system

Abstract

We provide the system. [Solution] Means for collecting data on individuals' daily activities in an educational environment, A method using artificial intelligence to analyze the collected data and detect signs of stress, A means for transmitting a notification to a person who will receive a notification corresponding to the detected signs of stress, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the educational field, problems such as bullying, school refusal, and violent behavior are often caused by the mental stress of children and students. However, it is difficult to detect and respond to the signs of such stress at an early stage. Conventional countermeasures mainly rely on human intervention, and effective responses may not be carried out until the problems become apparent. There is a need to develop a system that can detect subtle changes in children and students without overlooking them and provide early support.

Means for Solving the Problems

[0005] This invention provides a system that detects signs of mental stress by collecting and analyzing data obtained from the daily activities of students in an educational environment. The system includes data collection means, analysis means utilizing generation AI, and notification means, and analyzes students' written and behavioral data, as well as their memory history, to detect abnormalities early. Based on these results, notifications are sent to educators, enabling prompt and appropriate support.

[0006] The "educational environment" refers to the space in which students engage in learning activities on a daily basis, including schools and learning facilities.

[0007] "Individual daily activity data" refers to written information, behavioral patterns, and information about daily habits such as forgetfulness that students input or record during their school life.

[0008] "Means of collection" refers to methods or devices for acquiring the above data using tablets, computers, IoT devices, etc.

[0009] "Means of analysis" refers to processes and devices that use artificial intelligence to detect potential stress or abnormal behavior based on collected data.

[0010] "Artificial intelligence" refers to algorithms that operate on computers and make specific judgments or predictions through data learning and pattern recognition.

[0011] "Signs of stress" are patterns and behavioral changes that indicate mental burden or anxiety, and are characteristics that are detected as abnormalities in normal learning activities.

[0012] "Means of notification" refer to the processes and systems for communicating analysis results to educators, and these are carried out via email or application notifications. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] To implement this invention, a system will be constructed that uses terminals placed within the educational environment and a central server to monitor and analyze the daily activities of students. The terminals will have the functionality to acquire students' text input, behavioral data, and records of forgetfulness in real time. As a result, the terminals will capture the contents of notebooks, essays, and reflection papers, and record the status of forgotten items based on teacher input. In addition, IoT devices can be linked to monitor behavioral patterns in classrooms and on the school grounds.

[0035] This data is periodically encrypted and sent to a server. The server sets up an environment to analyze the received data and utilizes a pre-trained artificial intelligence model. This model performs sentiment analysis on text data and identifies pattern changes in behavioral data. If an anomaly is detected as a sign of stress as a result of this process, relevant educators are automatically notified.

[0036] As a concrete example, suppose student A's handwriting in the notes they submit during class has recently become messy. This information is captured by a device and sent to a server. The server's AI analyzes this information and compares it with past data to determine that the recent messy handwriting is abnormal. This abnormality is notified to the teacher, allowing for an early warning that student A may be experiencing some kind of stress. Based on this, the teacher can then take steps to support student A and prepare to offer individual counseling.

[0037] The entire system is equipped with infrastructure adaptable to the educational environment and is properly managed in accordance with guidelines for handling student data in a secure and privacy-conscious manner. In this way, a system is created that effectively promotes attention to the mental health of students in educational settings.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The device monitors students' activities in school life and collects text input (notes, essays, reflections), behavioral data (travel patterns, interactions with friends, etc.), and records of memory loss. This includes a process of acquiring data using input devices and sensors.

[0041] Step 2:

[0042] The device temporarily stores the collected data in its internal memory. This stored data is encrypted at a specified frequency and prepared to be sent to the server using a secure communication protocol.

[0043] Step 3:

[0044] The server receives data sent from the terminal and stores it in the database. The database then performs preprocessing to allow for comparison with past data and pattern analysis.

[0045] Step 4:

[0046] The server applies artificial intelligence models to the stored data to perform sentiment analysis and behavioral pattern analysis. This analysis aims to identify signs and anomalies of stress based on pre-defined criteria.

[0047] Step 5:

[0048] The server triggers an alert based on the anomaly detection information obtained from the analysis results. This alert is sent to teachers and counselors associated with the student in question.

[0049] Step 6:

[0050] The user (teacher or counselor) receives a notification from the server and reviews its contents. The notification contains detailed information about signs or abnormalities of stress, which they use to consider how to respond to the student. For example, they may prepare to initiate specific support measures, such as planning a meeting with the student.

[0051] (Example 1)

[0052] 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."

[0053] Maintaining the mental health of students and proactively identifying and addressing excessive stress is crucial in the educational environment. However, traditional methods have made it difficult to accurately grasp changes in students' daily activities and signs of stress, making it challenging for teachers and educators to respond at the appropriate time. To address this challenge, there is a need for a method that efficiently and accurately detects signs of stress and promptly notifies educators.

[0054] 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.

[0055] In this invention, the server includes means for acquiring individual text data and behavioral information in an educational environment, means for encrypting the acquired data and transmitting it to a central information processing device, and means for analyzing emotional evaluation and changes in behavioral patterns using a generated AI model based on the data acquired by the central information processing device. This enables efficient detection of signs of stress in students and prompt notification and response to educators.

[0056] The term "educational environment" refers to the setting in schools and other learning facilities where students learn and engage in activities, and the environment in which teachers and educators provide guidance.

[0057] "Personal text data" refers to text information that students input or write within the educational environment, and this includes notes, essays, and reflection papers.

[0058] "Behavioral information" refers to data on the physical movements and habitual activities of students in classrooms, schoolyards, etc., and is information acquired by IoT devices.

[0059] "Encryption" refers to the process of converting data into an invisible format to protect it from third parties, and is essential for secure communication.

[0060] A "central information processing system" refers to a server or computer used to receive and analyze data transmitted from an educational environment.

[0061] A "generative AI model" refers to an artificial intelligence model used to analyze the sentiment evaluation and behavioral pattern fluctuations of text data, and is pre-trained based on data.

[0062] "Emotional assessment" refers to the process of analyzing the emotional state of students from text data and identifying positive or negative emotional tendencies.

[0063] "Variations in behavioral patterns" refers to the process of identifying abnormal or exceptional behavior by comparing it with normal behavioral information.

[0064] Detecting an "abnormality" refers to finding and identifying unusual signs or patterns of stress from data on daily activities.

[0065] "Responsible personnel" refers to educators or teachers who are responsible for receiving notifications of anomalies and taking appropriate action.

[0066] To implement this invention, a system will be constructed that effectively monitors the learning and activities of students by utilizing digital tools and network infrastructure in the educational environment. Specifically, it will be implemented using the following hardware and software.

[0067] First, devices are deployed within educational facilities. These include computer terminals, tablets, and IoT devices used by students, which capture students' text input and physical actions. The devices, for example, transmit the collected data via Wi-Fi to a server using an encryption protocol. This encryption ensures the security of the data.

[0068] The server functions as a central information processing unit, acquiring and analyzing received data. The data is analyzed using generative AI models to identify sentiment evaluations of text data and pattern variations in behavioral data. By using appropriate software (e.g., machine learning libraries and data analysis tools), rapid and efficient analysis is possible.

[0069] For example, a generative AI model might identify signs of stress by comparing recent behavioral data with past data, based on a prompt such as, "How can we detect changes that suggest a decline in mood from recent behavioral data?" This example prompt demonstrates the effectiveness of the AI's analysis process.

[0070] Based on this analytical data, users, such as teachers and educators, can identify students' stress and learning-related problems early on and provide opportunities for individualized guidance and counseling. If an anomaly is detected, the server will send appropriate notifications to educators, enabling prompt action and implementation of corrective measures.

[0071] As described above, this invention is an effective system for supporting the mental health of students in educational settings.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The terminal collects text input and behavioral information from students. Input includes text data entered by students and movement patterns recorded by IoT devices. The terminal collects this data and stores it locally. Specifically, it records key input in real time when students are writing notes or compositions, and receives behavioral information from wearable devices and classroom sensors.

[0075] Step 2:

[0076] The terminal sends the collected data to the server using an encryption protocol. The input consists of text data and behavioral information collected in step 1. The terminal encrypts this data and transfers it to the server via a secure communication channel. Specifically, it uses the TLS / SSL protocol and sends the data in batch processing to prevent information leakage.

[0077] Step 3:

[0078] The server inputs the received data into a generative AI model for analysis. The input consists of encrypted text data and behavioral information. The server decrypts this data and uses the generative AI model to analyze emotional evaluations and changes in behavioral patterns. Specifically, it uses natural language processing techniques to calculate emotional scores from text and identifies abnormal patterns from behavioral data.

[0079] Step 4:

[0080] The server sends a notification to the responsible party if an anomaly is detected. The input is the analysis results from step 3. The server evaluates the analysis results, and if it determines an anomaly is detected, it sends an alert to the education personnel. Specifically, it uses email and app notifications to enable the responsible party to take immediate action.

[0081] (Application Example 1)

[0082] 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."

[0083] In modern society, there is a need to detect changes in people's feelings and awareness early on in their daily lives and prevent mental health problems. However, existing systems have the challenge of not being able to monitor these changes in real time and respond quickly. In particular, it is difficult to notice invisible stress and psychological changes in residential areas, and if appropriate measures are not taken, it can lead to serious problems.

[0084] 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.

[0085] In this invention, the server includes a device for collecting information on an individual's daily activities within a residential area, a device using artificial intelligence that analyzes the collected information to detect changes in feelings and consciousness, and a device for transmitting warnings to relevant parties in accordance with the detected changes in feelings and consciousness. This makes it possible to detect psychological changes in an individual within a residential area at an early stage, enabling the maintenance and improvement of mental health.

[0086] A "residential area" refers to the place where an individual conducts their daily life and the surrounding environment, and mainly includes residential areas and nearby public spaces.

[0087] "Individual daily activities" refer to actions, behaviors, and thoughts that an individual performs on a daily basis, and specifically include habitual actions such as typing text, moving around, and inattentiveness.

[0088] A "device for collecting information" is a device that uses sensor devices, computer systems, etc., to acquire activity data and environmental information of a target individual.

[0089] An "artificial intelligence-based device" refers to a system that incorporates artificial intelligence technologies such as machine learning and deep learning for data analysis, and is used to detect changes in emotions and consciousness.

[0090] A "device for transmitting warnings to relevant parties" is a device that has a means of communication to quickly transmit abnormal data or events to the relevant people.

[0091] "Changes in feelings and awareness" refer to changes in an individual's internal emotions and psychological state, and these changes can be observed in the data.

[0092] This invention provides a system for monitoring an individual's daily activities in their living area and detecting changes in their mood and awareness early on. The server runs a program that collects data from sensor devices and smartphones. These devices acquire information about the individual's daily activities, such as their movement routes and the objects they come into contact with.

[0093] The server uses Python as its programming language and employs AI frameworks such as TENSORFLOW® or PyTorch to analyze the collected data. This analysis allows a generative AI model to detect patterns indicating changes in emotions and consciousness. Specifically, it understands changes in a person's mental state through emotion analysis and behavioral pattern analysis. If any abnormalities are detected, a notification is promptly sent to parents or relevant departments.

[0094] This technology operates on cloud infrastructure such as Amazon Web Services (AWS®) and Google Cloud to ensure secure and efficient data processing within a cloud environment. The overall system guarantees real-time data analysis and rapid notification, with a particular focus on protecting the mental health of minors.

[0095] As a concrete example, data on a child's behavior while playing in a park is recorded on a smartphone, and if an abnormal behavioral pattern is detected, a message is sent to the parent or guardian prompting them to check on the child's safety. An example of this prompt message is given to the AI ​​model: "Based on recent behavioral patterns, please detect if there has been a change in the feelings or awareness of a particular individual." In this way, the system provides safety and security in the field.

[0096] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0097] Step 1:

[0098] The device uses sensor equipment to collect data on an individual's daily activities within their living area. This data includes behavioral data such as text input and movement paths, transmitted via smartphones or smart glasses. For example, a sensor might record a child running around in a park. The input data is transmitted to a server using a secure protocol.

[0099] Step 2:

[0100] The server receives the collected data and formats it as preprocessing for AI analysis. It checks for data deficiencies and performs processing to impute missing values ​​as needed. For example, if data for a certain time period is missing, the imputation algorithm appropriately fills in the missing data. The output is a dataset formatted for analysis.

