system

Abstract

We provide the system. [Solution] Means for collecting learner behavioral data, A means of using an artificial intelligence model to analyze the aforementioned collected data, A means for identifying learning difficulties based on the aforementioned analysis, A means for automatically generating individualized instruction plans and supplementary materials to address the identified learning difficulties of learners, A means for notifying the generated instruction plan and teaching materials, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that uses an artificial intelligence model for collecting and analyzing learner behavior data. This system can identify learners' learning difficulties and automatically generate and notify them of individualized instruction plans and supplementary materials that address those difficulties. Furthermore, this system can monitor the progress of the notified instruction plans and materials and adjust them based on the results. This makes it possible to provide optimized learning support for each learner and improve learning efficiency and equal learning opportunities.

[0006] A "learner" refers to an individual whose purpose in educational activities is to acquire knowledge and skills.

[0007] "Behavioral data" refers to recorded information about a series of activities that learners engage in within the educational environment.

[0008] An "artificial intelligence model" refers to a collection of computer algorithms used to analyze collected data and extract specific patterns or insights.

[0009] "Learning difficulties" refer to the barriers to understanding and acquiring skills that learners face in a particular field or skill.

[0010] An "individualized instruction plan" refers to a special educational program designed based on the individual needs of each learner.

[0011] "Supplementary materials" refer to additional educational resources used to deepen learners' understanding.

[0012] "Automatic generation" refers to the process by which a computer system creates documents or plans based on pre-configured algorithms without manual intervention.

[0013] "Notification" refers to the act of communicating information generated by a system to relevant parties.

[0014] "Progress monitoring" refers to the process of continuously observing and evaluating the learning activities and achievements of learners.

[0015] "Adjustment" refers to the act of changing and optimizing existing plans and teaching materials based on the obtained feedback and evaluation.

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] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0019] In the following embodiments, a 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, a 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, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[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 relates to a system that provides effective support tailored to the individual needs of learners in diverse educational environments. This system utilizes the functions of a server, a terminal, and a user, and improves learning efficiency by utilizing learner behavior data.

[0038] The server plays a central role, collecting learner behavior data in real time from various educational platforms. This data includes learners' online activity history, test results, homework submissions, and grades. The collected data is organized and stored in a database on the server.

[0039] The server then analyzes the stored data using an artificial intelligence model. This AI model can capture characteristics from learners' behavior and performance, and identify individual learning difficulties. For example, if a learner repeatedly shows low performance in a particular area of ​​mathematics, the server will identify a learning difficulty in that area as a warning.

[0040] Based on the analysis results, the server automatically generates an individualized tutoring plan and related supplementary materials. This plan is designed to address the identified learning difficulties, and the AI ​​model suggests materials that suit the learner's learning style. For example, it can generate a plan that includes video materials and practice problems to help understand specific types of math problems.

[0041] The server then notifies the user of the generated plan and supplementary materials. The terminal provides an interface that allows the user to review the plan and materials, making them easily accessible to learners. Through this interface, teachers and parents can quickly make necessary changes and provide feedback.

[0042] Users can also review the progress monitored by the system and evaluate the effectiveness of the instruction plan and materials on the learners. Based on this progress, the server re-analyzes and adjusts the instruction plan and materials. This enables continuous support tailored to the individual needs of the learners.

[0043] In this way, this system utilizes learner behavior data to enable efficient and effective individualized instruction. It aims to improve learning efficiency and provide equal learning opportunities in educational settings.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects learner behavior data in real time through the educational platform's API. This data includes login times, learning material viewing history, test results, and homework submission status. The collected data is organized and stored in a database.

[0047] Step 2:

[0048] The server performs data cleansing on the collected data. Specifically, it prepares the data for analysis by imputing missing values, correcting outliers, and standardizing data formats.

[0049] Step 3:

[0050] The server feeds the cleansed data into an artificial intelligence model for analysis. The AI ​​model extracts features from the learner's behavior patterns and performance to identify specific learning difficulties. This process identifies areas where improvement is needed in specific subjects or skills.

[0051] Step 4:

[0052] The server automatically generates personalized tutoring plans and supplementary materials to address learning difficulties identified by the AI ​​model. These plans include video materials, quizzes, and additional reading materials, all designed to aid learners' understanding.

[0053] Step 5:

[0054] The server notifies the user of the generated individualized tutoring plan and supplementary materials. The terminal displays the plan on the device of the user (teacher or parent) who received the notification, providing an interface that learners can access.

[0055] Step 6:

[0056] Users review the lesson plans and materials provided through their devices, and provide feedback and adjustments tailored to their learners. If necessary, they can request additional adjustments to the lesson plan.

[0057] Step 7:

[0058] The server continuously monitors learners' progress as part of the program. Based on the monitoring results, the effectiveness of the instruction plan is evaluated, and the plan and materials are readjusted as needed. This process further optimizes learning support.

[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] In today's diverse educational environment, efficiently providing individualized instruction tailored to the needs of each learner is challenging. Traditional methods require teachers to manually manage student progress and address individual challenges, resulting in time-consuming and labor-intensive processes that prevent providing optimal support to all learners. In this context, there is a need to develop methods that support learners in learning effectively and ensuring equal opportunities.

[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 means for collecting learner behavioral data from multiple educational platforms, means for using an artificial intelligence model to analyze the collected data, and means for identifying learning difficulties based on the analysis. This enables the automatic generation of individualized instruction plans and supplementary materials tailored to the needs of each learner, as well as optimal educational support through continuous effectiveness measurement and adjustment.

[0064] "Learner behavior data" refers to data that includes the history of activities performed by learners on the educational platform, their deliverables, and evaluation results.

[0065] An "educational platform" is a system or service that learners use to conduct educational activities online.

[0066] An "artificial intelligence model" is a computer program that learns patterns using large amounts of data and performs specific tasks.

[0067] "Learning difficulties" refer to the challenges or obstacles that learners face in acquiring specific learning content or skills.

[0068] An "individualized instruction plan" is an educational program designed to suit the individual needs and learning styles of each student.

[0069] "Supplementary materials" are additional learning resources that learners use to overcome specific learning difficulties.

[0070] A "user interface" is a system that provides users with screens and pathways to access and operate the system's functions.

[0071] "Evaluation" refers to the criteria and observations used to measure the impact and effectiveness of the provided instructional plan and supplementary materials on learners.

[0072] This invention is a system for providing educational support tailored to the individual needs of learners. The system utilizes behavioral data obtained from diverse educational platforms and employs artificial intelligence technology to enable optimal instruction for each learner.

[0073] The server plays a central role in this system, collecting learner behavior data online in real time. This collection process includes the ability to retrieve data via multiple APIs, enabling the integration of data from various educational applications. The collected data includes important learning information such as learners' online activity history, test results, homework submission status, and grades. The server stores, organizes, and manages this data in a database.

[0074] Next, the server analyzes the data using an artificial intelligence model. The AI ​​model uses Tensorflow®, a Python machine learning library. This model extracts features from the data and identifies the learner's learning difficulties. Based on this identified information, the server generates a personalized tutoring plan and supplementary materials tailored to the learner. For example, the generated tutoring plan may include video materials and practice exercises, specifically designed to focus on areas where the learner struggles.

[0075] The device provides an interface that allows users to easily access generated lesson plans and materials. This interface on the device functions as a web application developed using React. Learners, teachers, and parents can use this interface to view lesson plans, check progress, and provide feedback.

[0076] Users evaluate the impact of the lesson plan on learners and input their feedback into the system. This feedback is sent to the server and used for subsequent data analysis and plan adjustments. This ensures that lesson plans and materials continuously adapt to the learners' current situations.

[0077] For example, if a learner is having difficulty acquiring foreign language grammar, this system analyzes data from the learner's past grammar tests and homework to identify areas of weakness. Next, it suggests supplementary materials such as grammar video lessons and practice exercises to help the learner overcome those identified weaknesses.

[0078] The following prompt statements can be used as example inputs to a generative AI model:

[0079] "The learner's behavioral data is shown below. Please generate an individualized tutoring plan based on this data. Data: Online activity history, test results, assignment submission history, grades. Area of ​​focus: Understanding of geometry problems in mathematics."

[0080] This system allows learners to receive educational support tailored to their own learning style, and enables teachers and parents to effectively support students' learning.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server collects learner behavior data. Inputs include online activity history, test results, and homework submission status, retrieved via APIs from multiple educational platforms. This data is initially filtered to remove duplicates and irrelevant information. The output is organized data, which is stored in a database. The server efficiently manages this data through a relational database management system.

[0084] Step 2:

[0085] The server uses the stored data to perform analysis with an artificial intelligence model. The input is the organized learner data obtained in Step 1. The server uses Python's TensorFlow to pass this data to the AI ​​model and identify the learning difficulties the learners are facing. Specific data processing includes normalization and feature selection. The output is information about the identified learning difficulties and the learners' challenges based on them.

[0086] Step 3:

[0087] The server automatically generates individualized instruction plans and supplementary materials based on identified learning difficulties. The input is the learning difficulty information obtained in Step 2. The server selects appropriate materials from its educational content database and constructs the plan. Specific data calculations include matching with user profiles and prioritizing materials. The output is an instruction plan and materials optimized for each learner.

[0088] Step 4:

[0089] The server notifies the user of the generated lesson plan and materials. The input is the lesson plan and materials created in step 3. The server sends this to the terminal through the user interface. Specific actions include sending notification emails and in-app notifications. The output is the lesson plan information sent to the learner's terminal.

[0090] Step 5:

[0091] The terminal provides an interface that allows the user to view the lesson plans and materials they have received. The input is the lesson plan and material information sent from the server in step 4. The terminal functions as a web application and uses React to provide the user with an intuitive interface. The output is the display state of the plans that the user can access.

[0092] Step 6:

[0093] The user evaluates the effectiveness of the provided lesson plan and materials and monitors progress. Inputs are the lesson plan displayed in step 5 and the learning activities performed by the user. The user inputs feedback into the system and provides suggestions for improvement to continue effective learning support. Outputs are evaluation results and feedback information.

[0094] Step 7:

[0095] The server adjusts the lesson plan and materials based on user feedback and progress data. The input is the feedback information obtained in step 6. The server analyzes the data again using the AI ​​model and updates the lesson content as needed. Specific actions include adding materials and changing teaching methods. The output is the newly adjusted lesson plan.

[0096] (Application Example 1)

[0097] 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."

[0098] In today's educational environment, there is a need to effectively and efficiently identify and resolve the unique learning difficulties faced by individual learners, but there are challenges in establishing an appropriate system to achieve this. Traditional methods have made it difficult to provide adaptive instruction tailored to the individual needs of learners, and furthermore, they have not been able to monitor and adjust their progress in real time. In addition, there is a lack of means to provide learners with personalized educational experiences by utilizing virtual spaces.

[0099] 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.

[0100] In this invention, the server includes a device for collecting learner behavioral data, a device for using intelligent functions to analyze the collected data, and a device for identifying learning difficulties based on the analysis. This makes it possible to generate individualized instruction plans for each learner and provide a personalized educational experience in a virtual space.

[0101] "Learner behavioral data" refers to a collection of learning-related information, such as online activity history, test results, and homework submission status.

[0102] "Intelligent function" refers to the ability to analyze data using artificial intelligence technology and identify potential problems.

[0103] "Learning difficulties" refer to situations or challenges in which learners find it difficult to achieve results in a particular field or task.

[0104] An "individualized instruction plan" refers to an educational plan that addresses the specific difficulties of a particular learner and is tailored to their learning style.

[0105] "Supplementary materials" refer to textbooks and workbooks provided to help learners understand areas in which they have difficulty.

[0106] A "notification device" refers to a means of informing learners or instructors about generated lesson plans and materials.

[0107] A "virtual space" refers to a three-dimensional virtual environment created by a computer, where users can have interactive experiences.

[0108] "Educational materials" refer to teaching materials and educational content used by learners to acquire specific knowledge or skills.

[0109] A "user" refers to an individual who uses this system to access learning plans and educational materials and engage in learning.

[0110] The system according to the present invention collects and analyzes learner behavioral data and creates individualized instruction plans to overcome specific learning difficulties. The server is used to collect learners' learning history and achievements from various educational platforms and organize and store them in a database. The server also analyzes the data using a generative AI model to automatically identify learner-specific difficulties.

[0111] Based on these analysis results, the server creates individualized instruction plans and supplementary materials and notifies the user. The terminal provides an interface that allows the user to review the generated plan and proceed with learning based on it. This interface enables users (teachers and parents) to provide quick feedback and adjust the plan as needed.

[0112] Users can engage in educational experiences within a virtual reality environment via a virtual space. Because the content is delivered in a virtual space, learners can engage in real-time, interactive learning. This system is primarily implemented using a server-side application based on Django and a client-side VR application utilizing Unity.

[0113] For example, if AI analysis identifies a learner's lack of understanding in the field of history, they will be provided with an experience that deepens their understanding through the recreation of historical events in a virtual space. Such an experience can be generated using the prompt, "Generate a VR learning experience about ancient civilizations, including a VR tour themed on Egyptian ruins."

