system

Abstract

We provide a system that contributes to improving health management in educational institutions. [Solution] A system within an educational institution that includes data collection means for acquiring environmental data, data collection means for acquiring individual health data of students, data integration means for integrating and pre-processing the collected data, analysis means for analyzing the pre-processed data and predicting health risks, notification means for notifying or warning relevant parties based on the analysis results, information provision means for supporting health operations, information provision means for providing parents with information on the health status of students, and analysis means for analyzing overall health data and creating reports for administrators.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times when the importance of health management in educational institutions is increasing, there is a lack of means to comprehensively monitor environmental factors and the health status of individual students. In conventional methods, environmental data and individual health data are separated, and as a result, it is difficult to predict and respond to health risks quickly and appropriately. Also, there is not enough means to efficiently provide information to guardians and administrators of educational institutions. Therefore, there is a need for a new system to effectively reduce health risks in educational settings and provide accurate information to administrators and guardians in a timely manner.

Means for Solving the Problems

[0005] This invention comprises data collection means for acquiring environmental data and data collection means for acquiring individual student health data. Data integration means integrates and preprocesses this data, ensuring it is processed in a unified format. Furthermore, analysis means analyze the preprocessed data to predict health risks, enabling real-time risk identification and analysis. Notification means enable immediate countermeasures by notifying or warning relevant parties based on the analysis results, and information provision means support health operations, facilitating efficient management. In addition, information provision means provide parents with information on their children's health status, strengthening collaboration with families. Analysis means that analyze school-wide health data and generate reports for administrators contribute to improving health management in educational institutions.

[0006] An "educational institution" is a facility or organization that provides learning and education for students, and includes schools, universities, and other similar institutions.

[0007] "Environmental data" refers to numerical information that indicates the physical and chemical conditions of a specific location, including temperature, humidity, carbon dioxide concentration, and noise levels.

[0008] "Individual student health data" refers to information that indicates the health status of each student, and includes physiological measurements such as body temperature, heart rate, and activity level.

[0009] "Data collection means" refers to devices and systems used to acquire environmental data and individual health data.

[0010] "Data integration means" refers to a function that organizes multiple collected data into a unified format and processes them as a single dataset.

[0011] "Preprocessing" refers to processes performed on collected data to improve its quality, such as removing outliers and standardizing the format.

[0012] "Analysis means" refers to a function that analyzes collected and integrated data using a specific algorithm and derives results.

[0013] "Health risk" refers to an indicator that shows the likelihood that a particular environment or individual's health condition may lead to the development of disease or deterioration of health.

[0014] "Notification means" refers to a function that delivers information and warnings based on analysis results to specific recipients.

[0015] "Health services" refer to the work of managing the health of students in educational institutions and providing medical care as needed.

[0016] "Information provision means" refers to a function that provides information tailored to a specific user's purpose.

[0017] "Analytical tools" refer to functions that statistically or algorithmically analyze large amounts of data and provide the results in the form of reports or visuals. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

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

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

[0021] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

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

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

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

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

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] The system of the present invention is designed to effectively manage health in educational institutions and integrates various data collection, analysis, and provision functions. Specific embodiments of the present invention are described below.

[0040] First, the server continuously collects environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed throughout the school. Meanwhile, terminals obtain individual health data such as body temperature, heart rate, and activity level from health monitoring apps used by students and send it to the server. This data is integrated on the server, the format of each data item is standardized, and it is stored in a database.

[0041] Next, the server uses the integrated dataset to predict health risks. It utilizes a generative AI model to predict the risk of infectious disease outbreaks and student health problems, generating visualized results. If the prediction results exceed a predetermined threshold, the server sends a warning or prompt to action to the relevant parties' terminals.

[0042] Furthermore, the terminals in the school infirmary display information on students with abnormal health conditions based on data from the server. This allows health staff to perform quick and appropriate triage. The terminals also provide information to facilitate referrals to medical institutions as needed.

[0043] For users who are parents, the server generates regular reports on the student's health status and sends them via email or a dedicated app. These reports include the student's health trends and important points to help parents take appropriate action.

[0044] Finally, the server centrally analyzes health data from across the school and generates a report for administrators. Administrators can use this report to develop and implement data-driven improvements to health management.

[0045] As described above, the system according to the present invention will revolutionize health management in educational institutions by effectively utilizing environmental data and individual health data.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server acquires environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed on campus. The acquired data is transmitted to a database in real time.

[0049] Step 2:

[0050] The device collects individual student health data, such as body temperature, heart rate, and activity level, from the health monitoring app used by the student and sends it to the server. The transmitted data is then integrated on the server.

[0051] Step 3:

[0052] The server converts the collected environmental and individual health data into a unified format and stores it in a database. During this process, it detects data anomalies and corrects them appropriately.

[0053] Step 4:

[0054] The server analyzes the integrated data and runs an AI model specifically designed to predict the likelihood of infectious disease outbreaks and health problems. This model assesses the risk by comparing it to historical data.

[0055] Step 5:

[0056] The server sends notifications to educational institution personnel based on AI-generated predictions. In particular, if an anomaly is detected, an immediate warning is sent to the terminal to prompt action.

[0057] Step 6:

[0058] The terminal displays student health information based on data transmitted from the server to assist health room staff with triage. If necessary, the server provides referral information to external medical institutions.

[0059] Step 7:

[0060] Parents, who are users, receive periodic reports from the server regarding their child's health. These reports include recent health trends and advice.

[0061] Step 8:

[0062] The server analyzes health data across the entire school and generates a comprehensive report for administrators. This report is delivered to administrators' terminals in a visual format and provides information to help improve health management.

[0063] (Example 1)

[0064] 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."

[0065] Traditionally, student health management in educational institutions relied on periodic health checkups and observations by teachers and staff, making it difficult to grasp students' health status in real time and thus hindering the rapid detection of early signs of infectious diseases or illness. Furthermore, there was a lack of efficient methods for integrating and analyzing environmental information with individual student health data, which hindered improvements in the accuracy of health management. Additionally, there was insufficient information provided to parents to effectively understand their children's health status and take appropriate action.

[0066] 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.

[0067] In this invention, the server includes means for collecting information to acquire environmental information within an educational facility, means for using a generative AI model that predicts future health risks based on past data, and means for generating prompts to instruct the AI ​​model based on prompt sentences. This enables real-time prediction of health risks, and by efficiently integrating and analyzing environmental information and individual health information, rapid and appropriate health management can be achieved, allowing parents and teachers to take necessary actions in a timely manner.

[0068] "Educational facilities" refer to places where students learn and receive education, and include institutions such as schools and universities.

[0069] "Environmental information" refers to data about the environment inside or around an educational facility, including temperature, humidity, carbon dioxide concentration, and noise levels.

[0070] "Individual health information" refers to data about an individual student's health status, including vital data such as body temperature, heart rate, and activity level.

[0071] "Information gathering means" refers to devices or functions for acquiring environmental information and individual health information, and consists of sensors and applications.

[0072] "Information integration means" refers to a function that integrates collected environmental information and individual health information and processes them to convert them into a standard format.

[0073] "Machine learning methods" consist of algorithms and models used to analyze large amounts of data and predict health risks, and utilize generative AI models.

[0074] "Communication methods" refer to functions for notifying relevant parties of information from a server, and include email and push notifications.

[0075] "Information presentation means" refers to a function that visually displays information regarding students' health status and health risks, enabling health staff and other relevant parties to quickly grasp the situation.

[0076] "Information generation means" refers to a function that generates periodic reports to inform parents about the student's health status, including health trends and precautions.

[0077] "Information analysis tools" refer to functions that centrally analyze overall health information and create reports for facility managers, thereby supporting the formulation of improvement measures.

[0078] A "generative AI model" refers to an artificial intelligence model that performs predictions and generation based on large amounts of data, and is used to predict health risks.

[0079] "Prompt generation means" refers to a function that creates prompt statements to give specific instructions to an AI model.

[0080] This invention is a system for efficiently managing student health in educational facilities. It collects, integrates, and analyzes environmental information and individual health information, and provides this information to relevant parties. The system is configured and implemented as follows.

[0081] The server collects environmental information using sensors placed throughout the school. Specifically, it utilizes general-purpose sensors that measure temperature, humidity, carbon dioxide concentration, and noise levels. The server also uses MySQL® or PostgreSQL as database software to efficiently store and manage the information.

[0082] The system uses students' smartphones and wearable devices to acquire individual health information. For example, it collects body temperature, heart rate, and activity levels using devices such as Apple Watch and Fitbit, and transmits the data to a server via a dedicated application.

[0083] The server integrates the collected information and uses a generative AI model to predict health risks. The AI ​​model utilizes machine learning frameworks such as TENSORFLOW® and PyTorch, enabling predictions based on historical data. An example of a prompt used to instruct the AI ​​model would be, "Predict the risk of infectious diseases next week based on historical data."

[0084] The server notifies relevant parties based on the analysis results. For example, if carbon dioxide levels rise, it will send a notification recommending "ventilating the classroom." Notifications are sent via email or a dedicated application.

[0085] The server generates and periodically sends reports summarizing the student's health status to the user (parent). These reports include health trends and important points to consider, helping parents understand and act accordingly. They are available in digital format and delivered via email or a dedicated app.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The server collects environmental information.

[0089] The input consists of data such as temperature, humidity, carbon dioxide concentration, and noise level, acquired from sensors within the school. The server polls and receives this data at regular intervals. The output is raw environmental information that is temporarily stored in a database. This step includes the specific operation of the sensors transmitting the measured data to the server via wireless communication.

[0090] Step 2:

[0091] The device collects individual health information from students.

[0092] The input consists of body temperature, heart rate, and activity level measured by smartphones or wearable devices. The device collects this data either through a health monitoring app entered by the student or automatically. The output is individual health information uploaded to a server in a standard format. Specifically, the device acquires data from wearable devices using Bluetooth or Wi-Fi, processes it in the app, and then transmits it.

[0093] Step 3:

[0094] The server performs information integration.

[0095] The input consists of environmental and individual health information obtained from steps 1 and 2. The server converts them to a standard format (e.g., JSON) and integrates and stores them in a database. The output is a parseable integrated dataset. This step involves specific actions to integrate data in different formats and form a consistent dataset.

[0096] Step 4:

[0097] The server performs health risk predictions.

[0098] The input is an integrated dataset. The server utilizes a generative AI model to analyze the collected data and predict health risks. The output is a predictive report showing the percentage of infection risk and likelihood of illness in the following week. Specifically, it includes the generation of prompts used as instructions for the AI ​​model, and the AI's actions to perform analysis based on historical data.

[0099] Step 5:

[0100] The server sends a notification based on the analysis results.

[0101] The input is the health risk prediction report obtained in step 4. The server notifies relevant parties if the risk exceeds a threshold. The output is a warning message sent as an email or app notification. For example, a notification might be sent recommending actions such as opening windows because the carbon dioxide concentration in the classroom is high.

[0102] Step 6:

[0103] We provide health status reports to the user, who is also a parent.

[0104] The input is data showing students' health trends. The server uses this information to create periodic reports and send them to parents. The output is a PDF report provided via email or a dedicated app. Specifically, the report is generated using a template engine and distributed using an email sending system.

[0105] Step 7:

[0106] The server analyzes overall health information and generates a report for administrators.

[0107] The input is an integrated dataset for the entire educational facility. The server utilizes a generative AI model to aggregate the data and perform an overall analysis. The output is a report for administrators to evaluate and improve the facility's health management. Specifically, this includes creating a visual report using tables and graphs and providing it to the administrator as a PDF.

[0108] (Application Example 1)

[0109] 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."

[0110] In factories and facilities, it is crucial to monitor the safety of the work environment and the health of workers in real time to ensure a safe working environment. However, conventional technologies lacked sufficient mechanisms for quickly collecting environmental and individual health information, and for predicting and responding to risks based on that information. As a result, problems such as health problems among workers and decreased production efficiency have occurred.

[0111] 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.

[0112] In this invention, the server includes information gathering means for acquiring environmental information, information gathering means for acquiring individual health information of workers, information integration means for integrating and pre-processing the collected information, analysis means for analyzing the pre-processed information and predicting health risks, notification means for notifying or warning relevant parties based on the analysis results, and information processing means for evaluating safety. This enables real-time monitoring of the work environment and the health status of workers, facilitating rapid risk management and ensuring a safe work environment.

[0113] "Facilities" refers to all places and buildings where business or production activities take place, including factories and workshops.

[0114] "Environmental information" refers to data about the physical conditions within a facility, such as temperature, humidity, and noise levels.

[0115] "Information gathering means" refers to sensors and devices used to collect environmental information and individual health information.

[0116] A "worker" is a person who performs duties or tasks within a facility and is subject to safety management and health monitoring.

[0117] "Individual health information" refers to health data collected for each worker, including heart rate, body temperature, and activity level.

[0118] "Information integration means" refers to a means that has the function of centrally managing collected environmental information and individual health information and preparing it in a state that can be analyzed.

[0119] "Preprocessing" refers to the process of preparing data into a format suitable for analysis, such as standardizing data and imputing missing values, which is carried out using information integration tools.

[0120] "Analysis methods" refer to software and algorithms used to predict health risks based on collected information.

[0121] "Notification means" refers to methods and technologies for communicating necessary information to stakeholders and workers based on analysis results.

[0122] "Information processing means for evaluating safety" refers to processes and systems for evaluating the safety of the work environment based on collected and analyzed data.

[0123] The system implementing this invention ensures a safe working environment within a facility. The server continuously acquires environmental information such as temperature, humidity, and noise levels from multiple environmental sensors. In addition, it collects individual health information such as heart rate and activity level from wearable devices worn by workers. This data is integrated on the server, pre-processed, and then stored in a database.

[0124] The server analyzes integrated information using a generation AI model to assess health risks and work environment safety in real time. If risks exceed certain thresholds, it sends a warning to relevant personnel's terminals via a notification system, urging them to take specific action. This notification includes appropriate countermeasures and points to note.

[0125] The server also generates and provides administrators with comprehensive, data-driven workplace environment assessment reports. This enables facility managers to develop and implement effective, data-driven safety measures.

[0126] For example, if the temperature in the factory rises sharply, the server can analyze the data and, if an increase in a worker's heart rate is detected, immediately send a notification suggesting a break. This not only protects the health of workers but also helps maintain production efficiency.

