system

Abstract

We provide the system. [Solution] A means of obtaining learner appearance data, A means for analyzing the appearance data to identify the learner's emotional state, A means for generating information for the learner based on the said emotional state, Means for presenting the information to learners, A means of adjusting the learning plan according to the learner's progress, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional learning support technologies, it has been difficult to flexibly respond according to the individual understanding levels and emotional states of learners, and there has been a technical problem that the difficulties and questions faced by learners cannot be appropriately supported. As a result, there has been a problem that the learning motivation of learners has decreased and effective learning has not progressed.

Means for Solving the Problems

[0005] The present invention provides means for resolving in real time the difficulties and questions of learners by acquiring appearance data of learners, analyzing the data to identify the emotional states of learners, and generating and presenting optimal information for learners based on the emotional states. Further, it is a system that dynamically grasps the progress of learners and adjusts the learning plan accordingly to achieve a learning progress suitable for individual learners.

[0006] A "learner" is an individual who is trying to acquire knowledge and skills in the educational process.

[0007] "External data" refers to information obtained through sight and hearing, such as the learner's facial expressions and movements.

[0008] "Emotional state" refers to the state of a learner's current emotions and psychological responses.

[0009] "Generating information" means creating appropriate content to provide to learners based on the analysis results.

[0010] "Progress" refers to the state or history of a learner's position within the educational process.

[0011] "Adjusting the learning plan" means changing the teaching method and content based on the learner's progress. [Brief explanation of the drawing]

[0012] [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 the data processing device and 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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of 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

[0013] 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.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] 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.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention provides a system that offers real-time educational support tailored to the individual needs of learners. This system uses learner appearance data to identify their emotional state and dynamically adjusts learning content and plans based on the learner's progress.

[0034] System Overview

[0035] 1. User begins learning

[0036] When a user starts a learning session using their device, the device's camera and microphone activate to capture the user's facial expressions, movements, and voice.

[0037] 2. Acquisition and transmission of appearance data

[0038] The device processes the captured appearance data in real time and sends it to the server. This data includes information such as the user's facial movements, voice tone, and speed.

[0039] 3. Data analysis and identification of emotional state

[0040] The server analyzes the received data and uses a generative AI model to identify the user's emotional state. It identifies whether the user is confused, satisfied, etc., and predicts their response to specific learning tasks.

[0041] 4. Generating appropriate learning content

[0042] The server generates explanatory materials and supplementary learning content based on the user's emotional state. For example, if a user is struggling with a new mathematical concept, it will provide a review of the relevant foundational knowledge or a visual guide.

[0043] 5. Dynamic adjustment of the learning plan

[0044] The server monitors the user's progress and adjusts the learning plan according to their learning progress. If a particular topic is taking too long, it provides additional practice exercises on that topic.

[0045] 6. Providing feedback

[0046] The terminal displays explanations and supplementary questions sent from the server to the user. It also notifies the user of any changes to their learning plan, clearly indicating what they should do next.

[0047] Thus, the present invention provides flexible and efficient learning support tailored to each individual learner. Specifically, it enables the rapid identification of delays in understanding that occur when solving mathematical problems and provides supplementary materials suitable for the user. This allows users to continue effective learning at their own pace.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The user starts a learning session using their device. The device activates its built-in camera and microphone and is configured to capture the user's facial expressions, movements, and voice in real time.

[0051] Step 2:

[0052] The device transmits captured visual data to the server in real time. This data includes the user's facial expressions, voice tone, and speed information.

[0053] Step 3:

[0054] The server applies a generative AI model based on the received visual data to identify the user's emotional state. The server identifies emotions such as confusion and interest and analyzes the user's response to the learning content.

[0055] Step 4:

[0056] The server generates learning content tailored to the user based on the analysis results. If it determines that the user is struggling with a specific problem, it creates supplementary explanations and visuals for that section.

[0057] Step 5:

[0058] The server evaluates the user's learning progress and adjusts the learning plan as needed. Based on the user's progress data, it adds supplementary practice exercises and materials to reinforce necessary topics.

[0059] Step 6:

[0060] The device provides the user with new learning content and an adjusted learning plan sent from the server. The user can use this feedback to further their learning.

[0061] This entire process allows the system to provide customized, real-time learning support for each user.

[0062] (Example 1)

[0063] 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."

[0064] Providing effective, real-time educational support tailored to the individual needs of learners is a challenging task. Conventional systems struggle to instantly reflect changes in learners' behavior and emotions, making it difficult to adjust learning plans accordingly, potentially leading to decreased learner motivation. Therefore, there is a need for technology that can provide flexible and responsive learning support to each individual learner.

[0065] 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.

[0066] In this invention, the server includes means for acquiring learner behavioral data, means for analyzing the behavioral data to identify the learner's emotional state, and means for generating educational resources based on the emotional state. This enables flexible and effective learning support tailored to the individual circumstances of each learner.

[0067] "Learner behavior data" refers to information about learners' physical movements and vocal expressions—information about their behavior that can be observed from the outside.

[0068] "Emotional state" refers to the learner's feelings and mental state, encompassing psychological conditions such as happiness, anxiety, and concentration.

[0069] "Educational resources" refer to all information media that support learning, such as teaching materials, supplementary materials, and interactive tools provided to learners.

[0070] "Dynamic adjustment" refers to the fact that learning plans and content are not fixed in place, but can be flexibly changed according to the learner's progress and condition.

[0071] "Real-time" refers to a state where the system processes data instantly and can quickly provide feedback and adjustments tailored to the learner's situation.

[0072] This invention is a learning support system that provides personalized, real-time educational support to learners. This system utilizes the user's terminal and server to analyze the learner's emotional state and flexibly generate and adjust learning content based on that analysis.

[0073] Hardware and software configuration

[0074] terminal

[0075] The user's device is equipped with a camera and microphone, which capture the user's facial expressions, movements, and voice during learning sessions. The processing system includes software that supports real-time data analysis. This data is then transmitted from the device to the server.

[0076] server

[0077] The server is equipped with a generative AI model and performs advanced data analysis. Based on the data received from the user, the server identifies the user's emotional state and generates learning resources accordingly. It also tracks the learning progress and dynamically adjusts the learning plan as needed.

[0078] Data processing and calculations

[0079] When the server receives data from the user, it analyzes the data using a generative AI model. This model identifies the user's emotional state during learning from their facial movements and voice tone. As a result, the generated information is fed back to the user through the device.

[0080] Specific example

[0081] For example, if a user is confused by a mathematical concept, basic visual guides and additional practice problems are presented according to their emotional state. This allows users to learn efficiently at their own pace.

[0082] Example of a prompt

[0083] "Identify the user's current emotional state based on their facial expressions and voice data. How is the user feeling? Identify states such as confusion or satisfaction."

[0084] As described above, this invention makes it possible to provide flexible and effective learning support tailored to individual learners.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The device captures the user's movements and voice. The user starts a learning session, and the camera and microphone are activated. As input, data on the user's facial expressions, movements, and voice are collected. This raw data is ready to be sent to the server in real time.

[0088] Step 2:

[0089] The device sends the collected data to the server. Converting the data to an appropriate format before sending it to the server enables efficient data processing. The input is data such as captured facial movements and voice tones, and the output is communication with the server.

[0090] Step 3:

[0091] The server analyzes the received data. The server uses a generative AI model to evaluate the data in order to identify the user's emotional state. The input is action and voice data sent from the terminal, and the output is an identification result of the emotional state (e.g., confused, satisfied).

[0092] Step 4:

[0093] The server generates learning resources based on emotional states. Based on the results of the AI ​​model, it creates learning content optimized for each individual user. It receives identified emotional states as input and generates appropriate educational resources (visual guides, supplementary materials, etc.) as output.

[0094] Step 5:

[0095] The server dynamically adjusts the learning plan. The server monitors the user's progress and updates the learning plan as needed. It receives user learning progress data as input and an updated learning plan as output.

[0096] Step 6:

[0097] The device presents the user with generated feedback and learning resources. The device communicates output information from the server to the user, providing clear instructions for the next steps. It receives educational resources and feedback from the server as input, and presents them to the user visually or audibly as output.

[0098] (Application Example 1)

[0099] 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."

[0100] When providing educational support to individual learners, there is a challenge in immediately grasping their emotional state and providing appropriate supplementary materials. Furthermore, there is a need for dynamic adjustments to learning plans based on the learner's progress. In particular, real-time data acquisition and analysis are crucial for realizing such educational support in the home environment.

[0101] 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.

[0102] In this invention, the server includes means for acquiring observational data of learners, means for analyzing the observational data to identify the learners' emotional state, and means for generating supplementary materials for the learners based on the emotional state. This makes it possible to provide personalized educational support in real time.

[0103] A "learner" is an individual who is eligible to receive educational support.

[0104] "Observational data" refers to information collected in real time, such as learners' facial expressions, gestures, and voice.

[0105] "Emotional state" refers to a learner's psychological and emotional condition and is used to evaluate their response to learning.

[0106] "Supplementary materials" are additional educational content or guides provided to help learners understand the material.

[0107] An "educational plan" is a system of learning content and schedules formulated based on the learner's progress.

[0108] "Dynamic generation methods" refer to methods of sequentially creating necessary information and materials in response to real-time data.

[0109] To implement this invention, a system is built in which a server and a terminal work together to provide optimal educational support to learners. Learners begin learning activities using a terminal equipped with a camera and microphone. The terminal acquires observational data such as the learner's facial expressions and voice in real time and transmits it to the server. The server uses a generative AI model to analyze the received data. As a specific example, the EmotionAPI is used to identify the emotional state.

[0110] Based on the analysis results, the server dynamically generates supplementary materials appropriate to the learner's emotional state. For example, if it determines that the learner is confused, it immediately generates a visual guide or practice exercises on the relevant foundational knowledge and sends them to the device. The device then presents these materials to the learner.

[0111] Furthermore, the server continuously monitors the learner's progress and adjusts the educational plan as needed. Through all these processes, an optimized educational experience can be provided for each learner.

[0112] As a concrete example, a server analyzes a student who sighs while studying mathematics and generates a visual guide to basic calculations. An example of a prompt would be, "Analyze the user's emotions and select the appropriate visual material if they appear confused." This prompt manages the process by which the generating AI model selects appropriate materials.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user starts a learning session using the device. The device activates its camera and microphone to acquire observational data in real time, including the learner's facial expressions, voice tone, and movements. The input is the learner's visual data, and the output is this data in digital format. The device prepares this data for the next processing step.

[0116] Step 2:

[0117] The terminal transmits the acquired observation data to the server. The input is the digital observation data collected by the terminal, and the output is the data transfer to the server. The server receives and stores this data and prepares it for the next analysis step.

[0118] Step 3:

[0119] The server uses a generative AI model to analyze the received observation data. The input is observation data stored on the server, and a model such as EmotionAPI analyzes the data. In this step, the server performs calculations to identify emotional states and judge changes in the learner's mood and emotions. The output is the evaluation result of the emotional state.