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server collects learner behavior data from various educational platforms. Specifically, it automatically retrieves online activity history, test results, homework submission status, and other data, and stores it in a database. The input is log data from each platform, and the output is information stored in a formatted manner in the database.

[0117] Step 2:

[0118] The server launches a generative AI model to analyze the collected data. This model takes learner data as input and identifies learning difficulties based on behavior and performance. In this step, data preprocessing is performed, including imputation of missing values ​​and normalization, and the output is an analysis that highlights specific problem areas.

[0119] Step 3:

[0120] The server automatically generates corresponding individualized instruction plans and supplementary materials based on the analysis results. In this step, the generating AI model uses prompts to generate learning materials and instruction plans, which are then organized into a user-optimized set of teaching materials. The input is the identified results from the analysis, and the output is a customized instruction plan and teaching material package.

[0121] Step 4:

[0122] The server notifies the terminal of the generated lesson plan and supplementary materials. This notification is often sent via email or push notification. The input is the generated lesson plan and materials, and the output is a concise notification format accessible to the user.

[0123] Step 5:

[0124] Users review their individualized instruction plan and supplementary materials on their device and begin their educational experience according to their learning style. Specifically, they wear a VR headset and immerse themselves in the content in real time. The input is the learning materials provided by the server, and the output is the user's educational experience.

[0125] Step 6:

[0126] The server monitors the user's learning progress. This is achieved by collecting and analyzing user interaction and behavioral data within the VR environment. The input is real-time user data, and the output is a progress report.

[0127] Step 7:

[0128] The server adjusts the lesson plan and supplementary materials as needed based on monitoring results. This adjustment is based on learner needs and responses, generating a new, optimized lesson plan. The input is a progress report, and the output is the adjusted lesson plan and materials.

[0129] 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.

[0130] This invention is an advanced system for supporting learners' learning processes, optimizing individualized instruction by collecting and analyzing learner behavioral and emotional data. This system functions through three main components: a server, a terminal, and a user.

[0131] The server collects learner behavior data from the learning platform. This data includes access frequency, learning material viewing history, test results, and homework submission timing. In addition, an emotion engine is used to obtain emotional data from learners' videos and audio. This emotional data includes emotional responses extracted from facial expressions and speech during learning.

[0132] Next, the server analyzes the collected behavioral and emotional data through an artificial intelligence model. The AI ​​model explores the correlation between the learner's learning behavior and emotional state to accurately identify the situations in which learning difficulties arise. For example, detecting a high level of stress in response to difficult math problems might lead to the determination that specialized instruction is needed for that topic.

[0133] Based on this analysis, the server automatically generates individualized instruction plans and supplementary materials. These plans are created considering not only the learner's cognitive needs but also their emotional needs, including relaxation techniques to alleviate stress and incentives to boost motivation. For example, a learner struggling with a specific area of ​​mathematics might be provided with practice problems of progressively increasing difficulty, along with relaxation videos.

[0134] The generated lesson plans and materials are notified to the user from the server. The terminal displays this information on the user's device, supporting learners in following the plan. Teachers and parents can monitor learners' progress in real time through the terminal and provide feedback as needed.

[0135] This system is expected to improve learning efficiency and satisfaction by providing an instructional approach that takes learners' emotions into account and making the learning experience more personalized.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The server collects learner behavior data in real time from the learning platform and associated devices. This includes information such as learning material viewing history, login status, and test results.

[0139] Step 2:

[0140] The server acquires emotional data from the learner's device via the camera and microphone using an emotion engine. This allows the server to collect emotional states such as stress and excitement from the learner's facial expressions and tone of voice during the learning process.

[0141] Step 3:

[0142] The server integrates the collected behavioral and emotional data and analyzes it using an artificial intelligence model. This analysis identifies the situations in which learners are experiencing decreased learning efficiency or emotional instability.

[0143] Step 4:

[0144] The server automatically generates individualized instruction plans and supplementary materials based on the analysis results. This generation process also includes break times and relaxation content tailored to the learner's needs to reduce stress.

[0145] Step 5:

[0146] The server notifies the user of the lesson plan and teaching materials generated via the terminal. The terminal provides an interface on the user's device that allows them to view the detailed plan.

[0147] Step 6:

[0148] Users review the provided plan and send feedback to the server via their device as needed. This allows for continuous adaptation and improvement.

[0149] Step 7:

[0150] The server monitors user feedback and learner progress, and fine-tunes the instruction plan as needed. Learners' emotional states are also continuously monitored to optimize the learning experience.

[0151] (Example 2)

[0152] 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".

[0153] In today's educational environment, providing individualized instruction that addresses the diverse needs of each learner is challenging. In particular, there is a lack of effective technical tools to consider not only the learner's knowledge acquisition but also their emotional state during the learning process. As a result, learning efficiency may decline, and motivation may be lost. To address these challenges, automated generation and monitoring of personalized instruction plans are necessary.

[0154] 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.

[0155] In this invention, the server includes means for collecting learner behavioral and emotional information, means for using an artificial intelligence model for analyzing the collected information, means for automatically generating individualized instruction plans and supplementary materials that address the learner's identified learning difficulties, means for notifying the user of the generated instruction plans and materials, and means for displaying the notified instruction plans and materials on the learner's device. This makes it possible to quickly and efficiently provide an optimal instruction plan that is tailored to the learner's individual needs and emotional state.

[0156] A "learner" refers to a person who has a specific educational objective and is acquiring knowledge or skills.

[0157] "Behavioral information" refers to data related to learners' behavioral history, such as their access frequency, viewing history of learning materials, test results, and homework submission timing.

[0158] "Emotional information" refers to data indicating the emotional state obtained from the learner's facial expressions, voice, etc.

[0159] An "artificial intelligence model" refers to an algorithm or computational model used to analyze data and recognize patterns in the learner's behavior and emotional state.

[0160] An "individualized instruction plan" refers to instructional content and progress plans optimized according to each learner's characteristics and learning situation.

[0161] "Supplementary materials" refer to materials provided to complement or reinforce specific learning items.

[0162] "Notification" refers to the act of informing learners and relevant parties of information generated by the system.

[0163] "Device" refers to information processing equipment or devices used by learners, and typically includes personal computers, tablets, smartphones, etc.

[0164] This system is designed to support learners' efficient learning. It consists of three main elements: a server, a terminal, and a user. It generates and provides individualized instruction plans by collecting and analyzing learner behavioral and emotional information.

[0165] The server accesses the learning platform to collect learner behavioral information. The software used includes database access libraries and API interfaces. Furthermore, an emotion analysis engine is run to obtain emotional information. This is used to analyze facial expressions and voice tone from the learner's video and audio data to determine their emotional state. Specifically, Pandas and TensorFlow are used to build the AI ​​model, and OpenCV and related NVIDIA libraries are used for the emotion engine.

[0166] The server analyzes this data using an artificial intelligence model to understand the learner's behavioral and emotional patterns. This identifies individual learning difficulties, and personalized instruction plans and supplementary materials are automatically generated accordingly.

[0167] The terminal receives notifications sent from the server and displays lesson plans and teaching material information on the user's device. This device is typically a personal computer, tablet, or smartphone. A dedicated application or web interface is used for display.

[0168] Users progress through their learning according to the displayed instruction plan. Feedback and comments received during learning are also recorded via the device and used to further improve the system.

[0169] For example, if a learner is having difficulty in a particular area of ​​mathematics, the system provides practice problems with gradually increasing difficulty levels, along with videos introducing relaxation techniques. In this way, the system takes the learner's emotional state into consideration, making it possible to learn while reducing stress.

[0170] An example of an input prompt for a generative AI model is: "Analyze the learner's behavioral and emotional data to generate an optimal individualized tutoring plan. Specifically, consider learning material viewing history, test results, and emotional state."

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The server retrieves learner behavior information from the learning platform. Using learner account information as input, it collects data such as access frequency, learning material viewing history, and test results via an API. The server organizes this data chronologically and stores it in a database. The output is learner behavior information organized in a format necessary for analysis.

[0174] Step 2:

[0175] The server uses an emotion analysis engine to acquire learner emotional information. It uses the learner's video and audio data as input. This data is processed in real time, performing facial expression analysis and voice tone analysis. The output includes numerical data and categorical information indicating the learner's emotional state during learning.

[0176] Step 3:

[0177] The server inputs collected behavioral and emotional information into an artificial intelligence model for data analysis. The server preprocesses this data using Pandas and TensorFlow, converting it into a format suitable for the AI ​​model. The AI ​​model analyzes the data, examining the learner's behavioral and emotional patterns. The output identifies specific learning difficulties and stressors.

[0178] Step 4:

[0179] The server automatically generates individualized instruction plans and supplementary materials based on the analysis results of the AI ​​model. The AI ​​model's analysis results are used as input. The generation program combines information tailored to the learner's needs to create learning tasks and relaxation activities. The output includes a set of specific learning tasks and links to media content.

[0180] Step 5:

[0181] The server notifies the user of the generated lesson plan and teaching materials and sends them to the device. Notifications are sent via email or in-app messages. The input is the generated plan and lesson content, and the output is the lesson plan information sent to the user.

[0182] Step 6:

[0183] The terminal displays the lesson plan and teaching materials received from the server on the user's device. The input consists of the plan and materials received as notifications. The terminal provides this information using a dedicated app or web interface. The output is a learning instruction display screen available to the user.

[0184] (Application Example 2)

[0185] 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".

[0186] In modern brick-and-mortar stores, there is a demand for efficient and personalized product recommendations to customers. However, traditional methods struggle to provide appropriate recommendations based on customer interests and emotions. Furthermore, systems that analyze customer needs in real time and propose optimal services are limited.

[0187] 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.

[0188] In this invention, the server includes means for collecting learner behavior data, means for using an information processing model for analyzing the collected data, means for identifying learning difficulties based on the analysis, means for acquiring user behavior information and emotional state in physical stores, and means for analyzing user interests based on the acquired information and providing individually suitable products and services. This enables efficient and personalized product suggestions and service provision to customers in physical stores.

[0189] "Learner behavior data" refers to information related to learning activities, such as the frequency of user access, history of viewing learning materials, and test results.

[0190] An "information processing model" is an artificial intelligence algorithm that analyzes collected data and makes decisions and suggestions based on that analysis.

[0191] "Learning difficulties" refer to obstacles or problems that learners face in understanding and progressing through specific learning content.

[0192] An "individualized instruction plan" is a set of instructional procedures that are tailored to the specific needs and difficulties of each learner, and are designed to reflect their progress.

[0193] "Supplementary materials" are teaching materials and resources provided in addition to help learners understand and progress.

[0194] "User behavior information" refers to data related to purchasing activities, including the amount of time customers spend in physical stores and their product browsing history.

[0195] "Emotional state" refers to the emotional response that can be inferred from the user's facial expressions and speech.

[0196] "Customer interest" refers to a state in which a customer shows a strong interest in a particular product or service.

[0197] "Means of providing goods and services" refers to methods and devices for conveying information about goods and services that have been analyzed as suitable for the customer.

[0198] This invention is a system for improving the customer experience in physical stores. The server receives user behavior information and emotional state acquired from smart glasses and other mobile devices, and performs real-time analysis based on this information. User behavior information includes browsing history and time spent in the store, while emotional state includes emotional responses such as facial expressions and voice, which are judged using the camera function of the smart glasses.

[0199] The server analyzes this data using an information processing model to identify the user's interests. This process utilizes an AI model powered by Python and TensorFlow. Based on the interests identified through the analysis, the server generates information to provide products and services tailored to the user and displays it on the smart glasses' display or other devices.

[0200] For example, if a customer enters a bookstore, puts on smart glasses, and stops in front of a bookshelf of a specific genre, the system might analyze their behavior and facial expressions to determine that they are interested in mystery novels. In this case, the system would display a list of recommended mystery novels and sample reading coupons in real time on the smart glasses' display.

[0201] An example of a prompt message is, "Analyze the customer's eye-tracking and facial expression data to identify the products they are most interested in and generate personalized recommendations." This prompt allows the server to efficiently analyze customer interests and provide appropriate information.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user wears smart glasses and moves around within a physical store. The smart glasses acquire the user's gaze and facial expression data in real time through cameras and sensors. The input data at this time includes the direction of gaze and facial expression.

[0205] Step 2:

[0206] The terminal sends the acquired gaze data and facial expression data to the server. The server receives this data and stores it in a database. The input data consists of gaze and facial expression information, and based on this, an initial dataset of behavioral and emotional information is obtained.

[0207] Step 3:

[0208] The server uses Python and TensorFlow to analyze the input data. The processing involves estimating emotions from gaze focus and facial expressions to identify the user's current interests and emotional state. The output includes the product categories of interest and the emotion evaluation results.

[0209] Step 4:

[0210] The server uses an AI model to generate recommendations for the most suitable products and services for the user based on the analysis results. The input is the analysis results, and the output is a list of recommendations based on the user's interests.

[0211] Step 5:

[0212] The server sends the generated recommendations to the device, where they are displayed on the user's smart glasses. The user receives relevant product and service information in real time through the smart glasses' display. The output is the information displayed on the smart glasses.

