system

Abstract

We provide the system. [Solution] A device means for acquiring behavioral information, facial expression information, and voice information, A computing device means that integrates and analyzes the acquired information in real time, A generation device means that generates educational content suitable for individual learners based on the analysis results, A system including a display device for presenting generated educational content to learners.

Description

Technical Field

[0005]

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, 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 the educational field, while individual guidance according to the learning styles and abilities of individual learners is required, there is a problem that it is difficult for a uniform teaching method to meet the diverse needs of learners. In particular, for learners with learning disabilities or communication disorders, sufficient educational support has not been provided by conventional methods. As a result, the potential abilities of learners cannot be fully brought out, and there are variations in the quality of education.

Means for Solving the Problems

[0005] This invention combines a device that acquires learner behavioral information, facial expression information, and voice information with a computing device that integrates and analyzes this information in real time, thereby comprehensively understanding the learner's learning progress. Furthermore, by providing a generation device that generates educational content suitable for each individual learner based on the analysis results, and a display device that presents the generated educational content to the learner, an individualized learning experience is provided to each learner. This realizes effective educational support that meets the needs of each learner.

[0006] "Behavioral information" refers to data about the participants' actions, input operations, and behavioral patterns.

[0007] "Facial expression information" refers to data that indicates the emotional state and reactions based on the facial expressions of the participants.

[0008] "Audio information" refers to data related to the participant's voice, such as pronunciation, speaking style, and tone.

[0009] A "device" is a set of hardware or software components designed to perform a specific function.

[0010] "Real-time" refers to a method where data is processed simultaneously with its generation.

[0011] A "processing unit" is a device used to process data and perform calculations and analyses.

[0012] "Analysis" is the process of breaking down collected data into easily understandable information and clarifying its meaning.

[0013] A "generator" is a device or program that creates new data or content based on specific inputs.

[0014] "Educational content" refers to information and learning materials provided to cultivate learners' understanding and abilities.

[0015] The "display device" is a device for visually presenting information.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which 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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying out the Invention

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

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

[0019] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

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

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that provides personalized education tailored to the diverse learning needs of students, and primarily involves server, terminal, and user-based operations. Specific embodiments of this invention are described below.

[0038] server

[0039] The server plays a central role in receiving behavioral, facial, and voice data collected from learners, and integrating and analyzing this data in real time. This includes various algorithms for evaluating learners' learning styles, interests, and concentration levels. The analysis results form the basis for generating educational content within the server.

[0040] For example, if the server detects a decrease in a participant's concentration from their facial expression, it can immediately adjust the difficulty level of the content and provide new, more challenging problems.

[0041] terminal

[0042] The device continuously acquires information about the learner through sensors such as cameras and microphones. The acquired data is pre-processed in real time before being sent to the server. It also plays a role in displaying educational content generated from the server to the learner.

[0043] For example, if analysis reveals that a particular student finds audio explanations easier to understand, the device will be configured to present learning content with enhanced audio guidance.

[0044] User (student)

[0045] Users interact with learning content provided through their devices. All data generated when users solve problems or manipulate content is collected again through the device and used for further analysis on the server.

[0046] For example, if a user demonstrates sufficient understanding of a particular task, the device will adjust to allow them to move on to the next stage of learning. By establishing a feedback loop that adapts to the learning progress in this way, it is possible to continuously optimize the learning experience for each individual student.

[0047] Comprehensive operation

[0048] This system combines these elements to enable learning tailored to the individual needs of each student. In particular, real-time information gathering and analysis, and adaptive content generation and delivery contribute to maximizing students' potential. This allows for the comprehensive provision of personalized educational experiences, creating an educational platform that can accommodate students with diverse learning backgrounds.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The device collects information on the participant's behavior, facial expressions, and voice via various sensors. The collected data is pre-processed, including noise reduction.

[0052] Step 2:

[0053] The terminal sends pre-processed data to the server. The data is encrypted in real time and transferred over a secure network.

[0054] Step 3:

[0055] The server integrates the received data and performs real-time analysis using a multi-layer neural network. This analysis evaluates the learner's learning style, concentration level, interests, and other factors.

[0056] Step 4:

[0057] The server generates educational content optimized for each student based on the analysis results. Here, a generative AI model is used to design content that meets the individual needs of each student.

[0058] Step 5:

[0059] The server sends the generated educational content to the device. Content delivery is in real time, ensuring that the learner's learning experience is not interrupted.

[0060] Step 6:

[0061] The device presents learning content to the learner. Information is delivered in the most effective way for the learner, including audio guidance, visual feedback, and interactive controls.

[0062] Step 7:

[0063] Users interact with the presented content and work on learning tasks. Their responses and results are collected again via the device and used for the next processing cycle.

[0064] Step 8:

[0065] The server receives feedback based on new data from users, updates the content, and adaptively improves it for the next learning cycle. This functions as a continuous learning process.

[0066] (Example 1)

[0067] 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."

[0068] In today's educational environment, there is a need to identify the diverse needs of individual learners in real time and provide them with the optimal educational experience. However, conventional systems have struggled to effectively monitor learners' behavior and status and to flexibly adapt learning content.

[0069] 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.

[0070] In this invention, the server includes information acquisition means for acquiring various types of information, processing means for pre-processing the acquired information and transmitting the data, and analysis means for integrating and analyzing the acquired information in real time. This makes it possible to realize a system that can evaluate the status of individual learners and flexibly generate and provide appropriate educational materials.

[0071] "Information acquisition methods" refer to means of collecting various data such as learners' behavior, facial expressions, and voice.

[0072] "Processing means" refers to methods for pre-processing acquired raw data, such as noise reduction and format conversion, to prepare it for secure transmission to a server.

[0073] "Analysis means" refers to a method for integrating and analyzing transmitted data to evaluate learners' interests, concentration levels, and learning styles.

[0074] "Content generation means" refers to methods for generating educational materials that are optimal for individual learners based on analysis results.

[0075] "Display means" refers to the means of appropriately presenting generated educational materials to learners.

[0076] An "information processing infrastructure" is a foundation for executing various processes on a computer network, including the cloud.

[0077] This invention is a system for providing personalized educational experiences that can meet the diverse needs of learners. This system primarily involves the collaboration of a server, terminal, and user, and utilizes information technology to optimize the learning process.

[0078] The server plays a central role, analyzing various types of information (behavior, facial expressions, voice, etc.) in real time to evaluate the learner's state. Generative AI models are used for analysis, and content is suggested and generated using prompt sentences. An example of such a prompt sentence is, "Suggest activities to improve concentration."

[0079] The device implements information acquisition mechanisms to obtain data from learners. This includes sensors such as cameras and microphones to collect learners' facial expressions and voices in real time. The collected data is preprocessed and sent to a server. The device also displays generated educational content and provides a user interface that learners can interact with.

[0080] Users (learners) interact with individually customized educational content via their devices. Based on the learners' interactions, the server re-analyzes the data to further optimize the learning experience. The learners' actions, such as solving problems or referring to learning materials, are collected by the server as feedback and used to inform educational strategies in the next session.

[0081] This system makes it possible to provide individualized education to each learner, thus addressing the diverse educational needs of today.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The device acquires learner behavior, facial expressions, and voice information in real time using sensors (camera, microphone). For example, it uses the camera to capture the learner's facial expressions and the microphone to collect their voice tone. Inputs include the learner's facial expression data and voice data. Outputs the acquired raw data.

[0085] Step 2:

[0086] The terminal performs preprocessing such as noise reduction and color correction on the acquired raw data. This prepares the data for analysis. The input is the acquired raw data, and the output is processed, well-formed data. Specific operations include filtering to remove background noise from audio data.

[0087] Step 3:

[0088] The terminal sends pre-processed data to the server via a secure protocol. The input is the pre-processed data, and the output is the server's reception status. The operation involves data transfer and verification that there is no leakage.

[0089] Step 4:

[0090] The server analyzes the received data using a generative AI model. At this stage, it generates prompt sentences and evaluates the learner's interest and concentration level. The input is the transmitted well-formed data, and the output is the analysis results and the generated prompt sentences. A specific example of its operation is the generation of a prompt sentence such as, "Suggest an activity to improve concentration."

[0091] Step 5:

[0092] The server generates optimal educational content for learners based on the analysis results. Inputs include analysis results and prompts, and output is customized educational content. A specific example of its operation is adjusting the difficulty level according to the learner's level of concentration.

[0093] Step 6:

[0094] The server sends the generated educational content to the terminal, which then displays it to the learner. The input is the generated content, and the output is the educational material displayed on the terminal. The operation requires the creation of an interface in an easy-to-learn format.

[0095] Step 7:

[0096] Users interact with educational content presented on their devices. For example, a user solves a problem and understands the provided explanation. The input is the presented content, and the output is feedback data such as the user's answers and access logs. Specific actions include entering answers into answer fields and manipulating the content.

[0097] Step 8:

[0098] The server performs more precise analysis based on user feedback data and uses this information to generate content for the next learning session. The input is feedback data, and the output is new analysis results and a subsequent learning plan based on those results. The process involves updating the database and adjusting the analysis algorithm.

[0099] (Application Example 1)

[0100] 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."

[0101] To provide personalized information experiences, detailed information analysis based on users' gaze, facial expressions, and voice is required. However, current systems face the challenge of being unable to analyze this information in real time and immediately present the most suitable information content to users.

[0102] 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.

[0103] In this invention, the server includes a device means for acquiring gaze information, facial expression information, and voice information; a computing device means for integrating and analyzing the acquired information in real time; and a generation device means for generating information content suitable for individual users based on the analysis results. This makes it possible to provide information content optimized for users in real time.

[0104] "Eye-gaze information" refers to data that indicates the direction of a user's gaze and the focus of their viewpoint.

[0105] "Facial expression information" refers to data that shows the user's facial expressions and emotions.

[0106] "Audio information" refers to audio data that records the tone and content of the user's voice.

[0107] "Acquisition device" refers to hardware used to collect eye-tracking information, facial expression information, and voice information.

[0108] A "processing unit" is a computer device used to process and analyze acquired information in real time.

[0109] A "generator" is a device that automatically creates content tailored to individual users based on analysis results.

[0110] A "display device" is hardware used to present generated content to a user visually or audibly.

[0111] The system that realizes this invention includes a server, a terminal, and a user.

[0112] The server is a core device that receives eye-tracking information, facial expression information, and voice information. This information includes gaze direction, emotions, and voice content, and the server analyzes it in real time. A dedicated algorithm is used for analysis, and it is desirable to use a deep learning model such as TENSORFLOW® as a framework. Based on the analysis results, information content optimized for the user is generated. The generated content is adjusted according to the user's interests and transmitted from the server to the terminal.

[0113] The terminal is a device equipped with sensors such as a camera and microphone, which acquires the user's gaze information, facial expression information, and voice information. These devices preprocess the data and then transmit the information to the server. They also support the user experience by displaying content transmitted from the server to the user.

