 【0494】 Step 6: 【0495】 The device notifies the user of the generated list of suggestions. The notification includes special advice that takes emotions into consideration, guiding the user in self-care. 【0496】 Step 7: 【0497】 Users can plan their next meal based on the notifications. They can use the system's feedback to make choices that improve their nutritional status and emotional balance. 【0498】 (Example 2) 【0499】 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." 【0500】 Traditional nutrition management systems simply evaluate and suggest nutritional status based on meal content, without considering the user's emotional state. Therefore, they fail to offer meal suggestions that address the user's emotional state, and issues such as stress and motivation are not taken into account. This results in low user acceptance of meal suggestions and makes it difficult to ensure nutritional balance. 【0501】 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. 【0502】 In this invention, the server includes means for inputting meal content and emotional state, means for formatting and transmitting the input meal data and emotional data, and means for analyzing the transmitted data, evaluating nutritional status, and performing a nutritional evaluation that takes emotional state into consideration. This makes it possible to suggest food ingredients and nutritional supplements that reflect the user's emotional state. 【0503】 "Meal content" refers to all information regarding the food consumed by the user and how it is prepared. 【0504】 "Emotional state" refers to information that indicates the psychological state, such as stress or joy, that a user is experiencing at a particular point in time. 【0505】 "Means for input" refers to the interface through which users provide information about their meals and emotional state to the system. 【0506】 "Means of formatting and transmitting" refers to the process of converting input data into a standard format and transmitting it in a form that can be processed within the system. 【0507】 "Means for analyzing and evaluating nutritional status" refers to a process or technology for analyzing and evaluating a user's nutritional status using dietary data and emotional data. 【0508】 "Suggestion generation means" refers to a function or process that creates suggestions for improving the user's diet based on analyzed data. 【0509】 "Means of notification" refers to an interface or method for communicating generated suggestions to users. 【0510】 This invention is a system for meal management that provides nutritional management that takes into account the user's emotional state. The user inputs their current emotional state along with their meal details through a digital input device. Suitable devices include smartphones and tablet terminals. This allows the user to utilize the system within their daily life. 【0511】 The terminal converts the input information into a standard format. A dedicated application is used for this process to ensure data format consistency. The formatted data is then sent to the server using a secure communication protocol. 【0512】 The server analyzes the received data. This process utilizes a generative AI model to analyze the collected dietary data and emotional state in detail. The use of an emotion engine enables nutritional analysis that reflects the user's emotional state. For example, if the user is stressed, foods with relaxation effects will be identified. 【0513】 The suggestion generation system constructs nutritional suggestions for the user based on analyzed data. This allows users to receive specific suggestions that take into account not only their current nutritional status but also their emotional state. The device notifies the user of these suggestions to help them make daily meal choices. 【0514】 For example, if a user enters "I'm tired today," the server will generate a plan suggesting foods that help with fatigue recovery. This allows the user to make choices based on their own health condition. An example of a prompt might be something like, "Please explain in detail how to suggest foods with relaxation effects based on the user's emotional state." 【0515】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0516】 Step 1: 【0517】 Users input their meal details and emotional state through the application. Specifically, they record daily ingredients, quantities, cooking methods, and their emotions at the time (e.g., stress, happiness). The entered information is stored on the device as meal record data and emotional data. 【0518】 Step 2: 【0519】 The terminal formats the entered meal record data and emotion data. This process converts the data into a standard format to facilitate subsequent processing. Specifically, it normalizes emotion values ​​and tags food categories. As a result, it outputs formatted data. 【0520】 Step 3: 【0521】 The terminal sends formatted data to the server. The transmission uses a secure communication protocol to maintain data confidentiality and integrity. After transmission, the server receives the data. 【0522】 Step 4: 【0523】 The server analyzes the received formatted data. The analysis uses a generative AI model to assess the user's nutritional status using dietary and emotional data. Specifically, it outputs evaluation results such as "calcium deficiency" or "high stress levels." 【0524】 Step 5: 【0525】 The server generates meal suggestions based on the analysis results. The suggestion generation process also considers emotional data to create a list of meals suitable for the user's current physical and mental state. Output includes a "list of ingredients to choose for your next meal" and "recommended recipes." 【0526】 Step 6: 【0527】 The device notifies the user of meal suggestions provided by the server. These notifications include specific ingredients, cooking methods, and even emotional support advice. Users can receive this information and incorporate it into their daily eating habits. 【0528】 (Application Example 2) 【0529】 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." 