[0213] This entire process allows users to efficiently find products and services they are interested in within physical stores.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention relates to a system that provides effective support tailored to the individual needs of learners in diverse educational environments. This system utilizes the functions of a server, a terminal, and a user, and improves learning efficiency by utilizing learner behavior data.

[0231] The server plays a central role, collecting learner behavior data in real time from various educational platforms. This data includes learners' online activity history, test results, homework submissions, and grades. The collected data is organized and stored in a database on the server.

[0232] The server then analyzes the stored data using an artificial intelligence model. This AI model can capture characteristics from learners' behavior and performance, and identify individual learning difficulties. For example, if a learner repeatedly shows low performance in a particular area of ​​mathematics, the server will identify a learning difficulty in that area as a warning.

[0233] Based on the analysis results, the server automatically generates an individualized tutoring plan and related supplementary materials. This plan is designed to address the identified learning difficulties, and the AI ​​model suggests materials that suit the learner's learning style. For example, it can generate a plan that includes video materials and practice problems to help understand specific types of math problems.

[0234] The server then notifies the user of the generated plan and supplementary materials. The terminal provides an interface that allows the user to review the plan and materials, making them easily accessible to learners. Through this interface, teachers and parents can quickly make necessary changes and provide feedback.

[0235] Users can also review the progress monitored by the system and evaluate the effectiveness of the instruction plan and materials on the learners. Based on this progress, the server re-analyzes and adjusts the instruction plan and materials. This enables continuous support tailored to the individual needs of the learners.

[0236] In this way, this system utilizes learner behavior data to enable efficient and effective individualized instruction. It aims to improve learning efficiency and provide equal learning opportunities in educational settings.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The server collects learner behavior data in real time through the educational platform's API. This data includes login times, learning material viewing history, test results, and homework submission status. The collected data is organized and stored in a database.

[0240] Step 2:

[0241] The server performs data cleansing on the collected data. Specifically, it prepares the data for analysis by imputing missing values, correcting outliers, and standardizing data formats.

[0242] Step 3:

[0243] The server feeds the cleansed data into an artificial intelligence model for analysis. The AI ​​model extracts features from the learner's behavior patterns and performance to identify specific learning difficulties. This process identifies areas where improvement is needed in specific subjects or skills.

[0244] Step 4:

[0245] The server automatically generates personalized tutoring plans and supplementary materials to address learning difficulties identified by the AI ​​model. These plans include video materials, quizzes, and additional reading materials, all designed to aid learners' understanding.

[0246] Step 5:

[0247] The server notifies the user of the generated individualized tutoring plan and supplementary materials. The terminal displays the plan on the device of the user (teacher or parent) who received the notification, providing an interface that learners can access.

[0248] Step 6:

[0249] Users review the lesson plans and materials provided through their devices, and provide feedback and adjustments tailored to their learners. If necessary, they can request additional adjustments to the lesson plan.

[0250] Step 7:

[0251] The server continuously monitors learners' progress as part of the program. Based on the monitoring results, the effectiveness of the instruction plan is evaluated, and the plan and materials are readjusted as needed. This process further optimizes learning support.

[0252] (Example 1)

[0253] 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."

[0254] In today's diverse educational environment, efficiently providing individualized instruction tailored to the needs of each learner is challenging. Traditional methods require teachers to manually manage student progress and address individual challenges, resulting in time-consuming and labor-intensive processes that prevent providing optimal support to all learners. In this context, there is a need to develop methods that support learners in learning effectively and ensuring equal opportunities.

[0255] 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.

[0256] In this invention, the server includes means for collecting learner behavioral data from multiple educational platforms, means for using an artificial intelligence model to analyze the collected data, and means for identifying learning difficulties based on the analysis. This enables the automatic generation of individualized instruction plans and supplementary materials tailored to the needs of each learner, as well as optimal educational support through continuous effectiveness measurement and adjustment.

[0257] "Learner behavior data" refers to data that includes the history of activities performed by learners on the educational platform, their deliverables, and evaluation results.

[0258] An "educational platform" is a system or service that learners use to conduct educational activities online.

[0259] An "artificial intelligence model" is a computer program that learns patterns using large amounts of data and performs specific tasks.

[0260] "Learning difficulties" refer to the challenges or obstacles that learners face in acquiring specific learning content or skills.

[0261] An "individualized instruction plan" is an educational program designed to suit the individual needs and learning styles of each student.

[0262] "Supplementary materials" are additional learning resources that learners use to overcome specific learning difficulties.

[0263] A "user interface" is a system that provides users with screens and pathways to access and operate the system's functions.

[0264] "Evaluation" refers to the criteria and observations used to measure the impact and effectiveness of the provided instructional plan and supplementary materials on learners.

[0265] This invention is a system for providing educational support tailored to the individual needs of learners. The system utilizes behavioral data obtained from diverse educational platforms and employs artificial intelligence technology to enable optimal instruction for each learner.

[0266] The server plays a central role in this system, collecting learner behavior data online in real time. This collection process includes the ability to retrieve data via multiple APIs, enabling the integration of data from various educational applications. The collected data includes important learning information such as learners' online activity history, test results, homework submission status, and grades. The server stores, organizes, and manages this data in a database.

[0267] Next, the server analyzes the data using an artificial intelligence model. The AI ​​model uses TensorFlow, a Python machine learning library. This model extracts features from the data and identifies the learner's learning difficulties. Based on this identified information, the server generates a personalized tutoring plan and supplementary materials tailored to the learner. For example, the generated tutoring plan might include video materials and practice exercises, specifically designed to focus on areas where the learner struggles.

[0268] The device provides an interface that allows users to easily access generated lesson plans and materials. This interface on the device functions as a web application developed using React. Learners, teachers, and parents can use this interface to view lesson plans, check progress, and provide feedback.

[0269] Users evaluate the impact of the lesson plan on learners and input their feedback into the system. This feedback is sent to the server and used for subsequent data analysis and plan adjustments. This ensures that lesson plans and materials continuously adapt to the learners' current situations.

[0270] For example, if a learner is having difficulty acquiring foreign language grammar, this system analyzes data from the learner's past grammar tests and homework to identify areas of weakness. Next, it suggests supplementary materials such as grammar video lessons and practice exercises to help the learner overcome those identified weaknesses.

[0271] The following prompt statements can be used as example inputs to a generative AI model:

[0272] "The learner's behavioral data is shown below. Please generate an individualized tutoring plan based on this data. Data: Online activity history, test results, assignment submission history, grades. Area of ​​focus: Understanding of geometry problems in mathematics."

[0273] This system allows learners to receive educational support tailored to their own learning style, and enables teachers and parents to effectively support students' learning.

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

[0275] Step 1:

[0276] The server collects learner behavior data. Inputs include online activity history, test results, and homework submission status, retrieved via APIs from multiple educational platforms. This data is initially filtered to remove duplicates and irrelevant information. The output is organized data, which is stored in a database. The server efficiently manages this data through a relational database management system.

[0277] Step 2:

[0278] The server uses the stored data to perform analysis with an artificial intelligence model. The input is the organized learner data obtained in Step 1. The server uses Python's TensorFlow to pass this data to the AI ​​model and identify the learning difficulties the learners are facing. Specific data processing includes normalization and feature selection. The output is information about the identified learning difficulties and the learners' challenges based on them.

[0279] Step 3:

[0280] The server automatically generates an individualized guidance plan and supplementary teaching materials based on the identified learning difficulties. The input is the information regarding the learning difficulties obtained in Step 2. The server selects appropriate teaching materials from the database of educational content and constructs the plan. Specific data operations include matching with the user profile and prioritizing the teaching materials. The output is the guidance plan and teaching materials optimized for each learner.

[0281] Step 4:

[0282] The server notifies the user of the generated guidance plan and teaching materials. The input is the guidance plan and teaching materials created in Step 3. The server sends this through the user interface to the terminal. Specific operations include sending notification emails and in-application notifications. The output is the information of the educational plan sent to the learner's terminal.

[0283] Step 5:

[0284] The terminal provides an interface for the user to view the received guidance plan and teaching materials. The input is the information of the guidance plan and teaching materials sent from the server in Step 4. The terminal functions as a web application and uses React to provide a user-friendly operation screen. The output is the display state of the plan accessible to the user.

[0285] Step 6:

[0286] The user evaluates the effectiveness of the provided guidance plan and teaching materials and monitors the progress. The input is the guidance plan displayed in Step 5 and the learning activities performed by the user. The user inputs feedback into the system and provides improvement suggestions for continuous effective learning support. The output is the evaluation result and feedback information.

[0287] Step 7:

[0288] The server adjusts the lesson plan and materials based on user feedback and progress data. The input is the feedback information obtained in step 6. The server analyzes the data again using the AI ​​model and updates the lesson content as needed. Specific actions include adding materials and changing teaching methods. The output is the newly adjusted lesson plan.

[0289] (Application Example 1)

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

[0291] In today's educational environment, there is a need to effectively and efficiently identify and resolve the unique learning difficulties faced by individual learners, but there are challenges in establishing an appropriate system to achieve this. Traditional methods have made it difficult to provide adaptive instruction tailored to the individual needs of learners, and furthermore, they have not been able to monitor and adjust their progress in real time. In addition, there is a lack of means to provide learners with personalized educational experiences by utilizing virtual spaces.

[0292] 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.

[0293] In this invention, the server includes a device for collecting learner behavioral data, a device for using intelligent functions to analyze the collected data, and a device for identifying learning difficulties based on the analysis. This makes it possible to generate individualized instruction plans for each learner and provide a personalized educational experience in a virtual space.

[0294] "Learner behavioral data" refers to a collection of learning-related information, such as online activity history, test results, and homework submission status.

[0295] "Intelligent function" refers to the ability to analyze data using artificial intelligence technology and identify potential problems.

[0296] "Learning difficulties" refer to situations or challenges in which learners find it difficult to achieve results in a particular field or task.

[0297] An "individualized instruction plan" refers to an educational plan that addresses the specific difficulties of a particular learner and is tailored to their learning style.

[0298] "Supplementary materials" refer to textbooks and workbooks provided to help learners understand areas in which they have difficulty.

[0299] A "notification device" refers to a means of informing learners or instructors about generated lesson plans and materials.

[0300] A "virtual space" refers to a three-dimensional virtual environment created by a computer, where users can have interactive experiences.

[0301] "Educational materials" refer to teaching materials and educational content used by learners to acquire specific knowledge or skills.

[0302] A "user" refers to an individual who uses this system to access learning plans and educational materials and engage in learning.

[0303] The system according to the present invention collects and analyzes learner behavioral data and creates individualized instruction plans to overcome specific learning difficulties. The server is used to collect learners' learning history and achievements from various educational platforms and organize and store them in a database. The server also analyzes the data using a generative AI model to automatically identify learner-specific difficulties.

[0304] Based on this analysis result, the server creates an individualized guidance plan and supplementary learning materials and notifies the user. The terminal provides an interface that allows the user to view the plan generated by the user and proceed with learning based on that plan. Through this interface, the user (teacher or guardian) can provide feedback promptly and adjust the plan as needed.

[0305] Users can have an educational experience within a virtual reality environment through a virtual space. Since the content is implemented in the virtual space, real-time interactive learning is possible for learners. The implementation of this system is mainly constructed by a server-side application based on Django and a client-side VR application using Unity.

[0306] As a specific example, if it is identified by AI analysis that a certain learner lacks understanding in the field of history, an experience to deepen understanding is provided through the reproduction of historical events within the virtual space. Such an experience is generated using the prompt text "Generate a VR learning experience related to ancient civilizations, including a VR tour themed on the ruins of Egypt."

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

[0308] Step 1:

[0309] The server collects the behavior data of the learners from each educational platform. Specifically, it automatically obtains online activity histories, test results, homework submission status, etc., and stores them in a database. The input is the log data from each platform, and the output is the information stored in an organized form in the database.

[0310] Step 2:

[0311] The server launches a generative AI model to analyze the collected data. This model takes learner data as input and identifies learning difficulties based on behavior and performance. In this step, data preprocessing is performed, including imputation of missing values ​​and normalization, and the output is an analysis that highlights specific problem areas.

[0312] Step 3:

[0313] The server automatically generates corresponding individualized instruction plans and supplementary materials based on the analysis results. In this step, the generating AI model uses prompts to generate learning materials and instruction plans, which are then organized into a user-optimized set of teaching materials. The input is the identified results from the analysis, and the output is a customized instruction plan and teaching material package.

[0314] Step 4:

[0315] The server notifies the terminal of the generated lesson plan and supplementary materials. This notification is often sent via email or push notification. The input is the generated lesson plan and materials, and the output is a concise notification format accessible to the user.

[0316] Step 5:

[0317] Users review their individualized instruction plan and supplementary materials on their device and begin their educational experience according to their learning style. Specifically, they wear a VR headset and immerse themselves in the content in real time. The input is the learning materials provided by the server, and the output is the user's educational experience.