[0114] Users interact with the content provided using their devices. For example, if they are interested in a product, product information is displayed where they look, and an audio explanation of its details is played. In this process, the user's responses are again captured as data, and the system continuously optimizes the content through a feedback loop.

[0115] As a concrete example, in a virtual store, when a user looks at a specific product, its usage instructions and features are displayed on smart glasses. If the user shows interest, additional information about related accessories and usage recipes is also provided. In this process, a generative AI model is used to generate content about the product of interest based on the prompt text.

[0116] Examples of prompt messages include, "Generate detailed information about the product the customer is interested in," or "Generate information about related accessories."

[0117] This makes it possible to instantly provide users with personalized information experiences.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The device acquires eye-tracking, facial expression, and audio information using its camera and microphone. The input consists of real-time video and audio data, which is pre-processed and sent to the server in a specific format. Pre-processing includes noise reduction and data format conversion.

[0121] Step 2:

[0122] The server receives information transmitted from the terminal. Based on the received data, it analyzes the direction of gaze, facial features, and content of speech. A generative AI model is used for the analysis, and machine learning frameworks such as TensorFlow are employed. The output is an analysis result that evaluates the user's interest and level of attention.

[0123] Step 3:

[0124] The server generates informational content tailored to the user based on the analysis results. This generation process involves inputting prompts into a generation AI model, which then creates personalized content considering various factors. The generated content can take the form of text, video, or audio.

[0125] Step 4:

[0126] The server sends the generated content to the terminal. The content sent includes information designed to attract the user's interest and introductions to related products. Targeted content is created using prompt messages.

[0127] Step 5:

[0128] The device displays received content to the user. It provides information to the user in real time using screens and audio. If the user shows interest in a product, it has a function to display further details.

[0129] Step 6:

[0130] The user interacts with the content provided through the device. Their responses (direction of gaze, changes in facial expression, voice input) are then captured again as data on the device.

[0131] Step 7:

[0132] The newly acquired information is sent back from the device to the server, forming a feedback loop. This allows the server to further optimize the content and continuously improve the user experience.

[0133] 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.

[0134] This invention provides a system incorporating an emotion engine to more deeply personalize the learning experience of students. This invention is primarily realized through the interaction of a server, a terminal, and a user. Its specific form is described below.

[0135] server

[0136] The server integrates behavioral information, facial expression information, and voice information transmitted from the terminal, and the emotion engine plays a central role in analyzing the learner's emotional state based on this information. The emotional data recognized by the emotion engine is processed in real time on the server and used to generate appropriate educational content. The server adjusts content according to the user's emotions, for example, by increasing activities that can take advantage of the user's concentration if the user is excited.

[0137] terminal

[0138] The device uses its camera and microphone to collect real-time facial and audio information from the learner. This information is immediately sent to the server and used for sentiment analysis. It also displays educational content generated and transmitted from the server, providing feedback to the user. The device ensures an interactive experience tailored to the learner's situation.

[0139] User (student)

[0140] Users interact with learning content presented through their devices. The learner's emotional state is analyzed by an emotion engine, allowing them to receive a learning experience optimized for their current emotions. For example, if the system determines the user is tired, relaxing sounds or other calming elements are played from the device to reduce stress during learning.

[0141] Overall operation

[0142] This system uses an emotion engine to link learners' emotions with their learning, providing a learning environment that is more tailored to each individual learner. By adjusting educational content in real time based on learners' emotional data, learners can learn at a pace that best suits their own learning style. This maximizes learner effectiveness and enables flexible responses to diverse educational needs.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The device captures the participant's facial expressions with its camera and records their voice with its microphone. The acquired data is temporarily stored on the device.

[0146] Step 2:

[0147] The device transmits stored facial expression and voice information to the server in real time. The data is encrypted and sent via a secure communication protocol.

[0148] Step 3:

[0149] Based on the received data, the server activates an emotion engine to analyze the learner's emotional state. Specifically, the analysis is performed by integrating a facial expression recognition algorithm and a voice analysis algorithm.

[0150] Step 4:

[0151] The server uses the results of the emotion engine analysis to generate educational content optimized for the learner's current emotional state. For example, if a learner is feeling stressed, it will select content that promotes relaxation.

[0152] Step 5:

[0153] The server sends the generated educational content to the terminal. This process is performed in real time, ensuring no delay in the learner's experience.

[0154] Step 6:

[0155] The device presents the received content to the user. It provides an environment that facilitates learning while considering the user's emotions through visual and auditory means.

[0156] Step 7:

[0157] The user learns by interacting with the presented content. During this process, new facial and audio information is collected by the device.

[0158] Step 8:

[0159] The device sends the newly collected data to the server. Based on this information, the server uses the emotion engine to perform further analysis, enabling continuous learning support.

[0160] (Example 2)

[0161] 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".

[0162] In recent years, there has been a growing demand for learning support systems that provide educational content tailored to individual learners. However, conventional systems have struggled to reflect learners' mental states and biometric information in real time, limiting their ability to provide personalized learning experiences. A solution is needed to address this challenge, accurately capture learners' emotional states, and deliver optimized content at the appropriate time.

[0163] 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.

[0164] In this invention, the server includes means for collecting biometric information using a sensing device, information processing means for integrating and analyzing the collected data, and generation means for creating educational materials optimized for the learner's mental state based on the analysis results. This makes it possible to generate and provide optimal learning content that is tailored to the individual psychological state of the learner.

[0165] "Means for collecting biological information using a sensing device" refers to a device that detects a learner's biological responses in real time and captures them as digital information.

[0166] An "information processing device for integrating and analyzing collected data" is a device that combines multiple acquired biometric information into a single dataset and evaluates the learner's mental state using an analysis algorithm.

[0167] A "generating device that creates educational materials optimized for the learner's mental state based on analysis results" is a device that dynamically generates the most suitable educational content for individual learners based on data analyzed by an information processing device.

[0168] A "display device" is a device that presents generated educational materials to learners in a visual or other form, thereby enabling an interactive learning experience.

[0169] A "distributed information processing environment" is an environment that utilizes computing resources on a network, such as cloud computing, to efficiently execute the processes of information collection, analysis, generation, and provision.

[0170] "Dynamic adjustment" means that the system automatically changes the learning pace and content delivery method in a timely manner according to the learner's real-time status.

[0171] This invention is a system that analyzes a learner's biometric information in real time and provides appropriate learning content based on that analysis. This system is primarily realized through the interaction of a server, a terminal, and a user.

[0172] The server plays a central role, receiving and integrating data transmitted from sensing devices and analyzing the learner's emotional state using an emotion engine. This process utilizes a generative AI model, enabling more precise emotional judgments. The analyzed data is used in real-time to generate learning materials, customizing the materials according to the user's mental state. For example, based on the learner's data, a prompt such as "What activity would you recommend for a student experiencing decreased concentration?" is generated, and the AI ​​model suggests the most suitable activity.

[0173] The device functions as a tool that collects learner facial expression and voice data using a camera and microphone. This real-time data is immediately sent to a server for analysis. The device also functions as a display for generated educational content. User feedback is obtained through the device, further enhancing the interactive learning experience.

[0174] Users can interact with personalized learning content displayed on their device. This allows users to enjoy a learning environment optimized for their current mental state. For example, if a learner needs to relax, the device may play appropriate music to reduce learning stress.

[0175] This system leverages a distributed information processing environment to efficiently execute various processes. This improves the accuracy and immediacy of personalized learning, resulting in an optimal learning experience for each learner.

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

[0177] Step 1:

[0178] The device uses a camera and microphone to collect learner facial and audio data in real time. This process captures eye movements and facial tension from facial expressions, and voice tone and tempo from audio. Video and audio data are acquired as input, and this data serves as foundational data for the next step.

[0179] Step 2:

[0180] The device immediately transmits the collected data to the server. The server receives this data and integrates it from different data formats. The inputs are video and audio data from the device, and the output is integrated biometric data. This data is used as material for emotion analysis.

[0181] Step 3:

[0182] The server uses integrated biometric data to perform analysis with an emotion engine. Here, a generative AI model is applied to identify the learner's mental state from the data. The input data is integrated biometric data, and the output is an emotion evaluation, for example, "the learner is lacking concentration." Specifically, a deep learning algorithm compares the current state with past data to accurately understand it.

[0183] Step 4:

[0184] The server dynamically generates educational content tailored to the learner's emotions based on the results of emotion analysis. Here too, a generative AI model is utilized to automatically generate content using prompts that correspond to the user's state. The input is the result of emotion analysis, and the output is personalized educational content. For example, if it is determined that the user's concentration is low, relaxing educational materials will be generated.

[0185] Step 5:

[0186] The terminal displays educational content sent from the server to the user and provides feedback. The input is the generated educational content, to which the user reacts. The output is the user's feedback, which the terminal sends back to the server as the basis for new data collection. This enables real-time interaction between the learner and the system.

[0187] (Application Example 2)

[0188] 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".

[0189] In food delivery, providing services tailored to the customer's emotional state can improve the customer experience and increase service satisfaction. However, traditional services can only offer a uniform approach, making it difficult to personalize services to meet the diverse emotions and needs of customers.

[0190] 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.

[0191] In this invention, the server includes a device means for acquiring behavioral data, facial expression data, and voice data; a computing device means for integrating and analyzing the acquired data in real time; and a generation device means for generating service content suitable for individual users based on the analysis results. This makes it possible to provide personalized food delivery services based on the emotional state of the customer.

[0192] "Behavioral data" refers to information about a user's actions and activities, including information about body movements and movement.

[0193] "Facial expression data" refers to information about a user's facial expressions, which is used to infer their emotional state.

[0194] "Voice data" refers to information about the user's speech and tone of voice, and by analyzing this data, it is possible to understand their emotions and intentions.

[0195] "Acquisition device" refers to hardware for collecting behavioral data, facial expression data, and voice data in real time.

[0196] A "computational device for integration and analysis" refers to a computer system that combines acquired data and analyzes it.

[0197] A "generating device" refers to a computer system that creates user-specific services and content based on analyzed data.

[0198] A "display device" refers to a device used to present generated service content to users visually or audibly.

[0199] "Cloud" refers to an environment that provides services that make computer resources and data available via the internet.

[0200] "Continuous monitoring" refers to the process of continuously acquiring user information and constantly evaluating that data in response to changing circumstances.

[0201] "Adaptive adjustment" refers to the act of dynamically changing the content of the services provided based on the information acquired.

[0202] The system implementing this invention collects and analyzes data in real time in order to provide personalized services based on the user's emotional state. Its specific form is described below.

[0203] The server integrates and analyzes information transmitted from devices that acquire user behavior data, facial expression data, and voice data in the cloud. This analysis uses software that acts as an emotion analysis engine (for example, Google® Cloud Vision API or Microsoft® Azure® Face API). This software extracts features from the data collected from cameras and microphones and performs analysis in real time.