[0120] Step 4:

[0121] The server dynamically generates appropriate supplementary materials based on the emotional state assessment results. The input is the emotional state assessment results, and the output is the specific supplementary materials to be provided to the learner. These include visual guides and practice exercises, and are configured to support the learner's learning needs.

[0122] Step 5:

[0123] The server generates supplementary materials and sends them to the terminal. The input is dynamically generated supplementary materials, and the output is the transmission of these materials to the terminal. The terminal receives these materials and prepares them to be displayed to the learner.

[0124] Step 6:

[0125] The terminal presents the received supplementary materials to the user. The input is the supplementary materials received by the terminal, and the output is the learner obtaining the materials visually or aurally. The terminal presents the materials in a format suitable for the learner, supporting their learning.

[0126] Step 7:

[0127] The server continuously monitors the learner's progress and adjusts the teaching plan as needed. The input is the learner's current progress data, and the output is the latest teaching plan. This plan is modified according to individual needs and adjusted to optimize learning.

[0128] 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.

[0129] This invention relates to an educational support system that incorporates an emotion engine that recognizes user emotions in real time and applies them to learning support. The system aims to identify the learner's emotions and emotional state and provide appropriate learning content and plans based on that.

[0130] System Overview

[0131] 1. User begins learning

[0132] When a user starts a learning session using their device, the device's built-in camera and microphone are activated. This allows the user's facial expressions, movements, and voice to be captured in real time.

[0133] 2. Acquisition of appearance data and sentiment data

[0134] The device sends captured visual data to the server. The server uses this data to activate an emotion engine and analyze the user's emotional state from their facial expressions and voice.

[0135] 3. Analysis using an emotion engine

[0136] The emotion engine on the server analyzes the user's facial expressions and voice patterns to identify emotional states such as stress, excitement, and concentration. This allows for a detailed understanding of the user's responses to learning tasks.

[0137] 4. Content Creation and Delivery

[0138] The server generates optimal learning content for the user based on the analysis results from the emotion engine. For example, if the user shows signs of impatience, it will slow down the pace and provide more detailed explanations.

[0139] 5. Dynamic adjustment of the learning plan

[0140] The server dynamically adjusts the learning plan based on the user's current emotional state. For example, if the server determines that the user is highly focused, it will move on to learning more advanced topics.

[0141] 6. Feedback and Motivation

[0142] The device presents the user with learning content and plans sent from the server, and provides feedback based on their progress. Furthermore, by utilizing the analysis results from the emotion engine, it contributes to maintaining and improving motivation.

[0143] For example, if a user's concentration wanes while solving a math problem, the system analyzes their emotional state and adjusts the difficulty of the problem or suggests content that promotes relaxation. In this way, the present invention aims to personalize the user's learning experience and support effective learning.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The user starts a learning session using their device. The device activates its built-in camera and microphone and is configured to capture the user's facial expressions, movements, and voice in real time.

[0147] Step 2:

[0148] The device transmits user appearance and voice data to the server in real time. This data includes the user's facial expressions, voice tone, and speed information.

[0149] Step 3:

[0150] The server uses the received data to activate the emotion engine and analyze the user's emotional state. Specifically, it analyzes subtle changes in the user's facial expressions and the emotional tone of their voice to identify levels of stress and concentration.

[0151] Step 4:

[0152] The server generates appropriate learning content based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it might add easy-to-understand explanations or animations to make the learning process smoother.

[0153] Step 5:

[0154] The server dynamically adjusts the learning plan according to the user's emotional state. If it determines that concentration is being maintained, adjustments such as maintaining or accelerating the learning pace will be made.

[0155] Step 6:

[0156] The device displays learning content generated from the server and a revised learning plan to the user. The user then follows these instructions to effectively progress with their learning.

[0157] This processing step allows the system to leverage the user's emotional state and provide flexible and effective learning support tailored to each learner's needs.

[0158] (Example 2)

[0159] 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".

[0160] Conventional educational support systems were unable to adjust educational content in real time, taking into account the learner's emotional state, making it difficult to provide effective individualized learning. Furthermore, the lack of technology to dynamically adapt learning plans based on the learner's real-time emotions and reactions limited the ability to maintain learner motivation and improve concentration.

[0161] 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.

[0162] In this invention, the server includes means for acquiring learner appearance data and voice data, means for identifying emotional state, and means for generating personalized educational information. This enables real-time analysis of the learner's emotional state and the provision of individually adapted learning content.

[0163] "Appearance data" refers to visual information about the learner's facial expressions and body movements.

[0164] "Audio data" refers to auditory information related to the learner's voice tone, volume, and the content of their speech.

[0165] "Emotional state" refers to the learner's internal state, such as stress, excitement, and concentration, which are inferred from external and auditory data.

[0166] "Personalized educational information" refers to learning content and materials that are tailored based on the learner's current emotional state.

[0167] "Presenting visually and aurally" means conveying information to learners through on-screen displays and audio.

[0168] "Adjusting a learning plan" means changing the content and pace of learning in accordance with the learner's progress and emotional changes.

[0169] This invention utilizes emotion recognition functionality as part of an educational support system, and takes the form of data exchange between the user, terminal, and server. Specific embodiments are described below.

[0170] The user starts a learning session using a device with the learning application installed. The device acquires appearance data and audio data by capturing the learner's facial expressions and movements with its camera and recording their voice with its microphone. This data is transmitted to the server in real time.

[0171] The server activates an emotion engine based on the received data to analyze the user's emotional state. This emotion engine utilizes image recognition technology, such as TENSORFLOW®, and uses a speech analysis algorithm to analyze speech. This allows the server to classify the user's emotional state into characteristics such as stress, excitement, and concentration.

[0172] Based on the analysis results, the server generates and sends user-optimized educational content to the device. For example, if the user is feeling impatient, the system will slow down the pace and select materials that provide more detailed explanations.

[0173] The device presents content transmitted from the server to the user visually and audibly. Specifically, it displays information on the screen and provides audio guidance as needed. Furthermore, it monitors the user's learning progress and follows instructions from the server to dynamically adjust the learning plan in response to changes in the user's emotions.

[0174] As a concrete example, consider a situation where a user is learning the procedures for a scientific experiment. If the system determines that the user is facing difficulties, it will immediately present content that promotes understanding, such as a video with step-by-step explanations.

[0175] An example of a prompt for a generative AI model is: "Analyze the user's facial expression and voice data to identify and analyze emotional states that influence learning progress, and provide optimized learning content."

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The user launches an educational support application on their device and begins a learning session. The device uses its camera and microphone to capture the user's facial expressions and voice in real time. During this process, the user's facial image and voice recording data are acquired as input.

[0179] Step 2:

[0180] The terminal sends captured visual and audio data to the server. The input here is the raw data obtained from the user, and the output is the process of transferring this data to the server.

[0181] Step 3:

[0182] The server stores the received data and activates the emotion engine for analysis. The input consists of user facial image and audio data, and an emotion classification model is used to output emotional states such as stress, excitement, and concentration. This process includes facial feature analysis and audio feature extraction.

[0183] Step 4:

[0184] The server generates educational content optimized for the learner based on the analyzed emotional state. Here, it takes an emotional state (e.g., low concentration) as input and outputs specific learning materials and content to supplement the learning process (e.g., easy problem sets or visual aids).

[0185] Step 5:

[0186] The server sends the generated educational content to the terminal. The input is the generated content, and the output is the process of displaying it on the terminal in a format that the user can view and use to progress in their learning.

[0187] Step 6:

[0188] The terminal presents content received from the server to the user and monitors the user's learning progress. It then sends user feedback and progress data back to the server to help adjust the next learning plan. Specifically, it automatically displays quizzes and surveys to check the user's understanding.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0191] Conventional learning support systems provide general learning content without considering the learner's emotional state, making it difficult to achieve an individualized learning experience. Furthermore, they fail to provide appropriate learning content tailored to the learner's concentration and stress levels, resulting in insufficient improvement in learning efficiency and maintenance of motivation.

[0192] 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.

[0193] In this invention, the server includes means for acquiring the learner's appearance information, means for analyzing the appearance information to identify the learner's emotional state, and means for dynamically selecting and providing diverse content for the learner based on the emotional state. This makes it possible to provide a personalized learning experience that is tailored to the learner's emotional state.

[0194] "External appearance information" refers to data that includes the learner's facial expressions and vocal characteristics, and is acquired to identify their emotional state.

[0195] "Emotional state" refers to the learner's feelings and psychological state, including stress, concentration, and excitement.

[0196] "Data" refers to learning materials and content generated based on the learner's emotional state, and is provided to offer specific learning support.

[0197] "Progress status" refers to information indicating how far a learner has progressed in their studies, and is used to adjust future learning plans.

[0198] "Dynamic selection" refers to a process that determines and provides appropriate content in real time based on the learner's emotional state and learning progress.

[0199] "Diverse content" refers to educational materials and media of varying formats and difficulty levels, which are provided flexibly according to the learner's needs.

[0200] The system for carrying out this invention includes a series of processes for analyzing the learner's emotional state in real time and providing an individualized learning experience. This process proceeds as follows:

[0201] First, when a user begins learning using the device, the device activates its built-in camera and microphone to acquire appearance information of the user's facial expressions and voice. This acquired appearance information is sent to the server via the network. The server uses Python®-based machine learning libraries such as OpenCV and TensorFlow to analyze the facial expressions and voice patterns and identify the learner's emotional state, such as stress, excitement, and concentration.

[0202] Based on this emotional state, the server selects a variety of content optimized for the learner. For example, if concentration is low, it provides relaxation videos to reduce stress, and if concentration is high, it delivers more challenging learning videos.

[0203] For example, if a user shows signs of fatigue while watching a history video, the server can detect this and insert a short break video to help them regain their concentration. This mechanism aims to provide users with a comfortable and effective learning environment.

[0204] A concrete example of a prompt message might be something like, "If a user is feeling stressed while watching a history video, what are some ways to help them relax? For example, what kind of content would you recommend?"

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

[0206] Step 1:

[0207] The device activates its camera and microphone when the user starts a learning session. This captures the user's facial expressions and voice in real time. The captured data is necessary to determine the user's emotional state. The input is the user's video and audio, and the output is the captured visual information.

[0208] Step 2:

[0209] The terminal sends the acquired appearance information to the server. The server receives this data and prepares to analyze it. During the data transfer process, the data is converted to the appropriate data format and sent. The input is the appearance information from the terminal, and the output is the raw data received by the server.

[0210] Step 3:

[0211] The server uses the received appearance information to launch Python-based machine learning libraries (such as OpenCV and TensorFlow). The server utilizes these libraries to analyze the user's emotional state from facial expression data and voice patterns. The input is raw data, and the output is the analysis result indicating the user's emotional state.

[0212] Step 4:

[0213] Based on the analysis results, the server selects a variety of content optimized for the user. For example, if the user is stressed, the server recommends a relaxation video. The input is the analysis results, and the output is information about the recommended content.