[0101] Step 3:

[0102] The server uses a generative AI model to perform sentiment and behavioral pattern analysis on the formatted data. Specifically, it uses TensorFlow to detect anomalies based on specific indicators while comparing them with past data. The analysis is performed according to the prompt "Detect whether there has been a change in the feelings or awareness of a specific individual based on their recent behavioral patterns." The output is an analysis result that includes indicators showing anomalies or possibilities.

[0103] Step 4:

[0104] If an anomaly is detected, the server immediately generates a warning message and sends it to the relevant parties. The notification is delivered to parents and educators as a text message or app notification. For example, a message such as "Your child is in an unstable state. Please check on them" might be generated. The output is the warning message that was sent.

[0105] Step 5:

[0106] The user takes the necessary actions based on the received warning message. Specifically, this involves checking the health status of the person concerned and, if necessary, consulting a specialist. The output is an appropriate response and countermeasure to the warning.

[0107] 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.

[0108] To implement this invention, multiple terminals and a central server are placed within the educational environment. The terminals monitor the daily activities of students in real time and collect various data related to educational activities. The collected data includes text input information, behavioral data, and a history of forgetfulness. Furthermore, this invention adds a function that analyzes the emotional state from the user's voice tone and facial expressions by combining it with an emotion engine.

[0109] The terminal retrieves this data and temporarily stores it in its internal memory. The stored data is then prepared to be sent to the server via a secure communication protocol. The server receives the data sent from the terminal and stores it in its database.

[0110] The server is equipped with artificial intelligence specialized in data analysis, and uses a novel method, combined with an emotion engine, to perform sentiment analysis of text data and analysis of behavioral patterns. This emotion engine analyzes the user's emotional state and integrates that information with other data to perform more accurate stress assessments.

[0111] To give a concrete example, the emotion engine detects that student B appears nervous during a classroom presentation, and that their voice tone is higher than usual. The device also captures notes and essays written by student B as text data, while simultaneously recording their movements within the classroom as behavioral data. This data is sent to a server and analyzed by artificial intelligence and the emotion engine. As a result, a combination of emotional changes and abnormal behavioral patterns is recognized as signs of stress. This recognition result is automatically notified to the teacher or counselor in charge.

[0112] In this way, the present invention enables multifaceted monitoring of students in educational settings and, by combining all data, including changes in emotions, allows for prompt and appropriate support. This promotes early intervention for students' mental health.

[0113] The following describes the processing flow.

[0114] Step 1:

[0115] The device monitors written information and daily activities of students, and uses an emotion engine to acquire emotional data from voice tone and facial expressions. This includes a process of collecting data in real time using microphones and cameras.

[0116] Step 2:

[0117] The device temporarily stores the collected text data, behavioral data, and emotional data in its internal memory. The stored data is then prepared to be sent to the server in a secure format using encryption technology.

[0118] Step 3:

[0119] The server receives data sent from the terminal and stores it in the database. This organizes the data into a state where it can be analyzed.

[0120] Step 4:

[0121] The server analyzes the stored data using an artificial intelligence model and an emotion engine. The emotion engine analyzes emotional data obtained from voice tone and facial expressions, and tracks changes in emotional state.

[0122] Step 5:

[0123] The server integrates the analysis results and comprehensively evaluates signs of stress by analyzing the sentiment of text data, abnormal behavioral patterns, and changes in emotional state. Based on this evaluation, if an anomaly is detected, it triggers a notification to educators.

[0124] Step 6:

[0125] The user (teacher or counselor) receives notifications from the server and reviews detailed analysis results. The notifications include indicators of emotional changes and behavioral abnormalities, which they use to plan actions for the student. They prepare to begin appropriate support by scheduling meetings or contacting parents.

[0126] (Example 2)

[0127] 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".

[0128] In today's educational environment, managing students' mental health is considered crucial, but traditional methods make it difficult to quickly and efficiently monitor individual students' emotional states and signs of stress. In particular, there is a need for a system that can detect abnormalities in students' emotional changes and behavioral patterns early and take appropriate action. To address this challenge, technology is needed that comprehensively analyzes multifaceted data on individual students to detect abnormalities in their emotions and behavior.

[0129] 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.

[0130] In this invention, the server includes a device for collecting information on an individual's daily activities within an educational facility, a device using artificial intelligence to analyze the collected information and evaluate their emotional state, and a device for transferring information to a person responsible for receiving specific notifications based on the evaluation of the emotional state. This makes it possible to comprehensively monitor the mental health of students in educational settings, detect changes in emotions and behavior early, and provide appropriate support quickly.

[0131] "Within an educational facility" refers to a place where a specific educational activity takes place, and includes environments such as schools and learning facilities.

[0132] "Individual daily activity information" refers to various information about each student's daily learning and life, and includes data such as written information, action information, and a history of memory impairment.

[0133] "Data collection devices" refer to equipment and software used to acquire activity information from students within educational facilities, and include sensors and digital devices.

[0134] "Artificial intelligence that analyzes and evaluates emotional states" refers to a system that uses collected information and machine learning and data analysis techniques to evaluate changes in the emotions and behaviors of individual students.

[0135] "Persons responsible for receiving specific notifications" refers to individuals within an educational institution who are responsible for managing and supporting students' health, including teachers and counselors.

[0136] A "device for transferring information" refers to a mechanism or system that provides a means of communication to quickly inform the recipient of the analyzed results.

[0137] The system for implementing this invention consists of multiple terminals and a central server located within an educational facility. The terminals include digital devices for collecting information on students' daily activities in real time, acquiring text information, motion information, and a history of memory impairment. This is achieved by combining sensors, cameras, and microphones. The terminals also feature an emotion engine for emotion analysis, which analyzes the emotional state of students from their voice tone and facial expressions.

[0138] The device temporarily stores the collected data in its internal memory. This data is then prepared to be sent to the server using a secure communication protocol. Specifically, protocols such as TLS / SSL are used to ensure that the data is transmitted securely.

[0139] The server stores the received data in an integrated database and analyzes the information using an artificial intelligence model specifically designed for data analysis. This AI combines emotion analysis and behavioral pattern analysis to assess students' stress levels. By utilizing a generative AI model to identify changes in emotions and abnormal behavioral patterns, the server can detect signs of stress early on.

[0140] The resulting data is immediately communicated to the teacher or counselor. This allows for a swift response to maintain the student's mental health. This system is useful for improving student mental health management in educational institutions and for prompt intervention when problems arise.

[0141] For example, if a student shows signs of agitation during a classroom presentation, the device captures changes in voice tone and facial expressions in real time, sends this information to a server for analysis, and detects signs of stress. Based on this, the person in charge can be quickly notified.

[0142] An example of a prompt might be: "Design a method to analyze students' stress levels and respond quickly through terminals and servers used in educational facilities. What technologies and protocols would be suitable?" This prompt is used in the process of suggesting appropriate methods to the generative AI model.

[0143] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0144] Step 1:

[0145] The device collects student text information, behavioral information, and a history of memory impairment in real time. Specifically, it captures input from the keyboard and touchscreen, and acquires the student's facial expressions and voice tone through the camera and microphone. This data is received as input and temporarily stored in internal memory.

[0146] Step 2:

[0147] The device analyzes emotional states from data acquired using an emotion engine. Based on input voice tone and facial expression information, it applies a machine learning algorithm to estimate the emotional state. The estimation results are stored as emotional data within the device.

[0148] Step 3:

[0149] The terminal prepares to send analyzed sentiment data and other activity information to the server using a secure communication protocol (e.g., TLS / SSL). It references data in its internal memory as input and creates a data package.

[0150] Step 4:

[0151] The server receives data sent from the terminal. The received data, including text information, behavioral information, and sentiment data, is stored in a database. The server checks the received data and performs processing to maintain data integrity.

[0152] Step 5:

[0153] The server performs data analysis using a generative AI model. It takes information stored in the database as input and performs calculations to identify changes in emotional state and abnormal behavioral patterns. As output, it generates a stress assessment result.

[0154] Step 6:

[0155] If the server detects signs of stress based on the analysis results, it will notify the teacher or counselor in charge. It uses the analysis results as input and sends notifications via email or a dedicated app. The notification information is sent out as output.

[0156] This series of processes makes it possible to analyze changes in students' emotions and behavior within educational facilities in real time and provide appropriate support quickly.

[0157] (Application Example 2)

[0158] 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".

[0159] There is a challenge in identifying the mental health of students in educational settings at an early stage and providing appropriate and prompt support. Traditional methods often rely on direct observation and judgments based on limited data, and there is a need to improve their accuracy. Furthermore, objective and continuous data collection and analysis regarding stress and mental state are insufficient.

[0160] 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.

[0161] In this invention, the server includes means for monitoring an individual's daily activities using an autonomous device, means for collecting facial expression data and voice data and transmitting them to a central memory device, and information processing means for performing emotion analysis using a generative model based on the transmitted data. This makes it possible to precisely grasp signs of an individual's stress and quickly notify the administrator when support is needed.

[0162] "Educational environment" refers to the place where educational activities take place or the elements that constitute that place, including classrooms where students learn and the entire school.

[0163] An "autonomous device" is a device that operates on its own without external intervention and performs a predetermined function; in this context, it specifically refers to a device that monitors student activities.

[0164] "Facial expression data" refers to data that digitally represents changes and characteristics of an individual's face, and is used to analyze the emotional states contained in facial expressions.

[0165] "Audio data" refers to data that digitally records an individual's voice, and is used to analyze emotions from the tone and pitch of the sound.

[0166] A "central memory device" is a device or system for centrally recording and storing data received from multiple devices via a network.

[0167] A "generative model" is a type of artificial intelligence that uses algorithms to generate new data and insights based on input data, and is typically used for data analysis and pattern recognition.

[0168] "Emotional analysis" refers to a method of analyzing an individual's emotional state from their facial expressions, voice, text, etc., and identifying that state.

[0169] "Information processing means" refers to a means of receiving data and performing calculations and analyses based on that data, and usually refers to a computer or its software.

[0170] An "administrator" is an individual or organization responsible for overseeing a system or process and managing personnel and resources as needed.

[0171] "Support" means providing appropriate advice and assistance to individuals facing challenges, and in educational settings, it particularly refers to promoting the mental health of children and students.

[0172] The system for implementing this invention is configured to accurately grasp the mental state of students in an educational environment and to provide appropriate support quickly. The system components include an autonomous device, a central memory device, and an information processing device including a generative model.

[0173] The devices operate autonomously within the educational environment, monitoring the daily activities of students. Specifically, the devices collect facial expression and voice data using input devices such as cameras and microphones. This data is transmitted in real time to a central memory device. The central memory device records and stores data received from multiple devices via the network.

[0174] The server is responsible for analyzing data collected from the central memory. This analysis utilizes a generative model, specifically an AI-powered emotion analysis system. Based on facial expression and voice data, the server leverages this generative AI model to analyze changes in an individual's emotional state. If the analysis detects signs of stress, the server automatically sends a warning to the administrator via a notification system. This notification provides foundational data for teachers and counselors to provide prompt and appropriate support to students.

[0175] As a concrete example, suppose a student faces a difficult problem during class, and their facial expression shows confusion. The device captures their facial expression with a camera and also collects subtle changes in their voice. The server analyzes this data using emotion analysis AI and detects that the student is experiencing high levels of stress. This information is then sent to the teacher, enabling follow-up with the student after class.

[0176] An example of a prompt to a generative AI model would be, "Based on the student's facial expression and voice data, analyze how stressed this student is." This prompt allows the AI ​​model to perform an appropriate analysis.

[0177] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0178] Step 1:

[0179] The device continuously monitors an individual's daily activities in the educational environment. Equipped with a camera and microphone, these sensors collect individual facial and voice data in real time. Input is raw facial and voice data obtained from the individual, while output is primary digital data.

[0180] Step 2:

[0181] Data collected by the terminal is transmitted to the central memory via a secure communication protocol. The terminal's task is to format the data appropriately and transfer it quickly. The input is facial expression data and voice data stored on the terminal itself, and the output is the formatted data sent to the central memory.

[0182] Step 3:

[0183] The server periodically retrieves data stored in central memory. Here, the server organizes the retrieved data in preparation for analysis and generates prompts to pass it to the sentiment analysis AI model. The input consists of various data from central memory, and the output is a dataset ready for analysis.

[0184] Step 4:

[0185] The server uses a generative AI model to perform sentiment analysis based on prompt messages. The server determines the individual's emotional state based on the received data and searches for signs of stress. The input is pre-prepared data for analysis, and the output is the analysis result indicating the emotional state.