[0204] Furthermore, the server generates personalized service content for each individual user based on the analysis results. The generated content is adaptively adjusted according to the user's emotional state and presented via a display device. In this case, the display device is often a smartphone or tablet.

[0205] Considering a scenario where a user orders food delivery, one application would be for the delivery person to analyze the user's facial expressions using their smartphone camera upon arrival at their home to understand their emotional state for the day. Based on these results, the server would offer special service offers or suggest customized menus. For example, if the system determines that the user is tired, it might suggest a complimentary refreshing drink.

[0206] An example of a prompt message to an actual generation AI model is, "Please suggest a refreshing service to provide if the user of this application is determined to be tired." In this way, the present invention makes it possible to provide optimal services in real time, taking into account the individual emotional state of the user.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The device acquires user behavior data, facial expression data, and voice data. This acquisition uses the smartphone's camera and microphone. Input is image data from the camera and voice data from the microphone, and output is formalized behavior data, facial expression data, and voice data. This data is then transmitted to the server in real time.

[0210] Step 2:

[0211] The server integrates the received behavioral data, facial expression data, and voice data, and performs analysis using a cloud-based emotion analysis engine. The input is data acquired from the terminal, and the output is the analyzed emotional state data. As part of the data processing, features of the user's face are extracted from the image data, and tone and speed are extracted from the voice data.

[0212] Step 3:

[0213] The server generates service content tailored to individual users based on the analysis results. The input is emotional state data, and the output is specialized service content or special offers. Specifically, it uses an AI model to suggest optimal menus and calculate benefits based on emotional state.

[0214] Step 4:

[0215] The server sends the generated service content to the terminal. The input is the service content, and the output is the content of the offer proposed to the user. Specifically, it sends a notification to the user's smartphone and prepares to display the content in the appropriate format.

[0216] Step 5:

[0217] Users react to service content presented through their devices, making selections and placing orders as needed. The input is the displayed service content, and the output is the user's selections and feedback. Specifically, if the user accepts an offer, a coupon applicable to their next order is activated.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention is a system that provides personalized education tailored to the diverse learning needs of students, and primarily involves server, terminal, and user-based operations. Specific embodiments of this invention are described below.

[0235] server

[0236] The server plays a central role in receiving behavioral, facial, and voice data collected from learners, and integrating and analyzing this data in real time. This includes various algorithms for evaluating learners' learning styles, interests, and concentration levels. The analysis results form the basis for generating educational content within the server.

[0237] For example, if the server detects a decrease in a participant's concentration from their facial expression, it can immediately adjust the difficulty level of the content and provide new, more challenging problems.

[0238] terminal

[0239] The device continuously acquires information about the learner through sensors such as cameras and microphones. The acquired data is pre-processed in real time before being sent to the server. It also plays a role in displaying educational content generated from the server to the learner.

[0240] For example, if analysis reveals that a particular student finds audio explanations easier to understand, the device will be configured to present learning content with enhanced audio guidance.

[0241] User (student)

[0242] Users interact with learning content provided through their devices. All data generated when users solve problems or manipulate content is collected again through the device and used for further analysis on the server.

[0243] For example, if a user demonstrates sufficient understanding of a particular task, the device will adjust to allow them to move on to the next stage of learning. By establishing a feedback loop that adapts to the learning progress in this way, it is possible to continuously optimize the learning experience for each individual student.

[0244] Comprehensive operation

[0245] This system combines these elements to enable learning tailored to the individual needs of each student. In particular, real-time information gathering and analysis, and adaptive content generation and delivery contribute to maximizing students' potential. This allows for the comprehensive provision of personalized educational experiences, creating an educational platform that can accommodate students with diverse learning backgrounds.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] The device collects information on the participant's behavior, facial expressions, and voice via various sensors. The collected data is pre-processed, including noise reduction.

[0249] Step 2:

[0250] The terminal sends pre-processed data to the server. The data is encrypted in real time and transferred over a secure network.

[0251] Step 3:

[0252] The server integrates the received data and performs real-time analysis using a multi-layer neural network. This analysis evaluates the learner's learning style, concentration level, interests, and other factors.

[0253] Step 4:

[0254] The server generates educational content optimized for each student based on the analysis results. Here, a generative AI model is used to design content that meets the individual needs of each student.

[0255] Step 5:

[0256] The server sends the generated educational content to the device. Content delivery is in real time, ensuring that the learner's learning experience is not interrupted.

[0257] Step 6:

[0258] The device presents learning content to the learner. Information is delivered in the most effective way for the learner, including audio guidance, visual feedback, and interactive controls.

[0259] Step 7:

[0260] Users interact with the presented content and work on learning tasks. Their responses and results are collected again via the device and used for the next processing cycle.

[0261] Step 8:

[0262] The server receives feedback based on new data from users, updates the content, and adaptively improves it for the next learning cycle. This functions as a continuous learning process.

[0263] (Example 1)

[0264] 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."

[0265] In today's educational environment, there is a need to identify the diverse needs of individual learners in real time and provide them with the optimal educational experience. However, conventional systems have struggled to effectively monitor learners' behavior and status and to flexibly adapt learning content.

[0266] 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.

[0267] In this invention, the server includes information acquisition means for acquiring various types of information, processing means for pre-processing the acquired information and transmitting the data, and analysis means for integrating and analyzing the acquired information in real time. This makes it possible to realize a system that can evaluate the status of individual learners and flexibly generate and provide appropriate educational materials.

[0268] "Information acquisition methods" refer to means of collecting various data such as learners' behavior, facial expressions, and voice.

[0269] "Processing means" refers to methods for pre-processing acquired raw data, such as noise reduction and format conversion, to prepare it for secure transmission to a server.

[0270] "Analysis means" refers to a method for integrating and analyzing transmitted data to evaluate learners' interests, concentration levels, and learning styles.

[0271] "Content generation means" refers to methods for generating educational materials that are optimal for individual learners based on analysis results.

[0272] "Display means" refers to the means of appropriately presenting generated educational materials to learners.

[0273] An "information processing infrastructure" is a foundation for executing various processes on a computer network, including the cloud.

[0274] This invention is a system for providing personalized educational experiences that can meet the diverse needs of learners. This system primarily involves the collaboration of a server, terminal, and user, and utilizes information technology to optimize the learning process.

[0275] The server plays a central role, analyzing various types of information (behavior, facial expressions, voice, etc.) in real time to evaluate the learner's state. Generative AI models are used for analysis, and content is suggested and generated using prompt sentences. An example of such a prompt sentence is, "Suggest activities to improve concentration."

[0276] The device implements information acquisition mechanisms to obtain data from learners. This includes sensors such as cameras and microphones to collect learners' facial expressions and voices in real time. The collected data is preprocessed and sent to a server. The device also displays generated educational content and provides a user interface that learners can interact with.

[0277] Users (learners) interact with individually customized educational content via their devices. Based on the learners' interactions, the server re-analyzes the data to further optimize the learning experience. The learners' actions, such as solving problems or referring to learning materials, are collected by the server as feedback and used to inform educational strategies in the next session.

[0278] This system makes it possible to provide individualized education to each learner, thus addressing the diverse educational needs of today.

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] The device acquires learner behavior, facial expressions, and voice information in real time using sensors (camera, microphone). For example, it uses the camera to capture the learner's facial expressions and the microphone to collect their voice tone. Inputs include the learner's facial expression data and voice data. Outputs the acquired raw data.

[0282] Step 2:

[0283] The terminal performs preprocessing such as noise removal and color tone correction on the acquired raw data. As a result, data suitable for analysis is prepared. The input is the acquired raw data, and the output is the processed and formatted data. Specific operations include filtering to remove background noise from audio data.

[0284] Step 3:

[0285] The terminal sends the preprocessed data to the server through a secure protocol. The input is the preprocessed data, and the output is the reception status at the server. The operation is to perform data transfer and confirm that there is no leakage.

[0286] Step 4:

[0287] The server analyzes the received data using a generative AI model. At this stage, a prompt sentence is generated to evaluate the learner's interest and concentration. The input is the transferred and formatted data, and the output is the analysis result and the generated prompt sentence. Specific operations include generating a prompt sentence such as "Propose activities to enhance concentration".

[0288] Step 5:

[0289] The server generates optimal educational content for the learner based on the analysis result. The input includes the analysis result and the prompt sentence, and the output is the customized educational content. Specific operations include adjusting the difficulty level according to the learner's concentration.

[0290] Step 6:

[0291] The server sends the generated educational content to the terminal, which then displays it to the learner. The input is the generated content, and the output is the educational material displayed on the terminal. The operation requires the creation of an interface in an easy-to-learn format.

[0292] Step 7:

[0293] Users interact with educational content presented on their devices. For example, a user solves a problem and understands the provided explanation. The input is the presented content, and the output is feedback data such as the user's answers and access logs. Specific actions include entering answers into answer fields and manipulating the content.

[0294] Step 8:

[0295] The server performs more precise analysis based on user feedback data and uses this information to generate content for the next learning session. The input is feedback data, and the output is new analysis results and a subsequent learning plan based on those results. The process involves updating the database and adjusting the analysis algorithm.

[0296] (Application Example 1)

[0297] 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."

[0298] To provide personalized information experiences, detailed information analysis based on users' gaze, facial expressions, and voice is required. However, current systems face the challenge of being unable to analyze this information in real time and immediately present the most suitable information content to users.

[0299] 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.

[0300] In this invention, the server includes device means for acquiring line-of-sight information, expression information, and voice information, arithmetic device means for integrating and analyzing the acquired information in real time, and generation device means for generating information content suitable for individual users based on the analysis result. As a result, it becomes possible to provide information content optimized for the user in real time.

[0301] The "line-of-sight information" is data indicating the direction of the user's line of sight and the focus of the viewpoint.

[0302] The "expression information" is data indicating the expression and emotion of the user's face.

[0303] The "voice information" is voice data recording the tone and content of the user's voice.

[0304] The "device for acquisition" refers to the hardware for collecting line-of-sight information, expression information, and voice information.

[0305] The "arithmetic device" is a computer device for processing and analyzing the acquired information in real time.

[0306] The "generation device" is a device for automatically creating content suitable for individual users based on the analysis result.

[0307] The "display device" is hardware for presenting the generated content to the user visually or aurally.

[0308] The system for realizing this invention includes a server, a terminal, and a user.

[0309] The server is the core device that receives eye-tracking information, facial expression information, and voice information. This information includes gaze direction, emotion, and voice content, and the server analyzes it in real time. A dedicated algorithm is used for analysis, and it is desirable to use a deep learning model such as TensorFlow as a framework. Based on the analysis results, information content optimized for the user is generated. The generated content is adjusted according to the user's interests and transmitted from the server to the terminal.

[0310] The terminal is a device equipped with sensors such as a camera and microphone, which acquires the user's gaze information, facial expression information, and voice information. These devices preprocess the data and then transmit the information to the server. They also support the user experience by displaying content transmitted from the server to the user.

