system

Abstract

We provide the system. [Solution] Means for collecting user input data, A means of analyzing collected data to identify users' learning needs, A means of automatically generating user-appropriate learning materials using generative artificial intelligence technology, A means of distributing the generated educational materials to the user's terminal, A means of monitoring the user's learning progress and evaluating learning outcomes, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, there are significant disparities in access to education and its quality around the world. In particular, it is difficult for the conventional education system to adequately respond to learners with economic constraints, lack of regional educational infrastructure, or learning disabilities. As a result, many learners are unable to fully develop their potential, which has contributed to the widening of the education gap. The present invention aims to eliminate such an education gap and provide an education environment optimized for each learner.

Means for Solving the Problems

[0005] This invention provides a technology that collects user input data, analyzes the collected data to identify the user's learning needs, and automatically generates learning materials suitable for the user using generative artificial intelligence technology, and delivers the generated materials to the user's terminal. This system further improves access to education by monitoring the user's learning progress and evaluating learning outcomes. In addition, by translating the learning materials into multiple languages ​​based on generative artificial intelligence technology and providing them in visual and auditory formats, it can accommodate learners with different language backgrounds and learners with visual and auditory difficulties. By monitoring the user's learning progress in real time and providing additional materials or feedback as needed, it realizes flexible learning support tailored to individual learners.

[0006] "Input data" refers to information provided by the user, including profile information, learning history, and learning needs.

[0007] "Analysis" is the process of processing and evaluating collected data to identify users' learning needs and level of understanding.

[0008] "Generative artificial intelligence technology" refers to technologies that include machine learning and natural language processing, and are used to generate educational materials and analyze data.

[0009] "Educational materials" refer to various forms of teaching materials provided to support user learning, and include text, images, audio, and video.

[0010] "Distribution" refers to the process of providing generated educational materials electronically to user terminals.

[0011] "Monitoring" refers to the act of continuously checking a user's learning progress and understanding their level of comprehension and learning speed.

[0012] "Evaluation" is an analytical method that measures the user's learning outcomes based on collected learning data and uses that information to guide the next learning step.

[0013] "Translation" refers to the process of converting information provided in one language into a different language to make it understandable.

[0014] "Visual and auditory formats" refer to formats in which information is conveyed using images, sounds, etc., and users receive the information through their sight and hearing.

[0015] "Monitoring" is the process of tracking users' learning activities in real time and immediately capturing any changes.

[0016] "Feedback" is the act of providing users with information about their learning progress and achievements, and communicating areas for improvement and successes. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0018] 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.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system implemented on an education-related digital platform that provides a personalized educational experience to support user learning. The system performs the following main functions:

[0039] First, users access a digital platform and enter their personal profile information and past learning history. This information is collected by a server and used in subsequent processes. The server analyzes this data to identify the user's learning needs. This analysis uses generative artificial intelligence technology, taking into account the user's past learning data and trends to clarify the necessary learning topics.

[0040] Next, the server generates the most suitable learning materials for the user based on the analysis results. These generated materials come in various formats, including text, images, audio, and video, and are provided to suit the user's learning style. The generated materials are delivered to the user via their device. The user can then proceed with their learning using the materials displayed on their device.

[0041] The server monitors the user's learning progress in real time and evaluates their understanding based on their input and learning history. Based on this evaluation, the server provides feedback to the user to improve learning efficiency. The feedback includes areas where understanding is insufficient and the content that should be studied next, depending on the user's progress.

[0042] Furthermore, the server translates the learning materials into multiple languages, making them available in the user's chosen language. This ensures that high-quality education is provided to learners from diverse cultural backgrounds. Accessibility is also enhanced by providing audio materials for visually impaired users and video materials with subtitles for hearing-impaired users.

[0043] As a concrete example, consider a middle school student learning mathematics. Suppose the user is having difficulty understanding algebra. The system recognizes this information from the user's learning history and generates visually easy-to-understand video materials on solving basic equations. After learning, the server conducts a quiz-style test to confirm the user's understanding and then suggests the next learning step. This entire process is dynamically adjusted according to the user's learning progress, providing an optimal learning experience.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users log in to the digital platform and enter their profile information and learning history. This includes age, grade level, subjects of interest, and past performance.

[0047] Step 2:

[0048] The server collects data entered by the user and stores it in a database. Based on the collected data, it starts an analysis to identify the user's learning needs.

[0049] Step 3:

[0050] The server uses generative artificial intelligence technology to analyze the collected data. This analysis identifies the learning themes and areas that the user needs to strengthen. For example, it identifies weaknesses in specific subjects and finds ways to improve them.

[0051] Step 4:

[0052] The server automatically generates the most suitable learning materials for the user based on the analysis results. These materials are created in various formats, including text, images, audio, and video, and are customized according to the user's preferences and needs.

[0053] Step 5:

[0054] The server converts the generated learning materials into the appropriate format and delivers them to the user's device. Since the materials are provided according to the user's learning style, a smooth learning experience is possible.

[0055] Step 6:

[0056] Users learn using learning materials provided on their devices. The user's learning activities are recorded in real time by the device.

[0057] Step 7:

[0058] The server continuously monitors the user's learning progress and evaluates their level of understanding based on that data. This evaluation identifies areas where further support is needed.

[0059] Step 8:

[0060] The server provides feedback to the user regarding the evaluation results. This feedback includes information and advice to guide further learning. For example, it may recommend additional learning materials on specific topics.

[0061] Step 9:

[0062] The server translates the learning materials according to the language selected by the user, making it multilingual. This process allows the system to accommodate users who speak different languages.

[0063] Step 10:

[0064] As the user progresses, the server continuously provides real-time feedback and additional learning materials. The learning content for the next step is adjusted as needed.

[0065] (Example 1)

[0066] 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."

[0067] In today's educational environment, there is a demand for personalized learning experiences that meet the diverse needs of learners. However, existing educational systems generally rely on the use of standardized materials, making it difficult to provide effective feedback tailored to the individual needs and progress of each learner. Therefore, to improve learning efficiency, it is necessary to provide dynamic educational materials that are tailored to individual learning styles and progress.

[0068] 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.

[0069] In this invention, the server includes means for collecting input information from users, means for analyzing the collected information and identifying learning needs, and means for automatically generating suitable educational materials using generative artificial intelligence technology. This enables the provision of educational materials optimized for individual learners, as well as real-time monitoring and feedback on learning progress.

[0070] "Users" refers to individuals or groups who use the educational system to engage in learning activities.

[0071] "Input information" refers to all data collected when a user accesses the platform, including profile information and learning history.

[0072] "Analysis" is an information processing process that uses input information collected by the server to identify the user's learning needs and trends.

[0073] "Learning needs" refer to the specific educational materials or instruction required by users to learn efficiently.

[0074] "Generative artificial intelligence technology" is a technology that uses artificial intelligence to generate optimal educational materials based on the user's needs and past behavior.

[0075] "Educational materials" refer to teaching materials and learning content provided to support users' learning, and include formats such as text, images, audio, and video.

[0076] "Distribution" refers to the process of sending generated educational materials to the user's device, making them easily accessible to the user.

[0077] A "terminal" is an electronic device used by users to access educational systems and utilize educational materials.

[0078] "Progress" refers to the progress of a user's learning activities, including their learning speed and level of understanding.

[0079] "Evaluation" is the process of measuring a user's learning outcomes and determining how well they understand the material.

[0080] "Multilingual" means supporting multiple different languages, allowing users to access educational materials in their chosen language.

[0081] "Visual and hearing impairments" refer to conditions involving limitations or difficulties related to vision or hearing, which necessitate the provision of specific forms of educational materials.

[0082] A description of the embodiment for carrying out the invention will be provided.

[0083] This invention relates to a system implemented in an educational digital platform, aiming to personalize the user's learning experience. The system primarily consists of a server, terminals, and a user interface.

[0084] The server is responsible for collecting user input information. This input information includes the user's profile and past learning history, and is stored in a database. This data forms the basis for information analysis.

[0085] Next, the server uses a generative AI model to analyze the collected information. Specifically, it identifies the user's learning needs based on their learning tendencies and behavioral history. This process makes it possible to extract different learning themes and skill sets for each user.

[0086] The server then generates optimal educational materials based on the identified learning needs. These materials are provided in various formats, including text, images, audio, and video. For example, interactive infographics can be generated for users who prefer visual learning.

[0087] The terminal receives generated educational materials and provides them to the user. Through the terminal, users can access the educational materials on the system and progress at their own pace. The server monitors the terminal's data in real time and provides feedback based on that progress. This enables feedback that improves the user's understanding and learning efficiency.

[0088] For users with visual or hearing impairments, the server enhances accessibility by providing audio materials and videos with subtitles. The materials are also translated into multiple languages, and a feature allows users to access them in their chosen language.

[0089] As a concrete example, consider the case of a middle school student learning mathematics. If the student is having difficulty with a specific area of ​​mathematics, such as algebra, the server analyzes their learning history and generates easy-to-understand video content. Then, a quiz-style test is presented to check their understanding, and the next learning steps are dynamically suggested based on the data.

[0090] For example, a possible prompt for a generative AI model might be, "Identify the learning needs of a specific user and provide a personalized learning plan."

[0091] In this way, the system provides an educational experience optimized for each individual user, thereby improving learning efficiency and achievement.

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] Users access the digital platform and enter their personal profile information and past learning history. This information becomes the input data sent to the platform. As an initial step, the server receives this data, converts it to a specific format, and stores it in a database. This lays the foundation necessary for subsequent data analysis processes.

[0095] Step 2:

[0096] The server extracts user data from the database and performs analysis using a generative AI model. This analysis process uses the input learning history and profile information to identify the user's learning needs. Specifically, it evaluates past performance and study time to clarify which areas require learning. The output of this analysis is a list of identified learning needs.

[0097] Step 3:

[0098] The server generates educational materials based on identified learning needs. To do this, it sends a prompt to a generative AI model saying, "Generate materials suitable for the user," and creates materials in various formats such as text, images, audio, and video. In this generation process, the input is a list of learning needs, and the output is educational materials in various formats.

[0099] Step 4:

[0100] The server sends the generated educational materials to the terminal. The terminal appropriately displays the received materials and provides them to the user. For example, it allows users to view interactive graphics and videos on the screen of a smartphone or tablet. The input to this process is the data of the educational materials, and the output is the materials displayed in the user interface.

[0101] Step 5:

[0102] The server monitors the user's learning progress in real time. By collecting and analyzing learning activities and the accuracy of responses, it evaluates the user's understanding and progress. The input at this stage is the user's learning behavior data, and the output is the progress evaluation result.

[0103] Step 6:

[0104] The server generates and provides feedback to the user based on the evaluation results. This feedback includes areas for improvement and what to learn next. It also generates and provides additional educational materials as needed. In this process, the input is the progress evaluation results, and the output is the feedback and additional learning materials.

[0105] The above outlines the specific processing steps of this system.

[0106] (Application Example 1)

[0107] 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."

[0108] Traditional education systems typically provide the same learning materials to all students, resulting in insufficient individualization tailored to each student's level of understanding and learning style. Furthermore, a lack of appropriate feedback based on learning progress makes effective learning support difficult. Additionally, language barriers and physical limitations prevent everyone from having equal learning opportunities.

[0109] 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.

[0110] In this invention, the server includes means for collecting information data from users, means for analyzing the collected information and identifying the user's learning requirements, and means for automatically generating educational content suitable for the user using generative information processing technology. This enables the provision of personalized learning materials tailored to individual learners and dynamic adjustment of the learning experience in real time.

[0111] "Means of collecting user information data" refers to a mechanism that accumulates user-provided profile information and learning history and stores it in a database.

[0112] "Means of analyzing collected information and identifying user learning requirements" refers to a process that uses generated information processing technology based on collected information to diagnose user needs and clarify the learning themes required for each learner.

[0113] "A means of automatically generating user-friendly educational content using generative information processing technology" refers to a system that utilizes generative AI technology to generate educational materials in formats such as text, images, audio, and video, tailored to the user's characteristics and needs.

[0114] "Means for delivering generated educational content to user devices" refers to a function that sends created educational materials to the user's device to support their learning.

[0115] "Means for monitoring users' learning progress and evaluating their results" refers to methods that track users' learning activities in real time, measure their level of understanding and results, and provide appropriate feedback.

[0116] "Means of dynamically adjusting the learning experience according to the learner's level of understanding" refers to a function that flexibly changes the learning content and methods based on the user's understanding and progress, thereby achieving optimal learning.

[0117] The system that realizes this invention provides learners with an individualized learning experience using educational support robots and terminals. First, it collects information data provided by the user and sends it to a cloud server for storage. The server analyzes this data using a generation AI model to identify the user's learning requirements. As a result, the most suitable educational content for the user is automatically generated.

[0118] The generated educational content is available in various formats, including text, images, audio, and video, and is delivered to users' devices or robots. The devices use displays and audio output functions to present information in a way that is intuitively easy for learners to understand.

