system

Abstract

We provide the system. [Solution] A means for collecting learner activity information and storing it in an information storage unit, A data analysis unit means for analyzing collected information and evaluating the learner's level of understanding, A method for selecting learning materials or assignments that are optimal for learners based on evaluation results, A means of providing selected learning materials or assignments to learners' devices, A communication means for sending the answer results and learning activities received from the learner's terminal as feedback to the server, Based on the information obtained, a mobile communication method is provided to update learning materials in real time via a device, regardless of the learner's location. A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is required to improve the efficiency of the learning process by managing the progress according to the understanding level of each learner and providing optimal teaching materials and tasks. In the conventional learning environment, it is difficult to provide individual support for learners with diverse levels and needs, and there is also a problem of increasing the burden on teachers. The purpose of this invention is to utilize AI technology to grasp the understanding level of each learner in real time and automate the provision of appropriate teaching materials and tasks based on that information, thereby providing an efficient and individualized learning environment.

Means for Solving the Problems

[0005] This invention provides a system that collects and analyzes learner activity data, and selects and provides the most suitable teaching materials or assignments based on the analysis results. Specifically, a data storage unit stores learner activity data, and a data analysis unit analyzes it. Based on this, a teaching material selection means selects the most suitable teaching materials or assignments for the learner, and a delivery means distributes them to the learner's terminal. Furthermore, a communication means receives feedback from the learner, and the difficulty level of assignments and the content of teacher management are adjusted according to the learner's level of understanding. In this way, the system flexibly responds to the needs of each learner and aims to improve the quality of learning.

[0006] A "learner" is an individual who participates in educational activities and seeks to acquire knowledge and skills.

[0007] "Activity data" refers to data that includes information such as the operations performed by learners within the system and the status of their responses to assignments.

[0008] A "data storage unit" is an area or mechanism for storing collected activity data and making it accessible as needed.

[0009] The "Data Analysis Department" is a department or module that processes collected activity data and has the function of evaluating learners' understanding and learning progress.

[0010] A "method for selecting teaching materials" is a system for selecting the most suitable teaching materials and assignments for learners based on analysis results.

[0011] "Delivery method" refers to technology that has the function of transmitting and displaying selected teaching materials and assignments on the devices used by learners.

[0012] "Communication means" refers to a system or interface for exchanging information between a learner's terminal and a server.

[0013] "Understanding" refers to the degree to which a learner has acquired knowledge and skills regarding a particular topic or concept.

[0014] "Adjustment of problem difficulty" means appropriately changing the difficulty of problems presented according to the learner's level of understanding.

[0015] "Teacher's management content" refers to the information and functions for a teacher to grasp the progress and understanding level of learners and formulate a guidance plan based on this.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] [[ID=4​​It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] 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.

[0020] 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.

[0021] 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, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention aims to realize a system that monitors learners' performance in real time and provides individually optimized learning materials and assignments based on that performance. The system consists of three components: a server, a terminal, and a user, and features AI-based data analysis and an adaptive learning material delivery mechanism.

[0038] The server first collects data on the learner's daily activities. This includes study time, correctness of answers, and operation logs for learning content. The server uses this information to analyze the learner's learning performance and diagnose their level of understanding using an AI model. Based on the results, the server selects the most suitable learning materials and tasks for each individual learner. These selected materials are then sent from the server to the terminal and provided to the learner.

[0039] The terminal displays learning materials and assignments received from the server to the learner. The learner can access the materials and complete assignments via the terminal. The terminal is equipped with an answer input interface and has the function to send the learner's entered answers and activity history to the server in real time.

[0040] Users (learners) work on the provided materials and assignments and input their answers. Through assignments whose difficulty level is automatically adjusted according to their understanding, users can continuously deepen their learning. Furthermore, users (teachers) can log into the system and monitor students' progress and understanding. Based on this information, teachers can adjust additional instruction and learning plans as needed.

[0041] As a concrete example, consider a case where a learner is struggling with a specific area of ​​mathematics. The server analyzes this data and selects additional problem sets or educational videos to reinforce the learner's weak areas. This material is instantly sent to the device, allowing the learner to begin supplementary learning immediately. In this way, providing learners with real-time adaptive learning opportunities enables efficient learning tailored to individual learning needs.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server collects learner activity data and stores it in a database. This data includes learning behavior, response time, and correct / incorrect answers. The server manages this data in a structured format.

[0045] Step 2:

[0046] The server inputs the collected data into an AI model and performs analysis. This analysis evaluates the learner's current level of understanding, areas of difficulty, and learning progress. The AI ​​model measures the learner's growth by comparing it with past data.

[0047] Step 3:

[0048] The server selects the most suitable learning materials and assignments for each learner based on the analysis results. Selection criteria include the learner's level of understanding, past performance, and the relevance of the topic. The selected materials are designated as the next content the learner should work on.

[0049] Step 4:

[0050] The server sends the selected learning materials and assignments to the device. This prepares the device to present the latest learning materials to the learner. The server tracks the usage of the learning materials by recording logs of material distribution.

[0051] Step 5:

[0052] The terminal displays learning materials and assignments received from the server to the learner. The terminal provides an interactive learning experience through its user interface. The learner progresses through their learning using the presented materials.

[0053] Step 6:

[0054] Users (learners) access learning materials using their devices and work on assignments. Users can enter their answers and refer to hints and explanations within the system as needed.

[0055] Step 7:

[0056] The device sends user answers and learning activity logs to the server in real time. The device formats the log data appropriately to ensure it reaches the server quickly.

[0057] Step 8:

[0058] The server immediately processes the received log data and updates the learner's performance. The server then uses this data for data analysis for the next learning cycle.

[0059] (Example 1)

[0060] 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."

[0061] Modern education systems require individualized support tailored to each learner's level of understanding and progress. However, traditional systems have struggled to respond in real time to the diverse levels of understanding of learners and to dynamically provide appropriate learning materials. This limits the maximization of learning efficiency and the improvement of educational quality, hindering continuous improvement in understanding.

[0062] 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.

[0063] In this invention, the server includes an information gathering means for collecting digital information obtained from learners' educational activities and storing it in an information storage unit, an information analysis unit for analyzing the collected information and evaluating the depth of the learners' understanding, and a resource selection means for selecting the most suitable educational resources or tasks for the learners based on the evaluation results. This makes it possible to provide each learner with an individually optimized educational experience in real time, thereby improving learning efficiency and the quality of education.

[0064] "Information gathering means" refers to the function of collecting digital information obtained from learners' educational activities and storing it in the information storage unit.

[0065] The "information analysis unit" is a function that analyzes collected information and evaluates the depth of the learner's understanding.

[0066] A "resource selection method" is a function for selecting the most suitable educational resources or assignments for learners based on evaluation results.

[0067] "Supply means" refers to the function of supplying selected educational resources or assignments to the learner's device.

[0068] "Communication means" refers to a function for transmitting answer results and records of learning activities obtained from the learner's device to the main device as feedback.

[0069] "Adaptive measures" refer to functions for dynamically updating educational resources in real time based on learners' activities.

[0070] "Generation means" refers to a function that performs recommendations using a generative AI model.

[0071] To implement this invention, three main players—a server, a terminal, and a user—work together. The server acts as the central hub for information processing, collecting digital information related to the learner's educational activities and storing it in the information storage unit. The server manages this information using database systems such as MySQL® or MongoDB. The stored data is analyzed using AI frameworks such as Tensorflow® or PyTorch. The analysis results are then used with a generative AI model to help select the optimal educational resources for the learner. In this process, the AI ​​is provided with prompts such as, "Evaluate the learner's performance and suggest necessary learning materials."

[0072] The terminal's role is to present educational resources and assignments sent from the server to the learner. It uses HTML / CSS as its user interface and is designed to be easily understood by the learner on the terminal. Furthermore, the terminal utilizes a REST API to send the learner's input, including answers and learning activity data, to the server in real time.

[0073] Users (learners) work on the provided learning materials and assignments using their devices. They can check their learning progress by entering their answers, and the system automatically adjusts the difficulty level. Users (teachers) can also use the dashboard provided by this system to check the learners' progress and understanding, and adjust the teaching content as needed. This functionality is achieved by utilizing a JavaScript® framework (e.g., React).

[0074] This invention makes it possible to provide learners with personalized educational opportunities in real time, thereby improving the quality and efficiency of education.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server collects learner activity data. Inputs include learning time, the correctness of answers to assignments, and logs of learning content usage. This data is stored in a database to prepare for subsequent analysis. MySQL and MongoDB are used for database management. The output is the collected data, properly structured and ready for use in subsequent analysis processes.

[0078] Step 2:

[0079] The server analyzes the collected activity data using a generating AI model. It receives learner activity data stored in a database as input and performs analysis using data analysis tools such as "TensorFlow" and "PyTorch." Specifically, the model evaluates the learner's performance and performs data processing to determine their level of understanding. The output provides an evaluation of the learner's understanding, which is then used to select learning materials for the next step.

[0080] Step 3:

[0081] The server selects the most suitable educational resources for the learner based on the evaluation results. The evaluation results obtained in the previous step are used as input. An AI algorithm is used for resource selection, taking a specific prompt, "Please suggest materials to reinforce weaknesses in a specific area," as input to determine which materials to recommend. The output is a list of materials and assignments to be provided to the learner.

[0082] Step 4:

[0083] The server sends the selected educational resources to the terminal. The input in this step is the list of learning materials generated in the previous step. The server uses the HTTP protocol to send the materials to the learner's terminal. The terminal receives them and prepares them for display in a format accessible to the learner. The output is that the educational resources have been successfully delivered to the terminal.

[0084] Step 5:

[0085] The terminal presents learning materials and assignments received from the server to the learner. Learning material data is passed to the terminal as input and displayed using a user interface based on HTML / CSS, etc. Learners can then use this to work on assignments. The output is the learner accessing the problems and submitting their answers.

[0086] Step 6:

[0087] The user (learner) works on the presented learning materials and assignments and enters their answers. The answers and learning actions entered by the user are sent to the server in real time via the device. As output, the user's answer data and activity history are stored again in the database and used as feedback for future learning plans.

[0088] Step 7:

[0089] The user (teacher) logs into the system to check the learners' progress. The system receives student progress and comprehension information as input data from the server. A teacher dashboard is displayed using a JavaScript framework, providing information for teachers to adjust their teaching content and learning plans. The output is the adjustment of the teaching plan based on the students' comprehension levels obtained by the teacher.

[0090] (Application Example 1)

[0091] 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."

[0092] There is a growing need to provide optimal learning materials in real time, regardless of location, tailored to each learner's level of understanding and learning progress. However, conventional systems struggle to provide the most suitable materials in a timely manner, taking into account the learner's specific situation and progress. Furthermore, there is a need for flexible solutions to ensure learners can continue their studies consistently even when they are in different environments, such as while traveling. A means to solve these challenges was therefore necessary.

[0093] 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.

[0094] In this invention, the server includes means for collecting learner activity information and storing it in an information storage unit; data analysis unit means for analyzing the collected information and evaluating the learner's level of understanding; material selection means for selecting the most suitable learning materials or assignments for the learner based on the evaluation results; and mobile communication means for updating learning materials in real time via a device, regardless of the learner's location, based on the obtained information. This makes it possible to always provide the most suitable learning materials according to the learner's progress, enabling flexible learning that is not bound by location.

[0095] The term "learner" refers to a person who engages in educational activities and learns.

[0096] "Activity information" refers to various data generated during the learning process, including learning history, answers, and operation logs.

[0097] The "information storage unit" is a storage device that stores collected activity information and keeps it in an easily accessible state.

[0098] The "Data Analysis Department" refers to the system component that processes collected activity information and analyzes learners' understanding and progress.

[0099] "Methods for selecting learning materials" refer to the methods and processes for selecting the most suitable learning materials and assignments based on the analysis results of learners.

[0100] "Learner terminals" refer to computers and smart devices that learners directly operate and use to conduct learning activities.

[0101] "Communication methods" refer to the technologies and protocols that enable the transmission and reception of information between a terminal and a server, and are carried out via wired or wireless networks.

[0102] "Mobile communication means" refers to communication technology that has the function of sending and receiving data in real time and updating learning content, no matter where the learner is.

[0103] The system for realizing this invention primarily consists of three components: a server, a terminal, and a user. The server is located in a data center and uses AI technologies such as Python and TensorFlow to collect learner activity information and perform data analysis. Specifically, it collects activity information such as the learner's learning history, operation logs, and answer results in real time and stores them in a database system.

[0104] Based on this information, the server uses a generated AI model to evaluate the learner's level of understanding and selects the most suitable learning materials and assignments based on the results. The selected materials and assignments are then sent from the server to the learner's device via Wi-Fi or mobile communication.

[0105] On the device side, learning materials and assignments received are displayed to the learner using a mobile SDK developed in Java (registered trademark). The learning materials are updated in real time via the mobile network, ensuring access from anywhere the learner is located. Learners can access these materials and complete assignments using their devices. Users (learners and teachers) can also log into the system to check progress and learning data.

[0106] As a concrete example, when a user launches the app on their smartphone while waiting for a train at a station, the latest learning data is sent to the device, and the AI ​​model provides the most suitable quizzes and supplementary materials on the spot. In this way, learners can effectively continue their studies even when they are out and about.

[0107] An example of a prompt to a generative AI model is: "This learner has a weak understanding of a specific area of ​​mathematics. Based on their past learning history, recommend materials to reinforce this area."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server collects activity information from learners. Specifically, it receives learning history, operation logs, and answer results from the learner's terminal and stores them in a database. The data format is standardized to JSON and sent to the server via an API.