[0311] Users interact with the content provided using their devices. For example, if they are interested in a product, product information is displayed where they look, and an audio explanation of its details is played. In this process, the user's responses are again captured as data, and the system continuously optimizes the content through a feedback loop.

[0312] As a concrete example, in a virtual store, when a user looks at a specific product, its usage instructions and features are displayed on smart glasses. If the user shows interest, additional information about related accessories and usage recipes is also provided. In this process, a generative AI model is used to generate content about the product of interest based on the prompt text.

[0313] Examples of prompt messages include, "Generate detailed information about the product the customer is interested in," or "Generate information about related accessories."

[0314] This makes it possible to instantly provide users with personalized information experiences.

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The device acquires eye-tracking, facial expression, and audio information using its camera and microphone. The input consists of real-time video and audio data, which is pre-processed and sent to the server in a specific format. Pre-processing includes noise reduction and data format conversion.

[0318] Step 2:

[0319] The server receives information transmitted from the terminal. Based on the received data, it analyzes the direction of gaze, facial features, and content of speech. A generative AI model is used for the analysis, and machine learning frameworks such as TensorFlow are employed. The output is an analysis result that evaluates the user's interest and level of attention.

[0320] Step 3:

[0321] The server generates informational content tailored to the user based on the analysis results. This generation process involves inputting prompts into a generation AI model, which then creates personalized content considering various factors. The generated content can take the form of text, video, or audio.

[0322] Step 4:

[0323] The server sends the generated content to the terminal. The content sent includes information designed to attract the user's interest and introductions to related products. Targeted content is created using prompt messages.

[0324] Step 5:

[0325] The device displays received content to the user. It provides information to the user in real time using screens and audio. If the user shows interest in a product, it has a function to display further details.

[0326] Step 6:

[0327] The user interacts with the content provided through the device. Their responses (direction of gaze, changes in facial expression, voice input) are then captured again as data on the device.

[0328] Step 7:

[0329] The newly acquired information is sent back from the device to the server, forming a feedback loop. This allows the server to further optimize the content and continuously improve the user experience.

[0330] 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.

[0331] This invention provides a system incorporating an emotion engine to more deeply personalize the learning experience of students. This invention is primarily realized through the interaction of a server, a terminal, and a user. Its specific form is described below.

[0332] server

[0333] The server integrates behavioral information, facial expression information, and voice information transmitted from the terminal, and the emotion engine plays a central role in analyzing the learner's emotional state based on this information. The emotional data recognized by the emotion engine is processed in real time on the server and used to generate appropriate educational content. The server adjusts content according to the user's emotions, for example, by increasing activities that can take advantage of the user's concentration if the user is excited.

[0334] terminal

[0335] The device uses its camera and microphone to collect real-time facial and audio information from the learner. This information is immediately sent to the server and used for sentiment analysis. It also displays educational content generated and transmitted from the server, providing feedback to the user. The device ensures an interactive experience tailored to the learner's situation.

[0336] User (student)

[0337] Users interact with learning content presented through their devices. The learner's emotional state is analyzed by an emotion engine, allowing them to receive a learning experience optimized for their current emotions. For example, if the system determines the user is tired, relaxing sounds or other calming elements are played from the device to reduce stress during learning.

[0338] Overall operation

[0339] This system uses an emotion engine to link learners' emotions with their learning, providing a learning environment that is more tailored to each individual learner. By adjusting educational content in real time based on learners' emotional data, learners can learn at a pace that best suits their own learning style. This maximizes learner effectiveness and enables flexible responses to diverse educational needs.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] The device captures the participant's facial expressions with its camera and records their voice with its microphone. The acquired data is temporarily stored on the device.

[0343] Step 2:

[0344] The device transmits stored facial expression and voice information to the server in real time. The data is encrypted and sent via a secure communication protocol.

[0345] Step 3:

[0346] Based on the received data, the server activates an emotion engine to analyze the learner's emotional state. Specifically, the analysis is performed by integrating a facial expression recognition algorithm and a voice analysis algorithm.

[0347] Step 4:

[0348] The server uses the results of the emotion engine analysis to generate educational content optimized for the learner's current emotional state. For example, if a learner is feeling stressed, it will select content that promotes relaxation.

[0349] Step 5:

[0350] The server sends the generated educational content to the terminal. This process is performed in real time, ensuring no delay in the learner's experience.

[0351] Step 6:

[0352] The device presents the received content to the user. It provides an environment that facilitates learning while considering the user's emotions through visual and auditory means.

[0353] Step 7:

[0354] The user learns by interacting with the presented content. During this process, new facial and audio information is collected by the device.

[0355] Step 8:

[0356] The device sends the newly collected data to the server. Based on this information, the server uses the emotion engine to perform further analysis, enabling continuous learning support.

[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] In recent years, there has been a growing demand for learning support systems that provide educational content tailored to individual learners. However, conventional systems have struggled to reflect learners' mental states and biometric information in real time, limiting their ability to provide personalized learning experiences. A solution is needed to address this challenge, accurately capture learners' emotional states, and deliver optimized content at the appropriate time.

[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 collecting biometric information using a sensing device, information processing means for integrating and analyzing the collected data, and generation means for creating educational materials optimized for the learner's mental state based on the analysis results. This makes it possible to generate and provide optimal learning content that is tailored to the individual psychological state of the learner.

[0362] "Means for collecting biological information using a sensing device" refers to a device that detects a learner's biological responses in real time and captures them as digital information.

[0363] An "information processing device for integrating and analyzing collected data" is a device that combines multiple acquired biometric information into a single dataset and evaluates the learner's mental state using an analysis algorithm.

[0364] A "generating device that creates educational materials optimized for the learner's mental state based on analysis results" is a device that dynamically generates the most suitable educational content for individual learners based on data analyzed by an information processing device.

[0365] A "display device" is a device that presents generated educational materials to learners in a visual or other form, thereby enabling an interactive learning experience.

[0366] A "distributed information processing environment" is an environment that utilizes computing resources on a network, such as cloud computing, to efficiently execute the processes of information collection, analysis, generation, and provision.

[0367] "Dynamic adjustment" means that the system automatically changes the learning pace and content delivery method in a timely manner according to the learner's real-time status.

[0368] This invention is a system that analyzes a learner's biometric information in real time and provides appropriate learning content based on that analysis. This system is primarily realized through the interaction of a server, a terminal, and a user.

[0369] The server plays a central role, receiving and integrating data transmitted from sensing devices and analyzing the learner's emotional state using an emotion engine. This process utilizes a generative AI model, enabling more precise emotional judgments. The analyzed data is used in real-time to generate learning materials, customizing the materials according to the user's mental state. For example, based on the learner's data, a prompt such as "What activity would you recommend for a student experiencing decreased concentration?" is generated, and the AI ​​model suggests the most suitable activity.

[0370] The device functions as a tool that collects learner facial expression and voice data using a camera and microphone. This real-time data is immediately sent to a server for analysis. The device also functions as a display for generated educational content. User feedback is obtained through the device, further enhancing the interactive learning experience.

[0371] Users can interact with personalized learning content displayed on their device. This allows users to enjoy a learning environment optimized for their current mental state. For example, if a learner needs to relax, the device may play appropriate music to reduce learning stress.

[0372] This system leverages a distributed information processing environment to efficiently execute various processes. This improves the accuracy and immediacy of personalized learning, resulting in an optimal learning experience for each learner.

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] The device uses a camera and microphone to collect learner facial and audio data in real time. This process captures eye movements and facial tension from facial expressions, and voice tone and tempo from audio. Video and audio data are acquired as input, and this data serves as foundational data for the next step.

[0376] Step 2:

[0377] The device immediately transmits the collected data to the server. The server receives this data and integrates it from different data formats. The inputs are video and audio data from the device, and the output is integrated biometric data. This data is used as material for emotion analysis.

[0378] Step 3:

[0379] The server uses integrated biometric data to perform analysis with an emotion engine. Here, a generative AI model is applied to identify the learner's mental state from the data. The input data is integrated biometric data, and the output is an emotion evaluation, for example, "the learner is lacking concentration." Specifically, a deep learning algorithm compares the current state with past data to accurately understand it.

[0380] Step 4:

[0381] The server dynamically generates educational content tailored to the learner's emotions based on the results of emotion analysis. Here too, a generative AI model is utilized to automatically generate content using prompts that correspond to the user's state. The input is the result of emotion analysis, and the output is personalized educational content. For example, if it is determined that the user's concentration is low, relaxing educational materials will be generated.

[0382] Step 5:

[0383] The terminal displays educational content sent from the server to the user and provides feedback. The input is the generated educational content, to which the user reacts. The output is the user's feedback, which the terminal sends back to the server as the basis for new data collection. This enables real-time interaction between the learner and the system.

[0384] (Application Example 2)

[0385] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0386] In food delivery, providing services tailored to the customer's emotional state can improve the customer experience and increase service satisfaction. However, traditional services can only offer a uniform approach, making it difficult to personalize services to meet the diverse emotions and needs of customers.

[0387] 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.

[0388] In this invention, the server includes a device means for acquiring behavioral data, facial expression data, and voice data; a computing device means for integrating and analyzing the acquired data in real time; and a generation device means for generating service content suitable for individual users based on the analysis results. This makes it possible to provide personalized food delivery services based on the emotional state of the customer.

[0389] "Behavioral data" refers to information about a user's actions and activities, including information about body movements and movement.

[0390] "Facial expression data" refers to information about a user's facial expressions, which is used to infer their emotional state.

[0391] "Voice data" refers to information about the user's speech and tone of voice, and by analyzing this data, it is possible to understand their emotions and intentions.

[0392] "Acquisition device" refers to hardware for collecting behavioral data, facial expression data, and voice data in real time.

[0393] A "computational device for integration and analysis" refers to a computer system that combines acquired data and analyzes it.

[0394] A "generating device" refers to a computer system that creates user-specific services and content based on analyzed data.

[0395] A "display device" refers to a device used to present generated service content to users visually or audibly.

[0396] "Cloud" refers to an environment that provides services that make computer resources and data available via the internet.

[0397] "Continuous monitoring" refers to the process of continuously acquiring user information and constantly evaluating that data in response to changing circumstances.

[0398] "Adaptive adjustment" refers to the act of dynamically changing the content of the services provided based on the information acquired.

[0399] The system implementing this invention collects and analyzes data in real time in order to provide personalized services based on the user's emotional state. Its specific form is described below.

[0400] The server integrates and analyzes information transmitted from devices that acquire user behavior data, facial expression data, and voice data in the cloud. This analysis uses software that acts as an emotion analysis engine (for example, Google Cloud Vision API or Microsoft Azure Face API). This software extracts features from the data collected from cameras and microphones and performs analysis in real time.

