system

Abstract

We provide the system. [Solution] A means of receiving user input data and analyzing the corresponding emotions using natural language processing, A means for aggregating data from multiple biosensors to detect changes in emotional state, A means for generating and providing feedback to the user based on the analysis results and detection results, A means of sharing and exchanging information among users while maintaining anonymity, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Unexamined Patent Application Publication No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In modern society, mental illnesses and mental health problems are increasing, and many people need effective support. However, there is a problem that it is difficult to access experts immediately, and there are restrictions in terms of region and time, so many patients cannot receive appropriate support. In addition, platforms for users to safely communicate about mental health with each other are also limited. 【Means for Solving the Problems】 【0005】 This invention provides a system that analyzes a user's voice or text input using natural language processing to recognize emotions. This system collects the user's biometric data in real time using multiple biosensors and detects changes in their emotional state. Based on these analysis results, it provides feedback and suggestions to promote relaxation. Furthermore, it includes a means for users to share information and interact while maintaining anonymity. In this way, it is possible to comprehensively support mental health, transcending time and geographical constraints. 【0006】 "User input data" refers to information that users provide to the system, particularly information related to emotions in voice or text format. 【0007】 "Natural language processing" refers to the technology that enables computers to understand, interpret, and generate human language, and specifically the process of extracting emotions and intentions from text and audio data. 【0008】 "Means of analyzing emotions" refers to a part of a system that can perform a process to identify the type and intensity of emotions based on the user's input data. 【0009】 A "biosensor" is a device that measures a user's physical condition and is used to detect things like heart rate, body temperature, and activity level. 【0010】 "Means for detecting changes in emotional state" refers to a part of a system that analyzes data from biosensors to identify the user's mental state and emotional fluctuations. 【0011】 "Means for generating and providing feedback" refers to system functions that create and present appropriate advice and information to users based on analyzed sentiment data. 【0012】 "Means of sharing and exchanging information while maintaining anonymity" refers to a part of a system that has functions that allow users to safely exchange information and experiences related to mental health without exposing their personal information. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] 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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined. 【Embodiments for Carrying Out the Invention】 【0014】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, a 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. 【0017】 In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0019】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0020】 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." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 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. 【0024】 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). 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0031】 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. 【0032】 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. 【0033】 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". 【0034】 This invention is a system designed to comprehensively support the mental health of users. The system aims to provide appropriate feedback to users based on emotion analysis and monitoring of biometric information. 【0035】 Emotional input and analysis 【0036】 Users can input their emotions into the device in voice or text format. After receiving the input, the device sends the data to the server. The server uses natural language processing techniques to analyze the emotions, classifying them as positive, negative, or neutral, and identifying specific emotions (e.g., joy, sadness, anger). This analysis is further used to provide insights into the user's emotions. 【0037】 Monitoring of biometric data 【0038】 This system uses multiple biosensors to collect real-time biometric data from the user. This data includes heart rate, body temperature, and physical activity level, and is periodically transmitted to a server via the device. The server analyzes the collected data to detect changes in the user's emotional state and increases in stress levels. 【0039】 Provide feedback 【0040】 Based on the information analyzed about the user, the server generates feedback. This feedback takes into account the user's current emotional state and may include suggestions for relaxation or recommendations for breaks as needed. The generated feedback is notified to the user via their device, and the user can incorporate it into their daily life. 【0041】 Community support 【0042】 The system also promotes safe interaction among users. Users can share their feelings and experiences anonymously with other users and receive community support regarding their mental health. This interaction is monitored by the server and managed to maintain safety and comfort. 【0043】 As a concrete example, suppose a user types the text "I'm very tired today." The server recognizes this input as "fatigue" and, after checking the biometric data, if an elevated heart rate is detected, it provides feedback such as "We recommend you take a deep breath and rest." In this way, users can receive real-time support and maintain their mental health in their daily lives. 【0044】 The following describes the processing flow. 【0045】 Step 1: 【0046】 The user uses their device to input their emotions via voice or text. This allows data about the emotions to be captured and temporarily stored on the device. 【0047】 Step 2: 【0048】 The device sends the entered emotion data to the server. The data is encrypted before transmission to protect privacy. 【0049】 Step 3: 【0050】 The server performs natural language processing on the received emotion data to analyze the emotions. As a result of the analysis, the polarity of the emotion and the specific emotional state are determined. 【0051】 Step 4: 【0052】 The device collects data such as heart rate, body temperature, and activity level from biosensors and transmits it to the server in real time. 【0053】 Step 5: 【0054】 The server analyzes the received biometric data to assess the user's current emotional state and stress level. 【0055】 Step 6: 【0056】 The server generates appropriate feedback based on the analysis results. This feedback may include suggestions for relaxation or recommendations for rest. 【0057】 Step 7: 【0058】 The device notifies the user of the feedback it generates. The user receives the feedback and uses it in their daily life. 【0059】 Step 8: 【0060】 Users can use their devices to share emotions and experiences with other users while maintaining anonymity. The server monitors interactions within the community to maintain safety and comfort. 【0061】 (Example 1) 【0062】 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." 【0063】 In recent years, with the growing interest in mental health, there is a need to accurately understand the mental state of individual users and provide appropriate support. However, conventional systems lack sufficient integration of emotional analysis and biometric information monitoring, making it difficult to provide accurate feedback based on the user's condition. Furthermore, methods for ensuring anonymity when users share information with others remain inadequate. 【0064】 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. 【0065】 In this invention, the server includes means for receiving user input information and analyzing the corresponding emotions using a generation program, means for aggregating information from multiple biodetectors and detecting changes in state, and means for notifying the generated feedback via an information terminal. This enables the provision of personalized feedback in real time, thereby maintaining mental health. Furthermore, by providing a method for sharing information with others safely while maintaining anonymity, it is possible to promote communication and ensure security. 【0066】 "User input information" refers to data about the user's emotions and state of mind, provided in audio or text format. 【0067】 A "generative program" is a computational procedure that analyzes user input information to classify and recognize emotions. 【0068】 "Means for analyzing emotions" refers to functions that use natural language processing or generative AI models to identify emotional characteristics from input information. 【0069】 A "biometric detector" is a device used to collect physiological data such as heart rate, body temperature, and activity level. 【0070】 "Means for detecting changes in state" refers to a function that grasps changes in psychological or physiological state from aggregated biological information. 【0071】 "Means for generating feedback" refers to methods for providing users with optimized suggestions and information based on analysis results and detection results. 【0072】 An "information terminal" is an electronic device used by users to input information and receive feedback. 【0073】 The "anonymity-preserving function" is a mechanism that allows information sharing with others while ensuring that users' personal information and identities are not identified. 【0074】 This invention is a system that comprehensively supports the mental health of users, and its implementation takes the following forms. 【0075】 This system is based on emotion analysis and biometric monitoring. Users can input their emotions in voice or text format using a device. The device receives this emotion data and securely transmits it to the server using encryption technology. This process employs state-of-the-art encryption technology to protect the confidentiality of the information and the privacy of the user. 【0076】 The server uses a generative AI model to perform natural language processing and analyze the input emotion data. Specifically, if the input is something like "I'm not feeling well today," the server classifies it as negative and further identifies it as a more specific emotion such as "sadness" or "fatigue." To achieve this, the server utilizes a pre-trained emotion analysis database and algorithms. 【0077】 Furthermore, this system uses biometric sensors to collect biometric data such as heart rate, body temperature, and physical activity level in real time. This biometric data is periodically transmitted to a server via the terminal. The server analyzes the collected biometric data to detect changes in the user's emotional state and increases in stress levels. 【0078】 Based on analyzed emotional and biometric data, the server generates personalized feedback for each user. For example, it might offer specific suggestions such as, "You seem tired, so we recommend taking a deep breath and resting." This feedback is notified to the user via their device, and the user can then incorporate these suggestions into their daily life. 【0079】 Furthermore, the system maintains anonymity, providing a secure platform where users can share their emotions and experiences with others. The server monitors this platform, maintaining a safe and secure environment for use. 【0080】 An example of a prompt might be: "Classify the emotion expressed in the user's input text as positive, negative, neutral, or a specific emotion (e.g., joy, sadness, anger), and determine if appropriate feedback is needed." This prompt allows the server to perform appropriate analysis and provide feedback. 【0081】 In this way, the present invention makes it possible to provide individual users with real-time emotional support and feedback based on biometric data, thereby improving their mental health. 【0082】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0083】 Step 1: 【0084】 The user inputs their emotions using the device. This input can be in voice or text format. When the user says, "I'm tired from work today," the device converts the voice data into text. This converted text data becomes the input for the next process. 【0085】 Step 2: 【0086】 The terminal sends the acquired text data to the server. This communication is encrypted for security. The server analyzes the received text data using natural language processing technology. Specifically, it utilizes a generative AI model to classify the input emotion as positive, negative, or neutral using prompt sentences. This classification result becomes the output of the next process. 【0087】 Step 3: 【0088】 The server receives biometric data periodically transmitted from the terminal through multiple biosensors. This biometric data includes heart rate, body temperature, and physical activity level. The server analyzes this data to determine the stress level. If fluctuations in biometric data suggest increased stress, a warning is output for the next processing step. 【0089】 Step 4: 【0090】 The server generates appropriate feedback for each individual user based on the emotional classification results and the analysis of biometric data. For example, it might generate a message such as, "Your current stress level is high, so we recommend you take a short break." This feedback becomes the output of the next process. 【0091】 Step 5: 【0092】 The generated feedback is notified to the user via the device. The device presents the feedback to the user via pop-up notifications or voice messages. This allows the user to receive advice tailored to their situation in real time. 【0093】 Step 6: 【0094】 If a user wishes, the system provides a platform where they can anonymously share their emotions and experiences with other users. The platform is monitored by servers to ensure security. Shared information entered by users is treated as data provided to other users. 【0095】 (Application Example 1) 【0096】 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." 【0097】 In modern urban life, it is difficult for residents to effectively manage the stress and emotional fluctuations they experience on a daily basis and maintain their mental health. This is considered a problem because it leads to decreased productivity and a deterioration in quality of life. Currently, there is a lack of means to provide customized support for individual users and real-time feedback adapted to the urban environment. 【0098】 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. 【0099】 In this invention, the server includes means for receiving user input information and analyzing the corresponding emotion using natural language processing, means for aggregating data from multiple biometric information acquisition devices and detecting changes in emotional state, and means for generating feedback according to the situation in the urban environment and notifying the user. As a result, users living in urban areas can receive relaxation suggestions and support tailored to their individual emotional state in real time. 【0100】 "User input information" refers to data in voice or text format that users transmit to the system. 【0101】 "Natural language processing" is a field of computer science that refers to the technology of analyzing human language and understanding its meaning. 【0102】 "Means for analyzing emotions" refers to a processing device or algorithm that identifies and classifies emotions based on user input information. 【0103】 A "biometric information acquisition device" refers to sensors used to acquire physiological data such as the user's heart rate, body temperature, and physical activity level. 【0104】 "Means for detecting changes in emotional state" refers to functions or devices that analyze changes in biometric information and identify changes in the user's mental state. 【0105】 "Means for generating and providing instructions" refers to methods and devices for creating and communicating instructions and advice to users in an easily understandable format based on analysis results. 【0106】 "Means of sharing and interacting with information among users while maintaining anonymity" refers to methods and functions that allow users to exchange information and emotions with other users while protecting their privacy. 【0107】 "Context-sensitive feedback in urban environments" refers to information that provides advice and suggestions appropriate to the environment, based on the user's location information and the urban environment. 【0108】 The system that realizes this invention operates through interaction between a server, a terminal, and a user. The user inputs emotions into the terminal in the form of voice or text. The terminal receives this input data and sends it to the server. 【0109】 The server uses natural language processing techniques to analyze the user's emotions. This analysis process identifies the type and intensity of the emotions and classifies them as positive, negative, neutral, etc. The Python speech_recognition library and other natural language processing libraries are used in this process. 【0110】 Next, the user's biometric data is transmitted from multiple biometric acquisition devices to a server via the terminal. The server aggregates this biometric data and analyzes heart rate, body temperature, physical activity level, etc., to detect changes in the user's emotional state. Machine learning algorithms are used for this data analysis. 【0111】 Based on these analysis results, the server generates feedback for the user. This feedback includes relaxation suggestions tailored to the individual's emotional state and information relevant to the urban environment. The feedback is sent to the terminal, and the user can receive it in real time. 