[0186] Step 5:

[0187] The server determines whether signs of stress have been detected and automatically notifies the administrator if an anomaly is recognized. The server's task is to provide the administrator with information in an appropriate format and encourage a prompt response. The input is the analysis results that may indicate stress, and the output is the warning notification sent to the administrator.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Second Embodiment]

[0192] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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".

[0204] To implement this invention, a system will be constructed that uses terminals placed within the educational environment and a central server to monitor and analyze the daily activities of students. The terminals will have the functionality to acquire students' text input, behavioral data, and records of forgetfulness in real time. As a result, the terminals will capture the contents of notebooks, essays, and reflection papers, and record the status of forgotten items based on teacher input. In addition, IoT devices can be linked to monitor behavioral patterns in classrooms and on the school grounds.

[0205] This data is periodically encrypted and sent to a server. The server sets up an environment to analyze the received data and utilizes a pre-trained artificial intelligence model. This model performs sentiment analysis on text data and identifies pattern changes in behavioral data. If an anomaly is detected as a sign of stress as a result of this process, relevant educators are automatically notified.

[0206] As a concrete example, suppose student A's handwriting in the notes they submit during class has recently become messy. This information is captured by a device and sent to a server. The server's AI analyzes this information and compares it with past data to determine that the recent messy handwriting is abnormal. This abnormality is notified to the teacher, allowing for an early warning that student A may be experiencing some kind of stress. Based on this, the teacher can then take steps to support student A and prepare to offer individual counseling.

[0207] The entire system is equipped with infrastructure adaptable to the educational environment and is properly managed in accordance with guidelines for handling student data in a secure and privacy-conscious manner. In this way, a system is created that effectively promotes attention to the mental health of students in educational settings.

[0208] The following describes the processing flow.

[0209] Step 1:

[0210] The device monitors students' activities in school life and collects text input (notes, essays, reflections), behavioral data (travel patterns, interactions with friends, etc.), and records of memory loss. This includes a process of acquiring data using input devices and sensors.

[0211] Step 2:

[0212] The device temporarily stores the collected data in its internal memory. This stored data is encrypted at a specified frequency and prepared to be sent to the server using a secure communication protocol.

[0213] Step 3:

[0214] The server receives data sent from the terminal and stores it in the database. The database then performs preprocessing to allow for comparison with past data and pattern analysis.

[0215] Step 4:

[0216] The server applies artificial intelligence models to the stored data to perform sentiment analysis and behavioral pattern analysis. This analysis aims to identify signs and anomalies of stress based on pre-defined criteria.

[0217] Step 5:

[0218] The server triggers an alert based on the anomaly detection information obtained from the analysis results. This alert is sent to teachers and counselors associated with the student in question.

[0219] Step 6:

[0220] The user (teacher or counselor) receives a notification from the server and reviews its contents. The notification contains detailed information about signs or abnormalities of stress, which they use to consider how to respond to the student. For example, they may prepare to initiate specific support measures, such as planning a meeting with the student.

[0221] (Example 1)

[0222] 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."

[0223] Maintaining the mental health of students and proactively identifying and addressing excessive stress is crucial in the educational environment. However, traditional methods have made it difficult to accurately grasp changes in students' daily activities and signs of stress, making it challenging for teachers and educators to respond at the appropriate time. To address this challenge, there is a need for a method that efficiently and accurately detects signs of stress and promptly notifies educators.

[0224] 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.

[0225] In this invention, the server includes means for acquiring individual text data and behavioral information in an educational environment, means for encrypting the acquired data and transmitting it to a central information processing device, and means for analyzing emotional evaluation and changes in behavioral patterns using a generated AI model based on the data acquired by the central information processing device. This enables efficient detection of signs of stress in students and prompt notification and response to educators.

[0226] The term "educational environment" refers to the setting in schools and other learning facilities where students learn and engage in activities, and the environment in which teachers and educators provide guidance.

[0227] "Personal text data" refers to text information that students input or write within the educational environment, and this includes notes, essays, and reflection papers.

[0228] "Behavioral information" refers to data on the physical movements and habitual activities of students in classrooms, schoolyards, etc., and is information acquired by IoT devices.

[0229] "Encryption" refers to the process of converting data into an invisible format to protect it from third parties, and is essential for secure communication.

[0230] A "central information processing system" refers to a server or computer used to receive and analyze data transmitted from an educational environment.

[0231] A "generative AI model" refers to an artificial intelligence model used to analyze the sentiment evaluation and behavioral pattern fluctuations of text data, and is pre-trained based on data.

[0232] "Emotional assessment" refers to the process of analyzing the emotional state of students from text data and identifying positive or negative emotional tendencies.

[0233] "Variations in behavioral patterns" refers to the process of identifying abnormal or exceptional behavior by comparing it with normal behavioral information.

[0234] Detecting an "abnormality" refers to finding and identifying unusual signs or patterns of stress from data on daily activities.

[0235] "Responsible personnel" refers to educators or teachers who are responsible for receiving notifications of anomalies and taking appropriate action.

[0236] To implement this invention, a system will be constructed that effectively monitors the learning and activities of students by utilizing digital tools and network infrastructure in the educational environment. Specifically, it will be implemented using the following hardware and software.

[0237] First, devices are deployed within educational facilities. These include computer terminals, tablets, and IoT devices used by students, which capture students' text input and physical actions. The devices, for example, transmit the collected data via Wi-Fi to a server using an encryption protocol. This encryption ensures the security of the data.

[0238] The server functions as a central information processing unit, acquiring and analyzing received data. The data is analyzed using generative AI models to identify sentiment evaluations of text data and pattern variations in behavioral data. By using appropriate software (e.g., machine learning libraries and data analysis tools), rapid and efficient analysis is possible.

[0239] For example, a generative AI model might identify signs of stress by comparing recent behavioral data with past data, based on a prompt such as, "How can we detect changes that suggest a decline in mood from recent behavioral data?" This example prompt demonstrates the effectiveness of the AI's analysis process.

[0240] Based on this analytical data, users, such as teachers and educators, can identify students' stress and learning-related problems early on and provide opportunities for individualized guidance and counseling. If an anomaly is detected, the server will send appropriate notifications to educators, enabling prompt action and implementation of corrective measures.

[0241] As described above, this invention is an effective system for supporting the mental health of students in educational settings.

[0242] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0243] Step 1:

[0244] The terminal collects text input and behavioral information from students. Input includes text data entered by students and movement patterns recorded by IoT devices. The terminal collects this data and stores it locally. Specifically, it records key input in real time when students are writing notes or compositions, and receives behavioral information from wearable devices and classroom sensors.

[0245] Step 2:

[0246] The terminal sends the collected data to the server using an encryption protocol. The input consists of text data and behavioral information collected in step 1. The terminal encrypts this data and transfers it to the server via a secure communication channel. Specifically, it uses the TLS / SSL protocol and sends the data in batch processing to prevent information leakage.

[0247] Step 3:

[0248] The server inputs the received data into a generative AI model for analysis. The input consists of encrypted text data and behavioral information. The server decrypts this data and uses the generative AI model to analyze emotional evaluations and changes in behavioral patterns. Specifically, it uses natural language processing techniques to calculate emotional scores from text and identifies abnormal patterns from behavioral data.

[0249] Step 4:

[0250] The server sends a notification to the responsible party if an anomaly is detected. The input is the analysis results from step 3. The server evaluates the analysis results, and if it determines an anomaly is detected, it sends an alert to the education personnel. Specifically, it uses email and app notifications to enable the responsible party to take immediate action.

[0251] (Application Example 1)

[0252] 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."

[0253] In modern society, there is a need to detect changes in people's feelings and awareness early on in their daily lives and prevent mental health problems. However, existing systems have the challenge of not being able to monitor these changes in real time and respond quickly. In particular, it is difficult to notice invisible stress and psychological changes in residential areas, and if appropriate measures are not taken, it can lead to serious problems.

[0254] 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.

[0255] In this invention, the server includes a device for collecting information on an individual's daily activities within a residential area, a device using artificial intelligence that analyzes the collected information to detect changes in feelings and consciousness, and a device for transmitting warnings to relevant parties in accordance with the detected changes in feelings and consciousness. This makes it possible to detect psychological changes in an individual within a residential area at an early stage, enabling the maintenance and improvement of mental health.

[0256] A "residential area" refers to the place where an individual conducts their daily life and the surrounding environment, and mainly includes residential areas and nearby public spaces.

[0257] "Individual daily activities" refer to actions, behaviors, and thoughts that an individual performs on a daily basis, and specifically include habitual actions such as typing text, moving around, and inattentiveness.

[0258] A "device for collecting information" is a device that uses sensor devices, computer systems, etc., to acquire activity data and environmental information of a target individual.

[0259] An "artificial intelligence-based device" refers to a system that incorporates artificial intelligence technologies such as machine learning and deep learning for data analysis, and is used to detect changes in emotions and consciousness.

[0260] A "device for transmitting warnings to relevant parties" is a device that has a means of communication to quickly transmit abnormal data or events to the relevant people.

[0261] "Changes in feelings and awareness" refer to changes in an individual's internal emotions and psychological state, and these changes can be observed in the data.

[0262] This invention provides a system for monitoring an individual's daily activities in their living area and detecting changes in their mood and awareness early on. The server runs a program that collects data from sensor devices and smartphones. These devices acquire information about the individual's daily activities, such as their movement routes and the objects they come into contact with.

[0263] The server uses Python as its programming language and employs AI frameworks such as TensorFlow or PyTorch to analyze the collected data. This analysis allows a generative AI model to detect patterns indicating changes in emotions and consciousness. Specifically, it understands changes in a person's mental state through emotion analysis and behavioral pattern analysis. If any abnormalities are detected, a notification is promptly sent to parents or relevant departments.

[0264] This technology operates on cloud infrastructure such as Amazon Web Services (AWS) and Google Cloud to ensure secure and efficient data processing within a cloud environment. The overall system guarantees real-time data analysis and rapid notification, with a particular focus on protecting the mental health of minors.

[0265] As a concrete example, data on a child's behavior while playing in a park is recorded on a smartphone, and if an abnormal behavioral pattern is detected, a message is sent to the parent or guardian prompting them to check on the child's safety. An example of this prompt message is given to the AI ​​model: "Based on recent behavioral patterns, please detect if there has been a change in the feelings or awareness of a particular individual." In this way, the system provides safety and security in the field.

[0266] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0267] Step 1:

[0268] The device uses sensor equipment to collect data on an individual's daily activities within their living area. This data includes behavioral data such as text input and movement paths, transmitted via smartphones or smart glasses. For example, a sensor might record a child running around in a park. The input data is transmitted to a server using a secure protocol.

[0269] Step 2:

[0270] The server receives the collected data and formats it as preprocessing for AI analysis. It checks for data deficiencies and performs processing to impute missing values ​​as needed. For example, if data for a certain time period is missing, the imputation algorithm appropriately fills in the missing data. The output is a dataset formatted for analysis.

[0271] Step 3:

[0272] The server uses a generative AI model to perform sentiment and behavioral pattern analysis on the formatted data. Specifically, it uses TensorFlow to detect anomalies based on specific indicators while comparing them with past data. The analysis is performed according to the prompt "Detect whether there has been a change in the feelings or awareness of a specific individual based on their recent behavioral patterns." The output is an analysis result that includes indicators showing anomalies or possibilities.

[0273] Step 4:

[0274] If an anomaly is detected, the server immediately generates a warning message and sends it to the relevant parties. The notification is delivered to parents and educators as a text message or app notification. For example, a message such as "Your child is in an unstable state. Please check on them" might be generated. The output is the warning message that was sent.

[0275] Step 5:

[0276] The user takes the necessary actions based on the received warning message. Specifically, this involves checking the health status of the person concerned and, if necessary, consulting a specialist. The output is an appropriate response and countermeasure to the warning.

[0277] 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.

[0278] To implement this invention, multiple terminals and a central server are placed within the educational environment. The terminals monitor the daily activities of students in real time and collect various data related to educational activities. The collected data includes text input information, behavioral data, and a history of forgetfulness. Furthermore, this invention adds a function that analyzes the emotional state from the user's voice tone and facial expressions by combining it with an emotion engine.

[0279] The terminal retrieves this data and temporarily stores it in its internal memory. The stored data is then prepared to be sent to the server via a secure communication protocol. The server receives the data sent from the terminal and stores it in its database.

[0280] The server is equipped with artificial intelligence specialized in data analysis, and uses a novel method, combined with an emotion engine, to perform sentiment analysis of text data and analysis of behavioral patterns. This emotion engine analyzes the user's emotional state and integrates that information with other data to perform more accurate stress assessments.