[0318] Step 6:

[0319] The server monitors the user's learning progress. This is achieved by collecting and analyzing user interaction and behavioral data within the VR environment. The input is real-time user data, and the output is a progress report.

[0320] Step 7:

[0321] The server adjusts the lesson plan and supplementary materials as needed based on monitoring results. This adjustment is based on learner needs and responses, generating a new, optimized lesson plan. The input is a progress report, and the output is the adjusted lesson plan and materials.

[0322] 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.

[0323] This invention is an advanced system for supporting learners' learning processes, optimizing individualized instruction by collecting and analyzing learner behavioral and emotional data. This system functions through three main components: a server, a terminal, and a user.

[0324] The server collects learner behavior data from the learning platform. This data includes access frequency, learning material viewing history, test results, and homework submission timing. In addition, an emotion engine is used to obtain emotional data from learners' videos and audio. This emotional data includes emotional responses extracted from facial expressions and speech during learning.

[0325] Next, the server analyzes the collected behavioral and emotional data through an artificial intelligence model. The AI ​​model explores the correlation between the learner's learning behavior and emotional state to accurately identify the situations in which learning difficulties arise. For example, detecting a high level of stress in response to difficult math problems might lead to the determination that specialized instruction is needed for that topic.

[0326] Based on this analysis, the server automatically generates individualized instruction plans and supplementary materials. These plans are created considering not only the learner's cognitive needs but also their emotional needs, including relaxation techniques to alleviate stress and incentives to boost motivation. For example, a learner struggling with a specific area of ​​mathematics might be provided with practice problems of progressively increasing difficulty, along with relaxation videos.

[0327] The generated lesson plans and materials are notified to the user from the server. The terminal displays this information on the user's device, supporting learners in following the plan. Teachers and parents can monitor learners' progress in real time through the terminal and provide feedback as needed.

[0328] This system is expected to improve learning efficiency and satisfaction by providing an instructional approach that takes learners' emotions into account and making the learning experience more personalized.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The server collects learner behavior data in real time from the learning platform and associated devices. This includes information such as learning material viewing history, login status, and test results.

[0332] Step 2:

[0333] The server acquires emotional data from the learner's device via the camera and microphone using an emotion engine. This allows the server to collect emotional states such as stress and excitement from the learner's facial expressions and tone of voice during the learning process.

[0334] Step 3:

[0335] The server integrates the collected behavioral and emotional data and analyzes it using an artificial intelligence model. This analysis identifies the situations in which learners are experiencing decreased learning efficiency or emotional instability.

[0336] Step 4:

[0337] The server automatically generates individualized instruction plans and supplementary materials based on the analysis results. This generation process also includes break times and relaxation content tailored to the learner's needs to reduce stress.

[0338] Step 5:

[0339] The server notifies the user of the lesson plan and teaching materials generated via the terminal. The terminal provides an interface on the user's device that allows them to view the detailed plan.

[0340] Step 6:

[0341] Users review the provided plan and send feedback to the server via their device as needed. This allows for continuous adaptation and improvement.

[0342] Step 7:

[0343] The server monitors user feedback and learner progress, and fine-tunes the instruction plan as needed. Learners' emotional states are also continuously monitored to optimize the learning experience.

[0344] (Example 2)

[0345] 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".

[0346] In today's educational environment, providing individualized instruction that addresses the diverse needs of each learner is challenging. In particular, there is a lack of effective technical tools to consider not only the learner's knowledge acquisition but also their emotional state during the learning process. As a result, learning efficiency may decline, and motivation may be lost. To address these challenges, automated generation and monitoring of personalized instruction plans are necessary.

[0347] 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.

[0348] In this invention, the server includes means for collecting learner behavioral and emotional information, means for using an artificial intelligence model for analyzing the collected information, means for automatically generating individualized instruction plans and supplementary materials that address the learner's identified learning difficulties, means for notifying the user of the generated instruction plans and materials, and means for displaying the notified instruction plans and materials on the learner's device. This makes it possible to quickly and efficiently provide an optimal instruction plan that is tailored to the learner's individual needs and emotional state.

[0349] A "learner" refers to a person who has a specific educational objective and is acquiring knowledge or skills.

[0350] "Behavioral information" refers to data related to learners' behavioral history, such as their access frequency, viewing history of learning materials, test results, and homework submission timing.

[0351] "Emotional information" refers to data indicating the emotional state obtained from the learner's facial expressions, voice, etc.

[0352] An "artificial intelligence model" refers to an algorithm or computational model used to analyze data and recognize patterns in the learner's behavior and emotional state.

[0353] An "individualized instruction plan" refers to instructional content and progress plans optimized according to each learner's characteristics and learning situation.

[0354] "Supplementary materials" refer to materials provided to complement or reinforce specific learning items.

[0355] "Notification" refers to the act of informing learners and relevant parties of information generated by the system.

[0356] "Device" refers to information processing equipment or devices used by learners, and typically includes personal computers, tablets, smartphones, etc.

[0357] This system is designed to support learners' efficient learning. It consists of three main elements: a server, a terminal, and a user. It generates and provides individualized instruction plans by collecting and analyzing learner behavioral and emotional information.

[0358] The server accesses the learning platform to collect learner behavioral information. The software used includes database access libraries and API interfaces. Furthermore, an emotion analysis engine is run to obtain emotional information. This is used to analyze facial expressions and voice tone from the learner's video and audio data to determine their emotional state. Specifically, Pandas and TensorFlow are used to build the AI ​​model, and OpenCV and related NVIDIA libraries are used for the emotion engine.

[0359] The server analyzes this data using an artificial intelligence model to understand the learner's behavioral and emotional patterns. This identifies individual learning difficulties, and personalized instruction plans and supplementary materials are automatically generated accordingly.

[0360] The terminal receives notifications sent from the server and displays lesson plans and teaching material information on the user's device. This device is typically a personal computer, tablet, or smartphone. A dedicated application or web interface is used for display.

[0361] Users progress through their learning according to the displayed instruction plan. Feedback and comments received during learning are also recorded via the device and used to further improve the system.

[0362] For example, if a learner is having difficulty in a particular area of ​​mathematics, the system provides practice problems with gradually increasing difficulty levels, along with videos introducing relaxation techniques. In this way, the system takes the learner's emotional state into consideration, making it possible to learn while reducing stress.

[0363] An example of an input prompt for a generative AI model is: "Analyze the learner's behavioral and emotional data to generate an optimal individualized tutoring plan. Specifically, consider learning material viewing history, test results, and emotional state."

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The server retrieves learner behavior information from the learning platform. Using learner account information as input, it collects data such as access frequency, learning material viewing history, and test results via an API. The server organizes this data chronologically and stores it in a database. The output is learner behavior information organized in a format necessary for analysis.

[0367] Step 2:

[0368] The server uses an emotion analysis engine to acquire learner emotional information. It uses the learner's video and audio data as input. This data is processed in real time, performing facial expression analysis and voice tone analysis. The output includes numerical data and categorical information indicating the learner's emotional state during learning.

[0369] Step 3:

[0370] The server inputs collected behavioral and emotional information into an artificial intelligence model for data analysis. The server preprocesses this data using Pandas and TensorFlow, converting it into a format suitable for the AI ​​model. The AI ​​model analyzes the data, examining the learner's behavioral and emotional patterns. The output identifies specific learning difficulties and stressors.

[0371] Step 4:

[0372] The server automatically generates individualized instruction plans and supplementary materials based on the analysis results of the AI ​​model. The AI ​​model's analysis results are used as input. The generation program combines information tailored to the learner's needs to create learning tasks and relaxation activities. The output includes a set of specific learning tasks and links to media content.

[0373] Step 5:

[0374] The server notifies the user of the generated lesson plan and teaching materials and sends them to the device. Notifications are sent via email or in-app messages. The input is the generated plan and lesson content, and the output is the lesson plan information sent to the user.

[0375] Step 6:

[0376] The terminal displays the lesson plan and teaching materials received from the server on the user's device. The input consists of the plan and materials received as notifications. The terminal provides this information using a dedicated app or web interface. The output is a learning instruction display screen available to the user.

[0377] (Application Example 2)

[0378] 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."

[0379] In modern brick-and-mortar stores, there is a demand for efficient and personalized product recommendations to customers. However, traditional methods struggle to provide appropriate recommendations based on customer interests and emotions. Furthermore, systems that analyze customer needs in real time and propose optimal services are limited.

[0380] 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.

[0381] In this invention, the server includes means for collecting learner behavior data, means for using an information processing model for analyzing the collected data, means for identifying learning difficulties based on the analysis, means for acquiring user behavior information and emotional state in physical stores, and means for analyzing user interests based on the acquired information and providing individually suitable products and services. This enables efficient and personalized product suggestions and service provision to customers in physical stores.

[0382] "Learner behavior data" refers to information related to learning activities, such as the frequency of user access, history of viewing learning materials, and test results.

[0383] An "information processing model" is an artificial intelligence algorithm that analyzes collected data and makes decisions and suggestions based on that analysis.

[0384] "Learning difficulties" refer to obstacles or problems that learners face in understanding and progressing through specific learning content.

[0385] An "individualized instruction plan" is a set of instructional procedures that are tailored to the specific needs and difficulties of each learner, and are designed to reflect their progress.

[0386] "Supplementary materials" are teaching materials and resources provided in addition to help learners understand and progress.

[0387] "User behavior information" refers to data related to purchasing activities, including the amount of time customers spend in physical stores and their product browsing history.

[0388] "Emotional state" refers to the emotional response that can be inferred from the user's facial expressions and speech.

[0389] "Customer interest" refers to a state in which a customer shows a strong interest in a particular product or service.

[0390] "Means of providing goods and services" refers to methods and devices for conveying information about goods and services that have been analyzed as suitable for the customer.

[0391] This invention is a system for improving the customer experience in physical stores. The server receives user behavior information and emotional state acquired from smart glasses and other mobile devices, and performs real-time analysis based on this information. User behavior information includes browsing history and time spent in the store, while emotional state includes emotional responses such as facial expressions and voice, which are judged using the camera function of the smart glasses.

[0392] The server analyzes this data using an information processing model to identify the user's interests. This process utilizes an AI model powered by Python and TensorFlow. Based on the interests identified through the analysis, the server generates information to provide products and services tailored to the user and displays it on the smart glasses' display or other devices.

[0393] For example, if a customer enters a bookstore, puts on smart glasses, and stops in front of a bookshelf of a specific genre, the system might analyze their behavior and facial expressions to determine that they are interested in mystery novels. In this case, the system would display a list of recommended mystery novels and sample reading coupons in real time on the smart glasses' display.

[0394] An example of a prompt message is, "Analyze the customer's eye-tracking and facial expression data to identify the products they are most interested in and generate personalized recommendations." This prompt allows the server to efficiently analyze customer interests and provide appropriate information.

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The user wears smart glasses and moves around within a physical store. The smart glasses acquire the user's gaze and facial expression data in real time through cameras and sensors. The input data at this time includes the direction of gaze and facial expression.

[0398] Step 2:

[0399] The terminal sends the acquired gaze data and facial expression data to the server. The server receives this data and stores it in a database. The input data consists of gaze and facial expression information, and based on this, an initial dataset of behavioral and emotional information is obtained.

[0400] Step 3:

[0401] The server uses Python and TensorFlow to analyze the input data. The processing involves estimating emotions from gaze focus and facial expressions to identify the user's current interests and emotional state. The output includes the product categories of interest and the emotion evaluation results.

[0402] Step 4:

[0403] The server uses an AI model to generate recommendations for the most suitable products and services for the user based on the analysis results. The input is the analysis results, and the output is a list of recommendations based on the user's interests.

[0404] Step 5:

[0405] The server sends the generated recommendations to the device, where they are displayed on the user's smart glasses. The user receives relevant product and service information in real time through the smart glasses' display. The output is the information displayed on the smart glasses.

[0406] This entire process allows users to efficiently find products and services they are interested in within physical stores.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] This invention relates to a system that provides effective support tailored to the individual needs of learners in diverse educational environments. This system utilizes the functions of a server, a terminal, and a user, and improves learning efficiency by utilizing learner behavior data.

[0424] The server plays a central role, collecting learner behavior data in real time from various educational platforms. This data includes learners' online activity history, test results, homework submissions, and grades. The collected data is organized and stored in a database on the server.

[0425] The server then analyzes the stored data using an artificial intelligence model. This AI model can capture characteristics from learners' behavior and performance, and identify individual learning difficulties. For example, if a learner repeatedly shows low performance in a particular area of ​​mathematics, the server will identify a learning difficulty in that area as a warning.

[0426] Based on the analysis results, the server automatically generates an individualized tutoring plan and related supplementary materials. This plan is designed to address the identified learning difficulties, and the AI ​​model suggests materials that suit the learner's learning style. For example, it can generate a plan that includes video materials and practice problems to help understand specific types of math problems.

[0427] The server then notifies the user of the generated plan and supplementary materials. The terminal provides an interface that allows the user to review the plan and materials, making them easily accessible to learners. Through this interface, teachers and parents can quickly make necessary changes and provide feedback.