[0111] Step 2:

[0112] The server performs data analysis on the collected activity information. Based on the learning history and logs received as input, it performs tensor analysis and evaluates the level of understanding. An AI model using TensorFlow is responsible for this analysis and calculates the learner's proficiency level as output.

[0113] Step 3:

[0114] The server selects the most suitable learning materials and assignments based on the acquired proficiency information. In this process, prompt sentences are generated using the output from the previous data analysis and input into the generative AI model. As a result, a list of learning materials appropriate for the learner is generated.

[0115] Step 4:

[0116] The server sends the selected learning materials to the device. The learning material data is delivered to the learner's device via a RESTful API. During this process, the content of the learning materials is dynamically updated and displayed immediately on the device.

[0117] Step 5:

[0118] Users access the provided learning materials via their devices and complete the assignments. Their answers and additional learning activities are sent to the server in real time. This data is then stored again in the database for use in subsequent analyses.

[0119] Step 6:

[0120] The server displays learner progress and comprehension data through an administration screen accessible to teachers. The administration screen is web-based and accessible after secure authentication. Teachers can use this information to further develop individualized instruction plans.

[0121] 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.

[0122] This invention is a system that recognizes the emotional state of learners and utilizes it in the learning process. The system consists of three main elements: a server, a terminal, and a user, and provides a more personalized learning experience, particularly by including an emotion engine.

[0123] The server collects emotional data from the emotion engine, including facial expressions, voice, and input patterns, along with the learner's activity data. This data is stored in a data storage unit and used to evaluate the learner's level of understanding. The server uses an AI model to simultaneously analyze the learner's understanding and emotions, comprehensively evaluating the learner's state. Based on this evaluation, the server determines the most suitable learning materials and assignments for the learner.

[0124] The selection of learning materials reflects the learner's current emotional state. For example, if a learner is feeling stressed, it's possible to adjust the difficulty level or select materials in a relaxing format. The selected materials and assignments are sent from the server to the user's (learner's) device, and the user deals with them through the device.

[0125] The terminal not only provides learners with learning materials and assignments sent from the server, but also analyzes the user's emotions using an emotion engine. Specifically, it uses a webcam and microphone to analyze the user's facial expressions and voice, and evaluates their emotional state in real time. As a result, if the user's mood changes during learning, new data is immediately collected and sent to the server.

[0126] Users (learners) engage with the presented materials and assignments, and their emotional state is evaluated as part of the learning process. This system provides continuous feedback to help learners maintain a relaxed state to deepen their understanding. Furthermore, users (teachers) can monitor learners' emotional changes and adjust learning materials and instructional content as needed.

[0127] This invention enables adaptive learning that takes learners' emotions into account, resulting in an efficient and effective learning experience. The system distinguishes itself from conventional learning support systems by incorporating emotional data in addition to analyzing learning data.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The device captures the learner's facial expressions and voice in real time into its emotion engine. It uses cameras and microphones to collect this data and recognize the user's current emotional state.

[0131] Step 2:

[0132] The device sends the recognized emotion data to the server. This prepares the server to receive and store both activity data and emotion data simultaneously.

[0133] Step 3:

[0134] The server stores activity data and emotion data in its data storage unit and inputs it into the AI ​​model. The model analyzes the learner's level of understanding and current emotional state to perform an overall performance evaluation.

[0135] Step 4:

[0136] Based on the analysis results, the server selects the most suitable learning materials and assignments for the learner. The selection process also takes into account the learner's emotional state, and content is adjusted to alleviate stress and to an appropriate level of difficulty.

[0137] Step 5:

[0138] The server sends selected learning materials and assignments to the terminal. The materials may include feedback that takes into account the user's emotional state.

[0139] Step 6:

[0140] The terminal displays learning materials and assignments received from the server to the learner. It is designed to be intuitive to use through its interface.

[0141] Step 7:

[0142] Users (learners) access the presented learning materials and work on assignments. An interactive learning experience tailored to their emotional state is provided to maintain learner motivation.

[0143] Step 8:

[0144] The server continuously analyzes the answer results and updated sentiment data received through the terminals, and continuously optimizes the learning process.

[0145] Step 9:

[0146] Users (teachers) can monitor learners' progress and emotional state through reports provided by the server, and adjust their teaching plans as needed to provide effective learning support.

[0147] (Example 2)

[0148] 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".

[0149] Traditional learning support systems only evaluated learners' learning progress and did not take their emotional state into consideration, which could hinder learners' interest and motivation. This could lead to insufficient selection of optimal learning materials and assignments, potentially reducing learning effectiveness. Furthermore, it was difficult for teachers to quickly grasp and respond to changes in learners' emotions. Therefore, there was a need for an effective learning support system that took learners' emotions into account.

[0150] 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.

[0151] In this invention, the server includes means for collecting learner activity information and storing it in an information recording unit, means for analyzing the collected information and evaluating the learner's level of understanding and emotional state, and means for selecting the most appropriate educational materials or assignments for the learner based on the evaluation results. This enables the selection of optimal teaching materials and feedback according to the learner's level of understanding and emotional state.

[0152] A "learner" is an individual who engages in learning, and who acquires knowledge and deepens their understanding through educational materials and assignments.

[0153] "Activity information" refers to data that records learners' actions and responses related to learning, whether online or offline.

[0154] The "Information Recording Unit" refers to a data storage system for centrally accumulating and managing learners' activity and emotional information.

[0155] The "Information Analysis Unit" refers to computer programs and algorithms that process accumulated activity and emotional information to analyze learners' comprehension levels and emotional states.

[0156] "Emotional state" is an indicator that shows the learner's psychological or emotional situation and reactions, and is based on information obtained from facial expressions, voice, and behavioral patterns.

[0157] "Material selection method" refers to a process or system for selecting the most appropriate educational materials and assignments for learners based on analyzed information.

[0158] "Means of delivery" refers to communication functions and technologies for displaying or distributing selected educational materials and assignments to learners' devices.

[0159] "Communication means" refers to network functions that enable data communication between learner terminals and servers, and for sending and receiving feedback and analysis results.

[0160] "Emotional analysis unit means" refers to technology for recognizing and analyzing the learner's emotional state in real time during learning and updating the data as needed.

[0161] This invention provides an adaptive learning support system that takes into account the emotional state of learners, and includes server, terminal, and user elements.

[0162] The server is responsible for collecting learner activity information and emotional data. Specifically, it stores learning progress and usage records sent from clients in a database. For collecting emotional data, it uses a webcam and microphone connected to the terminal, recognizes facial expressions using libraries such as OpenCV, and performs speech analysis. This data is stored in the information storage unit on the server. Next, the server analyzes the collected data using a generative AI model. It uses TensorFlow or PyTorch to simultaneously evaluate the learner's comprehension level and emotional state.

[0163] The server selects the most suitable educational materials and assignments based on evaluation results from an AI model. This process reflects the learner's emotional state and has the flexibility to adjust difficulty levels as needed. The selected materials are delivered to the device via the HTTP protocol.

[0164] The terminal displays learning materials provided by the server to the learner and also performs real-time sentiment analysis. It continuously monitors the user's facial expressions and voice using a webcam and microphone, and feeds back the analyzed sentiment data to the server. This information helps learners continue learning in a relaxed state.

[0165] As users (learners) work on learning materials and assignments presented through their devices, their emotional state is also treated as a learning element. For example, if a user is feeling stressed, the system may provide learning materials that promote relaxation. An example of utilizing a generative AI model is a prompt message such as, "Analyze the learner's emotional state and generate relaxing learning materials appropriate to their level of understanding."

[0166] This system enables adaptive learning that takes into account learners' emotions and comprehension levels, resulting in a more effective educational experience. Unlike conventional learning systems, it utilizes emotional data, allowing for flexible responses tailored to the individual needs of learners.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server collects learner activity and emotion data from the terminal. Its inputs include facial video and audio data captured by the terminal, as well as log data related to learning progress. Specifically, it uses a webcam and microphone to record the learner's facial expressions and speech in real time and sends this data to the server. The output is raw activity and emotion data stored in a database.

[0170] Step 2:

[0171] The server uses a generative AI model to analyze the collected data. As input, accumulated activity and sentiment data are retrieved from a database. The server processes this data using machine learning frameworks such as TensorFlow and PyTorch to evaluate the learner's comprehension and emotional state. Specifically, it performs data cleansing, feature extraction, and prediction using the AI ​​model. The output is the evaluation result regarding the learner's comprehension and emotional state.

[0172] Step 3:

[0173] The server selects the most appropriate educational materials and assignments based on the analysis results. Evaluation results from an AI model are used as input. The server applies predefined rules and machine learning models to select the most appropriate materials for the learner's state. Specifically, it performs a material filtering and selection process from the material database. The output is the selected educational materials and assignments.

[0174] Step 4:

[0175] The server provides selected learning materials and assignments to the terminal. Selected learning material data is retrieved from the server as input. The server sends the learning materials and assignments to the terminal using the HTTP protocol. Specifically, it packages and transmits the learning material data. The output is the learning material displayed to the user visually or audibly on the terminal.

[0176] Step 5:

[0177] The device presents the provided learning materials to the user and continuously analyzes the user's emotions during learning. Inputs include learning materials sent from the server and the user's current learning status. The device uses its active webcam and microphone to analyze the user's facial expressions and voice, capturing changes in emotions. Specifically, it displays learning materials on the UI and executes a real-time emotion analysis module. Output is the transmission of feedback data to the server.

[0178] Step 6:

[0179] The user engages with learning materials and assignments presented on the device. The input consists of the learning materials and assignments displayed on the device. The user improves their understanding by solving problems and viewing the materials. Specifically, the user interacts with the learning materials, and the resulting data is sent from the device to the server. The output consists of the user's activity results and feedback data.

[0180] (Application Example 2)

[0181] 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".

[0182] In modern learning and electronic payment systems, there is a need to provide efficient and effective experiences by appropriately considering the user's emotional state. However, conventional systems often fail to consider emotional changes, resulting in information and suggestions that are not suited to the user. This can lead to a diminished user experience and potentially hinder learning motivation and consumer behavior. Therefore, a method is needed that recognizes the user's emotional state in real time and provides optimal information and suggestions accordingly.

[0183] 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.

[0184] In this invention, the server includes a component that collects learner activity information and stores it in an information storage unit, an information analysis unit component that analyzes the collected information and evaluates the learner's level of understanding, and an emotion analysis component that recognizes the user's emotional state and provides optimal information or suggestions based on that emotional state. This makes it possible to provide suggestions and information that are adapted to the user's emotional state, thereby improving the user experience.

[0185] "Learner activity information" refers to data about the actions and operations performed by learners, and is used to evaluate the learning process.

[0186] An "information storage unit" is a storage device for temporarily or long-term storage of collected data.

[0187] The "information analysis unit components" are those that analyze collected data and have the function of evaluating the learner's level of understanding and emotional state.

[0188] The "material selection component" is responsible for selecting the most suitable learning materials or assignments for learners based on the analysis results.

[0189] "Delivery components" refer to components that have the function of delivering selected learning materials or assignments to the learner's device.

[0190] A "communication component" is a device that has the function of sending answer results and learning actions from the learner's terminal to the server.

[0191] "Emotional state" refers to the emotional state of a learner or user, and is a psychological state that includes specific emotions or moods.

[0192] "Emotional analysis components" are those that recognize and analyze the emotional state of the user from their facial expressions, voice, etc.

[0193] The term "instructor" refers to someone who has the role of providing guidance and instructions to learners or users.

[0194] "Management components" are those that allow instructors to monitor the progress of learners and users and adjust the content of instruction accordingly.

[0195] System Configuration

[0196] This invention is a learning and information provision system that takes into account the user's emotional state. The system mainly consists of three elements: a server, a terminal, and a user. The server collects learner activity information and stores it in an information storage unit. Furthermore, the server uses an information analysis unit component to analyze the collected data and evaluate the learner's level of understanding and emotional state. Based on the evaluation, a material selection component selects the most suitable learning materials and information for the learner, and a provisioning component delivers them to the terminal.

[0197] Recognition of emotional states

[0198] The user's emotional state is recognized in real time using the device's camera and microphone. An emotion analysis component analyzes the user's facial expressions and voice to estimate their current emotion. The system feeds the results of the emotion analysis back to the server, which is then used to provide new information.

[0199] Hardware and software

[0200] The hardware used includes smartphones and tablets. The software utilizes the Emotion API from Microsoft® Azure® Cognitive Services as the emotion analysis engine, and TensorFlow for the AI ​​model.

[0201] Specific example

[0202] As a concrete example, when a learner is studying a new unit, the server analyzes the learner's facial expressions and, if it determines that the learner is experiencing a high cognitive load, prioritizes selecting learning materials in a format that will help them relax. For instance, it could recommend review materials in a quiz format that helps reduce stress. This approach optimizes the user experience during learning.

[0203] Examples of prompts for generative AI models

[0204] "We want to provide the most relevant information based on the user's emotional state. Please tell us what kind of educational materials and information would be most effective considering their current emotional state."

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The user begins learning using the device. At this time, the device's camera and microphone are activated, recording the user's facial expressions and voice in real time. The input is the user's image and audio data, and the output is the recorded raw data.

[0208] Step 2:

[0209] The device sends recorded image and audio data to an emotion analysis engine. The emotion analysis engine analyzes this data and evaluates the user's emotional state. The input is raw image and audio data, and the engine performs feature extraction and emotion estimation on the data. The output is the user's emotional state (e.g., joy, stress, anxiety).

[0210] Step 3:

[0211] The server receives emotional state and learner activity information transmitted from the terminal. The input consists of the user's emotional state data and activity information. The server's AI model integrates this data and evaluates the user's learning progress. The output is an overall evaluation of the user's learning comprehension and emotional state.