[0127] An example of a prompt message for the generated AI model would be a specific instruction such as, "Current environmental data: temperature 35°C, humidity 70%. Worker A's heart rate: 100 bpm, work time: 4 hours. Predict the need for a break suggestion." This would allow the server to utilize the AI ​​model to respond efficiently and quickly.

[0128] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0129] Step 1:

[0130] The server continuously acquires environmental information such as temperature, humidity, and noise levels from environmental sensors installed within the facility. It receives data from environmental sensors as input and temporarily stores it in memory.

[0131] Step 2:

[0132] The server acquires individual health information, such as heart rate and activity level, from wearable devices worn by workers. It receives real-time data from the wearable devices as input and temporarily stores it in memory, similar to environmental information.

[0133] Step 3:

[0134] The server integrates collected environmental and individual health information and formats it into a format that can be stored in a database. A data conversion algorithm unifies the data in different formats and sends it to the destination database.

[0135] Step 4:

[0136] The server analyzes the integrated information using a generation AI model. The integrated data is passed to the AI ​​model as input, and the results of health risk and work environment safety assessments are obtained as output. In this process, the AI ​​model makes predictions based on the trained data.

[0137] Step 5:

[0138] Based on the analysis results, the server sends notifications to relevant parties' terminals to alert them and prompt them to take action if the risk exceeds a certain threshold. It generates an alert message as output and distributes it to the relevant parties' terminals. The message content may be dynamically generated.

[0139] Step 6:

[0140] The server analyzes the entire data and generates an analytical report for administrators. It uses the entire integrated dataset as input and produces a detailed report for the administrator dashboard as output. This report is visually represented using data visualization tools.

[0141] Step 7:

[0142] Based on the generated reports, users make adjustments to create a more efficient and secure work environment. They refer to the reports provided by administrators as input, notify the company of specific improvement measures, and put them into action.

[0143] 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.

[0144] The system of this invention, in addition to the conventional health management functions used in educational institutions, takes into account the emotional state of users, enabling more precise health risk prediction and individualized responses. This system integrates an emotion engine into various functions for integrating environmental data and individual student health data and analyzing them in real time.

[0145] First, the server receives data from environmental sensors installed in the classroom, as well as individual health data from students' health monitoring apps. This includes conventional physiological data such as body temperature, heart rate, and activity level. This data is integrated on the server and converted into a standardized format.

[0146] Next, the emotion engine of the present invention analyzes emotional data from the student's facial expressions, tone of voice, and speech content collected by the terminal, and transmits this data to the server. The emotion engine specifically detects emotional patterns that indicate signs of stress or anxiety and provides this information to the server.

[0147] The server processes this data holistically and uses AI models to predict health risks. The inclusion of emotional data enables a comprehensive health assessment, including students' mental health. For example, if a particular student shows signs of stress, additional information is added to comprehensively assess their physical and mental health risks, allowing for a more detailed understanding of their risk level.

[0148] Furthermore, based on the predicted health risks, the server sends customized health advice to relevant teachers and parents. For example, students who are experiencing low moods will receive suggestions for personalized relaxation techniques and activities to alleviate their symptoms.

[0149] Finally, the server conducts extensive analysis, including emotional data, to create a comprehensive health report for educational institutions. This report includes data showing trends in students' emotional states and their impact on health risks, guiding administrators to take appropriate action.

[0150] Thus, by combining an emotion engine, the present invention makes it possible to manage health in educational institutions more effectively.

[0151] The following describes the processing flow.

[0152] Step 1:

[0153] The server acquires environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed in each classroom within the school. This data is transmitted to the server in real time and stored in a database.

[0154] Step 2:

[0155] The device collects individual health data such as body temperature, heart rate, and activity level through a health monitoring app used by students. The device sends this data to a server to build an integrated dataset.

[0156] Step 3:

[0157] The device acquires emotional data by analyzing students' facial expressions and voices. Using an emotion engine, it identifies emotional states such as stress and anxiety and sends this information to a server. This emotional data is then integrated as part of a health assessment.

[0158] Step 4:

[0159] The server integrates collected environmental data, individual health data, and emotional data, cleans the data, and then uses an AI model to predict health risks. This allows for a comprehensive assessment of risks such as infectious disease risk and mental health status.

[0160] Step 5:

[0161] Based on the analysis results, the server generates alerts and health advice. In particular, if emotional data is included, relaxation techniques will be suggested for students at high stress-related risk. These notifications are sent to the devices of teachers and parents.

[0162] Step 6:

[0163] Parents, acting as users, receive reports on their child's health status sent from the server. These reports include recent emotional states and health trends, and provide advice on daily coping strategies.

[0164] Step 7:

[0165] The server analyzes health and emotional data across the entire school and generates a comprehensive health report for administrators. This report serves as a crucial guideline for the educational institution's health management strategy. The report details trends in emotional states and risk assessments.

[0166] (Example 2)

[0167] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0168] Traditional health management systems primarily rely on physiological data for assessment, but this has the drawback of potentially overlooking changes in mental health. Furthermore, real-time integration and analysis of acquired data are difficult, making it challenging to provide individualized support to each member. This often prevents rapid and accurate prediction and response to health risks. Additionally, limited information provision makes it difficult to quickly provide necessary countermeasures to stakeholders.

[0169] 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.

[0170] In this invention, the server includes information gathering means for acquiring environmental information, information gathering means for acquiring individual health information of its members, and information integration means for integrating and standardizing the acquired information. This enables the real-time integration of data containing both environmental and individual health information, and allows for highly accurate prediction of health risks based on the integrated data, as well as the provision of prompt and appropriate individual responses.

[0171] "Environmental information" refers to data that indicates the surrounding conditions, such as temperature, humidity, carbon dioxide concentration, and noise levels in classrooms, workspaces, and other areas.

[0172] "Information gathering means" refers to methods and technologies for acquiring data using various sensors and applications.

[0173] "Individual health information" refers to data that indicates the health status of each member, and includes, for example, body temperature, heart rate, and activity level.

[0174] "Information integration means" refers to the process of converting collected data into a single standardized format, making it available for continuous or intermittent use.

[0175] "Emotional analysis means" refers to a method of detecting the emotional state of members using facial expression analysis and voice analysis technologies, and processing that data.

[0176] "Analysis means" refers to methods or techniques for calculating and predicting health risks, etc., using collected standardized data.

[0177] "Notification means" refers to methods and systems for communicating necessary information to relevant parties based on analysis results.

[0178] "Means of providing advice" refers to methods or systems for providing reference information and instructions to support health-related work.

[0179] "Information provision methods" refer to means of distributing important health information to parents and other relevant parties, including email and apps.

[0180] "Evaluation tools" refer to functions or systems that analyze collected and integrated data and provide it to administrators in the form of reports or similar documents.

[0181] This invention is a system for comprehensively managing the health status of members within an educational institution. The system mainly consists of a server, terminals, and a generative AI model.

[0182] First, the server acquires environmental information from environmental sensors installed in classrooms and workspaces. This includes temperature, humidity, and noise levels. The server uses an API to continuously collect this environmental data via the internet. The server also acquires individual health information from wearable devices worn by students and dedicated health monitoring apps. This individual health information includes body temperature, heart rate, and activity level.

[0183] The terminal records the facial expressions and voices of the members in real time and performs emotion analysis. Using the camera and microphone built into the terminal, emotion analysis software analyzes the data using a machine learning model. Specific software used includes TensorFlow. The results of the emotion analysis are sent to the server as emotion categories, such as whether the member is experiencing stress.

[0184] The server integrates the received environmental information, individual health information, and emotional information, and converts it into a standardized data format (e.g., CSV format) using, for example, the Pandas library. This ensures that the data is stored in a consistent format and is easily analyzable.

[0185] Next, the server uses a generative AI model to analyze the rich data and predict health risks. This prediction is based on historical data and current physiological and emotional states, and the server prompts the prediction model with a message such as, "Input the student's current health data and emotional state, and predict the health risks for the next week." Based on this prompt, the AI ​​model performs a risk assessment and generates appropriate actions and advice for each member's health status.

[0186] Users receive health advice generated via the server through email or a dedicated app. This allows teachers and parents to take appropriate action based on the health status of their members. Furthermore, the server regularly generates detailed health reports for administrators, which supports overall health management.

[0187] For example, if a student's stress level rises during an exam period, a prompt such as "Please suggest ways to manage the student's stress during the exam period" can be input to the generating AI model to obtain specific stress management strategies.

[0188] This invention makes it possible to monitor the physiological and psychological health status of members in real time, enabling optimal health management.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The server acquires environmental data from environmental sensors installed in the classroom. It receives raw data from the sensors (e.g., temperature, humidity, noise level) as input and saves this data in a standardized format as output. Specifically, each data point is given the same timestamp and converted from JSON format to CSV format. This process involves collecting data every 30 seconds using the sensor's API and performing data conversion using the Python Pandas library.

[0192] Step 2:

[0193] The device transmits health data acquired from students to a server. It takes physiological data (e.g., body temperature, heart rate, activity level) from a wearable device as input and transmits this data to the server as output. This process involves a health monitoring app installed on the device receiving data via BLE connection and transmitting it to the server in real time via the HTTPS protocol.

[0194] Step 3:

[0195] The device acquires data on students' facial expressions and voices and performs emotion analysis. It takes video and audio data from the camera and microphone as input and sends the analyzed emotional state to the server as output. Specifically, a machine learning model using TensorFlow analyzes this raw data and classifies it into emotional categories such as stress and joy. This analysis result is provided to the server as data related to the psychological health status of the participants.

[0196] Step 4:

[0197] The server integrates all acquired data and predicts health risks using a generative AI model. Standardized physiological, environmental, and emotional data are used as input, and the output is each student's health risk level. This analysis uses the prompt "Input the student's current health data and emotional state, and predict their health risk for the next week" to the prediction model. The AI ​​model calculates the health risk, taking past health data into consideration.

[0198] Step 5:

[0199] The server notifies relevant parties based on the health risk prediction results. It receives predicted risk data as input and sends customized health advice to relevant parties (e.g., teachers, parents) as output. Specifically, it sends messages such as, "Mr. / Ms. XX needs activities that promote relaxation," via email or a dedicated app notification.

[0200] Step 6:

[0201] The server analyzes overall health data and generates health reports for administrators. Using all health data and risk assessment results as input, it produces a comprehensive health report in PDF format as output, including statistical trends and recommendations. The analysis results, including patterns highlighted by machine learning models, are regularly updated and available for administrator download.

[0202] (Application Example 2)

[0203] 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".

[0204] The problem that this invention aims to solve is to enable more detailed prediction of health risks and effective responses by considering individual emotional states in addition to conventional physiological data in health management within educational institutions. Conventional health management systems have relied solely on physiological data for evaluation, and therefore have not adequately covered mental health states, particularly emotional aspects such as stress and anxiety. This has resulted in difficulties in understanding and responding to the overall health status of students.

[0205] 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.

[0206] In this invention, the server includes an information gathering device for acquiring environmental information, an information gathering device for acquiring individual health information, an information integration device for integrating and pre-processing the collected information, an analysis device for analyzing the pre-processed information and predicting health risks, an emotion evaluation device for evaluating the emotional state by analyzing the user's facial expressions and voice, a notification device for notifying or warning relevant parties based on health risks and emotional state, an information provision device for supporting health activities, an information provision device for providing individual health status to guardians, and an analysis device for analyzing overall health data and emotional data and creating reports for administrators. This enables detailed health assessment, including the emotional state of students, and realizes more precise health risk prediction and individualized responses.

[0207] "Environmental information" refers to data that indicates the state of the learning environment within an educational institution, such as air quality, temperature, humidity, and noise level.

[0208] "Individual health information" refers to data indicating each student's physiological and physical condition, such as body temperature, heart rate, and activity level.

[0209] An "information gathering device" refers to equipment such as sensors and devices used to acquire environmental information and individual health information.

[0210] An "information integration device" is a system or software for integrating and pre-processing collected environmental information and individual health information.

[0211] An "analysis device" is hardware and software used to analyze pre-processed data and predict health risks.

[0212] An "emotion evaluation device" is a system that evaluates a user's emotional state by analyzing their facial expressions and voice.

[0213] A "notification device" is a device or system used to issue notifications or warnings to relevant parties based on analysis results.

[0214] An "information provision device" is a system for providing health information for the purpose of health activities and communicating information to parents and guardians.

[0215] An "analytical device" is software or a device used to analyze overall health and emotional data and generate reports for administrators.

[0216] To implement this invention, a health and emotional management system is required for educational institutions and security services. This system consists of three main elements: a server, terminals, and users.

[0217] The server is connected to data collection devices for acquiring environmental and individual health information. These devices include sensors and equipment that indicate the state of the learning environment, such as temperature sensors and heart rate monitors. The server integrates the collected data and performs preprocessing using an information integration device. This integrated data is then analyzed using a generative AI model to predict health risks for students and users.

[0218] The device works in conjunction with an emotion assessment device that analyzes the user's facial expressions and voice in real time. The device has a built-in camera and microphone, and uses image processing libraries such as OpenCV for facial expression analysis and speech recognition software (e.g., Google® Speech-to-Text) for voice analysis. This makes it possible to assess the user's emotional state.

[0219] The user receives appropriate feedback through a notification device based on the generated content. Notifications are based on health risks and emotional state and include suggestions for relaxation methods and activities. For example, a prompt message might appear such as, "Based on your current facial expression and tone of voice, your stress level is elevated. A 10-minute walk in a lush park is an effective way to relax. Why not give it a try?"

[0220] This system enables personalized support in educational institutions and on-site settings, allowing for a comprehensive assessment and improvement of users' physiological and mental health.

[0221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0222] Step 1:

[0223] The server receives environmental information (temperature, humidity, noise level, etc.) and individual health information (body temperature, heart rate, etc.) from the information collection device. Standardizing this data and unifying the format enables subsequent processing.

[0224] Step 2:

[0225] The server integrates standardized data using an information integration device and performs preprocessing. This includes data manipulation such as noise reduction and missing value imputation. As a result, the data is in a format suitable for analysis. This data is then passed on to the next analysis device.

[0226] Step 3:

[0227] The terminal processes real-time video and audio acquired from the user using an emotion evaluation device. It collects facial expression data with a camera, performs image analysis with OpenCV, and estimates the emotional state. Additionally, it collects audio with a microphone, converts the audio to text using speech recognition software, and analyzes the tone. The results of this analysis are then sent to a server.