【0530】 Traditional nutrition management systems focus on recording and analyzing meals, but fail to consider the user's emotional state, making it difficult to improve emotional satisfaction and mental health. Therefore, there is a need to develop new systems that harmonize users' emotions with their eating habits and contribute to improving their quality of life. 【0531】 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. 【0532】 In this invention, the server includes input means for recording the contents of meals and emotional states, analysis means for analyzing the recorded meal data and emotional data to evaluate nutritional status and emotional state, and suggestion generation means for generating food products and nutritional supplements to be suggested based on the nutritional evaluation and emotional state. This makes it possible to realize a healthy diet that takes into account the user's nutritional and emotional balance. 【0533】 "Input means" refers to a device or method used by a user to record the contents of a meal and their emotional state. 【0534】 "Analysis means" refers to a device or method that evaluates recorded meal data and emotional data to analyze nutritional status and emotional state. 【0535】 A "proposal generation means" is an apparatus or method that generates recommended foods or nutritional supplements based on nutritional evaluations and emotional states obtained through analysis. 【0536】 "Notification means" refers to a device or method for communicating the generated proposal to the user. 【0537】 This system records the user's diet and emotional state, and through analysis, provides health management that considers the balance between nutrition and emotions. The system mainly consists of input means, analysis means, suggestion generation means, and notification means. 【0538】 The input method involves users recording their meal content and emotional state, and utilizes devices such as smartphones and tablets. Users use these devices to input specific meal details and their emotional state (stress, joy, etc.). 【0539】 The input data is formatted on the terminal and then sent to the server. On the server, the analysis system uses AI analysis tools such as TensorFlow to evaluate the nutritional and emotional status based on the recorded meal and emotional data. From this evaluation result, suggestions for maintaining an optimal nutritional balance are generated. The suggestion generation system lists recommended foods and nutritional supplements based on the nutritional evaluation and emotional status. 【0540】 The notification system is used to inform the user of generated suggestions. These notifications are displayed on the device via the display or audio output. For example, if the user is feeling stressed, it may suggest oatmeal for breakfast, as it is known to be effective in reducing stress. 【0541】 Through the operation of this system, users can maintain a healthy lifestyle that takes into account the balance between nutrition and emotional well-being. For example, by using prompts such as, "What foods do you recommend when a user is feeling stressed? Please also provide a specific meal plan," it is possible to obtain more effective suggestions. 【0542】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0543】 Step 1: 【0544】 The user uses a terminal to input details about their meal and emotional state. This input includes specific dish names, ingredients, quantities, and their emotional state for the day (e.g., stress, joy). This data is then sent directly to the next processing step. 【0545】 Step 2: 【0546】 The terminal formats the meal details and emotional state entered by the user. This formatted data includes standardized meal information and emotional scores, which are then converted for transmission to the server. The output is sent to the server as formatted data. 【0547】 Step 3: 【0548】 The server analyzes the received formatted data. The analysis uses a generative AI model to evaluate dietary and emotional data, analyzing the user's nutritional and emotional state. From this analysis, a list of necessary nutrients and foods that can improve emotional well-being is generated. The output is a nutritional assessment and a list of suggested foods based on the analysis results. 【0549】 Step 4: 【0550】 The server generates a list of appropriate foods and nutritional supplements to suggest to the user based on the analysis results. It utilizes a generative AI model to select the ingredients and products that best suit the user's current nutritional and emotional state. The output is a list of suggested foods and nutritional supplements. 【0551】 Step 5: 【0552】 The terminal notifies the user of a list of suggestions received from the server. The notification is displayed on the terminal's screen and can also be communicated via voice output. The user can use this information as a reference when selecting their next meal. The output consists of suggestions for food and nutritional supplements for the user. 【0553】 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. 【0554】 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. 【0555】 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. 【0556】 [Fourth Embodiment] 【0557】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0558】 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. 【0559】 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). 【0560】 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. 【0561】 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. 【0562】 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). 【0563】 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. 【0564】 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. 【0565】 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. 【0566】 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. 【0567】 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. 【0568】 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. 【0569】 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". 【0570】 This invention is a system for effectively managing nutrition and is designed to support users' daily eating habits. By integrating functions such as meal recording, analysis, suggestions, and notifications, this system helps users maintain a healthy nutritional balance. 