[0214] Step 5:

[0215] The server sends the selected content to the device. The device receives this content and presents it to the user. This personalizes the user's learning experience and promotes effective learning. The input is the information of the selected content, and the output is the learning content displayed on the device.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention provides a system that offers real-time educational support tailored to the individual needs of learners. This system uses learner appearance data to identify their emotional state and dynamically adjusts learning content and plans based on the learner's progress.

[0233] System Overview

[0234] 1. User begins learning

[0235] When a user starts a learning session using their device, the device's camera and microphone activate to capture the user's facial expressions, movements, and voice.

[0236] 2. Acquisition and transmission of appearance data

[0237] The device processes the captured appearance data in real time and sends it to the server. This data includes information such as the user's facial movements, voice tone, and speed.

[0238] 3. Data analysis and identification of emotional state

[0239] The server analyzes the received data and uses a generative AI model to identify the user's emotional state. It identifies whether the user is confused, satisfied, etc., and predicts their response to specific learning tasks.

[0240] 4. Generating appropriate learning content

[0241] The server generates explanatory materials and supplementary learning content based on the user's emotional state. For example, if a user is struggling with a new mathematical concept, it will provide a review of the relevant foundational knowledge or a visual guide.

[0242] 5. Dynamic adjustment of the learning plan

[0243] The server monitors the user's progress and adjusts the learning plan according to their learning progress. If a particular topic is taking too long, it provides additional practice exercises on that topic.

[0244] 6. Providing feedback

[0245] The terminal displays explanations and supplementary questions sent from the server to the user. It also notifies the user of any changes to their learning plan, clearly indicating what they should do next.

[0246] Thus, the present invention provides flexible and efficient learning support tailored to each individual learner. Specifically, it enables the rapid identification of delays in understanding that occur when solving mathematical problems and provides supplementary materials suitable for the user. This allows users to continue effective learning at their own pace.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The user starts a learning session using their device. The device activates its built-in camera and microphone and is configured to capture the user's facial expressions, movements, and voice in real time.

[0250] Step 2:

[0251] The device transmits captured visual data to the server in real time. This data includes the user's facial expressions, voice tone, and speed information.

[0252] Step 3:

[0253] The server applies a generative AI model based on the received visual data to identify the user's emotional state. The server identifies emotions such as confusion and interest and analyzes the user's response to the learning content.

[0254] Step 4:

[0255] The server generates learning content tailored to the user based on the analysis results. If it determines that the user is struggling with a specific problem, it creates supplementary explanations and visuals for that section.

[0256] Step 5:

[0257] The server evaluates the user's learning progress and adjusts the learning plan as needed. Based on the user's progress data, it adds supplementary practice exercises and materials to reinforce necessary topics.

[0258] Step 6:

[0259] The device provides the user with new learning content and an adjusted learning plan sent from the server. The user can use this feedback to further their learning.

[0260] This entire process allows the system to provide customized, real-time learning support for each user.

[0261] (Example 1)

[0262] 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."

[0263] Providing effective, real-time educational support tailored to the individual needs of learners is a challenging task. Conventional systems struggle to instantly reflect changes in learners' behavior and emotions, making it difficult to adjust learning plans accordingly, potentially leading to decreased learner motivation. Therefore, there is a need for technology that can provide flexible and responsive learning support to each individual learner.

[0264] 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.

[0265] In this invention, the server includes means for acquiring learner behavioral data, means for analyzing the behavioral data to identify the learner's emotional state, and means for generating educational resources based on the emotional state. This enables flexible and effective learning support tailored to the individual circumstances of each learner.

[0266] "Learner behavior data" refers to information about learners' physical movements and vocal expressions—information about their behavior that can be observed from the outside.

[0267] "Emotional state" refers to the learner's feelings and mental state, encompassing psychological conditions such as happiness, anxiety, and concentration.

[0268] "Educational resources" refer to all information media that support learning, such as teaching materials, supplementary materials, and interactive tools provided to learners.

[0269] "Dynamic adjustment" refers to the fact that learning plans and content are not fixed in place, but can be flexibly changed according to the learner's progress and condition.

[0270] "Real-time" refers to a state where the system processes data instantly and can quickly provide feedback and adjustments tailored to the learner's situation.

[0271] This invention is a learning support system that provides personalized, real-time educational support to learners. This system utilizes the user's terminal and server to analyze the learner's emotional state and flexibly generate and adjust learning content based on that analysis.

[0272] Hardware and software configuration

[0273] terminal

[0274] The user's device is equipped with a camera and microphone, which capture the user's facial expressions, movements, and voice during learning sessions. The processing system includes software that supports real-time data analysis. This data is then transmitted from the device to the server.

[0275] server

[0276] The server is equipped with a generative AI model and performs advanced data analysis. Based on the data received from the user, the server identifies the user's emotional state and generates learning resources accordingly. It also tracks the learning progress and dynamically adjusts the learning plan as needed.

[0277] Data processing and calculation

[0278] When the server receives data from the user, it analyzes the data using a generative AI model. This model identifies the emotional state in learning from the user's facial movements and vocal tones. As a result, the generated information is fed back to the user through the terminal again.

[0279] Specific example

[0280] For example, when the user is confused about a mathematical concept, basic visual guides and additional practice problems are presented according to the emotional state. This provision enables the user to learn efficiently according to their own learning speed.

[0281] Example of prompt sentence

[0282] "Please identify the current emotional state from the user's facial expression and voice data. How does the user feel? Please identify states such as confusion or satisfaction."

[0283] As described above, according to this invention, it is possible to provide flexible and effective learning support corresponding to individual learners.

[0284] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0285] Step 1:

[0286] The terminal captures the user's actions and voice. The user starts a learning session, and the camera and microphone are activated. As input, the user's facial expression, movement, and voice data are collected. These raw data are ready to be sent to the server in real time.

[0287] Step 2:

[0288] The device sends the collected data to the server. Converting the data to an appropriate format before sending it to the server enables efficient data processing. The input is data such as captured facial movements and voice tones, and the output is communication with the server.

[0289] Step 3:

[0290] The server analyzes the received data. The server uses a generative AI model to evaluate the data in order to identify the user's emotional state. The input is action and voice data sent from the terminal, and the output is an identification result of the emotional state (e.g., confused, satisfied).

[0291] Step 4:

[0292] The server generates learning resources based on emotional states. Based on the results of the AI ​​model, it creates learning content optimized for each individual user. It receives identified emotional states as input and generates appropriate educational resources (visual guides, supplementary materials, etc.) as output.

[0293] Step 5:

[0294] The server dynamically adjusts the learning plan. The server monitors the user's progress and updates the learning plan as needed. It receives user learning progress data as input and an updated learning plan as output.

[0295] Step 6:

[0296] The device presents the user with generated feedback and learning resources. The device communicates output information from the server to the user, providing clear instructions for the next steps. It receives educational resources and feedback from the server as input, and presents them to the user visually or audibly as output.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] When providing educational support to individual learners, there is a challenge in immediately grasping their emotional state and providing appropriate supplementary materials. Furthermore, there is a need for dynamic adjustments to learning plans based on the learner's progress. In particular, real-time data acquisition and analysis are crucial for realizing such educational support in the home environment.

[0300] 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.

[0301] In this invention, the server includes means for acquiring observational data of learners, means for analyzing the observational data to identify the learners' emotional state, and means for generating supplementary materials for the learners based on the emotional state. This makes it possible to provide personalized educational support in real time.

[0302] A "learner" is an individual who is eligible to receive educational support.

[0303] "Observational data" refers to information collected in real time, such as learners' facial expressions, gestures, and voice.

[0304] "Emotional state" refers to a learner's psychological and emotional condition and is used to evaluate their response to learning.

[0305] "Supplementary materials" are additional educational content or guides provided to help learners understand the material.

[0306] An "educational plan" is a system of learning content and schedules formulated based on the learner's progress.

[0307] The "means for dynamically generating" refers to a method of sequentially creating necessary information and materials according to real-time data.

[0308] To implement this invention, mainly a system is constructed in which a server and a terminal cooperate to provide optimal educational support for learners. The learner starts learning activities using a terminal equipped with a camera and a microphone. The terminal acquires observation data such as the learner's expression and voice in real time and transmits this to the server. The server uses a generative AI model to analyze the received data. As a specific example, EmotionAPI is used to identify the emotional state.

[0309] Based on the analysis results, the server dynamically generates auxiliary materials suitable for the learner's emotional state. For example, if it is determined that the learner is confused, visual guides and practice problems of the corresponding basic knowledge are immediately generated and transmitted to the terminal. The terminal presents these materials to the learner.

[0310] Furthermore, the server continuously monitors the progress of the learner and adjusts the educational plan as needed. Through all these processes, an optimized educational experience can be provided for each learner.

[0311] As a specific example, the server analyzes a learner who sighed during math study and generates a visual guide for basic calculations. An example of a prompt sentence is "Analyze the user's emotion and select the corresponding visual teaching material if they feel confused." By using this prompt, the generative AI model manages the process of selecting appropriate materials.

[0312] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0313] Step 1:

[0314] The user starts a learning session using the device. The device activates its camera and microphone to acquire observational data in real time, including the learner's facial expressions, voice tone, and movements. The input is the learner's visual data, and the output is this data in digital format. The device prepares this data for the next processing step.

[0315] Step 2:

[0316] The terminal transmits the acquired observation data to the server. The input is the digital observation data collected by the terminal, and the output is the data transfer to the server. The server receives and stores this data and prepares it for the next analysis step.

[0317] Step 3:

[0318] The server uses a generative AI model to analyze the received observation data. The input is observation data stored on the server, and a model such as EmotionAPI analyzes the data. In this step, the server performs calculations to identify emotional states and judge changes in the learner's mood and emotions. The output is the evaluation result of the emotional state.

[0319] Step 4:

[0320] The server dynamically generates appropriate supplementary materials based on the emotional state assessment results. The input is the emotional state assessment results, and the output is the specific supplementary materials to be provided to the learner. These include visual guides and practice exercises, and are configured to support the learner's learning needs.

[0321] Step 5:

[0322] The server generates supplementary materials and sends them to the terminal. The input is dynamically generated supplementary materials, and the output is the transmission of these materials to the terminal. The terminal receives these materials and prepares them to be displayed to the learner.

[0323] Step 6:

[0324] The terminal presents the received supplementary materials to the user. The input is the supplementary materials received by the terminal, and the output is the learner obtaining the materials visually or aurally. The terminal presents the materials in a format suitable for the learner, supporting their learning.

[0325] Step 7:

[0326] The server continuously monitors the learner's progress and adjusts the teaching plan as needed. The input is the learner's current progress data, and the output is the latest teaching plan. This plan is modified according to individual needs and adjusted to optimize learning.

[0327] 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.

[0328] This invention relates to an educational support system that incorporates an emotion engine that recognizes user emotions in real time and applies them to learning support. The system aims to identify the learner's emotions and emotional state and provide appropriate learning content and plans based on that.