【0112】 Furthermore, users are provided with a feature that allows them to share their emotions and experiences with other users while maintaining anonymity. This feature is monitored by the server and designed to protect privacy. 【0113】 For example, if a user inputs "I've been feeling stressed at work lately," the server analyzes this and suggests relaxation methods at a nearby park. An example of a prompt to input into the generating AI model would be, "I've been feeling stressed at work lately, so could you recommend some relaxation methods?" 【0114】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0115】 Step 1: 【0116】 The user inputs their emotions into the device in voice or text format. The device receives this input and sends it to the server as input data. At this stage, the input is the user's direct expression of emotion, and the output is the emotion data sent to the server. 【0117】 Step 2: 【0118】 The server analyzes the received emotional data using natural language processing techniques. This analysis classifies the input emotional data and outputs categories such as positive, negative, and neutral. A natural language processing library is used for the analysis to identify the emotions. 【0119】 Step 3: 【0120】 The user's biosensor data is transmitted to the server via the terminal. This input includes heart rate, body temperature, and physical activity level. The server aggregates this data, analyzes each item using statistical methods, and detects changes in emotional state. The output is data indicating changes in emotional state. 【0121】 Step 4: 【0122】 The server generates specific feedback for the user based on emotion classification obtained from natural language processing and the results of biometric data analysis. The input is the analysis results of emotion data and biometric data, and the output is the feedback information provided to the user. This feedback may include, for example, relaxation methods for stress reduction or suggestions for appropriate behavior in urban environments. 【0123】 Step 5: 【0124】 The generated feedback information is sent to the device and notified to the user in real time. The input is the generated feedback information, and the output is the notification displayed on the user's device. Specifically, this involves displaying alerts and messages on the device. 【0125】 Step 6: 【0126】 Users can use their devices to share emotions and experiences with other users while maintaining anonymity. This feature involves user-generated information as input, and secure information sharing with other users as output. The server monitors this interaction to ensure privacy and security. 【0127】 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. 【0128】 This invention is a user support system that incorporates an emotion engine to enhance emotion recognition. This system collects and analyzes emotional and biometric data to support the user's mental health and provides feedback. Furthermore, it enables anonymous interaction among users. 【0129】 Emotional input and analysis 【0130】 Users input emotion-related data via voice or text through their device. The device sends this data to a server. The server uses an emotion engine to analyze the received data and identify the corresponding emotion. The emotion engine utilizes machine learning algorithms, resulting in improved accuracy in identifying emotions. 【0131】 Monitoring of biometric data 【0132】 The device collects biometric data such as heart rate, body temperature, and physical activity levels through multiple sensors and transmits it to a server. The server analyzes the received biometric data and continuously monitors changes in emotional state. 【0133】 Provide feedback 【0134】 The server utilizes an emotion engine to perform real-time sentiment analysis and provide immediate feedback to the user. This feedback includes suggestions for relaxation tailored to the user's emotional state and recommendations for appropriate rest. The generated feedback is then notified to the user from their device. 【0135】 Community support 【0136】 The system also provides a platform where users can anonymously share their emotions and experiences. A server manages this interaction platform, ensuring users can exchange information securely. An emotion engine analyzes the emotional elements within these interactions and enhances support as needed. 【0137】 For example, if a user voice-inputs "I'm frustrated with work today," the device sends this information to the server. The server analyzes the emotion of frustration through its emotion engine and evaluates the stress level based on biometric data. Based on this, it generates feedback such as "Try taking a few minutes of deep breathing" and notifies the user. In this way, the system can continuously support the user's emotional state and encourage appropriate actions. 【0138】 The following describes the processing flow. 【0139】 Step 1: 【0140】 The user inputs their emotions into the device in voice or text format. This captures raw data about the user's emotions. 【0141】 Step 2: 【0142】 The device sends the entered emotion data to the server. This transmission is encrypted to maintain data security. 【0143】 Step 3: 【0144】 The server inputs the received emotion data into the emotion engine. The emotion engine analyzes the data based on a machine learning model to identify the user's emotional state. 【0145】 Step 4: 【0146】 The server records the analysis results and compares them with the user's past emotional history to evaluate their current emotional tendencies. 【0147】 Step 5: 【0148】 The device collects biometric data such as heart rate and body temperature from sensors and sends it to a server. 【0149】 Step 6: 【0150】 The server analyzes biometric data and assesses changes in the user's physiological state. If signs of stress or tension are detected, it determines whether intervention is necessary. 【0151】 Step 7: 【0152】 The server generates appropriate feedback based on the analysis results from the emotion engine and biometric data. This feedback may include relaxation techniques or suggestions for rest. 【0153】 Step 8: 【0154】 The device notifies the user of the feedback it generates. The user receives this feedback and uses it in a practical way in their daily activities. 【0155】 Step 9: 【0156】 Users can share and interact with other users, sharing their emotions and experiences, through their devices while maintaining anonymity. 【0157】 Step 10: 【0158】 The server manages user interactions and uses an emotion engine to analyze emotional data within those communications, thereby gaining insights for better support. 【0159】 (Example 2) 【0160】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0161】 In modern society, there is a need for rapid and effective support for users' emotional states and mental health. However, conventional methods are insufficient for analyzing individual emotions and aggregating biometric data, and further improvements are necessary. Furthermore, the lack of community support through safe and anonymous information exchange is also a problem. 【0162】 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. 【0163】 In this invention, the server includes means for receiving user information and analyzing the corresponding emotions using machine learning, means for aggregating information from multiple biometric measurement devices and monitoring changes in emotional state, and means for generating and providing recommendations to the user based on the analysis results and monitoring results. This makes it possible to accurately analyze the user's emotional state and provide appropriate feedback in real time, thereby effectively supporting the user's mental health. 【0164】 "User information" refers to the voice or text data entered by the user, and serves as the basic data for analyzing their emotional state. 【0165】 Machine learning is a technique in which computers learn from data and automatically discover specific patterns and insights, and it forms the basis of models used for analyzing emotions. 【0166】 A "biometric measurement device" is a device equipped with sensors to collect biometric data such as heart rate, body temperature, and activity level, and this data is used to monitor emotional states. 【0167】 "Recommendations" refer to suggestions for actions and choices provided to the user based on analyzed emotional and biometric data. 【0168】 "Analysis results" refer to data that shows the classification and trends of emotions obtained from user information processed by machine learning. 【0169】 "Monitoring results" refer to information about changes in emotional states and their understanding, obtained based on the analysis of data collected through biometric measurement devices. 【0170】 "Exchanging and interacting information" refers to the act of users mutually exchanging data and experiences anonymously, and supporting each other. 【0171】 The following describes embodiments for carrying out the invention. 【0172】 This user support system analyzes the user's emotional state and provides appropriate feedback. The system mainly consists of a server, terminals, and multiple biometric measurement devices. 【0173】 The server plays a central role in collecting and analyzing user information. Through the terminal, users input emotional information in voice or text format. This information is processed on the terminal and sent to the server. On the server, an emotion engine is activated using machine learning libraries such as TENSORFLOW® to perform data analysis. This identifies the user's emotional state. 【0174】 The biometric measurement device worn by the user measures biometric data such as heart rate, body temperature, and activity level using sensors, and collects this data on a terminal. This data is also transmitted to a server, allowing for real-time monitoring of changes in health status and stress levels. 【0175】 The analysis results and monitoring data obtained instantly are integrated on the server side to generate specific recommendations for the user. These recommendations include relaxation suggestions and health maintenance advice tailored to the user's current emotions and physical state. The generated recommendations are then notified to the user via their device. 【0176】 Furthermore, the server also provides a platform where users can exchange information anonymously. Here, users can share their emotions and experiences with others and support each other. Interactions on this platform are also analyzed by the emotion engine, and support is enhanced as needed. 【0177】 For example, if a user enters "I'm feeling stressed today," the server uses an emotion engine to identify that emotion and evaluates the level of stress based on corresponding biometric data. Based on this, feedback such as "We recommend you try a short meditation" is delivered to the user. 【0178】 An example of a prompt to input into a generative AI model is, "Using the sentiment data obtained from the user, please tell me how you can generate the most appropriate feedback for that person." This prompt serves as a guide for the AI ​​to derive the most effective recommendations based on the user's situation. 【0179】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0180】 Step 1: 【0181】 Users input emotional information in voice or text format using their smartphones or PCs. The input data is received by the device. The device then converts this information into an appropriate digital format and sends it to the server. For example, if a user says "I'm nervous," the voice data is converted into text data and passed on to the next analysis step. 【0182】 Step 2: 【0183】 The server receives emotion data sent from the terminal and uses machine learning libraries to run the emotion engine. This engine performs natural language processing to identify emotions from the input data. Specifically, it applies an emotion analysis algorithm to the received text data to extract emotional states such as "tension." This analysis generates emotion labels, which are then sent to the next process. 【0184】 Step 3: 【0185】 Simultaneously, the device collects biometric data such as heart rate, body temperature, and activity level from a biometric measurement device. This data is transmitted to the device via Bluetooth or Wi-Fi and then forwarded to a server. The server analyzes this data in real time to evaluate the user's physiological state. Using data analysis software, for example, it determines whether an elevated heart rate is associated with a state of stress. As a result, the evaluated biometric data, along with emotion labels, is used to generate feedback. 【0186】 Step 4: 【0187】 The server generates user-specific feedback based on emotion labels and biometric data. This process utilizes an AI model to provide recommendations, such as those aimed at reducing tension. The server generates feedback such as "Try taking a few minutes of deep breathing" and prepares to notify the device. 【0188】 Step 5: 【0189】 The device receives feedback messages sent from the server and displays them on the user interface. This feedback allows the user to learn specific recommended actions and stress reduction strategies. For example, a graphic showing relaxation techniques may be displayed on the screen. 【0190】 Step 6: 【0191】 The server provides a community platform where users can exchange information anonymously. On this platform, the server performs sentiment analysis based on user posts and provides further support as needed. Users can support each other by sharing their feelings and experiences with other users. 【0192】 (Application Example 2) 【0193】 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". 【0194】 In modern society, people are often exposed to excessive stress and emotional fluctuations, creating a need for efficient mental health care. However, many support systems struggle to accurately understand users' emotional states and provide individually optimized feedback. In addition, there is a need for further improvement in technologies that enable interaction tailored to users' emotional states within the home. 【0195】 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. 【0196】 In this invention, the server includes means for receiving user input data and analyzing the corresponding emotion using language interpretation technology, means for aggregating data from multiple biosensors and detecting changes in emotional state, means for generating and providing feedback to the user based on the analysis results and detection results, and means for additionally providing a human sharing function that has information input and image display functions and provides support according to the user's emotional state. This enables appropriate emotional care optimized for each individual user. 【0197】 "User input data" refers to information that users provide through voice or text, indicating their emotions and state of mind. 【0198】 "Language interpretation technology" refers to natural language processing technology used to analyze emotions from user input data. 【0199】 A "biosensor" is a general term for devices and technologies used to measure the body's activity levels, such as heart rate and body temperature. 【0200】 "Changes in emotional state" refers to fluctuations in a user's emotions over time, and the goal is to understand the user's mental health state by detecting these changes. 【0201】 "Means of generating and providing feedback" refers to methods and technologies for communicating appropriate advice and support to users based on their analyzed emotional state. 【0202】 "Human sharing functionality" refers to interactive features that enable users to appropriately communicate and respond based on their emotions. 【0203】 The system that realizes this application consists of a program that has the ability to receive and analyze user input data and the function to collect biometric information. The system functions effectively in robots used in the home. 【0204】 The server first receives emotion data entered by the user via voice or text. By using natural language processing as a language interpretation technique, it analyzes the user's emotions from the input data with high accuracy. For language analysis, it utilizes Google Cloud's Speech-to-Text API and NLP libraries. 【0205】 The server then processes data from biosensors. Specifically, it uses heart rate and body temperature measuring devices connected to Raspberry Pi or Arduino. This allows for the aggregation of biometric data in real time and continuous monitoring of changes in the user's emotional state. Once this data is aggregated, the server generates appropriate feedback for the user based on the emotion analysis results and biometric information, and the robot provides this feedback to the user using voice and a display. This feedback includes suggestions for relaxation and emotionally appropriate interactions. 【0206】 As a concrete example, consider a situation where a user is stressed because they are under pressure to meet a project deadline. If the user inputs "I'm busy and restless right now," the server analyzes the emotional state as "stressed." Based on this, the robot offers a suggestion: "How about doing some 5 minutes of stretching?" In this way, the system suggests ways to relax to the user and helps reduce stress. 【0207】 Examples of prompts for a generative AI model: 【0208】 "Analyze the emotions the user is currently feeling and suggest appropriate feedback." 【0209】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0210】 Step 1: 【0211】 The user inputs voice or text information via the terminal. The terminal receives this data and sends it to the server as initial input data for the process. 【0212】 Step 2: 【0213】 The server analyzes the received user input data using language interpretation techniques. Specifically, it applies natural language processing (NLP) algorithms to identify the user's emotional state. In this process, natural language text or audio data is used as input, and the emotional state is identified as output. 【0214】 Step 3: 【0215】 The biosensors built into the device continuously measure the user's heart rate and body temperature. The device sends this biometric data to a server, which is used as information to monitor changes in the user's emotional state. 