[0228] Step 4:

[0229] The server inputs integrated health data and emotional data from devices into a generating AI model for data analysis. The model evaluates current health risks and potential emotional impacts. The analysis results are output as a risk assessment.

[0230] Step 5:

[0231] Users receive feedback via a notification device based on analysis results from the server. For example, if their stress level is high, a notification will be displayed with suggestions for relaxation methods and activities. This allows users to check their condition in real time and adjust their behavior accordingly.

[0232] Step 6:

[0233] Ultimately, the server uses an analytical instrument to aggregate health and emotional data from all users and generates a compiled report for administrators. This report allows educational institutions and facilities to understand the overall health status of their users and take appropriate action.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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".

[0250] The system of the present invention is designed to effectively manage health in educational institutions and integrates various data collection, analysis, and provision functions. Specific embodiments of the present invention are described below.

[0251] First, the server continuously collects environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed throughout the school. Meanwhile, terminals obtain individual health data such as body temperature, heart rate, and activity level from health monitoring apps used by students and send it to the server. This data is integrated on the server, the format of each data item is standardized, and it is stored in a database.

[0252] Next, the server uses the integrated dataset to predict health risks. It utilizes a generative AI model to predict the risk of infectious disease outbreaks and student health problems, generating visualized results. If the prediction results exceed a predetermined threshold, the server sends a warning or prompt to action to the relevant parties' terminals.

[0253] Furthermore, the terminals in the school infirmary display information on students with abnormal health conditions based on data from the server. This allows health staff to perform quick and appropriate triage. The terminals also provide information to facilitate referrals to medical institutions as needed.

[0254] For users who are parents, the server generates regular reports on the student's health status and sends them via email or a dedicated app. These reports include the student's health trends and important points to help parents take appropriate action.

[0255] Finally, the server centrally analyzes health data from across the school and generates a report for administrators. Administrators can use this report to develop and implement data-driven improvements to health management.

[0256] As described above, the system according to the present invention will revolutionize health management in educational institutions by effectively utilizing environmental data and individual health data.

[0257] The following describes the processing flow.

[0258] Step 1:

[0259] The server acquires environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed on campus. The acquired data is transmitted to a database in real time.

[0260] Step 2:

[0261] The device collects individual student health data, such as body temperature, heart rate, and activity level, from the health monitoring app used by the student and sends it to the server. The transmitted data is then integrated on the server.

[0262] Step 3:

[0263] The server converts the collected environmental and individual health data into a unified format and stores it in a database. During this process, it detects data anomalies and corrects them appropriately.

[0264] Step 4:

[0265] The server analyzes the integrated data and runs an AI model specifically designed to predict the likelihood of infectious disease outbreaks and health problems. This model assesses the risk by comparing it to historical data.

[0266] Step 5:

[0267] The server sends notifications to educational institution personnel based on AI-generated predictions. In particular, if an anomaly is detected, an immediate warning is sent to the terminal to prompt action.

[0268] Step 6:

[0269] The terminal displays student health information based on data transmitted from the server to assist health room staff with triage. If necessary, the server provides referral information to external medical institutions.

[0270] Step 7:

[0271] Parents, who are users, receive periodic reports from the server regarding their child's health. These reports include recent health trends and advice.

[0272] Step 8:

[0273] The server analyzes health data across the entire school and generates a comprehensive report for administrators. This report is delivered to administrators' terminals in a visual format and provides information to help improve health management.

[0274] (Example 1)

[0275] 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."

[0276] Traditionally, student health management in educational institutions relied on periodic health checkups and observations by teachers and staff, making it difficult to grasp students' health status in real time and thus hindering the rapid detection of early signs of infectious diseases or illness. Furthermore, there was a lack of efficient methods for integrating and analyzing environmental information with individual student health data, which hindered improvements in the accuracy of health management. Additionally, there was insufficient information provided to parents to effectively understand their children's health status and take appropriate action.

[0277] 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.

[0278] In this invention, the server includes means for collecting information to acquire environmental information within an educational facility, means for using a generative AI model that predicts future health risks based on past data, and means for generating prompts to instruct the AI ​​model based on prompt sentences. This enables real-time prediction of health risks, and by efficiently integrating and analyzing environmental information and individual health information, rapid and appropriate health management can be achieved, allowing parents and teachers to take necessary actions in a timely manner.

[0279] "Educational facilities" refer to places where students learn and receive education, and include institutions such as schools and universities.

[0280] "Environmental information" refers to data related to the environment inside or around an educational facility, including temperature, humidity, carbon dioxide concentration, noise level, etc.

[0281] "Individual health information" refers to data related to the health status of individual students, including vital data such as body temperature, heart rate, and activity level.

[0282] "Information collection means" refers to devices or functions for acquiring environmental information and individual health information, which are composed of sensors and applications.

[0283] "Information integration means" refers to the function of integrating the collected environmental information and individual health information and performing processing to convert it into a standard format.

[0284] "Machine learning means" refers to algorithms and models used to analyze a large amount of data and predict health risks from it, and utilizes generative AI models.

[0285] "Communication means" refers to the function of notifying information from the server to relevant parties, including email and push notifications.

[0286] "Information presentation means" refers to the function of visually displaying information related to students' health status and health risks so that healthcare staff and relevant parties can quickly grasp the situation.

[0287] "Information generation means" refers to the function of generating regular reports to report students' health status to guardians, including health trends and precautions.

[0288] "Information analysis means" refers to the function of intensively analyzing overall health information and creating reports for facility managers, and supports the formulation of improvement measures.

[0289] A "generative AI model" refers to an artificial intelligence model that performs predictions and generation based on large amounts of data, and is used to predict health risks.

[0290] "Prompt generation means" refers to a function that creates prompt statements to give specific instructions to an AI model.

[0291] This invention is a system for efficiently managing student health in educational facilities. It collects, integrates, and analyzes environmental and individual health information, and provides this information to relevant parties. The system is configured and implemented as follows:

[0292] The server collects environmental information using sensors placed throughout the school. Specifically, it utilizes general-purpose sensors that measure temperature, humidity, carbon dioxide concentration, and noise levels. The server also uses MySQL or PostgreSQL as database software to efficiently store and manage the information.

[0293] The system uses students' smartphones and wearable devices to acquire individual health information. For example, it collects body temperature, heart rate, and activity levels using devices such as Apple Watch and Fitbit, and transmits the data to a server via a dedicated application.

[0294] The server integrates the collected information and uses a generative AI model to predict health risks. The AI ​​model utilizes machine learning frameworks such as TensorFlow and PyTorch, enabling predictions based on historical data. An example of a prompt used to instruct the AI ​​model would be, "Predict the risk of infectious diseases next week based on historical data."

[0295] The server notifies relevant parties based on the analysis results. For example, if carbon dioxide levels rise, it will send a notification recommending "ventilating the classroom." Notifications are sent via email or a dedicated application.

[0296] The server generates and periodically sends reports summarizing the student's health status to the user (parent). These reports include health trends and important points to consider, helping parents understand and act accordingly. They are available in digital format and delivered via email or a dedicated app.

[0297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0298] Step 1:

[0299] The server collects environmental information.

[0300] The input consists of data such as temperature, humidity, carbon dioxide concentration, and noise level, acquired from sensors within the school. The server polls and receives this data at regular intervals. The output is raw environmental information that is temporarily stored in a database. This step includes the specific operation of the sensors transmitting the measured data to the server via wireless communication.

[0301] Step 2:

[0302] The device collects individual health information from students.

[0303] The input consists of body temperature, heart rate, and activity level measured by smartphones or wearable devices. The device collects this data either through a health monitoring app entered by the student or automatically. The output is individual health information uploaded to a server in a standard format. Specifically, the device acquires data from wearable devices using Bluetooth or Wi-Fi, processes it in the app, and then transmits it.

[0304] Step 3:

[0305] The server performs information integration.

[0306] The input is the environmental information and individual health information obtained from Step 1 and Step 2. The server converts them into a standard format (e.g., JSON format), integrates and stores them in the database. The output is an analyzable integrated dataset. This step includes specific operations to integrate data in different formats and form a consistent dataset.

[0307] Step 4:

[0308] The server performs a prediction of health risks.

[0309] The input is the integrated dataset. The server utilizes a generative AI model, analyzes the collected data, and predicts health risks. The output is a prediction report indicating the percentage of the risk of infectious diseases and the possibility of poor physical condition in the next week. Specifically, it includes the operation of generating a prompt text used as an instruction to the AI model and the AI performing an analysis based on past data.

[0310] Step 5:

[0311] The server sends a notification based on the analysis result.

[0312] The input is the health risk prediction report obtained in Step 4. The server notifies the relevant persons when the risk exceeds the threshold. The output is a warning message sent as an email or an app notification. For example, a notification recommending actions such as opening the window because the carbon dioxide concentration in the classroom is high is sent.

[0313] Step 6:

[0314] Provide a health status report to the guardian who is the user.

[0315] The input is data showing students' health trends. The server uses this information to create periodic reports and send them to parents. The output is a PDF report provided via email or a dedicated app. Specifically, the report is generated using a template engine and distributed using an email sending system.

[0316] Step 7:

[0317] The server analyzes overall health information and generates a report for administrators.

[0318] The input is an integrated dataset for the entire educational facility. The server utilizes a generative AI model to aggregate the data and perform an overall analysis. The output is a report for administrators to evaluate and improve the facility's health management. Specifically, this includes creating a visual report using tables and graphs and providing it to the administrator as a PDF.

[0319] (Application Example 1)

[0320] 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."

[0321] In factories and facilities, it is crucial to monitor the safety of the work environment and the health of workers in real time to ensure a safe working environment. However, conventional technologies lacked sufficient mechanisms for quickly collecting environmental and individual health information, and for predicting and responding to risks based on that information. As a result, problems such as health problems among workers and decreased production efficiency have occurred.

[0322] 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.

[0323] In this invention, the server includes information gathering means for acquiring environmental information, information gathering means for acquiring individual health information of workers, information integration means for integrating and pre-processing the collected information, analysis means for analyzing the pre-processed information and predicting health risks, notification means for notifying or warning relevant parties based on the analysis results, and information processing means for evaluating safety. This enables real-time monitoring of the work environment and the health status of workers, facilitating rapid risk management and ensuring a safe work environment.

[0324] "Facilities" refers to all places and buildings where business or production activities take place, including factories and workshops.

[0325] "Environmental information" refers to data about the physical conditions within a facility, such as temperature, humidity, and noise levels.

[0326] "Information gathering means" refers to sensors and devices used to collect environmental information and individual health information.

[0327] A "worker" is a person who performs duties or tasks within a facility and is subject to safety management and health monitoring.

[0328] "Individual health information" refers to health data collected for each worker, including heart rate, body temperature, and activity level.

[0329] "Information integration means" refers to a means that has the function of centrally managing collected environmental information and individual health information and preparing it in a state that can be analyzed.

[0330] "Preprocessing" refers to the process of preparing data into a format suitable for analysis, such as standardizing data and imputing missing values, which is carried out using information integration tools.

[0331] "Analysis methods" refer to software and algorithms used to predict health risks based on collected information.

[0332] "Notification means" refers to methods and technologies for communicating necessary information to stakeholders and workers based on analysis results.

[0333] "Information processing means for evaluating safety" refers to processes and systems for evaluating the safety of the work environment based on collected and analyzed data.

[0334] The system implementing this invention ensures a safe working environment within a facility. The server continuously acquires environmental information such as temperature, humidity, and noise levels from multiple environmental sensors. In addition, it collects individual health information such as heart rate and activity level from wearable devices worn by workers. This data is integrated on the server, pre-processed, and then stored in a database.

[0335] The server analyzes integrated information using a generation AI model to assess health risks and work environment safety in real time. If risks exceed certain thresholds, it sends a warning to relevant personnel's terminals via a notification system, urging them to take specific action. This notification includes appropriate countermeasures and points to note.

[0336] The server also generates and provides administrators with comprehensive, data-driven workplace environment assessment reports. This enables facility managers to develop and implement effective, data-driven safety measures.

[0337] For example, if the temperature in the factory rises sharply, the server can analyze the data and, if an increase in a worker's heart rate is detected, immediately send a notification suggesting a break. This not only protects the health of workers but also helps maintain production efficiency.

[0338] An example of a prompt message for the generated AI model would be a specific instruction such as, "Current environmental data: temperature 35°C, humidity 70%. Worker A's heart rate: 100 bpm, work time: 4 hours. Predict the need for a break suggestion." This would allow the server to utilize the AI ​​model to respond efficiently and quickly.

[0339] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0340] Step 1:

[0341] The server continuously acquires environmental information such as temperature, humidity, and noise levels from environmental sensors installed within the facility. It receives data from environmental sensors as input and temporarily stores it in memory.

[0342] Step 2:

[0343] The server acquires individual health information, such as heart rate and activity level, from wearable devices worn by workers. It receives real-time data from the wearable devices as input and temporarily stores it in memory, similar to environmental information.

[0344] Step 3:

[0345] The server integrates collected environmental and individual health information and formats it into a format that can be stored in a database. A data conversion algorithm unifies the data in different formats and sends it to the destination database.

[0346] Step 4:

[0347] The server analyzes the integrated information using a generation AI model. The integrated data is passed to the AI ​​model as input, and the results of health risk and work environment safety assessments are obtained as output. In this process, the AI ​​model makes predictions based on the trained data.

[0348] Step 5:

[0349] Based on the analysis results, the server sends notifications to relevant parties' terminals to alert them and prompt them to take action if the risk exceeds a certain threshold. It generates an alert message as output and distributes it to the relevant parties' terminals. The message content may be dynamically generated.

[0350] Step 6:

[0351] The server analyzes the entire data and generates an analytical report for administrators. It uses the entire integrated dataset as input and produces a detailed report for the administrator dashboard as output. This report is visually represented using data visualization tools.

[0352] Step 7:

[0353] Based on the generated reports, users make adjustments to create a more efficient and secure work environment. They refer to the reports provided by administrators as input, notify the company of specific improvement measures, and put them into action.

[0354] 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.

[0355] The system of this invention, in addition to the conventional health management functions used in educational institutions, takes into account the emotional state of users, enabling more precise health risk prediction and individualized responses. This system integrates an emotion engine into various functions for integrating environmental data and individual student health data and analyzing them in real time.