[0329] System Overview

[0330] 1. User begins learning

[0331] When a user starts a learning session using their device, the device's built-in camera and microphone are activated. This allows the user's facial expressions, movements, and voice to be captured in real time.

[0332] 2. Acquisition of appearance data and sentiment data

[0333] The device sends captured visual data to the server. The server uses this data to activate an emotion engine and analyze the user's emotional state from their facial expressions and voice.

[0334] 3. Analysis using an emotion engine

[0335] The emotion engine on the server analyzes the user's facial expressions and voice patterns to identify emotional states such as stress, excitement, and concentration. This allows for a detailed understanding of the user's responses to learning tasks.

[0336] 4. Content Creation and Delivery

[0337] The server generates optimal learning content for the user based on the analysis results from the emotion engine. For example, if the user shows signs of impatience, it will slow down the pace and provide more detailed explanations.

[0338] 5. Dynamic adjustment of the learning plan

[0339] The server dynamically adjusts the learning plan based on the user's current emotional state. For example, if the server determines that the user is highly focused, it will move on to learning more advanced topics.

[0340] 6. Feedback and Motivation

[0341] The device presents the user with learning content and plans sent from the server, and provides feedback based on their progress. Furthermore, by utilizing the analysis results from the emotion engine, it contributes to maintaining and improving motivation.

[0342] For example, if a user's concentration wanes while solving a math problem, the system analyzes their emotional state and adjusts the difficulty of the problem or suggests content that promotes relaxation. In this way, the present invention aims to personalize the user's learning experience and support effective learning.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] The user starts a learning session using their device. The device activates its built-in camera and microphone and is configured to capture the user's facial expressions, movements, and voice in real time.

[0346] Step 2:

[0347] The device transmits user appearance and voice data to the server in real time. This data includes the user's facial expressions, voice tone, and speed information.

[0348] Step 3:

[0349] The server uses the received data to activate the emotion engine and analyze the user's emotional state. Specifically, it analyzes subtle changes in the user's facial expressions and the emotional tone of their voice to identify levels of stress and concentration.

[0350] Step 4:

[0351] The server generates appropriate learning content based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it might add easy-to-understand explanations or animations to make the learning process smoother.

[0352] Step 5:

[0353] The server dynamically adjusts the learning plan according to the user's emotional state. If it determines that concentration is being maintained, adjustments such as maintaining or accelerating the learning pace will be made.

[0354] Step 6:

[0355] The device displays learning content generated from the server and a revised learning plan to the user. The user then follows these instructions to effectively progress with their learning.

[0356] This processing step allows the system to leverage the user's emotional state and provide flexible and effective learning support tailored to each learner's needs.

[0357] (Example 2)

[0358] 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".

[0359] Conventional educational support systems were unable to adjust educational content in real time, taking into account the learner's emotional state, making it difficult to provide effective individualized learning. Furthermore, the lack of technology to dynamically adapt learning plans based on the learner's real-time emotions and reactions limited the ability to maintain learner motivation and improve concentration.

[0360] 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.

[0361] In this invention, the server includes means for acquiring learner appearance data and voice data, means for identifying emotional state, and means for generating personalized educational information. This enables real-time analysis of the learner's emotional state and the provision of individually adapted learning content.

[0362] "Appearance data" refers to visual information about the learner's facial expressions and body movements.

[0363] "Audio data" refers to auditory information related to the learner's voice tone, volume, and the content of their speech.

[0364] "Emotional state" refers to the learner's internal state, such as stress, excitement, and concentration, which are inferred from external and auditory data.

[0365] "Personalized educational information" refers to learning content and materials that are tailored based on the learner's current emotional state.

[0366] "Presenting visually and aurally" means conveying information to learners through on-screen displays and audio.

[0367] "Adjusting a learning plan" means changing the content and pace of learning in accordance with the learner's progress and emotional changes.

[0368] This invention utilizes emotion recognition functionality as part of an educational support system, and takes the form of data exchange between the user, terminal, and server. Specific embodiments are described below.

[0369] The user starts a learning session using a device with the learning application installed. The device acquires appearance data and audio data by capturing the learner's facial expressions and movements with its camera and recording their voice with its microphone. This data is transmitted to the server in real time.

[0370] The server activates an emotion engine based on the received data to analyze the user's emotional state. This emotion engine utilizes image recognition technology, such as TensorFlow, and uses speech analysis algorithms to analyze speech. This allows the server to classify the user's emotional state based on characteristics such as stress, excitement, and concentration.

[0371] Based on the analysis results, the server generates and sends user-optimized educational content to the device. For example, if the user is feeling impatient, the system will slow down the pace and select materials that provide more detailed explanations.

[0372] The device presents content transmitted from the server to the user visually and audibly. Specifically, it displays information on the screen and provides audio guidance as needed. Furthermore, it monitors the user's learning progress and follows instructions from the server to dynamically adjust the learning plan in response to changes in the user's emotions.

[0373] As a concrete example, consider a situation where a user is learning the procedures for a scientific experiment. If the system determines that the user is facing difficulties, it will immediately present content that promotes understanding, such as a video with step-by-step explanations.

[0374] An example of a prompt for a generative AI model is: "Analyze the user's facial expression and voice data to identify and analyze emotional states that influence learning progress, and provide optimized learning content."

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The user launches an educational support application on their device and begins a learning session. The device uses its camera and microphone to capture the user's facial expressions and voice in real time. During this process, the user's facial image and voice recording data are acquired as input.

[0378] Step 2:

[0379] The terminal sends captured visual and audio data to the server. The input here is the raw data obtained from the user, and the output is the process of transferring this data to the server.

[0380] Step 3:

[0381] The server stores the received data and activates the emotion engine for analysis. The input consists of user facial image and audio data, and an emotion classification model is used to output emotional states such as stress, excitement, and concentration. This process includes facial feature analysis and audio feature extraction.

[0382] Step 4:

[0383] The server generates educational content optimized for the learner based on the analyzed emotional state. Here, it takes an emotional state (e.g., low concentration) as input and outputs specific learning materials and content to supplement the learning process (e.g., easy problem sets or visual aids).

[0384] Step 5:

[0385] The server sends the generated educational content to the terminal. The input is the generated content, and the output is the process of displaying it on the terminal in a format that the user can view and use to progress in their learning.

[0386] Step 6:

[0387] The terminal presents content received from the server to the user and monitors the user's learning progress. It then sends user feedback and progress data back to the server to help adjust the next learning plan. Specifically, it automatically displays quizzes and surveys to check the user's understanding.

[0388] (Application Example 2)

[0389] 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".

[0390] Conventional learning support systems provide general learning content without considering the learner's emotional state, making it difficult to achieve an individualized learning experience. Furthermore, they fail to provide appropriate learning content tailored to the learner's concentration and stress levels, resulting in insufficient improvement in learning efficiency and maintenance of motivation.

[0391] 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.

[0392] In this invention, the server includes means for acquiring the learner's appearance information, means for analyzing the appearance information to identify the learner's emotional state, and means for dynamically selecting and providing diverse content for the learner based on the emotional state. This makes it possible to provide a personalized learning experience that is tailored to the learner's emotional state.

[0393] "External appearance information" refers to data that includes the learner's facial expressions and vocal characteristics, and is acquired to identify their emotional state.

[0394] "Emotional state" refers to the learner's feelings and psychological state, including stress, concentration, and excitement.

[0395] "Data" refers to learning materials and content generated based on the learner's emotional state, and is provided to offer specific learning support.

[0396] "Progress status" refers to information indicating how far a learner has progressed in their studies, and is used to adjust future learning plans.

[0397] "Dynamic selection" refers to a process that determines and provides appropriate content in real time based on the learner's emotional state and learning progress.

[0398] "Diverse content" refers to educational materials and media of varying formats and difficulty levels, which are provided flexibly according to the learner's needs.

[0399] The system for carrying out this invention includes a series of processes for analyzing the learner's emotional state in real time and providing an individualized learning experience. This process proceeds as follows:

[0400] First, when a user begins learning using the device, the device activates its built-in camera and microphone to acquire appearance information of the user's facial expressions and voice. This acquired appearance information is sent to the server via the network. The server uses Python-based machine learning libraries such as OpenCV and TensorFlow to analyze the facial expressions and voice patterns and identify the learner's emotional state, such as stress, excitement, and concentration.

[0401] Based on this emotional state, the server selects a variety of content optimized for the learner. For example, if concentration is low, it provides relaxation videos to reduce stress, and if concentration is high, it delivers more challenging learning videos.

[0402] For example, if a user shows signs of fatigue while watching a history video, the server can detect this and insert a short break video to help them regain their concentration. This mechanism aims to provide users with a comfortable and effective learning environment.

[0403] A concrete example of a prompt message might be something like, "If a user is feeling stressed while watching a history video, what are some ways to help them relax? For example, what kind of content would you recommend?"

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

[0405] Step 1:

[0406] The device activates its camera and microphone when the user starts a learning session. This captures the user's facial expressions and voice in real time. The captured data is necessary to determine the user's emotional state. The input is the user's video and audio, and the output is the captured visual information.

[0407] Step 2:

[0408] The terminal sends the acquired appearance information to the server. The server receives this data and prepares to analyze it. During the data transfer process, the data is converted to the appropriate data format and sent. The input is the appearance information from the terminal, and the output is the raw data received by the server.

[0409] Step 3:

[0410] The server uses the received appearance information to launch Python-based machine learning libraries (such as OpenCV and TensorFlow). The server utilizes these libraries to analyze the user's emotional state from facial expression data and voice patterns. The input is raw data, and the output is the analysis result indicating the user's emotional state.

[0411] Step 4:

[0412] Based on the analysis results, the server selects a variety of content optimized for the user. For example, if the user is stressed, the server recommends a relaxation video. The input is the analysis results, and the output is information about the recommended content.

[0413] Step 5:

[0414] The server sends the selected content to the device. The device receives this content and presents it to the user. This personalizes the user's learning experience and promotes effective learning. The input is the information of the selected content, and the output is the learning content displayed on the device.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] This invention provides a system that offers real-time educational support tailored to the individual needs of learners. This system uses learner appearance data to identify their emotional state and dynamically adjusts learning content and plans based on the learner's progress.

[0432] System Overview

[0433] 1. User begins learning

[0434] When a user starts a learning session using their device, the device's camera and microphone activate to capture the user's facial expressions, movements, and voice.

[0435] 2. Acquisition and transmission of appearance data

[0436] The device processes the captured appearance data in real time and sends it to the server. This data includes information such as the user's facial movements, voice tone, and speed.

[0437] 3. Data analysis and identification of emotional state

[0438] The server analyzes the received data and uses a generative AI model to identify the user's emotional state. It identifies whether the user is confused, satisfied, etc., and predicts their response to specific learning tasks.

[0439] 4. Generating appropriate learning content

[0440] The server generates explanatory materials and supplementary learning content based on the user's emotional state. For example, if a user is struggling with a new mathematical concept, it will provide a review of the relevant foundational knowledge or a visual guide.