【0571】 Users first register their meal details in the application on their device. This input can be done via text, photos, or voice input, and it is recommended to include the names of the ingredients and their quantities. 【0572】 The terminal sends the data entered by the user to the server. The data sent includes detailed information about the meal and the date and time of registration. 【0573】 The server uses AI to analyze the received data. This analysis references the nutrients in each food item from a database and calculates the total amount of nutrients the user has consumed. Based on the analysis results, the server evaluates the user's current nutritional balance and identifies any nutrient deficiencies or excesses. 【0574】 Once the evaluation is complete, the server will identify recommended foods and dishes for the next meal. This suggestion will provide effective options to improve the individual's nutritional status and may include information on nutritional supplements if necessary. 【0575】 The device notifies the user of these suggestions. These notifications are delivered via push messages or in-app notification tabs, allowing users to easily access and review the content. This enables users to obtain information to make appropriate food choices. 【0576】 For example, if a user registers bread and coffee for breakfast, the server analyzes this and determines that the user is deficient in vitamins. The server then lists recommended fruits and vegetables for lunch, and if protein is lacking, it suggests foods like tofu or chicken. This information is notified to the user via their device, allowing them to make informed meal choices and maintain overall health. 【0577】 Thus, the system of the present invention supports individual nutritional management through AI analysis and is an effective means of achieving long-term health maintenance. 【0578】 The following describes the processing flow. 【0579】 Step 1: 【0580】 Users enter their meal details into the application. They can record their meals by writing detailed text descriptions or by uploading photos. 【0581】 Step 2: 【0582】 The terminal formats the entered meal data and sends it to the server. This data includes ingredient names, quantities, user ID, and date and time information. 【0583】 Step 3: 【0584】 The server inputs the received meal data into an AI module for analysis. The AI ​​module retrieves nutritional information for each food item from the database and calculates the total amount of each nutrient consumed by the user. 【0585】 Step 4: 【0586】 The server uses the calculated nutritional data to assess the user's nutritional status. It identifies nutrient deficiencies or excesses and lists nutrients that require particular attention. 【0587】 Step 5: 【0588】 The server calculates recommended ingredients and dishes for the next meal based on the nutritional assessment. It also takes into account seasonal ingredients and potential nutritional supplements. 【0589】 Step 6: 【0590】 The server organizes recommended ingredients and recipe information and generates a notification message. This message may also include information about necessary nutritional supplements. 【0591】 Step 7: 【0592】 The device notifies the user of the generated notification message. The user can then use this information to plan their next meal. 【0593】 (Example 1) 【0594】 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". 【0595】 Managing a healthy nutritional balance efficiently while addressing the diversity and variability of individual users' dietary habits is challenging. Conventional methods involve the cumbersome task of recording meal contents, and furthermore, they fail to adequately assess nutritional status or provide suggestions for improvement in subsequent meals. 【0596】 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. 【0597】 In this invention, the server includes recording means, analysis means, suggestion generation means using a generative AI model, and notification means. This allows users to easily record their meal contents and analyze their nutritional status using the AI ​​model, enabling personalized nutritional management. By receiving suggestions quickly, improvements to eating habits can be made efficiently. 【0598】 "Information input means" refers to methods and devices that allow users to record the contents of their meals. Specifically, this includes text input, image recognition, and voice input. 【0599】 A "processing unit" refers to a computing device or program used for data analysis and evaluation. It primarily functions as a server or computer system. 【0600】 "Analysis methods" refer to methods and devices that evaluate nutrients based on recorded dietary information. This is done using AI models and databases. 【0601】 "Suggestion generation means" refers to a method or apparatus that generates food and nutritional supplement options suitable for the next meal based on nutritional assessment. It utilizes a generation AI model. 【0602】 A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to analyze data and make optimal suggestions for a specific purpose. 【0603】 "Notification means" refers to methods or devices used to inform users of generated suggestions. This is primarily done through push notifications and in-app notifications. 【0604】 "Communication means" refers to methods and devices for exchanging information with other devices or systems. This is done via networks or the internet. 【0605】 The system of this invention efficiently manages the user's daily eating habits, integrating the functions of information input, data analysis, suggestion generation, and notification. The implementation method will be described below, specifying the hardware and software. 【0606】 Users install and use a dedicated application on their smartphones, tablets, or other devices. Through this application, users record their meals. For example, users can enter the names and quantities of ingredients using the text input function, or take photos of their meals using the camera function and identify the ingredients through image recognition. It is also possible to register meal details using the voice input function. 