system

Abstract

We provide the system. [Solution] An information gathering means that includes a device for detecting physical and vocal information of learners and for acquiring the collected information, Information analysis means for analyzing information obtained from the aforementioned information gathering means and evaluating the learner's cognitive state and attentiveness, Based on the evaluation results of the information analysis means, an educational resource provision means for dynamically generating and providing educational resources suited to learners, A notification means for generating a report on the learner's learning progress and level of understanding and providing it to the instructor or guardian, Control means for controlling a mechanical device that assists learners in their studies at home, 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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional education system, it is difficult to grasp the understanding degree and concentration of individual learners in real time, and as a result, there is a problem that individualized guidance and optimized teaching material provision are not sufficiently carried out. In addition, with the spread of online education, it has become difficult for teachers and parents to effectively monitor the progress of learners and provide feedback at appropriate times. As a result, the learning effect decreases and it is difficult to maintain the motivation of learners.

Means for Solving the Problems

[0005] This invention provides a system that solves these problems by providing a data collection means that acquires the collected data using sensors to detect the learner's facial expressions, posture, gaze, and voice, and by providing a data analysis means that evaluates the learner's level of understanding and concentration based on the data obtained. Furthermore, a material provision means dynamically generates and provides learning materials optimized for the learner based on the evaluation results, and provides a report on the learner's progress and level of understanding to educators or guardians via a notification means. This system enables real-time data analysis by using machine learning algorithms, and aims to improve learning effectiveness by suggesting breaks when concentration decreases and providing interactive feedback.

[0006] "Data collection means" refers to equipment or functions that use sensors to detect a learner's facial expressions, posture, gaze, and voice, and acquire this information as digital data.

[0007] "Data analysis means" refers to a process or function that uses machine learning algorithms to process data in order to evaluate the learner's state based on acquired data, and to estimate comprehension and concentration levels in real time.

[0008] "Methods for providing learning materials" refers to a system or function that generates learning materials and learning activities optimized for learners based on the results of data analysis, and presents them to learners.

[0009] "Notification means" refers to a means or function for effectively reporting information about learners' learning progress and understanding to teachers and parents.

[0010] A "machine learning algorithm" refers to a mathematical model or method used to automatically learn patterns from data and perform predictions or classifications. [Brief explanation of the drawing]

[0011] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of 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]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered 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, and the like.

[0017] In the following embodiments, the numbered 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), and the like.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, 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.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention provides an embodiment of an educational system that optimizes the learner's learning experience. This system monitors the learner's state, performs real-time data analysis, and presents appropriate learning materials based on the results.

[0033] First, the device uses sensors placed in the classroom or learning environment to continuously capture the learner's facial expressions, posture, gaze, and voice. This collects a wealth of data about the learner's state. This data is then transmitted to a server via the network.

[0034] The server preprocesses the received data and applies machine learning algorithms to evaluate the learner's comprehension and concentration level. Specifically, it analyzes multiple data points by integrating them, such as determining emotional states from facial expressions, estimating the direction of attention from eye movements, and detecting emotional changes from tone of voice.

[0035] Based on the evaluation results, the server generates learning materials tailored to the learner. For example, if a learner's understanding of a particular topic is insufficient, supplementary materials related to that topic are prepared. If the server determines that the learner's concentration is waning, it offers interactive and engaging quizzes or suggests breaks. This generated content is then presented to the learner via their device.

[0036] Furthermore, the server generates detailed reports, including learner progress and comprehension levels, and provides these to users (educators and parents). Users can utilize these reports to provide feedback to learners and adjust their teaching strategies.

[0037] For example, if a learner is learning a particular mathematical concept and the device detects that their facial expression indicates confusion, the server immediately analyzes this data and provides supplementary materials such as additional video tutorials or practice problems. Furthermore, based on numerical results, encouraging messages are displayed to motivate the learner as they attempt to solve problems.

[0038] In this way, this system provides education tailored to the individual circumstances of each learner, maximizing the quality and effectiveness of learning.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device activates its sensors and begins capturing the learner's facial expressions, posture, gaze, and voice in real time. The collected data undergoes initial noise reduction and basic data format conversion.

[0042] Step 2:

[0043] The device sends data to the server over the network. During this process, the data is encrypted for security purposes and transmitted in an appropriate packet format.

[0044] Step 3:

[0045] The server receives the data and performs data preprocessing. Specifically, this includes adjusting image data, cleaning audio data, and normalizing eye-tracking data.

[0046] Step 4:

[0047] The server uses an AI model to analyze pre-processed data and estimate the learner's comprehension and concentration levels. This includes processes such as analyzing emotions from facial expressions, evaluating the level of gaze focus, and detecting changes in tone from audio data.

[0048] Step 5:

[0049] The server generates learning materials and feedback tailored to the learner based on the analysis results. For topics deemed to have insufficient understanding, it creates supplementary materials and interactive learning content.

[0050] Step 6:

[0051] The server sends generated learning materials and feedback information to the terminal and displays them on the learner's interface to encourage them to continue learning.

[0052] Step 7:

[0053] The server collects learner progress data and generates reports for teachers and parents. These reports include comprehension scores and study time statistics, which are useful for developing individualized learning plans.

[0054] Step 8:

[0055] Users (teachers and parents) access reports provided by the server to check the learner's progress. They can provide feedback and instructions on the next learning steps as needed.

[0056] (Example 1)

[0057] 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."

[0058] Traditional educational systems have struggled to appropriately identify the varying levels of understanding and concentration among learners and provide personalized learning support tailored to their individual circumstances. Conventional methods often failed to grasp learners' states in real time and provide appropriate learning materials in a timely manner, resulting in poor learning outcomes. This invention aims to solve these problems and optimize the learner's experience.

[0059] 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.

[0060] In this invention, the server includes data acquisition means, information analysis means, and material supply means. This makes it possible to grasp the learner's status in real time and provide optimized learning materials tailored to that situation.

[0061] "Data acquisition means" refers to a device that detects the learner's facial expressions, posture, gaze, and voice, and collects this information.

[0062] "Information analysis means" refers to techniques for processing information obtained from data acquisition means to determine the learner's level of understanding and concentration.

[0063] "Material supply means" refers to a method of dynamically generating and presenting learning materials that are suitable for the learner's situation based on the determination results of the information analysis means.

[0064] "Information transmission means" refers to a function that generates documents detailing learners' progress and level of understanding, and reports these to educators or parents.

[0065] An "artificial intelligence algorithm" is a technical method used in information analysis to process learner data and determine their level of understanding and concentration.

[0066] "Two-way feedback" refers to a method that includes interactive responses, such as suggesting a break to the learner when it is determined that their concentration level is low.

[0067] The embodiments for carrying out this invention are described in detail below.

[0068] The device continuously captures the learner's facial expressions, posture, gaze, and voice using sensors placed in the learning environment. This includes high-resolution cameras and microphones that can accurately capture various aspects of the learner's state. The acquired data is transmitted to the server in real time.

[0069] The server preprocesses the received data and analyzes it using machine learning algorithms. This process utilizes software such as Python's OpenCV library and TENSORFLOW®. This allows the server to determine the learner's level of comprehension and concentration. Specifically, it analyzes facial expressions to determine emotional states and eye-tracking to estimate the direction of attention. It also analyzes voice tone through speech analysis to capture changes in emotion.

[0070] Based on the analysis results, the server generates learning materials tailored to the learner. For example, for topics where understanding is lacking, it provides related supplementary video tutorials and practice exercises. Furthermore, if it determines that the learner's concentration is waning, it offers interactive quizzes and suggests breaks to maintain their interest.

[0071] Furthermore, the server generates detailed reports on learners' progress and understanding, which are provided to users (educators and parents). Users can use this information to provide effective feedback to learners and adjust teaching strategies.

[0072] For example, if a learner is studying a specific mathematical concept and the device detects confusion from their facial expression, the server will immediately analyze this and generate and provide additional video tutorials or practice problems. By utilizing prompts with a generative AI model, new learning content can be prepared quickly. An example of a prompt might be, "Please suggest effective learning support for a confused student."

[0073] In this way, this system can provide education tailored to the individual needs of learners and significantly improve the quality of the learning experience.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The device uses a high-resolution camera and microphone to capture the learner's facial expressions, posture, gaze, and voice. The input for this process is the learner's real-time movements and sounds in the learning environment. This data is output and collected as video and audio data. Specifically, the device's sensors continuously capture data and convert it into a format that can be immediately analyzed.

[0077] Step 2:

[0078] The terminal transmits collected video and audio data to a server via the network. Input data is transmitted in real time and securely stored on the server as output. Specific operations include procedures to ensure security and privacy by using secure communication protocols for data transfer.

[0079] Step 3:

[0080] The server preprocesses the received data. The input consists of raw video and audio data, and data processing such as noise reduction and format conversion is performed. The output is clean data ready for analysis. The specific operation of this step involves applying a preprocessing algorithm using a Python library.

[0081] Step 4:

[0082] The server performs analysis on pre-processed data using machine learning algorithms. The input is clean data, and data calculations are performed to determine the learner's level of understanding and concentration. The output is a numerical value or evaluation result indicating the learner's current state. The specific operation includes analysis processing using a deep learning model, and each data point is integrated and analyzed.

[0083] Step 5:

[0084] The server generates learning materials tailored to the learner based on the analysis results. The input is the analysis results, and new learning materials are generated using a generative AI model in response to prompt sentences. For example, additional tutorials are generated for topics where the learner has a low level of understanding. The output is personalized learning content. The specific actions in this step include prompt input to the generative AI model.

[0085] Step 6:

[0086] The server generates learning materials and sends them to the learner's device for presentation. The input is the generated learning content, and the output is the learning materials displayed on the learner's device. Specific operations include formatting the learning materials and optimizing their display.

[0087] Step 7:

[0088] The server generates reports on learners' progress and understanding. Inputs include past data analysis results and learning content, while output is a report-style document for educators and parents. Specific operations include visualization of progress data and automated report generation.

[0089] Step 8:

[0090] The user uses the report to adjust feedback and teaching strategies for learners. The input is the report provided by the server, and the output is the teaching plan and feedback content for the learners. The specific actions in this step include report evaluation and the development of teaching strategies.

[0091] (Application Example 1)

[0092] 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."

[0093] Traditional education systems have struggled to provide dynamic and adaptive learning support tailored to the individual characteristics of each learner, and have been particularly unable to offer effective support for home study. This has led to challenges such as decreased learning efficiency and difficulty in maintaining motivation.

[0094] 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.

[0095] In this invention, the server includes information gathering means, information analysis means, educational resource provision means, and control means for controlling the mechanical device. This makes it possible to provide optimal educational resources according to the learner's learning status, and to provide appropriate and individualized learning support even within the home.

[0096] "Information gathering means" refers to devices and technologies for detecting and acquiring information about learners' physical and vocal characteristics.

[0097] "Information analysis means" refers to devices and technologies used to analyze collected information and evaluate the learner's cognitive state and attentiveness.

[0098] "Educational resource provision means" refers to devices and technologies that dynamically generate and provide educational resources optimized for learners based on the results of information analysis.

[0099] "Notification means" refers to devices or technologies that generate reports on learners' learning progress and understanding and provide them to instructors or guardians.

[0100] "Control means" refers to devices and technologies for controlling mechanical devices that assist learners in their studies within the home.

[0101] In this embodiment of the invention, the terminal first uses various sensors (e.g., facial recognition camera, eye-tracking sensor, microphone) placed within the learning environment to detect the learner's state in real time. This allows for the continuous acquisition of information regarding the learner's physical and vocal characteristics. The acquired information is then transmitted to a server via the network.

[0102] The server is responsible for information analysis, storing collected information in a database and analyzing it using machine learning libraries (e.g., TensorFlow, OpenCV). The analysis evaluates the learner's cognitive state and attentiveness. Specific examples include determining emotional states from facial expressions, estimating concentration levels from eye movements, and detecting emotional changes from voice tone.

[0103] Based on the analysis results, the server executes an algorithm for providing educational resources. This algorithm dynamically generates optimized educational resources (e.g., supplementary materials, quizzes, break suggestions) and presents them to learners via their devices. Furthermore, the server has the function of generating reports on learners' learning progress and comprehension levels and notifying instructors or parents.

[0104] For example, if a learner is solving a math problem and the device detects their confused expression, the server analyzes this data and prepares an appropriate video tutorial. Also, if the system determines that the learner is losing focus, the robot provides interactive feedback such as, "How about taking a short break next?"