[0356] First, the server receives data from environmental sensors installed in the classroom, as well as individual health data from students' health monitoring apps. This includes conventional physiological data such as body temperature, heart rate, and activity level. This data is integrated on the server and converted into a standardized format.

[0357] Next, the emotion engine of the present invention analyzes emotional data from the student's facial expressions, tone of voice, and speech content collected by the terminal, and transmits this data to the server. The emotion engine specifically detects emotional patterns that indicate signs of stress or anxiety and provides this information to the server.

[0358] The server processes this data holistically and uses AI models to predict health risks. The inclusion of emotional data enables a comprehensive health assessment, including students' mental health. For example, if a particular student shows signs of stress, additional information is added to comprehensively assess their physical and mental health risks, allowing for a more detailed understanding of their risk level.

[0359] Furthermore, based on the predicted health risks, the server sends customized health advice to relevant teachers and parents. For example, students who are experiencing low moods will receive suggestions for personalized relaxation techniques and activities to alleviate their symptoms.

[0360] Finally, the server conducts extensive analysis, including emotional data, to create a comprehensive health report for educational institutions. This report includes data showing trends in students' emotional states and their impact on health risks, guiding administrators to take appropriate action.

[0361] Thus, by combining an emotion engine, the present invention makes it possible to manage health in educational institutions more effectively.

[0362] The following describes the processing flow.

[0363] Step 1:

[0364] The server acquires environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed in each classroom within the school. This data is transmitted to the server in real time and stored in a database.

[0365] Step 2:

[0366] The device collects individual health data such as body temperature, heart rate, and activity level through a health monitoring app used by students. The device sends this data to a server to build an integrated dataset.

[0367] Step 3:

[0368] The device acquires emotional data by analyzing students' facial expressions and voices. Using an emotion engine, it identifies emotional states such as stress and anxiety and sends this information to a server. This emotional data is then integrated as part of a health assessment.

[0369] Step 4:

[0370] The server integrates collected environmental data, individual health data, and emotional data, cleans the data, and then uses an AI model to predict health risks. This allows for a comprehensive assessment of risks such as infectious disease risk and mental health status.

[0371] Step 5:

[0372] Based on the analysis results, the server generates alerts and health advice. In particular, if emotional data is included, relaxation techniques will be suggested for students at high stress-related risk. These notifications are sent to the devices of teachers and parents.

[0373] Step 6:

[0374] Parents, acting as users, receive reports on their child's health status sent from the server. These reports include recent emotional states and health trends, and provide advice on daily coping strategies.

[0375] Step 7:

[0376] The server analyzes health and emotional data across the entire school and generates a comprehensive health report for administrators. This report serves as a crucial guideline for the educational institution's health management strategy. The report details trends in emotional states and risk assessments.

[0377] (Example 2)

[0378] 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".

[0379] Traditional health management systems primarily rely on physiological data for assessment, but this has the drawback of potentially overlooking changes in mental health. Furthermore, real-time integration and analysis of acquired data are difficult, making it challenging to provide individualized support to each member. This often prevents rapid and accurate prediction and response to health risks. Additionally, limited information provision makes it difficult to quickly provide necessary countermeasures to stakeholders.

[0380] 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.

[0381] In this invention, the server includes information gathering means for acquiring environmental information, information gathering means for acquiring individual health information of its members, and information integration means for integrating and standardizing the acquired information. This enables the real-time integration of data containing both environmental and individual health information, and allows for highly accurate prediction of health risks based on the integrated data, as well as the provision of prompt and appropriate individual responses.

[0382] "Environmental information" refers to data that indicates the surrounding conditions, such as temperature, humidity, carbon dioxide concentration, and noise levels in classrooms, workspaces, and other areas.

[0383] "Information gathering means" refers to methods and technologies for acquiring data using various sensors and applications.

[0384] "Individual health information" refers to data that indicates the health status of each member, and includes, for example, body temperature, heart rate, and activity level.

[0385] "Information integration means" refers to the process of converting collected data into a single standardized format, making it available for continuous or intermittent use.

[0386] "Emotional analysis means" refers to a method of detecting the emotional state of members using facial expression analysis and voice analysis technologies, and processing that data.

[0387] "Analysis means" refers to methods or techniques for calculating and predicting health risks, etc., using collected standardized data.

[0388] "Notification means" refers to methods and systems for communicating necessary information to relevant parties based on analysis results.

[0389] "Means of providing advice" refers to methods or systems for providing reference information and instructions to support health-related work.

[0390] "Information provision methods" refer to means of distributing important health information to parents and other relevant parties, including email and apps.

[0391] "Evaluation tools" refer to functions or systems that analyze collected and integrated data and provide it to administrators in the form of reports or similar documents.

[0392] This invention is a system for comprehensively managing the health status of members within an educational institution. The system mainly consists of a server, terminals, and a generative AI model.

[0393] First, the server acquires environmental information from environmental sensors installed in classrooms and workspaces. This includes temperature, humidity, and noise levels. The server uses an API to continuously collect this environmental data via the internet. The server also acquires individual health information from wearable devices worn by students and dedicated health monitoring apps. This individual health information includes body temperature, heart rate, and activity level.

[0394] The terminal records the facial expressions and voices of the members in real time and performs emotion analysis. Using the camera and microphone built into the terminal, emotion analysis software analyzes the data using a machine learning model. Specific software used includes TensorFlow. The results of the emotion analysis are sent to the server as emotion categories, such as whether the member is experiencing stress.

[0395] The server integrates the received environmental information, individual health information, and emotional information, and converts it into a standardized data format (e.g., CSV format) using, for example, the Pandas library. This ensures that the data is stored in a consistent format and is easily analyzable.

[0396] Next, the server uses a generative AI model to analyze the rich data and predict health risks. This prediction is based on historical data and current physiological and emotional states, and the server prompts the prediction model with a message such as, "Input the student's current health data and emotional state, and predict the health risks for the next week." Based on this prompt, the AI ​​model performs a risk assessment and generates appropriate actions and advice for each member's health status.

[0397] Users receive health advice generated via the server through email or a dedicated app. This allows teachers and parents to take appropriate action based on the health status of their members. Furthermore, the server regularly generates detailed health reports for administrators, which supports overall health management.

[0398] For example, if a student's stress level rises during an exam period, a prompt such as "Please suggest ways to manage the student's stress during the exam period" can be input to the generating AI model to obtain specific stress management strategies.

[0399] This invention makes it possible to monitor the physiological and psychological health status of members in real time, enabling optimal health management.

[0400] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0401] Step 1:

[0402] The server acquires environmental data from environmental sensors installed in the classroom. It receives raw data from the sensors (e.g., temperature, humidity, noise level) as input and saves this data in a standardized format as output. Specifically, each data point is given the same timestamp and converted from JSON format to CSV format. This process involves collecting data every 30 seconds using the sensor's API and performing data conversion using the Python Pandas library.

[0403] Step 2:

[0404] The device transmits health data acquired from students to a server. It takes physiological data (e.g., body temperature, heart rate, activity level) from a wearable device as input and transmits this data to the server as output. This process involves a health monitoring app installed on the device receiving data via BLE connection and transmitting it to the server in real time via the HTTPS protocol.

[0405] Step 3:

[0406] The device acquires data on students' facial expressions and voices and performs emotion analysis. It takes video and audio data from the camera and microphone as input and sends the analyzed emotional state to the server as output. Specifically, a machine learning model using TensorFlow analyzes this raw data and classifies it into emotional categories such as stress and joy. This analysis result is provided to the server as data related to the psychological health status of the participants.

[0407] Step 4:

[0408] The server integrates all acquired data and predicts health risks using a generative AI model. Standardized physiological, environmental, and emotional data are used as input, and the output is each student's health risk level. This analysis uses the prompt "Input the student's current health data and emotional state, and predict their health risk for the next week" to the prediction model. The AI ​​model calculates the health risk, taking past health data into consideration.

[0409] Step 5:

[0410] The server notifies relevant parties based on the health risk prediction results. It receives predicted risk data as input and sends customized health advice to relevant parties (e.g., teachers, parents) as output. Specifically, it sends messages such as, "Mr. / Ms. XX needs activities that promote relaxation," via email or a dedicated app notification.

[0411] Step 6:

[0412] The server analyzes overall health data and generates health reports for administrators. Using all health data and risk assessment results as input, it produces a comprehensive health report in PDF format as output, including statistical trends and recommendations. The analysis results, including patterns highlighted by machine learning models, are regularly updated and available for administrator download.

[0413] (Application Example 2)

[0414] 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."

[0415] The problem that this invention aims to solve is to enable more detailed prediction of health risks and effective responses by considering individual emotional states in addition to conventional physiological data in health management within educational institutions. Conventional health management systems have relied solely on physiological data for evaluation, and therefore have not adequately covered mental health states, particularly emotional aspects such as stress and anxiety. This has resulted in difficulties in understanding and responding to the overall health status of students.

[0416] 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.

[0417] In this invention, the server includes an information gathering device for acquiring environmental information, an information gathering device for acquiring individual health information, an information integration device for integrating and pre-processing the collected information, an analysis device for analyzing the pre-processed information and predicting health risks, an emotion evaluation device for evaluating the emotional state by analyzing the user's facial expressions and voice, a notification device for notifying or warning relevant parties based on health risks and emotional state, an information provision device for supporting health activities, an information provision device for providing individual health status to guardians, and an analysis device for analyzing overall health data and emotional data and creating reports for administrators. This enables detailed health assessment, including the emotional state of students, and realizes more precise health risk prediction and individualized responses.

[0418] "Environmental information" refers to data that indicates the state of the learning environment within an educational institution, such as air quality, temperature, humidity, and noise level.

[0419] "Individual health information" refers to data indicating each student's physiological and physical condition, such as body temperature, heart rate, and activity level.

[0420] An "information gathering device" refers to equipment such as sensors and devices used to acquire environmental information and individual health information.

[0421] An "information integration device" is a system or software for integrating and pre-processing collected environmental information and individual health information.

[0422] An "analysis device" is hardware and software used to analyze pre-processed data and predict health risks.

[0423] An "emotion evaluation device" is a system that evaluates a user's emotional state by analyzing their facial expressions and voice.

[0424] A "notification device" is a device or system used to issue notifications or warnings to relevant parties based on analysis results.

[0425] An "information provision device" is a system for providing health information for the purpose of health activities and communicating information to parents and guardians.

[0426] An "analytical device" is software or a device used to analyze overall health and emotional data and generate reports for administrators.

[0427] To implement this invention, a health and emotional management system is required for educational institutions and security services. This system consists of three main elements: a server, terminals, and users.

[0428] The server is connected to data collection devices for acquiring environmental and individual health information. These devices include sensors and equipment that indicate the state of the learning environment, such as temperature sensors and heart rate monitors. The server integrates the collected data and performs preprocessing using an information integration device. This integrated data is then analyzed using a generative AI model to predict health risks for students and users.

[0429] The device works in conjunction with an emotion assessment device that analyzes the user's facial expressions and voice in real time. The device has a built-in camera and microphone, and uses image processing libraries such as OpenCV for facial expression analysis and speech recognition software (e.g., Google Speech-to-Text) for voice analysis. This enables the assessment of the user's emotional state.

[0430] The user receives appropriate feedback through a notification device based on the generated content. Notifications are based on health risks and emotional state and include suggestions for relaxation methods and activities. For example, a prompt message might appear such as, "Based on your current facial expression and tone of voice, your stress level is elevated. A 10-minute walk in a lush park is an effective way to relax. Why not give it a try?"

[0431] This system enables personalized support in educational institutions and on-site settings, allowing for a comprehensive assessment and improvement of users' physiological and mental health.

[0432] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0433] Step 1:

[0434] The server receives environmental information (temperature, humidity, noise level, etc.) and individual health information (body temperature, heart rate, etc.) from the information collection device. Standardizing this data and unifying the format enables subsequent processing.

[0435] Step 2:

[0436] The server integrates standardized data using an information integration device and performs preprocessing. This includes data manipulation such as noise reduction and missing value imputation. As a result, the data is in a format suitable for analysis. This data is then passed on to the next analysis device.

[0437] Step 3:

[0438] The terminal processes real-time video and audio acquired from the user using an emotion evaluation device. It collects facial expression data with a camera, performs image analysis with OpenCV, and estimates the emotional state. Additionally, it collects audio with a microphone, converts the audio to text using speech recognition software, and analyzes the tone. The results of this analysis are then sent to a server.

[0439] Step 4:

[0440] The server inputs integrated health data and emotional data from devices into a generating AI model for data analysis. The model evaluates current health risks and potential emotional impacts. The analysis results are output as a risk assessment.

[0441] Step 5:

[0442] Users receive feedback via a notification device based on analysis results from the server. For example, if their stress level is high, a notification will be displayed with suggestions for relaxation methods and activities. This allows users to check their condition in real time and adjust their behavior accordingly.

[0443] Step 6:

[0444] Ultimately, the server uses an analytical instrument to aggregate health and emotional data from all users and generates a compiled report for administrators. This report allows educational institutions and facilities to understand the overall health status of their users and take appropriate action.

[0445] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

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

[0448] [Third Embodiment]

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

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

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

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

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

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

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

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

[0457] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0461] The system of the present invention is designed to effectively manage health in educational institutions and integrates various data collection, analysis, and provision functions. Specific embodiments of the present invention are described below.

[0462] First, the server continuously collects environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed throughout the school. Meanwhile, terminals obtain individual health data such as body temperature, heart rate, and activity level from health monitoring apps used by students and send it to the server. This data is integrated on the server, the format of each data item is standardized, and it is stored in a database.

[0463] Next, the server uses the integrated dataset to predict health risks. It utilizes a generative AI model to predict the risk of infectious disease outbreaks and student health problems, generating visualized results. If the prediction results exceed a predetermined threshold, the server sends a warning or prompt to action to the relevant parties' terminals.

[0464] Furthermore, the terminals in the school infirmary display information on students with abnormal health conditions based on data from the server. This allows health staff to perform quick and appropriate triage. The terminals also provide information to facilitate referrals to medical institutions as needed.

[0465] For users who are parents, the server generates regular reports on the student's health status and sends them via email or a dedicated app. These reports include the student's health trends and important points to help parents take appropriate action.