[0119] Furthermore, the server monitors the user's learning progress in real time and evaluates the results based on progress and understanding. Additional learning materials and feedback are automatically provided as needed. In this way, a dynamic learning experience tailored to each individual learner is possible. In addition, the learning materials are translated into multiple languages ​​by a generative AI model and presented in visual and auditory formats, enabling education that accommodates diverse cultures and physical limitations.

[0120] For example, if a user requests to "learn how to find the area of ​​a triangle," the AI ​​model will generate visually animated learning materials and a detailed explanatory video, and deliver them to the device. Thus, an example of a prompt would be, "I would like to learn how to find the area of ​​a triangle; please generate visual learning materials."

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The user enters a learning prompt into the device, for example, "I would like to learn how to find the area of ​​a triangle; please generate visual learning materials." The device sends this prompt to the server. The input data includes the user's request and past learning history.

[0124] Step 2:

[0125] The server passes the received prompt message and the user's past learning history data to the generating AI model to identify the user's learning requirements. Data processing here includes clarifying the request content through text analysis and extracting learning needs through analysis of past history. The output is the identified learning theme and request content.

[0126] Step 3:

[0127] The server uses a generative AI model to automatically generate educational content optimized for the user. In this process, the specified learning theme and format are used as input, and the generated text, image, audio, and video content is output. The generative AI model utilizes natural language processing and image generation technologies to generate the content.

[0128] Step 4:

[0129] The server delivers the generated educational content to the user's device. The device receives this content and provides it to the learner by displaying it on the screen or playing it back using an audio output device. The output here refers to the visual and auditory presentation of the content to the user on the device.

[0130] Step 5:

[0131] Users access learning content through their devices. During the learning process, the device continuously collects user activity logs and responses. This feedback data is used to evaluate the user's learning progress.

[0132] Step 6:

[0133] The server periodically analyzes user progress data sent from terminals and generates additional learning materials and feedback as needed. This data processing includes machine learning algorithms for analyzing progress and assessing comprehension. The output is tailored learning content and new feedback.

[0134] 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.

[0135] This invention is a personalized learning support system that takes user emotions into consideration. By incorporating an emotion engine, it recognizes the user's psychological state in real time and further improves the learning experience. Specific embodiments of the system are shown below.

[0136] First, users access the system through a digital platform and input their personal information and past learning data. The server receives this information and stores it in a database. This information is used to customize learning materials and identify learning needs. In addition, user emotion data is collected in real time through facial recognition technology and voice analysis.

[0137] The server analyzes data obtained from users to identify their learning needs, and then uses AI to generate learning materials optimized for each user. In this process, an emotion engine evaluates the user's mental state in real time, and this information is also reflected in the creation of the learning materials. For example, if the system detects that a user is feeling stressed, it will provide more relaxing learning materials and adjust the learning pace to ensure comfort.

[0138] The generated learning materials are provided in various media formats (text, audio, video, etc.) and delivered to the user's device. The user uses these materials to progress through the learning process. During this time, the emotion engine monitors the user's reactions and evaluates not only their comprehension but also their emotional changes. Based on this data, the server adjusts the learning content and pace and provides feedback.

[0139] As a concrete example, consider a language learning program. If the emotion engine detects that the user is feeling fatigued or frustrated, the server will provide listening materials with a lower difficulty level. This is to prevent the user from becoming discouraged and losing interest in learning.

[0140] Furthermore, user sentiment data can be used to provide more personalized recommendations. For example, if a user finds certain content easy to understand and enjoyable, additional related learning materials can be provided to improve learning efficiency. This allows learners to maintain motivation and progress effectively in their studies.

[0141] Thus, the present invention aims to improve the quality of learning and the user experience by providing a system that takes user emotions into consideration.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] Users access a digital platform and enter their learning history and current learning goals. This includes information about subjects they are interested in and areas they struggle with.

[0145] Step 2:

[0146] The device sends user input data to a server and records its status in real time. Furthermore, it analyzes the user's facial expressions and tone of voice using an emotion engine and sends emotion data to the server.

[0147] Step 3:

[0148] The server analyzes data received from the user and assesses their learning needs and emotional state. Using AI technology, it identifies the optimal learning content tailored to the user's current psychological state.

[0149] Step 4:

[0150] The server automatically generates necessary learning materials using artificial intelligence technology. If the user is relaxed, it generates materials that are easy to concentrate on; if the user is stressed, it prepares content that is easier and more enjoyable.

[0151] Step 5:

[0152] The generated learning materials are converted into the format selected by the user (text, audio, or video) and delivered to the device. The user then uses the provided materials on the device to proceed with their learning.

[0153] Step 6:

[0154] The server monitors the user's reactions through an emotion engine while they are using the learning materials. This involves identifying changes in emotions from facial expressions and voice, and evaluating whether the learning content is effective.

[0155] Step 7:

[0156] If a change in the user's emotional state is detected, the server adjusts the learning materials and pace based on that change. For example, if the user shows satisfaction, it will encourage them to move on to the next, more challenging task.

[0157] Step 8:

[0158] The server evaluates learning outcomes along with sentiment data and provides feedback to the user. This feedback includes areas for improvement, areas of success, and advice for future learning sessions.

[0159] Step 9:

[0160] The system will analyze user sentiment data over the long term to recommend better learning materials and provide users with a continuous and optimal learning environment. The server aims to maximize user learning effectiveness through this process.

[0161] (Example 2)

[0162] 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".

[0163] A problem in modern education systems is the insufficient provision of learning support that takes into account the emotions and psychological states of individual learners. Traditional methods provide standardized materials, making it difficult to maximize learners' motivation and understanding. Furthermore, the lack of real-time feedback and adaptive learning support prevents the provision of an effective learning experience.

[0164] 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.

[0165] In this invention, the server includes means for receiving and recording information from the user, means for analyzing the recorded information to clarify the user's learning needs, and means for collecting and analyzing the user's emotional data using emotion analysis technology. This makes it possible to generate and deliver personalized learning materials that are adapted to the user's emotions and learning needs.

[0166] A "user" refers to an entity that utilizes the system and provides personal information or learning data.

[0167] "Information" includes personal data, emotional data, and data related to learning progress entered by the user.

[0168] A "generative artificial intelligence model" refers to software technology used to automatically generate learning materials that are adapted to the user's learning needs and emotional state.

[0169] "Emotion analysis technology" refers to a technology that determines a user's psychological state from their facial expressions, voice, etc., and collects and analyzes emotional data in real time.

[0170] "Educational materials" refer to educational content created for use by learners, and include various media formats such as text, audio, and video.

[0171] "Device" refers to hardware equipment used by users to advance their learning, including personal computers and smartphones.

[0172] "Progress" refers to an indicator that shows how much of the content a user has understood and achieved during the learning process.

[0173] "Feedback" refers to evaluations and advice provided by the system to the user regarding the learning content.

[0174] This invention is a personalized learning support system that takes into account the user's emotional state. This system improves the user's learning experience by combining a generative AI model and emotion analysis technology.

[0175] First, users access the digital platform using a personal computer or portable information terminal and input personal information and learning history. This information is sent to a server and stored in a database. The server uses video and audio data acquired in real time from the user's camera and microphone to evaluate the user's psychological state using sentiment analysis technology.

[0176] The server uses a generative AI model based on the received user information to generate personalized learning materials. For example, the generative AI model might be given a prompt such as, "Generate the most suitable learning materials based on the user's current emotional state." This model considers the user's learning needs and emotional state to select materials of appropriate difficulty and content.

[0177] The generated learning materials are provided in various media formats, including text, audio, and video, and delivered to the user's device. This allows the user to learn at a relaxed pace. The device monitors the user's responses during learning, and the server evaluates the learning progress and the suitability of the materials based on this.

[0178] As a concrete example, consider a user who is learning a language. When the server determines that the user is feeling fatigued or frustrated, it provides listening materials with adjusted difficulty levels. This kind of adaptive response helps maintain the learner's motivation and allows them to continue learning efficiently.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] Users log in to the digital platform using their personal computers or mobile devices and enter personal information and learning history. The entered data is sent to the server and stored in a database. In this process, the specific actions are filling out text input forms and clicking the submit button.

[0182] Step 2:

[0183] The server analyzes the collected user information to identify learning needs. This includes evaluating the type and difficulty level of necessary learning materials, taking into account the user's past learning history and current goals. Specifically, learning needs are identified by structuring the information using data processing algorithms and extracting features.

[0184] Step 3:

[0185] The server collects user emotional data in real time through cameras and microphones and analyzes this data using emotion analysis technology. The results of the analysis are output as data indicating the user's current psychological state. For example, by scanning facial expressions with facial recognition and evaluating voice tone with voice analysis, emotions such as stress and joy can be identified.

[0186] Step 4:

[0187] The server uses a generative AI model to generate learning materials based on the user's learning needs and emotional state. This process involves inputting collected data as prompts into the generative AI model, which then outputs the most suitable learning materials. Specifically, this includes sending a prompt message to the generative AI model such as, "Provide the most suitable learning materials considering the current emotional state."

[0188] Step 5:

[0189] The generated learning materials are delivered from the server to the user's device. These materials are in various formats, including text files, audio files, and video files, and the format is selected according to the user's learning style. Users access and use these materials on their devices to progress with their studies. Specific operations include downloading and streaming the learning materials.

[0190] Step 6:

[0191] The device monitors the user's responses during learning and feeds the collected data back to the server in real time. Based on this feedback, the server can evaluate the learning progress and adjust the learning materials and pace. Specifically, this involves continuous data capture using a camera and microphone.

[0192] (Application Example 2)

[0193] 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 device 14 will be referred to as the "terminal."

[0194] In modern e-commerce, improving the user's purchasing experience is a crucial challenge. In particular, providing appropriate suggestions in real time based on user emotions can support purchasing decisions and increase satisfaction. However, current systems lack the functionality to accurately analyze user emotion data and immediately suggest products and services based on that analysis.

[0195] 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.

[0196] In this invention, the server includes means for collecting user input information, means for analyzing the collected information and identifying user needs, and means for automatically generating user-appropriate content using generative artificial intelligence technology. This makes it possible to evaluate the user's emotions in real time and make purchase suggestions accordingly.

[0197] "Input information" refers to data provided by the user, including in the form of text, audio, images, and other formats.

[0198] "Demand" refers to the requests and desires that users have for the products and services they want.

[0199] "Generative artificial intelligence technology" is a technology that uses machine learning algorithms to analyze information and generate new content and solutions.

[0200] "Content" refers to information and media provided to users, delivered in electronic format.

[0201] A "device" is a device used by a user to receive information, and includes smartphones, tablets, and personal computers.

[0202] "Usage history" refers to the history of a user's actions and operations when using a particular service or product.

[0203] "Facial recognition technology" is a technique that uses computer vision to detect faces in images and analyze their characteristics.

[0204] "Speech analysis technology" is a technology that processes acoustic signals to identify meaning and emotion.

[0205] "Emotions" refer to the mental state or reactions that a user exhibits.

[0206] "Purchase experience" refers to the overall experience a user has when purchasing a product or service.

[0207] "Suggestion" refers to providing information and advice to encourage users to select or purchase products.

[0208] To implement this invention, users need to access the system using a device such as a smartphone and use devices and software that enable facial recognition and voice analysis. The device utilizes a camera and microphone to capture the user's facial expressions and voice in real time. Specifically, OpenCV is used for facial recognition and Librosa is used for voice analysis.

[0209] The server receives this data and analyzes the user's emotions using a generative AI model based on TENSORFLOW®. The analyzed emotion data is then used by generative artificial intelligence technology to suggest appropriate products and services. This makes it possible to provide users with a real-time, optimized purchasing experience.

[0210] As a concrete example, when a user is browsing products on their smartphone in a store, the system uses the camera and microphone to determine if they are experiencing stress based on their facial expressions and voice. For instance, if the system determines that a user is dissatisfied with the price of a particular product, it will immediately suggest similar, more reasonably priced items to support their purchase.

[0211] Examples of prompts include: "Explain how to suggest products based on user sentiment data and demonstrate how sentiment analysis influences purchasing decisions."

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The device captures the user's facial expressions in real time via a camera and collects audio data via a microphone. The input consists of video and audio data, and the output generates a dataset necessary for analysis. This data is then sent to the next step as initial information.

[0215] Step 2:

[0216] The server uses OpenCV to perform face recognition on video data received from the terminal. This process extracts facial features and converts them into digital data for emotion analysis. The input is video data, and the output is analyzed data showing the user's facial expressions.

[0217] Step 3:

[0218] Similarly, the server analyzes audio data using Librosa. It extracts tone and pitch from the acoustic signal and generates a base dataset for determining emotion. The input is audio data, and the output is analysis data containing features.

[0219] Step 4:

[0220] The server integrates the analysis data obtained in steps 2 and 3 and performs sentiment analysis using a generative AI model with TensorFlow. This sentiment analysis classifies the emotions expressed by the user and generates optimal purchase suggestions. The input is analysis data obtained from facial expressions and voice, and the output is the result of the sentiment classification.