[0105] An example of a prompt message might be something like, "Analyze the learner's facial expression data, and when a specific facial expression pattern is recognized, select and provide appropriate learning materials or encouraging messages." This allows learners to receive personalized learning support.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The terminal acquires real-time information on the learner's facial expressions, posture, gaze, and voice from sensors installed within the learning environment. The input is sensor data, and the output is a data packet containing the sensor data. This data packet is transmitted to the server via the network.

[0109] Step 2:

[0110] The server processes the received data packets and stores them in a database. The input is the data packets sent from the terminal, and the output is the stored data. Specifically, it formats the data and appropriately indexes it in the database.

[0111] Step 3:

[0112] The server analyzes stored data using machine learning algorithms. The input is sensor data stored in a database, and the output is an evaluation of the learner's perception state and attention level. The server uses TensorFlow, for example, to determine emotional states from facial expressions and estimate the focus of attention from gaze data.

[0113] Step 4:

[0114] The server selects the optimal educational resources using a generative AI model based on the analysis results and generates teaching materials. The input is learner state information obtained through analysis, and the output is dynamically generated educational resources. Specifically, it selects the generated teaching materials and creates content according to the format.

[0115] Step 5:

[0116] The terminal receives educational resources sent from the server and presents them to the learner. The input is educational resource data from the server, and the output is learning materials displayed to the learner. The terminal uses a display and speech synthesis capabilities to present the content.

[0117] Step 6:

[0118] The server generates reports on learners' progress and comprehension and notifies the user (educator or parent). The input is analyzed learner data, and the output is a report-formatted file. Specifically, the report is sent via email or platform notifications.

[0119] 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.

[0120] This invention provides a highly personalized educational support system for learners, incorporating an emotion recognition engine to deliver a richer and more effective learning experience. The system improves the quality of education by understanding the learner's emotional state and providing appropriate learning materials and feedback.

[0121] This system first features sensors in the terminal to capture the learner's facial expressions, posture, gaze, and voice in real time. This data is collected and sent to a server in an encrypted format.

[0122] The server analyzes the received data and uses machine learning algorithms and an emotion recognition engine to evaluate the learner's comprehension, concentration, and emotional state. The emotion recognition engine analyzes the learner's facial expressions and voice data to determine their emotions. For example, if a learner has an anxious expression, the server recognizes that emotion and determines that they may have difficulty understanding. This evaluation is then reflected in the presentation of learning materials.

[0123] Based on these evaluation results from the emotion recognition engine, the server dynamically generates learning materials optimized for the learner. For example, learners deemed to have a low level of understanding are provided with additional explanatory materials and practice exercises, and if their emotional state is negative, they are provided with relaxing learning content that induces positive emotions.

[0124] The presented learning materials are displayed to learners via their devices, facilitating their learning progress. Furthermore, the server generates reports on the learners' progress and understanding, providing them to the user (educator or parent) to support their learning.

[0125] As a concrete example, in a math class, when a student encounters a new concept, the device detects confusion from the student's facial expressions. This data is analyzed on a server, and if the emotion engine recognizes signs of stress or anxiety, the server immediately provides situation-appropriate mitigation measures, such as gamified practice problems or video tutorials. Information about this learning session is also saved as a report, allowing the user to review their progress later.

[0126] In this way, this system, which incorporates an emotion recognition engine, aims to improve learning effectiveness by providing a customized educational experience for each learner.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The device uses sensors to capture the learner's facial expressions, posture, gaze, and voice in real time. This data is immediately digitized and subjected to basic noise reduction as an initial processing step.

[0130] Step 2:

[0131] The device encrypts the captured data and then sends it to the server over the network. This ensures that the data is transferred securely and efficiently.

[0132] Step 3:

[0133] The server receives the data and performs preprocessing. Here, the data is shaped and normalized, and converted into a format suitable for analysis.

[0134] Step 4:

[0135] The server uses an emotion recognition engine and machine learning algorithms to perform detailed analysis of the data. Specifically, it identifies emotional states based on facial expression data and detects changes in emotion by analyzing tone and pitch from audio data. It also evaluates the learner's level of comprehension and concentration.

[0136] Step 5:

[0137] Based on the evaluation results, the server uses a learning material delivery system to generate learning materials and feedback optimized for the learner. If the learner is in a negative emotional state, it selects content with a relaxing effect, and if a lack of understanding is pointed out, it designs content that includes supplementary materials.

[0138] Step 6:

[0139] The server sends learning materials and feedback generated by the server to the user's device for display. This allows learners to progress through the learning process via interactive content.

[0140] Step 7:

[0141] The server generates detailed reports, including learners' learning progress and emotional changes, and provides them to the user (educator or parent). These reports help to understand the learners' qualitative and quantitative progress.

[0142] Step 8:

[0143] The user reviews the report provided by the server and evaluates the learner's progress and any necessary actions. If necessary, they adjust the instruction plan for the next learning session.

[0144] (Example 2)

[0145] 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".

[0146] Traditional educational support systems have struggled to grasp learners' emotions and comprehension levels in real time and to immediately provide the most appropriate learning materials accordingly. This has made it difficult to provide personalized learning experiences for each individual learner, resulting in a failure to maximize educational effectiveness.

[0147] 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.

[0148] In this invention, the server includes information gathering means, information analysis means, and teaching material presentation means. This makes it possible to collect and analyze characteristic data, including learners' facial expressions and voices, in real time, and to dynamically generate and provide optimized teaching materials based on that evaluation.

[0149] A "detection device" refers to hardware or software that detects a learner's facial expressions, posture, gaze, and voice in real time.

[0150] "Information gathering means" refers to the process and function of collecting characteristic data acquired by a detection device and converting it into a format usable within the system.

[0151] "Information analysis means" refers to the process and function of processing collected data and using machine learning algorithms, etc., to evaluate learners' emotional state, comprehension level, and concentration level.

[0152] "Method of presenting teaching materials" refers to the process and function of creating and providing teaching materials suitable for learners based on evaluation results obtained through information analysis methods.

[0153] "Communication means" refers to the process and function of generating reports on learners' learning progress and evaluation results, and presenting or transmitting them to educators.

[0154] This invention is an advanced educational support system for providing learners with individually optimized educational experiences. The system aims to improve learning effectiveness by understanding learners' emotional states in real time and dynamically providing appropriate learning materials.

[0155] The device is equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice. Specifically, it incorporates a high-precision camera and voice input device, enabling it to capture subtle changes in the learner's facial expressions and voice tone. This data is transmitted to the server in a securely encrypted format.

[0156] The server analyzes the received data in real time and uses machine learning algorithms and an emotion recognition engine to evaluate the learner's emotional state. This analysis uses a machine learning library based on Python, and based on the analysis results, it immediately determines the learner's level of understanding and concentration.

[0157] Next, the server uses the evaluation results to generate learning materials optimized for the learner. This process incorporates natural language processing technology, allowing for flexible adjustment of the material content according to the learner's level of understanding. The generated materials are then provided to the learner via their device.

[0158] As a concrete example, suppose a device detects that a student is struggling to understand a new concept in a math class. The server receives this data, determines that the student's understanding is low, and provides a combination of a simple explanatory video and practice problems.

[0159] Furthermore, once a learning session ends, the server generates progress information in report format and provides it to the user (educator or parent) to support learning. This allows educators to efficiently track the progress of individual learners and provide necessary support.

[0160] An example of a prompt message is, "Analyze the emotional state of the students based on their facial expression and voice data, and generate appropriate teaching materials."

[0161] In this way, the present invention provides educational materials tailored to the individual needs of learners, thereby realizing an effective learning experience.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The device detects the learner's facial expressions, posture, gaze, and voice in real time. Specifically, it uses a camera and microphone to capture the learner's facial movements, gaze direction, voice tone, and volume. This data serves as input necessary for inferring emotional state. The output is structured as encrypted data and sent securely to the server.

[0165] Step 2:

[0166] The server receives encrypted data sent from the terminal and first decrypts it. It then analyzes the received input data using machine learning algorithms and employs image processing and speech analysis technologies to evaluate the learner's emotional state, comprehension level, and concentration level. A generative AI model is used for this analysis, generating detailed evaluation information about the learner's state as output.

[0167] Step 3:

[0168] The server dynamically generates learning materials optimized for the learner based on the generated assessment information. Here, natural language processing technology is utilized to adjust the educational content to individual needs. For example, additional explanations and examples are added to items where understanding is low. The input to this process is assessment information, and the output is individually customized learning materials.

[0169] Step 4:

[0170] The terminal receives optimized learning materials sent from the server and presents them to the learner. The materials are displayed on the screen in an interactive format that the learner can manipulate. The input is learning material information from the server, and the output is a screen display to aid the learner's understanding. Immediate feedback is also provided in response to the learner's actions.

[0171] Step 5:

[0172] The server analyzes the activity log after the learning session ends and aggregates the learner's progress data. Based on this, it generates a report for educators. This report includes a history of the learner's understanding, progress, and emotional state. The input data is the activity log sent from the terminal during learning, and the output is a detailed progress report that users can later evaluate.

[0173] Step 6:

[0174] Users receive progress reports from the server and understand the learner's situation. This allows them to plan and implement appropriate instruction and support as needed. The input is reports from the server, and the output is an instruction plan based on understanding and progress. This ultimately leads to actions that enhance the educational effectiveness of the learner.

[0175] (Application Example 2)

[0176] 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".

[0177] Conventional learning support systems have struggled to appropriately understand learners' emotional states and provide learning materials accordingly, resulting in a failure to maximize learning effectiveness. This invention aims to provide a personalized learning experience tailored to learners' emotional states and levels of understanding, thereby promoting more effective learning.

[0178] 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.

[0179] In this invention, the server includes emotion recognition means, data collection means, and data analysis means. This makes it possible to evaluate the learner's emotional state and level of understanding in real time and provide optimized learning materials based on that evaluation.

[0180] "Emotion recognition means" refers to technology that analyzes the learner's facial expressions and voice data to determine their emotional state.

[0181] A "data collection means" is a system equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice, and to acquire the collected data.

[0182] "Data analysis methods" refer to techniques that analyze data obtained from data collection methods to evaluate learners' comprehension and concentration levels.

[0183] A "means of providing educational materials" refers to a system for dynamically generating and providing educational materials optimized for learners based on data analysis results and emotional states.

[0184] "Notification methods" refer to technologies that generate reports on learners' learning progress and comprehension levels, and provide this information to educators or parents.

[0185] The system for implementing this invention is an educational support system that analyzes the learner's emotional state in real time and provides optimal learning materials. First, the terminal is equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice. This data is transmitted to the server in a securely encrypted format.

[0186] The server processes the received data using emotion recognition and data analysis tools. Specifically, a machine learning algorithm built using Python and TensorFlow analyzes the data and evaluates the learner's comprehension, concentration, and emotional state. Based on these evaluation results, the learning material delivery tool generates optimized learning materials and provides them to the learner. The learning materials are dynamically created using Google® Cloud AI Platform.

[0187] Furthermore, the server organizes progress information and notifies users (educators and parents) in report format. This allows for centralized management of the learning process and timely monitoring of learners' understanding.

[0188] For example, if a student shows a confused expression during an online math class, this is detected, and the server performs an emotional assessment. Based on this assessment, relaxing learning content and supplementary materials are provided. An example of a prompt to the generative AI model in this process would be, "When learning progress slows down, what kind of content should be generated to increase the student's motivation?"

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The device uses sensors to acquire the learner's facial expressions, posture, gaze, and voice data in real time, encrypts this data, and sends it to the server. The input is various data of the learner acquired by the sensors, and the output is encrypted data. This ensures the security of personal information while providing the necessary data to the server.

[0192] Step 2:

[0193] The server decrypts encrypted data received from the terminal and analyzes the data using emotion recognition methods. Specifically, it uses a machine learning algorithm with Python and TensorFlow to identify the learner's emotional state. The input is the decrypted learner data, and the output is the analysis result regarding the learner's emotional state. This allows for a detailed evaluation of the learner's psychological state.

[0194] Step 3:

[0195] The server generates optimized learning materials using a learning material delivery system, based on the analyzed learner's emotional state and comprehension level. Here, Google Cloud AI Platform is used to dynamically create learning materials by combining necessary resources and content. The input is the analysis results of emotions and comprehension, and the output is customized learning materials. This enables the rapid delivery of learning materials tailored to the learner.