【0216】 Step 4: 【0217】 The server generates appropriate feedback for the user based on the analyzed emotional data and measured biometric data. It utilizes a generative AI model to create specific relaxation methods and behavioral suggestions. At this stage, the inputs are emotional data and biometric data, and the output is the specific feedback provided to the user. 【0218】 Step 5: 【0219】 The terminal transmits user feedback to the robot. The robot uses voice output devices and displays to provide feedback and interact with the user. This includes playing voice guides and displaying messages on the screen. 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 [Second Embodiment] 【0224】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0225】 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. 【0226】 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). 【0227】 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. 【0228】 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. 【0229】 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). 【0230】 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. 【0231】 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. 【0232】 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. 【0233】 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. 【0234】 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. 【0235】 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". 【0236】 This invention is a system designed to comprehensively support the mental health of users. The system aims to provide appropriate feedback to users based on emotion analysis and monitoring of biometric information. 【0237】 Emotional input and analysis 【0238】 Users can input their emotions into the device in voice or text format. After receiving the input, the device sends the data to the server. The server uses natural language processing techniques to analyze the emotions, classifying them as positive, negative, or neutral, and identifying specific emotions (e.g., joy, sadness, anger). This analysis is further used to provide insights into the user's emotions. 【0239】 Monitoring of biometric data 【0240】 This system uses multiple biosensors to collect real-time biometric data from the user. This data includes heart rate, body temperature, and physical activity level, and is periodically transmitted to a server via the device. The server analyzes the collected data to detect changes in the user's emotional state and increases in stress levels. 【0241】 Provide feedback 【0242】 Based on the information analyzed about the user, the server generates feedback. This feedback takes into account the user's current emotional state and may include suggestions for relaxation or recommendations for breaks as needed. The generated feedback is notified to the user via their device, and the user can incorporate it into their daily life. 【0243】 Community support 【0244】 The system also promotes safe interaction among users. Users can share their feelings and experiences anonymously with other users and receive community support regarding their mental health. This interaction is monitored by the server and managed to maintain safety and comfort. 【0245】 As a concrete example, suppose a user types the text "I'm very tired today." The server recognizes this input as "fatigue" and, after checking the biometric data, if an elevated heart rate is detected, it provides feedback such as "We recommend you take a deep breath and rest." In this way, users can receive real-time support and maintain their mental health in their daily lives. 【0246】 The following describes the processing flow. 【0247】 Step 1: 【0248】 The user uses their device to input their emotions via voice or text. This allows data about the emotions to be captured and temporarily stored on the device. 【0249】 Step 2: 【0250】 The device sends the entered emotion data to the server. The data is encrypted before transmission to protect privacy. 【0251】 Step 3: 【0252】 The server performs natural language processing on the received emotion data to analyze the emotions. As a result of the analysis, the polarity of the emotion and the specific emotional state are determined. 【0253】 Step 4: 【0254】 The device collects data such as heart rate, body temperature, and activity level from biosensors and transmits it to the server in real time. 【0255】 Step 5: 【0256】 The server analyzes the received biometric data to assess the user's current emotional state and stress level. 【0257】 Step 6: 【0258】 The server generates appropriate feedback based on the analysis results. This feedback may include suggestions for relaxation or recommendations for rest. 【0259】 Step 7: 【0260】 The device notifies the user of the feedback it generates. The user receives the feedback and uses it in their daily life. 【0261】 Step 8: 【0262】 Users can use their devices to share emotions and experiences with other users while maintaining anonymity. The server monitors interactions within the community to maintain safety and comfort. 【0263】 (Example 1) 【0264】 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." 【0265】 In recent years, with the growing interest in mental health, there is a need to accurately understand the mental state of individual users and provide appropriate support. However, conventional systems lack sufficient integration of emotional analysis and biometric information monitoring, making it difficult to provide accurate feedback based on the user's condition. Furthermore, methods for ensuring anonymity when users share information with others remain inadequate. 【0266】 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. 【0267】 In this invention, the server includes means for receiving user input information and analyzing the corresponding emotions using a generation program, means for aggregating information from multiple biodetectors and detecting changes in state, and means for notifying the generated feedback via an information terminal. This enables the provision of personalized feedback in real time, thereby maintaining mental health. Furthermore, by providing a method for sharing information with others safely while maintaining anonymity, it is possible to promote communication and ensure security. 【0268】 "User input information" refers to data about the user's emotions and state of mind, provided in audio or text format. 【0269】 A "generative program" is a computational procedure that analyzes user input information to classify and recognize emotions. 【0270】 "Means for analyzing emotions" refers to functions that use natural language processing or generative AI models to identify emotional characteristics from input information. 【0271】 A "biometric detector" is a device used to collect physiological data such as heart rate, body temperature, and activity level. 【0272】 "Means for detecting changes in state" refers to a function that grasps changes in psychological or physiological state from aggregated biological information. 【0273】 "Means for generating feedback" refers to methods for providing users with optimized suggestions and information based on analysis results and detection results. 【0274】 An "information terminal" is an electronic device used by users to input information and receive feedback. 【0275】 The "anonymity-preserving function" is a mechanism that allows information sharing with others while ensuring that users' personal information and identities are not identified. 【0276】 This invention is a system that comprehensively supports the mental health of users, and its implementation takes the following forms. 【0277】 This system is based on emotion analysis and biometric monitoring. Users can input their emotions in voice or text format using a device. The device receives this emotion data and securely transmits it to the server using encryption technology. This process employs state-of-the-art encryption technology to protect the confidentiality of the information and the privacy of the user. 【0278】 The server uses a generative AI model to perform natural language processing and analyze the input emotion data. Specifically, if the input is something like "I'm not feeling well today," the server classifies it as negative and further identifies it as a more specific emotion such as "sadness" or "fatigue." To achieve this, the server utilizes a pre-trained emotion analysis database and algorithms. 【0279】 Furthermore, this system uses biometric sensors to collect biometric data such as heart rate, body temperature, and physical activity level in real time. This biometric data is periodically transmitted to a server via the terminal. The server analyzes the collected biometric data to detect changes in the user's emotional state and increases in stress levels. 【0280】 Based on analyzed emotional and biometric data, the server generates personalized feedback for each user. For example, it might offer specific suggestions such as, "You seem tired, so we recommend taking a deep breath and resting." This feedback is notified to the user via their device, and the user can then incorporate these suggestions into their daily life. 【0281】 Furthermore, the system maintains anonymity, providing a secure platform where users can share their emotions and experiences with others. The server monitors this platform, maintaining a safe and secure environment for use. 【0282】 As an example of a prompt sentence, there is a form such as "Please classify the emotion indicated by the user's input text into positive, negative, neutral, or specific emotions (e.g., joy, sadness, anger), and determine whether appropriate feedback is needed." With this prompt, the server can perform appropriate analysis and provide feedback. 【0283】 In this way, the present invention can provide real-time emotion support and feedback based on biometric data to individual users, thereby improving mental health. 【0284】 The flow of specific processing in Example 1 will be described using FIG. 11. 【0285】 Step 1: 【0286】 The user inputs an emotion using a terminal. This input is made in voice or text form. If the user says "I'm tired from work today," the terminal converts the voice data into text. The converted text data becomes the input for the next processing. 【0287】 Step 2: 【0288】 The terminal transmits the acquired text data to the server. This communication is encrypted for security. The server analyzes the received text data using natural language processing technology. Specifically, it utilizes a generative AI model and uses a prompt sentence to classify the input emotion into either positive, negative, or neutral. This classification result becomes the output for the next processing. 【0289】 Step 3: 【0290】 The server periodically receives biometric data transmitted from the terminal through a plurality of biometric sensors. This biometric data includes heart rate, body temperature, and physical activity level. The server analyzes these data to grasp the stress level. If the variation in biometric data suggests an increase in stress, that warning becomes the output for the next processing. 【0291】 Step 4: 【0292】 The server generates appropriate feedback for each individual user based on the emotional classification results and the analysis of biometric data. For example, it might generate a message such as, "Your current stress level is high, so we recommend you take a short break." This feedback becomes the output of the next process. 【0293】 Step 5: 【0294】 The generated feedback is notified to the user via the device. The device presents the feedback to the user via pop-up notifications or voice messages. This allows the user to receive advice tailored to their situation in real time. 【0295】 Step 6: 【0296】 If a user wishes, the system provides a platform where they can anonymously share their emotions and experiences with other users. The platform is monitored by servers to ensure security. Shared information entered by users is treated as data provided to other users. 【0297】 (Application Example 1) 【0298】 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." 【0299】 In modern urban life, it is difficult for residents to effectively manage the stress and emotional fluctuations they experience on a daily basis and maintain their mental health. This is considered a problem because it leads to decreased productivity and a deterioration in quality of life. Currently, there is a lack of means to provide customized support for individual users and real-time feedback adapted to the urban environment. 【0300】 The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means. 【0301】 In this invention, the server includes means for receiving the input information of the user and analyzing the corresponding emotion by natural language processing, means for aggregating data from a plurality of biological information acquisition devices and detecting changes in the emotional state, and means for generating feedback according to the situation in the urban environment and notifying the user. Thereby, a user living in the city can receive real-time proposals and support for relaxation according to their individual emotional state. 【0302】 "The input information of the user" refers to data in the form of voice or text transmitted by the user to the system. 【0303】 "Natural language processing" is a field of computer science and refers to the technology of analyzing human language and understanding its meaning. 【0304】 "Means for analyzing emotions" means a processing device or algorithm for identifying and classifying emotions based on the input information of the user. 【0305】 "Biological information acquisition device" refers to sensors for acquiring physiological data such as the user's heart rate, body temperature, and physical activity level. 【0306】 "Means for detecting changes in the emotional state" means a function or device for analyzing changes in biological information and identifying changes in the user's mental state. 【0307】 "Means for generating and providing instructions" refers to a method or device for creating instructions and advice in an easy-to-understand form for the user based on the analysis results and transmitting them to the user. 【0308】 "Means for sharing and communicating information between users while maintaining anonymity" refers to a method or function that allows users to exchange information and emotions with other users while protecting privacy. 【0309】 "Context-sensitive feedback in urban environments" refers to information that provides advice and suggestions appropriate to the environment, based on the user's location information and the urban environment. 【0310】 The system that realizes this invention operates through interaction between a server, a terminal, and a user. The user inputs emotions into the terminal in the form of voice or text. The terminal receives this input data and sends it to the server. 【0311】 The server uses natural language processing techniques to analyze the user's emotions. This analysis process identifies the type and intensity of the emotions and classifies them as positive, negative, neutral, etc. The Python speech_recognition library and other natural language processing libraries are used in this process. 【0312】 Next, the user's biometric data is transmitted from multiple biometric acquisition devices to a server via the terminal. The server aggregates this biometric data and analyzes heart rate, body temperature, physical activity level, etc., to detect changes in the user's emotional state. Machine learning algorithms are used for this data analysis. 【0313】 Based on these analysis results, the server generates feedback for the user. This feedback includes relaxation suggestions tailored to the individual's emotional state and information relevant to the urban environment. The feedback is sent to the terminal, and the user can receive it in real time. 【0314】 Furthermore, users are provided with a feature that allows them to share their emotions and experiences with other users while maintaining anonymity. This feature is monitored by the server and designed to protect privacy. 【0315】 For example, if a user inputs "I've been feeling stressed at work lately," the server analyzes this and suggests relaxation methods at a nearby park. An example of a prompt to input into the generating AI model would be, "I've been feeling stressed at work lately, so could you recommend some relaxation methods?" 【0316】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0317】 Step 1: 【0318】 The user inputs their emotions into the device in voice or text format. The device receives this input and sends it to the server as input data. At this stage, the input is the user's direct expression of emotion, and the output is the emotion data sent to the server. 【0319】 Step 2: 【0320】 The server analyzes the received emotional data using natural language processing techniques. This analysis classifies the input emotional data and outputs categories such as positive, negative, and neutral. A natural language processing library is used for the analysis to identify the emotions. 【0321】 Step 3: 【0322】 The user's biosensor data is transmitted to the server via the terminal. This input includes heart rate, body temperature, and physical activity level. The server aggregates this data, analyzes each item using statistical methods, and detects changes in emotional state. The output is data indicating changes in emotional state. 【0323】 Step 4: 【0324】 The server generates specific feedback for the user based on emotion classification obtained from natural language processing and the results of biometric data analysis. The input is the analysis results of emotion data and biometric data, and the output is the feedback information provided to the user. This feedback may include, for example, relaxation methods for stress reduction or suggestions for appropriate behavior in urban environments. 【0325】 Step 5: 【0326】 The generated feedback information is sent to the device and notified to the user in real time. The input is the generated feedback information, and the output is the notification displayed on the user's device. Specifically, this involves displaying alerts and messages on the device. 