[0441] 5. Dynamic adjustment of the learning plan

[0442] The server monitors the user's progress and adjusts the learning plan according to their learning progress. If a particular topic is taking too long, it provides additional practice exercises on that topic.

[0443] 6. Providing feedback

[0444] The terminal displays explanations and supplementary questions sent from the server to the user. It also notifies the user of any changes to their learning plan, clearly indicating what they should do next.

[0445] Thus, the present invention provides flexible and efficient learning support tailored to each individual learner. Specifically, it enables the rapid identification of delays in understanding that occur when solving mathematical problems and provides supplementary materials suitable for the user. This allows users to continue effective learning at their own pace.

[0446] The following describes the processing flow.

[0447] Step 1:

[0448] The user starts a learning session using their device. The device activates its built-in camera and microphone and is configured to capture the user's facial expressions, movements, and voice in real time.

[0449] Step 2:

[0450] The device transmits captured visual data to the server in real time. This data includes the user's facial expressions, voice tone, and speed information.

[0451] Step 3:

[0452] The server applies a generative AI model based on the received visual data to identify the user's emotional state. The server identifies emotions such as confusion and interest and analyzes the user's response to the learning content.

[0453] Step 4:

[0454] The server generates learning content tailored to the user based on the analysis results. If it determines that the user is struggling with a specific problem, it creates supplementary explanations and visuals for that section.

[0455] Step 5:

[0456] The server evaluates the user's learning progress and adjusts the learning plan as needed. Based on the user's progress data, it adds supplementary practice exercises and materials to reinforce necessary topics.

[0457] Step 6:

[0458] The device provides the user with new learning content and an adjusted learning plan sent from the server. The user can use this feedback to further their learning.

[0459] This entire process allows the system to provide customized, real-time learning support for each user.

[0460] (Example 1)

[0461] 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."

[0462] Providing effective, real-time educational support tailored to the individual needs of learners is a challenging task. Conventional systems struggle to instantly reflect changes in learners' behavior and emotions, making it difficult to adjust learning plans accordingly, potentially leading to decreased learner motivation. Therefore, there is a need for technology that can provide flexible and responsive learning support to each individual learner.

[0463] 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.

[0464] In this invention, the server includes means for acquiring learner behavioral data, means for analyzing the behavioral data to identify the learner's emotional state, and means for generating educational resources based on the emotional state. This enables flexible and effective learning support tailored to the individual circumstances of each learner.

[0465] "Learner behavior data" refers to information about learners' physical movements and vocal expressions—information about their behavior that can be observed from the outside.

[0466] "Emotional state" refers to the learner's feelings and mental state, encompassing psychological conditions such as happiness, anxiety, and concentration.

[0467] "Educational resources" refer to all information media that support learning, such as teaching materials, supplementary materials, and interactive tools provided to learners.

[0468] "Dynamic adjustment" refers to the fact that learning plans and content are not fixed in place, but can be flexibly changed according to the learner's progress and condition.

[0469] "Real-time" refers to a state where the system processes data instantly and can quickly provide feedback and adjustments tailored to the learner's situation.

[0470] This invention is a learning support system that provides personalized, real-time educational support to learners. This system utilizes the user's terminal and server to analyze the learner's emotional state and flexibly generate and adjust learning content based on that analysis.

[0471] Hardware and software configuration

[0472] terminal

[0473] The user's device is equipped with a camera and microphone, which capture the user's facial expressions, movements, and voice during learning sessions. The processing system includes software that supports real-time data analysis. This data is then transmitted from the device to the server.

[0474] server

[0475] The server is equipped with a generative AI model and performs advanced data analysis. Based on the data received from the user, the server identifies the user's emotional state and generates learning resources accordingly. It also tracks the learning progress and dynamically adjusts the learning plan as needed.

[0476] Data processing and calculations

[0477] When the server receives data from the user, it analyzes the data using a generative AI model. This model identifies the user's emotional state during learning from their facial movements and voice tone. As a result, the generated information is fed back to the user through the device.

[0478] Specific example

[0479] For example, if a user is confused by a mathematical concept, basic visual guides and additional practice problems are presented according to their emotional state. This allows users to learn efficiently at their own pace.

[0480] Example of a prompt

[0481] "Identify the user's current emotional state based on their facial expressions and voice data. How is the user feeling? Identify states such as confusion or satisfaction."

[0482] As described above, this invention makes it possible to provide flexible and effective learning support tailored to individual learners.

[0483] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0484] Step 1:

[0485] The device captures the user's movements and voice. The user starts a learning session, and the camera and microphone are activated. As input, data on the user's facial expressions, movements, and voice are collected. This raw data is ready to be sent to the server in real time.

[0486] Step 2:

[0487] The device sends the collected data to the server. Converting the data to an appropriate format before sending it to the server enables efficient data processing. The input is data such as captured facial movements and voice tones, and the output is communication with the server.

[0488] Step 3:

[0489] The server analyzes the received data. The server uses a generative AI model to evaluate the data in order to identify the user's emotional state. The input is action and voice data sent from the terminal, and the output is an identification result of the emotional state (e.g., confused, satisfied).

[0490] Step 4:

[0491] The server generates learning resources based on emotional states. Based on the results of the AI ​​model, it creates learning content optimized for each individual user. It receives identified emotional states as input and generates appropriate educational resources (visual guides, supplementary materials, etc.) as output.

[0492] Step 5:

[0493] The server dynamically adjusts the learning plan. The server monitors the user's progress and updates the learning plan as needed. It receives user learning progress data as input and an updated learning plan as output.

[0494] Step 6:

[0495] The device presents the user with generated feedback and learning resources. The device communicates output information from the server to the user, providing clear instructions for the next steps. It receives educational resources and feedback from the server as input, and presents them to the user visually or audibly as output.

[0496] (Application Example 1)

[0497] 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."

[0498] When providing educational support to individual learners, there is a challenge in immediately grasping their emotional state and providing appropriate supplementary materials. Furthermore, there is a need for dynamic adjustments to learning plans based on the learner's progress. In particular, real-time data acquisition and analysis are crucial for realizing such educational support in the home environment.

[0499] 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.

[0500] In this invention, the server includes means for acquiring observational data of learners, means for analyzing the observational data to identify the learners' emotional state, and means for generating supplementary materials for the learners based on the emotional state. This makes it possible to provide personalized educational support in real time.

[0501] A "learner" is an individual who is eligible to receive educational support.

[0502] "Observational data" refers to information collected in real time, such as learners' facial expressions, gestures, and voice.

[0503] "Emotional state" refers to a learner's psychological and emotional condition and is used to evaluate their response to learning.

[0504] "Supplementary materials" are additional educational content or guides provided to help learners understand the material.

[0505] An "educational plan" is a system of learning content and schedules formulated based on the learner's progress.

[0506] "Dynamic generation methods" refer to methods of sequentially creating necessary information and materials in response to real-time data.

[0507] To implement this invention, a system is built in which a server and a terminal work together to provide optimal educational support to learners. Learners begin learning activities using a terminal equipped with a camera and microphone. The terminal acquires observational data such as the learner's facial expressions and voice in real time and transmits it to the server. The server uses a generative AI model to analyze the received data. As a specific example, the EmotionAPI is used to identify the emotional state.

[0508] Based on the analysis results, the server dynamically generates supplementary materials appropriate to the learner's emotional state. For example, if it determines that the learner is confused, it immediately generates a visual guide or practice exercises on the relevant foundational knowledge and sends them to the device. The device then presents these materials to the learner.

[0509] Furthermore, the server continuously monitors the learner's progress and adjusts the educational plan as needed. Through all these processes, an optimized educational experience can be provided for each learner.

[0510] As a concrete example, a server analyzes a student who sighs while studying mathematics and generates a visual guide to basic calculations. An example of a prompt would be, "Analyze the user's emotions and select the appropriate visual material if they appear confused." This prompt manages the process by which the generating AI model selects appropriate materials.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The user starts a learning session using the device. The device activates its camera and microphone to acquire observational data in real time, including the learner's facial expressions, voice tone, and movements. The input is the learner's visual data, and the output is this data in digital format. The device prepares this data for the next processing step.

[0514] Step 2:

[0515] The terminal transmits the acquired observation data to the server. The input is the digital observation data collected by the terminal, and the output is the data transfer to the server. The server receives and stores this data and prepares it for the next analysis step.

[0516] Step 3:

[0517] The server uses a generative AI model to analyze the received observation data. The input is observation data stored on the server, and a model such as EmotionAPI analyzes the data. In this step, the server performs calculations to identify emotional states and judge changes in the learner's mood and emotions. The output is the evaluation result of the emotional state.

[0518] Step 4:

[0519] The server dynamically generates appropriate supplementary materials based on the emotional state assessment results. The input is the emotional state assessment results, and the output is the specific supplementary materials to be provided to the learner. These include visual guides and practice exercises, and are configured to support the learner's learning needs.

[0520] Step 5:

[0521] The server generates supplementary materials and sends them to the terminal. The input is dynamically generated supplementary materials, and the output is the transmission of these materials to the terminal. The terminal receives these materials and prepares them to be displayed to the learner.

[0522] Step 6:

[0523] The terminal presents the received supplementary materials to the user. The input is the supplementary materials received by the terminal, and the output is the learner obtaining the materials visually or aurally. The terminal presents the materials in a format suitable for the learner, supporting their learning.

[0524] Step 7:

[0525] The server continuously monitors the learner's progress and adjusts the teaching plan as needed. The input is the learner's current progress data, and the output is the latest teaching plan. This plan is modified according to individual needs and adjusted to optimize learning.

[0526] 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.

[0527] This invention relates to an educational support system that incorporates an emotion engine that recognizes user emotions in real time and applies them to learning support. The system aims to identify the learner's emotions and emotional state and provide appropriate learning content and plans based on that.

[0528] System Overview

[0529] 1. User begins learning

[0530] When a user starts a learning session using their device, the device's built-in camera and microphone are activated. This allows the user's facial expressions, movements, and voice to be captured in real time.

[0531] 2. Acquisition of appearance data and sentiment data

[0532] The device sends captured visual data to the server. The server uses this data to activate an emotion engine and analyze the user's emotional state from their facial expressions and voice.

[0533] 3. Analysis using an emotion engine

[0534] The emotion engine on the server analyzes the user's facial expressions and voice patterns to identify emotional states such as stress, excitement, and concentration. This allows for a detailed understanding of the user's responses to learning tasks.

[0535] 4. Content Creation and Delivery

[0536] The server generates optimal learning content for the user based on the analysis results from the emotion engine. For example, if the user shows signs of impatience, it will slow down the pace and provide more detailed explanations.

[0537] 5. Dynamic adjustment of the learning plan

[0538] The server dynamically adjusts the learning plan based on the user's current emotional state. For example, if the server determines that the user is highly focused, it will move on to learning more advanced topics.

[0539] 6. Feedback and Motivation

[0540] The device presents the user with learning content and plans sent from the server, and provides feedback based on their progress. Furthermore, by utilizing the analysis results from the emotion engine, it contributes to maintaining and improving motivation.