[0196] Step 4:

[0197] The server sends the generated learning materials to the terminal and presents them to the learner. The terminal displays the provided materials on the learner's device. The input is the learning materials generated by the server, and the output is the display of the materials on the learner's terminal. This provides visual support to help the user progress through their learning effectively.

[0198] Step 5:

[0199] The server records the progress and comprehension of learning sessions and generates reports, which are then sent to parents and educators. The input is progress and comprehension information recorded during the learning session, and the output is a detailed learning report. This allows users to review the learner's understanding at a later date.

[0200] 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.

[0201] Data generation model 58 is a type of 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.

[0202] 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.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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".

[0216] This invention provides an embodiment of an educational system that optimizes the learner's learning experience. This system monitors the learner's state, performs real-time data analysis, and presents appropriate learning materials based on the results.

[0217] First, the device uses sensors placed in the classroom or learning environment to continuously capture the learner's facial expressions, posture, gaze, and voice. This collects a wealth of data about the learner's state. This data is then transmitted to a server via the network.

[0218] The server preprocesses the received data and applies machine learning algorithms to evaluate the learner's comprehension and concentration level. Specifically, it analyzes multiple data points by integrating them, such as determining emotional states from facial expressions, estimating the direction of attention from eye movements, and detecting emotional changes from tone of voice.

[0219] Based on the evaluation results, the server generates learning materials tailored to the learner. For example, if a learner's understanding of a particular topic is insufficient, supplementary materials related to that topic are prepared. If the server determines that the learner's concentration is waning, it offers interactive and engaging quizzes or suggests breaks. This generated content is then presented to the learner via their device.

[0220] Furthermore, the server generates detailed reports, including learner progress and comprehension levels, and provides these to users (educators and parents). Users can utilize these reports to provide feedback to learners and adjust their teaching strategies.

[0221] For example, if a learner is learning a particular mathematical concept and the device detects that their facial expression indicates confusion, the server immediately analyzes this data and provides supplementary materials such as additional video tutorials or practice problems. Furthermore, based on numerical results, encouraging messages are displayed to motivate the learner as they attempt to solve problems.

[0222] In this way, this system provides education tailored to the individual circumstances of each learner, maximizing the quality and effectiveness of learning.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The device activates its sensors and begins capturing the learner's facial expressions, posture, gaze, and voice in real time. The collected data undergoes initial noise reduction and basic data format conversion.

[0226] Step 2:

[0227] The device sends data to the server over the network. During this process, the data is encrypted for security purposes and transmitted in an appropriate packet format.

[0228] Step 3:

[0229] The server receives the data and performs data preprocessing. Specifically, this includes adjusting image data, cleaning audio data, and normalizing eye-tracking data.

[0230] Step 4:

[0231] The server uses an AI model to analyze pre-processed data and estimate the learner's comprehension and concentration levels. This includes processes such as analyzing emotions from facial expressions, evaluating the level of gaze focus, and detecting changes in tone from audio data.

[0232] Step 5:

[0233] The server generates learning materials and feedback tailored to the learner based on the analysis results. For topics deemed to have insufficient understanding, it creates supplementary materials and interactive learning content.

[0234] Step 6:

[0235] The server sends generated learning materials and feedback information to the terminal and displays them on the learner's interface to encourage them to continue learning.

[0236] Step 7:

[0237] The server collects learner progress data and generates reports for teachers and parents. These reports include comprehension scores and study time statistics, which are useful for developing individualized learning plans.

[0238] Step 8:

[0239] Users (teachers and parents) access reports provided by the server to check the learner's progress. They can provide feedback and instructions on the next learning steps as needed.

[0240] (Example 1)

[0241] 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."

[0242] Traditional educational systems have struggled to appropriately identify the varying levels of understanding and concentration among learners and provide personalized learning support tailored to their individual circumstances. Conventional methods often failed to grasp learners' states in real time and provide appropriate learning materials in a timely manner, resulting in poor learning outcomes. This invention aims to solve these problems and optimize the learner's experience.

[0243] 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.

[0244] In this invention, the server includes data acquisition means, information analysis means, and material supply means. This makes it possible to grasp the learner's status in real time and provide optimized learning materials tailored to that situation.

[0245] "Data acquisition means" refers to a device that detects the learner's facial expressions, posture, gaze, and voice, and collects this information.

[0246] "Information analysis means" refers to techniques for processing information obtained from data acquisition means to determine the learner's level of understanding and concentration.

[0247] "Material supply means" refers to a method of dynamically generating and presenting learning materials that are suitable for the learner's situation based on the determination results of the information analysis means.

[0248] "Information transmission means" refers to a function that generates documents detailing learners' progress and level of understanding, and reports these to educators or parents.

[0249] An "artificial intelligence algorithm" is a technical method used in information analysis to process learner data and determine their level of understanding and concentration.

[0250] "Two-way feedback" refers to a method that includes interactive responses, such as suggesting a break to the learner when it is determined that their concentration level is low.

[0251] The embodiments for carrying out this invention are described in detail below.

[0252] The device continuously captures the learner's facial expressions, posture, gaze, and voice using sensors placed in the learning environment. This includes high-resolution cameras and microphones that can accurately capture various aspects of the learner's state. The acquired data is transmitted to the server in real time.

[0253] The server preprocesses the received data and analyzes it using machine learning algorithms. This process utilizes software such as Python's OpenCV library and TensorFlow. This allows the server to determine the learner's level of comprehension and concentration. Specifically, it analyzes facial expressions to determine emotional states and eye-tracking to estimate the direction of attention. It also analyzes voice tone through speech analysis to capture changes in emotion.

[0254] Based on the analysis results, the server generates learning materials tailored to the learner. For example, for topics where understanding is lacking, it provides related supplementary video tutorials and practice exercises. Furthermore, if it determines that the learner's concentration is waning, it offers interactive quizzes and suggests breaks to maintain their interest.

[0255] Furthermore, the server generates detailed reports on learners' progress and understanding, which are provided to users (educators and parents). Users can use this information to provide effective feedback to learners and adjust teaching strategies.

[0256] For example, if a learner is studying a specific mathematical concept and the device detects confusion from their facial expression, the server will immediately analyze this and generate and provide additional video tutorials or practice problems. By utilizing prompts with a generative AI model, new learning content can be prepared quickly. An example of a prompt might be, "Please suggest effective learning support for a confused student."

[0257] In this way, this system can provide education tailored to the individual needs of learners and significantly improve the quality of the learning experience.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The device uses a high-resolution camera and microphone to capture the learner's facial expressions, posture, gaze, and voice. The input for this process is the learner's real-time movements and sounds in the learning environment. This data is output and collected as video and audio data. Specifically, the device's sensors continuously capture data and convert it into a format that can be immediately analyzed.

[0261] Step 2:

[0262] The terminal transmits collected video and audio data to a server via the network. Input data is transmitted in real time and securely stored on the server as output. Specific operations include procedures to ensure security and privacy by using secure communication protocols for data transfer.

[0263] Step 3:

[0264] The server preprocesses the received data. The input consists of raw video and audio data, and data processing such as noise reduction and format conversion is performed. The output is clean data ready for analysis. The specific operation of this step involves applying a preprocessing algorithm using a Python library.

[0265] Step 4:

[0266] The server performs analysis on pre-processed data using machine learning algorithms. The input is clean data, and data calculations are performed to determine the learner's level of understanding and concentration. The output is a numerical value or evaluation result indicating the learner's current state. The specific operation includes analysis processing using a deep learning model, and each data point is integrated and analyzed.

[0267] Step 5:

[0268] The server generates learning materials tailored to the learner based on the analysis results. The input is the analysis results, and new learning materials are generated using a generative AI model in response to prompt sentences. For example, additional tutorials are generated for topics where the learner has a low level of understanding. The output is personalized learning content. The specific actions in this step include prompt input to the generative AI model.

[0269] Step 6:

[0270] The server generates learning materials and sends them to the learner's device for presentation. The input is the generated learning content, and the output is the learning materials displayed on the learner's device. Specific operations include formatting the learning materials and optimizing their display.

[0271] Step 7:

[0272] The server generates reports on learners' progress and understanding. Inputs include past data analysis results and learning content, while output is a report-style document for educators and parents. Specific operations include visualization of progress data and automated report generation.

[0273] Step 8:

[0274] The user uses the report to adjust feedback and teaching strategies for learners. The input is the report provided by the server, and the output is the teaching plan and feedback content for the learners. The specific actions in this step include report evaluation and the development of teaching strategies.

[0275] (Application Example 1)

[0276] 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."

[0277] Traditional education systems have struggled to provide dynamic and adaptive learning support tailored to the individual characteristics of each learner, and have been particularly unable to offer effective support for home study. This has led to challenges such as decreased learning efficiency and difficulty in maintaining motivation.

[0278] 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.

[0279] In this invention, the server includes information gathering means, information analysis means, educational resource provision means, and control means for controlling the mechanical device. This makes it possible to provide optimal educational resources according to the learner's learning status, and to provide appropriate and individualized learning support even within the home.

[0280] "Information gathering means" refers to devices and technologies for detecting and acquiring information about learners' physical and vocal characteristics.

[0281] The "information analysis means" refers to devices or technologies for analyzing the collected information and evaluating the learner's cognitive state and attentiveness.

[0282] The "educational resource providing means" refers to devices or technologies for dynamically generating and providing educational resources optimized for the learner based on the results of information analysis.

[0283] The "notification means" refers to devices or technologies for generating in a report format the learner's learning progress and comprehension level and providing them to the instructor or guardian.

[0284] The "control means" refers to devices or technologies for controlling the mechanical devices that assist the learner's learning within the home.

[0285] In an embodiment of this invention, first, the terminal uses various sensors (e.g., facial recognition camera, eye-tracking sensor, microphone) arranged within the learning environment to detect the learner's state in real time. As a result, information regarding the learner's body and voice is continuously acquired. The acquired information is transmitted to the server via the network.

[0286] The server plays the role of information analysis, stores the collected information in a database, and analyzes it using machine learning libraries (e.g., TensorFlow, OpenCV). Through the analysis, the learner's cognitive state and attentiveness are evaluated. As specific examples, processes such as determining the emotional state from the facial expression, estimating the concentration level from the movement of the line of sight, and detecting emotional changes from the tone of voice are performed.

[0287] Based on the analysis results, the server executes an algorithm for providing educational resources. This algorithm dynamically generates optimized educational resources (e.g., supplementary teaching materials, quizzes, rest suggestions) and presents them to the learner through the terminal. Furthermore, the server has the function of generating in a report format the learner's learning progress and comprehension level and notifying the instructor or guardian.

[0288] For example, if a learner is solving a math problem and the device detects their confused expression, the server analyzes this data and prepares an appropriate video tutorial. Also, if the system determines that the learner is losing focus, the robot provides interactive feedback such as, "How about taking a short break next?"

[0289] An example of a prompt message might be something like, "Analyze the learner's facial expression data, and when a specific facial expression pattern is recognized, select and provide appropriate learning materials or encouraging messages." This allows learners to receive personalized learning support.

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The terminal acquires real-time information on the learner's facial expressions, posture, gaze, and voice from sensors installed within the learning environment. The input is sensor data, and the output is a data packet containing the sensor data. This data packet is transmitted to the server via the network.

[0293] Step 2:

[0294] The server processes the received data packets and stores them in a database. The input is the data packets sent from the terminal, and the output is the stored data. Specifically, it formats the data and appropriately indexes it in the database.

[0295] Step 3:

[0296] The server analyzes stored data using machine learning algorithms. The input is sensor data stored in a database, and the output is an evaluation of the learner's perception state and attention level. The server uses TensorFlow, for example, to determine emotional states from facial expressions and estimate the focus of attention from gaze data.

[0297] Step 4:

[0298] The server selects the optimal educational resources using a generative AI model based on the analysis results and generates teaching materials. The input is learner state information obtained through analysis, and the output is dynamically generated educational resources. Specifically, it selects the generated teaching materials and creates content according to the format.

[0299] Step 5:

[0300] The terminal receives educational resources sent from the server and presents them to the learner. The input is educational resource data from the server, and the output is learning materials displayed to the learner. The terminal uses a display and speech synthesis capabilities to present the content.

[0301] Step 6:

[0302] The server generates reports on learners' progress and comprehension and notifies the user (educator or parent). The input is analyzed learner data, and the output is a report-formatted file. Specifically, the report is sent via email or platform notifications.

[0303] 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.

[0304] The present invention is a highly personalized educational support system for learners, which combines an emotion recognition engine to provide a richer and more effective learning experience. This system improves the quality of education by grasping the emotional state of learners and providing corresponding teaching materials and feedback.