【0607】 The terminal sends the meal information entered by the user to the server. This communication uses data transfer technology via an internet connection. The information sent includes detailed meal information and the date and time of registration. 【0608】 The server includes a processing unit for analyzing received data and a database containing nutritional information for each food item. The received data is analyzed by an AI model to calculate the total amount of nutrients consumed by the user. In this process, the analysis also considers the user's past dietary information. 【0609】 Furthermore, the server uses a generative AI model to generate suggestions based on the analysis results. These suggestions include recommended foods and dishes for the next meal, tailored to the individual's nutritional status. For example, it might suggest oranges or broccoli to supplement a deficient vitamin C. This information is finally communicated to the user via a notification system through their device. 【0610】 As a concrete example, let's look at a prompt: "When a user has had bread and coffee for breakfast, please suggest recommended nutrients and ingredients for their next meal." Based on this prompt, the AI ​​model generates optimal suggestions. In this way, this system is designed to provide flexible nutritional management tailored to each user's eating habits. 【0611】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0612】 Step 1: 【0613】 Users record their meal details in an application on their device. Input methods include directly entering the names and quantities of ingredients as text, or using the camera function to take photos of the meal and utilizing image recognition technology. It is also possible to register meal details using voice input. This generates detailed meal information as input data. 【0614】 Step 2: 【0615】 The terminal processes the meal information entered by the user and sends it to the server. Specifically, it organizes the information into a data format that includes detailed meal information and the registration date and time, and sends it to the server using the HTTP protocol. In this case, the input is the user's meal information, and the output is the data sent to the server. 【0616】 Step 3: 【0617】 The server analyzes the received data using an AI model. It receives meal data sent to the server as input, references nutritional information for each food item from its internal database, and performs calculations to determine the total nutritional content. The output is the analysis result of the user's nutritional intake. 【0618】 Step 4: 【0619】 The server generates suggestions using a generative AI model based on the analysis results. The input is the user's nutritional status data obtained through the analysis. The generative AI model lists recommended foods and dishes to supplement any deficient nutrients, for example. The output is suggested information to refer to for the next meal. 【0620】 Step 5: 【0621】 The device notifies the user of the suggestion information received from the server. Specifically, this involves sending information to the user's device via push notification and displaying the suggestions in the app's notification tab. The input is the generated suggestion information, and the output shows the specific meal suggestions the user receives. 【0622】 (Application Example 1) 【0623】 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". 【0624】 In modern households, individualized nutritional management is required to maintain a healthy diet. However, it is a significant burden for individuals to meticulously record their daily meals, analyze their nutritional balance, and select appropriate ingredients. Furthermore, it is difficult for ordinary users without specialized knowledge to understand the nutritional analysis results and apply them to their daily lives. Therefore, there is a need for a system that can easily support nutritional management. 【0625】 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. 【0626】 In this invention, the server includes an input structure for recording meal information, an analysis structure for analyzing the recorded meal information and evaluating nutritional status, and a suggestion generation structure for generating suggested ingredients and nutritional supplements based on the nutritional evaluation. This makes it possible to easily collect meal data via a home device and immediately receive suggestions in a format that is easy for the user to understand. 【0627】 The "input structure for recording meal information" provides a function that allows users to record details of their daily meals using voice, text, or images. 【0628】 The "analysis structure" is a system that analyzes the nutrients contained in the food based on the input dietary information and has the function of evaluating the balance of nutrients consumed. 【0629】 The "proposal generation structure" refers to a system that, by referring to the results of the analysis structure, selects and proposes suitable ingredients and nutritional supplements for users to maintain a healthy nutritional balance. 【0630】 A "notification structure" is a system that provides a function to easily communicate generated suggestions to users through notifications, such as displaying them or outputting audio. 【0631】 "Consumer-use devices that collect data through interaction with users" are machines used in the home that collect dietary information while interacting with the user through voice and images. 【0632】 An "analysis device that analyzes input audio and image information" is a device that processes audio and image data acquired by consumer electronics to identify ingredients and determine their quantities. 【0633】 An "output device that displays or reads out analysis results" is a device that provides users with the results of the nutritional analysis in a visual or auditory way to aid their understanding. 【0634】 In the system implementing this invention, consumer electronics, a server, and a user terminal work together. The user inputs information about their daily meals via voice or images into the consumer electronics installed in their home. The consumer electronics uses voice processing software for voice recognition and image processing software for image recognition to analyze this input information. 