[0212] Step 4:

[0213] Based on the evaluation results, the server selects the optimal learning materials or information using material selection components. The input is the evaluation results, and the output is the selected learning materials or information determined by the material selection algorithm. These selections take into account the user's emotional state.

[0214] Step 5:

[0215] The server sends the selected learning materials or information to the terminal. The input is the selected material data, and the output is the data delivered to the terminal.

[0216] Step 6:

[0217] The terminal provides the user with learning materials and information received from the server. The user continues their learning activities based on this information. The input is the learning materials provided by the server, and the output is the progress of the user's learning activities.

[0218] Step 7:

[0219] When a user completes their learning activity, the device records the user's answers and learning behavior. The input consists of the user's answers and behavior data, while the output is the recorded learning data. This data is sent to the server as feedback and used to select future learning materials.

[0220] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0221] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0222] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0226] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0227] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0228] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0229] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0230] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0231] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0232] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0233] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0234] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0235] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0236] This invention aims to realize a system that monitors learners' performance in real time and provides individually optimized learning materials and assignments based on that performance. The system consists of three components: a server, a terminal, and a user, and features AI-based data analysis and an adaptive learning material delivery mechanism.

[0237] The server first collects data on the learner's daily activities. This includes study time, correctness of answers, and operation logs for learning content. The server uses this information to analyze the learner's learning performance and diagnose their level of understanding using an AI model. Based on the results, the server selects the most suitable learning materials and tasks for each individual learner. These selected materials are then sent from the server to the terminal and provided to the learner.

[0238] The terminal displays learning materials and assignments received from the server to the learner. The learner can access the materials and complete assignments via the terminal. The terminal is equipped with an answer input interface and has the function to send the learner's entered answers and activity history to the server in real time.

[0239] Users (learners) work on the provided materials and assignments and input their answers. Through assignments whose difficulty level is automatically adjusted according to their understanding, users can continuously deepen their learning. Furthermore, users (teachers) can log into the system and monitor students' progress and understanding. Based on this information, teachers can adjust additional instruction and learning plans as needed.

[0240] As a concrete example, consider a case where a learner is struggling with a specific area of ​​mathematics. The server analyzes this data and selects additional problem sets or educational videos to reinforce the learner's weak areas. This material is instantly sent to the device, allowing the learner to begin supplementary learning immediately. In this way, providing learners with real-time adaptive learning opportunities enables efficient learning tailored to individual learning needs.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server collects learner activity data and stores it in a database. This data includes learning behavior, response time, and correct / incorrect answers. The server manages this data in a structured format.

[0244] Step 2:

[0245] The server inputs the collected data into an AI model and performs analysis. This analysis evaluates the learner's current level of understanding, areas of difficulty, and learning progress. The AI ​​model measures the learner's growth by comparing it with past data.

[0246] Step 3:

[0247] The server selects the most suitable learning materials and assignments for each learner based on the analysis results. Selection criteria include the learner's level of understanding, past performance, and the relevance of the topic. The selected materials are designated as the next content the learner should work on.

[0248] Step 4:

[0249] The server sends the selected learning materials and assignments to the device. This prepares the device to present the latest learning materials to the learner. The server tracks the usage of the learning materials by recording logs of material distribution.

[0250] Step 5:

[0251] The terminal displays learning materials and assignments received from the server to the learner. The terminal provides an interactive learning experience through its user interface. The learner progresses through their learning using the presented materials.

[0252] Step 6:

[0253] Users (learners) access learning materials using their devices and work on assignments. Users can enter their answers and refer to hints and explanations within the system as needed.

[0254] Step 7:

[0255] The device sends user answers and learning activity logs to the server in real time. The device formats the log data appropriately to ensure it reaches the server quickly.

[0256] Step 8:

[0257] The server immediately processes the received log data and updates the learner's performance. The server then uses this data for data analysis for the next learning cycle.

[0258] (Example 1)

[0259] 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."

[0260] Modern education systems require individualized support tailored to each learner's level of understanding and progress. However, traditional systems have struggled to respond in real time to the diverse levels of understanding of learners and to dynamically provide appropriate learning materials. This limits the maximization of learning efficiency and the improvement of educational quality, hindering continuous improvement in understanding.

[0261] 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.

[0262] In this invention, the server includes an information gathering means for collecting digital information obtained from learners' educational activities and storing it in an information storage unit, an information analysis unit for analyzing the collected information and evaluating the depth of the learners' understanding, and a resource selection means for selecting the most suitable educational resources or tasks for the learners based on the evaluation results. This makes it possible to provide each learner with an individually optimized educational experience in real time, thereby improving learning efficiency and the quality of education.

[0263] "Information gathering means" refers to the function of collecting digital information obtained from learners' educational activities and storing it in the information storage unit.

[0264] The "information analysis unit" is a function that analyzes collected information and evaluates the depth of the learner's understanding.

[0265] A "resource selection method" is a function for selecting the most suitable educational resources or assignments for learners based on evaluation results.

[0266] "Supply means" refers to the function of supplying selected educational resources or assignments to the learner's device.

[0267] "Communication means" refers to a function for transmitting answer results and records of learning activities obtained from the learner's device to the main device as feedback.

[0268] "Adaptive measures" refer to functions for dynamically updating educational resources in real time based on learners' activities.

[0269] "Generation means" refers to a function that performs recommendations using a generative AI model.

[0270] To implement this invention, three main players—a server, a terminal, and a user—work together. The server acts as the central hub for information processing, collecting digital information related to the learner's educational activities and storing it in the information storage unit. The server manages this information using database systems such as MySQL or MongoDB. The stored data is analyzed using AI frameworks such as TensorFlow or PyTorch. The analysis results are then used with a generative AI model to help select the optimal educational resources for the learner. In this process, the AI ​​is provided with prompts such as, "Evaluate the learner's performance and suggest necessary learning materials."

[0271] The terminal's role is to present educational resources and assignments sent from the server to the learner. It uses HTML / CSS as its user interface and is designed to be easily understood by the learner on the terminal. Furthermore, the terminal utilizes a REST API to send the learner's input, including answers and learning activity data, to the server in real time.

[0272] Users (learners) work on the provided learning materials and assignments using their devices. They can check their learning progress by entering their answers, and the system automatically adjusts the difficulty level. Users (teachers) can also use the dashboard provided by this system to check the learners' progress and understanding, and adjust the teaching content as needed. This functionality is achieved by utilizing a JavaScript framework (e.g., React).

[0273] This invention makes it possible to provide learners with personalized educational opportunities in real time, thereby improving the quality and efficiency of education.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The server collects learner activity data. Inputs include learning time, the correctness of answers to assignments, and logs of learning content usage. This data is stored in a database to prepare for subsequent analysis. MySQL and MongoDB are used for database management. The output is the collected data, properly structured and ready for use in subsequent analysis processes.

[0277] Step 2:

[0278] The server analyzes the collected activity data using a generative AI model. It receives the activity data of the learners stored in the database as input and performs analysis using data analysis tools such as "TensorFlow" and "PyTorch". As a specific operation, the model evaluates the performance of the learners and performs data processing to determine the level of understanding. As output, an evaluation result regarding the level of understanding of the learners is obtained and utilized for textbook selection in the next step.

[0279] Step 3:

[0280] The server selects the optimal educational resources for the learners based on the evaluation results. It uses the evaluation results obtained in the previous step as input. For resource selection, an AI algorithm is used to determine the recommended teaching materials by inputting a specific prompt sentence, "Please propose teaching materials to reinforce the weaknesses in a specific field". The output is a list of teaching materials and tasks to be provided to the learners.

[0281] Step 4:

[0282] The server sends the selected educational resources to the terminal. The input for this step is the teaching material list generated in the previous step. The server uses the "HTTP protocol" to send the teaching materials to the learner's terminal. The terminal receives this and prepares to display it in a format accessible to the learner. The output is that the educational resources are properly distributed to the terminal.

[0283] Step 5:

[0284] The terminal presents the teaching materials and tasks received from the server to the learners. The teaching material data is passed to the terminal as input and displayed using a user interface such as "HTML / CSS". The learners can use this to work on the tasks. The output is that the learners access the questions and provide answers.

[0285] Step 6:

[0286] The user (learner) engages with the presented teaching materials and tasks and inputs answers. The answers and learning behaviors input by the user are transmitted to the server in real time by the terminal. As output, the user's answer data and behavior history are accumulated in the database again and utilized as feedback for future learning plans.

[0287] Step 7:

[0288] The user (teacher) logs in to the system to check the progress of the learner. As input data from the server, the teacher receives the progress and understanding information of the students. A teacher's dashboard is displayed using the "JavaScript framework", and information for the teacher to adjust the necessary guidance content and learning plan is provided. The output is the adjustment of the guidance plan based on the understanding status of the students obtained by the teacher.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] It is required to provide optimal learning materials in real time without depending on location according to the understanding and learning progress of individual learners. However, in conventional systems, it is difficult to provide optimal materials in a timely manner considering the specific situation and progress of learners. Also, flexible support is required for learners to continue learning uniformly even in different environments such as when they are away from home, and means to solve such problems have been needed.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0293] In this invention, the server includes means for collecting learner activity information and storing it in an information storage unit; data analysis unit means for analyzing the collected information and evaluating the learner's level of understanding; material selection means for selecting the most suitable learning materials or assignments for the learner based on the evaluation results; and mobile communication means for updating learning materials in real time via a device, regardless of the learner's location, based on the obtained information. This makes it possible to always provide the most suitable learning materials according to the learner's progress, enabling flexible learning that is not bound by location.

[0294] The term "learner" refers to a person who engages in educational activities and learns.

[0295] "Activity information" refers to various data generated during the learning process, including learning history, answers, and operation logs.

[0296] The "information storage unit" is a storage device that stores collected activity information and keeps it in an easily accessible state.

[0297] The "Data Analysis Department" refers to the system component that processes collected activity information and analyzes learners' understanding and progress.

[0298] "Methods for selecting learning materials" refer to the methods and processes for selecting the most suitable learning materials and assignments based on the analysis results of learners.

[0299] "Learner terminals" refer to computers and smart devices that learners directly operate and use to conduct learning activities.

[0300] "Communication methods" refer to the technologies and protocols that enable the transmission and reception of information between a terminal and a server, and are carried out via wired or wireless networks.

[0301] "Mobile communication means" refers to communication technology that has the function of sending and receiving data in real time and updating learning content, no matter where the learner is.

[0302] The system for realizing this invention mainly consists of three entities: a server, a terminal, and a user. The server is installed in a data center and uses AI technologies such as Python and TensorFlow to collect the activity information of learners and perform data analysis. Specifically, it collects in real-time the activity information such as the learning history, operation logs, and answer results of learners and stores them in a database system.

[0303] Based on this information, the server utilizes the generated AI model to evaluate the understanding level of learners and selects the most suitable learning materials and tasks based on the results. The selected materials and tasks are transmitted from the server to the learner's terminal via Wi-Fi or mobile communication.

[0304] On the terminal side, the received materials and tasks are displayed to the learners using a mobile SDK developed in Java. At this time, the learning materials are updated in real-time via the mobile network so that learners can use them anywhere. Learners can access these materials using the terminal and solve the tasks. Also, users (learners and teachers) can log in to the system to check the progress status and learning data.

[0305] As a specific example, when a user starts an app on their smartphone while waiting for a train at a station, the latest learning data is sent to the terminal, and the AI model provides the most suitable quizzes and supplementary materials on the spot. In this way, learners can effectively continue their learning even when they are outside.

[0306] An example of a prompt sentence for the generated AI model is "This learner has a shallow understanding in a specific field of mathematics. Based on the past learning history, please recommend materials to reinforce this field."

[0307] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0308] Step 1:

[0309] The server collects activity information from learners. Specifically, it receives learning history, operation logs, and answer results from the learner's terminal and stores them in a database. The data format is standardized to JSON and sent to the server via an API.

[0310] Step 2:

[0311] The server performs data analysis on the collected activity information. Based on the learning history and logs received as input, it performs tensor analysis and evaluates the level of understanding. An AI model using TensorFlow is responsible for this analysis and calculates the learner's proficiency level as output.

[0312] Step 3:

[0313] The server selects the most suitable learning materials and assignments based on the acquired proficiency information. In this process, prompt sentences are generated using the output from the previous data analysis and input into the generative AI model. As a result, a list of learning materials appropriate for the learner is generated.

[0314] Step 4:

[0315] The server sends the selected learning materials to the device. The learning material data is delivered to the learner's device via a RESTful API. During this process, the content of the learning materials is dynamically updated and displayed immediately on the device.

[0316] Step 5:

[0317] Users access the provided learning materials via their devices and complete the assignments. Their answers and additional learning activities are sent to the server in real time. This data is then stored again in the database for use in subsequent analyses.

[0318] Step 6:

[0319] The server displays learner progress and comprehension data through an administration screen accessible to teachers. The administration screen is web-based and accessible after secure authentication. Teachers can use this information to further develop individualized instruction plans.

[0320] 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.

[0321] This invention is a system that recognizes the emotional state of learners and utilizes it in the learning process. The system consists of three main elements: a server, a terminal, and a user, and provides a more personalized learning experience, particularly by including an emotion engine.

[0322] The server collects emotional data from the emotion engine, including facial expressions, voice, and input patterns, along with the learner's activity data. This data is stored in a data storage unit and used to evaluate the learner's level of understanding. The server uses an AI model to simultaneously analyze the learner's understanding and emotions, comprehensively evaluating the learner's state. Based on this evaluation, the server determines the most suitable learning materials and assignments for the learner.

[0323] The selection of learning materials reflects the learner's current emotional state. For example, if a learner is feeling stressed, it's possible to adjust the difficulty level or select materials in a relaxing format. The selected materials and assignments are sent from the server to the user's (learner's) device, and the user deals with them through the device.