[0305] This system first has sensors on the terminal for acquiring the learner's expression, posture, gaze, and voice in real time. This data is collected and transmitted to the server in an encrypted form.

[0306] The server analyzes the received data and uses machine learning algorithms and an emotion recognition engine to evaluate the learner's comprehension, concentration, and emotional state. The emotion recognition engine analyzes the learner's expression and voice data to determine the emotion. For example, if the learner has a worried expression, the server recognizes the emotion and determines that there may be a problem with comprehension. This evaluation is reflected in the presentation of teaching materials.

[0307] Based on these evaluation results by the emotion recognition engine, the server dynamically generates teaching materials optimized for the learner. For example, for learners with low comprehension, additional explanatory materials and practice questions are provided, and when the emotional state is negative, relaxing learning content that induces positive emotions is provided.

[0308] The presented teaching materials are displayed to the learner through the terminal to facilitate the progress of learning. Furthermore, the server generates the learner's progress information and comprehension as a report and provides it to the user (educator or guardian) to support learning.

[0309] As a concrete example, in a math class, when a student encounters a new concept, the device detects confusion from the student's facial expressions. This data is analyzed on a server, and if the emotion engine recognizes signs of stress or anxiety, the server immediately provides situation-appropriate mitigation measures, such as gamified practice problems or video tutorials. Information about this learning session is also saved as a report, allowing the user to review their progress later.

[0310] In this way, this system, which incorporates an emotion recognition engine, aims to improve learning effectiveness by providing a customized educational experience for each learner.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The device uses sensors to capture the learner's facial expressions, posture, gaze, and voice in real time. This data is immediately digitized and subjected to basic noise reduction as an initial processing step.

[0314] Step 2:

[0315] The device encrypts the captured data and then sends it to the server over the network. This ensures that the data is transferred securely and efficiently.

[0316] Step 3:

[0317] The server receives the data and performs preprocessing. Here, the data is shaped and normalized, and converted into a format suitable for analysis.

[0318] Step 4:

[0319] The server uses an emotion recognition engine and machine learning algorithms to perform detailed analysis of the data. Specifically, it identifies emotional states based on facial expression data and detects changes in emotion by analyzing tone and pitch from audio data. It also evaluates the learner's level of comprehension and concentration.

[0320] Step 5:

[0321] Based on the evaluation results, the server uses a learning material delivery system to generate learning materials and feedback optimized for the learner. If the learner is in a negative emotional state, it selects content with a relaxing effect, and if a lack of understanding is pointed out, it designs content that includes supplementary materials.

[0322] Step 6:

[0323] The server sends learning materials and feedback generated by the server to the user's device for display. This allows learners to progress through the learning process via interactive content.

[0324] Step 7:

[0325] The server generates detailed reports, including learners' learning progress and emotional changes, and provides them to the user (educator or parent). These reports help to understand the learners' qualitative and quantitative progress.

[0326] Step 8:

[0327] The user reviews the report provided by the server and evaluates the learner's progress and any necessary actions. If necessary, they adjust the instruction plan for the next learning session.

[0328] (Example 2)

[0329] 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".

[0330] Traditional educational support systems have struggled to grasp learners' emotions and comprehension levels in real time and to immediately provide the most appropriate learning materials accordingly. This has made it difficult to provide personalized learning experiences for each individual learner, resulting in a failure to maximize educational effectiveness.

[0331] 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.

[0332] In this invention, the server includes information gathering means, information analysis means, and teaching material presentation means. This makes it possible to collect and analyze characteristic data, including learners' facial expressions and voices, in real time, and to dynamically generate and provide optimized teaching materials based on that evaluation.

[0333] A "detection device" refers to hardware or software that detects a learner's facial expressions, posture, gaze, and voice in real time.

[0334] "Information gathering means" refers to the process and function of collecting characteristic data acquired by a detection device and converting it into a format usable within the system.

[0335] "Information analysis means" refers to the process and function of processing collected data and using machine learning algorithms, etc., to evaluate learners' emotional state, comprehension level, and concentration level.

[0336] "Method of presenting teaching materials" refers to the process and function of creating and providing teaching materials suitable for learners based on evaluation results obtained through information analysis methods.

[0337] "Communication means" refers to the process and function of generating reports on learners' learning progress and evaluation results, and presenting or transmitting them to educators.

[0338] This invention is an advanced educational support system for providing learners with individually optimized educational experiences. The system aims to improve learning effectiveness by understanding learners' emotional states in real time and dynamically providing appropriate learning materials.

[0339] The device is equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice. Specifically, it incorporates a high-precision camera and voice input device, enabling it to capture subtle changes in the learner's facial expressions and voice tone. This data is transmitted to the server in a securely encrypted format.

[0340] The server analyzes the received data in real time and uses machine learning algorithms and an emotion recognition engine to evaluate the learner's emotional state. This analysis uses a machine learning library based on Python, and based on the analysis results, it immediately determines the learner's level of understanding and concentration.

[0341] Next, the server uses the evaluation results to generate learning materials optimized for the learner. This process incorporates natural language processing technology, allowing for flexible adjustment of the material content according to the learner's level of understanding. The generated materials are then provided to the learner via their device.

[0342] As a concrete example, suppose a device detects that a student is struggling to understand a new concept in a math class. The server receives this data, determines that the student's understanding is low, and provides a combination of a simple explanatory video and practice problems.

[0343] Furthermore, once a learning session ends, the server generates progress information in report format and provides it to the user (educator or parent) to support learning. This allows educators to efficiently track the progress of individual learners and provide necessary support.

[0344] An example of a prompt message is, "Analyze the emotional state of the students based on their facial expression and voice data, and generate appropriate teaching materials."

[0345] In this way, the present invention provides educational materials tailored to the individual needs of learners, thereby realizing an effective learning experience.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The device detects the learner's facial expressions, posture, gaze, and voice in real time. Specifically, it uses a camera and microphone to capture the learner's facial movements, gaze direction, voice tone, and volume. This data serves as input necessary for inferring emotional state. The output is structured as encrypted data and sent securely to the server.

[0349] Step 2:

[0350] The server receives encrypted data sent from the terminal and first decrypts it. It then analyzes the received input data using machine learning algorithms and employs image processing and speech analysis technologies to evaluate the learner's emotional state, comprehension level, and concentration level. A generative AI model is used for this analysis, generating detailed evaluation information about the learner's state as output.

[0351] Step 3:

[0352] The server dynamically generates learning materials optimized for the learner based on the generated assessment information. Here, natural language processing technology is utilized to adjust the educational content to individual needs. For example, additional explanations and examples are added to items where understanding is low. The input to this process is assessment information, and the output is individually customized learning materials.

[0353] Step 4:

[0354] The terminal receives optimized learning materials sent from the server and presents them to the learner. The materials are displayed on the screen in an interactive format that the learner can manipulate. The input is learning material information from the server, and the output is a screen display to aid the learner's understanding. Immediate feedback is also provided in response to the learner's actions.

[0355] Step 5:

[0356] The server analyzes the activity log after the learning session ends and aggregates the learner's progress data. Based on this, it generates a report for educators. This report includes a history of the learner's understanding, progress, and emotional state. The input data is the activity log sent from the terminal during learning, and the output is a detailed progress report that users can later evaluate.

[0357] Step 6:

[0358] Users receive progress reports from the server and understand the learner's situation. This allows them to plan and implement appropriate instruction and support as needed. The input is reports from the server, and the output is an instruction plan based on understanding and progress. This ultimately leads to actions that enhance the educational effectiveness of the learner.

[0359] (Application Example 2)

[0360] 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."

[0361] Conventional learning support systems have struggled to appropriately understand learners' emotional states and provide learning materials accordingly, resulting in a failure to maximize learning effectiveness. This invention aims to provide a personalized learning experience tailored to learners' emotional states and levels of understanding, thereby promoting more effective learning.

[0362] 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.

[0363] In this invention, the server includes emotion recognition means, data collection means, and data analysis means. This makes it possible to evaluate the learner's emotional state and level of understanding in real time and provide optimized learning materials based on that evaluation.

[0364] "Emotion recognition means" refers to technology that analyzes the learner's facial expressions and voice data to determine their emotional state.

[0365] A "data collection means" is a system equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice, and to acquire the collected data.

[0366] "Data analysis methods" refer to techniques that analyze data obtained from data collection methods to evaluate learners' comprehension and concentration levels.

[0367] A "means of providing educational materials" refers to a system for dynamically generating and providing educational materials optimized for learners based on data analysis results and emotional states.

[0368] "Notification methods" refer to technologies that generate reports on learners' learning progress and comprehension levels, and provide this information to educators or parents.

[0369] The system for implementing this invention is an educational support system that analyzes the learner's emotional state in real time and provides optimal learning materials. First, the terminal is equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice. This data is transmitted to the server in a securely encrypted format.

[0370] The server processes the received data using emotion recognition and data analysis tools. Specifically, a machine learning algorithm built using Python and TensorFlow analyzes the data and evaluates the learner's comprehension, concentration, and emotional state. Based on these evaluation results, the learning material delivery tool generates optimized learning materials and provides them to the learner. The learning materials are dynamically created using Google Cloud AI Platform.

[0371] Furthermore, the server organizes progress information and notifies users (educators and parents) in report format. This allows for centralized management of the learning process and timely monitoring of learners' understanding.

[0372] For example, if a student shows a confused expression during an online math class, this is detected, and the server performs an emotional assessment. Based on this assessment, relaxing learning content and supplementary materials are provided. An example of a prompt to the generative AI model in this process would be, "When learning progress slows down, what kind of content should be generated to increase the student's motivation?"

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The device uses sensors to acquire the learner's facial expressions, posture, gaze, and voice data in real time, encrypts this data, and sends it to the server. The input is various data of the learner acquired by the sensors, and the output is encrypted data. This ensures the security of personal information while providing the necessary data to the server.

[0376] Step 2:

[0377] The server decrypts encrypted data received from the terminal and analyzes the data using emotion recognition methods. Specifically, it uses a machine learning algorithm with Python and TensorFlow to identify the learner's emotional state. The input is the decrypted learner data, and the output is the analysis result regarding the learner's emotional state. This allows for a detailed evaluation of the learner's psychological state.

[0378] Step 3:

[0379] The server generates optimized learning materials using a learning material delivery system, based on the analyzed learner's emotional state and comprehension level. Here, Google Cloud AI Platform is used to dynamically create learning materials by combining necessary resources and content. The input is the analysis results of emotions and comprehension, and the output is customized learning materials. This enables the rapid delivery of learning materials tailored to the learner.

[0380] Step 4:

[0381] The server sends the generated learning materials to the terminal and presents them to the learner. The terminal displays the provided materials on the learner's device. The input is the learning materials generated by the server, and the output is the display of the materials on the learner's terminal. This provides visual support to help the user progress through their learning effectively.

[0382] Step 5:

[0383] The server records the progress and comprehension of learning sessions and generates reports, which are then sent to parents and educators. The input is progress and comprehension information recorded during the learning session, and the output is a detailed learning report. This allows users to review the learner's understanding at a later date.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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".

[0400] This invention provides an embodiment of an educational system that optimizes the learner's learning experience. This system monitors the learner's state, performs real-time data analysis, and presents appropriate learning materials based on the results.

[0401] First, the device uses sensors placed in the classroom or learning environment to continuously capture the learner's facial expressions, posture, gaze, and voice. This collects a wealth of data about the learner's state. This data is then transmitted to a server via the network.

[0402] The server preprocesses the received data and applies machine learning algorithms to evaluate the learner's comprehension and concentration level. Specifically, it analyzes multiple data points by integrating them, such as determining emotional states from facial expressions, estimating the direction of attention from eye movements, and detecting emotional changes from tone of voice.

[0403] Based on the evaluation results, the server generates learning materials tailored to the learner. For example, if a learner's understanding of a particular topic is insufficient, supplementary materials related to that topic are prepared. If the server determines that the learner's concentration is waning, it offers interactive and engaging quizzes or suggests breaks. This generated content is then presented to the learner via their device.

[0404] Furthermore, the server generates detailed reports, including learner progress and comprehension levels, and provides these to users (educators and parents). Users can utilize these reports to provide feedback to learners and adjust their teaching strategies.