[0541] For example, if a user's concentration wanes while solving a math problem, the system analyzes their emotional state and adjusts the difficulty of the problem or suggests content that promotes relaxation. In this way, the present invention aims to personalize the user's learning experience and support effective learning.

[0542] The following describes the processing flow.

[0543] Step 1:

[0544] The user starts a learning session using their device. The device activates its built-in camera and microphone and is configured to capture the user's facial expressions, movements, and voice in real time.

[0545] Step 2:

[0546] The device transmits user appearance and voice data to the server in real time. This data includes the user's facial expressions, voice tone, and speed information.

[0547] Step 3:

[0548] The server uses the received data to activate the emotion engine and analyze the user's emotional state. Specifically, it analyzes subtle changes in the user's facial expressions and the emotional tone of their voice to identify levels of stress and concentration.

[0549] Step 4:

[0550] The server generates appropriate learning content based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it might add easy-to-understand explanations or animations to make the learning process smoother.

[0551] Step 5:

[0552] The server dynamically adjusts the learning plan according to the user's emotional state. If it determines that concentration is being maintained, adjustments such as maintaining or accelerating the learning pace will be made.

[0553] Step 6:

[0554] The device displays learning content generated from the server and a revised learning plan to the user. The user then follows these instructions to effectively progress with their learning.

[0555] This processing step allows the system to leverage the user's emotional state and provide flexible and effective learning support tailored to each learner's needs.

[0556] (Example 2)

[0557] 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."

[0558] Conventional educational support systems were unable to adjust educational content in real time, taking into account the learner's emotional state, making it difficult to provide effective individualized learning. Furthermore, the lack of technology to dynamically adapt learning plans based on the learner's real-time emotions and reactions limited the ability to maintain learner motivation and improve concentration.

[0559] 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.

[0560] In this invention, the server includes means for acquiring learner appearance data and voice data, means for identifying emotional state, and means for generating personalized educational information. This enables real-time analysis of the learner's emotional state and the provision of individually adapted learning content.

[0561] "Appearance data" refers to visual information about the learner's facial expressions and body movements.

[0562] "Audio data" refers to auditory information related to the learner's voice tone, volume, and the content of their speech.

[0563] "Emotional state" refers to the learner's internal state, such as stress, excitement, and concentration, which are inferred from external and auditory data.

[0564] "Personalized educational information" refers to learning content and materials that are tailored based on the learner's current emotional state.

[0565] "Presenting visually and aurally" means conveying information to learners through on-screen displays and audio.

[0566] "Adjusting a learning plan" means changing the content and pace of learning in accordance with the learner's progress and emotional changes.

[0567] This invention utilizes emotion recognition functionality as part of an educational support system, and takes the form of data exchange between the user, terminal, and server. Specific embodiments are described below.

[0568] The user starts a learning session using a device with the learning application installed. The device acquires appearance data and audio data by capturing the learner's facial expressions and movements with its camera and recording their voice with its microphone. This data is transmitted to the server in real time.

[0569] The server activates an emotion engine based on the received data to analyze the user's emotional state. This emotion engine utilizes image recognition technology, such as TensorFlow, and uses speech analysis algorithms to analyze speech. This allows the server to classify the user's emotional state based on characteristics such as stress, excitement, and concentration.

[0570] Based on the analysis results, the server generates and sends user-optimized educational content to the device. For example, if the user is feeling impatient, the system will slow down the pace and select materials that provide more detailed explanations.

[0571] The device presents content transmitted from the server to the user visually and audibly. Specifically, it displays information on the screen and provides audio guidance as needed. Furthermore, it monitors the user's learning progress and follows instructions from the server to dynamically adjust the learning plan in response to changes in the user's emotions.

[0572] As a concrete example, consider a situation where a user is learning the procedures for a scientific experiment. If the system determines that the user is facing difficulties, it will immediately present content that promotes understanding, such as a video with step-by-step explanations.

[0573] An example of a prompt for a generative AI model is: "Analyze the user's facial expression and voice data to identify and analyze emotional states that influence learning progress, and provide optimized learning content."

[0574] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0575] Step 1:

[0576] The user launches an educational support application on their device and begins a learning session. The device uses its camera and microphone to capture the user's facial expressions and voice in real time. During this process, the user's facial image and voice recording data are acquired as input.

[0577] Step 2:

[0578] The terminal sends captured visual and audio data to the server. The input here is the raw data obtained from the user, and the output is the process of transferring this data to the server.

[0579] Step 3:

[0580] The server stores the received data and activates the emotion engine for analysis. The input consists of user facial image and audio data, and an emotion classification model is used to output emotional states such as stress, excitement, and concentration. This process includes facial feature analysis and audio feature extraction.

[0581] Step 4:

[0582] The server generates educational content optimized for the learner based on the analyzed emotional state. Here, it takes an emotional state (e.g., low concentration) as input and outputs specific learning materials and content to supplement the learning process (e.g., easy problem sets or visual aids).

[0583] Step 5:

[0584] The server sends the generated educational content to the terminal. The input is the generated content, and the output is the process of displaying it on the terminal in a format that the user can view and use to progress in their learning.

[0585] Step 6:

[0586] The terminal presents content received from the server to the user and monitors the user's learning progress. It then sends user feedback and progress data back to the server to help adjust the next learning plan. Specifically, it automatically displays quizzes and surveys to check the user's understanding.

[0587] (Application Example 2)

[0588] 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."

[0589] Conventional learning support systems provide general learning content without considering the learner's emotional state, making it difficult to achieve an individualized learning experience. Furthermore, they fail to provide appropriate learning content tailored to the learner's concentration and stress levels, resulting in insufficient improvement in learning efficiency and maintenance of motivation.

[0590] 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.

[0591] In this invention, the server includes means for acquiring the learner's appearance information, means for analyzing the appearance information to identify the learner's emotional state, and means for dynamically selecting and providing diverse content for the learner based on the emotional state. This makes it possible to provide a personalized learning experience that is tailored to the learner's emotional state.

[0592] "External appearance information" refers to data that includes the learner's facial expressions and vocal characteristics, and is acquired to identify their emotional state.

[0593] "Emotional state" refers to the learner's feelings and psychological state, including stress, concentration, and excitement.

[0594] "Data" refers to learning materials and content generated based on the learner's emotional state, and is provided to offer specific learning support.

[0595] "Progress status" refers to information indicating how far a learner has progressed in their studies, and is used to adjust future learning plans.

[0596] "Dynamic selection" refers to a process that determines and provides appropriate content in real time based on the learner's emotional state and learning progress.

[0597] "Diverse content" refers to educational materials and media of varying formats and difficulty levels, which are provided flexibly according to the learner's needs.

[0598] The system for carrying out this invention includes a series of processes for analyzing the learner's emotional state in real time and providing an individualized learning experience. This process proceeds as follows:

[0599] First, when a user begins learning using the device, the device activates its built-in camera and microphone to acquire appearance information of the user's facial expressions and voice. This acquired appearance information is sent to the server via the network. The server uses Python-based machine learning libraries such as OpenCV and TensorFlow to analyze the facial expressions and voice patterns and identify the learner's emotional state, such as stress, excitement, and concentration.

[0600] Based on this emotional state, the server selects a variety of content optimized for the learner. For example, if concentration is low, it provides relaxation videos to reduce stress, and if concentration is high, it delivers more challenging learning videos.

[0601] For example, if a user shows signs of fatigue while watching a history video, the server can detect this and insert a short break video to help them regain their concentration. This mechanism aims to provide users with a comfortable and effective learning environment.

[0602] A concrete example of a prompt message might be something like, "If a user is feeling stressed while watching a history video, what are some ways to help them relax? For example, what kind of content would you recommend?"

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

[0604] Step 1:

[0605] The device activates its camera and microphone when the user starts a learning session. This captures the user's facial expressions and voice in real time. The captured data is necessary to determine the user's emotional state. The input is the user's video and audio, and the output is the captured visual information.

[0606] Step 2:

[0607] The terminal sends the acquired appearance information to the server. The server receives this data and prepares to analyze it. During the data transfer process, the data is converted to the appropriate data format and sent. The input is the appearance information from the terminal, and the output is the raw data received by the server.

[0608] Step 3:

[0609] The server uses the received appearance information to launch Python-based machine learning libraries (such as OpenCV and TensorFlow). The server utilizes these libraries to analyze the user's emotional state from facial expression data and voice patterns. The input is raw data, and the output is the analysis result indicating the user's emotional state.

[0610] Step 4:

[0611] Based on the analysis results, the server selects a variety of content optimized for the user. For example, if the user is stressed, the server recommends a relaxation video. The input is the analysis results, and the output is information about the recommended content.

[0612] Step 5:

[0613] The server sends the selected content to the device. The device receives this content and presents it to the user. This personalizes the user's learning experience and promotes effective learning. The input is the information of the selected content, and the output is the learning content displayed on the device.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] [Fourth Embodiment]

[0618] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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".

[0631] This invention provides a system that offers real-time educational support tailored to the individual needs of learners. This system uses learner appearance data to identify their emotional state and dynamically adjusts learning content and plans based on the learner's progress.

[0632] System Overview

[0633] 1. User begins learning

[0634] When a user starts a learning session using their device, the device's camera and microphone activate to capture the user's facial expressions, movements, and voice.

[0635] 2. Acquisition and transmission of appearance data

[0636] The device processes the captured appearance data in real time and sends it to the server. This data includes information such as the user's facial movements, voice tone, and speed.

[0637] 3. Data analysis and identification of emotional state

[0638] The server analyzes the received data and uses a generative AI model to identify the user's emotional state. It identifies whether the user is confused, satisfied, etc., and predicts their response to specific learning tasks.

[0639] 4. Generating appropriate learning content

[0640] The server generates explanatory materials and supplementary learning content based on the user's emotional state. For example, if a user is struggling with a new mathematical concept, it will provide a review of the relevant foundational knowledge or a visual guide.

[0641] 5. Dynamic adjustment of the learning plan

[0642] The server monitors the user's progress and adjusts the learning plan according to their learning progress. If a particular topic is taking too long, it provides additional practice exercises on that topic.

[0643] 6. Providing feedback

[0644] The terminal displays explanations and supplementary questions sent from the server to the user. It also notifies the user of any changes to their learning plan, clearly indicating what they should do next.

[0645] Thus, the present invention provides flexible and efficient learning support tailored to each individual learner. Specifically, it enables the rapid identification of delays in understanding that occur when solving mathematical problems and provides supplementary materials suitable for the user. This allows users to continue effective learning at their own pace.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The user starts a learning session using their device. The device activates its built-in camera and microphone and is configured to capture the user's facial expressions, movements, and voice in real time.

[0649] Step 2:

[0650] The device transmits captured visual data to the server in real time. This data includes the user's facial expressions, voice tone, and speed information.

[0651] Step 3:

[0652] The server applies a generative AI model based on the received visual data to identify the user's emotional state. The server identifies emotions such as confusion and interest and analyzes the user's response to the learning content.