[0281] As a specific example, when student B shows signs of nervousness during a presentation in the classroom, the emotion engine detects that the voice tone is higher than usual. The terminal captures the notes and compositions written by student B as text data, and on the other hand, records the movements within the classroom as behavior data. These data are transmitted to the server and analyzed by artificial intelligence and the emotion engine. As a result, a combination of emotional changes and abnormal behavior patterns is recognized as a sign of stress. This recognition result is automatically notified to the responsible teacher or counselor.

[0282] In this way, the present invention realizes multi-faceted monitoring of children and students in the educational field, and by combining all data including emotional changes, enables rapid and appropriate support. Thereby, it is possible to promote early response to the mental health of students.

[0283] The processing flow will be described below.

[0284] Step 1:

[0285] The terminal monitors the character information described by children and students and their daily behaviors, and uses an emotion engine to obtain emotion data from voice tones and expressions. This includes a process of collecting data in real time using a microphone and a camera.

[0286] Step 2:

[0287] The terminal temporarily stores the collected character data, behavior data, and emotion data in internal memory. The stored data is prepared to be transmitted to the server in a secure form using encryption technology.

[0288] Step 3:

[0289] The server receives the data transmitted from the terminal and stores it in a database. Thereby, the data is organized into an analyzable state.

[0290] Step 4:

[0291] The server analyzes the stored data using an artificial intelligence model and an emotion engine. The emotion engine analyzes emotional data obtained from voice tone and facial expressions, and tracks changes in emotional state.

[0292] Step 5:

[0293] The server integrates the analysis results and comprehensively evaluates signs of stress by analyzing the sentiment of text data, abnormal behavioral patterns, and changes in emotional state. Based on this evaluation, if an anomaly is detected, it triggers a notification to educators.

[0294] Step 6:

[0295] The user (teacher or counselor) receives notifications from the server and reviews detailed analysis results. The notifications include indicators of emotional changes and behavioral abnormalities, which they use to plan actions for the student. They prepare to begin appropriate support by scheduling meetings or contacting parents.

[0296] (Example 2)

[0297] 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".

[0298] In today's educational environment, managing students' mental health is considered crucial, but traditional methods make it difficult to quickly and efficiently monitor individual students' emotional states and signs of stress. In particular, there is a need for a system that can detect abnormalities in students' emotional changes and behavioral patterns early and take appropriate action. To address this challenge, technology is needed that comprehensively analyzes multifaceted data on individual students to detect abnormalities in their emotions and behavior.

[0299] 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.

[0300] In this invention, the server includes a device for collecting information on an individual's daily activities within an educational facility, a device using artificial intelligence to analyze the collected information and evaluate their emotional state, and a device for transferring information to a person responsible for receiving specific notifications based on the evaluation of the emotional state. This makes it possible to comprehensively monitor the mental health of students in educational settings, detect changes in emotions and behavior early, and provide appropriate support quickly.

[0301] "Within an educational facility" refers to a place where a specific educational activity takes place, and includes environments such as schools and learning facilities.

[0302] "Individual daily activity information" refers to various information about each student's daily learning and life, and includes data such as written information, action information, and a history of memory impairment.

[0303] "Data collection devices" refer to equipment and software used to acquire activity information from students within educational facilities, and include sensors and digital devices.

[0304] "Artificial intelligence that analyzes and evaluates emotional states" refers to a system that uses collected information and machine learning and data analysis techniques to evaluate changes in the emotions and behaviors of individual students.

[0305] "Persons responsible for receiving specific notifications" refers to individuals within an educational institution who are responsible for managing and supporting students' health, including teachers and counselors.

[0306] A "device for transferring information" refers to a mechanism or system that provides a means of communication to quickly inform the recipient of the analyzed results.

[0307] The system for implementing this invention consists of a plurality of terminals arranged within an educational facility and a central server. The terminals include digital devices for collecting students' daily activity information in real-time, acquiring character information, motion information, history of memory disorders, etc. This is achieved by combining sensors, cameras, and microphones. This terminal further includes an emotion engine for emotion analysis, which analyzes the emotional state from the students' voice tones and expressions.

[0308] The terminal temporarily stores the collected data in its internal memory. This data is prepared to be sent to the server using a secure communication protocol. Specifically, protocols such as TLS / SSL are used to ensure the secure transmission of data.

[0309] The server stores the received data in an integrated database and analyzes the information by leveraging an artificial intelligence model dedicated to data analysis. This artificial intelligence combines emotion analysis and motion pattern analysis to conduct stress evaluations of students. The server can utilize a generative AI model to identify changes in emotions and abnormalities in behavior patterns, thereby detecting signs of stress at an early stage.

[0310] The calculated data is immediately notified to the responsible teachers and counselors. This enables prompt responses to maintain the mental health of students. This mechanism is useful for improving the mental health management of students in educational facilities and for intervening promptly when problems occur.

[0311] As a specific example, when a student shows signs of nervousness during a presentation in the classroom, the terminal captures changes in voice tone and facial expressions in real-time, transmits that information to the server for analysis, and detects signs of stress based on it. Based on this, it can promptly notify the responsible person.

[0312] An example of a prompt might be: "Design a method to analyze students' stress levels and respond quickly through terminals and servers used in educational facilities. What technologies and protocols would be suitable?" This prompt is used in the process of suggesting appropriate methods to the generative AI model.

[0313] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0314] Step 1:

[0315] The device collects student text information, behavioral information, and a history of memory impairment in real time. Specifically, it captures input from the keyboard and touchscreen, and acquires the student's facial expressions and voice tone through the camera and microphone. This data is received as input and temporarily stored in internal memory.

[0316] Step 2:

[0317] The device analyzes emotional states from data acquired using an emotion engine. Based on input voice tone and facial expression information, it applies a machine learning algorithm to estimate the emotional state. The estimation results are stored as emotional data within the device.

[0318] Step 3:

[0319] The terminal prepares to send analyzed sentiment data and other activity information to the server using a secure communication protocol (e.g., TLS / SSL). It references data in its internal memory as input and creates a data package.

[0320] Step 4:

[0321] The server receives data sent from the terminal. The received data, including text information, behavioral information, and sentiment data, is stored in a database. The server checks the received data and performs processing to maintain data integrity.

[0322] Step 5:

[0323] The server performs data analysis using a generative AI model. It takes information stored in the database as input and performs calculations to identify changes in emotional state and abnormal behavioral patterns. As output, it generates a stress assessment result.

[0324] Step 6:

[0325] If the server detects signs of stress based on the analysis results, it will notify the teacher or counselor in charge. It uses the analysis results as input and sends notifications via email or a dedicated app. The notification information is sent out as output.

[0326] This series of processes makes it possible to analyze changes in students' emotions and behavior within educational facilities in real time and provide appropriate support quickly.

[0327] (Application Example 2)

[0328] 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."

[0329] There is a challenge in identifying the mental health of students in educational settings at an early stage and providing appropriate and prompt support. Traditional methods often rely on direct observation and judgments based on limited data, and there is a need to improve their accuracy. Furthermore, objective and continuous data collection and analysis regarding stress and mental state are insufficient.

[0330] 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.

[0331] In this invention, the server includes means for monitoring an individual's daily activities using an autonomous device, means for collecting facial expression data and voice data and transmitting them to a central memory device, and information processing means for performing emotion analysis using a generative model based on the transmitted data. This makes it possible to precisely grasp signs of an individual's stress and quickly notify the administrator when support is needed.

[0332] "Educational environment" refers to the place where educational activities take place or the elements that constitute that place, including classrooms where students learn and the entire school.

[0333] An "autonomous device" is a device that operates on its own without external intervention and performs a predetermined function; in this context, it specifically refers to a device that monitors student activities.

[0334] "Facial expression data" refers to data that digitally represents changes and characteristics of an individual's face, and is used to analyze the emotional states contained in facial expressions.

[0335] "Audio data" refers to data that digitally records an individual's voice, and is used to analyze emotions from the tone and pitch of the sound.

[0336] A "central memory device" is a device or system for centrally recording and storing data received from multiple devices via a network.

[0337] A "generative model" is a type of artificial intelligence that uses algorithms to generate new data and insights based on input data, and is typically used for data analysis and pattern recognition.

[0338] "Emotional analysis" refers to a method of analyzing an individual's emotional state from their facial expressions, voice, text, etc., and identifying that state.

[0339] "Information processing means" refers to a means of receiving data and performing calculations and analyses based on that data, and usually refers to a computer or its software.

[0340] An "administrator" is an individual or organization responsible for overseeing a system or process and managing personnel and resources as needed.

[0341] "Support" means providing appropriate advice and assistance to individuals facing challenges, and in educational settings, it particularly refers to promoting the mental health of children and students.

[0342] The system for implementing this invention is configured to accurately grasp the mental state of students in an educational environment and to provide appropriate support quickly. The system components include an autonomous device, a central memory device, and an information processing device including a generative model.

[0343] The devices operate autonomously within the educational environment, monitoring the daily activities of students. Specifically, the devices collect facial expression and voice data using input devices such as cameras and microphones. This data is transmitted in real time to a central memory device. The central memory device records and stores data received from multiple devices via the network.

[0344] The server is responsible for analyzing data collected from the central memory. This analysis utilizes a generative model, specifically an AI-powered emotion analysis system. Based on facial expression and voice data, the server leverages this generative AI model to analyze changes in an individual's emotional state. If the analysis detects signs of stress, the server automatically sends a warning to the administrator via a notification system. This notification provides foundational data for teachers and counselors to provide prompt and appropriate support to students.

[0345] As a concrete example, suppose a student faces a difficult problem during class, and their facial expression shows confusion. The device captures their facial expression with a camera and also collects subtle changes in their voice. The server analyzes this data using emotion analysis AI and detects that the student is experiencing high levels of stress. This information is then sent to the teacher, enabling follow-up with the student after class.

[0346] An example of a prompt to a generative AI model would be, "Based on the student's facial expression and voice data, analyze how stressed this student is." This prompt allows the AI ​​model to perform an appropriate analysis.

[0347] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0348] Step 1:

[0349] The device continuously monitors an individual's daily activities in the educational environment. Equipped with a camera and microphone, these sensors collect individual facial and voice data in real time. Input is raw facial and voice data obtained from the individual, while output is primary digital data.

[0350] Step 2:

[0351] Data collected by the terminal is transmitted to the central memory via a secure communication protocol. The terminal's task is to format the data appropriately and transfer it quickly. The input is facial expression data and voice data stored on the terminal itself, and the output is the formatted data sent to the central memory.

[0352] Step 3:

[0353] The server periodically retrieves data stored in central memory. Here, the server organizes the retrieved data in preparation for analysis and generates prompts to pass it to the sentiment analysis AI model. The input consists of various data from central memory, and the output is a dataset ready for analysis.

[0354] Step 4:

[0355] The server uses a generative AI model to perform sentiment analysis based on prompt messages. The server determines the individual's emotional state based on the received data and searches for signs of stress. The input is pre-prepared data for analysis, and the output is the analysis result indicating the emotional state.

[0356] Step 5:

[0357] The server determines whether signs of stress have been detected and automatically notifies the administrator if an anomaly is recognized. The server's task is to provide the administrator with information in an appropriate format and encourage a prompt response. The input is the analysis results that may indicate stress, and the output is the warning notification sent to the administrator.

[0358] 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.

[0359] 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.

[0360] 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.

[0361] [Third Embodiment]

[0362] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0363] 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.

[0364] 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).

[0365] 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.

[0366] 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.

[0367] 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).

[0368] 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.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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".

[0374] To implement this invention, a system will be constructed that uses terminals placed within the educational environment and a central server to monitor and analyze the daily activities of students. The terminals will have the functionality to acquire students' text input, behavioral data, and records of forgetfulness in real time. As a result, the terminals will capture the contents of notebooks, essays, and reflection papers, and record the status of forgotten items based on teacher input. In addition, IoT devices can be linked to monitor behavioral patterns in classrooms and on the school grounds.

[0375] This data is periodically encrypted and sent to a server. The server sets up an environment to analyze the received data and utilizes a pre-trained artificial intelligence model. This model performs sentiment analysis on text data and identifies pattern changes in behavioral data. If an anomaly is detected as a sign of stress as a result of this process, relevant educators are automatically notified.

[0376] As a concrete example, suppose student A's handwriting in the notes they submit during class has recently become messy. This information is captured by a device and sent to a server. The server's AI analyzes this information and compares it with past data to determine that the recent messy handwriting is abnormal. This abnormality is notified to the teacher, allowing for an early warning that student A may be experiencing some kind of stress. Based on this, the teacher can then take steps to support student A and prepare to offer individual counseling.