[0405] For example, if a learner is learning a particular mathematical concept and the device detects that their facial expression indicates confusion, the server immediately analyzes this data and provides supplementary materials such as additional video tutorials or practice problems. Furthermore, based on numerical results, encouraging messages are displayed to motivate the learner as they attempt to solve problems.

[0406] In this way, this system provides education tailored to the individual circumstances of each learner, maximizing the quality and effectiveness of learning.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The device activates its sensors and begins capturing the learner's facial expressions, posture, gaze, and voice in real time. The collected data undergoes initial noise reduction and basic data format conversion.

[0410] Step 2:

[0411] The device sends data to the server over the network. During this process, the data is encrypted for security purposes and transmitted in an appropriate packet format.

[0412] Step 3:

[0413] The server receives the data and performs data preprocessing. Specifically, this includes adjusting image data, cleaning audio data, and normalizing eye-tracking data.

[0414] Step 4:

[0415] The server uses an AI model to analyze pre-processed data and estimate the learner's comprehension and concentration levels. This includes processes such as analyzing emotions from facial expressions, evaluating the level of gaze focus, and detecting changes in tone from audio data.

[0416] Step 5:

[0417] The server generates learning materials and feedback tailored to the learner based on the analysis results. For topics deemed to have insufficient understanding, it creates supplementary materials and interactive learning content.

[0418] Step 6:

[0419] The server sends generated learning materials and feedback information to the terminal and displays them on the learner's interface to encourage them to continue learning.

[0420] Step 7:

[0421] The server collects learner progress data and generates reports for teachers and parents. These reports include comprehension scores and study time statistics, which are useful for developing individualized learning plans.

[0422] Step 8:

[0423] Users (teachers and parents) access reports provided by the server to check the learner's progress. They can provide feedback and instructions on the next learning steps as needed.

[0424] (Example 1)

[0425] 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."

[0426] Traditional educational systems have struggled to appropriately identify the varying levels of understanding and concentration among learners and provide personalized learning support tailored to their individual circumstances. Conventional methods often failed to grasp learners' states in real time and provide appropriate learning materials in a timely manner, resulting in poor learning outcomes. This invention aims to solve these problems and optimize the learner's experience.

[0427] 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.

[0428] In this invention, the server includes data acquisition means, information analysis means, and material supply means. This makes it possible to grasp the learner's status in real time and provide optimized learning materials tailored to that situation.

[0429] "Data acquisition means" refers to a device that detects the learner's facial expressions, posture, gaze, and voice, and collects this information.

[0430] "Information analysis means" refers to techniques for processing information obtained from data acquisition means to determine the learner's level of understanding and concentration.

[0431] "Material supply means" refers to a method of dynamically generating and presenting learning materials that are suitable for the learner's situation based on the determination results of the information analysis means.

[0432] "Information transmission means" refers to a function that generates documents detailing learners' progress and level of understanding, and reports these to educators or parents.

[0433] An "artificial intelligence algorithm" is a technical method used in information analysis to process learner data and determine their level of understanding and concentration.

[0434] "Two-way feedback" refers to a method that includes interactive responses, such as suggesting a break to the learner when it is determined that their concentration level is low.

[0435] The embodiments for carrying out this invention are described in detail below.

[0436] The device continuously captures the learner's facial expressions, posture, gaze, and voice using sensors placed in the learning environment. This includes high-resolution cameras and microphones that can accurately capture various aspects of the learner's state. The acquired data is transmitted to the server in real time.

[0437] The server preprocesses the received data and analyzes it using machine learning algorithms. This process utilizes software such as Python's OpenCV library and TensorFlow. This allows the server to determine the learner's level of comprehension and concentration. Specifically, it analyzes facial expressions to determine emotional states and eye-tracking to estimate the direction of attention. It also analyzes voice tone through speech analysis to capture changes in emotion.

[0438] Based on the analysis results, the server generates learning materials tailored to the learner. For example, for topics where understanding is lacking, it provides related supplementary video tutorials and practice exercises. Furthermore, if it determines that the learner's concentration is waning, it offers interactive quizzes and suggests breaks to maintain their interest.

[0439] Furthermore, the server generates detailed reports on learners' progress and understanding, which are provided to users (educators and parents). Users can use this information to provide effective feedback to learners and adjust teaching strategies.

[0440] For example, if a learner is studying a specific mathematical concept and the device detects confusion from their facial expression, the server will immediately analyze this and generate and provide additional video tutorials or practice problems. By utilizing prompts with a generative AI model, new learning content can be prepared quickly. An example of a prompt might be, "Please suggest effective learning support for a confused student."

[0441] In this way, this system can provide education tailored to the individual needs of learners and significantly improve the quality of the learning experience.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The device uses a high-resolution camera and microphone to capture the learner's facial expressions, posture, gaze, and voice. The input for this process is the learner's real-time movements and sounds in the learning environment. This data is output and collected as video and audio data. Specifically, the device's sensors continuously capture data and convert it into a format that can be immediately analyzed.

[0445] Step 2:

[0446] The terminal transmits collected video and audio data to a server via the network. Input data is transmitted in real time and securely stored on the server as output. Specific operations include procedures to ensure security and privacy by using secure communication protocols for data transfer.

[0447] Step 3:

[0448] The server preprocesses the received data. The input consists of raw video and audio data, and data processing such as noise reduction and format conversion is performed. The output is clean data ready for analysis. The specific operation of this step involves applying a preprocessing algorithm using a Python library.

[0449] Step 4:

[0450] The server performs analysis on pre-processed data using machine learning algorithms. The input is clean data, and data calculations are performed to determine the learner's level of understanding and concentration. The output is a numerical value or evaluation result indicating the learner's current state. The specific operation includes analysis processing using a deep learning model, and each data point is integrated and analyzed.

[0451] Step 5:

[0452] The server generates learning materials tailored to the learner based on the analysis results. The input is the analysis results, and new learning materials are generated using a generative AI model in response to prompt sentences. For example, additional tutorials are generated for topics where the learner has a low level of understanding. The output is personalized learning content. The specific actions in this step include prompt input to the generative AI model.

[0453] Step 6:

[0454] The server generates learning materials and sends them to the learner's device for presentation. The input is the generated learning content, and the output is the learning materials displayed on the learner's device. Specific operations include formatting the learning materials and optimizing their display.

[0455] Step 7:

[0456] The server generates reports on learners' progress and understanding. Inputs include past data analysis results and learning content, while output is a report-style document for educators and parents. Specific operations include visualization of progress data and automated report generation.

[0457] Step 8:

[0458] The user uses the report to adjust feedback and teaching strategies for learners. The input is the report provided by the server, and the output is the teaching plan and feedback content for the learners. The specific actions in this step include report evaluation and the development of teaching strategies.

[0459] (Application Example 1)

[0460] 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."

[0461] Traditional education systems have struggled to provide dynamic and adaptive learning support tailored to the individual characteristics of each learner, and have been particularly unable to offer effective support for home study. This has led to challenges such as decreased learning efficiency and difficulty in maintaining motivation.

[0462] 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.

[0463] In this invention, the server includes information gathering means, information analysis means, educational resource provision means, and control means for controlling the mechanical device. This makes it possible to provide optimal educational resources according to the learner's learning status, and to provide appropriate and individualized learning support even within the home.

[0464] "Information gathering means" refers to devices and technologies for detecting and acquiring information about learners' physical and vocal characteristics.

[0465] "Information analysis means" refers to devices and technologies used to analyze collected information and evaluate the learner's cognitive state and attentiveness.

[0466] "Educational resource provision means" refers to devices and technologies that dynamically generate and provide educational resources optimized for learners based on the results of information analysis.

[0467] "Notification means" refers to devices or technologies that generate reports on learners' learning progress and understanding and provide them to instructors or guardians.

[0468] "Control means" refers to devices and technologies for controlling mechanical devices that assist learners in their studies within the home.

[0469] In this embodiment of the invention, the terminal first uses various sensors (e.g., facial recognition camera, eye-tracking sensor, microphone) placed within the learning environment to detect the learner's state in real time. This allows for the continuous acquisition of information regarding the learner's physical and vocal characteristics. The acquired information is then transmitted to a server via the network.

[0470] The server is responsible for information analysis, storing collected information in a database and analyzing it using machine learning libraries (e.g., TensorFlow, OpenCV). The analysis evaluates the learner's cognitive state and attentiveness. Specific examples include determining emotional states from facial expressions, estimating concentration levels from eye movements, and detecting emotional changes from voice tone.

[0471] Based on the analysis results, the server executes an algorithm for providing educational resources. This algorithm dynamically generates optimized educational resources (e.g., supplementary materials, quizzes, break suggestions) and presents them to learners via their devices. Furthermore, the server has the function of generating reports on learners' learning progress and comprehension levels and notifying instructors or parents.

[0472] For example, if a learner is solving a math problem and the device detects their confused expression, the server analyzes this data and prepares an appropriate video tutorial. Also, if the system determines that the learner is losing focus, the robot provides interactive feedback such as, "How about taking a short break next?"

[0473] An example of a prompt message might be something like, "Analyze the learner's facial expression data, and when a specific facial expression pattern is recognized, select and provide appropriate learning materials or encouraging messages." This allows learners to receive personalized learning support.

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] The terminal acquires real-time information on the learner's facial expressions, posture, gaze, and voice from sensors installed within the learning environment. The input is sensor data, and the output is a data packet containing the sensor data. This data packet is transmitted to the server via the network.

[0477] Step 2:

[0478] The server processes the received data packets and stores them in a database. The input is the data packets sent from the terminal, and the output is the stored data. Specifically, it formats the data and appropriately indexes it in the database.

[0479] Step 3:

[0480] The server analyzes stored data using machine learning algorithms. The input is sensor data stored in a database, and the output is an evaluation of the learner's perception state and attention level. The server uses TensorFlow, for example, to determine emotional states from facial expressions and estimate the focus of attention from gaze data.

[0481] Step 4:

[0482] The server selects the optimal educational resources using a generative AI model based on the analysis results and generates teaching materials. The input is learner state information obtained through analysis, and the output is dynamically generated educational resources. Specifically, it selects the generated teaching materials and creates content according to the format.

[0483] Step 5:

[0484] The terminal receives educational resources sent from the server and presents them to the learner. The input is educational resource data from the server, and the output is learning materials displayed to the learner. The terminal uses a display and speech synthesis capabilities to present the content.

[0485] Step 6:

[0486] The server generates reports on learners' progress and comprehension and notifies the user (educator or parent). The input is analyzed learner data, and the output is a report-formatted file. Specifically, the report is sent via email or platform notifications.

[0487] 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.

[0488] This invention provides a highly personalized educational support system for learners, incorporating an emotion recognition engine to deliver a richer and more effective learning experience. The system improves the quality of education by understanding the learner's emotional state and providing appropriate learning materials and feedback.

[0489] This system first features sensors in the terminal to capture the learner's facial expressions, posture, gaze, and voice in real time. This data is collected and sent to a server in an encrypted format.

[0490] The server analyzes the received data and uses machine learning algorithms and an emotion recognition engine to evaluate the learner's comprehension, concentration, and emotional state. The emotion recognition engine analyzes the learner's facial expressions and voice data to determine their emotions. For example, if a learner has an anxious expression, the server recognizes that emotion and determines that they may have difficulty understanding. This evaluation is then reflected in the presentation of learning materials.

[0491] Based on these evaluation results from the emotion recognition engine, the server dynamically generates learning materials optimized for the learner. For example, learners deemed to have a low level of understanding are provided with additional explanatory materials and practice exercises, and if their emotional state is negative, they are provided with relaxing learning content that induces positive emotions.

[0492] The presented learning materials are displayed to learners via their devices, facilitating their learning progress. Furthermore, the server generates reports on the learners' progress and understanding, providing them to the user (educator or parent) to support their learning.

[0493] As a concrete example, in a math class, when a student encounters a new concept, the device detects confusion from the student's facial expressions. This data is analyzed on a server, and if the emotion engine recognizes signs of stress or anxiety, the server immediately provides situation-appropriate mitigation measures, such as gamified practice problems or video tutorials. Information about this learning session is also saved as a report, allowing the user to review their progress later.

[0494] In this way, this system, which incorporates an emotion recognition engine, aims to improve learning effectiveness by providing a customized educational experience for each learner.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The device uses sensors to capture the learner's facial expressions, posture, gaze, and voice in real time. This data is immediately digitized and subjected to basic noise reduction as an initial processing step.

[0498] Step 2:

[0499] The device encrypts the captured data and then sends it to the server over the network. This ensures that the data is transferred securely and efficiently.