[0466] Finally, the server centrally analyzes health data from across the school and generates a report for administrators. Administrators can use this report to develop and implement data-driven improvements to health management.

[0467] As described above, the system according to the present invention will revolutionize health management in educational institutions by effectively utilizing environmental data and individual health data.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The server acquires environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed on campus. The acquired data is transmitted to a database in real time.

[0471] Step 2:

[0472] The device collects individual student health data, such as body temperature, heart rate, and activity level, from the health monitoring app used by the student and sends it to the server. The transmitted data is then integrated on the server.

[0473] Step 3:

[0474] The server converts the collected environmental and individual health data into a unified format and stores it in a database. During this process, it detects data anomalies and corrects them appropriately.

[0475] Step 4:

[0476] The server analyzes the integrated data and runs an AI model specifically designed to predict the likelihood of infectious disease outbreaks and health problems. This model assesses the risk by comparing it to historical data.

[0477] Step 5:

[0478] The server sends notifications to educational institution personnel based on AI-generated predictions. In particular, if an anomaly is detected, an immediate warning is sent to the terminal to prompt action.

[0479] Step 6:

[0480] The terminal displays student health information based on data transmitted from the server to assist health room staff with triage. If necessary, the server provides referral information to external medical institutions.

[0481] Step 7:

[0482] Parents, who are users, receive periodic reports from the server regarding their child's health. These reports include recent health trends and advice.

[0483] Step 8:

[0484] The server analyzes health data across the entire school and generates a comprehensive report for administrators. This report is delivered to administrators' terminals in a visual format and provides information to help improve health management.

[0485] (Example 1)

[0486] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] Traditionally, student health management in educational institutions relied on periodic health checkups and observations by teachers and staff, making it difficult to grasp students' health status in real time and thus hindering the rapid detection of early signs of infectious diseases or illness. Furthermore, there was a lack of efficient methods for integrating and analyzing environmental information with individual student health data, which hindered improvements in the accuracy of health management. Additionally, there was insufficient information provided to parents to effectively understand their children's health status and take appropriate action.

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

[0489] In this invention, the server includes means for collecting information to acquire environmental information within an educational facility, means for using a generative AI model that predicts future health risks based on past data, and means for generating prompts to instruct the AI ​​model based on prompt sentences. This enables real-time prediction of health risks, and by efficiently integrating and analyzing environmental information and individual health information, rapid and appropriate health management can be achieved, allowing parents and teachers to take necessary actions in a timely manner.

[0490] "Educational facilities" refer to places where students learn and receive education, and include institutions such as schools and universities.

[0491] "Environmental information" refers to data about the environment inside or around an educational facility, including temperature, humidity, carbon dioxide concentration, and noise levels.

[0492] "Individual health information" refers to data about an individual student's health status, including vital data such as body temperature, heart rate, and activity level.

[0493] "Information gathering means" refers to devices or functions for acquiring environmental information and individual health information, and consists of sensors and applications.

[0494] "Information integration means" refers to a function that integrates collected environmental information and individual health information and processes them to convert them into a standard format.

[0495] "Machine learning methods" consist of algorithms and models used to analyze large amounts of data and predict health risks, and utilize generative AI models.

[0496] "Communication methods" refer to functions for notifying relevant parties of information from a server, and include email and push notifications.

[0497] "Information presentation means" refers to a function that visually displays information regarding students' health status and health risks, enabling health staff and other relevant parties to quickly grasp the situation.

[0498] "Information generation means" refers to a function that generates periodic reports to inform parents about the student's health status, including health trends and precautions.

[0499] "Information analysis tools" refer to functions that centrally analyze overall health information and create reports for facility managers, thereby supporting the formulation of improvement measures.

[0500] A "generative AI model" refers to an artificial intelligence model that performs predictions and generation based on large amounts of data, and is used to predict health risks.

[0501] "Prompt generation means" refers to a function that creates prompt statements to give specific instructions to an AI model.

[0502] This invention is a system for efficiently managing student health in educational facilities. It collects, integrates, and analyzes environmental and individual health information, and provides this information to relevant parties. The system is configured and implemented as follows:

[0503] The server collects environmental information using sensors placed throughout the school. Specifically, it utilizes general-purpose sensors that measure temperature, humidity, carbon dioxide concentration, and noise levels. The server also uses MySQL or PostgreSQL as database software to efficiently store and manage the information.

[0504] The system uses students' smartphones and wearable devices to acquire individual health information. For example, it collects body temperature, heart rate, and activity levels using devices such as Apple Watch and Fitbit, and transmits the data to a server via a dedicated application.

[0505] The server integrates the collected information and uses a generative AI model to predict health risks. The AI ​​model utilizes machine learning frameworks such as TensorFlow and PyTorch, enabling predictions based on historical data. An example of a prompt used to instruct the AI ​​model would be, "Predict the risk of infectious diseases next week based on historical data."

[0506] The server notifies relevant parties based on the analysis results. For example, if carbon dioxide levels rise, it will send a notification recommending "ventilating the classroom." Notifications are sent via email or a dedicated application.

[0507] The server generates and periodically sends reports summarizing the student's health status to the user (parent). These reports include health trends and important points to consider, helping parents understand and act accordingly. They are available in digital format and delivered via email or a dedicated app.

[0508] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0509] Step 1:

[0510] The server collects environmental information.

[0511] The input consists of data such as temperature, humidity, carbon dioxide concentration, and noise level, acquired from sensors within the school. The server polls and receives this data at regular intervals. The output is raw environmental information that is temporarily stored in a database. This step includes the specific operation of the sensors transmitting the measured data to the server via wireless communication.

[0512] Step 2:

[0513] The device collects individual health information from students.

[0514] The input consists of body temperature, heart rate, and activity level measured by smartphones or wearable devices. The device collects this data either through a health monitoring app entered by the student or automatically. The output is individual health information uploaded to a server in a standard format. Specifically, the device acquires data from wearable devices using Bluetooth or Wi-Fi, processes it in the app, and then transmits it.

[0515] Step 3:

[0516] The server performs information integration.

[0517] The input consists of environmental and individual health information obtained from steps 1 and 2. The server converts them to a standard format (e.g., JSON) and integrates and stores them in a database. The output is a parseable integrated dataset. This step involves specific actions to integrate data in different formats and form a consistent dataset.

[0518] Step 4:

[0519] The server performs health risk predictions.

[0520] The input is an integrated dataset. The server utilizes a generative AI model to analyze the collected data and predict health risks. The output is a predictive report showing the percentage of infection risk and likelihood of illness in the following week. Specifically, it includes the generation of prompts used as instructions for the AI ​​model, and the AI's actions to perform analysis based on historical data.

[0521] Step 5:

[0522] The server sends a notification based on the analysis results.

[0523] The input is the health risk prediction report obtained in step 4. The server notifies relevant parties if the risk exceeds a threshold. The output is a warning message sent as an email or app notification. For example, a notification might be sent recommending actions such as opening windows because the carbon dioxide concentration in the classroom is high.

[0524] Step 6:

[0525] We provide health status reports to the user, who is also a parent.

[0526] The input is data showing students' health trends. The server uses this information to create periodic reports and send them to parents. The output is a PDF report provided via email or a dedicated app. Specifically, the report is generated using a template engine and distributed using an email sending system.

[0527] Step 7:

[0528] The server analyzes overall health information and generates a report for administrators.

[0529] The input is an integrated dataset for the entire educational facility. The server utilizes a generative AI model to aggregate the data and perform an overall analysis. The output is a report for administrators to evaluate and improve the facility's health management. Specifically, this includes creating a visual report using tables and graphs and providing it to the administrator as a PDF.

[0530] (Application Example 1)

[0531] 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."

[0532] In factories and facilities, it is crucial to monitor the safety of the work environment and the health of workers in real time to ensure a safe working environment. However, conventional technologies lacked sufficient mechanisms for quickly collecting environmental and individual health information, and for predicting and responding to risks based on that information. As a result, problems such as health problems among workers and decreased production efficiency have occurred.

[0533] 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.

[0534] In this invention, the server includes information gathering means for acquiring environmental information, information gathering means for acquiring individual health information of workers, information integration means for integrating and pre-processing the collected information, analysis means for analyzing the pre-processed information and predicting health risks, notification means for notifying or warning relevant parties based on the analysis results, and information processing means for evaluating safety. This enables real-time monitoring of the work environment and the health status of workers, facilitating rapid risk management and ensuring a safe work environment.

[0535] "Facilities" refers to all places and buildings where business or production activities take place, including factories and workshops.

[0536] "Environmental information" refers to data about the physical conditions within a facility, such as temperature, humidity, and noise levels.

[0537] "Information gathering means" refers to sensors and devices used to collect environmental information and individual health information.

[0538] A "worker" is a person who performs duties or tasks within a facility and is subject to safety management and health monitoring.

[0539] "Individual health information" refers to health data collected for each worker, including heart rate, body temperature, and activity level.

[0540] "Information integration means" refers to a means that has the function of centrally managing collected environmental information and individual health information and preparing it in a state that can be analyzed.

[0541] "Preprocessing" refers to the process of preparing data into a format suitable for analysis, such as standardizing data and imputing missing values, which is carried out using information integration tools.

[0542] "Analysis methods" refer to software and algorithms used to predict health risks based on collected information.

[0543] "Notification means" refers to methods and technologies for communicating necessary information to stakeholders and workers based on analysis results.

[0544] "Information processing means for evaluating safety" refers to processes and systems for evaluating the safety of the work environment based on collected and analyzed data.

[0545] The system implementing this invention ensures a safe working environment within a facility. The server continuously acquires environmental information such as temperature, humidity, and noise levels from multiple environmental sensors. In addition, it collects individual health information such as heart rate and activity level from wearable devices worn by workers. This data is integrated on the server, pre-processed, and then stored in a database.

[0546] The server analyzes integrated information using a generation AI model to assess health risks and work environment safety in real time. If risks exceed certain thresholds, it sends a warning to relevant personnel's terminals via a notification system, urging them to take specific action. This notification includes appropriate countermeasures and points to note.

[0547] The server also generates and provides administrators with comprehensive, data-driven workplace environment assessment reports. This enables facility managers to develop and implement effective, data-driven safety measures.

[0548] For example, if the temperature in the factory rises sharply, the server can analyze the data and, if an increase in a worker's heart rate is detected, immediately send a notification suggesting a break. This not only protects the health of workers but also helps maintain production efficiency.

[0549] An example of a prompt message for the generated AI model would be a specific instruction such as, "Current environmental data: temperature 35°C, humidity 70%. Worker A's heart rate: 100 bpm, work time: 4 hours. Predict the need for a break suggestion." This would allow the server to utilize the AI ​​model to respond efficiently and quickly.

[0550] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0551] Step 1:

[0552] The server continuously acquires environmental information such as temperature, humidity, and noise levels from environmental sensors installed within the facility. It receives data from environmental sensors as input and temporarily stores it in memory.

[0553] Step 2:

[0554] The server acquires individual health information, such as heart rate and activity level, from wearable devices worn by workers. It receives real-time data from the wearable devices as input and temporarily stores it in memory, similar to environmental information.

[0555] Step 3:

[0556] The server integrates collected environmental and individual health information and formats it into a format that can be stored in a database. A data conversion algorithm unifies the data in different formats and sends it to the destination database.

[0557] Step 4:

[0558] The server analyzes the integrated information using a generation AI model. The integrated data is passed to the AI ​​model as input, and the results of health risk and work environment safety assessments are obtained as output. In this process, the AI ​​model makes predictions based on the trained data.

[0559] Step 5:

[0560] Based on the analysis results, the server sends notifications to relevant parties' terminals to alert them and prompt them to take action if the risk exceeds a certain threshold. It generates an alert message as output and distributes it to the relevant parties' terminals. The message content may be dynamically generated.

[0561] Step 6:

[0562] The server analyzes the entire data and generates an analytical report for administrators. It uses the entire integrated dataset as input and produces a detailed report for the administrator dashboard as output. This report is visually represented using data visualization tools.

[0563] Step 7:

[0564] Based on the generated reports, users make adjustments to create a more efficient and secure work environment. They refer to the reports provided by administrators as input, notify the company of specific improvement measures, and put them into action.

[0565] 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.

[0566] The system of this invention, in addition to the conventional health management functions used in educational institutions, takes into account the emotional state of users, enabling more precise health risk prediction and individualized responses. This system integrates an emotion engine into various functions for integrating environmental data and individual student health data and analyzing them in real time.

[0567] First, the server receives data from environmental sensors installed in the classroom, as well as individual health data from students' health monitoring apps. This includes conventional physiological data such as body temperature, heart rate, and activity level. This data is integrated on the server and converted into a standardized format.

[0568] Next, the emotion engine of the present invention analyzes emotional data from the student's facial expressions, tone of voice, and speech content collected by the terminal, and transmits this data to the server. The emotion engine specifically detects emotional patterns that indicate signs of stress or anxiety and provides this information to the server.

[0569] The server processes this data holistically and uses AI models to predict health risks. The inclusion of emotional data enables a comprehensive health assessment, including students' mental health. For example, if a particular student shows signs of stress, additional information is added to comprehensively assess their physical and mental health risks, allowing for a more detailed understanding of their risk level.

[0570] Furthermore, based on the predicted health risks, the server sends customized health advice to relevant teachers and parents. For example, students who are experiencing low moods will receive suggestions for personalized relaxation techniques and activities to alleviate their symptoms.

[0571] Finally, the server conducts extensive analysis, including emotional data, to create a comprehensive health report for educational institutions. This report includes data showing trends in students' emotional states and their impact on health risks, guiding administrators to take appropriate action.

[0572] Thus, by combining an emotion engine, the present invention makes it possible to manage health in educational institutions more effectively.

[0573] The following describes the processing flow.

[0574] Step 1:

[0575] The server acquires environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed in each classroom within the school. This data is transmitted to the server in real time and stored in a database.

[0576] Step 2:

[0577] The device collects individual health data such as body temperature, heart rate, and activity level through a health monitoring app used by students. The device sends this data to a server to build an integrated dataset.

[0578] Step 3:

[0579] The device acquires emotional data by analyzing students' facial expressions and voices. Using an emotion engine, it identifies emotional states such as stress and anxiety and sends this information to a server. This emotional data is then integrated as part of a health assessment.