【0327】 Step 6: 【0328】 Users can use their devices to share emotions and experiences with other users while maintaining anonymity. This feature involves user-generated information as input, and secure information sharing with other users as output. The server monitors this interaction to ensure privacy and security. 【0329】 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. 【0330】 This invention is a user support system that incorporates an emotion engine to enhance emotion recognition. This system collects and analyzes emotional and biometric data to support the user's mental health and provides feedback. Furthermore, it enables anonymous interaction among users. 【0331】 Emotional input and analysis 【0332】 Users input emotion-related data via voice or text through their device. The device sends this data to a server. The server uses an emotion engine to analyze the received data and identify the corresponding emotion. The emotion engine utilizes machine learning algorithms, resulting in improved accuracy in identifying emotions. 【0333】 Monitoring of biometric data 【0334】 The device collects biometric data such as heart rate, body temperature, and physical activity levels through multiple sensors and transmits it to a server. The server analyzes the received biometric data and continuously monitors changes in emotional state. 【0335】 Provide feedback 【0336】 The server utilizes an emotion engine to perform real-time sentiment analysis and provide immediate feedback to the user. This feedback includes suggestions for relaxation tailored to the user's emotional state and recommendations for appropriate rest. The generated feedback is then notified to the user from their device. 【0337】 Community support 【0338】 The system also provides a platform where users can anonymously share their emotions and experiences. A server manages this interaction platform, ensuring users can exchange information securely. An emotion engine analyzes the emotional elements within these interactions and enhances support as needed. 【0339】 For example, if a user voice-inputs "I'm frustrated with work today," the device sends this information to the server. The server analyzes the emotion of frustration through its emotion engine and evaluates the stress level based on biometric data. Based on this, it generates feedback such as "Try taking a few minutes of deep breathing" and notifies the user. In this way, the system can continuously support the user's emotional state and encourage appropriate actions. 【0340】 The following describes the processing flow. 【0341】 Step 1: 【0342】 The user inputs their emotions into the device in voice or text format. This captures raw data about the user's emotions. 【0343】 Step 2: 【0344】 The device sends the entered emotion data to the server. This transmission is encrypted to maintain data security. 【0345】 Step 3: 【0346】 The server inputs the received emotion data into the emotion engine. The emotion engine analyzes the data based on a machine learning model to identify the user's emotional state. 【0347】 Step 4: 【0348】 The server records the analysis results and compares them with the user's past emotional history to evaluate their current emotional tendencies. 【0349】 Step 5: 【0350】 The device collects biometric data such as heart rate and body temperature from sensors and sends it to a server. 【0351】 Step 6: 【0352】 The server analyzes biometric data and assesses changes in the user's physiological state. If signs of stress or tension are detected, it determines whether intervention is necessary. 【0353】 Step 7: 【0354】 The server generates appropriate feedback based on the analysis results from the emotion engine and biometric data. This feedback may include relaxation techniques or suggestions for rest. 【0355】 Step 8: 【0356】 The device notifies the user of the feedback it generates. The user receives this feedback and uses it in a practical way in their daily activities. 【0357】 Step 9: 【0358】 Users can share and interact with other users, sharing their emotions and experiences, through their devices while maintaining anonymity. 【0359】 Step 10: 【0360】 The server manages user interactions and uses an emotion engine to analyze emotional data within those communications, thereby gaining insights for better support. 【0361】 (Example 2) 【0362】 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". 【0363】 In modern society, there is a need for rapid and effective support for users' emotional states and mental health. However, conventional methods are insufficient for analyzing individual emotions and aggregating biometric data, and further improvements are necessary. Furthermore, the lack of community support through safe and anonymous information exchange is also a problem. 【0364】 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. 【0365】 In this invention, the server includes means for receiving user information and analyzing the corresponding emotions using machine learning, means for aggregating information from multiple biometric measurement devices and monitoring changes in emotional state, and means for generating and providing recommendations to the user based on the analysis results and monitoring results. This makes it possible to accurately analyze the user's emotional state and provide appropriate feedback in real time, thereby effectively supporting the user's mental health. 【0366】 "User information" refers to the voice or text data entered by the user, and serves as the basic data for analyzing their emotional state. 【0367】 Machine learning is a technique in which computers learn from data and automatically discover specific patterns and insights, and it forms the basis of models used for analyzing emotions. 【0368】 A "biometric measurement device" is a device equipped with sensors to collect biometric data such as heart rate, body temperature, and activity level, and this data is used to monitor emotional states. 【0369】 "Recommendations" refer to suggestions for actions and choices provided to the user based on analyzed emotional and biometric data. 【0370】 "Analysis results" refer to data that shows the classification and trends of emotions obtained from user information processed by machine learning. 【0371】 "Monitoring results" refer to information about changes in emotional states and their understanding, obtained based on the analysis of data collected through biometric measurement devices. 【0372】 "Exchanging and interacting information" refers to the act of users mutually exchanging data and experiences anonymously, and supporting each other. 【0373】 The following describes embodiments for carrying out the invention. 【0374】 This user support system analyzes the user's emotional state and provides appropriate feedback. The system mainly consists of a server, terminals, and multiple biometric measurement devices. 【0375】 The server plays a central role in collecting and analyzing user information. Through the terminal, users input emotional information in voice or text format. This information is processed on the terminal and sent to the server. On the server, an emotion engine is activated using machine learning libraries such as TensorFlow to perform data analysis. This identifies the user's emotional state. 【0376】 The biometric measurement device worn by the user measures biometric data such as heart rate, body temperature, and activity level using sensors, and collects this data on a terminal. This data is also transmitted to a server, allowing for real-time monitoring of changes in health status and stress levels. 【0377】 The analysis results and monitoring data obtained instantly are integrated on the server side to generate specific recommendations for the user. These recommendations include relaxation suggestions and health maintenance advice tailored to the user's current emotions and physical state. The generated recommendations are then notified to the user via their device. 【0378】 Furthermore, the server also provides a platform where users can exchange information anonymously. Here, users can share their emotions and experiences with others and support each other. Interactions on this platform are also analyzed by the emotion engine, and support is enhanced as needed. 【0379】 For example, if a user enters "I'm feeling stressed today," the server uses an emotion engine to identify that emotion and evaluates the level of stress based on corresponding biometric data. Based on this, feedback such as "We recommend you try a short meditation" is delivered to the user. 【0380】 An example of a prompt to input into a generative AI model is, "Using the sentiment data obtained from the user, please tell me how you can generate the most appropriate feedback for that person." This prompt serves as a guide for the AI ​​to derive the most effective recommendations based on the user's situation. 【0381】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0382】 Step 1: 【0383】 Users input emotional information in voice or text format using their smartphones or PCs. The input data is received by the device. The device then converts this information into an appropriate digital format and sends it to the server. For example, if a user says "I'm nervous," the voice data is converted into text data and passed on to the next analysis step. 【0384】 Step 2: 【0385】 The server receives emotion data sent from the terminal and uses machine learning libraries to run the emotion engine. This engine performs natural language processing to identify emotions from the input data. Specifically, it applies an emotion analysis algorithm to the received text data to extract emotional states such as "tension." This analysis generates emotion labels, which are then sent to the next process. 【0386】 Step 3: 【0387】 Simultaneously, the device collects biometric data such as heart rate, body temperature, and activity level from a biometric measurement device. This data is transmitted to the device via Bluetooth or Wi-Fi and then forwarded to a server. The server analyzes this data in real time to evaluate the user's physiological state. Using data analysis software, for example, it determines whether an elevated heart rate is associated with a state of stress. As a result, the evaluated biometric data, along with emotion labels, is used to generate feedback. 【0388】 Step 4: 【0389】 The server generates user-specific feedback based on emotion labels and biometric data. This process utilizes an AI model to provide recommendations, such as those aimed at reducing tension. The server generates feedback such as "Try taking a few minutes of deep breathing" and prepares to notify the device. 【0390】 Step 5: 【0391】 The device receives feedback messages sent from the server and displays them on the user interface. This feedback allows the user to learn specific recommended actions and stress reduction strategies. For example, a graphic showing relaxation techniques may be displayed on the screen. 【0392】 Step 6: 【0393】 The server provides a community platform where users can exchange information anonymously. On this platform, the server performs sentiment analysis based on user posts and provides further support as needed. Users can support each other by sharing their feelings and experiences with other users. 【0394】 (Application Example 2) 【0395】 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." 【0396】 In modern society, people are often exposed to excessive stress and emotional fluctuations, creating a need for efficient mental health care. However, many support systems struggle to accurately understand users' emotional states and provide individually optimized feedback. In addition, there is a need for further improvement in technologies that enable interaction tailored to users' emotional states within the home. 【0397】 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. 【0398】 In this invention, the server includes means for receiving user input data and analyzing the corresponding emotion using language interpretation technology, means for aggregating data from multiple biosensors and detecting changes in emotional state, means for generating and providing feedback to the user based on the analysis results and detection results, and means for additionally providing a human sharing function that has information input and image display functions and provides support according to the user's emotional state. This enables appropriate emotional care optimized for each individual user. 【0399】 "User input data" refers to information that users provide through voice or text, indicating their emotions and state of mind. 【0400】 "Language interpretation technology" refers to natural language processing technology used to analyze emotions from user input data. 【0401】 A "biosensor" is a general term for devices and technologies used to measure the body's activity levels, such as heart rate and body temperature. 【0402】 "Changes in emotional state" refers to fluctuations in a user's emotions over time, and the goal is to understand the user's mental health state by detecting these changes. 【0403】 "Means of generating and providing feedback" refers to methods and technologies for communicating appropriate advice and support to users based on their analyzed emotional state. 【0404】 "Human sharing functionality" refers to interactive features that enable users to appropriately communicate and respond based on their emotions. 【0405】 The system that realizes this application consists of a program that has the ability to receive and analyze user input data and the function to collect biometric information. The system functions effectively in robots used in the home. 【0406】 The server first receives emotion data entered by the user via voice or text. By using natural language processing as a language interpretation technique, it analyzes the user's emotions from the input data with high accuracy. For language analysis, it utilizes Google Cloud's Speech-to-Text API and NLP libraries. 【0407】 The server then processes data from biosensors. Specifically, it uses heart rate and body temperature measuring devices connected to Raspberry Pi or Arduino. This allows for the aggregation of biometric data in real time and continuous monitoring of changes in the user's emotional state. Once this data is aggregated, the server generates appropriate feedback for the user based on the emotion analysis results and biometric information, and the robot provides this feedback to the user using voice and a display. This feedback includes suggestions for relaxation and emotionally appropriate interactions. 【0408】 As a concrete example, consider a situation where a user is stressed because they are under pressure to meet a project deadline. If the user inputs "I'm busy and restless right now," the server analyzes the emotional state as "stressed." Based on this, the robot offers a suggestion: "How about doing some 5 minutes of stretching?" In this way, the system suggests ways to relax to the user and helps reduce stress. 【0409】 Examples of prompts for a generative AI model: 【0410】 "Analyze the emotions the user is currently feeling and suggest appropriate feedback." 【0411】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0412】 Step 1: 【0413】 The user inputs voice or text information via the terminal. The terminal receives this data and sends it to the server as initial input data for the process. 【0414】 Step 2: 【0415】 The server analyzes the received user input data using language interpretation techniques. Specifically, it applies natural language processing (NLP) algorithms to identify the user's emotional state. In this process, natural language text or audio data is used as input, and the emotional state is identified as output. 【0416】 Step 3: 【0417】 The biosensors built into the device continuously measure the user's heart rate and body temperature. The device sends this biometric data to a server, which is used as information to monitor changes in the user's emotional state. 【0418】 Step 4: 【0419】 The server generates appropriate feedback for the user based on the analyzed emotional data and measured biometric data. It utilizes a generative AI model to create specific relaxation methods and behavioral suggestions. At this stage, the inputs are emotional data and biometric data, and the output is the specific feedback provided to the user. 【0420】 Step 5: 【0421】 The terminal transmits user feedback to the robot. The robot uses voice output devices and displays to provide feedback and interact with the user. This includes playing voice guides and displaying messages on the screen. 【0422】 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. 【0423】 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. 【0424】 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. 【0425】 [Third Embodiment] 【0426】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0427】 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. 【0428】 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). 【0429】 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. 【0430】 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. 【0431】 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). 【0432】 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. 【0433】 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. 【0434】 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. 【0435】 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. 【0436】 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. 【0437】 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". 【0438】 This invention is a system designed to comprehensively support the mental health of users. The system aims to provide appropriate feedback to users based on emotion analysis and monitoring of biometric information. 【0439】 Emotional input and analysis 【0440】 Users can input their emotions into the device in voice or text format. After receiving the input, the device sends the data to the server. The server uses natural language processing techniques to analyze the emotions, classifying them as positive, negative, or neutral, and identifying specific emotions (e.g., joy, sadness, anger). This analysis is further used to provide insights into the user's emotions. 