[0500] Step 3:

[0501] The server receives the data and performs preprocessing. Here, the data is shaped and normalized, and converted into a format suitable for analysis.

[0502] Step 4:

[0503] The server uses an emotion recognition engine and machine learning algorithms to perform detailed analysis of the data. Specifically, it identifies emotional states based on facial expression data and detects changes in emotion by analyzing tone and pitch from audio data. It also evaluates the learner's level of comprehension and concentration.

[0504] Step 5:

[0505] Based on the evaluation results, the server uses a learning material delivery system to generate learning materials and feedback optimized for the learner. If the learner is in a negative emotional state, it selects content with a relaxing effect, and if a lack of understanding is pointed out, it designs content that includes supplementary materials.

[0506] Step 6:

[0507] The server sends learning materials and feedback generated by the server to the user's device for display. This allows learners to progress through the learning process via interactive content.

[0508] Step 7:

[0509] The server generates detailed reports, including learners' learning progress and emotional changes, and provides them to the user (educator or parent). These reports help to understand the learners' qualitative and quantitative progress.

[0510] Step 8:

[0511] The user reviews the report provided by the server and evaluates the learner's progress and any necessary actions. If necessary, they adjust the instruction plan for the next learning session.

[0512] (Example 2)

[0513] 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."

[0514] Traditional educational support systems have struggled to grasp learners' emotions and comprehension levels in real time and to immediately provide the most appropriate learning materials accordingly. This has made it difficult to provide personalized learning experiences for each individual learner, resulting in a failure to maximize educational effectiveness.

[0515] 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.

[0516] In this invention, the server includes information gathering means, information analysis means, and teaching material presentation means. This makes it possible to collect and analyze characteristic data, including learners' facial expressions and voices, in real time, and to dynamically generate and provide optimized teaching materials based on that evaluation.

[0517] A "detection device" refers to hardware or software that detects a learner's facial expressions, posture, gaze, and voice in real time.

[0518] "Information gathering means" refers to the process and function of collecting characteristic data acquired by a detection device and converting it into a format usable within the system.

[0519] "Information analysis means" refers to the process and function of processing collected data and using machine learning algorithms, etc., to evaluate learners' emotional state, comprehension level, and concentration level.

[0520] "Method of presenting teaching materials" refers to the process and function of creating and providing teaching materials suitable for learners based on evaluation results obtained through information analysis methods.

[0521] "Communication means" refers to the process and function of generating reports on learners' learning progress and evaluation results, and presenting or transmitting them to educators.

[0522] This invention is an advanced educational support system for providing learners with individually optimized educational experiences. The system aims to improve learning effectiveness by understanding learners' emotional states in real time and dynamically providing appropriate learning materials.

[0523] The device is equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice. Specifically, it incorporates a high-precision camera and voice input device, enabling it to capture subtle changes in the learner's facial expressions and voice tone. This data is transmitted to the server in a securely encrypted format.

[0524] The server analyzes the received data in real time and uses machine learning algorithms and an emotion recognition engine to evaluate the learner's emotional state. This analysis uses a machine learning library based on Python, and based on the analysis results, it immediately determines the learner's level of understanding and concentration.

[0525] Next, the server uses the evaluation results to generate learning materials optimized for the learner. This process incorporates natural language processing technology, allowing for flexible adjustment of the material content according to the learner's level of understanding. The generated materials are then provided to the learner via their device.

[0526] As a concrete example, suppose a device detects that a student is struggling to understand a new concept in a math class. The server receives this data, determines that the student's understanding is low, and provides a combination of a simple explanatory video and practice problems.

[0527] Furthermore, once a learning session ends, the server generates progress information in report format and provides it to the user (educator or parent) to support learning. This allows educators to efficiently track the progress of individual learners and provide necessary support.

[0528] An example of a prompt message is, "Analyze the emotional state of the students based on their facial expression and voice data, and generate appropriate teaching materials."

[0529] In this way, the present invention provides educational materials tailored to the individual needs of learners, thereby realizing an effective learning experience.

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] The device detects the learner's facial expressions, posture, gaze, and voice in real time. Specifically, it uses a camera and microphone to capture the learner's facial movements, gaze direction, voice tone, and volume. This data serves as input necessary for inferring emotional state. The output is structured as encrypted data and sent securely to the server.

[0533] Step 2:

[0534] The server receives encrypted data sent from the terminal and first decrypts it. It then analyzes the received input data using machine learning algorithms and employs image processing and speech analysis technologies to evaluate the learner's emotional state, comprehension level, and concentration level. A generative AI model is used for this analysis, generating detailed evaluation information about the learner's state as output.

[0535] Step 3:

[0536] The server dynamically generates learning materials optimized for the learner based on the generated assessment information. Here, natural language processing technology is utilized to adjust the educational content to individual needs. For example, additional explanations and examples are added to items where understanding is low. The input to this process is assessment information, and the output is individually customized learning materials.

[0537] Step 4:

[0538] The terminal receives optimized learning materials sent from the server and presents them to the learner. The materials are displayed on the screen in an interactive format that the learner can manipulate. The input is learning material information from the server, and the output is a screen display to aid the learner's understanding. Immediate feedback is also provided in response to the learner's actions.

[0539] Step 5:

[0540] The server analyzes the activity log after the learning session ends and aggregates the learner's progress data. Based on this, it generates a report for educators. This report includes a history of the learner's understanding, progress, and emotional state. The input data is the activity log sent from the terminal during learning, and the output is a detailed progress report that users can later evaluate.

[0541] Step 6:

[0542] Users receive progress reports from the server and understand the learner's situation. This allows them to plan and implement appropriate instruction and support as needed. The input is reports from the server, and the output is an instruction plan based on understanding and progress. This ultimately leads to actions that enhance the educational effectiveness of the learner.

[0543] (Application Example 2)

[0544] 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."

[0545] Conventional learning support systems have struggled to appropriately understand learners' emotional states and provide learning materials accordingly, resulting in a failure to maximize learning effectiveness. This invention aims to provide a personalized learning experience tailored to learners' emotional states and levels of understanding, thereby promoting more effective learning.

[0546] 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.

[0547] In this invention, the server includes emotion recognition means, data collection means, and data analysis means. This makes it possible to evaluate the learner's emotional state and level of understanding in real time and provide optimized learning materials based on that evaluation.

[0548] "Emotion recognition means" refers to technology that analyzes the learner's facial expressions and voice data to determine their emotional state.

[0549] A "data collection means" is a system equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice, and to acquire the collected data.

[0550] "Data analysis methods" refer to techniques that analyze data obtained from data collection methods to evaluate learners' comprehension and concentration levels.

[0551] A "means of providing educational materials" refers to a system for dynamically generating and providing educational materials optimized for learners based on data analysis results and emotional states.

[0552] "Notification methods" refer to technologies that generate reports on learners' learning progress and comprehension levels, and provide this information to educators or parents.

[0553] The system for implementing this invention is an educational support system that analyzes the learner's emotional state in real time and provides optimal learning materials. First, the terminal is equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice. This data is transmitted to the server in a securely encrypted format.

[0554] The server processes the received data using emotion recognition and data analysis tools. Specifically, a machine learning algorithm built using Python and TensorFlow analyzes the data and evaluates the learner's comprehension, concentration, and emotional state. Based on these evaluation results, the learning material delivery tool generates optimized learning materials and provides them to the learner. The learning materials are dynamically created using Google Cloud AI Platform.

[0555] Furthermore, the server organizes progress information and notifies users (educators and parents) in report format. This allows for centralized management of the learning process and timely monitoring of learners' understanding.

[0556] For example, if a student shows a confused expression during an online math class, this is detected, and the server performs an emotional assessment. Based on this assessment, relaxing learning content and supplementary materials are provided. An example of a prompt to the generative AI model in this process would be, "When learning progress slows down, what kind of content should be generated to increase the student's motivation?"

[0557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0558] Step 1:

[0559] The device uses sensors to acquire the learner's facial expressions, posture, gaze, and voice data in real time, encrypts this data, and sends it to the server. The input is various data of the learner acquired by the sensors, and the output is encrypted data. This ensures the security of personal information while providing the necessary data to the server.

[0560] Step 2:

[0561] The server decrypts encrypted data received from the terminal and analyzes the data using emotion recognition methods. Specifically, it uses a machine learning algorithm with Python and TensorFlow to identify the learner's emotional state. The input is the decrypted learner data, and the output is the analysis result regarding the learner's emotional state. This allows for a detailed evaluation of the learner's psychological state.

[0562] Step 3:

[0563] The server generates optimized learning materials using a learning material delivery system, based on the analyzed learner's emotional state and comprehension level. Here, Google Cloud AI Platform is used to dynamically create learning materials by combining necessary resources and content. The input is the analysis results of emotions and comprehension, and the output is customized learning materials. This enables the rapid delivery of learning materials tailored to the learner.

[0564] Step 4:

[0565] The server sends the generated learning materials to the terminal and presents them to the learner. The terminal displays the provided materials on the learner's device. The input is the learning materials generated by the server, and the output is the display of the materials on the learner's terminal. This provides visual support to help the user progress through their learning effectively.

[0566] Step 5:

[0567] The server records the progress and comprehension of learning sessions and generates reports, which are then sent to parents and educators. The input is progress and comprehension information recorded during the learning session, and the output is a detailed learning report. This allows users to review the learner's understanding at a later date.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] [Fourth Embodiment]

[0572] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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".

[0585] This invention provides an embodiment of an educational system that optimizes the learner's learning experience. This system monitors the learner's state, performs real-time data analysis, and presents appropriate learning materials based on the results.

[0586] First, the device uses sensors placed in the classroom or learning environment to continuously capture the learner's facial expressions, posture, gaze, and voice. This collects a wealth of data about the learner's state. This data is then transmitted to a server via the network.

[0587] The server preprocesses the received data and applies machine learning algorithms to evaluate the learner's comprehension and concentration level. Specifically, it analyzes multiple data points by integrating them, such as determining emotional states from facial expressions, estimating the direction of attention from eye movements, and detecting emotional changes from tone of voice.

[0588] Based on the evaluation results, the server generates learning materials tailored to the learner. For example, if a learner's understanding of a particular topic is insufficient, supplementary materials related to that topic are prepared. If the server determines that the learner's concentration is waning, it offers interactive and engaging quizzes or suggests breaks. This generated content is then presented to the learner via their device.

[0589] Furthermore, the server generates detailed reports, including learner progress and comprehension levels, and provides these to users (educators and parents). Users can utilize these reports to provide feedback to learners and adjust their teaching strategies.

[0590] For example, if a learner is learning a particular mathematical concept and the device detects that their facial expression indicates confusion, the server immediately analyzes this data and provides supplementary materials such as additional video tutorials or practice problems. Furthermore, based on numerical results, encouraging messages are displayed to motivate the learner as they attempt to solve problems.

[0591] In this way, this system provides education tailored to the individual circumstances of each learner, maximizing the quality and effectiveness of learning.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The device activates its sensors and begins capturing the learner's facial expressions, posture, gaze, and voice in real time. The collected data undergoes initial noise reduction and basic data format conversion.

[0595] Step 2:

[0596] The device sends data to the server over the network. During this process, the data is encrypted for security purposes and transmitted in an appropriate packet format.

[0597] Step 3:

[0598] The server receives the data and performs data preprocessing. Specifically, this includes adjusting image data, cleaning audio data, and normalizing eye-tracking data.

[0599] Step 4:

[0600] The server uses an AI model to analyze pre-processed data and estimate the learner's comprehension and concentration levels. This includes processes such as analyzing emotions from facial expressions, evaluating the level of gaze focus, and detecting changes in tone from audio data.

[0601] Step 5:

[0602] The server generates learning materials and feedback tailored to the learner based on the analysis results. For topics deemed to have insufficient understanding, it creates supplementary materials and interactive learning content.

[0603] Step 6:

[0604] The server sends generated learning materials and feedback information to the terminal and displays them on the learner's interface to encourage them to continue learning.

[0605] Step 7:

[0606] The server collects learner progress data and generates reports for teachers and parents. These reports include comprehension scores and study time statistics, which are useful for developing individualized learning plans.

[0607] Step 8:

[0608] Users (teachers and parents) access reports provided by the server to check the learner's progress. They can provide feedback and instructions on the next learning steps as needed.

[0609] (Example 1)

[0610] 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".