[0221] Step 5:

[0222] The server automatically selects suitable products and services for the user using generative artificial intelligence technology based on the emotion classification results. In this step, product information is created that matches the user's needs and current emotions. The input is the emotion classification result, and the output is the suggested product information.

[0223] Step 6:

[0224] Finally, the terminal displays suggestions received from the server to the user in real time, supporting the purchase. This allows the user to make purchasing decisions based on appropriate information. The input is product information, and the output is the suggested information displayed to the user.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention is a system implemented on an education-related digital platform that provides a personalized educational experience to support user learning. The system performs the following main functions:

[0242] First, users access a digital platform and enter their personal profile information and past learning history. This information is collected by a server and used in subsequent processes. The server analyzes this data to identify the user's learning needs. This analysis uses generative artificial intelligence technology, taking into account the user's past learning data and trends to clarify the necessary learning topics.

[0243] Next, the server generates the most suitable learning materials for the user based on the analysis results. These generated materials come in various formats, including text, images, audio, and video, and are provided to suit the user's learning style. The generated materials are delivered to the user via their device. The user can then proceed with their learning using the materials displayed on their device.

[0244] The server monitors the user's learning progress in real time and evaluates their understanding based on their input and learning history. Based on this evaluation, the server provides feedback to the user to improve learning efficiency. The feedback includes areas where understanding is insufficient and the content that should be studied next, depending on the user's progress.

[0245] Furthermore, the server translates the learning materials into multiple languages, making them available in the user's chosen language. This ensures that high-quality education is provided to learners from diverse cultural backgrounds. Accessibility is also enhanced by providing audio materials for visually impaired users and video materials with subtitles for hearing-impaired users.

[0246] As a concrete example, consider a middle school student learning mathematics. Suppose the user is having difficulty understanding algebra. The system recognizes this information from the user's learning history and generates visually easy-to-understand video materials on solving basic equations. After learning, the server conducts a quiz-style test to confirm the user's understanding and then suggests the next learning step. This entire process is dynamically adjusted according to the user's learning progress, providing an optimal learning experience.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] Users log in to the digital platform and enter their profile information and learning history. This includes age, grade level, subjects of interest, and past performance.

[0250] Step 2:

[0251] The server collects data entered by the user and stores it in a database. Based on the collected data, it starts an analysis to identify the user's learning needs.

[0252] Step 3:

[0253] The server uses generative artificial intelligence technology to analyze the collected data. This analysis identifies the learning themes and areas that the user needs to strengthen. For example, it identifies weaknesses in specific subjects and finds ways to improve them.

[0254] Step 4:

[0255] The server automatically generates the most suitable learning materials for the user based on the analysis results. These materials are created in various formats, including text, images, audio, and video, and are customized according to the user's preferences and needs.

[0256] Step 5:

[0257] The server converts the generated learning materials into the appropriate format and delivers them to the user's device. Since the materials are provided according to the user's learning style, a smooth learning experience is possible.

[0258] Step 6:

[0259] Users learn using learning materials provided on their devices. The user's learning activities are recorded in real time by the device.

[0260] Step 7:

[0261] The server continuously monitors the user's learning progress and evaluates their level of understanding based on that data. This evaluation identifies areas where further support is needed.

[0262] Step 8:

[0263] The server provides feedback to the user regarding the evaluation results. This feedback includes information and advice to guide further learning. For example, it may recommend additional learning materials on specific topics.

[0264] Step 9:

[0265] The server translates the learning materials according to the language selected by the user, making it multilingual. This process allows the system to accommodate users who speak different languages.

[0266] Step 10:

[0267] As the user progresses, the server continuously provides real-time feedback and additional learning materials. The learning content for the next step is adjusted as needed.

[0268] (Example 1)

[0269] 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."

[0270] In today's educational environment, there is a demand for personalized learning experiences that meet the diverse needs of learners. However, existing educational systems generally rely on the use of standardized materials, making it difficult to provide effective feedback tailored to the individual needs and progress of each learner. Therefore, to improve learning efficiency, it is necessary to provide dynamic educational materials that are tailored to individual learning styles and progress.

[0271] 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.

[0272] In this invention, the server includes means for collecting input information from users, means for analyzing the collected information and identifying learning needs, and means for automatically generating suitable educational materials using generative artificial intelligence technology. This enables the provision of educational materials optimized for individual learners, as well as real-time monitoring and feedback on learning progress.

[0273] "Users" refers to individuals or groups who use the educational system to engage in learning activities.

[0274] "Input information" refers to all data collected when a user accesses the platform, including profile information and learning history.

[0275] "Analysis" is an information processing process that uses input information collected by the server to identify the user's learning needs and trends.

[0276] "Learning needs" refer to the specific educational materials or instruction required by users to learn efficiently.

[0277] "Generative artificial intelligence technology" is a technology that uses artificial intelligence to generate optimal educational materials based on the user's needs and past behavior.

[0278] "Educational materials" refer to teaching materials and learning content provided to support users' learning, and include formats such as text, images, audio, and video.

[0279] "Distribution" refers to the process of sending generated educational materials to the user's device, making them easily accessible to the user.

[0280] A "terminal" is an electronic device used by users to access educational systems and utilize educational materials.

[0281] "Progress" refers to the progress of the user's learning activities, including the learning speed and understanding level, etc.

[0282] "Evaluation" is a process of measuring the user's learning achievements and judging how far the understanding has advanced.

[0283] "Multilingual" means corresponding to multiple different languages, and the educational materials can be used in the language selected by the user.

[0284] "Visual and auditory impairments" refer to the state of having restrictions or difficulties related to vision and hearing, which is a condition that requires the provision of specific forms of educational materials.

[0285] The form for implementing the invention will be described.

[0286] The present invention is a system implemented on an education-related digital platform, aiming to individualize the user's learning experience. The system mainly consists of a server, a terminal, and a user interface.

[0287] The server is responsible for collecting input information from the user. The input information includes the user's profile and past learning history, which are stored in a database. This data serves as the basis for information analysis.

[0288] Next, the server uses a generative AI model to analyze the collected information. Specifically, based on the learning tendency and behavior history, the server identifies the user's learning needs. Through this process, it is possible to extract different learning themes and skill sets for each user.

[0289] After that, the server generates optimal educational materials according to the identified learning needs. The teaching materials are provided in various forms such as text, images, audio, and video. For example, for users who prefer to learn visually, interactive infographics can be generated.

[0290] The terminal receives generated educational materials and provides them to the user. Through the terminal, users can access the educational materials on the system and progress at their own pace. The server monitors the terminal's data in real time and provides feedback based on that progress. This enables feedback that improves the user's understanding and learning efficiency.

[0291] For users with visual or hearing impairments, the server enhances accessibility by providing audio materials and videos with subtitles. The materials are also translated into multiple languages, and a feature allows users to access them in their chosen language.

[0292] As a concrete example, consider the case of a middle school student learning mathematics. If the student is having difficulty with a specific area of ​​mathematics, such as algebra, the server analyzes their learning history and generates easy-to-understand video content. Then, a quiz-style test is presented to check their understanding, and the next learning steps are dynamically suggested based on the data.

[0293] For example, a possible prompt for a generative AI model might be, "Identify the learning needs of a specific user and provide a personalized learning plan."

[0294] In this way, the system provides an educational experience optimized for each individual user, thereby improving learning efficiency and achievement.

[0295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0296] Step 1:

[0297] Users access the digital platform and enter their personal profile information and past learning history. This information becomes the input data sent to the platform. As an initial step, the server receives this data, converts it to a specific format, and stores it in a database. This lays the foundation necessary for subsequent data analysis processes.

[0298] Step 2:

[0299] The server extracts user data from the database and performs analysis using a generative AI model. This analysis process uses the input learning history and profile information to identify the user's learning needs. Specifically, it evaluates past performance and study time to clarify which areas require learning. The output of this analysis is a list of identified learning needs.

[0300] Step 3:

[0301] The server generates educational materials based on identified learning needs. To do this, it sends a prompt to a generative AI model saying, "Generate materials suitable for the user," and creates materials in various formats such as text, images, audio, and video. In this generation process, the input is a list of learning needs, and the output is educational materials in various formats.

[0302] Step 4:

[0303] The server sends the generated educational materials to the terminal. The terminal appropriately displays the received materials and provides them to the user. For example, it allows users to view interactive graphics and videos on the screen of a smartphone or tablet. The input to this process is the data of the educational materials, and the output is the materials displayed in the user interface.

[0304] Step 5:

[0305] The server monitors the user's learning progress in real time. By collecting and analyzing the learning activities and answer accuracy during learning, it evaluates the understanding and progress of learning. The input at this stage is the user's learning behavior data, and the output is the evaluation result of the progress.

[0306] Step 6:

[0307] The server creates feedback based on the evaluation results and provides it to the user. The feedback includes areas that need improvement and what to learn next. Also, additional educational materials are generated and provided as needed. In this process, the input is the evaluation result of the progress, and the output is the feedback and additional teaching materials.

[0308] The above are the specific processing steps of this system.

[0309] (Application Example 1)

[0310] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In a conventional education system, it is common to provide the same teaching materials to all learners, and there is a problem that individualization according to the understanding and learning style of each learner is not sufficiently carried out. Also, there is an issue that appropriate feedback according to the learning progress is lacking, making it difficult to provide effective learning support. Furthermore, there is also an issue that equal learning opportunities are not provided to all people due to language barriers and physical limitations.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0313] In this invention, the server includes means for collecting information data from users, means for analyzing the collected information and identifying the user's learning requirements, and means for automatically generating educational content suitable for the user using generative information processing technology. This enables the provision of personalized learning materials tailored to individual learners and dynamic adjustment of the learning experience in real time.

[0314] "Means of collecting user information data" refers to a mechanism that accumulates user-provided profile information and learning history and stores it in a database.

[0315] "Means of analyzing collected information and identifying user learning requirements" refers to a process that uses generated information processing technology based on collected information to diagnose user needs and clarify the learning themes required for each learner.

[0316] "A means of automatically generating user-friendly educational content using generative information processing technology" refers to a system that utilizes generative AI technology to generate educational materials in formats such as text, images, audio, and video, tailored to the user's characteristics and needs.

[0317] "Means for delivering generated educational content to user devices" refers to a function that sends created educational materials to the user's device to support their learning.

[0318] "Means for monitoring users' learning progress and evaluating their results" refers to methods that track users' learning activities in real time, measure their level of understanding and results, and provide appropriate feedback.

[0319] "Means of dynamically adjusting the learning experience according to the learner's level of understanding" refers to a function that flexibly changes the learning content and methods based on the user's understanding and progress, thereby achieving optimal learning.

[0320] The system that realizes this invention provides learners with an individualized learning experience using educational support robots and terminals. First, it collects information data provided by the user and sends it to a cloud server for storage. The server analyzes this data using a generation AI model to identify the user's learning requirements. As a result, the most suitable educational content for the user is automatically generated.

[0321] The generated educational content is available in various formats, including text, images, audio, and video, and is delivered to users' devices or robots. The devices use displays and audio output functions to present information in a way that is intuitively easy for learners to understand.

[0322] Furthermore, the server monitors the user's learning progress in real time and evaluates the results based on progress and understanding. Additional learning materials and feedback are automatically provided as needed. In this way, a dynamic learning experience tailored to each individual learner is possible. In addition, the learning materials are translated into multiple languages ​​by a generative AI model and presented in visual and auditory formats, enabling education that accommodates diverse cultures and physical limitations.

[0323] For example, if a user requests to "learn how to find the area of ​​a triangle," the AI ​​model will generate visually animated learning materials and a detailed explanatory video, and deliver them to the device. Thus, an example of a prompt would be, "I would like to learn how to find the area of ​​a triangle; please generate visual learning materials."

[0324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0325] Step 1:

[0326] The user enters a learning prompt into the device, for example, "I would like to learn how to find the area of ​​a triangle; please generate visual learning materials." The device sends this prompt to the server. The input data includes the user's request and past learning history.

[0327] Step 2:

[0328] The server passes the received prompt message and the user's past learning history data to the generating AI model to identify the user's learning requirements. Data processing here includes clarifying the request content through text analysis and extracting learning needs through analysis of past history. The output is the identified learning theme and request content.

[0329] Step 3:

[0330] The server uses a generative AI model to automatically generate educational content optimized for the user. In this process, the specified learning theme and format are used as input, and the generated text, image, audio, and video content is output. The generative AI model utilizes natural language processing and image generation technologies to generate the content.

[0331] Step 4:

[0332] The server delivers the generated educational content to the user's device. The device receives this content and provides it to the learner by displaying it on the screen or playing it back using an audio output device. The output here refers to the visual and auditory presentation of the content to the user on the device.

[0333] Step 5:

[0334] Users access learning content through their devices. During the learning process, the device continuously collects user activity logs and responses. This feedback data is used to evaluate the user's learning progress.