[0324] The terminal not only provides learners with learning materials and assignments sent from the server, but also analyzes the user's emotions using an emotion engine. Specifically, it uses a webcam and microphone to analyze the user's facial expressions and voice, and evaluates their emotional state in real time. As a result, if the user's mood changes during learning, new data is immediately collected and sent to the server.

[0325] Users (learners) engage with the presented materials and assignments, and their emotional state is evaluated as part of the learning process. This system provides continuous feedback to help learners maintain a relaxed state to deepen their understanding. Furthermore, users (teachers) can monitor learners' emotional changes and adjust learning materials and instructional content as needed.

[0326] This invention enables adaptive learning that takes learners' emotions into account, resulting in an efficient and effective learning experience. The system distinguishes itself from conventional learning support systems by incorporating emotional data in addition to analyzing learning data.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The device captures the learner's facial expressions and voice in real time into its emotion engine. It uses cameras and microphones to collect this data and recognize the user's current emotional state.

[0330] Step 2:

[0331] The device sends the recognized emotion data to the server. This prepares the server to receive and store both activity data and emotion data simultaneously.

[0332] Step 3:

[0333] The server stores activity data and emotion data in its data storage unit and inputs it into the AI ​​model. The model analyzes the learner's level of understanding and current emotional state to perform an overall performance evaluation.

[0334] Step 4:

[0335] Based on the analysis results, the server selects the most suitable learning materials and assignments for the learner. The selection process also takes into account the learner's emotional state, and content is adjusted to alleviate stress and to an appropriate level of difficulty.

[0336] Step 5:

[0337] The server sends selected learning materials and assignments to the terminal. The materials may include feedback that takes into account the user's emotional state.

[0338] Step 6:

[0339] The terminal displays learning materials and assignments received from the server to the learner. It is designed to be intuitive to use through its interface.

[0340] Step 7:

[0341] Users (learners) access the presented learning materials and work on assignments. An interactive learning experience tailored to their emotional state is provided to maintain learner motivation.

[0342] Step 8:

[0343] The server continuously analyzes the answer results and updated sentiment data received through the terminals, and continuously optimizes the learning process.

[0344] Step 9:

[0345] Users (teachers) can monitor learners' progress and emotional state through reports provided by the server, and adjust their teaching plans as needed to provide effective learning support.

[0346] (Example 2)

[0347] 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".

[0348] Traditional learning support systems only evaluated learners' learning progress and did not take their emotional state into consideration, which could hinder learners' interest and motivation. This could lead to insufficient selection of optimal learning materials and assignments, potentially reducing learning effectiveness. Furthermore, it was difficult for teachers to quickly grasp and respond to changes in learners' emotions. Therefore, there was a need for an effective learning support system that took learners' emotions into account.

[0349] 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.

[0350] In this invention, the server includes means for collecting learner activity information and storing it in an information recording unit, means for analyzing the collected information and evaluating the learner's level of understanding and emotional state, and means for selecting the most appropriate educational materials or assignments for the learner based on the evaluation results. This enables the selection of optimal teaching materials and feedback according to the learner's level of understanding and emotional state.

[0351] A "learner" is an individual who engages in learning, and who acquires knowledge and deepens their understanding through educational materials and assignments.

[0352] "Activity information" refers to data that records learners' actions and responses related to learning, whether online or offline.

[0353] The "Information Recording Unit" refers to a data storage system for centrally accumulating and managing learners' activity and emotional information.

[0354] The "Information Analysis Unit" refers to computer programs and algorithms that process accumulated activity and emotional information to analyze learners' comprehension levels and emotional states.

[0355] "Emotional state" is an indicator that shows the learner's psychological or emotional situation and reactions, and is based on information obtained from facial expressions, voice, and behavioral patterns.

[0356] "Material selection method" refers to a process or system for selecting the most appropriate educational materials and assignments for learners based on analyzed information.

[0357] "Means of delivery" refers to communication functions and technologies for displaying or distributing selected educational materials and assignments to learners' devices.

[0358] "Communication means" refers to network functions that enable data communication between learner terminals and servers, and for sending and receiving feedback and analysis results.

[0359] "Emotional analysis unit means" refers to technology for recognizing and analyzing the learner's emotional state in real time during learning and updating the data as needed.

[0360] This invention provides an adaptive learning support system that takes into account the emotional state of learners, and includes server, terminal, and user elements.

[0361] The server is responsible for collecting learner activity information and emotional data. Specifically, it stores learning progress and usage records sent from clients in a database. For collecting emotional data, it uses a webcam and microphone connected to the terminal, recognizes facial expressions using libraries such as OpenCV, and performs speech analysis. This data is stored in the information storage unit on the server. Next, the server analyzes the collected data using a generative AI model. It uses TensorFlow or PyTorch to simultaneously evaluate the learner's comprehension level and emotional state.

[0362] The server selects the most suitable educational materials and assignments based on evaluation results from an AI model. This process reflects the learner's emotional state and has the flexibility to adjust difficulty levels as needed. The selected materials are delivered to the device via the HTTP protocol.

[0363] The terminal displays learning materials provided by the server to the learner and also performs real-time sentiment analysis. It continuously monitors the user's facial expressions and voice using a webcam and microphone, and feeds back the analyzed sentiment data to the server. This information helps learners continue learning in a relaxed state.

[0364] As users (learners) work on learning materials and assignments presented through their devices, their emotional state is also treated as a learning element. For example, if a user is feeling stressed, the system may provide learning materials that promote relaxation. An example of utilizing a generative AI model is a prompt message such as, "Analyze the learner's emotional state and generate relaxing learning materials appropriate to their level of understanding."

[0365] This system enables adaptive learning that takes into account learners' emotions and comprehension levels, resulting in a more effective educational experience. Unlike conventional learning systems, it utilizes emotional data, allowing for flexible responses tailored to the individual needs of learners.

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The server collects learner activity and emotion data from the terminal. Its inputs include facial video and audio data captured by the terminal, as well as log data related to learning progress. Specifically, it uses a webcam and microphone to record the learner's facial expressions and speech in real time and sends this data to the server. The output is raw activity and emotion data stored in a database.

[0369] Step 2:

[0370] The server uses a generative AI model to analyze the collected data. As input, accumulated activity and sentiment data are retrieved from a database. The server processes this data using machine learning frameworks such as TensorFlow and PyTorch to evaluate the learner's comprehension and emotional state. Specifically, it performs data cleansing, feature extraction, and prediction using the AI ​​model. The output is the evaluation result regarding the learner's comprehension and emotional state.

[0371] Step 3:

[0372] The server selects the most appropriate educational materials and assignments based on the analysis results. Evaluation results from an AI model are used as input. The server applies predefined rules and machine learning models to select the most appropriate materials for the learner's state. Specifically, it performs a material filtering and selection process from the material database. The output is the selected educational materials and assignments.

[0373] Step 4:

[0374] The server provides selected learning materials and assignments to the terminal. Selected learning material data is retrieved from the server as input. The server sends the learning materials and assignments to the terminal using the HTTP protocol. Specifically, it packages and transmits the learning material data. The output is the learning material displayed to the user visually or audibly on the terminal.

[0375] Step 5:

[0376] The device presents the provided learning materials to the user and continuously analyzes the user's emotions during learning. Inputs include learning materials sent from the server and the user's current learning status. The device uses its active webcam and microphone to analyze the user's facial expressions and voice, capturing changes in emotions. Specifically, it displays learning materials on the UI and executes a real-time emotion analysis module. Output is the transmission of feedback data to the server.

[0377] Step 6:

[0378] The user engages with learning materials and assignments presented on the device. The input consists of the learning materials and assignments displayed on the device. The user improves their understanding by solving problems and viewing the materials. Specifically, the user interacts with the learning materials, and the resulting data is sent from the device to the server. The output consists of the user's activity results and feedback data.

[0379] (Application Example 2)

[0380] 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."

[0381] In modern learning and electronic payment systems, there is a need to provide efficient and effective experiences by appropriately considering the user's emotional state. However, conventional systems often fail to consider emotional changes, resulting in information and suggestions that are not suited to the user. This can lead to a diminished user experience and potentially hinder learning motivation and consumer behavior. Therefore, a method is needed that recognizes the user's emotional state in real time and provides optimal information and suggestions accordingly.

[0382] 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.

[0383] In this invention, the server includes a component that collects learner activity information and stores it in an information storage unit, an information analysis unit component that analyzes the collected information and evaluates the learner's level of understanding, and an emotion analysis component that recognizes the user's emotional state and provides optimal information or suggestions based on that emotional state. This makes it possible to provide suggestions and information that are adapted to the user's emotional state, thereby improving the user experience.

[0384] "Learner activity information" refers to data about the actions and operations performed by learners, and is used to evaluate the learning process.

[0385] An "information storage unit" is a storage device for temporarily or long-term storage of collected data.

[0386] The "information analysis unit components" are those that analyze collected data and have the function of evaluating the learner's level of understanding and emotional state.

[0387] The "material selection component" is responsible for selecting the most suitable learning materials or assignments for learners based on the analysis results.

[0388] "Delivery components" refer to components that have the function of delivering selected learning materials or assignments to the learner's device.

[0389] A "communication component" is a device that has the function of sending answer results and learning actions from the learner's terminal to the server.

[0390] "Emotional state" refers to the emotional state of a learner or user, and is a psychological state that includes specific emotions or moods.

[0391] "Emotional analysis components" are those that recognize and analyze the emotional state of the user from their facial expressions, voice, etc.

[0392] The term "instructor" refers to someone who has the role of providing guidance and instructions to learners or users.

[0393] "Management components" are those that allow instructors to monitor the progress of learners and users and adjust the content of instruction accordingly.

[0394] System Configuration

[0395] This invention is a learning and information provision system that takes into account the user's emotional state. The system mainly consists of three elements: a server, a terminal, and a user. The server collects learner activity information and stores it in an information storage unit. Furthermore, the server uses an information analysis unit component to analyze the collected data and evaluate the learner's level of understanding and emotional state. Based on the evaluation, a material selection component selects the most suitable learning materials and information for the learner, and a provisioning component delivers them to the terminal.

[0396] Recognition of emotional states

[0397] The user's emotional state is recognized in real time using the device's camera and microphone. An emotion analysis component analyzes the user's facial expressions and voice to estimate their current emotion. The system feeds the results of the emotion analysis back to the server, which is then used to provide new information.

[0398] Hardware and software

[0399] The hardware used includes smartphones and tablets. The software utilizes Microsoft Azure Cognitive Services' Emotion API as the emotion analysis engine and TensorFlow for the AI ​​model.

[0400] Specific example

[0401] As a concrete example, when a learner is studying a new unit, the server analyzes the learner's facial expressions and, if it determines that the learner is experiencing a high cognitive load, prioritizes selecting learning materials in a format that will help them relax. For instance, it could recommend review materials in a quiz format that helps reduce stress. This approach optimizes the user experience during learning.

[0402] Examples of prompts for generative AI models

[0403] "We want to provide the most relevant information based on the user's emotional state. Please tell us what kind of educational materials and information would be most effective considering their current emotional state."

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The user begins learning using the device. At this time, the device's camera and microphone are activated, recording the user's facial expressions and voice in real time. The input is the user's image and audio data, and the output is the recorded raw data.

[0407] Step 2:

[0408] The device sends recorded image and audio data to an emotion analysis engine. The emotion analysis engine analyzes this data and evaluates the user's emotional state. The input is raw image and audio data, and the engine performs feature extraction and emotion estimation on the data. The output is the user's emotional state (e.g., joy, stress, anxiety).

[0409] Step 3:

[0410] The server receives emotional state and learner activity information transmitted from the terminal. The input consists of the user's emotional state data and activity information. The server's AI model integrates this data and evaluates the user's learning progress. The output is an overall evaluation of the user's learning comprehension and emotional state.

[0411] Step 4:

[0412] Based on the evaluation results, the server selects the optimal learning materials or information using material selection components. The input is the evaluation results, and the output is the selected learning materials or information determined by the material selection algorithm. These selections take into account the user's emotional state.

[0413] Step 5:

[0414] The server sends the selected learning materials or information to the terminal. The input is the selected material data, and the output is the data delivered to the terminal.

[0415] Step 6:

[0416] The terminal provides the user with learning materials and information received from the server. The user continues their learning activities based on this information. The input is the learning materials provided by the server, and the output is the progress of the user's learning activities.

[0417] Step 7:

[0418] When a user completes their learning activity, the device records the user's answers and learning behavior. The input consists of the user's answers and behavior data, while the output is the recorded learning data. This data is sent to the server as feedback and used to select future learning materials.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention aims to realize a system that monitors learners' performance in real time and provides individually optimized learning materials and assignments based on that performance. The system consists of three components: a server, a terminal, and a user, and features AI-based data analysis and an adaptive learning material delivery mechanism.

[0436] The server first collects data on the learner's daily activities. This includes study time, correctness of answers, and operation logs for learning content. The server uses this information to analyze the learner's learning performance and diagnose their level of understanding using an AI model. Based on the results, the server selects the most suitable learning materials and tasks for each individual learner. These selected materials are then sent from the server to the terminal and provided to the learner.

[0437] The terminal displays learning materials and assignments received from the server to the learner. The learner can access the materials and complete assignments via the terminal. The terminal is equipped with an answer input interface and has the function to send the learner's entered answers and activity history to the server in real time.

[0438] Users (learners) work on the provided materials and assignments and input their answers. Through assignments whose difficulty level is automatically adjusted according to their understanding, users can continuously deepen their learning. Furthermore, users (teachers) can log into the system and monitor students' progress and understanding. Based on this information, teachers can adjust additional instruction and learning plans as needed.