【0635】 Consumer devices use analytical tools such as the Google Speech-to-Text API and the Google Cloud Vision API to convert input data into text and identifiable food information. The converted data is then sent to a server. 【0636】 On the server, the acquired meal data is analyzed using AI analysis software such as TensorFlow to analyze nutrients. The server evaluates the user's nutritional status and generates suggestions for foods and nutritional supplements to provide the necessary nutrients. 【0637】 The generated suggestions are sent via a notification structure to the user's terminal or consumer electronics output device. Users can review the suggestions through visual displays or audio output and use them to improve their daily diet. 【0638】 As a concrete example, suppose a user tells a consumer device that they ate eggs and toast for breakfast. The device sends this information to a server, which analyzes it and then provides the user with a suggestion such as, "You should add some fruit containing vitamin C to your next meal." 【0639】 Examples of prompts when using a generative AI model include: "What algorithm should be used to analyze the ingredients a user ate for breakfast and suggest a healthy lunch?" and "Please tell me an effective approach for users to manage their nutrition properly." 【0640】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0641】 Step 1: 【0642】 Users input meal information via voice or images into consumer devices installed in their homes. The input data includes information about the types and quantities of food the user has eaten. 【0643】 Step 2: 【0644】 Consumer devices convert user-input voice data into text data using the Google Speech-to-Text API. Image data is also analyzed using the Google Cloud Vision API to extract food names. The output of this step is text-based meal information. 【0645】 Step 3: 【0646】 The converted meal information is sent from the terminal to the server. This information includes detailed data such as the names and quantities of ingredients. The transmitted data is received by the server. 【0647】 Step 4: 【0648】 The server uses TensorFlow to analyze nutrients based on the received meal information. The input for the analysis is food ingredient data, and the output is an evaluation of the user's nutritional balance. In this step, nutritional deficiencies or excesses are identified. 【0649】 Step 5: 【0650】 The server uses an AI model based on the analysis results to generate suggestions for ingredients and nutritional supplements to recommend for the next meal. The output is a list of suggested ingredients and recipes. 【0651】 Step 6: 【0652】 The generated suggestions are sent to the user's terminal or consumer device through a notification structure. The output of this step is suggestion information in a format that can be confirmed visually and audibly by the user. 【0653】 Step 7: 【0654】 Based on the suggested information, users select their next meal and adjust the nutritional balance. This helps maintain a healthy diet. 【0655】 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. 【0656】 This invention is a system for recording and analyzing dietary content, as well as providing nutritional management that takes into account the user's emotional state. This makes it possible to harmonize not only nutritional intake, but also emotions and eating habits. 【0657】 This system comprehensively performs a series of operations including meal recording, nutritional analysis, emotion recognition, suggestion generation, and notifications. Users input their meal details into the application. The registered information includes not only the names and quantities of ingredients but also the emotional state. The emotional state includes the user's level of stress and joy. 【0658】 The device formats this data individually and sends it to the server. On the server, AI performs nutritional analysis based on the meal data. The analysis method has the ability to more flexibly adjust the necessary nutrients based on the user's emotional state. 【0659】 Specifically, an emotion engine is used to identify the user's current emotional state. This emotional data is then incorporated into the analysis, and in generating suggestions, nutritional balance is considered, along with recommendations for foods that support emotional improvement. For example, if the user is feeling stressed, foods that have a mood-boosting effect will be suggested. 【0660】 The suggestion generation mechanism generates a list containing information on recommended ingredients and dishes for the next meal, as well as nutritional supplements as needed, based on the analysis results and emotional state. The terminal notifies the user of these suggestions. The notification includes special advice that takes emotions into consideration, helping the user correct unbalanced nutrition and achieve emotional stability. 【0661】 For example, if a user records a decrease in their motivation to cook, the server will take that emotion into consideration and suggest easy-to-prepare, healthy recipes. This allows users to easily supplement their nutrition while maintaining their motivation. 【0662】 Thus, the system of the present invention aims to improve the user's quality of life by managing health from two aspects: nutrition and emotional well-being. 【0663】 The following describes the processing flow. 【0664】 Step 1: 【0665】 When users enter their meal details into the application, they also record their current emotional state. Emotions can be entered by selecting from a preset list of emotions or by setting the emotional level using a slider. 【0666】 Step 2: 【0667】 The terminal formats the entered meal and emotional data and sends it to the server. The transmitted data includes detailed meal information, emotional state, and associated timestamps. 【0668】 Step 3: 【0669】 The server passes the received data to the AI ​​analysis module. The AI ​​analyzes the meal data to calculate the nutrients consumed and adjusts for any nutritional deficiencies or excesses based on emotional data. 