[0335] Step 6:

[0336] The server periodically analyzes user progress data sent from terminals and generates additional learning materials and feedback as needed. This data processing includes machine learning algorithms for analyzing progress and assessing comprehension. The output is tailored learning content and new feedback.

[0337] 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.

[0338] This invention is a personalized learning support system that takes user emotions into consideration. By incorporating an emotion engine, it recognizes the user's psychological state in real time and further improves the learning experience. Specific embodiments of the system are shown below.

[0339] First, users access the system through a digital platform and input their personal information and past learning data. The server receives this information and stores it in a database. This information is used to customize learning materials and identify learning needs. In addition, user emotion data is collected in real time through facial recognition technology and voice analysis.

[0340] The server analyzes data obtained from users to identify their learning needs, and then uses AI to generate learning materials optimized for each user. In this process, an emotion engine evaluates the user's mental state in real time, and this information is also reflected in the creation of the learning materials. For example, if the system detects that a user is feeling stressed, it will provide more relaxing learning materials and adjust the learning pace to ensure comfort.

[0341] The generated learning materials are provided in various media formats (text, audio, video, etc.) and delivered to the user's device. The user uses these materials to progress through the learning process. During this time, the emotion engine monitors the user's reactions and evaluates not only their comprehension but also their emotional changes. Based on this data, the server adjusts the learning content and pace and provides feedback.

[0342] As a concrete example, consider a language learning program. If the emotion engine detects that the user is feeling fatigued or frustrated, the server will provide listening materials with a lower difficulty level. This is to prevent the user from becoming discouraged and losing interest in learning.

[0343] Furthermore, user sentiment data can be used to provide more personalized recommendations. For example, if a user finds certain content easy to understand and enjoyable, additional related learning materials can be provided to improve learning efficiency. This allows learners to maintain motivation and progress effectively in their studies.

[0344] Thus, the present invention aims to improve the quality of learning and the user experience by providing a system that takes user emotions into consideration.

[0345] The following describes the processing flow.

[0346] Step 1:

[0347] Users access a digital platform and enter their learning history and current learning goals. This includes information about subjects they are interested in and areas they struggle with.

[0348] Step 2:

[0349] The device sends user input data to a server and records its status in real time. Furthermore, it analyzes the user's facial expressions and tone of voice using an emotion engine and sends emotion data to the server.

[0350] Step 3:

[0351] The server analyzes data received from the user and assesses their learning needs and emotional state. Using AI technology, it identifies the optimal learning content tailored to the user's current psychological state.

[0352] Step 4:

[0353] The server automatically generates necessary learning materials using artificial intelligence technology. If the user is relaxed, it generates materials that are easy to concentrate on; if the user is stressed, it prepares content that is easier and more enjoyable.

[0354] Step 5:

[0355] The generated learning materials are converted into the format selected by the user (text, audio, or video) and delivered to the device. The user then uses the provided materials on the device to proceed with their learning.

[0356] Step 6:

[0357] The server monitors the user's reactions through an emotion engine while they are using the learning materials. This involves identifying changes in emotions from facial expressions and voice, and evaluating whether the learning content is effective.

[0358] Step 7:

[0359] If a change in the user's emotional state is detected, the server adjusts the learning materials and pace based on that change. For example, if the user shows satisfaction, it will encourage them to move on to the next, more challenging task.

[0360] Step 8:

[0361] The server evaluates learning outcomes along with sentiment data and provides feedback to the user. This feedback includes areas for improvement, areas of success, and advice for future learning sessions.

[0362] Step 9:

[0363] The system will analyze user sentiment data over the long term to recommend better learning materials and provide users with a continuous and optimal learning environment. The server aims to maximize user learning effectiveness through this process.

[0364] (Example 2)

[0365] 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".

[0366] A problem in modern education systems is the insufficient provision of learning support that takes into account the emotions and psychological states of individual learners. Traditional methods provide standardized materials, making it difficult to maximize learners' motivation and understanding. Furthermore, the lack of real-time feedback and adaptive learning support prevents the provision of an effective learning experience.

[0367] 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.

[0368] In this invention, the server includes means for receiving and recording information from the user, means for analyzing the recorded information to clarify the user's learning needs, and means for collecting and analyzing the user's emotional data using emotion analysis technology. This makes it possible to generate and deliver personalized learning materials that are adapted to the user's emotions and learning needs.

[0369] A "user" refers to an entity that utilizes the system and provides personal information or learning data.

[0370] "Information" includes personal data, emotional data, and data related to learning progress entered by the user.

[0371] A "generative artificial intelligence model" refers to software technology used to automatically generate learning materials that are adapted to the user's learning needs and emotional state.

[0372] "Emotion analysis technology" refers to a technology that determines a user's psychological state from their facial expressions, voice, etc., and collects and analyzes emotional data in real time.

[0373] "Educational materials" refer to educational content created for use by learners, and include various media formats such as text, audio, and video.

[0374] "Device" refers to hardware equipment used by users to advance their learning, including personal computers and smartphones.

[0375] "Progress" refers to an indicator that shows how much of the content a user has understood and achieved during the learning process.

[0376] "Feedback" refers to evaluations and advice provided by the system to the user regarding the learning content.

[0377] This invention is a personalized learning support system that takes into account the user's emotional state. This system improves the user's learning experience by combining a generative AI model and emotion analysis technology.

[0378] First, users access the digital platform using a personal computer or portable information terminal and input personal information and learning history. This information is sent to a server and stored in a database. The server uses video and audio data acquired in real time from the user's camera and microphone to evaluate the user's psychological state using sentiment analysis technology.

[0379] The server uses a generative AI model based on the received user information to generate personalized learning materials. For example, the generative AI model might be given a prompt such as, "Generate the most suitable learning materials based on the user's current emotional state." This model considers the user's learning needs and emotional state to select materials of appropriate difficulty and content.

[0380] The generated learning materials are provided in various media formats, including text, audio, and video, and delivered to the user's device. This allows the user to learn at a relaxed pace. The device monitors the user's responses during learning, and the server evaluates the learning progress and the suitability of the materials based on this.

[0381] As a concrete example, consider a user who is learning a language. When the server determines that the user is feeling fatigued or frustrated, it provides listening materials with adjusted difficulty levels. This kind of adaptive response helps maintain the learner's motivation and allows them to continue learning efficiently.

[0382] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0383] Step 1:

[0384] Users log in to the digital platform using their personal computers or mobile devices and enter personal information and learning history. The entered data is sent to the server and stored in a database. In this process, the specific actions are filling out text input forms and clicking the submit button.

[0385] Step 2:

[0386] The server analyzes the collected user information to identify learning needs. This includes evaluating the type and difficulty level of necessary learning materials, taking into account the user's past learning history and current goals. Specifically, learning needs are identified by structuring the information using data processing algorithms and extracting features.

[0387] Step 3:

[0388] The server collects user emotional data in real time through cameras and microphones and analyzes this data using emotion analysis technology. The results of the analysis are output as data indicating the user's current psychological state. For example, by scanning facial expressions with facial recognition and evaluating voice tone with voice analysis, emotions such as stress and joy can be identified.

[0389] Step 4:

[0390] The server uses a generative AI model to generate learning materials based on the user's learning needs and emotional state. This process involves inputting collected data as prompts into the generative AI model, which then outputs the most suitable learning materials. Specifically, this includes sending a prompt message to the generative AI model such as, "Provide the most suitable learning materials considering the current emotional state."

[0391] Step 5:

[0392] The generated learning materials are delivered from the server to the user's device. These materials are in various formats, including text files, audio files, and video files, and the format is selected according to the user's learning style. Users access and use these materials on their devices to progress with their studies. Specific operations include downloading and streaming the learning materials.

[0393] Step 6:

[0394] The device monitors the user's responses during learning and feeds the collected data back to the server in real time. Based on this feedback, the server can evaluate the learning progress and adjust the learning materials and pace. Specifically, this involves continuous data capture using a camera and microphone.

[0395] (Application Example 2)

[0396] 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 as the "terminal".

[0397] In modern e-commerce, improving the user's purchasing experience is a crucial challenge. In particular, providing appropriate suggestions in real time based on user emotions can support purchasing decisions and increase satisfaction. However, current systems lack the functionality to accurately analyze user emotion data and immediately suggest products and services based on that analysis.

[0398] 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.

[0399] In this invention, the server includes means for collecting user input information, means for analyzing the collected information and identifying user needs, and means for automatically generating user-appropriate content using generative artificial intelligence technology. This makes it possible to evaluate the user's emotions in real time and make purchase suggestions accordingly.

[0400] "Input information" refers to data provided by the user, including in the form of text, audio, images, and other formats.

[0401] "Demand" refers to the requests and desires that users have for the products and services they want.

[0402] "Generative artificial intelligence technology" is a technology that uses machine learning algorithms to analyze information and generate new content and solutions.

[0403] "Content" refers to information and media provided to users, delivered in electronic format.

[0404] A "device" is a device used by a user to receive information, and includes smartphones, tablets, and personal computers.

[0405] "Usage history" refers to the history of a user's actions and operations when using a particular service or product.

[0406] "Facial recognition technology" is a technique that uses computer vision to detect faces in images and analyze their characteristics.

[0407] "Speech analysis technology" is a technology that processes acoustic signals to identify meaning and emotion.

[0408] "Emotions" refer to the mental state or reactions that a user exhibits.

[0409] "Purchase experience" refers to the overall experience a user has when purchasing a product or service.

[0410] "Suggestion" refers to providing information and advice to encourage users to select or purchase products.

[0411] To implement this invention, users need to access the system using a device such as a smartphone and use devices and software that enable facial recognition and voice analysis. The device utilizes a camera and microphone to capture the user's facial expressions and voice in real time. Specifically, OpenCV is used for facial recognition and Librosa is used for voice analysis.

[0412] The server receives this data and analyzes the user's emotions using a generative AI model based on TensorFlow. The analyzed emotion data is then used by generative artificial intelligence technology to suggest appropriate products and services. This makes it possible to provide users with a real-time, optimized purchasing experience.

[0413] As a concrete example, when a user is browsing products on their smartphone in a store, the system uses the camera and microphone to determine if they are experiencing stress based on their facial expressions and voice. For instance, if the system determines that a user is dissatisfied with the price of a particular product, it will immediately suggest similar, more reasonably priced items to support their purchase.

[0414] Examples of prompts include: "Explain how to suggest products based on user sentiment data and demonstrate how sentiment analysis influences purchasing decisions."

[0415] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0416] Step 1:

[0417] The device captures the user's facial expressions in real time via a camera and collects audio data via a microphone. The input consists of video and audio data, and the output generates a dataset necessary for analysis. This data is then sent to the next step as initial information.

[0418] Step 2:

[0419] The server uses OpenCV to perform face recognition on video data received from the terminal. This process extracts facial features and converts them into digital data for emotion analysis. The input is video data, and the output is analyzed data showing the user's facial expressions.

[0420] Step 3:

[0421] Similarly, the server analyzes audio data using Librosa. It extracts tone and pitch from the acoustic signal and generates a base dataset for determining emotion. The input is audio data, and the output is analysis data containing features.

[0422] Step 4:

[0423] The server integrates the analysis data obtained in steps 2 and 3 and performs sentiment analysis using a generative AI model with TensorFlow. This sentiment analysis classifies the emotions expressed by the user and generates optimal purchase suggestions. The input is analysis data obtained from facial expressions and voice, and the output is the result of the sentiment classification.

[0424] Step 5:

[0425] The server automatically selects suitable products and services for the user using generative artificial intelligence technology based on the emotion classification results. In this step, product information is created that matches the user's needs and current emotions. The input is the emotion classification result, and the output is the suggested product information.

[0426] Step 6:

[0427] Finally, the terminal displays suggestions received from the server to the user in real time, supporting the purchase. This allows the user to make purchasing decisions based on appropriate information. The input is product information, and the output is the suggested information displayed to the user.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] [Third Embodiment]

[0432] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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".

[0444] This invention is a system implemented on an education-related digital platform that provides a personalized educational experience to support user learning. The system performs the following main functions:

[0445] First, users access a digital platform and enter their personal profile information and past learning history. This information is collected by a server and used in subsequent processes. The server analyzes this data to identify the user's learning needs. This analysis uses generative artificial intelligence technology, taking into account the user's past learning data and trends to clarify the necessary learning topics.

[0446] Next, the server generates the most suitable learning materials for the user based on the analysis results. These generated materials come in various formats, including text, images, audio, and video, and are provided to suit the user's learning style. The generated materials are delivered to the user via their device. The user can then proceed with their learning using the materials displayed on their device.

[0447] The server monitors the user's learning progress in real time and evaluates their understanding based on their input and learning history. Based on this evaluation, the server provides feedback to the user to improve learning efficiency. The feedback includes areas where understanding is insufficient and the content that should be studied next, depending on the user's progress.

[0448] Furthermore, the server translates the learning materials into multiple languages, making them available in the user's chosen language. This ensures that high-quality education is provided to learners from diverse cultural backgrounds. Accessibility is also enhanced by providing audio materials for visually impaired users and video materials with subtitles for hearing-impaired users.