[0611] Traditional educational systems have struggled to appropriately identify the varying levels of understanding and concentration among learners and provide personalized learning support tailored to their individual circumstances. Conventional methods often failed to grasp learners' states in real time and provide appropriate learning materials in a timely manner, resulting in poor learning outcomes. This invention aims to solve these problems and optimize the learner's experience.

[0612] 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.

[0613] In this invention, the server includes data acquisition means, information analysis means, and material supply means. This makes it possible to grasp the learner's status in real time and provide optimized learning materials tailored to that situation.

[0614] "Data acquisition means" refers to a device that detects the learner's facial expressions, posture, gaze, and voice, and collects this information.

[0615] "Information analysis means" refers to techniques for processing information obtained from data acquisition means to determine the learner's level of understanding and concentration.

[0616] "Material supply means" refers to a method of dynamically generating and presenting learning materials that are suitable for the learner's situation based on the determination results of the information analysis means.

[0617] "Information transmission means" refers to a function that generates documents detailing learners' progress and level of understanding, and reports these to educators or parents.

[0618] An "artificial intelligence algorithm" is a technical method used in information analysis to process learner data and determine their level of understanding and concentration.

[0619] "Two-way feedback" refers to a method that includes interactive responses, such as suggesting a break to the learner when it is determined that their concentration level is low.

[0620] The embodiments for carrying out this invention are described in detail below.

[0621] The device continuously captures the learner's facial expressions, posture, gaze, and voice using sensors placed in the learning environment. This includes high-resolution cameras and microphones that can accurately capture various aspects of the learner's state. The acquired data is transmitted to the server in real time.

[0622] The server preprocesses the received data and analyzes it using machine learning algorithms. This process utilizes software such as Python's OpenCV library and TensorFlow. This allows the server to determine the learner's level of comprehension and concentration. Specifically, it analyzes facial expressions to determine emotional states and eye-tracking to estimate the direction of attention. It also analyzes voice tone through speech analysis to capture changes in emotion.

[0623] Based on the analysis results, the server generates learning materials tailored to the learner. For example, for topics where understanding is lacking, it provides related supplementary video tutorials and practice exercises. Furthermore, if it determines that the learner's concentration is waning, it offers interactive quizzes and suggests breaks to maintain their interest.

[0624] Furthermore, the server generates detailed reports on learners' progress and understanding, which are provided to users (educators and parents). Users can use this information to provide effective feedback to learners and adjust teaching strategies.

[0625] For example, if a learner is studying a specific mathematical concept and the device detects confusion from their facial expression, the server will immediately analyze this and generate and provide additional video tutorials or practice problems. By utilizing prompts with a generative AI model, new learning content can be prepared quickly. An example of a prompt might be, "Please suggest effective learning support for a confused student."

[0626] In this way, this system can provide education tailored to the individual needs of learners and significantly improve the quality of the learning experience.

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] The device uses a high-resolution camera and microphone to capture the learner's facial expressions, posture, gaze, and voice. The input for this process is the learner's real-time movements and sounds in the learning environment. This data is output and collected as video and audio data. Specifically, the device's sensors continuously capture data and convert it into a format that can be immediately analyzed.

[0630] Step 2:

[0631] The terminal transmits collected video and audio data to a server via the network. Input data is transmitted in real time and securely stored on the server as output. Specific operations include procedures to ensure security and privacy by using secure communication protocols for data transfer.

[0632] Step 3:

[0633] The server preprocesses the received data. The input consists of raw video and audio data, and data processing such as noise reduction and format conversion is performed. The output is clean data ready for analysis. The specific operation of this step involves applying a preprocessing algorithm using a Python library.

[0634] Step 4:

[0635] The server performs analysis on pre-processed data using machine learning algorithms. The input is clean data, and data calculations are performed to determine the learner's level of understanding and concentration. The output is a numerical value or evaluation result indicating the learner's current state. The specific operation includes analysis processing using a deep learning model, and each data point is integrated and analyzed.

[0636] Step 5:

[0637] The server generates learning materials tailored to the learner based on the analysis results. The input is the analysis results, and new learning materials are generated using a generative AI model in response to prompt sentences. For example, additional tutorials are generated for topics where the learner has a low level of understanding. The output is personalized learning content. The specific actions in this step include prompt input to the generative AI model.

[0638] Step 6:

[0639] The server generates learning materials and sends them to the learner's device for presentation. The input is the generated learning content, and the output is the learning materials displayed on the learner's device. Specific operations include formatting the learning materials and optimizing their display.

[0640] Step 7:

[0641] The server generates reports on learners' progress and understanding. Inputs include past data analysis results and learning content, while output is a report-style document for educators and parents. Specific operations include visualization of progress data and automated report generation.

[0642] Step 8:

[0643] The user uses the report to adjust feedback and teaching strategies for learners. The input is the report provided by the server, and the output is the teaching plan and feedback content for the learners. The specific actions in this step include report evaluation and the development of teaching strategies.

[0644] (Application Example 1)

[0645] 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".

[0646] Traditional education systems have struggled to provide dynamic and adaptive learning support tailored to the individual characteristics of each learner, and have been particularly unable to offer effective support for home study. This has led to challenges such as decreased learning efficiency and difficulty in maintaining motivation.

[0647] 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.

[0648] In this invention, the server includes information gathering means, information analysis means, educational resource provision means, and control means for controlling the mechanical device. This makes it possible to provide optimal educational resources according to the learner's learning status, and to provide appropriate and individualized learning support even within the home.

[0649] "Information gathering means" refers to devices and technologies for detecting and acquiring information about learners' physical and vocal characteristics.

[0650] "Information analysis means" refers to devices and technologies used to analyze collected information and evaluate the learner's cognitive state and attentiveness.

[0651] "Educational resource provision means" refers to devices and technologies that dynamically generate and provide educational resources optimized for learners based on the results of information analysis.

[0652] "Notification means" refers to devices or technologies that generate reports on learners' learning progress and understanding and provide them to instructors or guardians.

[0653] "Control means" refers to devices and technologies for controlling mechanical devices that assist learners in their studies within the home.

[0654] In this embodiment of the invention, the terminal first uses various sensors (e.g., facial recognition camera, eye-tracking sensor, microphone) placed within the learning environment to detect the learner's state in real time. This allows for the continuous acquisition of information regarding the learner's physical and vocal characteristics. The acquired information is then transmitted to a server via the network.

[0655] The server is responsible for information analysis, storing collected information in a database and analyzing it using machine learning libraries (e.g., TensorFlow, OpenCV). The analysis evaluates the learner's cognitive state and attentiveness. Specific examples include determining emotional states from facial expressions, estimating concentration levels from eye movements, and detecting emotional changes from voice tone.

[0656] Based on the analysis results, the server executes an algorithm for providing educational resources. This algorithm dynamically generates optimized educational resources (e.g., supplementary materials, quizzes, break suggestions) and presents them to learners via their devices. Furthermore, the server has the function of generating reports on learners' learning progress and comprehension levels and notifying instructors or parents.

[0657] For example, if a learner is solving a math problem and the device detects their confused expression, the server analyzes this data and prepares an appropriate video tutorial. Also, if the system determines that the learner is losing focus, the robot provides interactive feedback such as, "How about taking a short break next?"

[0658] An example of a prompt message might be something like, "Analyze the learner's facial expression data, and when a specific facial expression pattern is recognized, select and provide appropriate learning materials or encouraging messages." This allows learners to receive personalized learning support.

[0659] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0660] Step 1:

[0661] The terminal acquires real-time information on the learner's facial expressions, posture, gaze, and voice from sensors installed within the learning environment. The input is sensor data, and the output is a data packet containing the sensor data. This data packet is transmitted to the server via the network.

[0662] Step 2:

[0663] The server processes the received data packets and stores them in a database. The input is the data packets sent from the terminal, and the output is the stored data. Specifically, it formats the data and appropriately indexes it in the database.

[0664] Step 3:

[0665] The server analyzes stored data using machine learning algorithms. The input is sensor data stored in a database, and the output is an evaluation of the learner's perception state and attention level. The server uses TensorFlow, for example, to determine emotional states from facial expressions and estimate the focus of attention from gaze data.

[0666] Step 4:

[0667] The server selects the optimal educational resources using a generative AI model based on the analysis results and generates teaching materials. The input is learner state information obtained through analysis, and the output is dynamically generated educational resources. Specifically, it selects the generated teaching materials and creates content according to the format.

[0668] Step 5:

[0669] The terminal receives educational resources sent from the server and presents them to the learner. The input is educational resource data from the server, and the output is learning materials displayed to the learner. The terminal uses a display and speech synthesis capabilities to present the content.

[0670] Step 6:

[0671] The server generates reports on learners' progress and comprehension and notifies the user (educator or parent). The input is analyzed learner data, and the output is a report-formatted file. Specifically, the report is sent via email or platform notifications.

[0672] 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.

[0673] This invention provides a highly personalized educational support system for learners, incorporating an emotion recognition engine to deliver a richer and more effective learning experience. The system improves the quality of education by understanding the learner's emotional state and providing appropriate learning materials and feedback.

[0674] This system first features sensors in the terminal to capture the learner's facial expressions, posture, gaze, and voice in real time. This data is collected and sent to a server in an encrypted format.

[0675] The server analyzes the received data and uses machine learning algorithms and an emotion recognition engine to evaluate the learner's comprehension, concentration, and emotional state. The emotion recognition engine analyzes the learner's facial expressions and voice data to determine their emotions. For example, if a learner has an anxious expression, the server recognizes that emotion and determines that they may have difficulty understanding. This evaluation is then reflected in the presentation of learning materials.

[0676] Based on these evaluation results from the emotion recognition engine, the server dynamically generates learning materials optimized for the learner. For example, learners deemed to have a low level of understanding are provided with additional explanatory materials and practice exercises, and if their emotional state is negative, they are provided with relaxing learning content that induces positive emotions.

[0677] The presented learning materials are displayed to learners via their devices, facilitating their learning progress. Furthermore, the server generates reports on the learners' progress and understanding, providing them to the user (educator or parent) to support their learning.

[0678] As a concrete example, in a math class, when a student encounters a new concept, the device detects confusion from the student's facial expressions. This data is analyzed on a server, and if the emotion engine recognizes signs of stress or anxiety, the server immediately provides situation-appropriate mitigation measures, such as gamified practice problems or video tutorials. Information about this learning session is also saved as a report, allowing the user to review their progress later.

[0679] In this way, this system, which incorporates an emotion recognition engine, aims to improve learning effectiveness by providing a customized educational experience for each learner.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The device uses sensors to capture the learner's facial expressions, posture, gaze, and voice in real time. This data is immediately digitized and subjected to basic noise reduction as an initial processing step.

[0683] Step 2:

[0684] The device encrypts the captured data and then sends it to the server over the network. This ensures that the data is transferred securely and efficiently.

[0685] Step 3:

[0686] The server receives the data and performs preprocessing. Here, the data is shaped and normalized, and converted into a format suitable for analysis.

[0687] Step 4:

[0688] The server uses an emotion recognition engine and machine learning algorithms to perform detailed analysis of the data. Specifically, it identifies emotional states based on facial expression data and detects changes in emotion by analyzing tone and pitch from audio data. It also evaluates the learner's level of comprehension and concentration.

[0689] Step 5:

[0690] Based on the evaluation results, the server uses a learning material delivery system to generate learning materials and feedback optimized for the learner. If the learner is in a negative emotional state, it selects content with a relaxing effect, and if a lack of understanding is pointed out, it designs content that includes supplementary materials.

[0691] Step 6:

[0692] The server sends learning materials and feedback generated by the server to the user's device for display. This allows learners to progress through the learning process via interactive content.

[0693] Step 7:

[0694] The server generates detailed reports, including learners' learning progress and emotional changes, and provides them to the user (educator or parent). These reports help to understand the learners' qualitative and quantitative progress.

[0695] Step 8:

[0696] The user reviews the report provided by the server and evaluates the learner's progress and any necessary actions. If necessary, they adjust the instruction plan for the next learning session.

[0697] (Example 2)

[0698] 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".

[0699] Traditional educational support systems have struggled to grasp learners' emotions and comprehension levels in real time and to immediately provide the most appropriate learning materials accordingly. This has made it difficult to provide personalized learning experiences for each individual learner, resulting in a failure to maximize educational effectiveness.

[0700] 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.

[0701] In this invention, the server includes information gathering means, information analysis means, and teaching material presentation means. This makes it possible to collect and analyze characteristic data, including learners' facial expressions and voices, in real time, and to dynamically generate and provide optimized teaching materials based on that evaluation.