[0401] Furthermore, the server generates personalized service content for each individual user based on the analysis results. The generated content is adaptively adjusted according to the user's emotional state and presented via a display device. In this case, the display device is often a smartphone or tablet.

[0402] Considering a scenario where a user orders food delivery, one application would be for the delivery person to analyze the user's facial expressions using their smartphone camera upon arrival at their home to understand their emotional state for the day. Based on these results, the server would offer special service offers or suggest customized menus. For example, if the system determines that the user is tired, it might suggest a complimentary refreshing drink.

[0403] An example of a prompt message to an actual generation AI model is, "Please suggest a refreshing service to provide if the user of this application is determined to be tired." In this way, the present invention makes it possible to provide optimal services in real time, taking into account the individual emotional state of the user.

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

[0405] Step 1:

[0406] The device acquires user behavior data, facial expression data, and voice data. This acquisition uses the smartphone's camera and microphone. Input is image data from the camera and voice data from the microphone, and output is formalized behavior data, facial expression data, and voice data. This data is then transmitted to the server in real time.

[0407] Step 2:

[0408] The server integrates the received behavioral data, facial expression data, and voice data, and performs analysis using a cloud-based emotion analysis engine. The input is data acquired from the terminal, and the output is the analyzed emotional state data. As part of the data processing, features of the user's face are extracted from the image data, and tone and speed are extracted from the voice data.

[0409] Step 3:

[0410] The server generates service content tailored to individual users based on the analysis results. The input is emotional state data, and the output is specialized service content or special offers. Specifically, it uses an AI model to suggest optimal menus and calculate benefits based on emotional state.

[0411] Step 4:

[0412] The server sends the generated service content to the terminal. The input is the service content, and the output is the content of the offer proposed to the user. Specifically, it sends a notification to the user's smartphone and prepares to display the content in the appropriate format.

[0413] Step 5:

[0414] Users react to service content presented through their devices, making selections and placing orders as needed. The input is the displayed service content, and the output is the user's selections and feedback. Specifically, if the user accepts an offer, a coupon applicable to their next order is activated.

[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 is a system that provides personalized education tailored to the diverse learning needs of students, and primarily involves server, terminal, and user-based operations. Specific embodiments of this invention are described below.

[0432] server

[0433] The server plays a central role in receiving behavioral, facial, and voice data collected from learners, and integrating and analyzing this data in real time. This includes various algorithms for evaluating learners' learning styles, interests, and concentration levels. The analysis results form the basis for generating educational content within the server.

[0434] For example, if the server detects a decrease in a participant's concentration from their facial expression, it can immediately adjust the difficulty level of the content and provide new, more challenging problems.

[0435] terminal

[0436] The device continuously acquires information about the learner through sensors such as cameras and microphones. The acquired data is pre-processed in real time before being sent to the server. It also plays a role in displaying educational content generated from the server to the learner.

[0437] For example, if analysis reveals that a particular student finds audio explanations easier to understand, the device will be configured to present learning content with enhanced audio guidance.

[0438] User (student)

[0439] Users interact with learning content provided through their devices. All data generated when users solve problems or manipulate content is collected again through the device and used for further analysis on the server.

[0440] For example, if a user demonstrates sufficient understanding of a particular task, the device will adjust to allow them to move on to the next stage of learning. By establishing a feedback loop that adapts to the learning progress in this way, it is possible to continuously optimize the learning experience for each individual student.

[0441] Comprehensive operation

[0442] This system combines these elements to enable learning tailored to the individual needs of each student. In particular, real-time information gathering and analysis, and adaptive content generation and delivery contribute to maximizing students' potential. This allows for the comprehensive provision of personalized educational experiences, creating an educational platform that can accommodate students with diverse learning backgrounds.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The device collects information on the participant's behavior, facial expressions, and voice via various sensors. The collected data is pre-processed, including noise reduction.

[0446] Step 2:

[0447] The terminal sends pre-processed data to the server. The data is encrypted in real time and transferred over a secure network.

[0448] Step 3:

[0449] The server integrates the received data and performs real-time analysis using a multi-layer neural network. This analysis evaluates the learner's learning style, concentration level, interests, and other factors.

[0450] Step 4:

[0451] The server generates educational content optimized for each student based on the analysis results. Here, a generative AI model is used to design content that meets the individual needs of each student.

[0452] Step 5:

[0453] The server sends the generated educational content to the device. Content delivery is in real time, ensuring that the learner's learning experience is not interrupted.

[0454] Step 6:

[0455] The device presents learning content to the learner. Information is delivered in the most effective way for the learner, including audio guidance, visual feedback, and interactive controls.

[0456] Step 7:

[0457] Users interact with the presented content and work on learning tasks. Their responses and results are collected again via the device and used for the next processing cycle.

[0458] Step 8:

[0459] The server receives feedback based on new data from users, updates the content, and adaptively improves it for the next learning cycle. This functions as a continuous learning process.

[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] In today's educational environment, there is a need to identify the diverse needs of individual learners in real time and provide them with the optimal educational experience. However, conventional systems have struggled to effectively monitor learners' behavior and status and to flexibly adapt learning content.

[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 information acquisition means for acquiring various types of information, processing means for pre-processing the acquired information and transmitting the data, and analysis means for integrating and analyzing the acquired information in real time. This makes it possible to realize a system that can evaluate the status of individual learners and flexibly generate and provide appropriate educational materials.

[0465] "Information acquisition methods" refer to means of collecting various data such as learners' behavior, facial expressions, and voice.

[0466] "Processing means" refers to methods for pre-processing acquired raw data, such as noise reduction and format conversion, to prepare it for secure transmission to a server.

[0467] "Analysis means" refers to a method for integrating and analyzing transmitted data to evaluate learners' interests, concentration levels, and learning styles.

[0468] "Content generation means" refers to methods for generating educational materials that are optimal for individual learners based on analysis results.

[0469] "Display means" refers to the means of appropriately presenting generated educational materials to learners.

[0470] An "information processing infrastructure" is a foundation for executing various processes on a computer network, including the cloud.

[0471] This invention is a system for providing personalized educational experiences that can meet the diverse needs of learners. This system primarily involves the collaboration of a server, terminal, and user, and utilizes information technology to optimize the learning process.

[0472] The server plays a central role, analyzing various types of information (behavior, facial expressions, voice, etc.) in real time to evaluate the learner's state. Generative AI models are used for analysis, and content is suggested and generated using prompt sentences. An example of such a prompt sentence is, "Suggest activities to improve concentration."

[0473] The device implements information acquisition mechanisms to obtain data from learners. This includes sensors such as cameras and microphones to collect learners' facial expressions and voices in real time. The collected data is preprocessed and sent to a server. The device also displays generated educational content and provides a user interface that learners can interact with.

[0474] Users (learners) interact with individually customized educational content via their devices. Based on the learners' interactions, the server re-analyzes the data to further optimize the learning experience. The learners' actions, such as solving problems or referring to learning materials, are collected by the server as feedback and used to inform educational strategies in the next session.

[0475] This system makes it possible to provide individualized education to each learner, thus addressing the diverse educational needs of today.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The device acquires learner behavior, facial expressions, and voice information in real time using sensors (camera, microphone). For example, it uses the camera to capture the learner's facial expressions and the microphone to collect their voice tone. Inputs include the learner's facial expression data and voice data. Outputs the acquired raw data.

[0479] Step 2:

[0480] The terminal performs preprocessing such as noise reduction and color correction on the acquired raw data. This prepares the data for analysis. The input is the acquired raw data, and the output is processed, well-formed data. Specific operations include filtering to remove background noise from audio data.

[0481] Step 3:

[0482] The terminal sends pre-processed data to the server via a secure protocol. The input is the pre-processed data, and the output is the server's reception status. The operation involves data transfer and verification that there is no leakage.

[0483] Step 4:

[0484] The server analyzes the received data using a generative AI model. At this stage, it generates prompt sentences and evaluates the learner's interest and concentration level. The input is the transmitted well-formed data, and the output is the analysis results and the generated prompt sentences. A specific example of its operation is the generation of a prompt sentence such as, "Suggest an activity to improve concentration."

[0485] Step 5:

[0486] The server generates optimal educational content for learners based on the analysis results. Inputs include analysis results and prompts, and output is customized educational content. A specific example of its operation is adjusting the difficulty level according to the learner's level of concentration.

[0487] Step 6:

[0488] The server sends the generated educational content to the terminal, which then displays it to the learner. The input is the generated content, and the output is the educational material displayed on the terminal. The operation requires the creation of an interface in an easy-to-learn format.

[0489] Step 7:

[0490] Users interact with educational content presented on their devices. For example, a user solves a problem and understands the provided explanation. The input is the presented content, and the output is feedback data such as the user's answers and access logs. Specific actions include entering answers into answer fields and manipulating the content.

[0491] Step 8:

[0492] The server performs more precise analysis based on user feedback data and uses this information to generate content for the next learning session. The input is feedback data, and the output is new analysis results and a subsequent learning plan based on those results. The process involves updating the database and adjusting the analysis algorithm.

[0493] (Application Example 1)

[0494] 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."

[0495] To provide personalized information experiences, detailed information analysis based on users' gaze, facial expressions, and voice is required. However, current systems face the challenge of being unable to analyze this information in real time and immediately present the most suitable information content to users.

[0496] 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.

[0497] In this invention, the server includes a device means for acquiring gaze information, facial expression information, and voice information; a computing device means for integrating and analyzing the acquired information in real time; and a generation device means for generating information content suitable for individual users based on the analysis results. This makes it possible to provide information content optimized for users in real time.

[0498] "Eye-gaze information" refers to data that indicates the direction of a user's gaze and the focus of their viewpoint.

[0499] "Facial expression information" refers to data that shows the user's facial expressions and emotions.

[0500] "Audio information" refers to audio data that records the tone and content of the user's voice.

[0501] "Acquisition device" refers to hardware used to collect eye-tracking information, facial expression information, and voice information.

[0502] A "processing unit" is a computer device used to process and analyze acquired information in real time.

[0503] A "generator" is a device that automatically creates content tailored to individual users based on analysis results.

[0504] A "display device" is hardware used to present generated content to a user visually or audibly.

[0505] The system that realizes this invention includes a server, a terminal, and a user.

[0506] The server is the core device that receives eye-tracking information, facial expression information, and voice information. This information includes gaze direction, emotion, and voice content, and the server analyzes it in real time. A dedicated algorithm is used for analysis, and it is desirable to use a deep learning model such as TensorFlow as a framework. Based on the analysis results, information content optimized for the user is generated. The generated content is adjusted according to the user's interests and transmitted from the server to the terminal.

[0507] The terminal is a device equipped with sensors such as a camera and microphone, which acquires the user's gaze information, facial expression information, and voice information. These devices preprocess the data and then transmit the information to the server. They also support the user experience by displaying content transmitted from the server to the user.