[0439] As a concrete example, consider a case where a learner is struggling with a specific area of ​​mathematics. The server analyzes this data and selects additional problem sets or educational videos to reinforce the learner's weak areas. This material is instantly sent to the device, allowing the learner to begin supplementary learning immediately. In this way, providing learners with real-time adaptive learning opportunities enables efficient learning tailored to individual learning needs.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The server collects learner activity data and stores it in a database. This data includes learning behavior, response time, and correct / incorrect answers. The server manages this data in a structured format.

[0443] Step 2:

[0444] The server inputs the collected data into an AI model and performs analysis. This analysis evaluates the learner's current level of understanding, areas of difficulty, and learning progress. The AI ​​model measures the learner's growth by comparing it with past data.

[0445] Step 3:

[0446] The server selects the most suitable learning materials and assignments for each learner based on the analysis results. Selection criteria include the learner's level of understanding, past performance, and the relevance of the topic. The selected materials are designated as the next content the learner should work on.

[0447] Step 4:

[0448] The server sends the selected learning materials and assignments to the device. This prepares the device to present the latest learning materials to the learner. The server tracks the usage of the learning materials by recording logs of material distribution.

[0449] Step 5:

[0450] The terminal displays learning materials and assignments received from the server to the learner. The terminal provides an interactive learning experience through its user interface. The learner progresses through their learning using the presented materials.

[0451] Step 6:

[0452] Users (learners) access learning materials using their devices and work on assignments. Users can enter their answers and refer to hints and explanations within the system as needed.

[0453] Step 7:

[0454] The device sends user answers and learning activity logs to the server in real time. The device formats the log data appropriately to ensure it reaches the server quickly.

[0455] Step 8:

[0456] The server immediately processes the received log data and updates the learner's performance. The server then uses this data for data analysis for the next learning cycle.

[0457] (Example 1)

[0458] 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."

[0459] Modern education systems require individualized support tailored to each learner's level of understanding and progress. However, traditional systems have struggled to respond in real time to the diverse levels of understanding of learners and to dynamically provide appropriate learning materials. This limits the maximization of learning efficiency and the improvement of educational quality, hindering continuous improvement in understanding.

[0460] 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.

[0461] In this invention, the server includes an information gathering means for collecting digital information obtained from learners' educational activities and storing it in an information storage unit, an information analysis unit for analyzing the collected information and evaluating the depth of the learners' understanding, and a resource selection means for selecting the most suitable educational resources or tasks for the learners based on the evaluation results. This makes it possible to provide each learner with an individually optimized educational experience in real time, thereby improving learning efficiency and the quality of education.

[0462] "Information gathering means" refers to the function of collecting digital information obtained from learners' educational activities and storing it in the information storage unit.

[0463] The "information analysis unit" is a function that analyzes collected information and evaluates the depth of the learner's understanding.

[0464] A "resource selection method" is a function for selecting the most suitable educational resources or assignments for learners based on evaluation results.

[0465] "Supply means" refers to the function of supplying selected educational resources or assignments to the learner's device.

[0466] "Communication means" refers to a function for transmitting answer results and records of learning activities obtained from the learner's device to the main device as feedback.

[0467] "Adaptive measures" refer to functions for dynamically updating educational resources in real time based on learners' activities.

[0468] "Generation means" refers to a function that performs recommendations using a generative AI model.

[0469] To implement this invention, three main players—a server, a terminal, and a user—work together. The server acts as the central hub for information processing, collecting digital information related to the learner's educational activities and storing it in the information storage unit. The server manages this information using database systems such as MySQL or MongoDB. The stored data is analyzed using AI frameworks such as TensorFlow or PyTorch. The analysis results are then used with a generative AI model to help select the optimal educational resources for the learner. In this process, the AI ​​is provided with prompts such as, "Evaluate the learner's performance and suggest necessary learning materials."

[0470] The terminal's role is to present educational resources and assignments sent from the server to the learner. It uses HTML / CSS as its user interface and is designed to be easily understood by the learner on the terminal. Furthermore, the terminal utilizes a REST API to send the learner's input, including answers and learning activity data, to the server in real time.

[0471] Users (learners) work on the provided learning materials and assignments using their devices. They can check their learning progress by entering their answers, and the system automatically adjusts the difficulty level. Users (teachers) can also use the dashboard provided by this system to check the learners' progress and understanding, and adjust the teaching content as needed. This functionality is achieved by utilizing a JavaScript framework (e.g., React).

[0472] This invention makes it possible to provide learners with personalized educational opportunities in real time, thereby improving the quality and efficiency of education.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The server collects learner activity data. Inputs include learning time, the correctness of answers to assignments, and logs of learning content usage. This data is stored in a database to prepare for subsequent analysis. MySQL and MongoDB are used for database management. The output is the collected data, properly structured and ready for use in subsequent analysis processes.

[0476] Step 2:

[0477] The server analyzes the collected activity data using a generating AI model. It receives learner activity data stored in a database as input and performs analysis using data analysis tools such as "TensorFlow" and "PyTorch." Specifically, the model evaluates the learner's performance and performs data processing to determine their level of understanding. The output provides an evaluation of the learner's understanding, which is then used to select learning materials for the next step.

[0478] Step 3:

[0479] The server selects the most suitable educational resources for the learner based on the evaluation results. The evaluation results obtained in the previous step are used as input. An AI algorithm is used for resource selection, taking a specific prompt, "Please suggest materials to reinforce weaknesses in a specific area," as input to determine which materials to recommend. The output is a list of materials and assignments to be provided to the learner.

[0480] Step 4:

[0481] The server sends the selected educational resources to the terminal. The input in this step is the list of learning materials generated in the previous step. The server uses the HTTP protocol to send the materials to the learner's terminal. The terminal receives them and prepares them for display in a format accessible to the learner. The output is that the educational resources have been successfully delivered to the terminal.

[0482] Step 5:

[0483] The terminal presents learning materials and assignments received from the server to the learner. Learning material data is passed to the terminal as input and displayed using a user interface based on HTML / CSS, etc. Learners can then use this to work on assignments. The output is the learner accessing the problems and submitting their answers.

[0484] Step 6:

[0485] The user (learner) works on the presented learning materials and assignments and enters their answers. The answers and learning actions entered by the user are sent to the server in real time via the device. As output, the user's answer data and activity history are stored again in the database and used as feedback for future learning plans.

[0486] Step 7:

[0487] The user (teacher) logs into the system to check the learners' progress. The system receives student progress and comprehension information as input data from the server. A teacher dashboard is displayed using a JavaScript framework, providing information for teachers to adjust their teaching content and learning plans. The output is the adjustment of the teaching plan based on the students' comprehension levels obtained by the teacher.

[0488] (Application Example 1)

[0489] 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."

[0490] There is a growing need to provide optimal learning materials in real time, regardless of location, tailored to each learner's level of understanding and learning progress. However, conventional systems struggle to provide the most suitable materials in a timely manner, taking into account the learner's specific situation and progress. Furthermore, there is a need for flexible solutions to ensure learners can continue their studies consistently even when they are in different environments, such as while traveling. A means to solve these challenges was therefore necessary.

[0491] 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.

[0492] In this invention, the server includes means for collecting learner activity information and storing it in an information storage unit; data analysis unit means for analyzing the collected information and evaluating the learner's level of understanding; material selection means for selecting the most suitable learning materials or assignments for the learner based on the evaluation results; and mobile communication means for updating learning materials in real time via a device, regardless of the learner's location, based on the obtained information. This makes it possible to always provide the most suitable learning materials according to the learner's progress, enabling flexible learning that is not bound by location.

[0493] The term "learner" refers to a person who engages in educational activities and learns.

[0494] "Activity information" refers to various data generated during the learning process, including learning history, answers, and operation logs.

[0495] The "information storage unit" is a storage device that stores collected activity information and keeps it in an easily accessible state.

[0496] The "Data Analysis Department" refers to the system component that processes collected activity information and analyzes learners' understanding and progress.

[0497] "Methods for selecting learning materials" refer to the methods and processes for selecting the most suitable learning materials and assignments based on the analysis results of learners.

[0498] "Learner terminals" refer to computers and smart devices that learners directly operate and use to conduct learning activities.

[0499] "Communication methods" refer to the technologies and protocols that enable the transmission and reception of information between a terminal and a server, and are carried out via wired or wireless networks.

[0500] "Mobile communication means" refers to communication technology that has the function of sending and receiving data in real time and updating learning content, no matter where the learner is.

[0501] The system for realizing this invention primarily consists of three components: a server, a terminal, and a user. The server is located in a data center and uses AI technologies such as Python and TensorFlow to collect learner activity information and perform data analysis. Specifically, it collects activity information such as the learner's learning history, operation logs, and answer results in real time and stores them in a database system.

[0502] Based on this information, the server uses a generated AI model to evaluate the learner's level of understanding and selects the most suitable learning materials and assignments based on the results. The selected materials and assignments are then sent from the server to the learner's device via Wi-Fi or mobile communication.

[0503] On the device side, learning materials and assignments received are displayed to learners using a mobile SDK developed in Java. The learning materials are updated in real time via the mobile network, ensuring access from anywhere. Learners can access these materials and complete assignments using their devices. Users (learners and teachers) can also log into the system to check progress and learning data.

[0504] As a concrete example, when a user launches the app on their smartphone while waiting for a train at a station, the latest learning data is sent to the device, and the AI ​​model provides the most suitable quizzes and supplementary materials on the spot. In this way, learners can effectively continue their studies even when they are out and about.

[0505] An example of a prompt to a generative AI model is: "This learner has a weak understanding of a specific area of ​​mathematics. Based on their past learning history, recommend materials to reinforce this area."

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The server collects activity information from learners. Specifically, it receives learning history, operation logs, and answer results from the learner's terminal and stores them in a database. The data format is standardized to JSON and sent to the server via an API.

[0509] Step 2:

[0510] The server performs data analysis on the collected activity information. Based on the learning history and logs received as input, it performs tensor analysis and evaluates the level of understanding. An AI model using TensorFlow is responsible for this analysis and calculates the learner's proficiency level as output.

[0511] Step 3:

[0512] The server selects the most suitable learning materials and assignments based on the acquired proficiency information. In this process, prompt sentences are generated using the output from the previous data analysis and input into the generative AI model. As a result, a list of learning materials appropriate for the learner is generated.

[0513] Step 4:

[0514] The server sends the selected learning materials to the device. The learning material data is delivered to the learner's device via a RESTful API. During this process, the content of the learning materials is dynamically updated and displayed immediately on the device.

[0515] Step 5:

[0516] Users access the provided learning materials via their devices and complete the assignments. Their answers and additional learning activities are sent to the server in real time. This data is then stored again in the database for use in subsequent analyses.

[0517] Step 6:

[0518] The server displays learner progress and comprehension data through an administration screen accessible to teachers. The administration screen is web-based and accessible after secure authentication. Teachers can use this information to further develop individualized instruction plans.

[0519] 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.

[0520] This invention is a system that recognizes the emotional state of learners and utilizes it in the learning process. The system consists of three main elements: a server, a terminal, and a user, and provides a more personalized learning experience, particularly by including an emotion engine.

[0521] The server collects emotional data from the emotion engine, including facial expressions, voice, and input patterns, along with the learner's activity data. This data is stored in a data storage unit and used to evaluate the learner's level of understanding. The server uses an AI model to simultaneously analyze the learner's understanding and emotions, comprehensively evaluating the learner's state. Based on this evaluation, the server determines the most suitable learning materials and assignments for the learner.

[0522] The selection of learning materials reflects the learner's current emotional state. For example, if a learner is feeling stressed, it's possible to adjust the difficulty level or select materials in a relaxing format. The selected materials and assignments are sent from the server to the user's (learner's) device, and the user deals with them through the device.

[0523] The terminal not only provides learners with learning materials and assignments sent from the server, but also analyzes the user's emotions using an emotion engine. Specifically, it uses a webcam and microphone to analyze the user's facial expressions and voice, and evaluates their emotional state in real time. As a result, if the user's mood changes during learning, new data is immediately collected and sent to the server.

[0524] Users (learners) engage with the presented materials and assignments, and their emotional state is evaluated as part of the learning process. This system provides continuous feedback to help learners maintain a relaxed state to deepen their understanding. Furthermore, users (teachers) can monitor learners' emotional changes and adjust learning materials and instructional content as needed.

[0525] This invention enables adaptive learning that takes learners' emotions into account, resulting in an efficient and effective learning experience. The system distinguishes itself from conventional learning support systems by incorporating emotional data in addition to analyzing learning data.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The device captures the learner's facial expressions and voice in real time into its emotion engine. It uses cameras and microphones to collect this data and recognize the user's current emotional state.

[0529] Step 2:

[0530] The device sends the recognized emotion data to the server. This prepares the server to receive and store both activity data and emotion data simultaneously.

[0531] Step 3:

[0532] The server stores activity data and emotion data in its data storage unit and inputs it into the AI ​​model. The model analyzes the learner's level of understanding and current emotional state to perform an overall performance evaluation.

[0533] Step 4:

[0534] Based on the analysis results, the server selects the most suitable learning materials and assignments for the learner. The selection process also takes into account the learner's emotional state, and content is adjusted to alleviate stress and to an appropriate level of difficulty.

[0535] Step 5:

[0536] The server sends selected learning materials and assignments to the terminal. The materials may include feedback that takes into account the user's emotional state.

[0537] Step 6:

[0538] The terminal displays learning materials and assignments received from the server to the learner. It is designed to be intuitive to use through its interface.

[0539] Step 7:

[0540] Users (learners) access the presented learning materials and work on assignments. An interactive learning experience tailored to their emotional state is provided to maintain learner motivation.

[0541] Step 8:

[0542] The server continuously analyzes the answer results and updated sentiment data received through the terminals, and continuously optimizes the learning process.