[0702] A "detection device" refers to hardware or software that detects a learner's facial expressions, posture, gaze, and voice in real time.

[0703] "Information gathering means" refers to the process and function of collecting characteristic data acquired by a detection device and converting it into a format usable within the system.

[0704] "Information analysis means" refers to the process and function of processing collected data and using machine learning algorithms, etc., to evaluate learners' emotional state, comprehension level, and concentration level.

[0705] "Method of presenting teaching materials" refers to the process and function of creating and providing teaching materials suitable for learners based on evaluation results obtained through information analysis methods.

[0706] "Communication means" refers to the process and function of generating reports on learners' learning progress and evaluation results, and presenting or transmitting them to educators.

[0707] This invention is an advanced educational support system for providing learners with individually optimized educational experiences. The system aims to improve learning effectiveness by understanding learners' emotional states in real time and dynamically providing appropriate learning materials.

[0708] The device is equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice. Specifically, it incorporates a high-precision camera and voice input device, enabling it to capture subtle changes in the learner's facial expressions and voice tone. This data is transmitted to the server in a securely encrypted format.

[0709] The server analyzes the received data in real time and uses machine learning algorithms and an emotion recognition engine to evaluate the learner's emotional state. This analysis uses a machine learning library based on Python, and based on the analysis results, it immediately determines the learner's level of understanding and concentration.

[0710] Next, the server uses the evaluation results to generate learning materials optimized for the learner. This process incorporates natural language processing technology, allowing for flexible adjustment of the material content according to the learner's level of understanding. The generated materials are then provided to the learner via their device.

[0711] As a concrete example, suppose a device detects that a student is struggling to understand a new concept in a math class. The server receives this data, determines that the student's understanding is low, and provides a combination of a simple explanatory video and practice problems.

[0712] Furthermore, once a learning session ends, the server generates progress information in report format and provides it to the user (educator or parent) to support learning. This allows educators to efficiently track the progress of individual learners and provide necessary support.

[0713] An example of a prompt message is, "Analyze the emotional state of the students based on their facial expression and voice data, and generate appropriate teaching materials."

[0714] In this way, the present invention provides educational materials tailored to the individual needs of learners, thereby realizing an effective learning experience.

[0715] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0716] Step 1:

[0717] The device detects the learner's facial expressions, posture, gaze, and voice in real time. Specifically, it uses a camera and microphone to capture the learner's facial movements, gaze direction, voice tone, and volume. This data serves as input necessary for inferring emotional state. The output is structured as encrypted data and sent securely to the server.

[0718] Step 2:

[0719] The server receives encrypted data sent from the terminal and first decrypts it. It then analyzes the received input data using machine learning algorithms and employs image processing and speech analysis technologies to evaluate the learner's emotional state, comprehension level, and concentration level. A generative AI model is used for this analysis, generating detailed evaluation information about the learner's state as output.

[0720] Step 3:

[0721] The server dynamically generates learning materials optimized for the learner based on the generated assessment information. Here, natural language processing technology is utilized to adjust the educational content to individual needs. For example, additional explanations and examples are added to items where understanding is low. The input to this process is assessment information, and the output is individually customized learning materials.

[0722] Step 4:

[0723] The terminal receives optimized learning materials sent from the server and presents them to the learner. The materials are displayed on the screen in an interactive format that the learner can manipulate. The input is learning material information from the server, and the output is a screen display to aid the learner's understanding. Immediate feedback is also provided in response to the learner's actions.

[0724] Step 5:

[0725] The server analyzes the activity log after the learning session ends and aggregates the learner's progress data. Based on this, it generates a report for educators. This report includes a history of the learner's understanding, progress, and emotional state. The input data is the activity log sent from the terminal during learning, and the output is a detailed progress report that users can later evaluate.

[0726] Step 6:

[0727] Users receive progress reports from the server and understand the learner's situation. This allows them to plan and implement appropriate instruction and support as needed. The input is reports from the server, and the output is an instruction plan based on understanding and progress. This ultimately leads to actions that enhance the educational effectiveness of the learner.

[0728] (Application Example 2)

[0729] 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".

[0730] Conventional learning support systems have struggled to appropriately understand learners' emotional states and provide learning materials accordingly, resulting in a failure to maximize learning effectiveness. This invention aims to provide a personalized learning experience tailored to learners' emotional states and levels of understanding, thereby promoting more effective learning.

[0731] 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.

[0732] In this invention, the server includes emotion recognition means, data collection means, and data analysis means. This makes it possible to evaluate the learner's emotional state and level of understanding in real time and provide optimized learning materials based on that evaluation.

[0733] "Emotion recognition means" refers to technology that analyzes the learner's facial expressions and voice data to determine their emotional state.

[0734] A "data collection means" is a system equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice, and to acquire the collected data.

[0735] "Data analysis methods" refer to techniques that analyze data obtained from data collection methods to evaluate learners' comprehension and concentration levels.

[0736] A "means of providing educational materials" refers to a system for dynamically generating and providing educational materials optimized for learners based on data analysis results and emotional states.

[0737] "Notification methods" refer to technologies that generate reports on learners' learning progress and comprehension levels, and provide this information to educators or parents.

[0738] The system for implementing this invention is an educational support system that analyzes the learner's emotional state in real time and provides optimal learning materials. First, the terminal is equipped with sensors to detect the learner's facial expressions, posture, gaze, and voice. This data is transmitted to the server in a securely encrypted format.

[0739] The server processes the received data using emotion recognition and data analysis tools. Specifically, a machine learning algorithm built using Python and TensorFlow analyzes the data and evaluates the learner's comprehension, concentration, and emotional state. Based on these evaluation results, the learning material delivery tool generates optimized learning materials and provides them to the learner. The learning materials are dynamically created using Google Cloud AI Platform.

[0740] Furthermore, the server organizes progress information and notifies users (educators and parents) in report format. This allows for centralized management of the learning process and timely monitoring of learners' understanding.

[0741] For example, if a student shows a confused expression during an online math class, this is detected, and the server performs an emotional assessment. Based on this assessment, relaxing learning content and supplementary materials are provided. An example of a prompt to the generative AI model in this process would be, "When learning progress slows down, what kind of content should be generated to increase the student's motivation?"

[0742] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0743] Step 1:

[0744] The device uses sensors to acquire the learner's facial expressions, posture, gaze, and voice data in real time, encrypts this data, and sends it to the server. The input is various data of the learner acquired by the sensors, and the output is encrypted data. This ensures the security of personal information while providing the necessary data to the server.

[0745] Step 2:

[0746] The server decrypts encrypted data received from the terminal and analyzes the data using emotion recognition methods. Specifically, it uses a machine learning algorithm with Python and TensorFlow to identify the learner's emotional state. The input is the decrypted learner data, and the output is the analysis result regarding the learner's emotional state. This allows for a detailed evaluation of the learner's psychological state.

[0747] Step 3:

[0748] The server generates optimized learning materials using a learning material delivery system, based on the analyzed learner's emotional state and comprehension level. Here, Google Cloud AI Platform is used to dynamically create learning materials by combining necessary resources and content. The input is the analysis results of emotions and comprehension, and the output is customized learning materials. This enables the rapid delivery of learning materials tailored to the learner.

[0749] Step 4:

[0750] The server sends the generated learning materials to the terminal and presents them to the learner. The terminal displays the provided materials on the learner's device. The input is the learning materials generated by the server, and the output is the display of the materials on the learner's terminal. This provides visual support to help the user progress through their learning effectively.

[0751] Step 5:

[0752] The server records the progress and comprehension of learning sessions and generates reports, which are then sent to parents and educators. The input is progress and comprehension information recorded during the learning session, and the output is a detailed learning report. This allows users to review the learner's understanding at a later date.

[0753] 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.

[0754] 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.

[0755] 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 robot 414.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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."

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0774] The following is further disclosed regarding the embodiments described above.

[0775] (Claim 1)

[0776] A data collection means equipped with sensors for detecting the learner's facial expressions, posture, gaze, and voice, and for acquiring the collected data,

[0777] A data analysis means for analyzing data obtained from the aforementioned data collection means and evaluating the learner's level of understanding and concentration,

[0778] A means for providing learning materials that dynamically generate and provide learning materials optimized for learners based on the evaluation results of the data analysis means,

[0779] A notification means for generating a report on the learner's learning progress and level of understanding and providing it to the educator or guardian,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, wherein the data analysis means processes the data using a machine learning algorithm.

[0783] (Claim 3)

[0784] The system according to claim 1, which includes interactive feedback suggesting a break when the means for providing educational materials evaluates that the learner's level of concentration is low.

[0785] "Example 1"

[0786] (Claim 1)

[0787] The system includes a detection device for acquiring the learner's facial expressions, posture, gaze, and voice, and a data acquisition means for accumulating this information.

[0788] Information analysis means for processing information obtained from the data acquisition means and determining the learner's level of understanding and concentration,

[0789] A material supply means for dynamically generating and presenting learning materials suitable for the learner based on the determination results by the information analysis means,

[0790] A means for transmitting information to report the learner's learning progress and level of understanding in document format to an educator or guardian,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, wherein the information analysis means processes the information using an artificial intelligence algorithm.

[0794] (Claim 3)

[0795] The system according to claim 1, which includes two-way feedback that suggests a break when the material supply means determines that the learner's level of concentration is low.

[0796] "Application Example 1"

[0797] (Claim 1)

[0798] An information gathering means that includes a device for detecting physical and vocal information of learners and for acquiring the collected information,

[0799] Information analysis means for analyzing information obtained from the aforementioned information gathering means and evaluating the learner's cognitive state and attentiveness,

[0800] Based on the evaluation results of the information analysis means, an educational resource provision means for dynamically generating and providing educational resources suited to learners,

[0801] A notification means for generating a report on the learner's learning progress and level of understanding and providing it to the instructor or guardian,

[0802] Control means for controlling a mechanical device that assists learners in their studies at home,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, wherein the information analysis means processes the information using a machine learning method.

[0806] (Claim 3)

[0807] The system according to claim 1, wherein the means for providing educational resources includes an interactive response that suggests a break if the learner's attentiveness is assessed as low, and the machine device provides visual and auditory assistance to the learner.

[0808] "Example 2 of combining an emotion engine"

[0809] (Claim 1)

[0810] A detection device for detecting learner characteristics and an information gathering means for acquiring the collected information,

[0811] Information analysis means for analyzing information obtained from the aforementioned information gathering means and evaluating the learner's state,

[0812] Based on the evaluation results of the information analysis means, a material presentation means for dynamically creating and presenting materials suitable for learners,

[0813] A communication means for generating a report on the learner's learning progress and status and providing it to educators,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, wherein the information analysis means processes the information using an algorithm.

[0817] (Claim 3)

[0818] The system according to claim 1, which includes interactive feedback suggesting a break when the material presentation means evaluates that the learner's level of concentration is low.

[0819] "Application example 2 when combining with an emotional engine"

[0820] (Claim 1)

[0821] An emotion recognition means for identifying the emotional state of learners,

[0822] A data collection means equipped with sensors for detecting the learner's facial expressions, posture, gaze, and voice, and for acquiring the collected data,

[0823] A data analysis means for analyzing data obtained from the aforementioned data collection means and evaluating the learner's level of understanding and concentration,

[0824] A means for dynamically generating and providing learning materials optimized for learners based on the evaluation results and emotional state of the aforementioned data analysis means,

[0825] A notification means for generating a report on the learner's learning progress and level of understanding and providing it to the educator or guardian,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the data analysis means processes the data using a machine learning algorithm and provides specific content when the learner shows confusion.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the means for providing educational materials includes interactive feedback that suggests relaxed learning content in accordance with the learner's emotional state. [Explanation of Symbols]

[0831] 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. An information gathering means that includes a device for detecting physical and vocal information of learners and for acquiring the collected information, Information analysis means for analyzing information obtained from the aforementioned information gathering means and evaluating the learner's cognitive state and attentiveness, Based on the evaluation results of the information analysis means, an educational resource provision means for dynamically generating and providing educational resources suited to learners, A notification means for generating a report on the learner's learning progress and level of understanding and providing it to the instructor or guardian, Control means for controlling a mechanical device that assists learners in their studies at home, A system that includes this.

2. The system according to claim 1, wherein the information analysis means processes the information using a machine learning method.

3. The system according to claim 1, wherein the means for providing educational resources includes an interactive response that suggests a break if the learner's attentiveness is assessed as low, and the machine device provides visual and auditory assistance to the learner.

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