[0508] Users interact with the content provided using their devices. For example, if they are interested in a product, product information is displayed where they look, and an audio explanation of its details is played. In this process, the user's responses are again captured as data, and the system continuously optimizes the content through a feedback loop.

[0509] As a concrete example, in a virtual store, when a user looks at a specific product, its usage instructions and features are displayed on smart glasses. If the user shows interest, additional information about related accessories and usage recipes is also provided. In this process, a generative AI model is used to generate content about the product of interest based on the prompt text.

[0510] Examples of prompt messages include, "Generate detailed information about the product the customer is interested in," or "Generate information about related accessories."

[0511] This makes it possible to instantly provide users with personalized information experiences.

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The device acquires eye-tracking, facial expression, and audio information using its camera and microphone. The input consists of real-time video and audio data, which is pre-processed and sent to the server in a specific format. Pre-processing includes noise reduction and data format conversion.

[0515] Step 2:

[0516] The server receives information transmitted from the terminal. Based on the received data, it analyzes the direction of gaze, facial features, and content of speech. A generative AI model is used for the analysis, and machine learning frameworks such as TensorFlow are employed. The output is an analysis result that evaluates the user's interest and level of attention.

[0517] Step 3:

[0518] The server generates informational content tailored to the user based on the analysis results. This generation process involves inputting prompts into a generation AI model, which then creates personalized content considering various factors. The generated content can take the form of text, video, or audio.

[0519] Step 4:

[0520] The server sends the generated content to the terminal. The content sent includes information designed to attract the user's interest and introductions to related products. Targeted content is created using prompt messages.

[0521] Step 5:

[0522] The device displays received content to the user. It provides information to the user in real time using screens and audio. If the user shows interest in a product, it has a function to display further details.

[0523] Step 6:

[0524] The user interacts with the content provided through the device. Their responses (direction of gaze, changes in facial expression, voice input) are then captured again as data on the device.

[0525] Step 7:

[0526] The newly acquired information is sent back from the device to the server, forming a feedback loop. This allows the server to further optimize the content and continuously improve the user experience.

[0527] 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.

[0528] This invention provides a system incorporating an emotion engine to more deeply personalize the learning experience of students. This invention is primarily realized through the interaction of a server, a terminal, and a user. Its specific form is described below.

[0529] server

[0530] The server integrates behavioral information, facial expression information, and voice information transmitted from the terminal, and the emotion engine plays a central role in analyzing the learner's emotional state based on this information. The emotional data recognized by the emotion engine is processed in real time on the server and used to generate appropriate educational content. The server adjusts content according to the user's emotions, for example, by increasing activities that can take advantage of the user's concentration if the user is excited.

[0531] terminal

[0532] The device uses its camera and microphone to collect real-time facial and audio information from the learner. This information is immediately sent to the server and used for sentiment analysis. It also displays educational content generated and transmitted from the server, providing feedback to the user. The device ensures an interactive experience tailored to the learner's situation.

[0533] User (student)

[0534] Users interact with learning content presented through their devices. The learner's emotional state is analyzed by an emotion engine, allowing them to receive a learning experience optimized for their current emotions. For example, if the system determines the user is tired, relaxing sounds or other calming elements are played from the device to reduce stress during learning.

[0535] Overall operation

[0536] This system uses an emotion engine to link learners' emotions with their learning, providing a learning environment that is more tailored to each individual learner. By adjusting educational content in real time based on learners' emotional data, learners can learn at a pace that best suits their own learning style. This maximizes learner effectiveness and enables flexible responses to diverse educational needs.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] The device captures the participant's facial expressions with its camera and records their voice with its microphone. The acquired data is temporarily stored on the device.

[0540] Step 2:

[0541] The device transmits stored facial expression and voice information to the server in real time. The data is encrypted and sent via a secure communication protocol.

[0542] Step 3:

[0543] Based on the received data, the server activates an emotion engine to analyze the learner's emotional state. Specifically, the analysis is performed by integrating a facial expression recognition algorithm and a voice analysis algorithm.

[0544] Step 4:

[0545] The server uses the results of the emotion engine analysis to generate educational content optimized for the learner's current emotional state. For example, if a learner is feeling stressed, it will select content that promotes relaxation.

[0546] Step 5:

[0547] The server sends the generated educational content to the terminal. This process is performed in real time, ensuring no delay in the learner's experience.

[0548] Step 6:

[0549] The device presents the received content to the user. It provides an environment that facilitates learning while considering the user's emotions through visual and auditory means.

[0550] Step 7:

[0551] The user learns by interacting with the presented content. During this process, new facial and audio information is collected by the device.

[0552] Step 8:

[0553] The device sends the newly collected data to the server. Based on this information, the server uses the emotion engine to perform further analysis, enabling continuous learning support.

[0554] (Example 2)

[0555] 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."

[0556] In recent years, there has been a growing demand for learning support systems that provide educational content tailored to individual learners. However, conventional systems have struggled to reflect learners' mental states and biometric information in real time, limiting their ability to provide personalized learning experiences. A solution is needed to address this challenge, accurately capture learners' emotional states, and deliver optimized content at the appropriate time.

[0557] 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.

[0558] In this invention, the server includes means for collecting biometric information using a sensing device, information processing means for integrating and analyzing the collected data, and generation means for creating educational materials optimized for the learner's mental state based on the analysis results. This makes it possible to generate and provide optimal learning content that is tailored to the individual psychological state of the learner.

[0559] "Means for collecting biological information using a sensing device" refers to a device that detects a learner's biological responses in real time and captures them as digital information.

[0560] An "information processing device for integrating and analyzing collected data" is a device that combines multiple acquired biometric information into a single dataset and evaluates the learner's mental state using an analysis algorithm.

[0561] A "generating device that creates educational materials optimized for the learner's mental state based on analysis results" is a device that dynamically generates the most suitable educational content for individual learners based on data analyzed by an information processing device.

[0562] A "display device" is a device that presents generated educational materials to learners in a visual or other form, thereby enabling an interactive learning experience.

[0563] A "distributed information processing environment" is an environment that utilizes computing resources on a network, such as cloud computing, to efficiently execute the processes of information collection, analysis, generation, and provision.

[0564] "Dynamic adjustment" means that the system automatically changes the learning pace and content delivery method in a timely manner according to the learner's real-time status.

[0565] This invention is a system that analyzes a learner's biometric information in real time and provides appropriate learning content based on that analysis. This system is primarily realized through the interaction of a server, a terminal, and a user.

[0566] The server plays a central role, receiving and integrating data transmitted from sensing devices and analyzing the learner's emotional state using an emotion engine. This process utilizes a generative AI model, enabling more precise emotional judgments. The analyzed data is used in real-time to generate learning materials, customizing the materials according to the user's mental state. For example, based on the learner's data, a prompt such as "What activity would you recommend for a student experiencing decreased concentration?" is generated, and the AI ​​model suggests the most suitable activity.

[0567] The device functions as a tool that collects learner facial expression and voice data using a camera and microphone. This real-time data is immediately sent to a server for analysis. The device also functions as a display for generated educational content. User feedback is obtained through the device, further enhancing the interactive learning experience.

[0568] Users can interact with personalized learning content displayed on their device. This allows users to enjoy a learning environment optimized for their current mental state. For example, if a learner needs to relax, the device may play appropriate music to reduce learning stress.

[0569] This system leverages a distributed information processing environment to efficiently execute various processes. This improves the accuracy and immediacy of personalized learning, resulting in an optimal learning experience for each learner.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The device uses a camera and microphone to collect learner facial and audio data in real time. This process captures eye movements and facial tension from facial expressions, and voice tone and tempo from audio. Video and audio data are acquired as input, and this data serves as foundational data for the next step.

[0573] Step 2:

[0574] The device immediately transmits the collected data to the server. The server receives this data and integrates it from different data formats. The inputs are video and audio data from the device, and the output is integrated biometric data. This data is used as material for emotion analysis.

[0575] Step 3:

[0576] The server uses integrated biometric data to perform analysis with an emotion engine. Here, a generative AI model is applied to identify the learner's mental state from the data. The input data is integrated biometric data, and the output is an emotion evaluation, for example, "the learner is lacking concentration." Specifically, a deep learning algorithm compares the current state with past data to accurately understand it.

[0577] Step 4:

[0578] The server dynamically generates educational content tailored to the learner's emotions based on the results of emotion analysis. Here too, a generative AI model is utilized to automatically generate content using prompts that correspond to the user's state. The input is the result of emotion analysis, and the output is personalized educational content. For example, if it is determined that the user's concentration is low, relaxing educational materials will be generated.

[0579] Step 5:

[0580] The terminal displays educational content sent from the server to the user and provides feedback. The input is the generated educational content, to which the user reacts. The output is the user's feedback, which the terminal sends back to the server as the basis for new data collection. This enables real-time interaction between the learner and the system.

[0581] (Application Example 2)

[0582] 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."

[0583] In food delivery, providing services tailored to the customer's emotional state can improve the customer experience and increase service satisfaction. However, traditional services can only offer a uniform approach, making it difficult to personalize services to meet the diverse emotions and needs of customers.

[0584] 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.

[0585] In this invention, the server includes a device means for acquiring behavioral data, facial expression data, and voice data; a computing device means for integrating and analyzing the acquired data in real time; and a generation device means for generating service content suitable for individual users based on the analysis results. This makes it possible to provide personalized food delivery services based on the emotional state of the customer.

[0586] "Behavioral data" refers to information about a user's actions and activities, including information about body movements and movement.

[0587] "Facial expression data" refers to information about a user's facial expressions, which is used to infer their emotional state.

[0588] "Voice data" refers to information about the user's speech and tone of voice, and by analyzing this data, it is possible to understand their emotions and intentions.

[0589] "Acquisition device" refers to hardware for collecting behavioral data, facial expression data, and voice data in real time.

[0590] A "computational device for integration and analysis" refers to a computer system that combines acquired data and analyzes it.

[0591] A "generating device" refers to a computer system that creates user-specific services and content based on analyzed data.

[0592] A "display device" refers to a device used to present generated service content to users visually or audibly.

[0593] "Cloud" refers to an environment that provides services that make computer resources and data available via the internet.

[0594] "Continuous monitoring" refers to the process of continuously acquiring user information and constantly evaluating that data in response to changing circumstances.

[0595] "Adaptive adjustment" refers to the act of dynamically changing the content of the services provided based on the information acquired.

[0596] The system implementing this invention collects and analyzes data in real time in order to provide personalized services based on the user's emotional state. Its specific form is described below.

[0597] The server integrates and analyzes information transmitted from devices that acquire user behavior data, facial expression data, and voice data in the cloud. This analysis uses software that acts as an emotion analysis engine (for example, Google Cloud Vision API or Microsoft Azure Face API). This software extracts features from the data collected from cameras and microphones and performs analysis in real time.

[0598] Furthermore, the server generates personalized service content for each individual user based on the analysis results. The generated content is adaptively adjusted according to the user's emotional state and presented via a display device. In this case, the display device is often a smartphone or tablet.