[0449] As a concrete example, consider a middle school student learning mathematics. Suppose the user is having difficulty understanding algebra. The system recognizes this information from the user's learning history and generates visually easy-to-understand video materials on solving basic equations. After learning, the server conducts a quiz-style test to confirm the user's understanding and then suggests the next learning step. This entire process is dynamically adjusted according to the user's learning progress, providing an optimal learning experience.

[0450] The following describes the processing flow.

[0451] Step 1:

[0452] Users log in to the digital platform and enter their profile information and learning history. This includes age, grade level, subjects of interest, and past performance.

[0453] Step 2:

[0454] The server collects data entered by the user and stores it in a database. Based on the collected data, it starts an analysis to identify the user's learning needs.

[0455] Step 3:

[0456] The server uses generative artificial intelligence technology to analyze the collected data. This analysis identifies the learning themes and areas that the user needs to strengthen. For example, it identifies weaknesses in specific subjects and finds ways to improve them.

[0457] Step 4:

[0458] The server automatically generates the most suitable learning materials for the user based on the analysis results. These materials are created in various formats, including text, images, audio, and video, and are customized according to the user's preferences and needs.

[0459] Step 5:

[0460] The server converts the generated learning materials into the appropriate format and delivers them to the user's device. Since the materials are provided according to the user's learning style, a smooth learning experience is possible.

[0461] Step 6:

[0462] Users learn using learning materials provided on their devices. The user's learning activities are recorded in real time by the device.

[0463] Step 7:

[0464] The server continuously monitors the user's learning progress and evaluates their level of understanding based on that data. This evaluation identifies areas where further support is needed.

[0465] Step 8:

[0466] The server provides feedback to the user regarding the evaluation results. This feedback includes information and advice to guide further learning. For example, it may recommend additional learning materials on specific topics.

[0467] Step 9:

[0468] The server translates the learning materials according to the language selected by the user, making it multilingual. This process allows the system to accommodate users who speak different languages.

[0469] Step 10:

[0470] As the user progresses, the server continuously provides real-time feedback and additional learning materials. The learning content for the next step is adjusted as needed.

[0471] (Example 1)

[0472] 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."

[0473] In today's educational environment, there is a demand for personalized learning experiences that meet the diverse needs of learners. However, existing educational systems generally rely on the use of standardized materials, making it difficult to provide effective feedback tailored to the individual needs and progress of each learner. Therefore, to improve learning efficiency, it is necessary to provide dynamic educational materials that are tailored to individual learning styles and progress.

[0474] 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.

[0475] In this invention, the server includes means for collecting input information from users, means for analyzing the collected information and identifying learning needs, and means for automatically generating suitable educational materials using generative artificial intelligence technology. This enables the provision of educational materials optimized for individual learners, as well as real-time monitoring and feedback on learning progress.

[0476] "Users" refers to individuals or groups who use the educational system to engage in learning activities.

[0477] "Input information" refers to all data collected when a user accesses the platform, including profile information and learning history.

[0478] "Analysis" is an information processing process that uses input information collected by the server to identify the user's learning needs and trends.

[0479] "Learning needs" refer to the specific educational materials or instruction required by users to learn efficiently.

[0480] "Generative artificial intelligence technology" is a technology that uses artificial intelligence to generate optimal educational materials based on the user's needs and past behavior.

[0481] "Educational materials" refer to teaching materials and learning content provided to support users' learning, and include formats such as text, images, audio, and video.

[0482] "Distribution" refers to the process of sending generated educational materials to the user's device, making them easily accessible to the user.

[0483] A "terminal" is an electronic device used by users to access educational systems and utilize educational materials.

[0484] "Progress" refers to the progress of a user's learning activities, including their learning speed and level of understanding.

[0485] "Evaluation" is the process of measuring a user's learning outcomes and determining how well they understand the material.

[0486] "Multilingual" means supporting multiple different languages, allowing users to access educational materials in their chosen language.

[0487] "Visual and hearing impairments" refer to conditions involving limitations or difficulties related to vision or hearing, which necessitate the provision of specific forms of educational materials.

[0488] A description of the embodiment for carrying out the invention will be provided.

[0489] This invention relates to a system implemented in an educational digital platform, aiming to personalize the user's learning experience. The system primarily consists of a server, terminals, and a user interface.

[0490] The server is responsible for collecting user input information. This input information includes the user's profile and past learning history, and is stored in a database. This data forms the basis for information analysis.

[0491] Next, the server uses a generative AI model to analyze the collected information. Specifically, it identifies the user's learning needs based on their learning tendencies and behavioral history. This process makes it possible to extract different learning themes and skill sets for each user.

[0492] The server then generates optimal educational materials based on the identified learning needs. These materials are provided in various formats, including text, images, audio, and video. For example, interactive infographics can be generated for users who prefer visual learning.

[0493] The terminal receives generated educational materials and provides them to the user. Through the terminal, users can access the educational materials on the system and progress at their own pace. The server monitors the terminal's data in real time and provides feedback based on that progress. This enables feedback that improves the user's understanding and learning efficiency.

[0494] For users with visual or hearing impairments, the server enhances accessibility by providing audio materials and videos with subtitles. The materials are also translated into multiple languages, and a feature allows users to access them in their chosen language.

[0495] As a concrete example, consider the case of a middle school student learning mathematics. If the student is having difficulty with a specific area of ​​mathematics, such as algebra, the server analyzes their learning history and generates easy-to-understand video content. Then, a quiz-style test is presented to check their understanding, and the next learning steps are dynamically suggested based on the data.

[0496] For example, a possible prompt for a generative AI model might be, "Identify the learning needs of a specific user and provide a personalized learning plan."

[0497] In this way, the system provides an educational experience optimized for each individual user, thereby improving learning efficiency and achievement.

[0498] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0499] Step 1:

[0500] Users access the digital platform and enter their personal profile information and past learning history. This information becomes the input data sent to the platform. As an initial step, the server receives this data, converts it to a specific format, and stores it in a database. This lays the foundation necessary for subsequent data analysis processes.

[0501] Step 2:

[0502] The server extracts user data from the database and performs analysis using a generative AI model. This analysis process uses the input learning history and profile information to identify the user's learning needs. Specifically, it evaluates past performance and study time to clarify which areas require learning. The output of this analysis is a list of identified learning needs.

[0503] Step 3:

[0504] The server generates educational materials based on identified learning needs. To do this, it sends a prompt to a generative AI model saying, "Generate materials suitable for the user," and creates materials in various formats such as text, images, audio, and video. In this generation process, the input is a list of learning needs, and the output is educational materials in various formats.

[0505] Step 4:

[0506] The server sends the generated educational materials to the terminal. The terminal appropriately displays the received materials and provides them to the user. For example, it allows users to view interactive graphics and videos on the screen of a smartphone or tablet. The input to this process is the data of the educational materials, and the output is the materials displayed in the user interface.

[0507] Step 5:

[0508] The server monitors the user's learning progress in real time. By collecting and analyzing learning activities and the accuracy of responses, it evaluates the user's understanding and progress. The input at this stage is the user's learning behavior data, and the output is the progress evaluation result.

[0509] Step 6:

[0510] The server generates and provides feedback to the user based on the evaluation results. This feedback includes areas for improvement and what to learn next. It also generates and provides additional educational materials as needed. In this process, the input is the progress evaluation results, and the output is the feedback and additional learning materials.

[0511] The above outlines the specific processing steps of this system.

[0512] (Application Example 1)

[0513] 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."

[0514] Traditional education systems typically provide the same learning materials to all students, resulting in insufficient individualization tailored to each student's level of understanding and learning style. Furthermore, a lack of appropriate feedback based on learning progress makes effective learning support difficult. Additionally, language barriers and physical limitations prevent everyone from having equal learning opportunities.

[0515] 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.

[0516] In this invention, the server includes means for collecting information data from users, means for analyzing the collected information and identifying the user's learning requirements, and means for automatically generating educational content suitable for the user using generative information processing technology. This enables the provision of personalized learning materials tailored to individual learners and dynamic adjustment of the learning experience in real time.

[0517] "Means of collecting user information data" refers to a mechanism that accumulates user-provided profile information and learning history and stores it in a database.

[0518] "Means of analyzing collected information and identifying user learning requirements" refers to a process that uses generated information processing technology based on collected information to diagnose user needs and clarify the learning themes required for each learner.

[0519] "A means of automatically generating user-friendly educational content using generative information processing technology" refers to a system that utilizes generative AI technology to generate educational materials in formats such as text, images, audio, and video, tailored to the user's characteristics and needs.

[0520] "Means for delivering generated educational content to user devices" refers to a function that sends created educational materials to the user's device to support their learning.

[0521] "Means for monitoring users' learning progress and evaluating their results" refers to methods that track users' learning activities in real time, measure their level of understanding and results, and provide appropriate feedback.

[0522] "Means of dynamically adjusting the learning experience according to the learner's level of understanding" refers to a function that flexibly changes the learning content and methods based on the user's understanding and progress, thereby achieving optimal learning.

[0523] The system that realizes this invention provides learners with an individualized learning experience using educational support robots and terminals. First, it collects information data provided by the user and sends it to a cloud server for storage. The server analyzes this data using a generation AI model to identify the user's learning requirements. As a result, the most suitable educational content for the user is automatically generated.

[0524] The generated educational content is available in various formats, including text, images, audio, and video, and is delivered to users' devices or robots. The devices use displays and audio output functions to present information in a way that is intuitively easy for learners to understand.

[0525] Furthermore, the server monitors the user's learning progress in real time and evaluates the results based on progress and understanding. Additional learning materials and feedback are automatically provided as needed. In this way, a dynamic learning experience tailored to each individual learner is possible. In addition, the learning materials are translated into multiple languages ​​by a generative AI model and presented in visual and auditory formats, enabling education that accommodates diverse cultures and physical limitations.

[0526] For example, if a user requests to "learn how to find the area of ​​a triangle," the AI ​​model will generate visually animated learning materials and a detailed explanatory video, and deliver them to the device. Thus, an example of a prompt would be, "I would like to learn how to find the area of ​​a triangle; please generate visual learning materials."

[0527] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0528] Step 1:

[0529] The user enters a learning prompt into the device, for example, "I would like to learn how to find the area of ​​a triangle; please generate visual learning materials." The device sends this prompt to the server. The input data includes the user's request and past learning history.

[0530] Step 2:

[0531] The server passes the received prompt message and the user's past learning history data to the generating AI model to identify the user's learning requirements. Data processing here includes clarifying the request content through text analysis and extracting learning needs through analysis of past history. The output is the identified learning theme and request content.

[0532] Step 3:

[0533] The server uses a generative AI model to automatically generate educational content optimized for the user. In this process, the specified learning theme and format are used as input, and the generated text, image, audio, and video content is output. The generative AI model utilizes natural language processing and image generation technologies to generate the content.

[0534] Step 4:

[0535] The server delivers the generated educational content to the user's device. The device receives this content and provides it to the learner by displaying it on the screen or playing it back using an audio output device. The output here refers to the visual and auditory presentation of the content to the user on the device.

[0536] Step 5:

[0537] Users access learning content through their devices. During the learning process, the device continuously collects user activity logs and responses. This feedback data is used to evaluate the user's learning progress.

[0538] Step 6:

[0539] The server periodically analyzes user progress data sent from terminals and generates additional learning materials and feedback as needed. This data processing includes machine learning algorithms for analyzing progress and assessing comprehension. The output is tailored learning content and new feedback.

[0540] 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.

[0541] This invention is a personalized learning support system that takes user emotions into consideration. By incorporating an emotion engine, it recognizes the user's psychological state in real time and further improves the learning experience. Specific embodiments of the system are shown below.

[0542] First, users access the system through a digital platform and input their personal information and past learning data. The server receives this information and stores it in a database. This information is used to customize learning materials and identify learning needs. In addition, user emotion data is collected in real time through facial recognition technology and voice analysis.

[0543] The server analyzes data obtained from users to identify their learning needs, and then uses AI to generate learning materials optimized for each user. In this process, an emotion engine evaluates the user's mental state in real time, and this information is also reflected in the creation of the learning materials. For example, if the system detects that a user is feeling stressed, it will provide more relaxing learning materials and adjust the learning pace to ensure comfort.

[0544] The generated learning materials are provided in various media formats (text, audio, video, etc.) and delivered to the user's device. The user uses these materials to progress through the learning process. During this time, the emotion engine monitors the user's reactions and evaluates not only their comprehension but also their emotional changes. Based on this data, the server adjusts the learning content and pace and provides feedback.

[0545] As a concrete example, consider a language learning program. If the emotion engine detects that the user is feeling fatigued or frustrated, the server will provide listening materials with a lower difficulty level. This is to prevent the user from becoming discouraged and losing interest in learning.

[0546] Furthermore, user sentiment data can be used to provide more personalized recommendations. For example, if a user finds certain content easy to understand and enjoyable, additional related learning materials can be provided to improve learning efficiency. This allows learners to maintain motivation and progress effectively in their studies.