[0377] The entire system is equipped with infrastructure adaptable to the educational environment and is properly managed in accordance with guidelines for handling student data in a secure and privacy-conscious manner. In this way, a system is created that effectively promotes attention to the mental health of students in educational settings.

[0378] The following describes the processing flow.

[0379] Step 1:

[0380] The device monitors students' activities in school life and collects text input (notes, essays, reflections), behavioral data (travel patterns, interactions with friends, etc.), and records of memory loss. This includes a process of acquiring data using input devices and sensors.

[0381] Step 2:

[0382] The device temporarily stores the collected data in its internal memory. This stored data is encrypted at a specified frequency and prepared to be sent to the server using a secure communication protocol.

[0383] Step 3:

[0384] The server receives data sent from the terminal and stores it in the database. The database then performs preprocessing to allow for comparison with past data and pattern analysis.

[0385] Step 4:

[0386] The server applies artificial intelligence models to the stored data to perform sentiment analysis and behavioral pattern analysis. This analysis aims to identify signs and anomalies of stress based on pre-defined criteria.

[0387] Step 5:

[0388] The server triggers an alert based on the anomaly detection information obtained from the analysis results. This alert is sent to teachers and counselors associated with the student in question.

[0389] Step 6:

[0390] The user (teacher or counselor) receives a notification from the server and reviews its contents. The notification contains detailed information about signs or abnormalities of stress, which they use to consider how to respond to the student. For example, they may prepare to initiate specific support measures, such as planning a meeting with the student.

[0391] (Example 1)

[0392] 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."

[0393] Maintaining the mental health of students and proactively identifying and addressing excessive stress is crucial in the educational environment. However, traditional methods have made it difficult to accurately grasp changes in students' daily activities and signs of stress, making it challenging for teachers and educators to respond at the appropriate time. To address this challenge, there is a need for a method that efficiently and accurately detects signs of stress and promptly notifies educators.

[0394] 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.

[0395] In this invention, the server includes means for acquiring individual text data and behavioral information in an educational environment, means for encrypting the acquired data and transmitting it to a central information processing device, and means for analyzing emotional evaluation and changes in behavioral patterns using a generated AI model based on the data acquired by the central information processing device. This enables efficient detection of signs of stress in students and prompt notification and response to educators.

[0396] The term "educational environment" refers to the setting in schools and other learning facilities where students learn and engage in activities, and the environment in which teachers and educators provide guidance.

[0397] "Personal text data" refers to text information that students input or write within the educational environment, and this includes notes, essays, and reflection papers.

[0398] "Behavioral information" refers to data on the physical movements and habitual activities of students in classrooms, schoolyards, etc., and is information acquired by IoT devices.

[0399] "Encryption" refers to the process of converting data into an invisible format to protect it from third parties, and is essential for secure communication.

[0400] A "central information processing system" refers to a server or computer used to receive and analyze data transmitted from an educational environment.

[0401] A "generative AI model" refers to an artificial intelligence model used to analyze the sentiment evaluation and behavioral pattern fluctuations of text data, and is pre-trained based on data.

[0402] "Emotional assessment" refers to the process of analyzing the emotional state of students from text data and identifying positive or negative emotional tendencies.

[0403] "Variations in behavioral patterns" refers to the process of identifying abnormal or exceptional behavior by comparing it with normal behavioral information.

[0404] Detecting an "abnormality" refers to finding and identifying unusual signs or patterns of stress from data on daily activities.

[0405] "Responsible personnel" refers to educators or teachers who are responsible for receiving notifications of anomalies and taking appropriate action.

[0406] To implement this invention, a system will be constructed that effectively monitors the learning and activities of students by utilizing digital tools and network infrastructure in the educational environment. Specifically, it will be implemented using the following hardware and software.

[0407] First, devices are deployed within educational facilities. These include computer terminals, tablets, and IoT devices used by students, which capture students' text input and physical actions. The devices, for example, transmit the collected data via Wi-Fi to a server using an encryption protocol. This encryption ensures the security of the data.

[0408] The server functions as a central information processing unit, acquiring and analyzing received data. The data is analyzed using generative AI models to identify sentiment evaluations of text data and pattern variations in behavioral data. By using appropriate software (e.g., machine learning libraries and data analysis tools), rapid and efficient analysis is possible.

[0409] For example, a generative AI model might identify signs of stress by comparing recent behavioral data with past data, based on a prompt such as, "How can we detect changes that suggest a decline in mood from recent behavioral data?" This example prompt demonstrates the effectiveness of the AI's analysis process.

[0410] Based on this analytical data, users, such as teachers and educators, can identify students' stress and learning-related problems early on and provide opportunities for individualized guidance and counseling. If an anomaly is detected, the server will send appropriate notifications to educators, enabling prompt action and implementation of corrective measures.

[0411] As described above, this invention is an effective system for supporting the mental health of students in educational settings.

[0412] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0413] Step 1:

[0414] The terminal collects text input and behavioral information from students. Input includes text data entered by students and movement patterns recorded by IoT devices. The terminal collects this data and stores it locally. Specifically, it records key input in real time when students are writing notes or compositions, and receives behavioral information from wearable devices and classroom sensors.

[0415] Step 2:

[0416] The terminal sends the collected data to the server using an encryption protocol. The input consists of text data and behavioral information collected in step 1. The terminal encrypts this data and transfers it to the server via a secure communication channel. Specifically, it uses the TLS / SSL protocol and sends the data in batch processing to prevent information leakage.

[0417] Step 3:

[0418] The server inputs the received data into a generative AI model for analysis. The input consists of encrypted text data and behavioral information. The server decrypts this data and uses the generative AI model to analyze emotional evaluations and changes in behavioral patterns. Specifically, it uses natural language processing techniques to calculate emotional scores from text and identifies abnormal patterns from behavioral data.

[0419] Step 4:

[0420] The server sends a notification to the responsible party if an anomaly is detected. The input is the analysis results from step 3. The server evaluates the analysis results, and if it determines an anomaly is detected, it sends an alert to the education personnel. Specifically, it uses email and app notifications to enable the responsible party to take immediate action.

[0421] (Application Example 1)

[0422] 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."

[0423] In modern society, there is a need to detect changes in people's feelings and awareness early on in their daily lives and prevent mental health problems. However, existing systems have the challenge of not being able to monitor these changes in real time and respond quickly. In particular, it is difficult to notice invisible stress and psychological changes in residential areas, and if appropriate measures are not taken, it can lead to serious problems.

[0424] 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.

[0425] In this invention, the server includes a device for collecting information on an individual's daily activities within a residential area, a device using artificial intelligence that analyzes the collected information to detect changes in feelings and consciousness, and a device for transmitting warnings to relevant parties in accordance with the detected changes in feelings and consciousness. This makes it possible to detect psychological changes in an individual within a residential area at an early stage, enabling the maintenance and improvement of mental health.

[0426] A "residential area" refers to the place where an individual conducts their daily life and the surrounding environment, and mainly includes residential areas and nearby public spaces.

[0427] "Individual daily activities" refer to actions, behaviors, and thoughts that an individual performs on a daily basis, and specifically include habitual actions such as typing text, moving around, and inattentiveness.

[0428] A "device for collecting information" is a device that uses sensor devices, computer systems, etc., to acquire activity data and environmental information of a target individual.

[0429] An "artificial intelligence-based device" refers to a system that incorporates artificial intelligence technologies such as machine learning and deep learning for data analysis, and is used to detect changes in emotions and consciousness.

[0430] A "device for transmitting warnings to relevant parties" is a device that has a means of communication to quickly transmit abnormal data or events to the relevant people.

[0431] "Changes in feelings and awareness" refer to changes in an individual's internal emotions and psychological state, and these changes can be observed in the data.

[0432] This invention provides a system for monitoring an individual's daily activities in their living area and detecting changes in their mood and awareness early on. The server runs a program that collects data from sensor devices and smartphones. These devices acquire information about the individual's daily activities, such as their movement routes and the objects they come into contact with.

[0433] The server uses Python as its programming language and employs AI frameworks such as TensorFlow or PyTorch to analyze the collected data. This analysis allows a generative AI model to detect patterns indicating changes in emotions and consciousness. Specifically, it understands changes in a person's mental state through emotion analysis and behavioral pattern analysis. If any abnormalities are detected, a notification is promptly sent to parents or relevant departments.

[0434] This technology operates on cloud infrastructure such as Amazon Web Services (AWS) and Google Cloud to ensure secure and efficient data processing within a cloud environment. The overall system guarantees real-time data analysis and rapid notification, with a particular focus on protecting the mental health of minors.

[0435] As a concrete example, data on a child's behavior while playing in a park is recorded on a smartphone, and if an abnormal behavioral pattern is detected, a message is sent to the parent or guardian prompting them to check on the child's safety. An example of this prompt message is given to the AI ​​model: "Based on recent behavioral patterns, please detect if there has been a change in the feelings or awareness of a particular individual." In this way, the system provides safety and security in the field.

[0436] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0437] Step 1:

[0438] The device uses sensor equipment to collect data on an individual's daily activities within their living area. This data includes behavioral data such as text input and movement paths, transmitted via smartphones or smart glasses. For example, a sensor might record a child running around in a park. The input data is transmitted to a server using a secure protocol.

[0439] Step 2:

[0440] The server receives the collected data and formats it as preprocessing for AI analysis. It checks for data deficiencies and performs processing to impute missing values ​​as needed. For example, if data for a certain time period is missing, the imputation algorithm appropriately fills in the missing data. The output is a dataset formatted for analysis.

[0441] Step 3:

[0442] The server uses a generative AI model to perform sentiment and behavioral pattern analysis on the formatted data. Specifically, it uses TensorFlow to detect anomalies based on specific indicators while comparing them with past data. The analysis is performed according to the prompt "Detect whether there has been a change in the feelings or awareness of a specific individual based on their recent behavioral patterns." The output is an analysis result that includes indicators showing anomalies or possibilities.

[0443] Step 4:

[0444] If an anomaly is detected, the server immediately generates a warning message and sends it to the relevant parties. The notification is delivered to parents and educators as a text message or app notification. For example, a message such as "Your child is in an unstable state. Please check on them" might be generated. The output is the warning message that was sent.

[0445] Step 5:

[0446] The user takes the necessary actions based on the received warning message. Specifically, this involves checking the health status of the person concerned and, if necessary, consulting a specialist. The output is an appropriate response and countermeasure to the warning.

[0447] 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.

[0448] To implement this invention, multiple terminals and a central server are placed within the educational environment. The terminals monitor the daily activities of students in real time and collect various data related to educational activities. The collected data includes text input information, behavioral data, and a history of forgetfulness. Furthermore, this invention adds a function that analyzes the emotional state from the user's voice tone and facial expressions by combining it with an emotion engine.

[0449] The terminal retrieves this data and temporarily stores it in its internal memory. The stored data is then prepared to be sent to the server via a secure communication protocol. The server receives the data sent from the terminal and stores it in its database.

[0450] The server is equipped with artificial intelligence specialized in data analysis, and uses a novel method, combined with an emotion engine, to perform sentiment analysis of text data and analysis of behavioral patterns. This emotion engine analyzes the user's emotional state and integrates that information with other data to perform more accurate stress assessments.

[0451] To give a concrete example, the emotion engine detects that student B appears nervous during a classroom presentation, and that their voice tone is higher than usual. The device also captures notes and essays written by student B as text data, while simultaneously recording their movements within the classroom as behavioral data. This data is sent to a server and analyzed by artificial intelligence and the emotion engine. As a result, a combination of emotional changes and abnormal behavioral patterns is recognized as signs of stress. This recognition result is automatically notified to the teacher or counselor in charge.

[0452] In this way, the present invention enables multifaceted monitoring of students in educational settings and, by combining all data, including changes in emotions, allows for prompt and appropriate support. This promotes early intervention for students' mental health.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] The device monitors written information and daily activities of students, and uses an emotion engine to acquire emotional data from voice tone and facial expressions. This includes a process of collecting data in real time using microphones and cameras.

[0456] Step 2:

[0457] The device temporarily stores the collected text data, behavioral data, and emotional data in its internal memory. The stored data is then prepared to be sent to the server in a secure format using encryption technology.

[0458] Step 3:

[0459] The server receives data sent from the terminal and stores it in the database. This organizes the data into a state where it can be analyzed.