[0580] Step 4:

[0581] The server integrates collected environmental data, individual health data, and emotional data, cleans the data, and then uses an AI model to predict health risks. This allows for a comprehensive assessment of risks such as infectious disease risk and mental health status.

[0582] Step 5:

[0583] Based on the analysis results, the server generates alerts and health advice. In particular, if emotional data is included, relaxation techniques will be suggested for students at high stress-related risk. These notifications are sent to the devices of teachers and parents.

[0584] Step 6:

[0585] Parents, acting as users, receive reports on their child's health status sent from the server. These reports include recent emotional states and health trends, and provide advice on daily coping strategies.

[0586] Step 7:

[0587] The server analyzes health and emotional data across the entire school and generates a comprehensive health report for administrators. This report serves as a crucial guideline for the educational institution's health management strategy. The report details trends in emotional states and risk assessments.

[0588] (Example 2)

[0589] 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."

[0590] Traditional health management systems primarily rely on physiological data for assessment, but this has the drawback of potentially overlooking changes in mental health. Furthermore, real-time integration and analysis of acquired data are difficult, making it challenging to provide individualized support to each member. This often prevents rapid and accurate prediction and response to health risks. Additionally, limited information provision makes it difficult to quickly provide necessary countermeasures to stakeholders.

[0591] 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.

[0592] In this invention, the server includes information gathering means for acquiring environmental information, information gathering means for acquiring individual health information of its members, and information integration means for integrating and standardizing the acquired information. This enables the real-time integration of data containing both environmental and individual health information, and allows for highly accurate prediction of health risks based on the integrated data, as well as the provision of prompt and appropriate individual responses.

[0593] "Environmental information" refers to data that indicates the surrounding conditions, such as temperature, humidity, carbon dioxide concentration, and noise levels in classrooms, workspaces, and other areas.

[0594] "Information gathering means" refers to methods and technologies for acquiring data using various sensors and applications.

[0595] "Individual health information" refers to data that indicates the health status of each member, and includes, for example, body temperature, heart rate, and activity level.

[0596] "Information integration means" refers to the process of converting collected data into a single standardized format, making it available for continuous or intermittent use.

[0597] "Emotional analysis means" refers to a method of detecting the emotional state of members using facial expression analysis and voice analysis technologies, and processing that data.

[0598] "Analysis means" refers to methods or techniques for calculating and predicting health risks, etc., using collected standardized data.

[0599] "Notification means" refers to methods and systems for communicating necessary information to relevant parties based on analysis results.

[0600] "Means of providing advice" refers to methods or systems for providing reference information and instructions to support health-related work.

[0601] "Information provision methods" refer to means of distributing important health information to parents and other relevant parties, including email and apps.

[0602] "Evaluation tools" refer to functions or systems that analyze collected and integrated data and provide it to administrators in the form of reports or similar documents.

[0603] This invention is a system for comprehensively managing the health status of members within an educational institution. The system mainly consists of a server, terminals, and a generative AI model.

[0604] First, the server acquires environmental information from environmental sensors installed in classrooms and workspaces. This includes temperature, humidity, and noise levels. The server uses an API to continuously collect this environmental data via the internet. The server also acquires individual health information from wearable devices worn by students and dedicated health monitoring apps. This individual health information includes body temperature, heart rate, and activity level.

[0605] The terminal records the facial expressions and voices of the members in real time and performs emotion analysis. Using the camera and microphone built into the terminal, emotion analysis software analyzes the data using a machine learning model. Specific software used includes TensorFlow. The results of the emotion analysis are sent to the server as emotion categories, such as whether the member is experiencing stress.

[0606] The server integrates the received environmental information, individual health information, and emotional information, and converts it into a standardized data format (e.g., CSV format) using, for example, the Pandas library. This ensures that the data is stored in a consistent format and is easily analyzable.

[0607] Next, the server uses a generative AI model to analyze the rich data and predict health risks. This prediction is based on historical data and current physiological and emotional states, and the server prompts the prediction model with a message such as, "Input the student's current health data and emotional state, and predict the health risks for the next week." Based on this prompt, the AI ​​model performs a risk assessment and generates appropriate actions and advice for each member's health status.

[0608] Users receive health advice generated via the server through email or a dedicated app. This allows teachers and parents to take appropriate action based on the health status of their members. Furthermore, the server regularly generates detailed health reports for administrators, which supports overall health management.

[0609] For example, if a student's stress level rises during an exam period, a prompt such as "Please suggest ways to manage the student's stress during the exam period" can be input to the generating AI model to obtain specific stress management strategies.

[0610] This invention makes it possible to monitor the physiological and psychological health status of members in real time, enabling optimal health management.

[0611] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0612] Step 1:

[0613] The server acquires environmental data from environmental sensors installed in the classroom. It receives raw data from the sensors (e.g., temperature, humidity, noise level) as input and saves this data in a standardized format as output. Specifically, each data point is given the same timestamp and converted from JSON format to CSV format. This process involves collecting data every 30 seconds using the sensor's API and performing data conversion using the Python Pandas library.

[0614] Step 2:

[0615] The device transmits health data acquired from students to a server. It takes physiological data (e.g., body temperature, heart rate, activity level) from a wearable device as input and transmits this data to the server as output. This process involves a health monitoring app installed on the device receiving data via BLE connection and transmitting it to the server in real time via the HTTPS protocol.

[0616] Step 3:

[0617] The device acquires data on students' facial expressions and voices and performs emotion analysis. It takes video and audio data from the camera and microphone as input and sends the analyzed emotional state to the server as output. Specifically, a machine learning model using TensorFlow analyzes this raw data and classifies it into emotional categories such as stress and joy. This analysis result is provided to the server as data related to the psychological health status of the participants.

[0618] Step 4:

[0619] The server integrates all acquired data and predicts health risks using a generative AI model. Standardized physiological, environmental, and emotional data are used as input, and the output is each student's health risk level. This analysis uses the prompt "Input the student's current health data and emotional state, and predict their health risk for the next week" to the prediction model. The AI ​​model calculates the health risk, taking past health data into consideration.

[0620] Step 5:

[0621] The server notifies relevant parties based on the health risk prediction results. It receives predicted risk data as input and sends customized health advice to relevant parties (e.g., teachers, parents) as output. Specifically, it sends messages such as, "Mr. / Ms. XX needs activities that promote relaxation," via email or a dedicated app notification.

[0622] Step 6:

[0623] The server analyzes overall health data and generates health reports for administrators. Using all health data and risk assessment results as input, it produces a comprehensive health report in PDF format as output, including statistical trends and recommendations. The analysis results, including patterns highlighted by machine learning models, are regularly updated and available for administrator download.

[0624] (Application Example 2)

[0625] 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."

[0626] The problem that this invention aims to solve is to enable more detailed prediction of health risks and effective responses by considering individual emotional states in addition to conventional physiological data in health management within educational institutions. Conventional health management systems have relied solely on physiological data for evaluation, and therefore have not adequately covered mental health states, particularly emotional aspects such as stress and anxiety. This has resulted in difficulties in understanding and responding to the overall health status of students.

[0627] 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.

[0628] In this invention, the server includes an information gathering device for acquiring environmental information, an information gathering device for acquiring individual health information, an information integration device for integrating and pre-processing the collected information, an analysis device for analyzing the pre-processed information and predicting health risks, an emotion evaluation device for evaluating the emotional state by analyzing the user's facial expressions and voice, a notification device for notifying or warning relevant parties based on health risks and emotional state, an information provision device for supporting health activities, an information provision device for providing individual health status to guardians, and an analysis device for analyzing overall health data and emotional data and creating reports for administrators. This enables detailed health assessment, including the emotional state of students, and realizes more precise health risk prediction and individualized responses.

[0629] "Environmental information" refers to data that indicates the state of the learning environment within an educational institution, such as air quality, temperature, humidity, and noise level.

[0630] "Individual health information" refers to data indicating each student's physiological and physical condition, such as body temperature, heart rate, and activity level.

[0631] An "information gathering device" refers to equipment such as sensors and devices used to acquire environmental information and individual health information.

[0632] An "information integration device" is a system or software for integrating and pre-processing collected environmental information and individual health information.

[0633] An "analysis device" is hardware and software used to analyze pre-processed data and predict health risks.

[0634] An "emotion evaluation device" is a system that evaluates a user's emotional state by analyzing their facial expressions and voice.

[0635] A "notification device" is a device or system used to issue notifications or warnings to relevant parties based on analysis results.

[0636] An "information provision device" is a system for providing health information for the purpose of health activities and communicating information to parents and guardians.

[0637] An "analytical device" is software or a device used to analyze overall health and emotional data and generate reports for administrators.

[0638] To implement this invention, a health and emotional management system is required for educational institutions and security services. This system consists of three main elements: a server, terminals, and users.

[0639] The server is connected to data collection devices for acquiring environmental and individual health information. These devices include sensors and equipment that indicate the state of the learning environment, such as temperature sensors and heart rate monitors. The server integrates the collected data and performs preprocessing using an information integration device. This integrated data is then analyzed using a generative AI model to predict health risks for students and users.

[0640] The device works in conjunction with an emotion assessment device that analyzes the user's facial expressions and voice in real time. The device has a built-in camera and microphone, and uses image processing libraries such as OpenCV for facial expression analysis and speech recognition software (e.g., Google Speech-to-Text) for voice analysis. This enables the assessment of the user's emotional state.

[0641] The user receives appropriate feedback through a notification device based on the generated content. Notifications are based on health risks and emotional state and include suggestions for relaxation methods and activities. For example, a prompt message might appear such as, "Based on your current facial expression and tone of voice, your stress level is elevated. A 10-minute walk in a lush park is an effective way to relax. Why not give it a try?"

[0642] This system enables personalized support in educational institutions and on-site settings, allowing for a comprehensive assessment and improvement of users' physiological and mental health.

[0643] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0644] Step 1:

[0645] The server receives environmental information (temperature, humidity, noise level, etc.) and individual health information (body temperature, heart rate, etc.) from the information collection device. Standardizing this data and unifying the format enables subsequent processing.

[0646] Step 2:

[0647] The server integrates standardized data using an information integration device and performs preprocessing. This includes data manipulation such as noise reduction and missing value imputation. As a result, the data is in a format suitable for analysis. This data is then passed on to the next analysis device.

[0648] Step 3:

[0649] The terminal processes real-time video and audio acquired from the user using an emotion evaluation device. It collects facial expression data with a camera, performs image analysis with OpenCV, and estimates the emotional state. Additionally, it collects audio with a microphone, converts the audio to text using speech recognition software, and analyzes the tone. The results of this analysis are then sent to a server.

[0650] Step 4:

[0651] The server inputs integrated health data and emotional data from devices into a generating AI model for data analysis. The model evaluates current health risks and potential emotional impacts. The analysis results are output as a risk assessment.

[0652] Step 5:

[0653] Users receive feedback via a notification device based on analysis results from the server. For example, if their stress level is high, a notification will be displayed with suggestions for relaxation methods and activities. This allows users to check their condition in real time and adjust their behavior accordingly.

[0654] Step 6:

[0655] Ultimately, the server uses an analytical instrument to aggregate health and emotional data from all users and generates a compiled report for administrators. This report allows educational institutions and facilities to understand the overall health status of their users and take appropriate action.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] [Fourth Embodiment]

[0660] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0661] 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.

[0662] 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).

[0663] 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.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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".

[0673] The system of the present invention is designed to effectively manage health in educational institutions and integrates various data collection, analysis, and provision functions. Specific embodiments of the present invention are described below.

[0674] First, the server continuously collects environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed throughout the school. Meanwhile, terminals obtain individual health data such as body temperature, heart rate, and activity level from health monitoring apps used by students and send it to the server. This data is integrated on the server, the format of each data item is standardized, and it is stored in a database.

[0675] Next, the server uses the integrated dataset to predict health risks. It utilizes a generative AI model to predict the risk of infectious disease outbreaks and student health problems, generating visualized results. If the prediction results exceed a predetermined threshold, the server sends a warning or prompt to action to the relevant parties' terminals.

[0676] Furthermore, the terminals in the school infirmary display information on students with abnormal health conditions based on data from the server. This allows health staff to perform quick and appropriate triage. The terminals also provide information to facilitate referrals to medical institutions as needed.

[0677] For users who are parents, the server generates regular reports on the student's health status and sends them via email or a dedicated app. These reports include the student's health trends and important points to help parents take appropriate action.

[0678] Finally, the server centrally analyzes health data from across the school and generates a report for administrators. Administrators can use this report to develop and implement data-driven improvements to health management.

[0679] As described above, the system according to the present invention will revolutionize health management in educational institutions by effectively utilizing environmental data and individual health data.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The server acquires environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed on campus. The acquired data is transmitted to a database in real time.

[0683] Step 2:

[0684] The device collects individual student health data, such as body temperature, heart rate, and activity level, from the health monitoring app used by the student and sends it to the server. The transmitted data is then integrated on the server.

[0685] Step 3:

[0686] The server converts the collected environmental and individual health data into a unified format and stores it in a database. During this process, it detects data anomalies and corrects them appropriately.

[0687] Step 4:

[0688] The server analyzes the integrated data and runs an AI model specifically designed to predict the likelihood of infectious disease outbreaks and health problems. This model assesses the risk by comparing it to historical data.

[0689] Step 5:

[0690] The server sends notifications to educational institution personnel based on AI-generated predictions. In particular, if an anomaly is detected, an immediate warning is sent to the terminal to prompt action.

[0691] Step 6:

[0692] The terminal displays student health information based on data transmitted from the server to assist health room staff with triage. If necessary, the server provides referral information to external medical institutions.

[0693] Step 7:

[0694] Parents, who are users, receive periodic reports from the server regarding their child's health. These reports include recent health trends and advice.

[0695] Step 8:

[0696] The server analyzes health data across the entire school and generates a comprehensive report for administrators. This report is delivered to administrators' terminals in a visual format and provides information to help improve health management.

[0697] (Example 1)

[0698] 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".

[0699] Traditionally, student health management in educational institutions relied on periodic health checkups and observations by teachers and staff, making it difficult to grasp students' health status in real time and thus hindering the rapid detection of early signs of infectious diseases or illness. Furthermore, there was a lack of efficient methods for integrating and analyzing environmental information with individual student health data, which hindered improvements in the accuracy of health management. Additionally, there was insufficient information provided to parents to effectively understand their children's health status and take appropriate action.