【0670】 Step 4: 【0671】 The server evaluates the user's nutritional status based on the analysis results and, taking into account their emotional state, determines the next recommended foods and dishes. In this process, foods that help alleviate stress and improve mood may also be selected. 【0672】 Step 5: 【0673】 The server analyzes the analysis results and emotional state through a suggestion generation mechanism and creates a list that includes recommended ingredients and dishes for the next meal. This list also includes information on relevant nutritional supplements. 【0674】 Step 6: 【0675】 The device notifies the user of the generated list of suggestions. The notification includes special advice that takes emotions into consideration, guiding the user in self-care. 【0676】 Step 7: 【0677】 Users can plan their next meal based on the notifications. They can use the system's feedback to make choices that improve their nutritional status and emotional balance. 【0678】 (Example 2) 【0679】 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". 【0680】 Traditional nutrition management systems simply evaluate and suggest nutritional status based on meal content, without considering the user's emotional state. Therefore, they fail to offer meal suggestions that address the user's emotional state, and issues such as stress and motivation are not taken into account. This results in low user acceptance of meal suggestions and makes it difficult to ensure nutritional balance. 【0681】 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. 【0682】 In this invention, the server includes means for inputting meal content and emotional state, means for formatting and transmitting the input meal data and emotional data, and means for analyzing the transmitted data, evaluating nutritional status, and performing a nutritional evaluation that takes emotional state into consideration. This makes it possible to suggest food ingredients and nutritional supplements that reflect the user's emotional state. 【0683】 "Meal content" refers to all information regarding the food consumed by the user and how it is prepared. 【0684】 "Emotional state" refers to information that indicates the psychological state, such as stress or joy, that a user is experiencing at a particular point in time. 【0685】 "Means for input" refers to the interface through which users provide information about their meals and emotional state to the system. 【0686】 "Means of formatting and transmitting" refers to the process of converting input data into a standard format and transmitting it in a form that can be processed within the system. 【0687】 "Means for analyzing and evaluating nutritional status" refers to a process or technology for analyzing and evaluating a user's nutritional status using dietary data and emotional data. 【0688】 "Suggestion generation means" refers to a function or process that creates suggestions for improving the user's diet based on analyzed data. 【0689】 "Means of notification" refers to an interface or method for communicating generated suggestions to users. 【0690】 This invention is a system for meal management that provides nutritional management that takes into account the user's emotional state. The user inputs their current emotional state along with their meal details through a digital input device. Suitable devices include smartphones and tablet terminals. This allows the user to utilize the system within their daily life. 【0691】 The terminal converts the input information into a standard format. A dedicated application is used for this process to ensure data format consistency. The formatted data is then sent to the server using a secure communication protocol. 【0692】 The server analyzes the received data. This process utilizes a generative AI model to analyze the collected dietary data and emotional state in detail. The use of an emotion engine enables nutritional analysis that reflects the user's emotional state. For example, if the user is stressed, foods with relaxation effects will be identified. 【0693】 The suggestion generation system constructs nutritional suggestions for the user based on analyzed data. This allows users to receive specific suggestions that take into account not only their current nutritional status but also their emotional state. The device notifies the user of these suggestions to help them make daily meal choices. 【0694】 For example, if a user enters "I'm tired today," the server will generate a plan suggesting foods that help with fatigue recovery. This allows the user to make choices based on their own health condition. An example of a prompt might be something like, "Please explain in detail how to suggest foods with relaxation effects based on the user's emotional state." 【0695】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0696】 Step 1: 【0697】 Users input their meal details and emotional state through the application. Specifically, they record daily ingredients, quantities, cooking methods, and their emotions at the time (e.g., stress, happiness). The entered information is stored on the device as meal record data and emotional data. 【0698】 Step 2: 【0699】 The terminal formats the entered meal record data and emotion data. This process converts the data into a standard format to facilitate subsequent processing. Specifically, it normalizes emotion values ​​and tags food categories. As a result, it outputs formatted data. 【0700】 Step 3: 【0701】 The terminal sends formatted data to the server. The transmission uses a secure communication protocol to maintain data confidentiality and integrity. After transmission, the server receives the data. 【0702】 Step 4: 【0703】 The server analyzes the received formatted data. The analysis uses a generative AI model to assess the user's nutritional status using dietary and emotional data. Specifically, it outputs evaluation results such as "calcium deficiency" or "high stress levels." 【0704】 Step 5: 【0705】 The server generates meal suggestions based on the analysis results. The suggestion generation process also considers emotional data to create a list of meals suitable for the user's current physical and mental state. Output includes a "list of ingredients to choose for your next meal" and "recommended recipes." 