[0599] Considering a scenario where a user orders food delivery, one application would be for the delivery person to analyze the user's facial expressions using their smartphone camera upon arrival at their home to understand their emotional state for the day. Based on these results, the server would offer special service offers or suggest customized menus. For example, if the system determines that the user is tired, it might suggest a complimentary refreshing drink.

[0600] An example of a prompt message to an actual generation AI model is, "Please suggest a refreshing service to provide if the user of this application is determined to be tired." In this way, the present invention makes it possible to provide optimal services in real time, taking into account the individual emotional state of the user.

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] The device acquires user behavior data, facial expression data, and voice data. This acquisition uses the smartphone's camera and microphone. Input is image data from the camera and voice data from the microphone, and output is formalized behavior data, facial expression data, and voice data. This data is then transmitted to the server in real time.

[0604] Step 2:

[0605] The server integrates the received behavioral data, facial expression data, and voice data, and performs analysis using a cloud-based emotion analysis engine. The input is data acquired from the terminal, and the output is the analyzed emotional state data. As part of the data processing, features of the user's face are extracted from the image data, and tone and speed are extracted from the voice data.

[0606] Step 3:

[0607] The server generates service content tailored to individual users based on the analysis results. The input is emotional state data, and the output is specialized service content or special offers. Specifically, it uses an AI model to suggest optimal menus and calculate benefits based on emotional state.

[0608] Step 4:

[0609] The server sends the generated service content to the terminal. The input is the service content, and the output is the content of the offer proposed to the user. Specifically, it sends a notification to the user's smartphone and prepares to display the content in the appropriate format.

[0610] Step 5:

[0611] Users react to service content presented through their devices, making selections and placing orders as needed. The input is the displayed service content, and the output is the user's selections and feedback. Specifically, if the user accepts an offer, a coupon applicable to their next order is activated.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] [Fourth Embodiment]

[0616] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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".

[0629] This invention is a system that provides personalized education tailored to the diverse learning needs of students, and primarily involves server, terminal, and user-based operations. Specific embodiments of this invention are described below.

[0630] server

[0631] The server plays a central role in receiving behavioral, facial, and voice data collected from learners, and integrating and analyzing this data in real time. This includes various algorithms for evaluating learners' learning styles, interests, and concentration levels. The analysis results form the basis for generating educational content within the server.

[0632] For example, if the server detects a decrease in a participant's concentration from their facial expression, it can immediately adjust the difficulty level of the content and provide new, more challenging problems.

[0633] terminal

[0634] The device continuously acquires information about the learner through sensors such as cameras and microphones. The acquired data is pre-processed in real time before being sent to the server. It also plays a role in displaying educational content generated from the server to the learner.

[0635] For example, if analysis reveals that a particular student finds audio explanations easier to understand, the device will be configured to present learning content with enhanced audio guidance.

[0636] User (student)

[0637] Users interact with learning content provided through their devices. All data generated when users solve problems or manipulate content is collected again through the device and used for further analysis on the server.

[0638] For example, if a user demonstrates sufficient understanding of a particular task, the device will adjust to allow them to move on to the next stage of learning. By establishing a feedback loop that adapts to the learning progress in this way, it is possible to continuously optimize the learning experience for each individual student.

[0639] Comprehensive operation

[0640] This system combines these elements to enable learning tailored to the individual needs of each student. In particular, real-time information gathering and analysis, and adaptive content generation and delivery contribute to maximizing students' potential. This allows for the comprehensive provision of personalized educational experiences, creating an educational platform that can accommodate students with diverse learning backgrounds.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] The device collects information on the participant's behavior, facial expressions, and voice via various sensors. The collected data is pre-processed, including noise reduction.

[0644] Step 2:

[0645] The terminal sends pre-processed data to the server. The data is encrypted in real time and transferred over a secure network.

[0646] Step 3:

[0647] The server integrates the received data and performs real-time analysis using a multi-layer neural network. This analysis evaluates the learner's learning style, concentration level, interests, and other factors.

[0648] Step 4:

[0649] The server generates educational content optimized for each student based on the analysis results. Here, a generative AI model is used to design content that meets the individual needs of each student.

[0650] Step 5:

[0651] The server sends the generated educational content to the device. Content delivery is in real time, ensuring that the learner's learning experience is not interrupted.

[0652] Step 6:

[0653] The device presents learning content to the learner. Information is delivered in the most effective way for the learner, including audio guidance, visual feedback, and interactive controls.

[0654] Step 7:

[0655] Users interact with the presented content and work on learning tasks. Their responses and results are collected again via the device and used for the next processing cycle.

[0656] Step 8:

[0657] The server receives feedback based on new data from users, updates the content, and adaptively improves it for the next learning cycle. This functions as a continuous learning process.

[0658] (Example 1)

[0659] 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".

[0660] In today's educational environment, there is a need to identify the diverse needs of individual learners in real time and provide them with the optimal educational experience. However, conventional systems have struggled to effectively monitor learners' behavior and status and to flexibly adapt learning content.

[0661] 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.

[0662] In this invention, the server includes information acquisition means for acquiring various types of information, processing means for pre-processing the acquired information and transmitting the data, and analysis means for integrating and analyzing the acquired information in real time. This makes it possible to realize a system that can evaluate the status of individual learners and flexibly generate and provide appropriate educational materials.

[0663] "Information acquisition methods" refer to means of collecting various data such as learners' behavior, facial expressions, and voice.

[0664] "Processing means" refers to methods for pre-processing acquired raw data, such as noise reduction and format conversion, to prepare it for secure transmission to a server.

[0665] "Analysis means" refers to a method for integrating and analyzing transmitted data to evaluate learners' interests, concentration levels, and learning styles.

[0666] "Content generation means" refers to methods for generating educational materials that are optimal for individual learners based on analysis results.

[0667] "Display means" refers to the means of appropriately presenting generated educational materials to learners.

[0668] An "information processing infrastructure" is a foundation for executing various processes on a computer network, including the cloud.

[0669] This invention is a system for providing personalized educational experiences that can meet the diverse needs of learners. This system primarily involves the collaboration of a server, terminal, and user, and utilizes information technology to optimize the learning process.

[0670] The server plays a central role, analyzing various types of information (behavior, facial expressions, voice, etc.) in real time to evaluate the learner's state. Generative AI models are used for analysis, and content is suggested and generated using prompt sentences. An example of such a prompt sentence is, "Suggest activities to improve concentration."

[0671] The device implements information acquisition mechanisms to obtain data from learners. This includes sensors such as cameras and microphones to collect learners' facial expressions and voices in real time. The collected data is preprocessed and sent to a server. The device also displays generated educational content and provides a user interface that learners can interact with.

[0672] Users (learners) interact with individually customized educational content via their devices. Based on the learners' interactions, the server re-analyzes the data to further optimize the learning experience. The learners' actions, such as solving problems or referring to learning materials, are collected by the server as feedback and used to inform educational strategies in the next session.

[0673] This system makes it possible to provide individualized education to each learner, thus addressing the diverse educational needs of today.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The device acquires learner behavior, facial expressions, and voice information in real time using sensors (camera, microphone). For example, it uses the camera to capture the learner's facial expressions and the microphone to collect their voice tone. Inputs include the learner's facial expression data and voice data. Outputs the acquired raw data.

[0677] Step 2:

[0678] The terminal performs preprocessing such as noise reduction and color correction on the acquired raw data. This prepares the data for analysis. The input is the acquired raw data, and the output is processed, well-formed data. Specific operations include filtering to remove background noise from audio data.

[0679] Step 3:

[0680] The terminal sends pre-processed data to the server via a secure protocol. The input is the pre-processed data, and the output is the server's reception status. The operation involves data transfer and verification that there is no leakage.

[0681] Step 4:

[0682] The server analyzes the received data using a generative AI model. At this stage, it generates prompt sentences and evaluates the learner's interest and concentration level. The input is the transmitted well-formed data, and the output is the analysis results and the generated prompt sentences. A specific example of its operation is the generation of a prompt sentence such as, "Suggest an activity to improve concentration."

[0683] Step 5:

[0684] The server generates optimal educational content for learners based on the analysis results. Inputs include analysis results and prompts, and output is customized educational content. A specific example of its operation is adjusting the difficulty level according to the learner's level of concentration.

[0685] Step 6:

[0686] The server sends the generated educational content to the terminal, which then displays it to the learner. The input is the generated content, and the output is the educational material displayed on the terminal. The operation requires the creation of an interface in an easy-to-learn format.

[0687] Step 7:

[0688] Users interact with educational content presented on their devices. For example, a user solves a problem and understands the provided explanation. The input is the presented content, and the output is feedback data such as the user's answers and access logs. Specific actions include entering answers into answer fields and manipulating the content.

[0689] Step 8:

[0690] The server performs more precise analysis based on user feedback data and uses this information to generate content for the next learning session. The input is feedback data, and the output is new analysis results and a subsequent learning plan based on those results. The process involves updating the database and adjusting the analysis algorithm.

[0691] (Application Example 1)

[0692] 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".

[0693] To provide personalized information experiences, detailed information analysis based on users' gaze, facial expressions, and voice is required. However, current systems face the challenge of being unable to analyze this information in real time and immediately present the most suitable information content to users.

[0694] 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.

[0695] In this invention, the server includes a device means for acquiring gaze information, facial expression information, and voice information; a computing device means for integrating and analyzing the acquired information in real time; and a generation device means for generating information content suitable for individual users based on the analysis results. This makes it possible to provide information content optimized for users in real time.

[0696] "Eye-gaze information" refers to data that indicates the direction of a user's gaze and the focus of their viewpoint.

[0697] "Facial expression information" refers to data that shows the user's facial expressions and emotions.

[0698] "Audio information" refers to audio data that records the tone and content of the user's voice.

[0699] "Acquisition device" refers to hardware used to collect eye-tracking information, facial expression information, and voice information.

[0700] A "processing unit" is a computer device used to process and analyze acquired information in real time.

[0701] A "generator" is a device that automatically creates content tailored to individual users based on analysis results.

[0702] A "display device" is hardware used to present generated content to a user visually or audibly.

[0703] The system that realizes this invention includes a server, a terminal, and a user.

[0704] The server is the core device that receives eye-tracking information, facial expression information, and voice information. This information includes gaze direction, emotion, and voice content, and the server analyzes it in real time. A dedicated algorithm is used for analysis, and it is desirable to use a deep learning model such as TensorFlow as a framework. Based on the analysis results, information content optimized for the user is generated. The generated content is adjusted according to the user's interests and transmitted from the server to the terminal.

[0705] The terminal is a device equipped with sensors such as a camera and microphone, which acquires the user's gaze information, facial expression information, and voice information. These devices preprocess the data and then transmit the information to the server. They also support the user experience by displaying content transmitted from the server to the user.