[0547] Thus, the present invention aims to improve the quality of learning and the user experience by providing a system that takes user emotions into consideration.

[0548] The following describes the processing flow.

[0549] Step 1:

[0550] Users access a digital platform and enter their learning history and current learning goals. This includes information about subjects they are interested in and areas they struggle with.

[0551] Step 2:

[0552] The device sends user input data to a server and records its status in real time. Furthermore, it analyzes the user's facial expressions and tone of voice using an emotion engine and sends emotion data to the server.

[0553] Step 3:

[0554] The server analyzes data received from the user and assesses their learning needs and emotional state. Using AI technology, it identifies the optimal learning content tailored to the user's current psychological state.

[0555] Step 4:

[0556] The server automatically generates necessary learning materials using artificial intelligence technology. If the user is relaxed, it generates materials that are easy to concentrate on; if the user is stressed, it prepares content that is easier and more enjoyable.

[0557] Step 5:

[0558] The generated learning materials are converted into the format selected by the user (text, audio, or video) and delivered to the device. The user then uses the provided materials on the device to proceed with their learning.

[0559] Step 6:

[0560] The server monitors the user's reactions through an emotion engine while they are using the learning materials. This involves identifying changes in emotions from facial expressions and voice, and evaluating whether the learning content is effective.

[0561] Step 7:

[0562] If a change in the user's emotional state is detected, the server adjusts the learning materials and pace based on that change. For example, if the user shows satisfaction, it will encourage them to move on to the next, more challenging task.

[0563] Step 8:

[0564] The server evaluates learning outcomes along with sentiment data and provides feedback to the user. This feedback includes areas for improvement, areas of success, and advice for future learning sessions.

[0565] Step 9:

[0566] The system will analyze user sentiment data over the long term to recommend better learning materials and provide users with a continuous and optimal learning environment. The server aims to maximize user learning effectiveness through this process.

[0567] (Example 2)

[0568] 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."

[0569] A problem in modern education systems is the insufficient provision of learning support that takes into account the emotions and psychological states of individual learners. Traditional methods provide standardized materials, making it difficult to maximize learners' motivation and understanding. Furthermore, the lack of real-time feedback and adaptive learning support prevents the provision of an effective learning experience.

[0570] 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.

[0571] In this invention, the server includes means for receiving and recording information from the user, means for analyzing the recorded information to clarify the user's learning needs, and means for collecting and analyzing the user's emotional data using emotion analysis technology. This makes it possible to generate and deliver personalized learning materials that are adapted to the user's emotions and learning needs.

[0572] A "user" refers to an entity that utilizes the system and provides personal information or learning data.

[0573] "Information" includes personal data, emotional data, and data related to learning progress entered by the user.

[0574] A "generative artificial intelligence model" refers to software technology used to automatically generate learning materials that are adapted to the user's learning needs and emotional state.

[0575] "Emotion analysis technology" refers to a technology that determines a user's psychological state from their facial expressions, voice, etc., and collects and analyzes emotional data in real time.

[0576] "Educational materials" refer to educational content created for use by learners, and include various media formats such as text, audio, and video.

[0577] "Device" refers to hardware equipment used by users to advance their learning, including personal computers and smartphones.

[0578] "Progress" refers to an indicator that shows how much of the content a user has understood and achieved during the learning process.

[0579] "Feedback" refers to evaluations and advice provided by the system to the user regarding the learning content.

[0580] This invention is a personalized learning support system that takes into account the user's emotional state. This system improves the user's learning experience by combining a generative AI model and emotion analysis technology.

[0581] First, users access the digital platform using a personal computer or portable information terminal and input personal information and learning history. This information is sent to a server and stored in a database. The server uses video and audio data acquired in real time from the user's camera and microphone to evaluate the user's psychological state using sentiment analysis technology.

[0582] The server uses a generative AI model based on the received user information to generate personalized learning materials. For example, the generative AI model might be given a prompt such as, "Generate the most suitable learning materials based on the user's current emotional state." This model considers the user's learning needs and emotional state to select materials of appropriate difficulty and content.

[0583] The generated learning materials are provided in various media formats, including text, audio, and video, and delivered to the user's device. This allows the user to learn at a relaxed pace. The device monitors the user's responses during learning, and the server evaluates the learning progress and the suitability of the materials based on this.

[0584] As a concrete example, consider a user who is learning a language. When the server determines that the user is feeling fatigued or frustrated, it provides listening materials with adjusted difficulty levels. This kind of adaptive response helps maintain the learner's motivation and allows them to continue learning efficiently.

[0585] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0586] Step 1:

[0587] Users log in to the digital platform using their personal computers or mobile devices and enter personal information and learning history. The entered data is sent to the server and stored in a database. In this process, the specific actions are filling out text input forms and clicking the submit button.

[0588] Step 2:

[0589] The server analyzes the collected user information to identify learning needs. This includes evaluating the type and difficulty level of necessary learning materials, taking into account the user's past learning history and current goals. Specifically, learning needs are identified by structuring the information using data processing algorithms and extracting features.

[0590] Step 3:

[0591] The server collects user emotional data in real time through cameras and microphones and analyzes this data using emotion analysis technology. The results of the analysis are output as data indicating the user's current psychological state. For example, by scanning facial expressions with facial recognition and evaluating voice tone with voice analysis, emotions such as stress and joy can be identified.

[0592] Step 4:

[0593] The server uses a generative AI model to generate learning materials based on the user's learning needs and emotional state. This process involves inputting collected data as prompts into the generative AI model, which then outputs the most suitable learning materials. Specifically, this includes sending a prompt message to the generative AI model such as, "Provide the most suitable learning materials considering the current emotional state."

[0594] Step 5:

[0595] The generated learning materials are delivered from the server to the user's device. These materials are in various formats, including text files, audio files, and video files, and the format is selected according to the user's learning style. Users access and use these materials on their devices to progress with their studies. Specific operations include downloading and streaming the learning materials.

[0596] Step 6:

[0597] The device monitors the user's responses during learning and feeds the collected data back to the server in real time. Based on this feedback, the server can evaluate the learning progress and adjust the learning materials and pace. Specifically, this involves continuous data capture using a camera and microphone.

[0598] (Application Example 2)

[0599] 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."

[0600] In modern e-commerce, improving the user's purchasing experience is a crucial challenge. In particular, providing appropriate suggestions in real time based on user emotions can support purchasing decisions and increase satisfaction. However, current systems lack the functionality to accurately analyze user emotion data and immediately suggest products and services based on that analysis.

[0601] 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.

[0602] In this invention, the server includes means for collecting user input information, means for analyzing the collected information and identifying user needs, and means for automatically generating user-appropriate content using generative artificial intelligence technology. This makes it possible to evaluate the user's emotions in real time and make purchase suggestions accordingly.

[0603] "Input information" refers to data provided by the user, including in the form of text, audio, images, and other formats.

[0604] "Demand" refers to the requests and desires that users have for the products and services they want.

[0605] "Generative artificial intelligence technology" is a technology that uses machine learning algorithms to analyze information and generate new content and solutions.

[0606] "Content" refers to information and media provided to users, delivered in electronic format.

[0607] A "device" is a device used by a user to receive information, and includes smartphones, tablets, and personal computers.

[0608] "Usage history" refers to the history of a user's actions and operations when using a particular service or product.

[0609] "Facial recognition technology" is a technique that uses computer vision to detect faces in images and analyze their characteristics.

[0610] "Speech analysis technology" is a technology that processes acoustic signals to identify meaning and emotion.

[0611] "Emotions" refer to the mental state or reactions that a user exhibits.

[0612] "Purchase experience" refers to the overall experience a user has when purchasing a product or service.

[0613] "Suggestion" refers to providing information and advice to encourage users to select or purchase products.

[0614] To implement this invention, users need to access the system using a device such as a smartphone and use devices and software that enable facial recognition and voice analysis. The device utilizes a camera and microphone to capture the user's facial expressions and voice in real time. Specifically, OpenCV is used for facial recognition and Librosa is used for voice analysis.

[0615] The server receives this data and analyzes the user's emotions using a generative AI model based on TensorFlow. The analyzed emotion data is then used by generative artificial intelligence technology to suggest appropriate products and services. This makes it possible to provide users with a real-time, optimized purchasing experience.

[0616] As a concrete example, when a user is browsing products on their smartphone in a store, the system uses the camera and microphone to determine if they are experiencing stress based on their facial expressions and voice. For instance, if the system determines that a user is dissatisfied with the price of a particular product, it will immediately suggest similar, more reasonably priced items to support their purchase.

[0617] Examples of prompts include: "Explain how to suggest products based on user sentiment data and demonstrate how sentiment analysis influences purchasing decisions."

[0618] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0619] Step 1:

[0620] The device captures the user's facial expressions in real time via a camera and collects audio data via a microphone. The input consists of video and audio data, and the output generates a dataset necessary for analysis. This data is then sent to the next step as initial information.

[0621] Step 2:

[0622] The server uses OpenCV to perform face recognition on video data received from the terminal. This process extracts facial features and converts them into digital data for emotion analysis. The input is video data, and the output is analyzed data showing the user's facial expressions.

[0623] Step 3:

[0624] Similarly, the server analyzes audio data using Librosa. It extracts tone and pitch from the acoustic signal and generates a base dataset for determining emotion. The input is audio data, and the output is analysis data containing features.

[0625] Step 4:

[0626] The server integrates the analysis data obtained in steps 2 and 3 and performs sentiment analysis using a generative AI model with TensorFlow. This sentiment analysis classifies the emotions expressed by the user and generates optimal purchase suggestions. The input is analysis data obtained from facial expressions and voice, and the output is the result of the sentiment classification.

[0627] Step 5:

[0628] The server automatically selects suitable products and services for the user using generative artificial intelligence technology based on the emotion classification results. In this step, product information is created that matches the user's needs and current emotions. The input is the emotion classification result, and the output is the suggested product information.

[0629] Step 6:

[0630] Finally, the terminal displays suggestions received from the server to the user in real time, supporting the purchase. This allows the user to make purchasing decisions based on appropriate information. The input is product information, and the output is the suggested information displayed to the user.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] [Fourth Embodiment]

[0635] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0636] 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.

[0637] 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).

[0638] 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.

[0639] 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.

[0640] 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).

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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".

[0648] This invention is a system implemented on an education-related digital platform that provides a personalized educational experience to support user learning. The system performs the following main functions:

[0649] First, users access a digital platform and enter their personal profile information and past learning history. This information is collected by a server and used in subsequent processes. The server analyzes this data to identify the user's learning needs. This analysis uses generative artificial intelligence technology, taking into account the user's past learning data and trends to clarify the necessary learning topics.

[0650] Next, the server generates the most suitable learning materials for the user based on the analysis results. These generated materials come in various formats, including text, images, audio, and video, and are provided to suit the user's learning style. The generated materials are delivered to the user via their device. The user can then proceed with their learning using the materials displayed on their device.

[0651] The server monitors the user's learning progress in real time and evaluates their understanding based on their input and learning history. Based on this evaluation, the server provides feedback to the user to improve learning efficiency. The feedback includes areas where understanding is insufficient and the content that should be studied next, depending on the user's progress.

[0652] Furthermore, the server translates the learning materials into multiple languages, making them available in the user's chosen language. This ensures that high-quality education is provided to learners from diverse cultural backgrounds. Accessibility is also enhanced by providing audio materials for visually impaired users and video materials with subtitles for hearing-impaired users.

[0653] As a concrete example, consider a middle school student learning mathematics. Suppose the user is having difficulty understanding algebra. The system recognizes this information from the user's learning history and generates visually easy-to-understand video materials on solving basic equations. After learning, the server conducts a quiz-style test to confirm the user's understanding and then suggests the next learning step. This entire process is dynamically adjusted according to the user's learning progress, providing an optimal learning experience.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] Users log in to the digital platform and enter their profile information and learning history. This includes age, grade level, subjects of interest, and past performance.

[0657] Step 2:

[0658] The server collects data entered by the user and stores it in a database. Based on the collected data, it starts an analysis to identify the user's learning needs.

[0659] Step 3:

[0660] The server uses generative artificial intelligence technology to analyze the collected data. This analysis identifies the learning themes and areas that the user needs to strengthen. For example, it identifies weaknesses in specific subjects and finds ways to improve them.

[0661] Step 4:

[0662] The server automatically generates the most suitable learning materials for the user based on the analysis results. These materials are created in various formats, including text, images, audio, and video, and are customized according to the user's preferences and needs.

[0663] Step 5:

[0664] The server converts the generated learning materials into the appropriate format and delivers them to the user's device. Since the materials are provided according to the user's learning style, a smooth learning experience is possible.

[0665] Step 6:

[0666] Users learn using learning materials provided on their devices. The user's learning activities are recorded in real time by the device.

[0667] Step 7:

[0668] The server continuously monitors the user's learning progress and evaluates their level of understanding based on that data. This evaluation identifies areas where further support is needed.