[0460] Step 4:

[0461] The server analyzes the stored data using an artificial intelligence model and an emotion engine. The emotion engine analyzes emotional data obtained from voice tone and facial expressions, and tracks changes in emotional state.

[0462] Step 5:

[0463] The server integrates the analysis results and comprehensively evaluates signs of stress by analyzing the sentiment of text data, abnormal behavioral patterns, and changes in emotional state. Based on this evaluation, if an anomaly is detected, it triggers a notification to educators.

[0464] Step 6:

[0465] The user (teacher or counselor) receives notifications from the server and reviews detailed analysis results. The notifications include indicators of emotional changes and behavioral abnormalities, which they use to plan actions for the student. They prepare to begin appropriate support by scheduling meetings or contacting parents.

[0466] (Example 2)

[0467] 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."

[0468] In today's educational environment, managing students' mental health is considered crucial, but traditional methods make it difficult to quickly and efficiently monitor individual students' emotional states and signs of stress. In particular, there is a need for a system that can detect abnormalities in students' emotional changes and behavioral patterns early and take appropriate action. To address this challenge, technology is needed that comprehensively analyzes multifaceted data on individual students to detect abnormalities in their emotions and behavior.

[0469] 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.

[0470] In this invention, the server includes a device for collecting information on an individual's daily activities within an educational facility, a device using artificial intelligence to analyze the collected information and evaluate their emotional state, and a device for transferring information to a person responsible for receiving specific notifications based on the evaluation of the emotional state. This makes it possible to comprehensively monitor the mental health of students in educational settings, detect changes in emotions and behavior early, and provide appropriate support quickly.

[0471] "Within an educational facility" refers to a place where a specific educational activity takes place, and includes environments such as schools and learning facilities.

[0472] "Individual daily activity information" refers to various information about each student's daily learning and life, and includes data such as written information, action information, and a history of memory impairment.

[0473] "Data collection devices" refer to equipment and software used to acquire activity information from students within educational facilities, and include sensors and digital devices.

[0474] "Artificial intelligence that analyzes and evaluates emotional states" refers to a system that uses collected information and machine learning and data analysis techniques to evaluate changes in the emotions and behaviors of individual students.

[0475] "Persons responsible for receiving specific notifications" refers to individuals within an educational institution who are responsible for managing and supporting students' health, including teachers and counselors.

[0476] A "device for transferring information" refers to a mechanism or system that provides a means of communication to quickly inform the recipient of the analyzed results.

[0477] The system for implementing this invention consists of multiple terminals and a central server located within an educational facility. The terminals include digital devices for collecting information on students' daily activities in real time, acquiring text information, motion information, and a history of memory impairment. This is achieved by combining sensors, cameras, and microphones. The terminals also feature an emotion engine for emotion analysis, which analyzes the emotional state of students from their voice tone and facial expressions.

[0478] The device temporarily stores the collected data in its internal memory. This data is then prepared to be sent to the server using a secure communication protocol. Specifically, protocols such as TLS / SSL are used to ensure that the data is transmitted securely.

[0479] The server stores the received data in an integrated database and analyzes the information using an artificial intelligence model specifically designed for data analysis. This AI combines emotion analysis and behavioral pattern analysis to assess students' stress levels. By utilizing a generative AI model to identify changes in emotions and abnormal behavioral patterns, the server can detect signs of stress early on.

[0480] The resulting data is immediately communicated to the teacher or counselor. This allows for a swift response to maintain the student's mental health. This system is useful for improving student mental health management in educational institutions and for prompt intervention when problems arise.

[0481] For example, if a student shows signs of agitation during a classroom presentation, the device captures changes in voice tone and facial expressions in real time, sends this information to a server for analysis, and detects signs of stress. Based on this, the person in charge can be quickly notified.

[0482] An example of a prompt might be: "Design a method to analyze students' stress levels and respond quickly through terminals and servers used in educational facilities. What technologies and protocols would be suitable?" This prompt is used in the process of suggesting appropriate methods to the generative AI model.

[0483] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0484] Step 1:

[0485] The device collects student text information, behavioral information, and a history of memory impairment in real time. Specifically, it captures input from the keyboard and touchscreen, and acquires the student's facial expressions and voice tone through the camera and microphone. This data is received as input and temporarily stored in internal memory.

[0486] Step 2:

[0487] The device analyzes emotional states from data acquired using an emotion engine. Based on input voice tone and facial expression information, it applies a machine learning algorithm to estimate the emotional state. The estimation results are stored as emotional data within the device.

[0488] Step 3:

[0489] The terminal prepares to send analyzed sentiment data and other activity information to the server using a secure communication protocol (e.g., TLS / SSL). It references data in its internal memory as input and creates a data package.

[0490] Step 4:

[0491] The server receives data sent from the terminal. The received data, including text information, behavioral information, and sentiment data, is stored in a database. The server checks the received data and performs processing to maintain data integrity.

[0492] Step 5:

[0493] The server performs data analysis using a generative AI model. It takes information stored in the database as input and performs calculations to identify changes in emotional state and abnormal behavioral patterns. As output, it generates a stress assessment result.

[0494] Step 6:

[0495] If the server detects signs of stress based on the analysis results, it will notify the teacher or counselor in charge. It uses the analysis results as input and sends notifications via email or a dedicated app. The notification information is sent out as output.

[0496] This series of processes makes it possible to analyze changes in students' emotions and behavior within educational facilities in real time and provide appropriate support quickly.

[0497] (Application Example 2)

[0498] 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."

[0499] There is a challenge in identifying the mental health of students in educational settings at an early stage and providing appropriate and prompt support. Traditional methods often rely on direct observation and judgments based on limited data, and there is a need to improve their accuracy. Furthermore, objective and continuous data collection and analysis regarding stress and mental state are insufficient.

[0500] 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.

[0501] In this invention, the server includes means for monitoring an individual's daily activities using an autonomous device, means for collecting facial expression data and voice data and transmitting them to a central memory device, and information processing means for performing emotion analysis using a generative model based on the transmitted data. This makes it possible to precisely grasp signs of an individual's stress and quickly notify the administrator when support is needed.

[0502] "Educational environment" refers to the place where educational activities take place or the elements that constitute that place, including classrooms where students learn and the entire school.

[0503] An "autonomous device" is a device that operates on its own without external intervention and performs a predetermined function; in this context, it specifically refers to a device that monitors student activities.

[0504] "Facial expression data" refers to data that digitally represents changes and characteristics of an individual's face, and is used to analyze the emotional states contained in facial expressions.

[0505] "Audio data" refers to data that digitally records an individual's voice, and is used to analyze emotions from the tone and pitch of the sound.

[0506] A "central memory device" is a device or system for centrally recording and storing data received from multiple devices via a network.

[0507] A "generative model" is a type of artificial intelligence that uses algorithms to generate new data and insights based on input data, and is typically used for data analysis and pattern recognition.

[0508] "Emotional analysis" refers to a method of analyzing an individual's emotional state from their facial expressions, voice, text, etc., and identifying that state.

[0509] "Information processing means" refers to a means of receiving data and performing calculations and analyses based on that data, and usually refers to a computer or its software.

[0510] An "administrator" is an individual or organization responsible for overseeing a system or process and managing personnel and resources as needed.

[0511] "Support" means providing appropriate advice and assistance to individuals facing challenges, and in educational settings, it particularly refers to promoting the mental health of children and students.

[0512] The system for implementing this invention is configured to accurately grasp the mental state of students in an educational environment and to provide appropriate support quickly. The system components include an autonomous device, a central memory device, and an information processing device including a generative model.

[0513] The devices operate autonomously within the educational environment, monitoring the daily activities of students. Specifically, the devices collect facial expression and voice data using input devices such as cameras and microphones. This data is transmitted in real time to a central memory device. The central memory device records and stores data received from multiple devices via the network.

[0514] The server is responsible for analyzing data collected from the central memory. This analysis utilizes a generative model, specifically an AI-powered emotion analysis system. Based on facial expression and voice data, the server leverages this generative AI model to analyze changes in an individual's emotional state. If the analysis detects signs of stress, the server automatically sends a warning to the administrator via a notification system. This notification provides foundational data for teachers and counselors to provide prompt and appropriate support to students.

[0515] As a concrete example, suppose a student faces a difficult problem during class, and their facial expression shows confusion. The device captures their facial expression with a camera and also collects subtle changes in their voice. The server analyzes this data using emotion analysis AI and detects that the student is experiencing high levels of stress. This information is then sent to the teacher, enabling follow-up with the student after class.

[0516] An example of a prompt to a generative AI model would be, "Based on the student's facial expression and voice data, analyze how stressed this student is." This prompt allows the AI ​​model to perform an appropriate analysis.

[0517] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0518] Step 1:

[0519] The device continuously monitors an individual's daily activities in the educational environment. Equipped with a camera and microphone, these sensors collect individual facial and voice data in real time. Input is raw facial and voice data obtained from the individual, while output is primary digital data.

[0520] Step 2:

[0521] Data collected by the terminal is transmitted to the central memory via a secure communication protocol. The terminal's task is to format the data appropriately and transfer it quickly. The input is facial expression data and voice data stored on the terminal itself, and the output is the formatted data sent to the central memory.

[0522] Step 3:

[0523] The server periodically retrieves data stored in central memory. Here, the server organizes the retrieved data in preparation for analysis and generates prompts to pass it to the sentiment analysis AI model. The input consists of various data from central memory, and the output is a dataset ready for analysis.

[0524] Step 4:

[0525] The server uses a generative AI model to perform sentiment analysis based on prompt messages. The server determines the individual's emotional state based on the received data and searches for signs of stress. The input is pre-prepared data for analysis, and the output is the analysis result indicating the emotional state.

[0526] Step 5:

[0527] The server determines whether signs of stress have been detected and automatically notifies the administrator if an anomaly is recognized. The server's task is to provide the administrator with information in an appropriate format and encourage a prompt response. The input is the analysis results that may indicate stress, and the output is the warning notification sent to the administrator.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] [Fourth Embodiment]

[0532] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0533] 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.

[0534] 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).

[0535] 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.

[0536] 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.

[0537] 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).

[0538] 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.

[0539] 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.

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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".

[0545] To implement this invention, a system will be constructed that uses terminals placed within the educational environment and a central server to monitor and analyze the daily activities of students. The terminals will have the functionality to acquire students' text input, behavioral data, and records of forgetfulness in real time. As a result, the terminals will capture the contents of notebooks, essays, and reflection papers, and record the status of forgotten items based on teacher input. In addition, IoT devices can be linked to monitor behavioral patterns in classrooms and on the school grounds.

[0546] This data is periodically encrypted and sent to a server. The server sets up an environment to analyze the received data and utilizes a pre-trained artificial intelligence model. This model performs sentiment analysis on text data and identifies pattern changes in behavioral data. If an anomaly is detected as a sign of stress as a result of this process, relevant educators are automatically notified.

[0547] As a concrete example, suppose student A's handwriting in the notes they submit during class has recently become messy. This information is captured by a device and sent to a server. The server's AI analyzes this information and compares it with past data to determine that the recent messy handwriting is abnormal. This abnormality is notified to the teacher, allowing for an early warning that student A may be experiencing some kind of stress. Based on this, the teacher can then take steps to support student A and prepare to offer individual counseling.

[0548] The entire system is equipped with infrastructure adaptable to the educational environment and is properly managed in accordance with guidelines for handling student data in a secure and privacy-conscious manner. In this way, a system is created that effectively promotes attention to the mental health of students in educational settings.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] The device monitors students' activities in school life and collects text input (notes, essays, reflections), behavioral data (travel patterns, interactions with friends, etc.), and records of memory loss. This includes a process of acquiring data using input devices and sensors.

[0552] Step 2:

[0553] The device temporarily stores the collected data in its internal memory. This stored data is encrypted at a specified frequency and prepared to be sent to the server using a secure communication protocol.

[0554] Step 3:

[0555] The server receives data sent from the terminal and stores it in the database. The database then performs preprocessing to allow for comparison with past data and pattern analysis.

[0556] Step 4:

[0557] The server applies artificial intelligence models to the stored data to perform sentiment analysis and behavioral pattern analysis. This analysis aims to identify signs and anomalies of stress based on pre-defined criteria.

[0558] Step 5:

[0559] The server triggers an alert based on the anomaly detection information obtained from the analysis results. This alert is sent to teachers and counselors associated with the student in question.

[0560] Step 6:

[0561] The user (teacher or counselor) receives a notification from the server and reviews its contents. The notification contains detailed information about signs or abnormalities of stress, which they use to consider how to respond to the student. For example, they may prepare to initiate specific support measures, such as planning a meeting with the student.