【0441】 Monitoring of biometric data 【0442】 This system uses multiple biosensors to collect real-time biometric data from the user. This data includes heart rate, body temperature, and physical activity level, and is periodically transmitted to a server via the device. The server analyzes the collected data to detect changes in the user's emotional state and increases in stress levels. 【0443】 Provide feedback 【0444】 Based on the information analyzed about the user, the server generates feedback. This feedback takes into account the user's current emotional state and may include suggestions for relaxation or recommendations for breaks as needed. The generated feedback is notified to the user via their device, and the user can incorporate it into their daily life. 【0445】 Community support 【0446】 The system also promotes safe interaction among users. Users can share their feelings and experiences anonymously with other users and receive community support regarding their mental health. This interaction is monitored by the server and managed to maintain safety and comfort. 【0447】 As a concrete example, suppose a user types the text "I'm very tired today." The server recognizes this input as "fatigue" and, after checking the biometric data, if an elevated heart rate is detected, it provides feedback such as "We recommend you take a deep breath and rest." In this way, users can receive real-time support and maintain their mental health in their daily lives. 【0448】 The following describes the processing flow. 【0449】 Step 1: 【0450】 The user uses their device to input their emotions via voice or text. This allows data about the emotions to be captured and temporarily stored on the device. 【0451】 Step 2: 【0452】 The device sends the entered emotion data to the server. The data is encrypted before transmission to protect privacy. 【0453】 Step 3: 【0454】 The server performs natural language processing on the received emotion data to analyze the emotions. As a result of the analysis, the polarity of the emotion and the specific emotional state are determined. 【0455】 Step 4: 【0456】 The device collects data such as heart rate, body temperature, and activity level from biosensors and transmits it to the server in real time. 【0457】 Step 5: 【0458】 The server analyzes the received biometric data to assess the user's current emotional state and stress level. 【0459】 Step 6: 【0460】 The server generates appropriate feedback based on the analysis results. This feedback may include suggestions for relaxation or recommendations for rest. 【0461】 Step 7: 【0462】 The device notifies the user of the feedback it generates. The user receives the feedback and uses it in their daily life. 【0463】 Step 8: 【0464】 Users can use their devices to share emotions and experiences with other users while maintaining anonymity. The server monitors interactions within the community to maintain safety and comfort. 【0465】 (Example 1) 【0466】 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." 【0467】 In recent years, with the growing interest in mental health, there is a need to accurately understand the mental state of individual users and provide appropriate support. However, conventional systems lack sufficient integration of emotional analysis and biometric information monitoring, making it difficult to provide accurate feedback based on the user's condition. Furthermore, methods for ensuring anonymity when users share information with others remain inadequate. 【0468】 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. 【0469】 In this invention, the server includes means for receiving user input information and analyzing the corresponding emotions using a generation program, means for aggregating information from multiple biodetectors and detecting changes in state, and means for notifying the generated feedback via an information terminal. This enables the provision of personalized feedback in real time, thereby maintaining mental health. Furthermore, by providing a method for sharing information with others safely while maintaining anonymity, it is possible to promote communication and ensure security. 【0470】 "User input information" refers to data about the user's emotions and state of mind, provided in audio or text format. 【0471】 A "generative program" is a computational procedure that analyzes user input information to classify and recognize emotions. 【0472】 "Means for analyzing emotions" refers to functions that use natural language processing or generative AI models to identify emotional characteristics from input information. 【0473】 A "biometric detector" is a device used to collect physiological data such as heart rate, body temperature, and activity level. 【0474】 "Means for detecting changes in state" refers to a function that grasps changes in psychological or physiological state from aggregated biological information. 【0475】 "Means for generating feedback" refers to methods for providing users with optimized suggestions and information based on analysis results and detection results. 【0476】 An "information terminal" is an electronic device used by users to input information and receive feedback. 【0477】 The "anonymity-preserving function" is a mechanism that allows information sharing with others while ensuring that users' personal information and identities are not identified. 【0478】 This invention is a system that comprehensively supports the mental health of users, and its implementation takes the following forms. 【0479】 This system is based on emotion analysis and biometric monitoring. Users can input their emotions in voice or text format using a device. The device receives this emotion data and securely transmits it to the server using encryption technology. This process employs state-of-the-art encryption technology to protect the confidentiality of the information and the privacy of the user. 【0480】 The server uses a generative AI model to perform natural language processing and analyze the input emotion data. Specifically, if the input is something like "I'm not feeling well today," the server classifies it as negative and further identifies it as a more specific emotion such as "sadness" or "fatigue." To achieve this, the server utilizes a pre-trained emotion analysis database and algorithms. 【0481】 Furthermore, this system uses biometric sensors to collect biometric data such as heart rate, body temperature, and physical activity level in real time. This biometric data is periodically transmitted to a server via the terminal. The server analyzes the collected biometric data to detect changes in the user's emotional state and increases in stress levels. 【0482】 Based on analyzed emotional and biometric data, the server generates personalized feedback for each user. For example, it might offer specific suggestions such as, "You seem tired, so we recommend taking a deep breath and resting." This feedback is notified to the user via their device, and the user can then incorporate these suggestions into their daily life. 【0483】 Furthermore, the system maintains anonymity, providing a secure platform where users can share their emotions and experiences with others. The server monitors this platform, maintaining a safe and secure environment for use. 【0484】 An example of a prompt might be: "Classify the emotion expressed in the user's input text as positive, negative, neutral, or a specific emotion (e.g., joy, sadness, anger), and determine if appropriate feedback is needed." This prompt allows the server to perform appropriate analysis and provide feedback. 【0485】 In this way, the present invention makes it possible to provide individual users with real-time emotional support and feedback based on biometric data, thereby improving their mental health. 【0486】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0487】 Step 1: 【0488】 The user inputs their emotions using the device. This input can be in voice or text format. When the user says, "I'm tired from work today," the device converts the voice data into text. This converted text data becomes the input for the next process. 【0489】 Step 2: 【0490】 The terminal sends the acquired text data to the server. This communication is encrypted for security. The server analyzes the received text data using natural language processing technology. Specifically, it utilizes a generative AI model to classify the input emotion as positive, negative, or neutral using prompt sentences. This classification result becomes the output of the next process. 【0491】 Step 3: 【0492】 The server receives biometric data periodically transmitted from the terminal through multiple biosensors. This biometric data includes heart rate, body temperature, and physical activity level. The server analyzes this data to determine the stress level. If fluctuations in biometric data suggest increased stress, a warning is output for the next processing step. 【0493】 Step 4: 【0494】 The server generates appropriate feedback for each individual user based on the emotional classification results and the analysis of biometric data. For example, it might generate a message such as, "Your current stress level is high, so we recommend you take a short break." This feedback becomes the output of the next process. 【0495】 Step 5: 【0496】 The generated feedback is notified to the user via the device. The device presents the feedback to the user via pop-up notifications or voice messages. This allows the user to receive advice tailored to their situation in real time. 【0497】 Step 6: 【0498】 If a user wishes, the system provides a platform where they can anonymously share their emotions and experiences with other users. The platform is monitored by servers to ensure security. Shared information entered by users is treated as data provided to other users. 【0499】 (Application Example 1) 【0500】 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." 【0501】 In modern urban life, it is difficult for residents to effectively manage the stress and emotional fluctuations they experience on a daily basis and maintain their mental health. This is considered a problem because it leads to decreased productivity and a deterioration in quality of life. Currently, there is a lack of means to provide customized support for individual users and real-time feedback adapted to the urban environment. 【0502】 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. 【0503】 In this invention, the server includes means for receiving user input information and analyzing the corresponding emotion using natural language processing, means for aggregating data from multiple biometric information acquisition devices and detecting changes in emotional state, and means for generating feedback according to the situation in the urban environment and notifying the user. As a result, users living in urban areas can receive relaxation suggestions and support tailored to their individual emotional state in real time. 【0504】 "User input information" refers to data in voice or text format that users transmit to the system. 【0505】 "Natural language processing" is a field of computer science that refers to the technology of analyzing human language and understanding its meaning. 【0506】 "Means for analyzing emotions" refers to a processing device or algorithm that identifies and classifies emotions based on user input information. 【0507】 A "biometric information acquisition device" refers to sensors used to acquire physiological data such as the user's heart rate, body temperature, and physical activity level. 【0508】 "Means for detecting changes in emotional state" refers to functions or devices that analyze changes in biometric information and identify changes in the user's mental state. 【0509】 "Means for generating and providing instructions" refers to methods and devices for creating and communicating instructions and advice to users in an easily understandable format based on analysis results. 【0510】 "Means of sharing and interacting with information among users while maintaining anonymity" refers to methods and functions that allow users to exchange information and emotions with other users while protecting their privacy. 【0511】 "Context-sensitive feedback in urban environments" refers to information that provides advice and suggestions appropriate to the environment, based on the user's location information and the urban environment. 【0512】 The system that realizes this invention operates through interaction between a server, a terminal, and a user. The user inputs emotions into the terminal in the form of voice or text. The terminal receives this input data and sends it to the server. 【0513】 The server uses natural language processing techniques to analyze the user's emotions. This analysis process identifies the type and intensity of the emotions and classifies them as positive, negative, neutral, etc. The Python speech_recognition library and other natural language processing libraries are used in this process. 【0514】 Next, the user's biometric data is transmitted from multiple biometric acquisition devices to a server via the terminal. The server aggregates this biometric data and analyzes heart rate, body temperature, physical activity level, etc., to detect changes in the user's emotional state. Machine learning algorithms are used for this data analysis. 【0515】 Based on these analysis results, the server generates feedback for the user. This feedback includes relaxation suggestions tailored to the individual's emotional state and information relevant to the urban environment. The feedback is sent to the terminal, and the user can receive it in real time. 【0516】 Furthermore, users are provided with a feature that allows them to share their emotions and experiences with other users while maintaining anonymity. This feature is monitored by the server and designed to protect privacy. 【0517】 For example, if a user inputs "I've been feeling stressed at work lately," the server analyzes this and suggests relaxation methods at a nearby park. An example of a prompt to input into the generating AI model would be, "I've been feeling stressed at work lately, so could you recommend some relaxation methods?" 【0518】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0519】 Step 1: 【0520】 The user inputs their emotions into the device in voice or text format. The device receives this input and sends it to the server as input data. At this stage, the input is the user's direct expression of emotion, and the output is the emotion data sent to the server. 【0521】 Step 2: 【0522】 The server analyzes the received emotional data using natural language processing techniques. This analysis classifies the input emotional data and outputs categories such as positive, negative, and neutral. A natural language processing library is used for the analysis to identify the emotions. 【0523】 Step 3: 【0524】 The user's biosensor data is transmitted to the server via the terminal. This input includes heart rate, body temperature, and physical activity level. The server aggregates this data, analyzes each item using statistical methods, and detects changes in emotional state. The output is data indicating changes in emotional state. 【0525】 Step 4: 【0526】 The server generates specific feedback for the user based on emotion classification obtained from natural language processing and the results of biometric data analysis. The input is the analysis results of emotion data and biometric data, and the output is the feedback information provided to the user. This feedback may include, for example, relaxation methods for stress reduction or suggestions for appropriate behavior in urban environments. 【0527】 Step 5: 【0528】 The generated feedback information is sent to the device and notified to the user in real time. The input is the generated feedback information, and the output is the notification displayed on the user's device. Specifically, this involves displaying alerts and messages on the device. 【0529】 Step 6: 【0530】 Users can use their devices to share emotions and experiences with other users while maintaining anonymity. This feature involves user-generated information as input, and secure information sharing with other users as output. The server monitors this interaction to ensure privacy and security. 【0531】 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. 【0532】 This invention is a user support system that incorporates an emotion engine to enhance emotion recognition. This system collects and analyzes emotional and biometric data to support the user's mental health and provides feedback. Furthermore, it enables anonymous interaction among users. 【0533】 Emotional input and analysis 【0534】 Users input emotion-related data via voice or text through their device. The device sends this data to a server. The server uses an emotion engine to analyze the received data and identify the corresponding emotion. The emotion engine utilizes machine learning algorithms, resulting in improved accuracy in identifying emotions. 【0535】 Monitoring of biometric data 【0536】 The device collects biometric data such as heart rate, body temperature, and physical activity levels through multiple sensors and transmits it to a server. The server analyzes the received biometric data and continuously monitors changes in emotional state. 【0537】 Provide feedback 【0538】 The server utilizes an emotion engine to perform real-time sentiment analysis and provide immediate feedback to the user. This feedback includes suggestions for relaxation tailored to the user's emotional state and recommendations for appropriate rest. The generated feedback is then notified to the user from their device. 【0539】 Community support 【0540】 The system also provides a platform where users can anonymously share their emotions and experiences. A server manages this interaction platform, ensuring users can exchange information securely. An emotion engine analyzes the emotional elements within these interactions and enhances support as needed. 【0541】 For example, if a user voice-inputs "I'm frustrated with work today," the device sends this information to the server. The server analyzes the emotion of frustration through its emotion engine and evaluates the stress level based on biometric data. Based on this, it generates feedback such as "Try taking a few minutes of deep breathing" and notifies the user. In this way, the system can continuously support the user's emotional state and encourage appropriate actions. 