[0653] Step 4:

[0654] The server generates learning content tailored to the user based on the analysis results. If it determines that the user is struggling with a specific problem, it creates supplementary explanations and visuals for that section.

[0655] Step 5:

[0656] The server evaluates the user's learning progress and adjusts the learning plan as needed. Based on the user's progress data, it adds supplementary practice exercises and materials to reinforce necessary topics.

[0657] Step 6:

[0658] The device provides the user with new learning content and an adjusted learning plan sent from the server. The user can use this feedback to further their learning.

[0659] This entire process allows the system to provide customized, real-time learning support for each user.

[0660] (Example 1)

[0661] 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".

[0662] Providing effective, real-time educational support tailored to the individual needs of learners is a challenging task. Conventional systems struggle to instantly reflect changes in learners' behavior and emotions, making it difficult to adjust learning plans accordingly, potentially leading to decreased learner motivation. Therefore, there is a need for technology that can provide flexible and responsive learning support to each individual learner.

[0663] 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.

[0664] In this invention, the server includes means for acquiring learner behavioral data, means for analyzing the behavioral data to identify the learner's emotional state, and means for generating educational resources based on the emotional state. This enables flexible and effective learning support tailored to the individual circumstances of each learner.

[0665] "Learner behavior data" refers to information about learners' physical movements and vocal expressions—information about their behavior that can be observed from the outside.

[0666] "Emotional state" refers to the learner's feelings and mental state, encompassing psychological conditions such as happiness, anxiety, and concentration.

[0667] "Educational resources" refer to all information media that support learning, such as teaching materials, supplementary materials, and interactive tools provided to learners.

[0668] "Dynamic adjustment" refers to the fact that learning plans and content are not fixed in place, but can be flexibly changed according to the learner's progress and condition.

[0669] "Real-time" refers to a state where the system processes data instantly and can quickly provide feedback and adjustments tailored to the learner's situation.

[0670] This invention is a learning support system that provides personalized, real-time educational support to learners. This system utilizes the user's terminal and server to analyze the learner's emotional state and flexibly generate and adjust learning content based on that analysis.

[0671] Hardware and software configuration

[0672] terminal

[0673] The user's device is equipped with a camera and microphone, which capture the user's facial expressions, movements, and voice during learning sessions. The processing system includes software that supports real-time data analysis. This data is then transmitted from the device to the server.

[0674] server

[0675] The server is equipped with a generative AI model and performs advanced data analysis. Based on the data received from the user, the server identifies the user's emotional state and generates learning resources accordingly. It also tracks the learning progress and dynamically adjusts the learning plan as needed.

[0676] Data processing and calculations

[0677] When the server receives data from the user, it analyzes the data using a generative AI model. This model identifies the user's emotional state during learning from their facial movements and voice tone. As a result, the generated information is fed back to the user through the device.

[0678] Specific example

[0679] For example, if a user is confused by a mathematical concept, basic visual guides and additional practice problems are presented according to their emotional state. This allows users to learn efficiently at their own pace.

[0680] Example of a prompt

[0681] "Identify the user's current emotional state based on their facial expressions and voice data. How is the user feeling? Identify states such as confusion or satisfaction."

[0682] As described above, this invention makes it possible to provide flexible and effective learning support tailored to individual learners.

[0683] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0684] Step 1:

[0685] The device captures the user's movements and voice. The user starts a learning session, and the camera and microphone are activated. As input, data on the user's facial expressions, movements, and voice are collected. This raw data is ready to be sent to the server in real time.

[0686] Step 2:

[0687] The device sends the collected data to the server. Converting the data to an appropriate format before sending it to the server enables efficient data processing. The input is data such as captured facial movements and voice tones, and the output is communication with the server.

[0688] Step 3:

[0689] The server analyzes the received data. The server uses a generative AI model to evaluate the data in order to identify the user's emotional state. The input is action and voice data sent from the terminal, and the output is an identification result of the emotional state (e.g., confused, satisfied).

[0690] Step 4:

[0691] The server generates learning resources based on emotional states. Based on the results of the AI ​​model, it creates learning content optimized for each individual user. It receives identified emotional states as input and generates appropriate educational resources (visual guides, supplementary materials, etc.) as output.

[0692] Step 5:

[0693] The server dynamically adjusts the learning plan. The server monitors the user's progress and updates the learning plan as needed. It receives user learning progress data as input and an updated learning plan as output.

[0694] Step 6:

[0695] The device presents the user with generated feedback and learning resources. The device communicates output information from the server to the user, providing clear instructions for the next steps. It receives educational resources and feedback from the server as input, and presents them to the user visually or audibly as output.

[0696] (Application Example 1)

[0697] 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".

[0698] When providing educational support to individual learners, there is a challenge in immediately grasping their emotional state and providing appropriate supplementary materials. Furthermore, there is a need for dynamic adjustments to learning plans based on the learner's progress. In particular, real-time data acquisition and analysis are crucial for realizing such educational support in the home environment.

[0699] 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.

[0700] In this invention, the server includes means for acquiring observational data of learners, means for analyzing the observational data to identify the learners' emotional state, and means for generating supplementary materials for the learners based on the emotional state. This makes it possible to provide personalized educational support in real time.

[0701] A "learner" is an individual who is eligible to receive educational support.

[0702] "Observational data" refers to information collected in real time, such as learners' facial expressions, gestures, and voice.

[0703] "Emotional state" refers to a learner's psychological and emotional condition and is used to evaluate their response to learning.

[0704] "Supplementary materials" are additional educational content or guides provided to help learners understand the material.

[0705] An "educational plan" is a system of learning content and schedules formulated based on the learner's progress.

[0706] "Dynamic generation methods" refer to methods of sequentially creating necessary information and materials in response to real-time data.

[0707] To implement this invention, a system is built in which a server and a terminal work together to provide optimal educational support to learners. Learners begin learning activities using a terminal equipped with a camera and microphone. The terminal acquires observational data such as the learner's facial expressions and voice in real time and transmits it to the server. The server uses a generative AI model to analyze the received data. As a specific example, the EmotionAPI is used to identify the emotional state.

[0708] Based on the analysis results, the server dynamically generates supplementary materials appropriate to the learner's emotional state. For example, if it determines that the learner is confused, it immediately generates a visual guide or practice exercises on the relevant foundational knowledge and sends them to the device. The device then presents these materials to the learner.

[0709] Furthermore, the server continuously monitors the learner's progress and adjusts the educational plan as needed. Through all these processes, an optimized educational experience can be provided for each learner.

[0710] As a concrete example, a server analyzes a student who sighs while studying mathematics and generates a visual guide to basic calculations. An example of a prompt would be, "Analyze the user's emotions and select the appropriate visual material if they appear confused." This prompt manages the process by which the generating AI model selects appropriate materials.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The user starts a learning session using the device. The device activates its camera and microphone to acquire observational data in real time, including the learner's facial expressions, voice tone, and movements. The input is the learner's visual data, and the output is this data in digital format. The device prepares this data for the next processing step.

[0714] Step 2:

[0715] The terminal transmits the acquired observation data to the server. The input is the digital observation data collected by the terminal, and the output is the data transfer to the server. The server receives and stores this data and prepares it for the next analysis step.

[0716] Step 3:

[0717] The server uses a generative AI model to analyze the received observation data. The input is observation data stored on the server, and a model such as EmotionAPI analyzes the data. In this step, the server performs calculations to identify emotional states and judge changes in the learner's mood and emotions. The output is the evaluation result of the emotional state.

[0718] Step 4:

[0719] The server dynamically generates appropriate supplementary materials based on the emotional state assessment results. The input is the emotional state assessment results, and the output is the specific supplementary materials to be provided to the learner. These include visual guides and practice exercises, and are configured to support the learner's learning needs.

[0720] Step 5:

[0721] The server generates supplementary materials and sends them to the terminal. The input is dynamically generated supplementary materials, and the output is the transmission of these materials to the terminal. The terminal receives these materials and prepares them to be displayed to the learner.

[0722] Step 6:

[0723] The terminal presents the received supplementary materials to the user. The input is the supplementary materials received by the terminal, and the output is the learner obtaining the materials visually or aurally. The terminal presents the materials in a format suitable for the learner, supporting their learning.

[0724] Step 7:

[0725] The server continuously monitors the learner's progress and adjusts the teaching plan as needed. The input is the learner's current progress data, and the output is the latest teaching plan. This plan is modified according to individual needs and adjusted to optimize learning.

[0726] 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.

[0727] This invention relates to an educational support system that incorporates an emotion engine that recognizes user emotions in real time and applies them to learning support. The system aims to identify the learner's emotions and emotional state and provide appropriate learning content and plans based on that.

[0728] System Overview

[0729] 1. User begins learning

[0730] When a user starts a learning session using their device, the device's built-in camera and microphone are activated. This allows the user's facial expressions, movements, and voice to be captured in real time.

[0731] 2. Acquisition of appearance data and sentiment data

[0732] The device sends captured visual data to the server. The server uses this data to activate an emotion engine and analyze the user's emotional state from their facial expressions and voice.

[0733] 3. Analysis using an emotion engine

[0734] The emotion engine on the server analyzes the user's facial expressions and voice patterns to identify emotional states such as stress, excitement, and concentration. This allows for a detailed understanding of the user's responses to learning tasks.

[0735] 4. Content Creation and Delivery

[0736] The server generates optimal learning content for the user based on the analysis results from the emotion engine. For example, if the user shows signs of impatience, it will slow down the pace and provide more detailed explanations.

[0737] 5. Dynamic adjustment of the learning plan

[0738] The server dynamically adjusts the learning plan based on the user's current emotional state. For example, if the server determines that the user is highly focused, it will move on to learning more advanced topics.

[0739] 6. Feedback and Motivation

[0740] The device presents the user with learning content and plans sent from the server, and provides feedback based on their progress. Furthermore, by utilizing the analysis results from the emotion engine, it contributes to maintaining and improving motivation.

[0741] For example, if a user's concentration wanes while solving a math problem, the system analyzes their emotional state and adjusts the difficulty of the problem or suggests content that promotes relaxation. In this way, the present invention aims to personalize the user's learning experience and support effective learning.

[0742] The following describes the processing flow.

[0743] Step 1:

[0744] The user starts a learning session using their device. The device activates its built-in camera and microphone and is configured to capture the user's facial expressions, movements, and voice in real time.

[0745] Step 2:

[0746] The device transmits user appearance and voice data to the server in real time. This data includes the user's facial expressions, voice tone, and speed information.

[0747] Step 3:

[0748] The server uses the received data to activate the emotion engine and analyze the user's emotional state. Specifically, it analyzes subtle changes in the user's facial expressions and the emotional tone of their voice to identify levels of stress and concentration.

[0749] Step 4:

[0750] The server generates appropriate learning content based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it might add easy-to-understand explanations or animations to make the learning process smoother.

[0751] Step 5:

[0752] The server dynamically adjusts the learning plan according to the user's emotional state. If it determines that concentration is being maintained, adjustments such as maintaining or accelerating the learning pace will be made.