[0700] 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.

[0701] In this invention, the server includes means for collecting information to acquire environmental information within an educational facility, means for using a generative AI model that predicts future health risks based on past data, and means for generating prompts to instruct the AI ​​model based on prompt sentences. This enables real-time prediction of health risks, and by efficiently integrating and analyzing environmental information and individual health information, rapid and appropriate health management can be achieved, allowing parents and teachers to take necessary actions in a timely manner.

[0702] "Educational facilities" refer to places where students learn and receive education, and include institutions such as schools and universities.

[0703] "Environmental information" refers to data about the environment inside or around an educational facility, including temperature, humidity, carbon dioxide concentration, and noise levels.

[0704] "Individual health information" refers to data about an individual student's health status, including vital data such as body temperature, heart rate, and activity level.

[0705] "Information gathering means" refers to devices or functions for acquiring environmental information and individual health information, and consists of sensors and applications.

[0706] "Information integration means" refers to a function that integrates collected environmental information and individual health information and processes them to convert them into a standard format.

[0707] "Machine learning methods" consist of algorithms and models used to analyze large amounts of data and predict health risks, and utilize generative AI models.

[0708] "Communication methods" refer to functions for notifying relevant parties of information from a server, and include email and push notifications.

[0709] "Information presentation means" refers to a function that visually displays information regarding students' health status and health risks, enabling health staff and other relevant parties to quickly grasp the situation.

[0710] "Information generation means" refers to a function that generates periodic reports to inform parents about the student's health status, including health trends and precautions.

[0711] "Information analysis tools" refer to functions that centrally analyze overall health information and create reports for facility managers, thereby supporting the formulation of improvement measures.

[0712] A "generative AI model" refers to an artificial intelligence model that performs predictions and generation based on large amounts of data, and is used to predict health risks.

[0713] "Prompt generation means" refers to a function that creates prompt statements to give specific instructions to an AI model.

[0714] This invention is a system for efficiently managing student health in educational facilities. It collects, integrates, and analyzes environmental and individual health information, and provides this information to relevant parties. The system is configured and implemented as follows:

[0715] The server collects environmental information using sensors placed throughout the school. Specifically, it utilizes general-purpose sensors that measure temperature, humidity, carbon dioxide concentration, and noise levels. The server also uses MySQL or PostgreSQL as database software to efficiently store and manage the information.

[0716] The system uses students' smartphones and wearable devices to acquire individual health information. For example, it collects body temperature, heart rate, and activity levels using devices such as Apple Watch and Fitbit, and transmits the data to a server via a dedicated application.

[0717] The server integrates the collected information and uses a generative AI model to predict health risks. The AI ​​model utilizes machine learning frameworks such as TensorFlow and PyTorch, enabling predictions based on historical data. An example of a prompt used to instruct the AI ​​model would be, "Predict the risk of infectious diseases next week based on historical data."

[0718] The server notifies relevant parties based on the analysis results. For example, if carbon dioxide levels rise, it will send a notification recommending "ventilating the classroom." Notifications are sent via email or a dedicated application.

[0719] The server generates and periodically sends reports summarizing the student's health status to the user (parent). These reports include health trends and important points to consider, helping parents understand and act accordingly. They are available in digital format and delivered via email or a dedicated app.

[0720] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0721] Step 1:

[0722] The server collects environmental information.

[0723] The input consists of data such as temperature, humidity, carbon dioxide concentration, and noise level, acquired from sensors within the school. The server polls and receives this data at regular intervals. The output is raw environmental information that is temporarily stored in a database. This step includes the specific operation of the sensors transmitting the measured data to the server via wireless communication.

[0724] Step 2:

[0725] The device collects individual health information from students.

[0726] The input consists of body temperature, heart rate, and activity level measured by smartphones or wearable devices. The device collects this data either through a health monitoring app entered by the student or automatically. The output is individual health information uploaded to a server in a standard format. Specifically, the device acquires data from wearable devices using Bluetooth or Wi-Fi, processes it in the app, and then transmits it.

[0727] Step 3:

[0728] The server performs information integration.

[0729] The input consists of environmental and individual health information obtained from steps 1 and 2. The server converts them to a standard format (e.g., JSON) and integrates and stores them in a database. The output is a parseable integrated dataset. This step involves specific actions to integrate data in different formats and form a consistent dataset.

[0730] Step 4:

[0731] The server performs health risk predictions.

[0732] The input is an integrated dataset. The server utilizes a generative AI model to analyze the collected data and predict health risks. The output is a predictive report showing the percentage of infection risk and likelihood of illness in the following week. Specifically, it includes the generation of prompts used as instructions for the AI ​​model, and the AI's actions to perform analysis based on historical data.

[0733] Step 5:

[0734] The server sends a notification based on the analysis results.

[0735] The input is the health risk prediction report obtained in step 4. The server notifies relevant parties if the risk exceeds a threshold. The output is a warning message sent as an email or app notification. For example, a notification might be sent recommending actions such as opening windows because the carbon dioxide concentration in the classroom is high.

[0736] Step 6:

[0737] We provide health status reports to the user, who is also a parent.

[0738] The input is data showing students' health trends. The server uses this information to create periodic reports and send them to parents. The output is a PDF report provided via email or a dedicated app. Specifically, the report is generated using a template engine and distributed using an email sending system.

[0739] Step 7:

[0740] The server analyzes overall health information and generates a report for administrators.

[0741] The input is an integrated dataset for the entire educational facility. The server utilizes a generative AI model to aggregate the data and perform an overall analysis. The output is a report for administrators to evaluate and improve the facility's health management. Specifically, this includes creating a visual report using tables and graphs and providing it to the administrator as a PDF.

[0742] (Application Example 1)

[0743] 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".

[0744] In factories and facilities, it is crucial to monitor the safety of the work environment and the health of workers in real time to ensure a safe working environment. However, conventional technologies lacked sufficient mechanisms for quickly collecting environmental and individual health information, and for predicting and responding to risks based on that information. As a result, problems such as health problems among workers and decreased production efficiency have occurred.

[0745] 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.

[0746] In this invention, the server includes information gathering means for acquiring environmental information, information gathering means for acquiring individual health information of workers, information integration means for integrating and pre-processing the collected information, analysis means for analyzing the pre-processed information and predicting health risks, notification means for notifying or warning relevant parties based on the analysis results, and information processing means for evaluating safety. This enables real-time monitoring of the work environment and the health status of workers, facilitating rapid risk management and ensuring a safe work environment.

[0747] "Facilities" refers to all places and buildings where business or production activities take place, including factories and workshops.

[0748] "Environmental information" refers to data about the physical conditions within a facility, such as temperature, humidity, and noise levels.

[0749] "Information gathering means" refers to sensors and devices used to collect environmental information and individual health information.

[0750] A "worker" is a person who performs duties or tasks within a facility and is subject to safety management and health monitoring.

[0751] "Individual health information" refers to health data collected for each worker, including heart rate, body temperature, and activity level.

[0752] "Information integration means" refers to a means that has the function of centrally managing collected environmental information and individual health information and preparing it in a state that can be analyzed.

[0753] "Preprocessing" refers to the process of preparing data into a format suitable for analysis, such as standardizing data and imputing missing values, which is carried out using information integration tools.

[0754] "Analysis methods" refer to software and algorithms used to predict health risks based on collected information.

[0755] "Notification means" refers to methods and technologies for communicating necessary information to stakeholders and workers based on analysis results.

[0756] "Information processing means for evaluating safety" refers to processes and systems for evaluating the safety of the work environment based on collected and analyzed data.

[0757] The system implementing this invention ensures a safe working environment within a facility. The server continuously acquires environmental information such as temperature, humidity, and noise levels from multiple environmental sensors. In addition, it collects individual health information such as heart rate and activity level from wearable devices worn by workers. This data is integrated on the server, pre-processed, and then stored in a database.

[0758] The server analyzes integrated information using a generation AI model to assess health risks and work environment safety in real time. If risks exceed certain thresholds, it sends a warning to relevant personnel's terminals via a notification system, urging them to take specific action. This notification includes appropriate countermeasures and points to note.

[0759] The server also generates and provides administrators with comprehensive, data-driven workplace environment assessment reports. This enables facility managers to develop and implement effective, data-driven safety measures.

[0760] For example, if the temperature in the factory rises sharply, the server can analyze the data and, if an increase in a worker's heart rate is detected, immediately send a notification suggesting a break. This not only protects the health of workers but also helps maintain production efficiency.

[0761] An example of a prompt message for the generated AI model would be a specific instruction such as, "Current environmental data: temperature 35°C, humidity 70%. Worker A's heart rate: 100 bpm, work time: 4 hours. Predict the need for a break suggestion." This would allow the server to utilize the AI ​​model to respond efficiently and quickly.

[0762] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0763] Step 1:

[0764] The server continuously acquires environmental information such as temperature, humidity, and noise levels from environmental sensors installed within the facility. It receives data from environmental sensors as input and temporarily stores it in memory.

[0765] Step 2:

[0766] The server acquires individual health information, such as heart rate and activity level, from wearable devices worn by workers. It receives real-time data from the wearable devices as input and temporarily stores it in memory, similar to environmental information.

[0767] Step 3:

[0768] The server integrates collected environmental and individual health information and formats it into a format that can be stored in a database. A data conversion algorithm unifies the data in different formats and sends it to the destination database.

[0769] Step 4:

[0770] The server analyzes the integrated information using a generation AI model. The integrated data is passed to the AI ​​model as input, and the results of health risk and work environment safety assessments are obtained as output. In this process, the AI ​​model makes predictions based on the trained data.

[0771] Step 5:

[0772] Based on the analysis results, the server sends notifications to relevant parties' terminals to alert them and prompt them to take action if the risk exceeds a certain threshold. It generates an alert message as output and distributes it to the relevant parties' terminals. The message content may be dynamically generated.

[0773] Step 6:

[0774] The server analyzes the entire data and generates an analytical report for administrators. It uses the entire integrated dataset as input and produces a detailed report for the administrator dashboard as output. This report is visually represented using data visualization tools.

[0775] Step 7:

[0776] Based on the generated reports, users make adjustments to create a more efficient and secure work environment. They refer to the reports provided by administrators as input, notify the company of specific improvement measures, and put them into action.

[0777] 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.

[0778] The system of this invention, in addition to the conventional health management functions used in educational institutions, takes into account the emotional state of users, enabling more precise health risk prediction and individualized responses. This system integrates an emotion engine into various functions for integrating environmental data and individual student health data and analyzing them in real time.

[0779] First, the server receives data from environmental sensors installed in the classroom, as well as individual health data from students' health monitoring apps. This includes conventional physiological data such as body temperature, heart rate, and activity level. This data is integrated on the server and converted into a standardized format.

[0780] Next, the emotion engine of the present invention analyzes emotional data from the student's facial expressions, tone of voice, and speech content collected by the terminal, and transmits this data to the server. The emotion engine specifically detects emotional patterns that indicate signs of stress or anxiety and provides this information to the server.

[0781] The server processes this data holistically and uses AI models to predict health risks. The inclusion of emotional data enables a comprehensive health assessment, including students' mental health. For example, if a particular student shows signs of stress, additional information is added to comprehensively assess their physical and mental health risks, allowing for a more detailed understanding of their risk level.

[0782] Furthermore, based on the predicted health risks, the server sends customized health advice to relevant teachers and parents. For example, students who are experiencing low moods will receive suggestions for personalized relaxation techniques and activities to alleviate their symptoms.

[0783] Finally, the server conducts extensive analysis, including emotional data, to create a comprehensive health report for educational institutions. This report includes data showing trends in students' emotional states and their impact on health risks, guiding administrators to take appropriate action.

[0784] Thus, by combining an emotion engine, the present invention makes it possible to manage health in educational institutions more effectively.

[0785] The following describes the processing flow.

[0786] Step 1:

[0787] The server acquires environmental data such as temperature, humidity, carbon dioxide concentration, and noise levels from environmental sensors installed in each classroom within the school. This data is transmitted to the server in real time and stored in a database.

[0788] Step 2:

[0789] The device collects individual health data such as body temperature, heart rate, and activity level through a health monitoring app used by students. The device sends this data to a server to build an integrated dataset.

[0790] Step 3:

[0791] The device acquires emotional data by analyzing students' facial expressions and voices. Using an emotion engine, it identifies emotional states such as stress and anxiety and sends this information to a server. This emotional data is then integrated as part of a health assessment.

[0792] Step 4:

[0793] The server integrates collected environmental data, individual health data, and emotional data, cleans the data, and then uses an AI model to predict health risks. This allows for a comprehensive assessment of risks such as infectious disease risk and mental health status.

[0794] Step 5:

[0795] Based on the analysis results, the server generates alerts and health advice. In particular, if emotional data is included, relaxation techniques will be suggested for students at high stress-related risk. These notifications are sent to the devices of teachers and parents.

[0796] Step 6:

[0797] Parents, acting as users, receive reports on their child's health status sent from the server. These reports include recent emotional states and health trends, and provide advice on daily coping strategies.

[0798] Step 7:

[0799] The server analyzes health and emotional data across the entire school and generates a comprehensive health report for administrators. This report serves as a crucial guideline for the educational institution's health management strategy. The report details trends in emotional states and risk assessments.

[0800] (Example 2)

[0801] 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".

[0802] Traditional health management systems primarily rely on physiological data for assessment, but this has the drawback of potentially overlooking changes in mental health. Furthermore, real-time integration and analysis of acquired data are difficult, making it challenging to provide individualized support to each member. This often prevents rapid and accurate prediction and response to health risks. Additionally, limited information provision makes it difficult to quickly provide necessary countermeasures to stakeholders.

[0803] 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.

[0804] In this invention, the server includes information gathering means for acquiring environmental information, information gathering means for acquiring individual health information of its members, and information integration means for integrating and standardizing the acquired information. This enables the real-time integration of data containing both environmental and individual health information, and allows for highly accurate prediction of health risks based on the integrated data, as well as the provision of prompt and appropriate individual responses.

[0805] "Environmental information" refers to data that indicates the surrounding conditions, such as temperature, humidity, carbon dioxide concentration, and noise levels in classrooms, workspaces, and other areas.

[0806] "Information gathering means" refers to methods and technologies for acquiring data using various sensors and applications.