[0562] (Example 1)

[0563] 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".

[0564] Maintaining the mental health of students and proactively identifying and addressing excessive stress is crucial in the educational environment. However, traditional methods have made it difficult to accurately grasp changes in students' daily activities and signs of stress, making it challenging for teachers and educators to respond at the appropriate time. To address this challenge, there is a need for a method that efficiently and accurately detects signs of stress and promptly notifies educators.

[0565] 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.

[0566] In this invention, the server includes means for acquiring individual text data and behavioral information in an educational environment, means for encrypting the acquired data and transmitting it to a central information processing device, and means for analyzing emotional evaluation and changes in behavioral patterns using a generated AI model based on the data acquired by the central information processing device. This enables efficient detection of signs of stress in students and prompt notification and response to educators.

[0567] The term "educational environment" refers to the setting in schools and other learning facilities where students learn and engage in activities, and the environment in which teachers and educators provide guidance.

[0568] "Personal text data" refers to text information that students input or write within the educational environment, and this includes notes, essays, and reflection papers.

[0569] "Behavioral information" refers to data on the physical movements and habitual activities of students in classrooms, schoolyards, etc., and is information acquired by IoT devices.

[0570] "Encryption" refers to the process of converting data into an invisible format to protect it from third parties, and is essential for secure communication.

[0571] A "central information processing system" refers to a server or computer used to receive and analyze data transmitted from an educational environment.

[0572] A "generative AI model" refers to an artificial intelligence model used to analyze the sentiment evaluation and behavioral pattern fluctuations of text data, and is pre-trained based on data.

[0573] "Emotional assessment" refers to the process of analyzing the emotional state of students from text data and identifying positive or negative emotional tendencies.

[0574] "Variations in behavioral patterns" refers to the process of identifying abnormal or exceptional behavior by comparing it with normal behavioral information.

[0575] Detecting an "abnormality" refers to finding and identifying unusual signs or patterns of stress from data on daily activities.

[0576] "Responsible personnel" refers to educators or teachers who are responsible for receiving notifications of anomalies and taking appropriate action.

[0577] To implement this invention, a system will be constructed that effectively monitors the learning and activities of students by utilizing digital tools and network infrastructure in the educational environment. Specifically, it will be implemented using the following hardware and software.

[0578] First, devices are deployed within educational facilities. These include computer terminals, tablets, and IoT devices used by students, which capture students' text input and physical actions. The devices, for example, transmit the collected data via Wi-Fi to a server using an encryption protocol. This encryption ensures the security of the data.

[0579] The server functions as a central information processing unit, acquiring and analyzing received data. The data is analyzed using generative AI models to identify sentiment evaluations of text data and pattern variations in behavioral data. By using appropriate software (e.g., machine learning libraries and data analysis tools), rapid and efficient analysis is possible.

[0580] For example, a generative AI model might identify signs of stress by comparing recent behavioral data with past data, based on a prompt such as, "How can we detect changes that suggest a decline in mood from recent behavioral data?" This example prompt demonstrates the effectiveness of the AI's analysis process.

[0581] Based on this analytical data, users, such as teachers and educators, can identify students' stress and learning-related problems early on and provide opportunities for individualized guidance and counseling. If an anomaly is detected, the server will send appropriate notifications to educators, enabling prompt action and implementation of corrective measures.

[0582] As described above, this invention is an effective system for supporting the mental health of students in educational settings.

[0583] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0584] Step 1:

[0585] The terminal collects text input and behavioral information from students. Input includes text data entered by students and movement patterns recorded by IoT devices. The terminal collects this data and stores it locally. Specifically, it records key input in real time when students are writing notes or compositions, and receives behavioral information from wearable devices and classroom sensors.

[0586] Step 2:

[0587] The terminal sends the collected data to the server using an encryption protocol. The input consists of text data and behavioral information collected in step 1. The terminal encrypts this data and transfers it to the server via a secure communication channel. Specifically, it uses the TLS / SSL protocol and sends the data in batch processing to prevent information leakage.

[0588] Step 3:

[0589] The server inputs the received data into a generative AI model for analysis. The input consists of encrypted text data and behavioral information. The server decrypts this data and uses the generative AI model to analyze emotional evaluations and changes in behavioral patterns. Specifically, it uses natural language processing techniques to calculate emotional scores from text and identifies abnormal patterns from behavioral data.

[0590] Step 4:

[0591] The server sends a notification to the responsible party if an anomaly is detected. The input is the analysis results from step 3. The server evaluates the analysis results, and if it determines an anomaly is detected, it sends an alert to the education personnel. Specifically, it uses email and app notifications to enable the responsible party to take immediate action.

[0592] (Application Example 1)

[0593] 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".

[0594] In modern society, there is a need to detect changes in people's feelings and awareness early on in their daily lives and prevent mental health problems. However, existing systems have the challenge of not being able to monitor these changes in real time and respond quickly. In particular, it is difficult to notice invisible stress and psychological changes in residential areas, and if appropriate measures are not taken, it can lead to serious problems.

[0595] 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.

[0596] In this invention, the server includes a device for collecting information on an individual's daily activities within a residential area, a device using artificial intelligence that analyzes the collected information to detect changes in feelings and consciousness, and a device for transmitting warnings to relevant parties in accordance with the detected changes in feelings and consciousness. This makes it possible to detect psychological changes in an individual within a residential area at an early stage, enabling the maintenance and improvement of mental health.

[0597] A "residential area" refers to the place where an individual conducts their daily life and the surrounding environment, and mainly includes residential areas and nearby public spaces.

[0598] "Individual daily activities" refer to actions, behaviors, and thoughts that an individual performs on a daily basis, and specifically include habitual actions such as typing text, moving around, and inattentiveness.

[0599] A "device for collecting information" is a device that uses sensor devices, computer systems, etc., to acquire activity data and environmental information of a target individual.

[0600] An "artificial intelligence-based device" refers to a system that incorporates artificial intelligence technologies such as machine learning and deep learning for data analysis, and is used to detect changes in emotions and consciousness.

[0601] A "device for transmitting warnings to relevant parties" is a device that has a means of communication to quickly transmit abnormal data or events to the relevant people.

[0602] "Changes in feelings and awareness" refer to changes in an individual's internal emotions and psychological state, and these changes can be observed in the data.

[0603] This invention provides a system for monitoring an individual's daily activities in their living area and detecting changes in their mood and awareness early on. The server runs a program that collects data from sensor devices and smartphones. These devices acquire information about the individual's daily activities, such as their movement routes and the objects they come into contact with.

[0604] The server uses Python as its programming language and employs AI frameworks such as TensorFlow or PyTorch to analyze the collected data. This analysis allows a generative AI model to detect patterns indicating changes in emotions and consciousness. Specifically, it understands changes in a person's mental state through emotion analysis and behavioral pattern analysis. If any abnormalities are detected, a notification is promptly sent to parents or relevant departments.

[0605] This technology operates on cloud infrastructure such as Amazon Web Services (AWS) and Google Cloud to ensure secure and efficient data processing within a cloud environment. The overall system guarantees real-time data analysis and rapid notification, with a particular focus on protecting the mental health of minors.

[0606] As a concrete example, data on a child's behavior while playing in a park is recorded on a smartphone, and if an abnormal behavioral pattern is detected, a message is sent to the parent or guardian prompting them to check on the child's safety. An example of this prompt message is given to the AI ​​model: "Based on recent behavioral patterns, please detect if there has been a change in the feelings or awareness of a particular individual." In this way, the system provides safety and security in the field.

[0607] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0608] Step 1:

[0609] The device uses sensor equipment to collect data on an individual's daily activities within their living area. This data includes behavioral data such as text input and movement paths, transmitted via smartphones or smart glasses. For example, a sensor might record a child running around in a park. The input data is transmitted to a server using a secure protocol.

[0610] Step 2:

[0611] The server receives the collected data and formats it as preprocessing for AI analysis. It checks for data deficiencies and performs processing to impute missing values ​​as needed. For example, if data for a certain time period is missing, the imputation algorithm appropriately fills in the missing data. The output is a dataset formatted for analysis.

[0612] Step 3:

[0613] The server uses a generative AI model to perform sentiment and behavioral pattern analysis on the formatted data. Specifically, it uses TensorFlow to detect anomalies based on specific indicators while comparing them with past data. The analysis is performed according to the prompt "Detect whether there has been a change in the feelings or awareness of a specific individual based on their recent behavioral patterns." The output is an analysis result that includes indicators showing anomalies or possibilities.

[0614] Step 4:

[0615] If an anomaly is detected, the server immediately generates a warning message and sends it to the relevant parties. The notification is delivered to parents and educators as a text message or app notification. For example, a message such as "Your child is in an unstable state. Please check on them" might be generated. The output is the warning message that was sent.

[0616] Step 5:

[0617] The user takes the necessary actions based on the received warning message. Specifically, this involves checking the health status of the person concerned and, if necessary, consulting a specialist. The output is an appropriate response and countermeasure to the warning.

[0618] 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.

[0619] To implement this invention, multiple terminals and a central server are placed within the educational environment. The terminals monitor the daily activities of students in real time and collect various data related to educational activities. The collected data includes text input information, behavioral data, and a history of forgetfulness. Furthermore, this invention adds a function that analyzes the emotional state from the user's voice tone and facial expressions by combining it with an emotion engine.

[0620] The terminal retrieves this data and temporarily stores it in its internal memory. The stored data is then prepared to be sent to the server via a secure communication protocol. The server receives the data sent from the terminal and stores it in its database.

[0621] The server is equipped with artificial intelligence specialized in data analysis, and uses a novel method, combined with an emotion engine, to perform sentiment analysis of text data and analysis of behavioral patterns. This emotion engine analyzes the user's emotional state and integrates that information with other data to perform more accurate stress assessments.

[0622] To give a concrete example, the emotion engine detects that student B appears nervous during a classroom presentation, and that their voice tone is higher than usual. The device also captures notes and essays written by student B as text data, while simultaneously recording their movements within the classroom as behavioral data. This data is sent to a server and analyzed by artificial intelligence and the emotion engine. As a result, a combination of emotional changes and abnormal behavioral patterns is recognized as signs of stress. This recognition result is automatically notified to the teacher or counselor in charge.

[0623] In this way, the present invention enables multifaceted monitoring of students in educational settings and, by combining all data, including changes in emotions, allows for prompt and appropriate support. This promotes early intervention for students' mental health.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The device monitors written information and daily activities of students, and uses an emotion engine to acquire emotional data from voice tone and facial expressions. This includes a process of collecting data in real time using microphones and cameras.

[0627] Step 2:

[0628] The device temporarily stores the collected text data, behavioral data, and emotional data in its internal memory. The stored data is then prepared to be sent to the server in a secure format using encryption technology.

[0629] Step 3:

[0630] The server receives data sent from the terminal and stores it in the database. This organizes the data into a state where it can be analyzed.

[0631] Step 4:

[0632] The server analyzes the stored data using an artificial intelligence model and an emotion engine. The emotion engine analyzes emotional data obtained from voice tone and facial expressions, and tracks changes in emotional state.

[0633] Step 5:

[0634] The server integrates the analysis results and comprehensively evaluates signs of stress by analyzing the sentiment of text data, abnormal behavioral patterns, and changes in emotional state. Based on this evaluation, if an anomaly is detected, it triggers a notification to educators.

[0635] Step 6:

[0636] The user (teacher or counselor) receives notifications from the server and reviews detailed analysis results. The notifications include indicators of emotional changes and behavioral abnormalities, which they use to plan actions for the student. They prepare to begin appropriate support by scheduling meetings or contacting parents.

[0637] (Example 2)

[0638] 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".

[0639] In today's educational environment, managing students' mental health is considered crucial, but traditional methods make it difficult to quickly and efficiently monitor individual students' emotional states and signs of stress. In particular, there is a need for a system that can detect abnormalities in students' emotional changes and behavioral patterns early and take appropriate action. To address this challenge, technology is needed that comprehensively analyzes multifaceted data on individual students to detect abnormalities in their emotions and behavior.

[0640] 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.

[0641] In this invention, the server includes a device for collecting information on an individual's daily activities within an educational facility, a device using artificial intelligence to analyze the collected information and evaluate their emotional state, and a device for transferring information to a person responsible for receiving specific notifications based on the evaluation of the emotional state. This makes it possible to comprehensively monitor the mental health of students in educational settings, detect changes in emotions and behavior early, and provide appropriate support quickly.