[0669] Step 8:

[0670] The server provides feedback to the user regarding the evaluation results. This feedback includes information and advice to guide further learning. For example, it may recommend additional learning materials on specific topics.

[0671] Step 9:

[0672] The server translates the learning materials according to the language selected by the user, making it multilingual. This process allows the system to accommodate users who speak different languages.

[0673] Step 10:

[0674] As the user progresses, the server continuously provides real-time feedback and additional learning materials. The learning content for the next step is adjusted as needed.

[0675] (Example 1)

[0676] 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".

[0677] In today's educational environment, there is a demand for personalized learning experiences that meet the diverse needs of learners. However, existing educational systems generally rely on the use of standardized materials, making it difficult to provide effective feedback tailored to the individual needs and progress of each learner. Therefore, to improve learning efficiency, it is necessary to provide dynamic educational materials that are tailored to individual learning styles and progress.

[0678] 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.

[0679] In this invention, the server includes means for collecting input information from users, means for analyzing the collected information and identifying learning needs, and means for automatically generating suitable educational materials using generative artificial intelligence technology. This enables the provision of educational materials optimized for individual learners, as well as real-time monitoring and feedback on learning progress.

[0680] "Users" refers to individuals or groups who use the educational system to engage in learning activities.

[0681] "Input information" refers to all data collected when a user accesses the platform, including profile information and learning history.

[0682] "Analysis" is an information processing process that uses input information collected by the server to identify the user's learning needs and trends.

[0683] "Learning needs" refer to the specific educational materials or instruction required by users to learn efficiently.

[0684] "Generative artificial intelligence technology" is a technology that uses artificial intelligence to generate optimal educational materials based on the user's needs and past behavior.

[0685] "Educational materials" refer to teaching materials and learning content provided to support users' learning, and include formats such as text, images, audio, and video.

[0686] "Distribution" refers to the process of sending generated educational materials to the user's device, making them easily accessible to the user.

[0687] A "terminal" is an electronic device used by users to access educational systems and utilize educational materials.

[0688] "Progress" refers to the progress of a user's learning activities, including their learning speed and level of understanding.

[0689] "Evaluation" is the process of measuring a user's learning outcomes and determining how well they understand the material.

[0690] "Multilingual" means supporting multiple different languages, allowing users to access educational materials in their chosen language.

[0691] "Visual and hearing impairments" refer to conditions involving limitations or difficulties related to vision or hearing, which necessitate the provision of specific forms of educational materials.

[0692] A description of the embodiment for carrying out the invention will be provided.

[0693] This invention relates to a system implemented in an educational digital platform, aiming to personalize the user's learning experience. The system primarily consists of a server, terminals, and a user interface.

[0694] The server is responsible for collecting user input information. This input information includes the user's profile and past learning history, and is stored in a database. This data forms the basis for information analysis.

[0695] Next, the server uses a generative AI model to analyze the collected information. Specifically, it identifies the user's learning needs based on their learning tendencies and behavioral history. This process makes it possible to extract different learning themes and skill sets for each user.

[0696] The server then generates optimal educational materials based on the identified learning needs. These materials are provided in various formats, including text, images, audio, and video. For example, interactive infographics can be generated for users who prefer visual learning.

[0697] The terminal receives generated educational materials and provides them to the user. Through the terminal, users can access the educational materials on the system and progress at their own pace. The server monitors the terminal's data in real time and provides feedback based on that progress. This enables feedback that improves the user's understanding and learning efficiency.

[0698] For users with visual or hearing impairments, the server enhances accessibility by providing audio materials and videos with subtitles. The materials are also translated into multiple languages, and a feature allows users to access them in their chosen language.

[0699] As a concrete example, consider the case of a middle school student learning mathematics. If the student is having difficulty with a specific area of ​​mathematics, such as algebra, the server analyzes their learning history and generates easy-to-understand video content. Then, a quiz-style test is presented to check their understanding, and the next learning steps are dynamically suggested based on the data.

[0700] For example, a possible prompt for a generative AI model might be, "Identify the learning needs of a specific user and provide a personalized learning plan."

[0701] In this way, the system provides an educational experience optimized for each individual user, thereby improving learning efficiency and achievement.

[0702] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0703] Step 1:

[0704] Users access the digital platform and enter their personal profile information and past learning history. This information becomes the input data sent to the platform. As an initial step, the server receives this data, converts it to a specific format, and stores it in a database. This lays the foundation necessary for subsequent data analysis processes.

[0705] Step 2:

[0706] The server extracts user data from the database and performs analysis using a generative AI model. This analysis process uses the input learning history and profile information to identify the user's learning needs. Specifically, it evaluates past performance and study time to clarify which areas require learning. The output of this analysis is a list of identified learning needs.

[0707] Step 3:

[0708] The server generates educational materials based on identified learning needs. To do this, it sends a prompt to a generative AI model saying, "Generate materials suitable for the user," and creates materials in various formats such as text, images, audio, and video. In this generation process, the input is a list of learning needs, and the output is educational materials in various formats.

[0709] Step 4:

[0710] The server sends the generated educational materials to the terminal. The terminal appropriately displays the received materials and provides them to the user. For example, it allows users to view interactive graphics and videos on the screen of a smartphone or tablet. The input to this process is the data of the educational materials, and the output is the materials displayed in the user interface.

[0711] Step 5:

[0712] The server monitors the user's learning progress in real time. By collecting and analyzing learning activities and the accuracy of responses, it evaluates the user's understanding and progress. The input at this stage is the user's learning behavior data, and the output is the progress evaluation result.

[0713] Step 6:

[0714] The server generates and provides feedback to the user based on the evaluation results. This feedback includes areas for improvement and what to learn next. It also generates and provides additional educational materials as needed. In this process, the input is the progress evaluation results, and the output is the feedback and additional learning materials.

[0715] The above outlines the specific processing steps of this system.

[0716] (Application Example 1)

[0717] 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".

[0718] Traditional education systems typically provide the same learning materials to all students, resulting in insufficient individualization tailored to each student's level of understanding and learning style. Furthermore, a lack of appropriate feedback based on learning progress makes effective learning support difficult. Additionally, language barriers and physical limitations prevent everyone from having equal learning opportunities.

[0719] 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.

[0720] In this invention, the server includes means for collecting information data from users, means for analyzing the collected information and identifying the user's learning requirements, and means for automatically generating educational content suitable for the user using generative information processing technology. This enables the provision of personalized learning materials tailored to individual learners and dynamic adjustment of the learning experience in real time.

[0721] "Means of collecting user information data" refers to a mechanism that accumulates user-provided profile information and learning history and stores it in a database.

[0722] "Means of analyzing collected information and identifying user learning requirements" refers to a process that uses generated information processing technology based on collected information to diagnose user needs and clarify the learning themes required for each learner.

[0723] "A means of automatically generating user-friendly educational content using generative information processing technology" refers to a system that utilizes generative AI technology to generate educational materials in formats such as text, images, audio, and video, tailored to the user's characteristics and needs.

[0724] "Means for delivering generated educational content to user devices" refers to a function that sends created educational materials to the user's device to support their learning.

[0725] "Means for monitoring users' learning progress and evaluating their results" refers to methods that track users' learning activities in real time, measure their level of understanding and results, and provide appropriate feedback.

[0726] "Means of dynamically adjusting the learning experience according to the learner's level of understanding" refers to a function that flexibly changes the learning content and methods based on the user's understanding and progress, thereby achieving optimal learning.

[0727] The system that realizes this invention provides learners with an individualized learning experience using educational support robots and terminals. First, it collects information data provided by the user and sends it to a cloud server for storage. The server analyzes this data using a generation AI model to identify the user's learning requirements. As a result, the most suitable educational content for the user is automatically generated.

[0728] The generated educational content is available in various formats, including text, images, audio, and video, and is delivered to users' devices or robots. The devices use displays and audio output functions to present information in a way that is intuitively easy for learners to understand.

[0729] Furthermore, the server monitors the user's learning progress in real time and evaluates the results based on progress and understanding. Additional learning materials and feedback are automatically provided as needed. In this way, a dynamic learning experience tailored to each individual learner is possible. In addition, the learning materials are translated into multiple languages ​​by a generative AI model and presented in visual and auditory formats, enabling education that accommodates diverse cultures and physical limitations.

[0730] For example, if a user requests to "learn how to find the area of ​​a triangle," the AI ​​model will generate visually animated learning materials and a detailed explanatory video, and deliver them to the device. Thus, an example of a prompt would be, "I would like to learn how to find the area of ​​a triangle; please generate visual learning materials."

[0731] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0732] Step 1:

[0733] The user enters a learning prompt into the device, for example, "I would like to learn how to find the area of ​​a triangle; please generate visual learning materials." The device sends this prompt to the server. The input data includes the user's request and past learning history.

[0734] Step 2:

[0735] The server passes the received prompt message and the user's past learning history data to the generating AI model to identify the user's learning requirements. Data processing here includes clarifying the request content through text analysis and extracting learning needs through analysis of past history. The output is the identified learning theme and request content.

[0736] Step 3:

[0737] The server uses a generative AI model to automatically generate educational content optimized for the user. In this process, the specified learning theme and format are used as input, and the generated text, image, audio, and video content is output. The generative AI model utilizes natural language processing and image generation technologies to generate the content.

[0738] Step 4:

[0739] The server delivers the generated educational content to the user's device. The device receives this content and provides it to the learner by displaying it on the screen or playing it back using an audio output device. The output here refers to the visual and auditory presentation of the content to the user on the device.

[0740] Step 5:

[0741] Users access learning content through their devices. During the learning process, the device continuously collects user activity logs and responses. This feedback data is used to evaluate the user's learning progress.

[0742] Step 6:

[0743] The server periodically analyzes user progress data sent from terminals and generates additional learning materials and feedback as needed. This data processing includes machine learning algorithms for analyzing progress and assessing comprehension. The output is tailored learning content and new feedback.

[0744] 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.

[0745] This invention is a personalized learning support system that takes user emotions into consideration. By incorporating an emotion engine, it recognizes the user's psychological state in real time and further improves the learning experience. Specific embodiments of the system are shown below.

[0746] First, users access the system through a digital platform and input their personal information and past learning data. The server receives this information and stores it in a database. This information is used to customize learning materials and identify learning needs. In addition, user emotion data is collected in real time through facial recognition technology and voice analysis.

[0747] The server analyzes data obtained from users to identify their learning needs, and then uses AI to generate learning materials optimized for each user. In this process, an emotion engine evaluates the user's mental state in real time, and this information is also reflected in the creation of the learning materials. For example, if the system detects that a user is feeling stressed, it will provide more relaxing learning materials and adjust the learning pace to ensure comfort.

[0748] The generated learning materials are provided in various media formats (text, audio, video, etc.) and delivered to the user's device. The user uses these materials to progress through the learning process. During this time, the emotion engine monitors the user's reactions and evaluates not only their comprehension but also their emotional changes. Based on this data, the server adjusts the learning content and pace and provides feedback.

[0749] As a concrete example, consider a language learning program. If the emotion engine detects that the user is feeling fatigued or frustrated, the server will provide listening materials with a lower difficulty level. This is to prevent the user from becoming discouraged and losing interest in learning.

[0750] Furthermore, user sentiment data can be used to provide more personalized recommendations. For example, if a user finds certain content easy to understand and enjoyable, additional related learning materials can be provided to improve learning efficiency. This allows learners to maintain motivation and progress effectively in their studies.

[0751] Thus, the present invention aims to improve the quality of learning and the user experience by providing a system that takes user emotions into consideration.

[0752] The following describes the processing flow.

[0753] Step 1:

[0754] Users access a digital platform and enter their learning history and current learning goals. This includes information about subjects they are interested in and areas they struggle with.

[0755] Step 2:

[0756] The device sends user input data to a server and records its status in real time. Furthermore, it analyzes the user's facial expressions and tone of voice using an emotion engine and sends emotion data to the server.

[0757] Step 3:

[0758] The server analyzes data received from the user and assesses their learning needs and emotional state. Using AI technology, it identifies the optimal learning content tailored to the user's current psychological state.

[0759] Step 4:

[0760] The server automatically generates necessary learning materials using artificial intelligence technology. If the user is relaxed, it generates materials that are easy to concentrate on; if the user is stressed, it prepares content that is easier and more enjoyable.

[0761] Step 5:

[0762] The generated learning materials are converted into the format selected by the user (text, audio, or video) and delivered to the device. The user then uses the provided materials on the device to proceed with their learning.

[0763] Step 6:

[0764] The server monitors the user's reactions through an emotion engine while they are using the learning materials. This involves identifying changes in emotions from facial expressions and voice, and evaluating whether the learning content is effective.

[0765] Step 7:

[0766] If a change in the user's emotional state is detected, the server adjusts the learning materials and pace based on that change. For example, if the user shows satisfaction, it will encourage them to move on to the next, more challenging task.

[0767] Step 8:

[0768] The server evaluates learning outcomes along with sentiment data and provides feedback to the user. This feedback includes areas for improvement, areas of success, and advice for future learning sessions.