[0543] Step 9:

[0544] Users (teachers) can monitor learners' progress and emotional state through reports provided by the server, and adjust their teaching plans as needed to provide effective learning support.

[0545] (Example 2)

[0546] 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."

[0547] Traditional learning support systems only evaluated learners' learning progress and did not take their emotional state into consideration, which could hinder learners' interest and motivation. This could lead to insufficient selection of optimal learning materials and assignments, potentially reducing learning effectiveness. Furthermore, it was difficult for teachers to quickly grasp and respond to changes in learners' emotions. Therefore, there was a need for an effective learning support system that took learners' emotions into account.

[0548] 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.

[0549] In this invention, the server includes means for collecting learner activity information and storing it in an information recording unit, means for analyzing the collected information and evaluating the learner's level of understanding and emotional state, and means for selecting the most appropriate educational materials or assignments for the learner based on the evaluation results. This enables the selection of optimal teaching materials and feedback according to the learner's level of understanding and emotional state.

[0550] A "learner" is an individual who engages in learning, and who acquires knowledge and deepens their understanding through educational materials and assignments.

[0551] "Activity information" refers to data that records learners' actions and responses related to learning, whether online or offline.

[0552] The "Information Recording Unit" refers to a data storage system for centrally accumulating and managing learners' activity and emotional information.

[0553] The "Information Analysis Unit" refers to computer programs and algorithms that process accumulated activity and emotional information to analyze learners' comprehension levels and emotional states.

[0554] "Emotional state" is an indicator that shows the learner's psychological or emotional situation and reactions, and is based on information obtained from facial expressions, voice, and behavioral patterns.

[0555] "Material selection method" refers to a process or system for selecting the most appropriate educational materials and assignments for learners based on analyzed information.

[0556] "Means of delivery" refers to communication functions and technologies for displaying or distributing selected educational materials and assignments to learners' devices.

[0557] "Communication means" refers to network functions that enable data communication between learner terminals and servers, and for sending and receiving feedback and analysis results.

[0558] "Emotional analysis unit means" refers to technology for recognizing and analyzing the learner's emotional state in real time during learning and updating the data as needed.

[0559] This invention provides an adaptive learning support system that takes into account the emotional state of learners, and includes server, terminal, and user elements.

[0560] The server is responsible for collecting learner activity information and emotional data. Specifically, it stores learning progress and usage records sent from clients in a database. For collecting emotional data, it uses a webcam and microphone connected to the terminal, recognizes facial expressions using libraries such as OpenCV, and performs speech analysis. This data is stored in the information storage unit on the server. Next, the server analyzes the collected data using a generative AI model. It uses TensorFlow or PyTorch to simultaneously evaluate the learner's comprehension level and emotional state.

[0561] The server selects the most suitable educational materials and assignments based on evaluation results from an AI model. This process reflects the learner's emotional state and has the flexibility to adjust difficulty levels as needed. The selected materials are delivered to the device via the HTTP protocol.

[0562] The terminal displays learning materials provided by the server to the learner and also performs real-time sentiment analysis. It continuously monitors the user's facial expressions and voice using a webcam and microphone, and feeds back the analyzed sentiment data to the server. This information helps learners continue learning in a relaxed state.

[0563] As users (learners) work on learning materials and assignments presented through their devices, their emotional state is also treated as a learning element. For example, if a user is feeling stressed, the system may provide learning materials that promote relaxation. An example of utilizing a generative AI model is a prompt message such as, "Analyze the learner's emotional state and generate relaxing learning materials appropriate to their level of understanding."

[0564] This system enables adaptive learning that takes into account learners' emotions and comprehension levels, resulting in a more effective educational experience. Unlike conventional learning systems, it utilizes emotional data, allowing for flexible responses tailored to the individual needs of learners.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The server collects learner activity and emotion data from the terminal. Its inputs include facial video and audio data captured by the terminal, as well as log data related to learning progress. Specifically, it uses a webcam and microphone to record the learner's facial expressions and speech in real time and sends this data to the server. The output is raw activity and emotion data stored in a database.

[0568] Step 2:

[0569] The server uses a generative AI model to analyze the collected data. As input, accumulated activity and sentiment data are retrieved from a database. The server processes this data using machine learning frameworks such as TensorFlow and PyTorch to evaluate the learner's comprehension and emotional state. Specifically, it performs data cleansing, feature extraction, and prediction using the AI ​​model. The output is the evaluation result regarding the learner's comprehension and emotional state.

[0570] Step 3:

[0571] The server selects the most appropriate educational materials and assignments based on the analysis results. Evaluation results from an AI model are used as input. The server applies predefined rules and machine learning models to select the most appropriate materials for the learner's state. Specifically, it performs a material filtering and selection process from the material database. The output is the selected educational materials and assignments.

[0572] Step 4:

[0573] The server provides selected learning materials and assignments to the terminal. Selected learning material data is retrieved from the server as input. The server sends the learning materials and assignments to the terminal using the HTTP protocol. Specifically, it packages and transmits the learning material data. The output is the learning material displayed to the user visually or audibly on the terminal.

[0574] Step 5:

[0575] The device presents the provided learning materials to the user and continuously analyzes the user's emotions during learning. Inputs include learning materials sent from the server and the user's current learning status. The device uses its active webcam and microphone to analyze the user's facial expressions and voice, capturing changes in emotions. Specifically, it displays learning materials on the UI and executes a real-time emotion analysis module. Output is the transmission of feedback data to the server.

[0576] Step 6:

[0577] The user engages with learning materials and assignments presented on the device. The input consists of the learning materials and assignments displayed on the device. The user improves their understanding by solving problems and viewing the materials. Specifically, the user interacts with the learning materials, and the resulting data is sent from the device to the server. The output consists of the user's activity results and feedback data.

[0578] (Application Example 2)

[0579] 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."

[0580] In modern learning and electronic payment systems, there is a need to provide efficient and effective experiences by appropriately considering the user's emotional state. However, conventional systems often fail to consider emotional changes, resulting in information and suggestions that are not suited to the user. This can lead to a diminished user experience and potentially hinder learning motivation and consumer behavior. Therefore, a method is needed that recognizes the user's emotional state in real time and provides optimal information and suggestions accordingly.

[0581] 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.

[0582] In this invention, the server includes a component that collects learner activity information and stores it in an information storage unit, an information analysis unit component that analyzes the collected information and evaluates the learner's level of understanding, and an emotion analysis component that recognizes the user's emotional state and provides optimal information or suggestions based on that emotional state. This makes it possible to provide suggestions and information that are adapted to the user's emotional state, thereby improving the user experience.

[0583] "Learner activity information" refers to data about the actions and operations performed by learners, and is used to evaluate the learning process.

[0584] An "information storage unit" is a storage device for temporarily or long-term storage of collected data.

[0585] The "information analysis unit components" are those that analyze collected data and have the function of evaluating the learner's level of understanding and emotional state.

[0586] The "material selection component" is responsible for selecting the most suitable learning materials or assignments for learners based on the analysis results.

[0587] "Delivery components" refer to components that have the function of delivering selected learning materials or assignments to the learner's device.

[0588] A "communication component" is a device that has the function of sending answer results and learning actions from the learner's terminal to the server.

[0589] "Emotional state" refers to the emotional state of a learner or user, and is a psychological state that includes specific emotions or moods.

[0590] "Emotional analysis components" are those that recognize and analyze the emotional state of the user from their facial expressions, voice, etc.

[0591] The term "instructor" refers to someone who has the role of providing guidance and instructions to learners or users.

[0592] "Management components" are those that allow instructors to monitor the progress of learners and users and adjust the content of instruction accordingly.

[0593] System Configuration

[0594] This invention is a learning and information provision system that takes into account the user's emotional state. The system mainly consists of three elements: a server, a terminal, and a user. The server collects learner activity information and stores it in an information storage unit. Furthermore, the server uses an information analysis unit component to analyze the collected data and evaluate the learner's level of understanding and emotional state. Based on the evaluation, a material selection component selects the most suitable learning materials and information for the learner, and a provisioning component delivers them to the terminal.

[0595] Recognition of emotional states

[0596] The user's emotional state is recognized in real time using the device's camera and microphone. An emotion analysis component analyzes the user's facial expressions and voice to estimate their current emotion. The system feeds the results of the emotion analysis back to the server, which is then used to provide new information.

[0597] Hardware and software

[0598] The hardware used includes smartphones and tablets. The software utilizes Microsoft Azure Cognitive Services' Emotion API as the emotion analysis engine and TensorFlow for the AI ​​model.

[0599] Specific example

[0600] As a concrete example, when a learner is studying a new unit, the server analyzes the learner's facial expressions and, if it determines that the learner is experiencing a high cognitive load, prioritizes selecting learning materials in a format that will help them relax. For instance, it could recommend review materials in a quiz format that helps reduce stress. This approach optimizes the user experience during learning.

[0601] Examples of prompts for generative AI models

[0602] "We want to provide the most relevant information based on the user's emotional state. Please tell us what kind of educational materials and information would be most effective considering their current emotional state."

[0603] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0604] Step 1:

[0605] The user begins learning using the device. At this time, the device's camera and microphone are activated, recording the user's facial expressions and voice in real time. The input is the user's image and audio data, and the output is the recorded raw data.

[0606] Step 2:

[0607] The device sends recorded image and audio data to an emotion analysis engine. The emotion analysis engine analyzes this data and evaluates the user's emotional state. The input is raw image and audio data, and the engine performs feature extraction and emotion estimation on the data. The output is the user's emotional state (e.g., joy, stress, anxiety).

[0608] Step 3:

[0609] The server receives emotional state and learner activity information transmitted from the terminal. The input consists of the user's emotional state data and activity information. The server's AI model integrates this data and evaluates the user's learning progress. The output is an overall evaluation of the user's learning comprehension and emotional state.

[0610] Step 4:

[0611] Based on the evaluation results, the server selects the optimal learning materials or information using material selection components. The input is the evaluation results, and the output is the selected learning materials or information determined by the material selection algorithm. These selections take into account the user's emotional state.

[0612] Step 5:

[0613] The server sends the selected learning materials or information to the terminal. The input is the selected material data, and the output is the data delivered to the terminal.

[0614] Step 6:

[0615] The terminal provides the user with learning materials and information received from the server. The user continues their learning activities based on this information. The input is the learning materials provided by the server, and the output is the progress of the user's learning activities.

[0616] Step 7:

[0617] When a user completes their learning activity, the device records the user's answers and learning behavior. The input consists of the user's answers and behavior data, while the output is the recorded learning data. This data is sent to the server as feedback and used to select future learning materials.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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".

[0635] This invention aims to realize a system that monitors learners' performance in real time and provides individually optimized learning materials and assignments based on that performance. The system consists of three components: a server, a terminal, and a user, and features AI-based data analysis and an adaptive learning material delivery mechanism.

[0636] The server first collects data on the learner's daily activities. This includes study time, correctness of answers, and operation logs for learning content. The server uses this information to analyze the learner's learning performance and diagnose their level of understanding using an AI model. Based on the results, the server selects the most suitable learning materials and tasks for each individual learner. These selected materials are then sent from the server to the terminal and provided to the learner.

[0637] The terminal displays learning materials and assignments received from the server to the learner. The learner can access the materials and complete assignments via the terminal. The terminal is equipped with an answer input interface and has the function to send the learner's entered answers and activity history to the server in real time.

[0638] Users (learners) work on the provided materials and assignments and input their answers. Through assignments whose difficulty level is automatically adjusted according to their understanding, users can continuously deepen their learning. Furthermore, users (teachers) can log into the system and monitor students' progress and understanding. Based on this information, teachers can adjust additional instruction and learning plans as needed.

[0639] As a concrete example, consider a case where a learner is struggling with a specific area of ​​mathematics. The server analyzes this data and selects additional problem sets or educational videos to reinforce the learner's weak areas. This material is instantly sent to the device, allowing the learner to begin supplementary learning immediately. In this way, providing learners with real-time adaptive learning opportunities enables efficient learning tailored to individual learning needs.

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] The server collects learner activity data and stores it in a database. This data includes learning behavior, response time, and correct / incorrect answers. The server manages this data in a structured format.

[0643] Step 2:

[0644] The server inputs the collected data into an AI model and performs analysis. This analysis evaluates the learner's current level of understanding, areas of difficulty, and learning progress. The AI ​​model measures the learner's growth by comparing it with past data.

[0645] Step 3:

[0646] The server selects the most suitable learning materials and assignments for each learner based on the analysis results. Selection criteria include the learner's level of understanding, past performance, and the relevance of the topic. The selected materials are designated as the next content the learner should work on.

[0647] Step 4:

[0648] The server sends the selected learning materials and assignments to the device. This prepares the device to present the latest learning materials to the learner. The server tracks the usage of the learning materials by recording logs of material distribution.

[0649] Step 5:

[0650] The terminal displays learning materials and assignments received from the server to the learner. The terminal provides an interactive learning experience through its user interface. The learner progresses through their learning using the presented materials.

[0651] Step 6:

[0652] Users (learners) access learning materials using their devices and work on assignments. Users can enter their answers and refer to hints and explanations within the system as needed.

[0653] Step 7:

[0654] The device sends user answers and learning activity logs to the server in real time. The device formats the log data appropriately to ensure it reaches the server quickly.

[0655] Step 8:

[0656] The server immediately processes the received log data and updates the learner's performance. The server then uses this data for data analysis for the next learning cycle.

[0657] (Example 1)

[0658] 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".

[0659] Modern education systems require individualized support tailored to each learner's level of understanding and progress. However, traditional systems have struggled to respond in real time to the diverse levels of understanding of learners and to dynamically provide appropriate learning materials. This limits the maximization of learning efficiency and the improvement of educational quality, hindering continuous improvement in understanding.