[0428] Users can also review the progress monitored by the system and evaluate the effectiveness of the instruction plan and materials on the learners. Based on this progress, the server re-analyzes and adjusts the instruction plan and materials. This enables continuous support tailored to the individual needs of the learners.

[0429] In this way, this system utilizes learner behavior data to enable efficient and effective individualized instruction. It aims to improve learning efficiency and provide equal learning opportunities in educational settings.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The server collects learner behavior data in real time through the educational platform's API. This data includes login times, learning material viewing history, test results, and homework submission status. The collected data is organized and stored in a database.

[0433] Step 2:

[0434] The server performs data cleansing on the collected data. Specifically, it prepares the data for analysis by imputing missing values, correcting outliers, and standardizing data formats.

[0435] Step 3:

[0436] The server feeds the cleansed data into an artificial intelligence model for analysis. The AI ​​model extracts features from the learner's behavior patterns and performance to identify specific learning difficulties. This process identifies areas where improvement is needed in specific subjects or skills.

[0437] Step 4:

[0438] The server automatically generates personalized tutoring plans and supplementary materials to address learning difficulties identified by the AI ​​model. These plans include video materials, quizzes, and additional reading materials, all designed to aid learners' understanding.

[0439] Step 5:

[0440] The server notifies the user of the generated individualized tutoring plan and supplementary materials. The terminal displays the plan on the device of the user (teacher or parent) who received the notification, providing an interface that learners can access.

[0441] Step 6:

[0442] Users review the lesson plans and materials provided through their devices, and provide feedback and adjustments tailored to their learners. If necessary, they can request additional adjustments to the lesson plan.

[0443] Step 7:

[0444] The server continuously monitors learners' progress as part of the program. Based on the monitoring results, the effectiveness of the instruction plan is evaluated, and the plan and materials are readjusted as needed. This process further optimizes learning support.

[0445] (Example 1)

[0446] 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."

[0447] In today's diverse educational environment, efficiently providing individualized instruction tailored to the needs of each learner is challenging. Traditional methods require teachers to manually manage student progress and address individual challenges, resulting in time-consuming and labor-intensive processes that prevent providing optimal support to all learners. In this context, there is a need to develop methods that support learners in learning effectively and ensuring equal opportunities.

[0448] 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.

[0449] In this invention, the server includes means for collecting learner behavioral data from multiple educational platforms, means for using an artificial intelligence model to analyze the collected data, and means for identifying learning difficulties based on the analysis. This enables the automatic generation of individualized instruction plans and supplementary materials tailored to the needs of each learner, as well as optimal educational support through continuous effectiveness measurement and adjustment.

[0450] "Learner behavior data" refers to data that includes the history of activities performed by learners on the educational platform, their deliverables, and evaluation results.

[0451] An "educational platform" is a system or service that learners use to conduct educational activities online.

[0452] An "artificial intelligence model" is a computer program that learns patterns using large amounts of data and performs specific tasks.

[0453] "Learning difficulties" refer to the challenges or obstacles that learners face in acquiring specific learning content or skills.

[0454] An "individualized instruction plan" is an educational program designed to suit the individual needs and learning styles of each student.

[0455] "Supplementary materials" are additional learning resources that learners use to overcome specific learning difficulties.

[0456] A "user interface" is a system that provides users with screens and pathways to access and operate the system's functions.

[0457] "Evaluation" refers to the criteria and observations used to measure the impact and effectiveness of the provided instructional plan and supplementary materials on learners.

[0458] This invention is a system for providing educational support tailored to the individual needs of learners. The system utilizes behavioral data obtained from diverse educational platforms and employs artificial intelligence technology to enable optimal instruction for each learner.

[0459] The server plays a central role in this system, collecting learner behavior data online in real time. This collection process includes the ability to retrieve data via multiple APIs, enabling the integration of data from various educational applications. The collected data includes important learning information such as learners' online activity history, test results, homework submission status, and grades. The server stores, organizes, and manages this data in a database.

[0460] Next, the server analyzes the data using an artificial intelligence model. The AI ​​model uses TensorFlow, a Python machine learning library. This model extracts features from the data and identifies the learner's learning difficulties. Based on this identified information, the server generates a personalized tutoring plan and supplementary materials tailored to the learner. For example, the generated tutoring plan might include video materials and practice exercises, specifically designed to focus on areas where the learner struggles.

[0461] The device provides an interface that allows users to easily access generated lesson plans and materials. This interface on the device functions as a web application developed using React. Learners, teachers, and parents can use this interface to view lesson plans, check progress, and provide feedback.

[0462] Users evaluate the impact of the lesson plan on learners and input their feedback into the system. This feedback is sent to the server and used for subsequent data analysis and plan adjustments. This ensures that lesson plans and materials continuously adapt to the learners' current situations.

[0463] For example, if a learner is having difficulty acquiring foreign language grammar, this system analyzes data from the learner's past grammar tests and homework to identify areas of weakness. Next, it suggests supplementary materials such as grammar video lessons and practice exercises to help the learner overcome those identified weaknesses.

[0464] The following prompt statements can be used as example inputs to a generative AI model:

[0465] "The learner's behavioral data is shown below. Please generate an individualized tutoring plan based on this data. Data: Online activity history, test results, assignment submission history, grades. Area of ​​focus: Understanding of geometry problems in mathematics."

[0466] This system allows learners to receive educational support tailored to their own learning style, and enables teachers and parents to effectively support students' learning.

[0467] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0468] Step 1:

[0469] The server collects learner behavior data. Inputs include online activity history, test results, and homework submission status, retrieved via APIs from multiple educational platforms. This data is initially filtered to remove duplicates and irrelevant information. The output is organized data, which is stored in a database. The server efficiently manages this data through a relational database management system.

[0470] Step 2:

[0471] The server uses the stored data to perform analysis with an artificial intelligence model. The input is the organized learner data obtained in Step 1. The server uses Python's TensorFlow to pass this data to the AI ​​model and identify the learning difficulties the learners are facing. Specific data processing includes normalization and feature selection. The output is information about the identified learning difficulties and the learners' challenges based on them.

[0472] Step 3:

[0473] The server automatically generates individualized instruction plans and supplementary materials based on identified learning difficulties. The input is the learning difficulty information obtained in Step 2. The server selects appropriate materials from its educational content database and constructs the plan. Specific data calculations include matching with user profiles and prioritizing materials. The output is an instruction plan and materials optimized for each learner.

[0474] Step 4:

[0475] The server notifies the user of the generated lesson plan and materials. The input is the lesson plan and materials created in step 3. The server sends this to the terminal through the user interface. Specific actions include sending notification emails and in-app notifications. The output is the lesson plan information sent to the learner's terminal.

[0476] Step 5:

[0477] The terminal provides an interface that allows the user to view the lesson plans and materials they have received. The input is the lesson plan and material information sent from the server in step 4. The terminal functions as a web application and uses React to provide the user with an intuitive interface. The output is the display state of the plans that the user can access.

[0478] Step 6:

[0479] The user evaluates the effectiveness of the provided lesson plan and materials and monitors progress. Inputs are the lesson plan displayed in step 5 and the learning activities performed by the user. The user inputs feedback into the system and provides suggestions for improvement to continue effective learning support. Outputs are evaluation results and feedback information.

[0480] Step 7:

[0481] The server adjusts the lesson plan and materials based on user feedback and progress data. The input is the feedback information obtained in step 6. The server analyzes the data again using the AI ​​model and updates the lesson content as needed. Specific actions include adding materials and changing teaching methods. The output is the newly adjusted lesson plan.

[0482] (Application Example 1)

[0483] 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."

[0484] In today's educational environment, there is a need to effectively and efficiently identify and resolve the unique learning difficulties faced by individual learners, but there are challenges in establishing an appropriate system to achieve this. Traditional methods have made it difficult to provide adaptive instruction tailored to the individual needs of learners, and furthermore, they have not been able to monitor and adjust their progress in real time. In addition, there is a lack of means to provide learners with personalized educational experiences by utilizing virtual spaces.

[0485] 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.

[0486] In this invention, the server includes a device for collecting learner behavioral data, a device for using intelligent functions to analyze the collected data, and a device for identifying learning difficulties based on the analysis. This makes it possible to generate individualized instruction plans for each learner and provide a personalized educational experience in a virtual space.

[0487] "Learner behavioral data" refers to a collection of learning-related information, such as online activity history, test results, and homework submission status.

[0488] "Intelligent function" refers to the ability to analyze data using artificial intelligence technology and identify potential problems.

[0489] "Learning difficulties" refer to situations or challenges in which learners find it difficult to achieve results in a particular field or task.

[0490] An "individualized instruction plan" refers to an educational plan that addresses the specific difficulties of a particular learner and is tailored to their learning style.

[0491] "Supplementary materials" refer to textbooks and workbooks provided to help learners understand areas in which they have difficulty.

[0492] A "notification device" refers to a means of informing learners or instructors about generated lesson plans and materials.

[0493] A "virtual space" refers to a three-dimensional virtual environment created by a computer, where users can have interactive experiences.

[0494] "Educational materials" refer to teaching materials and educational content used by learners to acquire specific knowledge or skills.

[0495] A "user" refers to an individual who uses this system to access learning plans and educational materials and engage in learning.

[0496] The system according to the present invention collects and analyzes learner behavioral data and creates individualized instruction plans to overcome specific learning difficulties. The server is used to collect learners' learning history and achievements from various educational platforms and organize and store them in a database. The server also analyzes the data using a generative AI model to automatically identify learner-specific difficulties.

[0497] Based on these analysis results, the server creates individualized instruction plans and supplementary materials and notifies the user. The terminal provides an interface that allows the user to review the generated plan and proceed with learning based on it. This interface enables users (teachers and parents) to provide quick feedback and adjust the plan as needed.

[0498] Users can engage in educational experiences within a virtual reality environment via a virtual space. Because the content is delivered in a virtual space, learners can engage in real-time, interactive learning. This system is primarily implemented using a server-side application based on Django and a client-side VR application utilizing Unity.

[0499] For example, if AI analysis identifies a learner's lack of understanding in the field of history, they will be provided with an experience that deepens their understanding through the recreation of historical events in a virtual space. Such an experience can be generated using the prompt, "Generate a VR learning experience about ancient civilizations, including a VR tour themed on Egyptian ruins."

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The server collects learner behavior data from various educational platforms. Specifically, it automatically retrieves online activity history, test results, homework submission status, and other data, and stores it in a database. The input is log data from each platform, and the output is information stored in a formatted manner in the database.

[0503] Step 2:

[0504] The server launches a generative AI model to analyze the collected data. This model takes learner data as input and identifies learning difficulties based on behavior and performance. In this step, data preprocessing is performed, including imputation of missing values ​​and normalization, and the output is an analysis that highlights specific problem areas.

[0505] Step 3:

[0506] The server automatically generates corresponding individualized instruction plans and supplementary materials based on the analysis results. In this step, the generating AI model uses prompts to generate learning materials and instruction plans, which are then organized into a user-optimized set of teaching materials. The input is the identified results from the analysis, and the output is a customized instruction plan and teaching material package.

[0507] Step 4:

[0508] The server notifies the terminal of the generated lesson plan and supplementary materials. This notification is often sent via email or push notification. The input is the generated lesson plan and materials, and the output is a concise notification format accessible to the user.

[0509] Step 5:

[0510] Users review their individualized instruction plan and supplementary materials on their device and begin their educational experience according to their learning style. Specifically, they wear a VR headset and immerse themselves in the content in real time. The input is the learning materials provided by the server, and the output is the user's educational experience.

[0511] Step 6:

[0512] The server monitors the user's learning progress. This is achieved by collecting and analyzing user interaction and behavioral data within the VR environment. The input is real-time user data, and the output is a progress report.

[0513] Step 7:

[0514] The server adjusts the lesson plan and supplementary materials as needed based on monitoring results. This adjustment is based on learner needs and responses, generating a new, optimized lesson plan. The input is a progress report, and the output is the adjusted lesson plan and materials.

[0515] 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.

[0516] This invention is an advanced system for supporting learners' learning processes, optimizing individualized instruction by collecting and analyzing learner behavioral and emotional data. This system functions through three main components: a server, a terminal, and a user.

[0517] The server collects learner behavior data from the learning platform. This data includes access frequency, learning material viewing history, test results, and homework submission timing. In addition, an emotion engine is used to obtain emotional data from learners' videos and audio. This emotional data includes emotional responses extracted from facial expressions and speech during learning.

[0518] Next, the server analyzes the collected behavioral and emotional data through an artificial intelligence model. The AI ​​model explores the correlation between the learner's learning behavior and emotional state to accurately identify the situations in which learning difficulties arise. For example, detecting a high level of stress in response to difficult math problems might lead to the determination that specialized instruction is needed for that topic.

[0519] Based on this analysis, the server automatically generates individualized instruction plans and supplementary materials. These plans are created considering not only the learner's cognitive needs but also their emotional needs, including relaxation techniques to alleviate stress and incentives to boost motivation. For example, a learner struggling with a specific area of ​​mathematics might be provided with practice problems of progressively increasing difficulty, along with relaxation videos.