[0769] Step 9:

[0770] The system will analyze user sentiment data over the long term to recommend better learning materials and provide users with a continuous and optimal learning environment. The server aims to maximize user learning effectiveness through this process.

[0771] (Example 2)

[0772] 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".

[0773] A problem in modern education systems is the insufficient provision of learning support that takes into account the emotions and psychological states of individual learners. Traditional methods provide standardized materials, making it difficult to maximize learners' motivation and understanding. Furthermore, the lack of real-time feedback and adaptive learning support prevents the provision of an effective learning experience.

[0774] 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.

[0775] In this invention, the server includes means for receiving and recording information from the user, means for analyzing the recorded information to clarify the user's learning needs, and means for collecting and analyzing the user's emotional data using emotion analysis technology. This makes it possible to generate and deliver personalized learning materials that are adapted to the user's emotions and learning needs.

[0776] A "user" refers to an entity that utilizes the system and provides personal information or learning data.

[0777] "Information" includes personal data, emotional data, and data related to learning progress entered by the user.

[0778] A "generative artificial intelligence model" refers to software technology used to automatically generate learning materials that are adapted to the user's learning needs and emotional state.

[0779] "Emotion analysis technology" refers to a technology that determines a user's psychological state from their facial expressions, voice, etc., and collects and analyzes emotional data in real time.

[0780] "Educational materials" refer to educational content created for use by learners, and include various media formats such as text, audio, and video.

[0781] "Device" refers to hardware equipment used by users to advance their learning, including personal computers and smartphones.

[0782] "Progress" refers to an indicator that shows how much of the content a user has understood and achieved during the learning process.

[0783] "Feedback" refers to evaluations and advice provided by the system to the user regarding the learning content.

[0784] This invention is a personalized learning support system that takes into account the user's emotional state. This system improves the user's learning experience by combining a generative AI model and emotion analysis technology.

[0785] First, users access the digital platform using a personal computer or portable information terminal and input personal information and learning history. This information is sent to a server and stored in a database. The server uses video and audio data acquired in real time from the user's camera and microphone to evaluate the user's psychological state using sentiment analysis technology.

[0786] The server uses a generative AI model based on the received user information to generate personalized learning materials. For example, the generative AI model might be given a prompt such as, "Generate the most suitable learning materials based on the user's current emotional state." This model considers the user's learning needs and emotional state to select materials of appropriate difficulty and content.

[0787] The generated learning materials are provided in various media formats, including text, audio, and video, and delivered to the user's device. This allows the user to learn at a relaxed pace. The device monitors the user's responses during learning, and the server evaluates the learning progress and the suitability of the materials based on this.

[0788] As a concrete example, consider a user who is learning a language. When the server determines that the user is feeling fatigued or frustrated, it provides listening materials with adjusted difficulty levels. This kind of adaptive response helps maintain the learner's motivation and allows them to continue learning efficiently.

[0789] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0790] Step 1:

[0791] Users log in to the digital platform using their personal computers or mobile devices and enter personal information and learning history. The entered data is sent to the server and stored in a database. In this process, the specific actions are filling out text input forms and clicking the submit button.

[0792] Step 2:

[0793] The server analyzes the collected user information to identify learning needs. This includes evaluating the type and difficulty level of necessary learning materials, taking into account the user's past learning history and current goals. Specifically, learning needs are identified by structuring the information using data processing algorithms and extracting features.

[0794] Step 3:

[0795] The server collects user emotional data in real time through cameras and microphones and analyzes this data using emotion analysis technology. The results of the analysis are output as data indicating the user's current psychological state. For example, by scanning facial expressions with facial recognition and evaluating voice tone with voice analysis, emotions such as stress and joy can be identified.

[0796] Step 4:

[0797] The server uses a generative AI model to generate learning materials based on the user's learning needs and emotional state. This process involves inputting collected data as prompts into the generative AI model, which then outputs the most suitable learning materials. Specifically, this includes sending a prompt message to the generative AI model such as, "Provide the most suitable learning materials considering the current emotional state."

[0798] Step 5:

[0799] The generated learning materials are delivered from the server to the user's device. These materials are in various formats, including text files, audio files, and video files, and the format is selected according to the user's learning style. Users access and use these materials on their devices to progress with their studies. Specific operations include downloading and streaming the learning materials.

[0800] Step 6:

[0801] The device monitors the user's responses during learning and feeds the collected data back to the server in real time. Based on this feedback, the server can evaluate the learning progress and adjust the learning materials and pace. Specifically, this involves continuous data capture using a camera and microphone.

[0802] (Application Example 2)

[0803] 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".

[0804] In modern e-commerce, improving the user's purchasing experience is a crucial challenge. In particular, providing appropriate suggestions in real time based on user emotions can support purchasing decisions and increase satisfaction. However, current systems lack the functionality to accurately analyze user emotion data and immediately suggest products and services based on that analysis.

[0805] 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.

[0806] In this invention, the server includes means for collecting user input information, means for analyzing the collected information and identifying user needs, and means for automatically generating user-appropriate content using generative artificial intelligence technology. This makes it possible to evaluate the user's emotions in real time and make purchase suggestions accordingly.

[0807] "Input information" refers to data provided by the user, including in the form of text, audio, images, and other formats.

[0808] "Demand" refers to the requests and desires that users have for the products and services they want.

[0809] "Generative artificial intelligence technology" is a technology that uses machine learning algorithms to analyze information and generate new content and solutions.

[0810] "Content" refers to information and media provided to users, delivered in electronic format.

[0811] A "device" is a device used by a user to receive information, and includes smartphones, tablets, and personal computers.

[0812] "Usage history" refers to the history of a user's actions and operations when using a particular service or product.

[0813] "Facial recognition technology" is a technique that uses computer vision to detect faces in images and analyze their characteristics.

[0814] "Speech analysis technology" is a technology that processes acoustic signals to identify meaning and emotion.

[0815] "Emotions" refer to the mental state or reactions that a user exhibits.

[0816] "Purchase experience" refers to the overall experience a user has when purchasing a product or service.

[0817] "Suggestion" refers to providing information and advice to encourage users to select or purchase products.

[0818] To implement this invention, users need to access the system using a device such as a smartphone and use devices and software that enable facial recognition and voice analysis. The device utilizes a camera and microphone to capture the user's facial expressions and voice in real time. Specifically, OpenCV is used for facial recognition and Librosa is used for voice analysis.

[0819] The server receives this data and analyzes the user's emotions using a generative AI model based on TensorFlow. The analyzed emotion data is then used by generative artificial intelligence technology to suggest appropriate products and services. This makes it possible to provide users with a real-time, optimized purchasing experience.

[0820] As a concrete example, when a user is browsing products on their smartphone in a store, the system uses the camera and microphone to determine if they are experiencing stress based on their facial expressions and voice. For instance, if the system determines that a user is dissatisfied with the price of a particular product, it will immediately suggest similar, more reasonably priced items to support their purchase.

[0821] Examples of prompts include: "Explain how to suggest products based on user sentiment data and demonstrate how sentiment analysis influences purchasing decisions."

[0822] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0823] Step 1:

[0824] The device captures the user's facial expressions in real time via a camera and collects audio data via a microphone. The input consists of video and audio data, and the output generates a dataset necessary for analysis. This data is then sent to the next step as initial information.

[0825] Step 2:

[0826] The server uses OpenCV to perform face recognition on video data received from the terminal. This process extracts facial features and converts them into digital data for emotion analysis. The input is video data, and the output is analyzed data showing the user's facial expressions.

[0827] Step 3:

[0828] Similarly, the server analyzes audio data using Librosa. It extracts tone and pitch from the acoustic signal and generates a base dataset for determining emotion. The input is audio data, and the output is analysis data containing features.

[0829] Step 4:

[0830] The server integrates the analysis data obtained in steps 2 and 3 and performs sentiment analysis using a generative AI model with TensorFlow. This sentiment analysis classifies the emotions expressed by the user and generates optimal purchase suggestions. The input is analysis data obtained from facial expressions and voice, and the output is the result of the sentiment classification.

[0831] Step 5:

[0832] The server automatically selects suitable products and services for the user using generative artificial intelligence technology based on the emotion classification results. In this step, product information is created that matches the user's needs and current emotions. The input is the emotion classification result, and the output is the suggested product information.

[0833] Step 6:

[0834] Finally, the terminal displays suggestions received from the server to the user in real time, supporting the purchase. This allows the user to make purchasing decisions based on appropriate information. The input is product information, and the output is the suggested information displayed to the user.

[0835] 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.

[0836] 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.

[0837] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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."

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] The following is further disclosed regarding the embodiments described above.

[0857] (Claim 1)

[0858] Means for collecting user input data,

[0859] A means of analyzing collected data to identify users' learning needs,

[0860] A means of automatically generating user-appropriate learning materials using generative artificial intelligence technology,

[0861] A means of distributing the generated educational materials to the user's terminal,

[0862] A means of monitoring the user's learning progress and evaluating learning outcomes,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, comprising means for translating educational materials into multiple languages ​​and providing them to the user in visual and auditory formats, based on generative artificial intelligence technology.

[0866] (Claim 3)

[0867] The system according to claim 1, comprising means for monitoring the user's learning progress in real time and providing additional learning materials or feedback as needed.

[0868] "Example 1"

[0869] (Claim 1)

[0870] Means for collecting user input information,

[0871] A means of analyzing the collected information and identifying learning needs,

[0872] A means of automatically generating suitable educational materials using generative artificial intelligence technology,

[0873] A means of distributing the generated educational materials to the terminal,

[0874] A means of monitoring users' learning progress and evaluating learning outcomes,

[0875] Means of translating teaching materials into multiple languages ​​and providing them in the selected language,

[0876] A means of providing educational materials in appropriate formats to users with visual and hearing impairments,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, comprising means for translating generated educational materials into multiple languages ​​and providing them to users in visual and auditory formats.

[0880] (Claim 3)

[0881] The system according to claim 1, comprising means for monitoring the user's learning progress in real time and providing additional educational materials or feedback as needed.

[0882] "Application Example 1"

[0883] (Claim 1)

[0884] Means of collecting information data from users,

[0885] A means of analyzing the collected information and identifying the user's learning requirements,

[0886] A means for automatically generating user-friendly educational content using generative information processing technology,

[0887] A means of delivering the generated educational content to the user's device,

[0888] A means of monitoring the user's learning progress and evaluating their results,

[0889] A means of dynamically adjusting the learning experience according to the learner's level of understanding,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, comprising means for converting educational content into multiple languages ​​and providing it to users in visual and auditory formats, based on generative information processing technology.

[0893] (Claim 3)

[0894] The system according to claim 1, comprising means for monitoring the user's learning progress in real time and providing additional educational content or advice as needed.

[0895] "Example 2 of combining an emotion engine"

[0896] (Claim 1)

[0897] A means of receiving and recording information from users,

[0898] A means of analyzing recorded information to clarify the user's learning needs,

[0899] A method for automatically creating educational materials that take into account the user's psychological state using a generative artificial intelligence model,

[0900] A means of collecting and analyzing user emotional data using emotion analysis technology,

[0901] A means of distributing the created teaching materials to the user's device,

[0902] A means of checking the user's learning progress and evaluating its effectiveness,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, comprising means for converting educational materials into multiple languages ​​and providing them to the user in visual and auditory formats, based on generative artificial intelligence technology.

[0906] (Claim 3)

[0907] The system according to claim 1, further comprising means for monitoring the user's learning progress in real time, providing additional learning materials or feedback as needed, and adapting to changes in the user's emotions.

[0908] "Application example 2 when combining with an emotional engine"

[0909] (Claim 1)

[0910] Means for collecting user input information,

[0911] A means of analyzing collected information to identify user needs,

[0912] A means of automatically generating user-friendly content using generative artificial intelligence technology,

[0913] A means of delivering the generated content to the user's terminal,

[0914] A means of monitoring user usage and evaluating usage results,

[0915] A method for evaluating user emotions in real time using facial recognition technology and voice analysis technology,

[0916] A means of making suggestions to optimize the purchasing experience based on user emotions,

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The system according to claim 1, comprising means for translating the generated content into multiple languages ​​and providing it to the user in visual and auditory formats.

[0920] (Claim 3)

[0921] The system according to claim 1, comprising means for monitoring user activity in real time and providing additional content or feedback as needed. [Explanation of symbols]

[0922] 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 user input data, A means of analyzing collected data to identify users' learning needs, A means of automatically generating user-appropriate learning materials using generative artificial intelligence technology, A means of distributing the generated educational materials to the user's terminal, A means of monitoring the user's learning progress and evaluating learning outcomes, A system that includes this.

2. The system according to claim 1, comprising means for translating educational materials into multiple languages ​​and providing them to the user in visual and auditory formats, based on generative artificial intelligence technology.

3. The system according to claim 1, comprising means for monitoring the user's learning progress in real time and providing additional learning materials or feedback as needed.

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