【0706】 Step 6: 【0707】 The device notifies the user of meal suggestions provided by the server. These notifications include specific ingredients, cooking methods, and even emotional support advice. Users can receive this information and incorporate it into their daily eating habits. 【0708】 (Application Example 2) 【0709】 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". 【0710】 Traditional nutrition management systems focus on recording and analyzing meals, but fail to consider the user's emotional state, making it difficult to improve emotional satisfaction and mental health. Therefore, there is a need to develop new systems that harmonize users' emotions with their eating habits and contribute to improving their quality of life. 【0711】 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. 【0712】 In this invention, the server includes input means for recording the contents of meals and emotional states, analysis means for analyzing the recorded meal data and emotional data to evaluate nutritional status and emotional state, and suggestion generation means for generating food products and nutritional supplements to be suggested based on the nutritional evaluation and emotional state. This makes it possible to realize a healthy diet that takes into account the user's nutritional and emotional balance. 【0713】 "Input means" refers to a device or method used by a user to record the contents of a meal and their emotional state. 【0714】 "Analysis means" refers to a device or method that evaluates recorded meal data and emotional data to analyze nutritional status and emotional state. 【0715】 A "proposal generation means" is an apparatus or method that generates recommended foods or nutritional supplements based on nutritional evaluations and emotional states obtained through analysis. 【0716】 "Notification means" refers to a device or method for communicating the generated proposal to the user. 【0717】 This system records the user's diet and emotional state, and through analysis, provides health management that considers the balance between nutrition and emotions. The system mainly consists of input means, analysis means, suggestion generation means, and notification means. 【0718】 The input method involves users recording their meal content and emotional state, and utilizes devices such as smartphones and tablets. Users use these devices to input specific meal details and their emotional state (stress, joy, etc.). 【0719】 The input data is formatted on the terminal and then sent to the server. On the server, the analysis system uses AI analysis tools such as TensorFlow to evaluate the nutritional and emotional status based on the recorded meal and emotional data. From this evaluation result, suggestions for maintaining an optimal nutritional balance are generated. The suggestion generation system lists recommended foods and nutritional supplements based on the nutritional evaluation and emotional status. 【0720】 The notification system is used to inform the user of generated suggestions. These notifications are displayed on the device via the display or audio output. For example, if the user is feeling stressed, it may suggest oatmeal for breakfast, as it is known to be effective in reducing stress. 【0721】 Through the operation of this system, users can maintain a healthy lifestyle that takes into account the balance between nutrition and emotional well-being. For example, by using prompts such as, "What foods do you recommend when a user is feeling stressed? Please also provide a specific meal plan," it is possible to obtain more effective suggestions. 【0722】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0723】 Step 1: 【0724】 The user uses a terminal to input details about their meal and emotional state. This input includes specific dish names, ingredients, quantities, and their emotional state for the day (e.g., stress, joy). This data is then sent directly to the next processing step. 【0725】 Step 2: 【0726】 The terminal formats the meal details and emotional state entered by the user. This formatted data includes standardized meal information and emotional scores, which are then converted for transmission to the server. The output is sent to the server as formatted data. 【0727】 Step 3: 【0728】 The server analyzes the received formatted data. The analysis uses a generative AI model to evaluate dietary and emotional data, analyzing the user's nutritional and emotional state. From this analysis, a list of necessary nutrients and foods that can improve emotional well-being is generated. The output is a nutritional assessment and a list of suggested foods based on the analysis results. 【0729】 Step 4: 【0730】 The server generates a list of appropriate foods and nutritional supplements to suggest to the user based on the analysis results. It utilizes a generative AI model to select the ingredients and products that best suit the user's current nutritional and emotional state. The output is a list of suggested foods and nutritional supplements. 【0731】 Step 5: 【0732】 The terminal notifies the user of a list of suggestions received from the server. The notification is displayed on the terminal's screen and can also be communicated via voice output. The user can use this information as a reference when selecting their next meal. The output consists of suggestions for food and nutritional supplements for the user. 【0733】 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. 【0734】 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. 【0735】 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. 【0736】 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. 【0737】 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. 【0738】 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. 【0739】 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. 【0740】 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. 【0741】 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." 【0742】 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. 【0743】 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. 【0744】 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. 【0745】 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. 