【0542】 The following describes the processing flow. 【0543】 Step 1: 【0544】 The user inputs their emotions into the device in voice or text format. This captures raw data about the user's emotions. 【0545】 Step 2: 【0546】 The device sends the entered emotion data to the server. This transmission is encrypted to maintain data security. 【0547】 Step 3: 【0548】 The server inputs the received emotion data into the emotion engine. The emotion engine analyzes the data based on a machine learning model to identify the user's emotional state. 【0549】 Step 4: 【0550】 The server records the analysis results and compares them with the user's past emotional history to evaluate their current emotional tendencies. 【0551】 Step 5: 【0552】 The device collects biometric data such as heart rate and body temperature from sensors and sends it to a server. 【0553】 Step 6: 【0554】 The server analyzes biometric data and assesses changes in the user's physiological state. If signs of stress or tension are detected, it determines whether intervention is necessary. 【0555】 Step 7: 【0556】 The server generates appropriate feedback based on the analysis results from the emotion engine and biometric data. This feedback may include relaxation techniques or suggestions for rest. 【0557】 Step 8: 【0558】 The device notifies the user of the feedback it generates. The user receives this feedback and uses it in a practical way in their daily activities. 【0559】 Step 9: 【0560】 Users can share and interact with other users, sharing their emotions and experiences, through their devices while maintaining anonymity. 【0561】 Step 10: 【0562】 The server manages user interactions and uses an emotion engine to analyze emotional data within those communications, thereby gaining insights for better support. 【0563】 (Example 2) 【0564】 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." 【0565】 In modern society, there is a need for rapid and effective support for users' emotional states and mental health. However, conventional methods are insufficient for analyzing individual emotions and aggregating biometric data, and further improvements are necessary. Furthermore, the lack of community support through safe and anonymous information exchange is also a problem. 【0566】 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. 【0567】 In this invention, the server includes means for receiving user information and analyzing the corresponding emotions using machine learning, means for aggregating information from multiple biometric measurement devices and monitoring changes in emotional state, and means for generating and providing recommendations to the user based on the analysis results and monitoring results. This makes it possible to accurately analyze the user's emotional state and provide appropriate feedback in real time, thereby effectively supporting the user's mental health. 【0568】 "User information" refers to the voice or text data entered by the user, and serves as the basic data for analyzing their emotional state. 【0569】 Machine learning is a technique in which computers learn from data and automatically discover specific patterns and insights, and it forms the basis of models used for analyzing emotions. 【0570】 A "biometric measurement device" is a device equipped with sensors to collect biometric data such as heart rate, body temperature, and activity level, and this data is used to monitor emotional states. 【0571】 "Recommendations" refer to suggestions for actions and choices provided to the user based on analyzed emotional and biometric data. 【0572】 "Analysis results" refer to data that shows the classification and trends of emotions obtained from user information processed by machine learning. 【0573】 "Monitoring results" refer to information about changes in emotional states and their understanding, obtained based on the analysis of data collected through biometric measurement devices. 【0574】 "Exchanging and interacting information" refers to the act of users mutually exchanging data and experiences anonymously, and supporting each other. 【0575】 The following describes embodiments for carrying out the invention. 【0576】 This user support system analyzes the user's emotional state and provides appropriate feedback. The system mainly consists of a server, terminals, and multiple biometric measurement devices. 【0577】 The server plays a central role in collecting and analyzing user information. Through the terminal, users input emotional information in voice or text format. This information is processed on the terminal and sent to the server. On the server, an emotion engine is activated using machine learning libraries such as TensorFlow to perform data analysis. This identifies the user's emotional state. 【0578】 The biometric measurement device worn by the user measures biometric data such as heart rate, body temperature, and activity level using sensors, and collects this data on a terminal. This data is also transmitted to a server, allowing for real-time monitoring of changes in health status and stress levels. 【0579】 The analysis results and monitoring data obtained instantly are integrated on the server side to generate specific recommendations for the user. These recommendations include relaxation suggestions and health maintenance advice tailored to the user's current emotions and physical state. The generated recommendations are then notified to the user via their device. 【0580】 Furthermore, the server also provides a platform where users can exchange information anonymously. Here, users can share their emotions and experiences with others and support each other. Interactions on this platform are also analyzed by the emotion engine, and support is enhanced as needed. 【0581】 For example, if a user enters "I'm feeling stressed today," the server uses an emotion engine to identify that emotion and evaluates the level of stress based on corresponding biometric data. Based on this, feedback such as "We recommend you try a short meditation" is delivered to the user. 【0582】 An example of a prompt to input into a generative AI model is, "Using the sentiment data obtained from the user, please tell me how you can generate the most appropriate feedback for that person." This prompt serves as a guide for the AI ​​to derive the most effective recommendations based on the user's situation. 【0583】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0584】 Step 1: 【0585】 Users input emotional information in voice or text format using their smartphones or PCs. The input data is received by the device. The device then converts this information into an appropriate digital format and sends it to the server. For example, if a user says "I'm nervous," the voice data is converted into text data and passed on to the next analysis step. 【0586】 Step 2: 【0587】 The server receives emotion data sent from the terminal and uses machine learning libraries to run the emotion engine. This engine performs natural language processing to identify emotions from the input data. Specifically, it applies an emotion analysis algorithm to the received text data to extract emotional states such as "tension." This analysis generates emotion labels, which are then sent to the next process. 【0588】 Step 3: 【0589】 Simultaneously, the device collects biometric data such as heart rate, body temperature, and activity level from a biometric measurement device. This data is transmitted to the device via Bluetooth or Wi-Fi and then forwarded to a server. The server analyzes this data in real time to evaluate the user's physiological state. Using data analysis software, for example, it determines whether an elevated heart rate is associated with a state of stress. As a result, the evaluated biometric data, along with emotion labels, is used to generate feedback. 【0590】 Step 4: 【0591】 The server generates user-specific feedback based on emotion labels and biometric data. This process utilizes an AI model to provide recommendations, such as those aimed at reducing tension. The server generates feedback such as "Try taking a few minutes of deep breathing" and prepares to notify the device. 【0592】 Step 5: 【0593】 The device receives feedback messages sent from the server and displays them on the user interface. This feedback allows the user to learn specific recommended actions and stress reduction strategies. For example, a graphic showing relaxation techniques may be displayed on the screen. 【0594】 Step 6: 【0595】 The server provides a community platform where users can exchange information anonymously. On this platform, the server performs sentiment analysis based on user posts and provides further support as needed. Users can support each other by sharing their feelings and experiences with other users. 【0596】 (Application Example 2) 【0597】 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." 【0598】 In modern society, people are often exposed to excessive stress and emotional fluctuations, creating a need for efficient mental health care. However, many support systems struggle to accurately understand users' emotional states and provide individually optimized feedback. In addition, there is a need for further improvement in technologies that enable interaction tailored to users' emotional states within the home. 【0599】 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. 【0600】 In this invention, the server includes means for receiving user input data and analyzing the corresponding emotion using language interpretation technology, means for aggregating data from multiple biosensors and detecting changes in emotional state, means for generating and providing feedback to the user based on the analysis results and detection results, and means for additionally providing a human sharing function that has information input and image display functions and provides support according to the user's emotional state. This enables appropriate emotional care optimized for each individual user. 【0601】 "User input data" refers to information that users provide through voice or text, indicating their emotions and state of mind. 【0602】 "Language interpretation technology" refers to natural language processing technology used to analyze emotions from user input data. 【0603】 A "biosensor" is a general term for devices and technologies used to measure the body's activity levels, such as heart rate and body temperature. 【0604】 "Changes in emotional state" refers to fluctuations in a user's emotions over time, and the goal is to understand the user's mental health state by detecting these changes. 【0605】 "Means of generating and providing feedback" refers to methods and technologies for communicating appropriate advice and support to users based on their analyzed emotional state. 【0606】 "Human sharing functionality" refers to interactive features that enable users to appropriately communicate and respond based on their emotions. 【0607】 The system that realizes this application consists of a program that has the ability to receive and analyze user input data and the function to collect biometric information. The system functions effectively in robots used in the home. 【0608】 The server first receives emotion data entered by the user via voice or text. By using natural language processing as a language interpretation technique, it analyzes the user's emotions from the input data with high accuracy. For language analysis, it utilizes Google Cloud's Speech-to-Text API and NLP libraries. 【0609】 The server then processes data from biosensors. Specifically, it uses heart rate and body temperature measuring devices connected to Raspberry Pi or Arduino. This allows for the aggregation of biometric data in real time and continuous monitoring of changes in the user's emotional state. Once this data is aggregated, the server generates appropriate feedback for the user based on the emotion analysis results and biometric information, and the robot provides this feedback to the user using voice and a display. This feedback includes suggestions for relaxation and emotionally appropriate interactions. 【0610】 As a concrete example, consider a situation where a user is stressed because they are under pressure to meet a project deadline. If the user inputs "I'm busy and restless right now," the server analyzes the emotional state as "stressed." Based on this, the robot offers a suggestion: "How about doing some 5 minutes of stretching?" In this way, the system suggests ways to relax to the user and helps reduce stress. 【0611】 Examples of prompts for a generative AI model: 【0612】 "Analyze the emotions the user is currently feeling and suggest appropriate feedback." 【0613】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0614】 Step 1: 【0615】 The user inputs voice or text information via the terminal. The terminal receives this data and sends it to the server as initial input data for the process. 【0616】 Step 2: 【0617】 The server analyzes the received user input data using language interpretation techniques. Specifically, it applies natural language processing (NLP) algorithms to identify the user's emotional state. In this process, natural language text or audio data is used as input, and the emotional state is identified as output. 【0618】 Step 3: 【0619】 The biosensors built into the device continuously measure the user's heart rate and body temperature. The device sends this biometric data to a server, which is used as information to monitor changes in the user's emotional state. 【0620】 Step 4: 【0621】 The server generates appropriate feedback for the user based on the analyzed emotional data and measured biometric data. It utilizes a generative AI model to create specific relaxation methods and behavioral suggestions. At this stage, the inputs are emotional data and biometric data, and the output is the specific feedback provided to the user. 【0622】 Step 5: 【0623】 The terminal transmits user feedback to the robot. The robot uses voice output devices and displays to provide feedback and interact with the user. This includes playing voice guides and displaying messages on the screen. 【0624】 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. 【0625】 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. 【0626】 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. 【0627】 [Fourth Embodiment] 【0628】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0629】 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. 【0630】 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). 【0631】 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. 【0632】 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. 【0633】 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). 【0634】 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. 【0635】 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. 【0636】 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. 【0637】 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. 【0638】 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. 【0639】 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. 【0640】 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". 【0641】 This invention is a system designed to comprehensively support the mental health of users. The system aims to provide appropriate feedback to users based on emotion analysis and monitoring of biometric information. 【0642】 Emotional input and analysis 【0643】 Users can input their emotions into the device in voice or text format. After receiving the input, the device sends the data to the server. The server uses natural language processing techniques to analyze the emotions, classifying them as positive, negative, or neutral, and identifying specific emotions (e.g., joy, sadness, anger). This analysis is further used to provide insights into the user's emotions. 【0644】 Monitoring of biometric data 【0645】 This system uses multiple biosensors to collect real-time biometric data from the user. This data includes heart rate, body temperature, and physical activity level, and is periodically transmitted to a server via the device. The server analyzes the collected data to detect changes in the user's emotional state and increases in stress levels. 【0646】 Provide feedback 【0647】 Based on the information analyzed about the user, the server generates feedback. This feedback takes into account the user's current emotional state and may include suggestions for relaxation or recommendations for breaks as needed. The generated feedback is notified to the user via their device, and the user can incorporate it into their daily life. 【0648】 Community support 【0649】 The system also promotes safe interaction among users. Users can share their feelings and experiences anonymously with other users and receive community support regarding their mental health. This interaction is monitored by the server and managed to maintain safety and comfort. 【0650】 As a concrete example, suppose a user types the text "I'm very tired today." The server recognizes this input as "fatigue" and, after checking the biometric data, if an elevated heart rate is detected, it provides feedback such as "We recommend you take a deep breath and rest." In this way, users can receive real-time support and maintain their mental health in their daily lives. 【0651】 The following describes the processing flow. 【0652】 Step 1: 【0653】 The user uses their device to input their emotions via voice or text. This allows data about the emotions to be captured and temporarily stored on the device. 【0654】 Step 2: 【0655】 The device sends the entered emotion data to the server. The data is encrypted before transmission to protect privacy. 【0656】 Step 3: 【0657】 The server performs natural language processing on the received emotion data to analyze the emotions. As a result of the analysis, the polarity of the emotion and the specific emotional state are determined. 【0658】 Step 4: 【0659】 The device collects data such as heart rate, body temperature, and activity level from biosensors and transmits it to the server in real time. 【0660】 Step 5: 【0661】 The server analyzes the received biometric data to assess the user's current emotional state and stress level. 