[0520] The generated lesson plans and materials are notified to the user from the server. The terminal displays this information on the user's device, supporting learners in following the plan. Teachers and parents can monitor learners' progress in real time through the terminal and provide feedback as needed.

[0521] This system is expected to improve learning efficiency and satisfaction by providing an instructional approach that takes learners' emotions into account and making the learning experience more personalized.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The server collects learner behavior data in real time from the learning platform and associated devices. This includes information such as learning material viewing history, login status, and test results.

[0525] Step 2:

[0526] The server acquires emotional data from the learner's device via the camera and microphone using an emotion engine. This allows the server to collect emotional states such as stress and excitement from the learner's facial expressions and tone of voice during the learning process.

[0527] Step 3:

[0528] The server integrates the collected behavioral and emotional data and analyzes it using an artificial intelligence model. This analysis identifies the situations in which learners are experiencing decreased learning efficiency or emotional instability.

[0529] Step 4:

[0530] The server automatically generates individualized instruction plans and supplementary materials based on the analysis results. This generation process also includes break times and relaxation content tailored to the learner's needs to reduce stress.

[0531] Step 5:

[0532] The server notifies the user of the lesson plan and teaching materials generated via the terminal. The terminal provides an interface on the user's device that allows them to view the detailed plan.

[0533] Step 6:

[0534] Users review the provided plan and send feedback to the server via their device as needed. This allows for continuous adaptation and improvement.

[0535] Step 7:

[0536] The server monitors user feedback and learner progress, and fine-tunes the instruction plan as needed. Learners' emotional states are also continuously monitored to optimize the learning experience.

[0537] (Example 2)

[0538] 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."

[0539] In today's educational environment, providing individualized instruction that addresses the diverse needs of each learner is challenging. In particular, there is a lack of effective technical tools to consider not only the learner's knowledge acquisition but also their emotional state during the learning process. As a result, learning efficiency may decline, and motivation may be lost. To address these challenges, automated generation and monitoring of personalized instruction plans are necessary.

[0540] 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.

[0541] In this invention, the server includes means for collecting learner behavioral and emotional information, means for using an artificial intelligence model for analyzing the collected information, means for automatically generating individualized instruction plans and supplementary materials that address the learner's identified learning difficulties, means for notifying the user of the generated instruction plans and materials, and means for displaying the notified instruction plans and materials on the learner's device. This makes it possible to quickly and efficiently provide an optimal instruction plan that is tailored to the learner's individual needs and emotional state.

[0542] A "learner" refers to a person who has a specific educational objective and is acquiring knowledge or skills.

[0543] "Behavioral information" refers to data related to learners' behavioral history, such as their access frequency, viewing history of learning materials, test results, and homework submission timing.

[0544] "Emotional information" refers to data indicating the emotional state obtained from the learner's facial expressions, voice, etc.

[0545] An "artificial intelligence model" refers to an algorithm or computational model used to analyze data and recognize patterns in the learner's behavior and emotional state.

[0546] An "individualized instruction plan" refers to instructional content and progress plans optimized according to each learner's characteristics and learning situation.

[0547] "Supplementary materials" refer to materials provided to complement or reinforce specific learning items.

[0548] "Notification" refers to the act of informing learners and relevant parties of information generated by the system.

[0549] "Device" refers to information processing equipment or devices used by learners, and typically includes personal computers, tablets, smartphones, etc.

[0550] This system is designed to support learners' efficient learning. It consists of three main elements: a server, a terminal, and a user. It generates and provides individualized instruction plans by collecting and analyzing learner behavioral and emotional information.

[0551] The server accesses the learning platform to collect learner behavioral information. The software used includes database access libraries and API interfaces. Furthermore, an emotion analysis engine is run to obtain emotional information. This is used to analyze facial expressions and voice tone from the learner's video and audio data to determine their emotional state. Specifically, Pandas and TensorFlow are used to build the AI ​​model, and OpenCV and related NVIDIA libraries are used for the emotion engine.

[0552] The server analyzes this data using an artificial intelligence model to understand the learner's behavioral and emotional patterns. This identifies individual learning difficulties, and personalized instruction plans and supplementary materials are automatically generated accordingly.

[0553] The terminal receives notifications sent from the server and displays lesson plans and teaching material information on the user's device. This device is typically a personal computer, tablet, or smartphone. A dedicated application or web interface is used for display.

[0554] Users progress through their learning according to the displayed instruction plan. Feedback and comments received during learning are also recorded via the device and used to further improve the system.

[0555] For example, if a learner is having difficulty in a particular area of ​​mathematics, the system provides practice problems with gradually increasing difficulty levels, along with videos introducing relaxation techniques. In this way, the system takes the learner's emotional state into consideration, making it possible to learn while reducing stress.

[0556] An example of an input prompt for a generative AI model is: "Analyze the learner's behavioral and emotional data to generate an optimal individualized tutoring plan. Specifically, consider learning material viewing history, test results, and emotional state."

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The server retrieves learner behavior information from the learning platform. Using learner account information as input, it collects data such as access frequency, learning material viewing history, and test results via an API. The server organizes this data chronologically and stores it in a database. The output is learner behavior information organized in a format necessary for analysis.

[0560] Step 2:

[0561] The server uses an emotion analysis engine to acquire learner emotional information. It uses the learner's video and audio data as input. This data is processed in real time, performing facial expression analysis and voice tone analysis. The output includes numerical data and categorical information indicating the learner's emotional state during learning.

[0562] Step 3:

[0563] The server inputs collected behavioral and emotional information into an artificial intelligence model for data analysis. The server preprocesses this data using Pandas and TensorFlow, converting it into a format suitable for the AI ​​model. The AI ​​model analyzes the data, examining the learner's behavioral and emotional patterns. The output identifies specific learning difficulties and stressors.

[0564] Step 4:

[0565] The server automatically generates individualized instruction plans and supplementary materials based on the analysis results of the AI ​​model. The AI ​​model's analysis results are used as input. The generation program combines information tailored to the learner's needs to create learning tasks and relaxation activities. The output includes a set of specific learning tasks and links to media content.

[0566] Step 5:

[0567] The server notifies the user of the generated lesson plan and teaching materials and sends them to the device. Notifications are sent via email or in-app messages. The input is the generated plan and lesson content, and the output is the lesson plan information sent to the user.

[0568] Step 6:

[0569] The terminal displays the lesson plan and teaching materials received from the server on the user's device. The input consists of the plan and materials received as notifications. The terminal provides this information using a dedicated app or web interface. The output is a learning instruction display screen available to the user.

[0570] (Application Example 2)

[0571] 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."

[0572] In modern brick-and-mortar stores, there is a demand for efficient and personalized product recommendations to customers. However, traditional methods struggle to provide appropriate recommendations based on customer interests and emotions. Furthermore, systems that analyze customer needs in real time and propose optimal services are limited.

[0573] 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.

[0574] In this invention, the server includes means for collecting learner behavior data, means for using an information processing model for analyzing the collected data, means for identifying learning difficulties based on the analysis, means for acquiring user behavior information and emotional state in physical stores, and means for analyzing user interests based on the acquired information and providing individually suitable products and services. This enables efficient and personalized product suggestions and service provision to customers in physical stores.

[0575] "Learner behavior data" refers to information related to learning activities, such as the frequency of user access, history of viewing learning materials, and test results.

[0576] An "information processing model" is an artificial intelligence algorithm that analyzes collected data and makes decisions and suggestions based on that analysis.

[0577] "Learning difficulties" refer to obstacles or problems that learners face in understanding and progressing through specific learning content.

[0578] An "individualized instruction plan" is a set of instructional procedures that are tailored to the specific needs and difficulties of each learner, and are designed to reflect their progress.

[0579] "Supplementary materials" are teaching materials and resources provided in addition to help learners understand and progress.

[0580] "User behavior information" refers to data related to purchasing activities, including the amount of time customers spend in physical stores and their product browsing history.

[0581] "Emotional state" refers to the emotional response that can be inferred from the user's facial expressions and speech.

[0582] "Customer interest" refers to a state in which a customer shows a strong interest in a particular product or service.

[0583] "Means of providing goods and services" refers to methods and devices for conveying information about goods and services that have been analyzed as suitable for the customer.

[0584] This invention is a system for improving the customer experience in physical stores. The server receives user behavior information and emotional state acquired from smart glasses and other mobile devices, and performs real-time analysis based on this information. User behavior information includes browsing history and time spent in the store, while emotional state includes emotional responses such as facial expressions and voice, which are judged using the camera function of the smart glasses.

[0585] The server analyzes this data using an information processing model to identify the user's interests. This process utilizes an AI model powered by Python and TensorFlow. Based on the interests identified through the analysis, the server generates information to provide products and services tailored to the user and displays it on the smart glasses' display or other devices.

[0586] For example, if a customer enters a bookstore, puts on smart glasses, and stops in front of a bookshelf of a specific genre, the system might analyze their behavior and facial expressions to determine that they are interested in mystery novels. In this case, the system would display a list of recommended mystery novels and sample reading coupons in real time on the smart glasses' display.

[0587] An example of a prompt message is, "Analyze the customer's eye-tracking and facial expression data to identify the products they are most interested in and generate personalized recommendations." This prompt allows the server to efficiently analyze customer interests and provide appropriate information.

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The user wears smart glasses and moves around within a physical store. The smart glasses acquire the user's gaze and facial expression data in real time through cameras and sensors. The input data at this time includes the direction of gaze and facial expression.

[0591] Step 2:

[0592] The terminal sends the acquired gaze data and facial expression data to the server. The server receives this data and stores it in a database. The input data consists of gaze and facial expression information, and based on this, an initial dataset of behavioral and emotional information is obtained.

[0593] Step 3:

[0594] The server uses Python and TensorFlow to analyze the input data. The processing involves estimating emotions from gaze focus and facial expressions to identify the user's current interests and emotional state. The output includes the product categories of interest and the emotion evaluation results.

[0595] Step 4:

[0596] The server uses an AI model to generate recommendations for the most suitable products and services for the user based on the analysis results. The input is the analysis results, and the output is a list of recommendations based on the user's interests.

[0597] Step 5:

[0598] The server sends the generated recommendations to the device, where they are displayed on the user's smart glasses. The user receives relevant product and service information in real time through the smart glasses' display. The output is the information displayed on the smart glasses.

[0599] This entire process allows users to efficiently find products and services they are interested in within physical stores.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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".

[0617] This invention relates to a system that provides effective support tailored to the individual needs of learners in diverse educational environments. This system utilizes the functions of a server, a terminal, and a user, and improves learning efficiency by utilizing learner behavior data.

[0618] The server plays a central role, collecting learner behavior data in real time from various educational platforms. This data includes learners' online activity history, test results, homework submissions, and grades. The collected data is organized and stored in a database on the server.

[0619] The server then analyzes the stored data using an artificial intelligence model. This AI model can capture characteristics from learners' behavior and performance, and identify individual learning difficulties. For example, if a learner repeatedly shows low performance in a particular area of ​​mathematics, the server will identify a learning difficulty in that area as a warning.

[0620] Based on the analysis results, the server automatically generates an individualized tutoring plan and related supplementary materials. This plan is designed to address the identified learning difficulties, and the AI ​​model suggests materials that suit the learner's learning style. For example, it can generate a plan that includes video materials and practice problems to help understand specific types of math problems.

[0621] The server then notifies the user of the generated plan and supplementary materials. The terminal provides an interface that allows the user to review the plan and materials, making them easily accessible to learners. Through this interface, teachers and parents can quickly make necessary changes and provide feedback.

[0622] Users can also review the progress monitored by the system and evaluate the effectiveness of the instruction plan and materials on the learners. Based on this progress, the server re-analyzes and adjusts the instruction plan and materials. This enables continuous support tailored to the individual needs of the learners.

[0623] In this way, this system utilizes learner behavior data to enable efficient and effective individualized instruction. It aims to improve learning efficiency and provide equal learning opportunities in educational settings.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The server collects learner behavior data in real time through the educational platform's API. This data includes login times, learning material viewing history, test results, and homework submission status. The collected data is organized and stored in a database.

[0627] Step 2:

[0628] The server performs data cleansing on the collected data. Specifically, it prepares the data for analysis by imputing missing values, correcting outliers, and standardizing data formats.

[0629] Step 3:

[0630] The server feeds the cleansed data into an artificial intelligence model for analysis. The AI ​​model extracts features from the learner's behavior patterns and performance to identify specific learning difficulties. This process identifies areas where improvement is needed in specific subjects or skills.

[0631] Step 4:

[0632] The server automatically generates personalized tutoring plans and supplementary materials to address learning difficulties identified by the AI ​​model. These plans include video materials, quizzes, and additional reading materials, all designed to aid learners' understanding.

[0633] Step 5:

[0634] The server notifies the user of the generated individualized tutoring plan and supplementary materials. The terminal displays the plan on the device of the user (teacher or parent) who received the notification, providing an interface that learners can access.

[0635] Step 6:

[0636] Users review the lesson plans and materials provided through their devices, and provide feedback and adjustments tailored to their learners. If necessary, they can request additional adjustments to the lesson plan.