【0746】 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. 【0747】 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. 【0748】 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. 【0749】 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. 【0750】 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. 【0751】 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. 【0752】 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. 【0753】 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. 【0754】 The following is further disclosed regarding the embodiments described above. 【0755】 (Claim 1) 【0756】 An input method for recording the contents of meals, 【0757】 An analytical means for analyzing recorded meal data to evaluate nutritional status, 【0758】 A proposal generation method for generating food ingredients and nutritional supplements that should be proposed based on nutritional evaluation, 【0759】 A notification mechanism to inform the user of the generated suggestions, 【0760】 A system that includes this. 【0761】 (Claim 2) 【0762】 The system according to claim 1, wherein the analysis means evaluates the nutritional status by taking into account the user's past meal data. 【0763】 (Claim 3) 【0764】 The system according to claim 1, wherein the suggestion generation means suggests ingredients while taking into account seasonal and regional characteristics. 【0765】 "Example 1" 【0766】 (Claim 1) 【0767】 A means of inputting information for recording the contents of meals, 【0768】 An analysis means that analyzes recorded meal information using a computing device and evaluates nutritional status, 【0769】 Based on the analysis results, a proposal generation means generates a list of food and nutritional supplement options using a generation AI model, 【0770】 A notification means for informing the user of the generated options via a communication means, 【0771】 A system that includes this. 【0772】 (Claim 2) 【0773】 The system according to claim 1, wherein the analysis means compares the user's past meal information with a database held in a computing device to evaluate the nutritional status. 【0774】 (Claim 3) 【0775】 The system according to claim 1, wherein the suggestion generation means uses a generation AI model to suggest food products based on seasonal and regional characteristics. 【0776】 "Application Example 1" 【0777】 (Claim 1) 【0778】 An input structure for recording meal information, 【0779】 An analytical structure that analyzes recorded dietary information to evaluate nutritional status, 【0780】 A proposal generation structure that generates suggested ingredients and nutritional supplements based on nutritional assessment, 【0781】 A notification structure that notifies the user of the generated suggestions, 【0782】 Consumer devices that collect data through interaction with users, 【0783】 An analysis device that analyzes input audio and image information, 【0784】 Output device that displays or reads out the analysis results, 【0785】 A system that includes this. 【0786】 (Claim 2) 【0787】 The system according to claim 1, wherein the analysis structure evaluates the nutritional status by taking into account the user's past meal information. 【0788】 (Claim 3) 【0789】 The system according to claim 1, wherein the proposal generation structure proposes ingredients while taking into account the characteristics of time and place. 【0790】 "Example 2 of combining an emotion engine" 【0791】 (Claim 1) 【0792】 A means of inputting meal content and emotional state, 【0793】 A means of formatting and transmitting entered meal data and emotional data, 【0794】 A means of analyzing transmitted data, evaluating nutritional status, and performing nutritional assessment that takes emotional status into consideration, 【0795】 A suggestion generation method that generates recommended foods and nutritional supplements based on nutritional assessment and emotional state, 【0796】 A means of notifying the user of the generated suggestions, 【0797】 A system that includes this. 【0798】 (Claim 2) 【0799】 The system according to claim 1, wherein the analysis means evaluates the nutritional status taking into account the user's past meal data and emotional state. 【0800】 (Claim 3) 【0801】 The system according to claim 1, wherein the suggestion generation means suggests ingredients considering seasonal and regional characteristics and the emotional state of the user. 【0802】 "Application example 2 when combining with an emotional engine" 【0803】 (Claim 1) 【0804】 An input method for recording the contents of meals and emotional state, 【0805】 An analytical means for evaluating nutritional status and emotional status by analyzing recorded meal data and emotional data, 【0806】 A suggestion generation means for generating food products and nutritional supplements to be suggested based on nutritional assessment and emotional state, 【0807】 A notification mechanism for informing the user of the generated suggestions, 【0808】 A system that includes this. 【0809】 (Claim 2) 【0810】 The system according to claim 1, wherein the analysis means evaluates the nutritional status by taking into account the user's past meal data and emotional data. 【0811】 (Claim 3) 【0812】 The system according to claim 1, wherein the suggestion generation means suggests food items considering seasonal and regional characteristics and the user's emotional state. [Explanation of symbols] 【0813】 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

[Claim 1] An input method for recording the contents of meals, An analytical means for analyzing recorded meal data to evaluate nutritional status, A proposal generation method for generating food ingredients and nutritional supplements that should be proposed based on nutritional evaluation, A notification mechanism to inform the user of the generated suggestions, A system that includes this. [Claim 2] The system according to claim 1, wherein the analysis means evaluates the nutritional status by taking into account the user's past meal data. [Claim 3] The system according to claim 1, wherein the suggestion generation means suggests ingredients while taking into account seasonal and regional characteristics.

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