[0642] "Within an educational facility" refers to a place where a specific educational activity takes place, and includes environments such as schools and learning facilities.

[0643] "Individual daily activity information" refers to various information about each student's daily learning and life, and includes data such as written information, action information, and a history of memory impairment.

[0644] "Data collection devices" refer to equipment and software used to acquire activity information from students within educational facilities, and include sensors and digital devices.

[0645] "Artificial intelligence that analyzes and evaluates emotional states" refers to a system that uses collected information and machine learning and data analysis techniques to evaluate changes in the emotions and behaviors of individual students.

[0646] "Persons responsible for receiving specific notifications" refers to individuals within an educational institution who are responsible for managing and supporting students' health, including teachers and counselors.

[0647] A "device for transferring information" refers to a mechanism or system that provides a means of communication to quickly inform the recipient of the analyzed results.

[0648] The system for implementing this invention consists of multiple terminals and a central server located within an educational facility. The terminals include digital devices for collecting information on students' daily activities in real time, acquiring text information, motion information, and a history of memory impairment. This is achieved by combining sensors, cameras, and microphones. The terminals also feature an emotion engine for emotion analysis, which analyzes the emotional state of students from their voice tone and facial expressions.

[0649] The device temporarily stores the collected data in its internal memory. This data is then prepared to be sent to the server using a secure communication protocol. Specifically, protocols such as TLS / SSL are used to ensure that the data is transmitted securely.

[0650] The server stores the received data in an integrated database and analyzes the information using an artificial intelligence model specifically designed for data analysis. This AI combines emotion analysis and behavioral pattern analysis to assess students' stress levels. By utilizing a generative AI model to identify changes in emotions and abnormal behavioral patterns, the server can detect signs of stress early on.

[0651] The resulting data is immediately communicated to the teacher or counselor. This allows for a swift response to maintain the student's mental health. This system is useful for improving student mental health management in educational institutions and for prompt intervention when problems arise.

[0652] For example, if a student shows signs of agitation during a classroom presentation, the device captures changes in voice tone and facial expressions in real time, sends this information to a server for analysis, and detects signs of stress. Based on this, the person in charge can be quickly notified.

[0653] An example of a prompt might be: "Design a method to analyze students' stress levels and respond quickly through terminals and servers used in educational facilities. What technologies and protocols would be suitable?" This prompt is used in the process of suggesting appropriate methods to the generative AI model.

[0654] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0655] Step 1:

[0656] The device collects student text information, behavioral information, and a history of memory impairment in real time. Specifically, it captures input from the keyboard and touchscreen, and acquires the student's facial expressions and voice tone through the camera and microphone. This data is received as input and temporarily stored in internal memory.

[0657] Step 2:

[0658] The device analyzes emotional states from data acquired using an emotion engine. Based on input voice tone and facial expression information, it applies a machine learning algorithm to estimate the emotional state. The estimation results are stored as emotional data within the device.

[0659] Step 3:

[0660] The terminal prepares to send analyzed sentiment data and other activity information to the server using a secure communication protocol (e.g., TLS / SSL). It references data in its internal memory as input and creates a data package.

[0661] Step 4:

[0662] The server receives data sent from the terminal. The received data, including text information, behavioral information, and sentiment data, is stored in a database. The server checks the received data and performs processing to maintain data integrity.

[0663] Step 5:

[0664] The server performs data analysis using a generative AI model. It takes information stored in the database as input and performs calculations to identify changes in emotional state and abnormal behavioral patterns. As output, it generates a stress assessment result.

[0665] Step 6:

[0666] If the server detects signs of stress based on the analysis results, it will notify the teacher or counselor in charge. It uses the analysis results as input and sends notifications via email or a dedicated app. The notification information is sent out as output.

[0667] This series of processes makes it possible to analyze changes in students' emotions and behavior within educational facilities in real time and provide appropriate support quickly.

[0668] (Application Example 2)

[0669] 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".

[0670] There is a challenge in identifying the mental health of students in educational settings at an early stage and providing appropriate and prompt support. Traditional methods often rely on direct observation and judgments based on limited data, and there is a need to improve their accuracy. Furthermore, objective and continuous data collection and analysis regarding stress and mental state are insufficient.

[0671] 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.

[0672] In this invention, the server includes means for monitoring an individual's daily activities using an autonomous device, means for collecting facial expression data and voice data and transmitting them to a central memory device, and information processing means for performing emotion analysis using a generative model based on the transmitted data. This makes it possible to precisely grasp signs of an individual's stress and quickly notify the administrator when support is needed.

[0673] "Educational environment" refers to the place where educational activities take place or the elements that constitute that place, including classrooms where students learn and the entire school.

[0674] An "autonomous device" is a device that operates on its own without external intervention and performs a predetermined function; in this context, it specifically refers to a device that monitors student activities.

[0675] "Facial expression data" refers to data that digitally represents changes and characteristics of an individual's face, and is used to analyze the emotional states contained in facial expressions.

[0676] "Audio data" refers to data that digitally records an individual's voice, and is used to analyze emotions from the tone and pitch of the sound.

[0677] A "central memory device" is a device or system for centrally recording and storing data received from multiple devices via a network.

[0678] A "generative model" is a type of artificial intelligence that uses algorithms to generate new data and insights based on input data, and is typically used for data analysis and pattern recognition.

[0679] "Emotional analysis" refers to a method of analyzing an individual's emotional state from their facial expressions, voice, text, etc., and identifying that state.

[0680] "Information processing means" refers to a means of receiving data and performing calculations and analyses based on that data, and usually refers to a computer or its software.

[0681] An "administrator" is an individual or organization responsible for overseeing a system or process and managing personnel and resources as needed.

[0682] "Support" means providing appropriate advice and assistance to individuals facing challenges, and in educational settings, it particularly refers to promoting the mental health of children and students.

[0683] The system for implementing this invention is configured to accurately grasp the mental state of students in an educational environment and to provide appropriate support quickly. The system components include an autonomous device, a central memory device, and an information processing device including a generative model.

[0684] The devices operate autonomously within the educational environment, monitoring the daily activities of students. Specifically, the devices collect facial expression and voice data using input devices such as cameras and microphones. This data is transmitted in real time to a central memory device. The central memory device records and stores data received from multiple devices via the network.

[0685] The server is responsible for analyzing data collected from the central memory. This analysis utilizes a generative model, specifically an AI-powered emotion analysis system. Based on facial expression and voice data, the server leverages this generative AI model to analyze changes in an individual's emotional state. If the analysis detects signs of stress, the server automatically sends a warning to the administrator via a notification system. This notification provides foundational data for teachers and counselors to provide prompt and appropriate support to students.

[0686] As a concrete example, suppose a student faces a difficult problem during class, and their facial expression shows confusion. The device captures their facial expression with a camera and also collects subtle changes in their voice. The server analyzes this data using emotion analysis AI and detects that the student is experiencing high levels of stress. This information is then sent to the teacher, enabling follow-up with the student after class.

[0687] An example of a prompt to a generative AI model would be, "Based on the student's facial expression and voice data, analyze how stressed this student is." This prompt allows the AI ​​model to perform an appropriate analysis.

[0688] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0689] Step 1:

[0690] The device continuously monitors an individual's daily activities in the educational environment. Equipped with a camera and microphone, these sensors collect individual facial and voice data in real time. Input is raw facial and voice data obtained from the individual, while output is primary digital data.

[0691] Step 2:

[0692] Data collected by the terminal is transmitted to the central memory via a secure communication protocol. The terminal's task is to format the data appropriately and transfer it quickly. The input is facial expression data and voice data stored on the terminal itself, and the output is the formatted data sent to the central memory.

[0693] Step 3:

[0694] The server periodically retrieves data stored in central memory. Here, the server organizes the retrieved data in preparation for analysis and generates prompts to pass it to the sentiment analysis AI model. The input consists of various data from central memory, and the output is a dataset ready for analysis.

[0695] Step 4:

[0696] The server uses a generative AI model to perform sentiment analysis based on prompt messages. The server determines the individual's emotional state based on the received data and searches for signs of stress. The input is pre-prepared data for analysis, and the output is the analysis result indicating the emotional state.

[0697] Step 5:

[0698] The server determines whether signs of stress have been detected and automatically notifies the administrator if an anomaly is recognized. The server's task is to provide the administrator with information in an appropriate format and encourage a prompt response. The input is the analysis results that may indicate stress, and the output is the warning notification sent to the administrator.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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."

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0720] The following is further disclosed regarding the embodiments described above.

[0721] (Claim 1)

[0722] Means for collecting data on individuals' daily activities in an educational environment,

[0723] A method using artificial intelligence to analyze the collected data and detect signs of stress,

[0724] A means for transmitting a notification to a person who will receive a notification corresponding to the detected signs of stress,

[0725] A system that includes this.

[0726] (Claim 2)

[0727] The system according to claim 1, characterized in that the data on an individual's daily activities in the aforementioned educational environment includes descriptive content, behavioral data, and a history of forgetfulness.

[0728] (Claim 3)

[0729] The system according to claim 1, characterized in that the artificial intelligence includes a generative model that performs emotion analysis and behavioral pattern analysis.

[0730] "Example 1"

[0731] (Claim 1)

[0732] Means for acquiring individual text data and behavioral information in an educational environment,

[0733] Means for encrypting the acquired data and transmitting it to a central information processing device,

[0734] A means for analyzing emotional evaluation and changes in behavioral patterns using a generated AI model based on data acquired by the aforementioned central information processing device,

[0735] A means of detecting anomalies based on the analysis results and notifying the person in charge of responding to those anomalies,

[0736] A system that includes this.

[0737] (Claim 2)

[0738] The system according to claim 1, characterized in that the data on an individual's daily activities in the aforementioned educational environment includes text input, behavioral information, and records of forgotten items.

[0739] (Claim 3)

[0740] The system according to claim 1, characterized in that the generated AI model has the function of identifying anomalies by comparing them with past data.

[0741] "Application Example 1"

[0742] (Claim 1)

[0743] A device for collecting information on an individual's daily activities within a residential area,

[0744] A device using artificial intelligence that analyzes the collected information to detect changes in feelings and consciousness,

[0745] A device that transmits a warning to the relevant parties in accordance with the detected changes in their feelings or awareness,

[0746] A system that includes this.

[0747] (Claim 2)

[0748] The system according to claim 1, characterized in that information on an individual's daily activities within the residential area includes text input, movement data, and a history of inattention.

[0749] (Claim 3)

[0750] The system according to claim 1, characterized in that the artificial intelligence includes a generative model that performs emotion analysis and behavioral pattern analysis.

[0751] "Example 2 of combining an emotion engine"

[0752] (Claim 1)

[0753] A device for collecting information on individuals' daily activities within educational facilities,

[0754] A device using artificial intelligence that analyzes the collected information and evaluates the emotional state,

[0755] A device that, based on the aforementioned assessment of emotional state, transfers information to a person responsible for receiving a specific notification,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, characterized in that the daily activity information of an individual within the aforementioned educational facility is integrated with textual information, action information, and a history of memory impairment.

[0759] (Claim 3)

[0760] The system according to claim 1, characterized in that the artificial intelligence includes a generative model that performs emotion analysis and behavior pattern analysis.

[0761] "Application example 2 when combining with an emotional engine"

[0762] (Claim 1)

[0763] Means including an autonomous device for monitoring an individual's daily activities in an educational environment,

[0764] The autonomous device includes means for collecting an individual's facial expressions and voice and transmitting them to a central memory device,

[0765] Information processing means equipped with a generative model that performs emotion analysis based on facial expression data and voice data transmitted to the central memory,

[0766] A means of analyzing signs of stress and notifying administrators who provide support to individuals of the analysis results,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, characterized in that the analysis process based on the facial expression data and voice data includes evaluating the stress level related to the challenges the individual is experiencing.

[0770] (Claim 3)

[0771] The system according to claim 1, characterized in that the notification means notifies the administrator of the point in time when support is needed based on the analysis results, as an event trigger. [Explanation of Symbols]

[0772] 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. Means for collecting data on individuals' daily activities in an educational environment, A method using artificial intelligence to analyze the collected data and detect signs of stress, A means for transmitting a notification to a person who will receive a notification corresponding to the detected signs of stress, A system that includes this.

2. The system according to claim 1, characterized in that the data on an individual's daily activities in the aforementioned educational environment includes descriptive content, behavioral data, and a history of forgetfulness.

3. The system according to claim 1, characterized in that the artificial intelligence includes a generative model that performs emotion analysis and behavioral pattern analysis.

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