[0807] "Individual health information" refers to data that indicates the health status of each member, and includes, for example, body temperature, heart rate, and activity level.

[0808] "Information integration means" refers to the process of converting collected data into a single standardized format, making it available for continuous or intermittent use.

[0809] "Emotional analysis means" refers to a method of detecting the emotional state of members using facial expression analysis and voice analysis technologies, and processing that data.

[0810] "Analysis means" refers to methods or techniques for calculating and predicting health risks, etc., using collected standardized data.

[0811] "Notification means" refers to methods and systems for communicating necessary information to relevant parties based on analysis results.

[0812] "Means of providing advice" refers to methods or systems for providing reference information and instructions to support health-related work.

[0813] "Information provision methods" refer to means of distributing important health information to parents and other relevant parties, including email and apps.

[0814] "Evaluation tools" refer to functions or systems that analyze collected and integrated data and provide it to administrators in the form of reports or similar documents.

[0815] This invention is a system for comprehensively managing the health status of members within an educational institution. The system mainly consists of a server, terminals, and a generative AI model.

[0816] First, the server acquires environmental information from environmental sensors installed in classrooms and workspaces. This includes temperature, humidity, and noise levels. The server uses an API to continuously collect this environmental data via the internet. The server also acquires individual health information from wearable devices worn by students and dedicated health monitoring apps. This individual health information includes body temperature, heart rate, and activity level.

[0817] The terminal records the facial expressions and voices of the members in real time and performs emotion analysis. Using the camera and microphone built into the terminal, emotion analysis software analyzes the data using a machine learning model. Specific software used includes TensorFlow. The results of the emotion analysis are sent to the server as emotion categories, such as whether the member is experiencing stress.

[0818] The server integrates the received environmental information, individual health information, and emotional information, and converts it into a standardized data format (e.g., CSV format) using, for example, the Pandas library. This ensures that the data is stored in a consistent format and is easily analyzable.

[0819] Next, the server uses a generative AI model to analyze the rich data and predict health risks. This prediction is based on historical data and current physiological and emotional states, and the server prompts the prediction model with a message such as, "Input the student's current health data and emotional state, and predict the health risks for the next week." Based on this prompt, the AI ​​model performs a risk assessment and generates appropriate actions and advice for each member's health status.

[0820] Users receive health advice generated via the server through email or a dedicated app. This allows teachers and parents to take appropriate action based on the health status of their members. Furthermore, the server regularly generates detailed health reports for administrators, which supports overall health management.

[0821] For example, if a student's stress level rises during an exam period, a prompt such as "Please suggest ways to manage the student's stress during the exam period" can be input to the generating AI model to obtain specific stress management strategies.

[0822] This invention makes it possible to monitor the physiological and psychological health status of members in real time, enabling optimal health management.

[0823] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0824] Step 1:

[0825] The server acquires environmental data from environmental sensors installed in the classroom. It receives raw data from the sensors (e.g., temperature, humidity, noise level) as input and saves this data in a standardized format as output. Specifically, each data point is given the same timestamp and converted from JSON format to CSV format. This process involves collecting data every 30 seconds using the sensor's API and performing data conversion using the Python Pandas library.

[0826] Step 2:

[0827] The device transmits health data acquired from students to a server. It takes physiological data (e.g., body temperature, heart rate, activity level) from a wearable device as input and transmits this data to the server as output. This process involves a health monitoring app installed on the device receiving data via BLE connection and transmitting it to the server in real time via the HTTPS protocol.

[0828] Step 3:

[0829] The device acquires data on students' facial expressions and voices and performs emotion analysis. It takes video and audio data from the camera and microphone as input and sends the analyzed emotional state to the server as output. Specifically, a machine learning model using TensorFlow analyzes this raw data and classifies it into emotional categories such as stress and joy. This analysis result is provided to the server as data related to the psychological health status of the participants.

[0830] Step 4:

[0831] The server integrates all acquired data and predicts health risks using a generative AI model. Standardized physiological, environmental, and emotional data are used as input, and the output is each student's health risk level. This analysis uses the prompt "Input the student's current health data and emotional state, and predict their health risk for the next week" to the prediction model. The AI ​​model calculates the health risk, taking past health data into consideration.

[0832] Step 5:

[0833] The server notifies relevant parties based on the health risk prediction results. It receives predicted risk data as input and sends customized health advice to relevant parties (e.g., teachers, parents) as output. Specifically, it sends messages such as, "Mr. / Ms. XX needs activities that promote relaxation," via email or a dedicated app notification.

[0834] Step 6:

[0835] The server analyzes overall health data and generates health reports for administrators. Using all health data and risk assessment results as input, it produces a comprehensive health report in PDF format as output, including statistical trends and recommendations. The analysis results, including patterns highlighted by machine learning models, are regularly updated and available for administrator download.

[0836] (Application Example 2)

[0837] 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".

[0838] The problem that this invention aims to solve is to enable more detailed prediction of health risks and effective responses by considering individual emotional states in addition to conventional physiological data in health management within educational institutions. Conventional health management systems have relied solely on physiological data for evaluation, and therefore have not adequately covered mental health states, particularly emotional aspects such as stress and anxiety. This has resulted in difficulties in understanding and responding to the overall health status of students.

[0839] 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.

[0840] In this invention, the server includes an information gathering device for acquiring environmental information, an information gathering device for acquiring individual health information, an information integration device for integrating and pre-processing the collected information, an analysis device for analyzing the pre-processed information and predicting health risks, an emotion evaluation device for evaluating the emotional state by analyzing the user's facial expressions and voice, a notification device for notifying or warning relevant parties based on health risks and emotional state, an information provision device for supporting health activities, an information provision device for providing individual health status to guardians, and an analysis device for analyzing overall health data and emotional data and creating reports for administrators. This enables detailed health assessment, including the emotional state of students, and realizes more precise health risk prediction and individualized responses.

[0841] "Environmental information" refers to data that indicates the state of the learning environment within an educational institution, such as air quality, temperature, humidity, and noise level.

[0842] "Individual health information" refers to data indicating each student's physiological and physical condition, such as body temperature, heart rate, and activity level.

[0843] An "information gathering device" refers to equipment such as sensors and devices used to acquire environmental information and individual health information.

[0844] An "information integration device" is a system or software for integrating and pre-processing collected environmental information and individual health information.

[0845] An "analysis device" is hardware and software used to analyze pre-processed data and predict health risks.

[0846] An "emotion evaluation device" is a system that evaluates a user's emotional state by analyzing their facial expressions and voice.

[0847] A "notification device" is a device or system used to issue notifications or warnings to relevant parties based on analysis results.

[0848] An "information provision device" is a system for providing health information for the purpose of health activities and communicating information to parents and guardians.

[0849] An "analytical device" is software or a device used to analyze overall health and emotional data and generate reports for administrators.

[0850] To implement this invention, a health and emotional management system is required for educational institutions and security services. This system consists of three main elements: a server, terminals, and users.

[0851] The server is connected to data collection devices for acquiring environmental and individual health information. These devices include sensors and equipment that indicate the state of the learning environment, such as temperature sensors and heart rate monitors. The server integrates the collected data and performs preprocessing using an information integration device. This integrated data is then analyzed using a generative AI model to predict health risks for students and users.

[0852] The device works in conjunction with an emotion assessment device that analyzes the user's facial expressions and voice in real time. The device has a built-in camera and microphone, and uses image processing libraries such as OpenCV for facial expression analysis and speech recognition software (e.g., Google Speech-to-Text) for voice analysis. This enables the assessment of the user's emotional state.

[0853] The user receives appropriate feedback through a notification device based on the generated content. Notifications are based on health risks and emotional state and include suggestions for relaxation methods and activities. For example, a prompt message might appear such as, "Based on your current facial expression and tone of voice, your stress level is elevated. A 10-minute walk in a lush park is an effective way to relax. Why not give it a try?"

[0854] This system enables personalized support in educational institutions and on-site settings, allowing for a comprehensive assessment and improvement of users' physiological and mental health.

[0855] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0856] Step 1:

[0857] The server receives environmental information (temperature, humidity, noise level, etc.) and individual health information (body temperature, heart rate, etc.) from the information collection device. Standardizing this data and unifying the format enables subsequent processing.

[0858] Step 2:

[0859] The server integrates standardized data using an information integration device and performs preprocessing. This includes data manipulation such as noise reduction and missing value imputation. As a result, the data is in a format suitable for analysis. This data is then passed on to the next analysis device.

[0860] Step 3:

[0861] The terminal processes real-time video and audio acquired from the user using an emotion evaluation device. It collects facial expression data with a camera, performs image analysis with OpenCV, and estimates the emotional state. Additionally, it collects audio with a microphone, converts the audio to text using speech recognition software, and analyzes the tone. The results of this analysis are then sent to a server.

[0862] Step 4:

[0863] The server inputs integrated health data and emotional data from devices into a generating AI model for data analysis. The model evaluates current health risks and potential emotional impacts. The analysis results are output as a risk assessment.

[0864] Step 5:

[0865] Users receive feedback via a notification device based on analysis results from the server. For example, if their stress level is high, a notification will be displayed with suggestions for relaxation methods and activities. This allows users to check their condition in real time and adjust their behavior accordingly.

[0866] Step 6:

[0867] Ultimately, the server uses an analytical instrument to aggregate health and emotional data from all users and generates a compiled report for administrators. This report allows educational institutions and facilities to understand the overall health status of their users and take appropriate action.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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."

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0889] The following is further disclosed regarding the embodiments described above.

[0890] (Claim 1)

[0891] Within educational institutions, data collection methods for acquiring environmental data,

[0892] A data collection method for obtaining individual health data of students,

[0893] A data integration means for integrating and preprocessing collected data,

[0894] An analytical means for analyzing pre-processed data and predicting health risks,

[0895] A notification mechanism for notifying or warning relevant parties based on the analysis results,

[0896] Information provision means to support public health services,

[0897] A means of providing information to parents regarding their child's health status,

[0898] A system that includes analytical tools for analyzing overall health data and generating reports for administrators.

[0899] (Claim 2)

[0900] The system according to claim 1, which enables real-time prediction of health risks.

[0901] (Claim 3)

[0902] The system according to claim 1, which provides triage support based on health data.

[0903] "Example 1"

[0904] (Claim 1)

[0905] Within educational facilities, means of collecting information to obtain environmental information,

[0906] Information gathering methods for obtaining individual health information of students,

[0907] Information integration means for integrating and standardizing collected information,

[0908] Machine learning methods for analyzing standardized information and predicting health risks,

[0909] A means of communication for notifying relevant parties based on the analysis results,

[0910] Information presentation tools to support public health services,

[0911] A means of generating information to provide parents with information on their child's health status,

[0912] Information analysis tools for creating reports for administrators that analyze overall health information,

[0913] A method using generative AI models that predict future health risks based on past data,

[0914] A prompt generation means for instructing an AI model based on a prompt statement,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, which enables real-time prediction of health risks.

[0918] (Claim 3)

[0919] The system according to claim 1, which provides support for priority determination based on health information.

[0920] "Application Example 1"

[0921] (Claim 1)

[0922] In a facility, information collection means for acquiring environmental information,

[0923] information collection means for acquiring individual health information of workers,

[0924] information integration means for integrating the collected information and performing pre - processing,

[0925] analysis means for analyzing the pre - processed information and predicting health risks,

[0926] notification means for notifying or warning relevant parties based on the analysis results,

[0927] information providing means for supporting healthcare operations,

[0928] information providing means for providing the health status of workers to managers,

[0929] analysis means for analyzing overall health information and creating analysis for managers,

[0930] acquiring and processing environmental and health information in real - time via a plurality of devices,

[0931] information processing means for evaluating safety,

[0932] notification means for prompting actions for workers based on the prediction results,

[0933] A system including the above.

[0934] (Claim 2)

[0935] The system according to claim 1, which enables real - time health risk prediction and work safety evaluation.

[0936] (Claim 3)

[0937] The system according to claim 1, which provides worker support based on health information and environmental information.

[0938] "Example 2 of combining an emotion engine"

[0939] (Claim 1)

[0940] Information gathering means for obtaining environmental information,

[0941] Information gathering methods for obtaining individual health information of members,

[0942] Information integration means for integrating and standardizing acquired information,

[0943] Analytical means for analyzing standardized information and predicting health risks,

[0944] An emotional analysis method for analyzing the emotional state of the members,

[0945] A notification mechanism for notifying or warning relevant parties based on the analysis results,

[0946] A means of providing advice to support health-related work,

[0947] A means of providing information to parents regarding the health status of their members,

[0948] A system that includes evaluation tools for analyzing overall health information and generating reports for administrators.

[0949] (Claim 2)

[0950] The system according to claim 1, characterized in that it enables real-time health risk assessment and includes a means for emotion analysis.

[0951] (Claim 3)

[0952] The system according to claim 1, which provides support for determining priorities based on health information and emotional state.

[0953] "Application example 2 when combining with an emotional engine"

[0954] (Claim 1)

[0955] Within an educational organization, an information gathering device for acquiring environmental information,

[0956] An information collection device for obtaining individual health information,

[0957] An information integration device that integrates and preprocesses the collected information,

[0958] An analysis device for analyzing pre-processed information and predicting health risks,

[0959] An emotion evaluation device for evaluating the emotional state by analyzing the user's facial expressions and voice,

[0960] A notification device for notifying or warning stakeholders based on health risks and emotional state,

[0961] Information provision device to support health activities,

[0962] An information provision device for providing parents with information on their child's health status,

[0963] A system including analytical instruments for analyzing overall health and emotional data and generating reports for administrators.

[0964] (Claim 2)

[0965] The system according to claim 1, which enables real-time risk prediction based on health risks and emotional state.

[0966] (Claim 3)

[0967] The system according to claim 1, which provides triage support based on health data and emotional data. [Explanation of Symbols]

[0968] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Within educational institutions, data collection methods for acquiring environmental data, A data collection method for obtaining individual health data of students, A data integration means for integrating and preprocessing collected data, An analytical means for analyzing pre-processed data and predicting health risks, A notification mechanism for notifying or warning relevant parties based on the analysis results, Information provision means to support public health services, A means of providing information to parents regarding their child's health status, A system that includes analytical tools for analyzing overall health data and generating reports for administrators.

2. The system according to claim 1, which enables real-time prediction of health risks.

3. The system according to claim 1, which provides triage support based on health data.

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