[0753] Step 6:

[0754] The device displays learning content generated from the server and a revised learning plan to the user. The user then follows these instructions to effectively progress with their learning.

[0755] This processing step allows the system to leverage the user's emotional state and provide flexible and effective learning support tailored to each learner's needs.

[0756] (Example 2)

[0757] 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".

[0758] Conventional educational support systems were unable to adjust educational content in real time, taking into account the learner's emotional state, making it difficult to provide effective individualized learning. Furthermore, the lack of technology to dynamically adapt learning plans based on the learner's real-time emotions and reactions limited the ability to maintain learner motivation and improve concentration.

[0759] 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.

[0760] In this invention, the server includes means for acquiring learner appearance data and voice data, means for identifying emotional state, and means for generating personalized educational information. This enables real-time analysis of the learner's emotional state and the provision of individually adapted learning content.

[0761] "Appearance data" refers to visual information about the learner's facial expressions and body movements.

[0762] "Audio data" refers to auditory information related to the learner's voice tone, volume, and the content of their speech.

[0763] "Emotional state" refers to the learner's internal state, such as stress, excitement, and concentration, which are inferred from external and auditory data.

[0764] "Personalized educational information" refers to learning content and materials that are tailored based on the learner's current emotional state.

[0765] "Presenting visually and aurally" means conveying information to learners through on-screen displays and audio.

[0766] "Adjusting a learning plan" means changing the content and pace of learning in accordance with the learner's progress and emotional changes.

[0767] This invention utilizes emotion recognition functionality as part of an educational support system, and takes the form of data exchange between the user, terminal, and server. Specific embodiments are described below.

[0768] The user starts a learning session using a device with the learning application installed. The device acquires appearance data and audio data by capturing the learner's facial expressions and movements with its camera and recording their voice with its microphone. This data is transmitted to the server in real time.

[0769] The server activates an emotion engine based on the received data to analyze the user's emotional state. This emotion engine utilizes image recognition technology, such as TensorFlow, and uses speech analysis algorithms to analyze speech. This allows the server to classify the user's emotional state based on characteristics such as stress, excitement, and concentration.

[0770] Based on the analysis results, the server generates and sends user-optimized educational content to the device. For example, if the user is feeling impatient, the system will slow down the pace and select materials that provide more detailed explanations.

[0771] The device presents content transmitted from the server to the user visually and audibly. Specifically, it displays information on the screen and provides audio guidance as needed. Furthermore, it monitors the user's learning progress and follows instructions from the server to dynamically adjust the learning plan in response to changes in the user's emotions.

[0772] As a concrete example, consider a situation where a user is learning the procedures for a scientific experiment. If the system determines that the user is facing difficulties, it will immediately present content that promotes understanding, such as a video with step-by-step explanations.

[0773] An example of a prompt for a generative AI model is: "Analyze the user's facial expression and voice data to identify and analyze emotional states that influence learning progress, and provide optimized learning content."

[0774] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0775] Step 1:

[0776] The user launches an educational support application on their device and begins a learning session. The device uses its camera and microphone to capture the user's facial expressions and voice in real time. During this process, the user's facial image and voice recording data are acquired as input.

[0777] Step 2:

[0778] The terminal sends captured visual and audio data to the server. The input here is the raw data obtained from the user, and the output is the process of transferring this data to the server.

[0779] Step 3:

[0780] The server stores the received data and activates the emotion engine for analysis. The input consists of user facial image and audio data, and an emotion classification model is used to output emotional states such as stress, excitement, and concentration. This process includes facial feature analysis and audio feature extraction.

[0781] Step 4:

[0782] The server generates educational content optimized for the learner based on the analyzed emotional state. Here, it takes an emotional state (e.g., low concentration) as input and outputs specific learning materials and content to supplement the learning process (e.g., easy problem sets or visual aids).

[0783] Step 5:

[0784] The server sends the generated educational content to the terminal. The input is the generated content, and the output is the process of displaying it on the terminal in a format that the user can view and use to progress in their learning.

[0785] Step 6:

[0786] The terminal presents content received from the server to the user and monitors the user's learning progress. It then sends user feedback and progress data back to the server to help adjust the next learning plan. Specifically, it automatically displays quizzes and surveys to check the user's understanding.

[0787] (Application Example 2)

[0788] 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".

[0789] Conventional learning support systems provide general learning content without considering the learner's emotional state, making it difficult to achieve an individualized learning experience. Furthermore, they fail to provide appropriate learning content tailored to the learner's concentration and stress levels, resulting in insufficient improvement in learning efficiency and maintenance of motivation.

[0790] 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.

[0791] In this invention, the server includes means for acquiring the learner's appearance information, means for analyzing the appearance information to identify the learner's emotional state, and means for dynamically selecting and providing diverse content for the learner based on the emotional state. This makes it possible to provide a personalized learning experience that is tailored to the learner's emotional state.

[0792] "External appearance information" refers to data that includes the learner's facial expressions and vocal characteristics, and is acquired to identify their emotional state.

[0793] "Emotional state" refers to the learner's feelings and psychological state, including stress, concentration, and excitement.

[0794] "Data" refers to learning materials and content generated based on the learner's emotional state, and is provided to offer specific learning support.

[0795] "Progress status" refers to information indicating how far a learner has progressed in their studies, and is used to adjust future learning plans.

[0796] "Dynamic selection" refers to a process that determines and provides appropriate content in real time based on the learner's emotional state and learning progress.

[0797] "Diverse content" refers to educational materials and media of varying formats and difficulty levels, which are provided flexibly according to the learner's needs.

[0798] The system for carrying out this invention includes a series of processes for analyzing the learner's emotional state in real time and providing an individualized learning experience. This process proceeds as follows:

[0799] First, when a user begins learning using the device, the device activates its built-in camera and microphone to acquire appearance information of the user's facial expressions and voice. This acquired appearance information is sent to the server via the network. The server uses Python-based machine learning libraries such as OpenCV and TensorFlow to analyze the facial expressions and voice patterns and identify the learner's emotional state, such as stress, excitement, and concentration.

[0800] Based on this emotional state, the server selects a variety of content optimized for the learner. For example, if concentration is low, it provides relaxation videos to reduce stress, and if concentration is high, it delivers more challenging learning videos.

[0801] For example, if a user shows signs of fatigue while watching a history video, the server can detect this and insert a short break video to help them regain their concentration. This mechanism aims to provide users with a comfortable and effective learning environment.

[0802] A concrete example of a prompt message might be something like, "If a user is feeling stressed while watching a history video, what are some ways to help them relax? For example, what kind of content would you recommend?"

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

[0804] Step 1:

[0805] The device activates its camera and microphone when the user starts a learning session. This captures the user's facial expressions and voice in real time. The captured data is necessary to determine the user's emotional state. The input is the user's video and audio, and the output is the captured visual information.

[0806] Step 2:

[0807] The terminal sends the acquired appearance information to the server. The server receives this data and prepares to analyze it. During the data transfer process, the data is converted to the appropriate data format and sent. The input is the appearance information from the terminal, and the output is the raw data received by the server.

[0808] Step 3:

[0809] The server uses the received appearance information to launch Python-based machine learning libraries (such as OpenCV and TensorFlow). The server utilizes these libraries to analyze the user's emotional state from facial expression data and voice patterns. The input is raw data, and the output is the analysis result indicating the user's emotional state.

[0810] Step 4:

[0811] Based on the analysis results, the server selects a variety of content optimized for the user. For example, if the user is stressed, the server recommends a relaxation video. The input is the analysis results, and the output is information about the recommended content.

[0812] Step 5:

[0813] The server sends the selected content to the device. The device receives this content and presents it to the user. This personalizes the user's learning experience and promotes effective learning. The input is the information of the selected content, and the output is the learning content displayed on the device.

[0814] 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.

[0815] 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.

[0816] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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 this memory.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A means of obtaining learner appearance data,

[0838] A means for analyzing the appearance data to identify the learner's emotional state,

[0839] A means for generating information for the learner based on the said emotional state,

[0840] Means for presenting the information to learners,

[0841] A means of adjusting the learning plan according to the learner's progress,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for acquiring and analyzing learner response data in real time.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for generating explanations and questions based on learners' error patterns.

[0847] "Example 1"

[0848] (Claim 1)

[0849] A means of acquiring learner behavior data,

[0850] A means for analyzing the motion data to identify the learner's emotional state,

[0851] A means for generating educational resources for learners based on the said emotional state,

[0852] Means for presenting the educational resources to learners,

[0853] A means of dynamically adjusting the learning plan according to the learner's progress,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, further comprising means for acquiring and analyzing learner response data in real time.

[0857] (Claim 3)

[0858] The system according to claim 1, further comprising means for generating explanations and questions based on learners' error patterns.

[0859] "Application Example 1"

[0860] (Claim 1)

[0861] Means for obtaining learner observation data,

[0862] A means for analyzing the observational data to identify the learner's emotional state,

[0863] A means for generating supplementary materials for learners based on the emotional state,

[0864] A device for presenting the supplementary materials to learners,

[0865] A means of adjusting the educational plan according to the learner's progress,

[0866] A means for dynamically generating information based on data acquired by a learning support device,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, further comprising means for acquiring and analyzing learner response data in real time.

[0870] (Claim 3)

[0871] The system according to claim 1, further comprising means for generating explanatory materials and questions based on learners' error patterns.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] A means of acquiring learner appearance data and audio data,

[0875] A means for analyzing the data to identify the learner's emotional state,

[0876] A means for generating personalized educational information based on emotional states,

[0877] Means for presenting the information to learners visually and aurally,

[0878] A means of dynamically adjusting the learning plan based on changes in the learner's emotions,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, further comprising means for acquiring and analyzing learner response data in real time.

[0882] (Claim 3)

[0883] The system according to claim 1, further comprising means for generating explanations and questions based on the learner's error patterns and emotional changes.

[0884] "Application example 2 when combining with an emotional engine"

[0885] (Claim 1)

[0886] Means for obtaining learner appearance information,

[0887] A means for analyzing the visual information to identify the learner's emotional state,

[0888] A means for generating data for learners based on the emotional state,

[0889] A means of presenting the data to the learner,

[0890] A means of adjusting the learning plan according to the learner's progress,

[0891] A means of dynamically selecting and providing diverse content based on the learner's emotional state,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, further comprising means for acquiring and analyzing learner response information in real time.

[0895] (Claim 3)

[0896] The system according to claim 1, further comprising means for generating explanations and questions based on the learner's error patterns, and means for providing relaxation information corresponding to the extracted emotional state. [Explanation of symbols]

[0897] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining learner appearance data, A means for analyzing the appearance data to identify the learner's emotional state, A means for generating information for the learner based on the said emotional state, Means for presenting the information to learners, A means of adjusting the learning plan according to the learner's progress, A system that includes this.

2. The system according to claim 1, further comprising means for acquiring and analyzing learner response data in real time.

3. The system according to claim 1, further comprising means for generating explanations and questions based on learners' error patterns.

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