【0662】 Step 6: 【0663】 The server generates appropriate feedback based on the analysis results. This feedback may include suggestions for relaxation or recommendations for rest. 【0664】 Step 7: 【0665】 The device notifies the user of the feedback it generates. The user receives the feedback and uses it in their daily life. 【0666】 Step 8: 【0667】 Users can use their devices to share emotions and experiences with other users while maintaining anonymity. The server monitors interactions within the community to maintain safety and comfort. 【0668】 (Example 1) 【0669】 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". 【0670】 In recent years, with the growing interest in mental health, there is a need to accurately understand the mental state of individual users and provide appropriate support. However, conventional systems lack sufficient integration of emotional analysis and biometric information monitoring, making it difficult to provide accurate feedback based on the user's condition. Furthermore, methods for ensuring anonymity when users share information with others remain inadequate. 【0671】 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. 【0672】 In this invention, the server includes means for receiving user input information and analyzing the corresponding emotions using a generation program, means for aggregating information from multiple biodetectors and detecting changes in state, and means for notifying the generated feedback via an information terminal. This enables the provision of personalized feedback in real time, thereby maintaining mental health. Furthermore, by providing a method for sharing information with others safely while maintaining anonymity, it is possible to promote communication and ensure security. 【0673】 "User input information" refers to data about the user's emotions and state of mind, provided in audio or text format. 【0674】 A "generative program" is a computational procedure that analyzes user input information to classify and recognize emotions. 【0675】 "Means for analyzing emotions" refers to functions that use natural language processing or generative AI models to identify emotional characteristics from input information. 【0676】 A "biometric detector" is a device used to collect physiological data such as heart rate, body temperature, and activity level. 【0677】 "Means for detecting changes in state" refers to a function that grasps changes in psychological or physiological state from aggregated biological information. 【0678】 "Means for generating feedback" refers to methods for providing users with optimized suggestions and information based on analysis results and detection results. 【0679】 An "information terminal" is an electronic device used by users to input information and receive feedback. 【0680】 The "anonymity-preserving function" is a mechanism that allows information sharing with others while ensuring that users' personal information and identities are not identified. 【0681】 This invention is a system that comprehensively supports the mental health of users, and its implementation takes the following forms. 【0682】 This system is based on emotion analysis and biometric monitoring. Users can input their emotions in voice or text format using a device. The device receives this emotion data and securely transmits it to the server using encryption technology. This process employs state-of-the-art encryption technology to protect the confidentiality of the information and the privacy of the user. 【0683】 The server uses a generative AI model to perform natural language processing and analyze the input emotion data. Specifically, if the input is something like "I'm not feeling well today," the server classifies it as negative and further identifies it as a more specific emotion such as "sadness" or "fatigue." To achieve this, the server utilizes a pre-trained emotion analysis database and algorithms. 【0684】 Furthermore, this system uses biometric sensors to collect biometric data such as heart rate, body temperature, and physical activity level in real time. This biometric data is periodically transmitted to a server via the terminal. The server analyzes the collected biometric data to detect changes in the user's emotional state and increases in stress levels. 【0685】 Based on analyzed emotional and biometric data, the server generates personalized feedback for each user. For example, it might offer specific suggestions such as, "You seem tired, so we recommend taking a deep breath and resting." This feedback is notified to the user via their device, and the user can then incorporate these suggestions into their daily life. 【0686】 Furthermore, the system maintains anonymity, providing a secure platform where users can share their emotions and experiences with others. The server monitors this platform, maintaining a safe and secure environment for use. 【0687】 An example of a prompt might be: "Classify the emotion expressed in the user's input text as positive, negative, neutral, or a specific emotion (e.g., joy, sadness, anger), and determine if appropriate feedback is needed." This prompt allows the server to perform appropriate analysis and provide feedback. 【0688】 In this way, the present invention makes it possible to provide individual users with real-time emotional support and feedback based on biometric data, thereby improving their mental health. 【0689】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0690】 Step 1: 【0691】 The user inputs their emotions using the device. This input can be in voice or text format. When the user says, "I'm tired from work today," the device converts the voice data into text. This converted text data becomes the input for the next process. 【0692】 Step 2: 【0693】 The terminal sends the acquired text data to the server. This communication is encrypted for security. The server analyzes the received text data using natural language processing technology. Specifically, it utilizes a generative AI model to classify the input emotion as positive, negative, or neutral using prompt sentences. This classification result becomes the output of the next process. 【0694】 Step 3: 【0695】 The server receives biometric data periodically transmitted from the terminal through multiple biosensors. This biometric data includes heart rate, body temperature, and physical activity level. The server analyzes this data to determine the stress level. If fluctuations in biometric data suggest increased stress, a warning is output for the next processing step. 【0696】 Step 4: 【0697】 The server generates appropriate feedback for each individual user based on the emotional classification results and the analysis of biometric data. For example, it might generate a message such as, "Your current stress level is high, so we recommend you take a short break." This feedback becomes the output of the next process. 【0698】 Step 5: 【0699】 The generated feedback is notified to the user via the device. The device presents the feedback to the user via pop-up notifications or voice messages. This allows the user to receive advice tailored to their situation in real time. 【0700】 Step 6: 【0701】 If a user wishes, the system provides a platform where they can anonymously share their emotions and experiences with other users. The platform is monitored by servers to ensure security. Shared information entered by users is treated as data provided to other users. 【0702】 (Application Example 1) 【0703】 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". 【0704】 In modern urban life, it is difficult for residents to effectively manage the stress and emotional fluctuations they experience on a daily basis and maintain their mental health. This is considered a problem because it leads to decreased productivity and a deterioration in quality of life. Currently, there is a lack of means to provide customized support for individual users and real-time feedback adapted to the urban environment. 【0705】 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. 【0706】 In this invention, the server includes means for receiving user input information and analyzing the corresponding emotion using natural language processing, means for aggregating data from multiple biometric information acquisition devices and detecting changes in emotional state, and means for generating feedback according to the situation in the urban environment and notifying the user. As a result, users living in urban areas can receive relaxation suggestions and support tailored to their individual emotional state in real time. 【0707】 "User input information" refers to data in voice or text format that users transmit to the system. 【0708】 "Natural language processing" is a field of computer science that refers to the technology of analyzing human language and understanding its meaning. 【0709】 "Means for analyzing emotions" refers to a processing device or algorithm that identifies and classifies emotions based on user input information. 【0710】 A "biometric information acquisition device" refers to sensors used to acquire physiological data such as the user's heart rate, body temperature, and physical activity level. 【0711】 "Means for detecting changes in emotional state" refers to functions or devices that analyze changes in biometric information and identify changes in the user's mental state. 【0712】 "Means for generating and providing instructions" refers to methods and devices for creating and communicating instructions and advice to users in an easily understandable format based on analysis results. 【0713】 "Means of sharing and interacting with information among users while maintaining anonymity" refers to methods and functions that allow users to exchange information and emotions with other users while protecting their privacy. 【0714】 "Context-sensitive feedback in urban environments" refers to information that provides advice and suggestions appropriate to the environment, based on the user's location information and the urban environment. 【0715】 The system that realizes this invention operates through interaction between a server, a terminal, and a user. The user inputs emotions into the terminal in the form of voice or text. The terminal receives this input data and sends it to the server. 【0716】 The server uses natural language processing techniques to analyze the user's emotions. This analysis process identifies the type and intensity of the emotions and classifies them as positive, negative, neutral, etc. The Python speech_recognition library and other natural language processing libraries are used in this process. 【0717】 Next, the user's biometric data is transmitted from multiple biometric acquisition devices to a server via the terminal. The server aggregates this biometric data and analyzes heart rate, body temperature, physical activity level, etc., to detect changes in the user's emotional state. Machine learning algorithms are used for this data analysis. 【0718】 Based on these analysis results, the server generates feedback for the user. This feedback includes relaxation suggestions tailored to the individual's emotional state and information relevant to the urban environment. The feedback is sent to the terminal, and the user can receive it in real time. 【0719】 Furthermore, users are provided with a feature that allows them to share their emotions and experiences with other users while maintaining anonymity. This feature is monitored by the server and designed to protect privacy. 【0720】 For example, if a user inputs "I've been feeling stressed at work lately," the server analyzes this and suggests relaxation methods at a nearby park. An example of a prompt to input into the generating AI model would be, "I've been feeling stressed at work lately, so could you recommend some relaxation methods?" 【0721】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0722】 Step 1: 【0723】 The user inputs their emotions into the device in voice or text format. The device receives this input and sends it to the server as input data. At this stage, the input is the user's direct expression of emotion, and the output is the emotion data sent to the server. 【0724】 Step 2: 【0725】 The server analyzes the received emotional data using natural language processing techniques. This analysis classifies the input emotional data and outputs categories such as positive, negative, and neutral. A natural language processing library is used for the analysis to identify the emotions. 【0726】 Step 3: 【0727】 The user's biosensor data is transmitted to the server via the terminal. This input includes heart rate, body temperature, and physical activity level. The server aggregates this data, analyzes each item using statistical methods, and detects changes in emotional state. The output is data indicating changes in emotional state. 【0728】 Step 4: 【0729】 The server generates specific feedback for the user based on emotion classification obtained from natural language processing and the results of biometric data analysis. The input is the analysis results of emotion data and biometric data, and the output is the feedback information provided to the user. This feedback may include, for example, relaxation methods for stress reduction or suggestions for appropriate behavior in urban environments. 【0730】 Step 5: 【0731】 The generated feedback information is sent to the device and notified to the user in real time. The input is the generated feedback information, and the output is the notification displayed on the user's device. Specifically, this involves displaying alerts and messages on the device. 【0732】 Step 6: 【0733】 Users can use their devices to share emotions and experiences with other users while maintaining anonymity. This feature involves user-generated information as input, and secure information sharing with other users as output. The server monitors this interaction to ensure privacy and security. 【0734】 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. 【0735】 This invention is a user support system that incorporates an emotion engine to enhance emotion recognition. This system collects and analyzes emotional and biometric data to support the user's mental health and provides feedback. Furthermore, it enables anonymous interaction among users. 【0736】 Emotional input and analysis 【0737】 Users input emotion-related data via voice or text through their device. The device sends this data to a server. The server uses an emotion engine to analyze the received data and identify the corresponding emotion. The emotion engine utilizes machine learning algorithms, resulting in improved accuracy in identifying emotions. 【0738】 Monitoring of biometric data 【0739】 The device collects biometric data such as heart rate, body temperature, and physical activity levels through multiple sensors and transmits it to a server. The server analyzes the received biometric data and continuously monitors changes in emotional state. 【0740】 Provide feedback 【0741】 The server utilizes an emotion engine to perform real-time sentiment analysis and provide immediate feedback to the user. This feedback includes suggestions for relaxation tailored to the user's emotional state and recommendations for appropriate rest. The generated feedback is then notified to the user from their device. 【0742】 Community support 【0743】 The system also provides a platform where users can anonymously share their emotions and experiences. A server manages this interaction platform, ensuring users can exchange information securely. An emotion engine analyzes the emotional elements within these interactions and enhances support as needed. 【0744】 For example, if a user voice-inputs "I'm frustrated with work today," the device sends this information to the server. The server analyzes the emotion of frustration through its emotion engine and evaluates the stress level based on biometric data. Based on this, it generates feedback such as "Try taking a few minutes of deep breathing" and notifies the user. In this way, the system can continuously support the user's emotional state and encourage appropriate actions. 【0745】 The following describes the processing flow. 【0746】 Step 1: 【0747】 The user inputs their emotions into the device in voice or text format. This captures raw data about the user's emotions. 【0748】 Step 2: 【0749】 The device sends the entered emotion data to the server. This transmission is encrypted to maintain data security. 【0750】 Step 3: 【0751】 The server inputs the received emotion data into the emotion engine. The emotion engine analyzes the data based on a machine learning model to identify the user's emotional state. 【0752】 Step 4: 【0753】 The server records the analysis results and compares them with the user's past emotional history to evaluate their current emotional tendencies. 【0754】 Step 5: 【0755】 The device collects biometric data such as heart rate and body temperature from sensors and sends it to a server. 【0756】 Step 6: 【0757】 The server analyzes biometric data and assesses changes in the user's physiological state. If signs of stress or tension are detected, it determines whether intervention is necessary. 【0758】 Step 7: 【0759】 The server generates appropriate feedback based on the analysis results from the emotion engine and biometric data. This feedback may include relaxation techniques or suggestions for rest. 【0760】 Step 8: 【0761】 The device notifies the user of the feedback it generates. The user receives this feedback and uses it in a practical way in their daily activities. 【0762】 Step 9: 【0763】 Users can share and interact with other users, sharing their emotions and experiences, through their devices while maintaining anonymity. 【0764】 Step 10: 【0765】 The server manages user interactions and uses an emotion engine to analyze emotional data within those communications, thereby gaining insights for better support. 【0766】 (Example 2) 【0767】 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". 【0768】 In modern society, there is a need for rapid and effective support for users' emotional states and mental health. However, conventional methods are insufficient for analyzing individual emotions and aggregating biometric data, and further improvements are necessary. Furthermore, the lack of community support through safe and anonymous information exchange is also a problem. 