[0660] 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.

[0661] In this invention, the server includes an information gathering means for collecting digital information obtained from learners' educational activities and storing it in an information storage unit, an information analysis unit for analyzing the collected information and evaluating the depth of the learners' understanding, and a resource selection means for selecting the most suitable educational resources or tasks for the learners based on the evaluation results. This makes it possible to provide each learner with an individually optimized educational experience in real time, thereby improving learning efficiency and the quality of education.

[0662] "Information gathering means" refers to the function of collecting digital information obtained from learners' educational activities and storing it in the information storage unit.

[0663] The "information analysis unit" is a function that analyzes collected information and evaluates the depth of the learner's understanding.

[0664] A "resource selection method" is a function for selecting the most suitable educational resources or assignments for learners based on evaluation results.

[0665] "Supply means" refers to the function of supplying selected educational resources or assignments to the learner's device.

[0666] "Communication means" refers to a function for transmitting answer results and records of learning activities obtained from the learner's device to the main device as feedback.

[0667] "Adaptive measures" refer to functions for dynamically updating educational resources in real time based on learners' activities.

[0668] "Generation means" refers to a function that performs recommendations using a generative AI model.

[0669] To implement this invention, three main players—a server, a terminal, and a user—work together. The server acts as the central hub for information processing, collecting digital information related to the learner's educational activities and storing it in the information storage unit. The server manages this information using database systems such as MySQL or MongoDB. The stored data is analyzed using AI frameworks such as TensorFlow or PyTorch. The analysis results are then used with a generative AI model to help select the optimal educational resources for the learner. In this process, the AI ​​is provided with prompts such as, "Evaluate the learner's performance and suggest necessary learning materials."

[0670] The terminal's role is to present educational resources and assignments sent from the server to the learner. It uses HTML / CSS as its user interface and is designed to be easily understood by the learner on the terminal. Furthermore, the terminal utilizes a REST API to send the learner's input, including answers and learning activity data, to the server in real time.

[0671] Users (learners) work on the provided learning materials and assignments using their devices. They can check their learning progress by entering their answers, and the system automatically adjusts the difficulty level. Users (teachers) can also use the dashboard provided by this system to check the learners' progress and understanding, and adjust the teaching content as needed. This functionality is achieved by utilizing a JavaScript framework (e.g., React).

[0672] This invention makes it possible to provide learners with personalized educational opportunities in real time, thereby improving the quality and efficiency of education.

[0673] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0674] Step 1:

[0675] The server collects learner activity data. Inputs include learning time, the correctness of answers to assignments, and logs of learning content usage. This data is stored in a database to prepare for subsequent analysis. MySQL and MongoDB are used for database management. The output is the collected data, properly structured and ready for use in subsequent analysis processes.

[0676] Step 2:

[0677] The server analyzes the collected activity data using a generating AI model. It receives learner activity data stored in a database as input and performs analysis using data analysis tools such as "TensorFlow" and "PyTorch." Specifically, the model evaluates the learner's performance and performs data processing to determine their level of understanding. The output provides an evaluation of the learner's understanding, which is then used to select learning materials for the next step.

[0678] Step 3:

[0679] The server selects the most suitable educational resources for the learner based on the evaluation results. The evaluation results obtained in the previous step are used as input. An AI algorithm is used for resource selection, taking a specific prompt, "Please suggest materials to reinforce weaknesses in a specific area," as input to determine which materials to recommend. The output is a list of materials and assignments to be provided to the learner.

[0680] Step 4:

[0681] The server sends the selected educational resources to the terminal. The input in this step is the list of learning materials generated in the previous step. The server uses the HTTP protocol to send the materials to the learner's terminal. The terminal receives them and prepares them for display in a format accessible to the learner. The output is that the educational resources have been successfully delivered to the terminal.

[0682] Step 5:

[0683] The terminal presents learning materials and assignments received from the server to the learner. Learning material data is passed to the terminal as input and displayed using a user interface based on HTML / CSS, etc. Learners can then use this to work on assignments. The output is the learner accessing the problems and submitting their answers.

[0684] Step 6:

[0685] The user (learner) works on the presented learning materials and assignments and enters their answers. The answers and learning actions entered by the user are sent to the server in real time via the device. As output, the user's answer data and activity history are stored again in the database and used as feedback for future learning plans.

[0686] Step 7:

[0687] The user (teacher) logs into the system to check the learners' progress. The system receives student progress and comprehension information as input data from the server. A teacher dashboard is displayed using a JavaScript framework, providing information for teachers to adjust their teaching content and learning plans. The output is the adjustment of the teaching plan based on the students' comprehension levels obtained by the teacher.

[0688] (Application Example 1)

[0689] 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".

[0690] There is a growing need to provide optimal learning materials in real time, regardless of location, tailored to each learner's level of understanding and learning progress. However, conventional systems struggle to provide the most suitable materials in a timely manner, taking into account the learner's specific situation and progress. Furthermore, there is a need for flexible solutions to ensure learners can continue their studies consistently even when they are in different environments, such as while traveling. A means to solve these challenges was therefore necessary.

[0691] 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.

[0692] In this invention, the server includes means for collecting learner activity information and storing it in an information storage unit; data analysis unit means for analyzing the collected information and evaluating the learner's level of understanding; material selection means for selecting the most suitable learning materials or assignments for the learner based on the evaluation results; and mobile communication means for updating learning materials in real time via a device, regardless of the learner's location, based on the obtained information. This makes it possible to always provide the most suitable learning materials according to the learner's progress, enabling flexible learning that is not bound by location.

[0693] The term "learner" refers to a person who engages in educational activities and learns.

[0694] "Activity information" refers to various data generated during the learning process, including learning history, answers, and operation logs.

[0695] The "information storage unit" is a storage device that stores collected activity information and keeps it in an easily accessible state.

[0696] The "Data Analysis Department" refers to the system component that processes collected activity information and analyzes learners' understanding and progress.

[0697] "Methods for selecting learning materials" refer to the methods and processes for selecting the most suitable learning materials and assignments based on the analysis results of learners.

[0698] "Learner terminals" refer to computers and smart devices that learners directly operate and use to conduct learning activities.

[0699] "Communication methods" refer to the technologies and protocols that enable the transmission and reception of information between a terminal and a server, and are carried out via wired or wireless networks.

[0700] "Mobile communication means" refers to communication technology that has the function of sending and receiving data in real time and updating learning content, no matter where the learner is.

[0701] The system for realizing this invention primarily consists of three components: a server, a terminal, and a user. The server is located in a data center and uses AI technologies such as Python and TensorFlow to collect learner activity information and perform data analysis. Specifically, it collects activity information such as the learner's learning history, operation logs, and answer results in real time and stores them in a database system.

[0702] Based on this information, the server uses a generated AI model to evaluate the learner's level of understanding and selects the most suitable learning materials and assignments based on the results. The selected materials and assignments are then sent from the server to the learner's device via Wi-Fi or mobile communication.

[0703] On the device side, learning materials and assignments received are displayed to learners using a mobile SDK developed in Java. The learning materials are updated in real time via the mobile network, ensuring access from anywhere. Learners can access these materials and complete assignments using their devices. Users (learners and teachers) can also log into the system to check progress and learning data.

[0704] As a concrete example, when a user launches the app on their smartphone while waiting for a train at a station, the latest learning data is sent to the device, and the AI ​​model provides the most suitable quizzes and supplementary materials on the spot. In this way, learners can effectively continue their studies even when they are out and about.

[0705] An example of a prompt to a generative AI model is: "This learner has a weak understanding of a specific area of ​​mathematics. Based on their past learning history, recommend materials to reinforce this area."

[0706] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0707] Step 1:

[0708] The server collects activity information from learners. Specifically, it receives learning history, operation logs, and answer results from the learner's terminal and stores them in a database. The data format is standardized to JSON and sent to the server via an API.

[0709] Step 2:

[0710] The server performs data analysis on the collected activity information. Based on the learning history and logs received as input, it performs tensor analysis and evaluates the level of understanding. An AI model using TensorFlow is responsible for this analysis and calculates the learner's proficiency level as output.

[0711] Step 3:

[0712] The server selects the most suitable learning materials and assignments based on the acquired proficiency information. In this process, prompt sentences are generated using the output from the previous data analysis and input into the generative AI model. As a result, a list of learning materials appropriate for the learner is generated.

[0713] Step 4:

[0714] The server sends the selected learning materials to the device. The learning material data is delivered to the learner's device via a RESTful API. During this process, the content of the learning materials is dynamically updated and displayed immediately on the device.

[0715] Step 5:

[0716] Users access the provided learning materials via their devices and complete the assignments. Their answers and additional learning activities are sent to the server in real time. This data is then stored again in the database for use in subsequent analyses.

[0717] Step 6:

[0718] The server displays learner progress and comprehension data through an administration screen accessible to teachers. The administration screen is web-based and accessible after secure authentication. Teachers can use this information to further develop individualized instruction plans.

[0719] 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.

[0720] This invention is a system that recognizes the emotional state of learners and utilizes it in the learning process. The system consists of three main elements: a server, a terminal, and a user, and provides a more personalized learning experience, particularly by including an emotion engine.

[0721] The server collects emotional data from the emotion engine, including facial expressions, voice, and input patterns, along with the learner's activity data. This data is stored in a data storage unit and used to evaluate the learner's level of understanding. The server uses an AI model to simultaneously analyze the learner's understanding and emotions, comprehensively evaluating the learner's state. Based on this evaluation, the server determines the most suitable learning materials and assignments for the learner.

[0722] The selection of learning materials reflects the learner's current emotional state. For example, if a learner is feeling stressed, it's possible to adjust the difficulty level or select materials in a relaxing format. The selected materials and assignments are sent from the server to the user's (learner's) device, and the user deals with them through the device.

[0723] The terminal not only provides learners with learning materials and assignments sent from the server, but also analyzes the user's emotions using an emotion engine. Specifically, it uses a webcam and microphone to analyze the user's facial expressions and voice, and evaluates their emotional state in real time. As a result, if the user's mood changes during learning, new data is immediately collected and sent to the server.

[0724] Users (learners) engage with the presented materials and assignments, and their emotional state is evaluated as part of the learning process. This system provides continuous feedback to help learners maintain a relaxed state to deepen their understanding. Furthermore, users (teachers) can monitor learners' emotional changes and adjust learning materials and instructional content as needed.

[0725] This invention enables adaptive learning that takes learners' emotions into account, resulting in an efficient and effective learning experience. The system distinguishes itself from conventional learning support systems by incorporating emotional data in addition to analyzing learning data.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The device captures the learner's facial expressions and voice in real time into its emotion engine. It uses cameras and microphones to collect this data and recognize the user's current emotional state.

[0729] Step 2:

[0730] The device sends the recognized emotion data to the server. This prepares the server to receive and store both activity data and emotion data simultaneously.

[0731] Step 3:

[0732] The server stores activity data and emotion data in its data storage unit and inputs it into the AI ​​model. The model analyzes the learner's level of understanding and current emotional state to perform an overall performance evaluation.

[0733] Step 4:

[0734] Based on the analysis results, the server selects the most suitable learning materials and assignments for the learner. The selection process also takes into account the learner's emotional state, and content is adjusted to alleviate stress and to an appropriate level of difficulty.

[0735] Step 5:

[0736] The server sends selected learning materials and assignments to the terminal. The materials may include feedback that takes into account the user's emotional state.

[0737] Step 6:

[0738] The terminal displays learning materials and assignments received from the server to the learner. It is designed to be intuitive to use through its interface.

[0739] Step 7:

[0740] Users (learners) access the presented learning materials and work on assignments. An interactive learning experience tailored to their emotional state is provided to maintain learner motivation.

[0741] Step 8:

[0742] The server continuously analyzes the answer results and updated sentiment data received through the terminals, and continuously optimizes the learning process.

[0743] Step 9:

[0744] Users (teachers) can monitor learners' progress and emotional state through reports provided by the server, and adjust their teaching plans as needed to provide effective learning support.

[0745] (Example 2)

[0746] 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".

[0747] Traditional learning support systems only evaluated learners' learning progress and did not take their emotional state into consideration, which could hinder learners' interest and motivation. This could lead to insufficient selection of optimal learning materials and assignments, potentially reducing learning effectiveness. Furthermore, it was difficult for teachers to quickly grasp and respond to changes in learners' emotions. Therefore, there was a need for an effective learning support system that took learners' emotions into account.

[0748] 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.

[0749] In this invention, the server includes means for collecting learner activity information and storing it in an information recording unit, means for analyzing the collected information and evaluating the learner's level of understanding and emotional state, and means for selecting the most appropriate educational materials or assignments for the learner based on the evaluation results. This enables the selection of optimal teaching materials and feedback according to the learner's level of understanding and emotional state.

[0750] A "learner" is an individual who engages in learning, and who acquires knowledge and deepens their understanding through educational materials and assignments.

[0751] "Activity information" refers to data that records learners' actions and responses related to learning, whether online or offline.

[0752] The "Information Recording Unit" refers to a data storage system for centrally accumulating and managing learners' activity and emotional information.

[0753] The "Information Analysis Unit" refers to computer programs and algorithms that process accumulated activity and emotional information to analyze learners' comprehension levels and emotional states.

[0754] "Emotional state" is an indicator that shows the learner's psychological or emotional situation and reactions, and is based on information obtained from facial expressions, voice, and behavioral patterns.

[0755] "Material selection method" refers to a process or system for selecting the most appropriate educational materials and assignments for learners based on analyzed information.

[0756] "Means of delivery" refers to communication functions and technologies for displaying or distributing selected educational materials and assignments to learners' devices.