[0637] Step 7:

[0638] The server continuously monitors learners' progress as part of the program. Based on the monitoring results, the effectiveness of the instruction plan is evaluated, and the plan and materials are readjusted as needed. This process further optimizes learning support.

[0639] (Example 1)

[0640] 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".

[0641] In today's diverse educational environment, efficiently providing individualized instruction tailored to the needs of each learner is challenging. Traditional methods require teachers to manually manage student progress and address individual challenges, resulting in time-consuming and labor-intensive processes that prevent providing optimal support to all learners. In this context, there is a need to develop methods that support learners in learning effectively and ensuring equal opportunities.

[0642] 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.

[0643] In this invention, the server includes means for collecting learner behavioral data from multiple educational platforms, means for using an artificial intelligence model to analyze the collected data, and means for identifying learning difficulties based on the analysis. This enables the automatic generation of individualized instruction plans and supplementary materials tailored to the needs of each learner, as well as optimal educational support through continuous effectiveness measurement and adjustment.

[0644] "Learner behavior data" refers to data that includes the history of activities performed by learners on the educational platform, their deliverables, and evaluation results.

[0645] An "educational platform" is a system or service that learners use to conduct educational activities online.

[0646] An "artificial intelligence model" is a computer program that learns patterns using large amounts of data and performs specific tasks.

[0647] "Learning difficulties" refer to the challenges or obstacles that learners face in acquiring specific learning content or skills.

[0648] An "individualized instruction plan" is an educational program designed to suit the individual needs and learning styles of each student.

[0649] "Supplementary materials" are additional learning resources that learners use to overcome specific learning difficulties.

[0650] A "user interface" is a system that provides users with screens and pathways to access and operate the system's functions.

[0651] "Evaluation" refers to the criteria and observations used to measure the impact and effectiveness of the provided instructional plan and supplementary materials on learners.

[0652] This invention is a system for providing educational support tailored to the individual needs of learners. The system utilizes behavioral data obtained from diverse educational platforms and employs artificial intelligence technology to enable optimal instruction for each learner.

[0653] The server plays a central role in this system, collecting learner behavior data online in real time. This collection process includes the ability to retrieve data via multiple APIs, enabling the integration of data from various educational applications. The collected data includes important learning information such as learners' online activity history, test results, homework submission status, and grades. The server stores, organizes, and manages this data in a database.

[0654] Next, the server analyzes the data using an artificial intelligence model. The AI ​​model uses TensorFlow, a Python machine learning library. This model extracts features from the data and identifies the learner's learning difficulties. Based on this identified information, the server generates a personalized tutoring plan and supplementary materials tailored to the learner. For example, the generated tutoring plan might include video materials and practice exercises, specifically designed to focus on areas where the learner struggles.

[0655] The device provides an interface that allows users to easily access generated lesson plans and materials. This interface on the device functions as a web application developed using React. Learners, teachers, and parents can use this interface to view lesson plans, check progress, and provide feedback.

[0656] Users evaluate the impact of the lesson plan on learners and input their feedback into the system. This feedback is sent to the server and used for subsequent data analysis and plan adjustments. This ensures that lesson plans and materials continuously adapt to the learners' current situations.

[0657] For example, if a learner is having difficulty acquiring foreign language grammar, this system analyzes data from the learner's past grammar tests and homework to identify areas of weakness. Next, it suggests supplementary materials such as grammar video lessons and practice exercises to help the learner overcome those identified weaknesses.

[0658] The following prompt statements can be used as example inputs to a generative AI model:

[0659] "The learner's behavioral data is shown below. Please generate an individualized tutoring plan based on this data. Data: Online activity history, test results, assignment submission history, grades. Area of ​​focus: Understanding of geometry problems in mathematics."

[0660] This system allows learners to receive educational support tailored to their own learning style, and enables teachers and parents to effectively support students' learning.

[0661] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0662] Step 1:

[0663] The server collects learner behavior data. Inputs include online activity history, test results, and homework submission status, retrieved via APIs from multiple educational platforms. This data is initially filtered to remove duplicates and irrelevant information. The output is organized data, which is stored in a database. The server efficiently manages this data through a relational database management system.

[0664] Step 2:

[0665] The server uses the stored data to perform analysis with an artificial intelligence model. The input is the organized learner data obtained in Step 1. The server uses Python's TensorFlow to pass this data to the AI ​​model and identify the learning difficulties the learners are facing. Specific data processing includes normalization and feature selection. The output is information about the identified learning difficulties and the learners' challenges based on them.

[0666] Step 3:

[0667] The server automatically generates individualized instruction plans and supplementary materials based on identified learning difficulties. The input is the learning difficulty information obtained in Step 2. The server selects appropriate materials from its educational content database and constructs the plan. Specific data calculations include matching with user profiles and prioritizing materials. The output is an instruction plan and materials optimized for each learner.

[0668] Step 4:

[0669] The server notifies the user of the generated lesson plan and materials. The input is the lesson plan and materials created in step 3. The server sends this to the terminal through the user interface. Specific actions include sending notification emails and in-app notifications. The output is the lesson plan information sent to the learner's terminal.

[0670] Step 5:

[0671] The terminal provides an interface that allows the user to view the lesson plans and materials they have received. The input is the lesson plan and material information sent from the server in step 4. The terminal functions as a web application and uses React to provide the user with an intuitive interface. The output is the display state of the plans that the user can access.

[0672] Step 6:

[0673] The user evaluates the effectiveness of the provided lesson plan and materials and monitors progress. Inputs are the lesson plan displayed in step 5 and the learning activities performed by the user. The user inputs feedback into the system and provides suggestions for improvement to continue effective learning support. Outputs are evaluation results and feedback information.

[0674] Step 7:

[0675] The server adjusts the lesson plan and materials based on user feedback and progress data. The input is the feedback information obtained in step 6. The server analyzes the data again using the AI ​​model and updates the lesson content as needed. Specific actions include adding materials and changing teaching methods. The output is the newly adjusted lesson plan.

[0676] (Application Example 1)

[0677] 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".

[0678] In today's educational environment, there is a need to effectively and efficiently identify and resolve the unique learning difficulties faced by individual learners, but there are challenges in establishing an appropriate system to achieve this. Traditional methods have made it difficult to provide adaptive instruction tailored to the individual needs of learners, and furthermore, they have not been able to monitor and adjust their progress in real time. In addition, there is a lack of means to provide learners with personalized educational experiences by utilizing virtual spaces.

[0679] 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.

[0680] In this invention, the server includes a device for collecting learner behavioral data, a device for using intelligent functions to analyze the collected data, and a device for identifying learning difficulties based on the analysis. This makes it possible to generate individualized instruction plans for each learner and provide a personalized educational experience in a virtual space.

[0681] "Learner behavioral data" refers to a collection of learning-related information, such as online activity history, test results, and homework submission status.

[0682] "Intelligent function" refers to the ability to analyze data using artificial intelligence technology and identify potential problems.

[0683] "Learning difficulties" refer to situations or challenges in which learners find it difficult to achieve results in a particular field or task.

[0684] An "individualized instruction plan" refers to an educational plan that addresses the specific difficulties of a particular learner and is tailored to their learning style.

[0685] "Supplementary materials" refer to textbooks and workbooks provided to help learners understand areas in which they have difficulty.

[0686] A "notification device" refers to a means of informing learners or instructors about generated lesson plans and materials.

[0687] A "virtual space" refers to a three-dimensional virtual environment created by a computer, where users can have interactive experiences.

[0688] "Educational materials" refer to teaching materials and educational content used by learners to acquire specific knowledge or skills.

[0689] A "user" refers to an individual who uses this system to access learning plans and educational materials and engage in learning.

[0690] The system according to the present invention collects and analyzes learner behavioral data and creates individualized instruction plans to overcome specific learning difficulties. The server is used to collect learners' learning history and achievements from various educational platforms and organize and store them in a database. The server also analyzes the data using a generative AI model to automatically identify learner-specific difficulties.

[0691] Based on these analysis results, the server creates individualized instruction plans and supplementary materials and notifies the user. The terminal provides an interface that allows the user to review the generated plan and proceed with learning based on it. This interface enables users (teachers and parents) to provide quick feedback and adjust the plan as needed.

[0692] Users can engage in educational experiences within a virtual reality environment via a virtual space. Because the content is delivered in a virtual space, learners can engage in real-time, interactive learning. This system is primarily implemented using a server-side application based on Django and a client-side VR application utilizing Unity.

[0693] For example, if AI analysis identifies a learner's lack of understanding in the field of history, they will be provided with an experience that deepens their understanding through the recreation of historical events in a virtual space. Such an experience can be generated using the prompt, "Generate a VR learning experience about ancient civilizations, including a VR tour themed on Egyptian ruins."

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] The server collects learner behavior data from various educational platforms. Specifically, it automatically retrieves online activity history, test results, homework submission status, and other data, and stores it in a database. The input is log data from each platform, and the output is information stored in a formatted manner in the database.

[0697] Step 2:

[0698] The server launches a generative AI model to analyze the collected data. This model takes learner data as input and identifies learning difficulties based on behavior and performance. In this step, data preprocessing is performed, including imputation of missing values ​​and normalization, and the output is an analysis that highlights specific problem areas.

[0699] Step 3:

[0700] The server automatically generates corresponding individualized instruction plans and supplementary materials based on the analysis results. In this step, the generating AI model uses prompts to generate learning materials and instruction plans, which are then organized into a user-optimized set of teaching materials. The input is the identified results from the analysis, and the output is a customized instruction plan and teaching material package.

[0701] Step 4:

[0702] The server notifies the terminal of the generated lesson plan and supplementary materials. This notification is often sent via email or push notification. The input is the generated lesson plan and materials, and the output is a concise notification format accessible to the user.

[0703] Step 5:

[0704] Users review their individualized instruction plan and supplementary materials on their device and begin their educational experience according to their learning style. Specifically, they wear a VR headset and immerse themselves in the content in real time. The input is the learning materials provided by the server, and the output is the user's educational experience.

[0705] Step 6:

[0706] The server monitors the user's learning progress. This is achieved by collecting and analyzing user interaction and behavioral data within the VR environment. The input is real-time user data, and the output is a progress report.

[0707] Step 7:

[0708] The server adjusts the lesson plan and supplementary materials as needed based on monitoring results. This adjustment is based on learner needs and responses, generating a new, optimized lesson plan. The input is a progress report, and the output is the adjusted lesson plan and materials.

[0709] 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.

[0710] This invention is an advanced system for supporting learners' learning processes, optimizing individualized instruction by collecting and analyzing learner behavioral and emotional data. This system functions through three main components: a server, a terminal, and a user.

[0711] The server collects learner behavior data from the learning platform. This data includes access frequency, learning material viewing history, test results, and homework submission timing. In addition, an emotion engine is used to obtain emotional data from learners' videos and audio. This emotional data includes emotional responses extracted from facial expressions and speech during learning.

[0712] Next, the server analyzes the collected behavioral and emotional data through an artificial intelligence model. The AI ​​model explores the correlation between the learner's learning behavior and emotional state to accurately identify the situations in which learning difficulties arise. For example, detecting a high level of stress in response to difficult math problems might lead to the determination that specialized instruction is needed for that topic.

[0713] Based on this analysis, the server automatically generates individualized instruction plans and supplementary materials. These plans are created considering not only the learner's cognitive needs but also their emotional needs, including relaxation techniques to alleviate stress and incentives to boost motivation. For example, a learner struggling with a specific area of ​​mathematics might be provided with practice problems of progressively increasing difficulty, along with relaxation videos.

[0714] The generated lesson plans and materials are notified to the user from the server. The terminal displays this information on the user's device, supporting learners in following the plan. Teachers and parents can monitor learners' progress in real time through the terminal and provide feedback as needed.

[0715] This system is expected to improve learning efficiency and satisfaction by providing an instructional approach that takes learners' emotions into account and making the learning experience more personalized.

[0716] The following describes the processing flow.

[0717] Step 1:

[0718] The server collects learner behavior data in real time from the learning platform and associated devices. This includes information such as learning material viewing history, login status, and test results.

[0719] Step 2:

[0720] The server acquires emotional data from the learner's device via the camera and microphone using an emotion engine. This allows the server to collect emotional states such as stress and excitement from the learner's facial expressions and tone of voice during the learning process.

[0721] Step 3:

[0722] The server integrates the collected behavioral and emotional data and analyzes it using an artificial intelligence model. This analysis identifies the situations in which learners are experiencing decreased learning efficiency or emotional instability.

[0723] Step 4:

[0724] The server automatically generates individualized instruction plans and supplementary materials based on the analysis results. This generation process also includes break times and relaxation content tailored to the learner's needs to reduce stress.

[0725] Step 5:

[0726] The server notifies the user of the lesson plan and teaching materials generated via the terminal. The terminal provides an interface on the user's device that allows them to view the detailed plan.

[0727] Step 6:

[0728] Users review the provided plan and send feedback to the server via their device as needed. This allows for continuous adaptation and improvement.