【0769】 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. 【0770】 In this invention, the server includes means for receiving user information and analyzing the corresponding emotions using machine learning, means for aggregating information from multiple biometric measurement devices and monitoring changes in emotional state, and means for generating and providing recommendations to the user based on the analysis results and monitoring results. This makes it possible to accurately analyze the user's emotional state and provide appropriate feedback in real time, thereby effectively supporting the user's mental health. 【0771】 "User information" refers to the voice or text data entered by the user, and serves as the basic data for analyzing their emotional state. 【0772】 Machine learning is a technique in which computers learn from data and automatically discover specific patterns and insights, and it forms the basis of models used for analyzing emotions. 【0773】 A "biometric measurement device" is a device equipped with sensors to collect biometric data such as heart rate, body temperature, and activity level, and this data is used to monitor emotional states. 【0774】 "Recommendations" refer to suggestions for actions and choices provided to the user based on analyzed emotional and biometric data. 【0775】 "Analysis results" refer to data that shows the classification and trends of emotions obtained from user information processed by machine learning. 【0776】 "Monitoring results" refer to information about changes in emotional states and their understanding, obtained based on the analysis of data collected through biometric measurement devices. 【0777】 "Exchanging and interacting information" refers to the act of users mutually exchanging data and experiences anonymously, and supporting each other. 【0778】 The following describes embodiments for carrying out the invention. 【0779】 This user support system analyzes the user's emotional state and provides appropriate feedback. The system mainly consists of a server, terminals, and multiple biometric measurement devices. 【0780】 The server plays a central role in collecting and analyzing user information. Through the terminal, users input emotional information in voice or text format. This information is processed on the terminal and sent to the server. On the server, an emotion engine is activated using machine learning libraries such as TensorFlow to perform data analysis. This identifies the user's emotional state. 【0781】 The biometric measurement device worn by the user measures biometric data such as heart rate, body temperature, and activity level using sensors, and collects this data on a terminal. This data is also transmitted to a server, allowing for real-time monitoring of changes in health status and stress levels. 【0782】 The analysis results and monitoring data obtained instantly are integrated on the server side to generate specific recommendations for the user. These recommendations include relaxation suggestions and health maintenance advice tailored to the user's current emotions and physical state. The generated recommendations are then notified to the user via their device. 【0783】 Furthermore, the server also provides a platform where users can exchange information anonymously. Here, users can share their emotions and experiences with others and support each other. Interactions on this platform are also analyzed by the emotion engine, and support is enhanced as needed. 【0784】 For example, if a user enters "I'm feeling stressed today," the server uses an emotion engine to identify that emotion and evaluates the level of stress based on corresponding biometric data. Based on this, feedback such as "We recommend you try a short meditation" is delivered to the user. 【0785】 An example of a prompt to input into a generative AI model is, "Using the sentiment data obtained from the user, please tell me how you can generate the most appropriate feedback for that person." This prompt serves as a guide for the AI ​​to derive the most effective recommendations based on the user's situation. 【0786】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0787】 Step 1: 【0788】 Users input emotional information in voice or text format using their smartphones or PCs. The input data is received by the device. The device then converts this information into an appropriate digital format and sends it to the server. For example, if a user says "I'm nervous," the voice data is converted into text data and passed on to the next analysis step. 【0789】 Step 2: 【0790】 The server receives emotion data sent from the terminal and uses machine learning libraries to run the emotion engine. This engine performs natural language processing to identify emotions from the input data. Specifically, it applies an emotion analysis algorithm to the received text data to extract emotional states such as "tension." This analysis generates emotion labels, which are then sent to the next process. 【0791】 Step 3: 【0792】 Simultaneously, the device collects biometric data such as heart rate, body temperature, and activity level from a biometric measurement device. This data is transmitted to the device via Bluetooth or Wi-Fi and then forwarded to a server. The server analyzes this data in real time to evaluate the user's physiological state. Using data analysis software, for example, it determines whether an elevated heart rate is associated with a state of stress. As a result, the evaluated biometric data, along with emotion labels, is used to generate feedback. 【0793】 Step 4: 【0794】 The server generates user-specific feedback based on emotion labels and biometric data. This process utilizes an AI model to provide recommendations, such as those aimed at reducing tension. The server generates feedback such as "Try taking a few minutes of deep breathing" and prepares to notify the device. 【0795】 Step 5: 【0796】 The device receives feedback messages sent from the server and displays them on the user interface. This feedback allows the user to learn specific recommended actions and stress reduction strategies. For example, a graphic showing relaxation techniques may be displayed on the screen. 【0797】 Step 6: 【0798】 The server provides a community platform where users can exchange information anonymously. On this platform, the server performs sentiment analysis based on user posts and provides further support as needed. Users can support each other by sharing their feelings and experiences with other users. 【0799】 (Application Example 2) 【0800】 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". 【0801】 In modern society, people are often exposed to excessive stress and emotional fluctuations, creating a need for efficient mental health care. However, many support systems struggle to accurately understand users' emotional states and provide individually optimized feedback. In addition, there is a need for further improvement in technologies that enable interaction tailored to users' emotional states within the home. 【0802】 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. 【0803】 In this invention, the server includes means for receiving user input data and analyzing the corresponding emotion using language interpretation technology, means for aggregating data from multiple biosensors and detecting changes in emotional state, means for generating and providing feedback to the user based on the analysis results and detection results, and means for additionally providing a human sharing function that has information input and image display functions and provides support according to the user's emotional state. This enables appropriate emotional care optimized for each individual user. 【0804】 "User input data" refers to information that users provide through voice or text, indicating their emotions and state of mind. 【0805】 "Language interpretation technology" refers to natural language processing technology used to analyze emotions from user input data. 【0806】 A "biosensor" is a general term for devices and technologies used to measure the body's activity levels, such as heart rate and body temperature. 【0807】 "Changes in emotional state" refers to fluctuations in a user's emotions over time, and the goal is to understand the user's mental health state by detecting these changes. 【0808】 "Means of generating and providing feedback" refers to methods and technologies for communicating appropriate advice and support to users based on their analyzed emotional state. 【0809】 "Human sharing functionality" refers to interactive features that enable users to appropriately communicate and respond based on their emotions. 【0810】 The system that realizes this application consists of a program that has the ability to receive and analyze user input data and the function to collect biometric information. The system functions effectively in robots used in the home. 【0811】 The server first receives emotion data entered by the user via voice or text. By using natural language processing as a language interpretation technique, it analyzes the user's emotions from the input data with high accuracy. For language analysis, it utilizes Google Cloud's Speech-to-Text API and NLP libraries. 【0812】 The server then processes data from biosensors. Specifically, it uses heart rate and body temperature measuring devices connected to Raspberry Pi or Arduino. This allows for the aggregation of biometric data in real time and continuous monitoring of changes in the user's emotional state. Once this data is aggregated, the server generates appropriate feedback for the user based on the emotion analysis results and biometric information, and the robot provides this feedback to the user using voice and a display. This feedback includes suggestions for relaxation and emotionally appropriate interactions. 【0813】 As a concrete example, consider a situation where a user is stressed because they are under pressure to meet a project deadline. If the user inputs "I'm busy and restless right now," the server analyzes the emotional state as "stressed." Based on this, the robot offers a suggestion: "How about doing some 5 minutes of stretching?" In this way, the system suggests ways to relax to the user and helps reduce stress. 【0814】 Examples of prompts for a generative AI model: 【0815】 "Analyze the emotions the user is currently feeling and suggest appropriate feedback." 【0816】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0817】 Step 1: 【0818】 The user inputs voice or text information via the terminal. The terminal receives this data and sends it to the server as initial input data for the process. 【0819】 Step 2: 【0820】 The server analyzes the received user input data using language interpretation techniques. Specifically, it applies natural language processing (NLP) algorithms to identify the user's emotional state. In this process, natural language text or audio data is used as input, and the emotional state is identified as output. 【0821】 Step 3: 【0822】 The biosensors built into the device continuously measure the user's heart rate and body temperature. The device sends this biometric data to a server, which is used as information to monitor changes in the user's emotional state. 【0823】 Step 4: 【0824】 The server generates appropriate feedback for the user based on the analyzed emotional data and measured biometric data. It utilizes a generative AI model to create specific relaxation methods and behavioral suggestions. At this stage, the inputs are emotional data and biometric data, and the output is the specific feedback provided to the user. 【0825】 Step 5: 【0826】 The terminal transmits user feedback to the robot. The robot uses voice output devices and displays to provide feedback and interact with the user. This includes playing voice guides and displaying messages on the screen. 【0827】 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. 【0828】 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. 【0829】 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. 【0830】 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. 【0831】 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. 【0832】 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. 【0833】 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. 【0834】 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. 【0835】 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." 【0836】 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. 【0837】 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. 【0838】 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. 【0839】 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. 【0840】 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. 【0841】 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. 【0842】 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. 【0843】 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. 【0844】 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. 【0845】 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. 【0846】 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. 【0847】 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 as being incorporated by reference. 【0848】 The following is further disclosed regarding the embodiments described above. 【0849】 (Claim 1) 【0850】 A means of receiving user input data and analyzing the corresponding emotions using natural language processing, 【0851】 A means for aggregating data from multiple biosensors to detect changes in emotional state, 【0852】 A means for generating and providing feedback to the user based on the analysis results and detection results, 【0853】 A means of sharing and exchanging information among users while maintaining anonymity, 【0854】 A system that includes this. 【0855】 (Claim 2) 【0856】 The system according to claim 1, wherein the user's input data is either voice or text. 【0857】 (Claim 3) 【0858】 The system according to claim 1, which provides a suggestion for rest as feedback. 【0859】 "Example 1" 【0860】 (Claim 1) 【0861】 A means of receiving user input information and analyzing the corresponding emotion using a generation program, 【0862】 A means for aggregating information from multiple biodetectors and detecting changes in state, 【0863】 A means for generating and providing individually optimized feedback based on analysis results and detection results, 【0864】 A means of providing a function that allows users to share and interact with each other while maintaining anonymity, 【0865】 A means of notifying the generated feedback via an information terminal, 【0866】 A system that includes this. 【0867】 (Claim 2) 【0868】 The system according to claim 1, wherein the user's input information is either voice or text. 【0869】 (Claim 3) 【0870】 The system according to claim 1, which provides a suggestion for rest as feedback. 【0871】 "Application Example 1" 【0872】 (Claim 1) 【0873】 A means of receiving user input information and analyzing the corresponding emotion using natural language processing, 【0874】 A means for aggregating data from multiple biometric information acquisition devices and detecting changes in emotional state, 【0875】 A means for generating and providing instructions to the user based on the analysis results and detection results, 【0876】 A means of sharing and exchanging information among users while maintaining anonymity, 【0877】 A means of generating and notifying users of feedback that is appropriate to the situation in an urban environment, 【0878】 A system that includes this. 【0879】 (Claim 2) 【0880】 The system according to claim 1, wherein the user input information is voice or text, and is used within a city. 【0881】 (Claim 3) 【0882】 The system according to claim 1, which provides environment-based relaxation suggestions as feedback. 【0883】 "Example 2 of combining an emotion engine" 【0884】 (Claim 1) 【0885】 A means of receiving user information and analyzing the corresponding emotions using machine learning, 【0886】 A means of aggregating information from multiple biometric measurement devices to monitor changes in emotional state, 【0887】 A means for generating and providing recommendations to users based on analysis results and monitoring results, 【0888】 A means of exchanging and interacting with data among users while maintaining anonymity, 【0889】 A system that includes this. 【0890】 (Claim 2) 【0891】 The system according to claim 1, wherein the user information is in voice or text. 【0892】 (Claim 3) 【0893】 The system according to claim 1, which recommends rest. 【0894】 "Application example 2 when combining with an emotional engine" 【0895】 (Claim 1) 【0896】 A means of receiving user input data and analyzing the corresponding emotion using language interpretation technology, 【0897】 A means for aggregating data from multiple biosensors to detect changes in emotional state, 【0898】 A means for generating and providing feedback to the user based on the analysis results and detection results, 【0899】 A means to provide additional human sharing functions that have information input and image display functions and provide support according to the user's emotional state, 【0900】 A means of sharing and exchanging information among users while maintaining anonymity, 【0901】 A system that includes this. 【0902】 (Claim 2) 【0903】 The system according to claim 1, wherein the user's input data is voice or text information. 【0904】 (Claim 3) 【0905】 The system according to claim 1, which provides relaxation suggestions as feedback. [Explanation of symbols] 【0906】 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] A means of receiving user input data and analyzing the corresponding emotions using natural language processing, A means for aggregating data from multiple biosensors to detect changes in emotional state, A means for generating and providing feedback to the user based on the analysis results and detection results, A means of sharing and exchanging information among users while maintaining anonymity, A system that includes this. [Claim 2] The system according to claim 1, wherein the user's input data is either voice or text. [Claim 3] The system according to claim 1, which provides a suggestion to take a break as feedback.

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