[0757] "Communication means" refers to network functions that enable data communication between learner terminals and servers, and for sending and receiving feedback and analysis results.

[0758] "Emotional analysis unit means" refers to technology for recognizing and analyzing the learner's emotional state in real time during learning and updating the data as needed.

[0759] This invention provides an adaptive learning support system that takes into account the emotional state of learners, and includes server, terminal, and user elements.

[0760] The server is responsible for collecting learner activity information and emotional data. Specifically, it stores learning progress and usage records sent from clients in a database. For collecting emotional data, it uses a webcam and microphone connected to the terminal, recognizes facial expressions using libraries such as OpenCV, and performs speech analysis. This data is stored in the information storage unit on the server. Next, the server analyzes the collected data using a generative AI model. It uses TensorFlow or PyTorch to simultaneously evaluate the learner's comprehension level and emotional state.

[0761] The server selects the most suitable educational materials and assignments based on evaluation results from an AI model. This process reflects the learner's emotional state and has the flexibility to adjust difficulty levels as needed. The selected materials are delivered to the device via the HTTP protocol.

[0762] The terminal displays learning materials provided by the server to the learner and also performs real-time sentiment analysis. It continuously monitors the user's facial expressions and voice using a webcam and microphone, and feeds back the analyzed sentiment data to the server. This information helps learners continue learning in a relaxed state.

[0763] As users (learners) work on learning materials and assignments presented through their devices, their emotional state is also treated as a learning element. For example, if a user is feeling stressed, the system may provide learning materials that promote relaxation. An example of utilizing a generative AI model is a prompt message such as, "Analyze the learner's emotional state and generate relaxing learning materials appropriate to their level of understanding."

[0764] This system enables adaptive learning that takes into account learners' emotions and comprehension levels, resulting in a more effective educational experience. Unlike conventional learning systems, it utilizes emotional data, allowing for flexible responses tailored to the individual needs of learners.

[0765] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0766] Step 1:

[0767] The server collects learner activity and emotion data from the terminal. Its inputs include facial video and audio data captured by the terminal, as well as log data related to learning progress. Specifically, it uses a webcam and microphone to record the learner's facial expressions and speech in real time and sends this data to the server. The output is raw activity and emotion data stored in a database.

[0768] Step 2:

[0769] The server uses a generative AI model to analyze the collected data. As input, accumulated activity and sentiment data are retrieved from a database. The server processes this data using machine learning frameworks such as TensorFlow and PyTorch to evaluate the learner's comprehension and emotional state. Specifically, it performs data cleansing, feature extraction, and prediction using the AI ​​model. The output is the evaluation result regarding the learner's comprehension and emotional state.

[0770] Step 3:

[0771] The server selects the most appropriate educational materials and assignments based on the analysis results. Evaluation results from an AI model are used as input. The server applies predefined rules and machine learning models to select the most appropriate materials for the learner's state. Specifically, it performs a material filtering and selection process from the material database. The output is the selected educational materials and assignments.

[0772] Step 4:

[0773] The server provides selected learning materials and assignments to the terminal. Selected learning material data is retrieved from the server as input. The server sends the learning materials and assignments to the terminal using the HTTP protocol. Specifically, it packages and transmits the learning material data. The output is the learning material displayed to the user visually or audibly on the terminal.

[0774] Step 5:

[0775] The device presents the provided learning materials to the user and continuously analyzes the user's emotions during learning. Inputs include learning materials sent from the server and the user's current learning status. The device uses its active webcam and microphone to analyze the user's facial expressions and voice, capturing changes in emotions. Specifically, it displays learning materials on the UI and executes a real-time emotion analysis module. Output is the transmission of feedback data to the server.

[0776] Step 6:

[0777] The user engages with learning materials and assignments presented on the device. The input consists of the learning materials and assignments displayed on the device. The user improves their understanding by solving problems and viewing the materials. Specifically, the user interacts with the learning materials, and the resulting data is sent from the device to the server. The output consists of the user's activity results and feedback data.

[0778] (Application Example 2)

[0779] 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".

[0780] In modern learning and electronic payment systems, there is a need to provide efficient and effective experiences by appropriately considering the user's emotional state. However, conventional systems often fail to consider emotional changes, resulting in information and suggestions that are not suited to the user. This can lead to a diminished user experience and potentially hinder learning motivation and consumer behavior. Therefore, a method is needed that recognizes the user's emotional state in real time and provides optimal information and suggestions accordingly.

[0781] 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.

[0782] In this invention, the server includes a component that collects learner activity information and stores it in an information storage unit, an information analysis unit component that analyzes the collected information and evaluates the learner's level of understanding, and an emotion analysis component that recognizes the user's emotional state and provides optimal information or suggestions based on that emotional state. This makes it possible to provide suggestions and information that are adapted to the user's emotional state, thereby improving the user experience.

[0783] "Learner activity information" refers to data about the actions and operations performed by learners, and is used to evaluate the learning process.

[0784] An "information storage unit" is a storage device for temporarily or long-term storage of collected data.

[0785] The "information analysis unit components" are those that analyze collected data and have the function of evaluating the learner's level of understanding and emotional state.

[0786] The "material selection component" is responsible for selecting the most suitable learning materials or assignments for learners based on the analysis results.

[0787] "Delivery components" refer to components that have the function of delivering selected learning materials or assignments to the learner's device.

[0788] A "communication component" is a device that has the function of sending answer results and learning actions from the learner's terminal to the server.

[0789] "Emotional state" refers to the emotional state of a learner or user, and is a psychological state that includes specific emotions or moods.

[0790] "Emotional analysis components" are those that recognize and analyze the emotional state of the user from their facial expressions, voice, etc.

[0791] The term "instructor" refers to someone who has the role of providing guidance and instructions to learners or users.

[0792] "Management components" are those that allow instructors to monitor the progress of learners and users and adjust the content of instruction accordingly.

[0793] System Configuration

[0794] This invention is a learning and information provision system that takes into account the user's emotional state. The system mainly consists of three elements: a server, a terminal, and a user. The server collects learner activity information and stores it in an information storage unit. Furthermore, the server uses an information analysis unit component to analyze the collected data and evaluate the learner's level of understanding and emotional state. Based on the evaluation, a material selection component selects the most suitable learning materials and information for the learner, and a provisioning component delivers them to the terminal.

[0795] Recognition of emotional states

[0796] The user's emotional state is recognized in real time using the device's camera and microphone. An emotion analysis component analyzes the user's facial expressions and voice to estimate their current emotion. The system feeds the results of the emotion analysis back to the server, which is then used to provide new information.

[0797] Hardware and software

[0798] The hardware used includes smartphones and tablets. The software utilizes Microsoft Azure Cognitive Services' Emotion API as the emotion analysis engine and TensorFlow for the AI ​​model.

[0799] Specific example

[0800] As a concrete example, when a learner is studying a new unit, the server analyzes the learner's facial expressions and, if it determines that the learner is experiencing a high cognitive load, prioritizes selecting learning materials in a format that will help them relax. For instance, it could recommend review materials in a quiz format that helps reduce stress. This approach optimizes the user experience during learning.

[0801] Examples of prompts for generative AI models

[0802] "We want to provide the most relevant information based on the user's emotional state. Please tell us what kind of educational materials and information would be most effective considering their current emotional state."

[0803] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0804] Step 1:

[0805] The user begins learning using the device. At this time, the device's camera and microphone are activated, recording the user's facial expressions and voice in real time. The input is the user's image and audio data, and the output is the recorded raw data.

[0806] Step 2:

[0807] The device sends recorded image and audio data to an emotion analysis engine. The emotion analysis engine analyzes this data and evaluates the user's emotional state. The input is raw image and audio data, and the engine performs feature extraction and emotion estimation on the data. The output is the user's emotional state (e.g., joy, stress, anxiety).

[0808] Step 3:

[0809] The server receives emotional state and learner activity information transmitted from the terminal. The input consists of the user's emotional state data and activity information. The server's AI model integrates this data and evaluates the user's learning progress. The output is an overall evaluation of the user's learning comprehension and emotional state.

[0810] Step 4:

[0811] Based on the evaluation results, the server selects the optimal learning materials or information using material selection components. The input is the evaluation results, and the output is the selected learning materials or information determined by the material selection algorithm. These selections take into account the user's emotional state.

[0812] Step 5:

[0813] The server sends the selected learning materials or information to the terminal. The input is the selected material data, and the output is the data delivered to the terminal.

[0814] Step 6:

[0815] The terminal provides the user with learning materials and information received from the server. The user continues their learning activities based on this information. The input is the learning materials provided by the server, and the output is the progress of the user's learning activities.

[0816] Step 7:

[0817] When a user completes their learning activity, the device records the user's answers and learning behavior. The input consists of the user's answers and behavior data, while the output is the recorded learning data. This data is sent to the server as feedback and used to select future learning materials.

[0818] 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.

[0819] 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.

[0820] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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."

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] (Claim 1)

[0841] A means for collecting learner activity data and storing it in a data storage unit,

[0842] A data analysis unit means for analyzing collected data and evaluating the learner's level of understanding,

[0843] A method for selecting learning materials or assignments that are optimal for learners based on evaluation results,

[0844] A means of providing selected learning materials or assignments to learners' devices,

[0845] A communication means for sending the answer results and learning behavior received from the learner's terminal as feedback to the server,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, characterized in that it adjusts the difficulty level of the tasks presented to the learner based on the assessed level of understanding.

[0849] (Claim 3)

[0850] The system according to claim 1, characterized in that it includes a management unit for teachers to check learners' progress and adjust the content of instruction.

[0851] "Example 1"

[0852] (Claim 1)

[0853] Information gathering means for collecting digital information obtained from learners' educational activities and storing it in an information storage unit,

[0854] Information analysis unit means for analyzing collected information and evaluating the depth of learners' understanding,

[0855] A resource selection method for selecting the most suitable educational resources or assignments for learners based on evaluation results,

[0856] A supply means for supplying selected educational resources or assignments to a learner's device,

[0857] A communication means for transmitting answer results and records of learning activities obtained from the learner device to the main device as feedback,

[0858] Adaptive means that dynamically update educational resources based on learner activity in real time,

[0859] A generation method that performs recommendations using a generative AI model,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, characterized in that it dynamically adjusts the difficulty level of the tasks presented to the learner based on the assessed depth of understanding.

[0863] (Claim 3)

[0864] The system according to claim 1, characterized in that it includes a monitoring unit for educators to check the progress of learners and adjust the content of instruction.

[0865] "Application Example 1"

[0866] (Claim 1)

[0867] A means for collecting learner activity information and storing it in an information storage unit,

[0868] A data analysis unit means for analyzing collected information and evaluating the learner's level of understanding,

[0869] A method for selecting learning materials or assignments that are optimal for learners based on evaluation results,

[0870] A means of providing selected learning materials or assignments to learners' devices,

[0871] A communication means for sending the answer results and learning activities received from the learner's terminal as feedback to the server,

[0872] Based on the information obtained, a mobile communication method is provided to update learning materials in real time via a device, regardless of the learner's location.

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, characterized in that it adjusts the difficulty level of the tasks presented to the learner based on the assessed level of understanding.

[0876] (Claim 3)

[0877] The system according to claim 1, characterized in that it includes a management unit for teachers to check learners' progress and adjust instructional information, and that it manages information using an external data center.

[0878] "Example 2 of combining an emotion engine"

[0879] (Claim 1)

[0880] A means for collecting learner activity information and storing it in the information recording unit,

[0881] Information analysis unit means for analyzing collected information and evaluating the learner's level of understanding and emotional state,

[0882] A method for selecting the most suitable educational materials or assignments for learners based on evaluation results,

[0883] A means of providing selected educational materials or assignments to learners' devices,

[0884] A communication means for sending answer results, learning behavior, and emotional information received from the learner's terminal as feedback to the server,

[0885] An emotion analysis unit means that analyzes the learner's emotional state in real time,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, characterized in that it adjusts the difficulty level of tasks presented to learners based on their assessed level of understanding and emotional state.

[0889] (Claim 3)

[0890] The system according to claim 1, characterized in that it includes a management unit for teachers to monitor learners' progress and emotional changes and adjust the content of instruction.

[0891] "Application example 2 of combining emotional engines"

[0892] (Claim 1)

[0893] A component that collects learner activity information and stores it in the information storage unit,

[0894] Information analysis unit components for analyzing collected information and evaluating learners' level of understanding,

[0895] A material selection component that selects the most suitable learning material or assignment for the learner based on the evaluation results,

[0896] A provisioning component that provides selected learning materials or assignments to the learner's device,

[0897] A communication component for sending the answer results and learning behavior received from the learner's terminal as feedback to the server,

[0898] An emotion analysis component that recognizes the user's emotional state and provides optimal information or suggestions based on that emotional state,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, characterized in that it adjusts the content and format of the information presented to the learner or user based on their emotional state.

[0902] (Claim 3)

[0903] The system according to claim 1, characterized in that it includes a management component for the instructor to check the progress of learners or users and adjust the content of instruction and suggestions. [Explanation of symbols]

[0904] 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. A means for collecting learner activity information and storing it in an information storage unit, A data analysis unit means for analyzing collected information and evaluating the learner's level of understanding, A method for selecting learning materials or assignments that are optimal for learners based on evaluation results, A means of providing selected learning materials or assignments to learners' devices, A communication means for sending the answer results and learning activities received from the learner's terminal as feedback to the server, Based on the information obtained, a mobile communication method is provided to update learning materials in real time via a device, regardless of the learner's location. A system that includes this.

2. The system according to claim 1, characterized in that the difficulty level of the tasks presented to the learner is adjusted based on the assessed level of understanding.

3. The system according to claim 1, characterized in that it includes a management unit for teachers to check learners' progress and adjust instructional information, and that it manages information using an external data center.

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