[0729] Step 7:

[0730] The server monitors user feedback and learner progress, and fine-tunes the instruction plan as needed. Learners' emotional states are also continuously monitored to optimize the learning experience.

[0731] (Example 2)

[0732] 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".

[0733] In today's educational environment, providing individualized instruction that addresses the diverse needs of each learner is challenging. In particular, there is a lack of effective technical tools to consider not only the learner's knowledge acquisition but also their emotional state during the learning process. As a result, learning efficiency may decline, and motivation may be lost. To address these challenges, automated generation and monitoring of personalized instruction plans are necessary.

[0734] 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.

[0735] In this invention, the server includes means for collecting learner behavioral and emotional information, means for using an artificial intelligence model for analyzing the collected information, means for automatically generating individualized instruction plans and supplementary materials that address the learner's identified learning difficulties, means for notifying the user of the generated instruction plans and materials, and means for displaying the notified instruction plans and materials on the learner's device. This makes it possible to quickly and efficiently provide an optimal instruction plan that is tailored to the learner's individual needs and emotional state.

[0736] A "learner" refers to a person who has a specific educational objective and is acquiring knowledge or skills.

[0737] "Behavioral information" refers to data related to learners' behavioral history, such as their access frequency, viewing history of learning materials, test results, and homework submission timing.

[0738] "Emotional information" refers to data indicating the emotional state obtained from the learner's facial expressions, voice, etc.

[0739] An "artificial intelligence model" refers to an algorithm or computational model used to analyze data and recognize patterns in the learner's behavior and emotional state.

[0740] An "individualized instruction plan" refers to instructional content and progress plans optimized according to each learner's characteristics and learning situation.

[0741] "Supplementary materials" refer to materials provided to complement or reinforce specific learning items.

[0742] "Notification" refers to the act of informing learners and relevant parties of information generated by the system.

[0743] "Device" refers to information processing equipment or devices used by learners, and typically includes personal computers, tablets, smartphones, etc.

[0744] This system is designed to support learners' efficient learning. It consists of three main elements: a server, a terminal, and a user. It generates and provides individualized instruction plans by collecting and analyzing learner behavioral and emotional information.

[0745] The server accesses the learning platform to collect learner behavioral information. The software used includes database access libraries and API interfaces. Furthermore, an emotion analysis engine is run to obtain emotional information. This is used to analyze facial expressions and voice tone from the learner's video and audio data to determine their emotional state. Specifically, Pandas and TensorFlow are used to build the AI ​​model, and OpenCV and related NVIDIA libraries are used for the emotion engine.

[0746] The server analyzes this data using an artificial intelligence model to understand the learner's behavioral and emotional patterns. This identifies individual learning difficulties, and personalized instruction plans and supplementary materials are automatically generated accordingly.

[0747] The terminal receives notifications sent from the server and displays lesson plans and teaching material information on the user's device. This device is typically a personal computer, tablet, or smartphone. A dedicated application or web interface is used for display.

[0748] Users progress through their learning according to the displayed instruction plan. Feedback and comments received during learning are also recorded via the device and used to further improve the system.

[0749] For example, if a learner is having difficulty in a particular area of ​​mathematics, the system provides practice problems with gradually increasing difficulty levels, along with videos introducing relaxation techniques. In this way, the system takes the learner's emotional state into consideration, making it possible to learn while reducing stress.

[0750] An example of an input prompt for a generative AI model is: "Analyze the learner's behavioral and emotional data to generate an optimal individualized tutoring plan. Specifically, consider learning material viewing history, test results, and emotional state."

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] The server retrieves learner behavior information from the learning platform. Using learner account information as input, it collects data such as access frequency, learning material viewing history, and test results via an API. The server organizes this data chronologically and stores it in a database. The output is learner behavior information organized in a format necessary for analysis.

[0754] Step 2:

[0755] The server uses an emotion analysis engine to acquire learner emotional information. It uses the learner's video and audio data as input. This data is processed in real time, performing facial expression analysis and voice tone analysis. The output includes numerical data and categorical information indicating the learner's emotional state during learning.

[0756] Step 3:

[0757] The server inputs collected behavioral and emotional information into an artificial intelligence model for data analysis. The server preprocesses this data using Pandas and TensorFlow, converting it into a format suitable for the AI ​​model. The AI ​​model analyzes the data, examining the learner's behavioral and emotional patterns. The output identifies specific learning difficulties and stressors.

[0758] Step 4:

[0759] The server automatically generates individualized instruction plans and supplementary materials based on the analysis results of the AI ​​model. The AI ​​model's analysis results are used as input. The generation program combines information tailored to the learner's needs to create learning tasks and relaxation activities. The output includes a set of specific learning tasks and links to media content.

[0760] Step 5:

[0761] The server notifies the user of the generated lesson plan and teaching materials and sends them to the device. Notifications are sent via email or in-app messages. The input is the generated plan and lesson content, and the output is the lesson plan information sent to the user.

[0762] Step 6:

[0763] The terminal displays the lesson plan and teaching materials received from the server on the user's device. The input consists of the plan and materials received as notifications. The terminal provides this information using a dedicated app or web interface. The output is a learning instruction display screen available to the user.

[0764] (Application Example 2)

[0765] 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".

[0766] In modern brick-and-mortar stores, there is a demand for efficient and personalized product recommendations to customers. However, traditional methods struggle to provide appropriate recommendations based on customer interests and emotions. Furthermore, systems that analyze customer needs in real time and propose optimal services are limited.

[0767] 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.

[0768] In this invention, the server includes means for collecting learner behavior data, means for using an information processing model for analyzing the collected data, means for identifying learning difficulties based on the analysis, means for acquiring user behavior information and emotional state in physical stores, and means for analyzing user interests based on the acquired information and providing individually suitable products and services. This enables efficient and personalized product suggestions and service provision to customers in physical stores.

[0769] "Learner behavior data" refers to information related to learning activities, such as the frequency of user access, history of viewing learning materials, and test results.

[0770] An "information processing model" is an artificial intelligence algorithm that analyzes collected data and makes decisions and suggestions based on that analysis.

[0771] "Learning difficulties" refer to obstacles or problems that learners face in understanding and progressing through specific learning content.

[0772] An "individualized instruction plan" is a set of instructional procedures that are tailored to the specific needs and difficulties of each learner, and are designed to reflect their progress.

[0773] "Supplementary materials" are teaching materials and resources provided in addition to help learners understand and progress.

[0774] "User behavior information" refers to data related to purchasing activities, including the amount of time customers spend in physical stores and their product browsing history.

[0775] "Emotional state" refers to the emotional response that can be inferred from the user's facial expressions and speech.

[0776] "Customer interest" refers to a state in which a customer shows a strong interest in a particular product or service.

[0777] "Means of providing goods and services" refers to methods and devices for conveying information about goods and services that have been analyzed as suitable for the customer.

[0778] This invention is a system for improving the customer experience in physical stores. The server receives user behavior information and emotional state acquired from smart glasses and other mobile devices, and performs real-time analysis based on this information. User behavior information includes browsing history and time spent in the store, while emotional state includes emotional responses such as facial expressions and voice, which are judged using the camera function of the smart glasses.

[0779] The server analyzes this data using an information processing model to identify the user's interests. This process utilizes an AI model powered by Python and TensorFlow. Based on the interests identified through the analysis, the server generates information to provide products and services tailored to the user and displays it on the smart glasses' display or other devices.

[0780] For example, if a customer enters a bookstore, puts on smart glasses, and stops in front of a bookshelf of a specific genre, the system might analyze their behavior and facial expressions to determine that they are interested in mystery novels. In this case, the system would display a list of recommended mystery novels and sample reading coupons in real time on the smart glasses' display.

[0781] An example of a prompt message is, "Analyze the customer's eye-tracking and facial expression data to identify the products they are most interested in and generate personalized recommendations." This prompt allows the server to efficiently analyze customer interests and provide appropriate information.

[0782] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0783] Step 1:

[0784] The user wears smart glasses and moves around within a physical store. The smart glasses acquire the user's gaze and facial expression data in real time through cameras and sensors. The input data at this time includes the direction of gaze and facial expression.

[0785] Step 2:

[0786] The terminal sends the acquired gaze data and facial expression data to the server. The server receives this data and stores it in a database. The input data consists of gaze and facial expression information, and based on this, an initial dataset of behavioral and emotional information is obtained.

[0787] Step 3:

[0788] The server uses Python and TensorFlow to analyze the input data. The processing involves estimating emotions from gaze focus and facial expressions to identify the user's current interests and emotional state. The output includes the product categories of interest and the emotion evaluation results.

[0789] Step 4:

[0790] The server uses an AI model to generate recommendations for the most suitable products and services for the user based on the analysis results. The input is the analysis results, and the output is a list of recommendations based on the user's interests.

[0791] Step 5:

[0792] The server sends the generated recommendations to the device, where they are displayed on the user's smart glasses. The user receives relevant product and service information in real time through the smart glasses' display. The output is the information displayed on the smart glasses.

[0793] This entire process allows users to efficiently find products and services they are interested in within physical stores.

[0794] 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.

[0795] 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.

[0796] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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."

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] The following is further disclosed regarding the embodiments described above.

[0816] (Claim 1)

[0817] Means for collecting learner behavioral data,

[0818] A means of using an artificial intelligence model to analyze the aforementioned collected data,

[0819] A means for identifying learning difficulties based on the aforementioned analysis,

[0820] A means for automatically generating individualized instruction plans and supplementary materials to address the identified learning difficulties of learners,

[0821] A means for notifying the generated instruction plan and teaching materials,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, further comprising means for monitoring the progress of the notified individualized instruction plan and supplementary materials.

[0825] (Claim 3)

[0826] The system according to claim 1, further comprising means for adjusting instructional plans and supplementary materials based on the monitored progress.

[0827] "Example 1"

[0828] (Claim 1)

[0829] A means of collecting learner behavioral data from multiple educational platforms,

[0830] A means of using an artificial intelligence model to analyze the aforementioned collected data,

[0831] A means for identifying learning difficulties based on the aforementioned analysis,

[0832] A means for automatically generating individualized instruction plans and supplementary materials that address learners' learning styles and identified learning difficulties,

[0833] A means for notifying the user of the generated instruction plan and teaching materials and making them available for review via a user interface,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, further comprising means for monitoring the progress of the notified individualized instruction plan and supplementary materials and for receiving evaluations from the user.

[0837] (Claim 3)

[0838] The system according to claim 1, further comprising means for adjusting instructional plans and supplementary materials based on the monitored progress and evaluation.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] A device for collecting learner behavior data,

[0842] A device that uses intelligent functions to analyze the collected data,

[0843] A device for identifying learning difficulties based on the aforementioned analysis,

[0844] A device that automatically generates individualized instruction plans and supplementary materials to address the identified learning difficulties of learners,

[0845] A device for notifying the generated instruction plan and materials,

[0846] A device that provides educational materials in a virtual space,

[0847] A device that enables users to have an educational experience in a virtual reality environment,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, further comprising a device for monitoring the progress of the notified individualized instruction plan and supplementary materials.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising a device for adjusting instructional plans and supplementary materials based on the monitored progress.

[0853] "Example 2 of combining an emotion engine"

[0854] (Claim 1)

[0855] Means for collecting learners' behavioral and emotional information,

[0856] A means of using an artificial intelligence model to analyze the collected information,

[0857] A means for identifying learning difficulties based on the aforementioned analysis,

[0858] A means for automatically generating individualized instruction plans and supplementary materials to address identified learning difficulties of learners,

[0859] A means for notifying the user of the generated lesson plan and teaching materials,

[0860] Means for displaying the notified lesson plan and teaching materials on the learner's device,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising means for monitoring the progress of the notified individualized instruction plan and supplementary materials.

[0864] (Claim 3)

[0865] The system according to claim 1, further comprising means for adjusting instructional plans and supplementary materials based on the monitored progress.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] Means for collecting learner behavioral data,

[0869] A means of using an information processing model for analyzing the collected data,

[0870] A means for identifying learning difficulties based on the aforementioned analysis,

[0871] A means for automatically generating individualized instruction plans and supplementary materials to address identified learning difficulties of learners,

[0872] Means for notifying the generated instruction plan and materials,

[0873] A means of acquiring user behavior information and emotional state in physical stores,

[0874] A means of analyzing user interests based on the information obtained and providing individually suitable products and services,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, further comprising means for monitoring the progress of the notified individualized instruction plan and supplementary materials.

[0878] (Claim 3)

[0879] The system according to claim 1, further comprising means for adjusting instructional plans and supplementary materials based on the monitored progress. [Explanation of Symbols]

[0880] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting learner behavioral data, A means of using an artificial intelligence model to analyze the aforementioned collected data, A means for identifying learning difficulties based on the aforementioned analysis, A means for automatically generating individualized instruction plans and supplementary materials to address the identified learning difficulties of learners, A means for notifying the generated instruction plan and teaching materials, A system that includes this.

2. The system according to claim 1, further comprising means for monitoring the progress of the notified individualized instruction plan and supplementary materials.

3. The system according to claim 1, further comprising means for adjusting the instruction plan and supplementary materials based on the monitored progress.

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