[0706] Users interact with the content provided using their devices. For example, if they are interested in a product, product information is displayed where they look, and an audio explanation of its details is played. In this process, the user's responses are again captured as data, and the system continuously optimizes the content through a feedback loop.

[0707] As a concrete example, in a virtual store, when a user looks at a specific product, its usage instructions and features are displayed on smart glasses. If the user shows interest, additional information about related accessories and usage recipes is also provided. In this process, a generative AI model is used to generate content about the product of interest based on the prompt text.

[0708] Examples of prompt messages include, "Generate detailed information about the product the customer is interested in," or "Generate information about related accessories."

[0709] This makes it possible to instantly provide users with personalized information experiences.

[0710] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0711] Step 1:

[0712] The device acquires eye-tracking, facial expression, and audio information using its camera and microphone. The input consists of real-time video and audio data, which is pre-processed and sent to the server in a specific format. Pre-processing includes noise reduction and data format conversion.

[0713] Step 2:

[0714] The server receives information transmitted from the terminal. Based on the received data, it analyzes the direction of gaze, facial features, and content of speech. A generative AI model is used for the analysis, and machine learning frameworks such as TensorFlow are employed. The output is an analysis result that evaluates the user's interest and level of attention.

[0715] Step 3:

[0716] The server generates informational content tailored to the user based on the analysis results. This generation process involves inputting prompts into a generation AI model, which then creates personalized content considering various factors. The generated content can take the form of text, video, or audio.

[0717] Step 4:

[0718] The server sends the generated content to the terminal. The content sent includes information designed to attract the user's interest and introductions to related products. Targeted content is created using prompt messages.

[0719] Step 5:

[0720] The device displays received content to the user. It provides information to the user in real time using screens and audio. If the user shows interest in a product, it has a function to display further details.

[0721] Step 6:

[0722] The user interacts with the content provided through the device. Their responses (direction of gaze, changes in facial expression, voice input) are then captured again as data on the device.

[0723] Step 7:

[0724] The newly acquired information is sent back from the device to the server, forming a feedback loop. This allows the server to further optimize the content and continuously improve the user experience.

[0725] 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.

[0726] This invention provides a system incorporating an emotion engine to more deeply personalize the learning experience of students. This invention is primarily realized through the interaction of a server, a terminal, and a user. Its specific form is described below.

[0727] server

[0728] The server integrates behavioral information, facial expression information, and voice information transmitted from the terminal, and the emotion engine plays a central role in analyzing the learner's emotional state based on this information. The emotional data recognized by the emotion engine is processed in real time on the server and used to generate appropriate educational content. The server adjusts content according to the user's emotions, for example, by increasing activities that can take advantage of the user's concentration if the user is excited.

[0729] terminal

[0730] The device uses its camera and microphone to collect real-time facial and audio information from the learner. This information is immediately sent to the server and used for sentiment analysis. It also displays educational content generated and transmitted from the server, providing feedback to the user. The device ensures an interactive experience tailored to the learner's situation.

[0731] User (student)

[0732] Users interact with learning content presented through their devices. The learner's emotional state is analyzed by an emotion engine, allowing them to receive a learning experience optimized for their current emotions. For example, if the system determines the user is tired, relaxing sounds or other calming elements are played from the device to reduce stress during learning.

[0733] Overall operation

[0734] This system uses an emotion engine to link learners' emotions with their learning, providing a learning environment that is more tailored to each individual learner. By adjusting educational content in real time based on learners' emotional data, learners can learn at a pace that best suits their own learning style. This maximizes learner effectiveness and enables flexible responses to diverse educational needs.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The device captures the participant's facial expressions with its camera and records their voice with its microphone. The acquired data is temporarily stored on the device.

[0738] Step 2:

[0739] The device transmits stored facial expression and voice information to the server in real time. The data is encrypted and sent via a secure communication protocol.

[0740] Step 3:

[0741] Based on the received data, the server activates an emotion engine to analyze the learner's emotional state. Specifically, the analysis is performed by integrating a facial expression recognition algorithm and a voice analysis algorithm.

[0742] Step 4:

[0743] The server uses the results of the emotion engine analysis to generate educational content optimized for the learner's current emotional state. For example, if a learner is feeling stressed, it will select content that promotes relaxation.

[0744] Step 5:

[0745] The server sends the generated educational content to the terminal. This process is performed in real time, ensuring no delay in the learner's experience.

[0746] Step 6:

[0747] The device presents the received content to the user. It provides an environment that facilitates learning while considering the user's emotions through visual and auditory means.

[0748] Step 7:

[0749] The user learns by interacting with the presented content. During this process, new facial and audio information is collected by the device.

[0750] Step 8:

[0751] The device sends the newly collected data to the server. Based on this information, the server uses the emotion engine to perform further analysis, enabling continuous learning support.

[0752] (Example 2)

[0753] 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".

[0754] In recent years, there has been a growing demand for learning support systems that provide educational content tailored to individual learners. However, conventional systems have struggled to reflect learners' mental states and biometric information in real time, limiting their ability to provide personalized learning experiences. A solution is needed to address this challenge, accurately capture learners' emotional states, and deliver optimized content at the appropriate time.

[0755] 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.

[0756] In this invention, the server includes means for collecting biometric information using a sensing device, information processing means for integrating and analyzing the collected data, and generation means for creating educational materials optimized for the learner's mental state based on the analysis results. This makes it possible to generate and provide optimal learning content that is tailored to the individual psychological state of the learner.

[0757] "Means for collecting biological information using a sensing device" refers to a device that detects a learner's biological responses in real time and captures them as digital information.

[0758] An "information processing device for integrating and analyzing collected data" is a device that combines multiple acquired biometric information into a single dataset and evaluates the learner's mental state using an analysis algorithm.

[0759] A "generating device that creates educational materials optimized for the learner's mental state based on analysis results" is a device that dynamically generates the most suitable educational content for individual learners based on data analyzed by an information processing device.

[0760] A "display device" is a device that presents generated educational materials to learners in a visual or other form, thereby enabling an interactive learning experience.

[0761] A "distributed information processing environment" is an environment that utilizes computing resources on a network, such as cloud computing, to efficiently execute the processes of information collection, analysis, generation, and provision.

[0762] "Dynamic adjustment" means that the system automatically changes the learning pace and content delivery method in a timely manner according to the learner's real-time status.

[0763] This invention is a system that analyzes a learner's biometric information in real time and provides appropriate learning content based on that analysis. This system is primarily realized through the interaction of a server, a terminal, and a user.

[0764] The server plays a central role, receiving and integrating data transmitted from sensing devices and analyzing the learner's emotional state using an emotion engine. This process utilizes a generative AI model, enabling more precise emotional judgments. The analyzed data is used in real-time to generate learning materials, customizing the materials according to the user's mental state. For example, based on the learner's data, a prompt such as "What activity would you recommend for a student experiencing decreased concentration?" is generated, and the AI ​​model suggests the most suitable activity.

[0765] The device functions as a tool that collects learner facial expression and voice data using a camera and microphone. This real-time data is immediately sent to a server for analysis. The device also functions as a display for generated educational content. User feedback is obtained through the device, further enhancing the interactive learning experience.

[0766] Users can interact with personalized learning content displayed on their device. This allows users to enjoy a learning environment optimized for their current mental state. For example, if a learner needs to relax, the device may play appropriate music to reduce learning stress.

[0767] This system leverages a distributed information processing environment to efficiently execute various processes. This improves the accuracy and immediacy of personalized learning, resulting in an optimal learning experience for each learner.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The device uses a camera and microphone to collect learner facial and audio data in real time. This process captures eye movements and facial tension from facial expressions, and voice tone and tempo from audio. Video and audio data are acquired as input, and this data serves as foundational data for the next step.

[0771] Step 2:

[0772] The device immediately transmits the collected data to the server. The server receives this data and integrates it from different data formats. The inputs are video and audio data from the device, and the output is integrated biometric data. This data is used as material for emotion analysis.

[0773] Step 3:

[0774] The server uses integrated biometric data to perform analysis with an emotion engine. Here, a generative AI model is applied to identify the learner's mental state from the data. The input data is integrated biometric data, and the output is an emotion evaluation, for example, "the learner is lacking concentration." Specifically, a deep learning algorithm compares the current state with past data to accurately understand it.

[0775] Step 4:

[0776] The server dynamically generates educational content tailored to the learner's emotions based on the results of emotion analysis. Here too, a generative AI model is utilized to automatically generate content using prompts that correspond to the user's state. The input is the result of emotion analysis, and the output is personalized educational content. For example, if it is determined that the user's concentration is low, relaxing educational materials will be generated.

[0777] Step 5:

[0778] The terminal displays educational content sent from the server to the user and provides feedback. The input is the generated educational content, to which the user reacts. The output is the user's feedback, which the terminal sends back to the server as the basis for new data collection. This enables real-time interaction between the learner and the system.

[0779] (Application Example 2)

[0780] 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".

[0781] In food delivery, providing services tailored to the customer's emotional state can improve the customer experience and increase service satisfaction. However, traditional services can only offer a uniform approach, making it difficult to personalize services to meet the diverse emotions and needs of customers.

[0782] 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.

[0783] In this invention, the server includes a device means for acquiring behavioral data, facial expression data, and voice data; a computing device means for integrating and analyzing the acquired data in real time; and a generation device means for generating service content suitable for individual users based on the analysis results. This makes it possible to provide personalized food delivery services based on the emotional state of the customer.

[0784] "Behavioral data" refers to information about a user's actions and activities, including information about body movements and movement.

[0785] "Facial expression data" refers to information about a user's facial expressions, which is used to infer their emotional state.

[0786] "Voice data" refers to information about the user's speech and tone of voice, and by analyzing this data, it is possible to understand their emotions and intentions.

[0787] "Acquisition device" refers to hardware for collecting behavioral data, facial expression data, and voice data in real time.

[0788] A "computational device for integration and analysis" refers to a computer system that combines acquired data and analyzes it.

[0789] A "generating device" refers to a computer system that creates user-specific services and content based on analyzed data.

[0790] A "display device" refers to a device used to present generated service content to users visually or audibly.

[0791] "Cloud" refers to an environment that provides services that make computer resources and data available via the internet.

[0792] "Continuous monitoring" refers to the process of continuously acquiring user information and constantly evaluating that data in response to changing circumstances.

[0793] "Adaptive adjustment" refers to the act of dynamically changing the content of the services provided based on the information acquired.

[0794] The system implementing this invention collects and analyzes data in real time in order to provide personalized services based on the user's emotional state. Its specific form is described below.

[0795] The server integrates and analyzes information transmitted from devices that acquire user behavior data, facial expression data, and voice data in the cloud. This analysis uses software that acts as an emotion analysis engine (for example, Google Cloud Vision API or Microsoft Azure Face API). This software extracts features from the data collected from cameras and microphones and performs analysis in real time.

[0796] Furthermore, the server generates personalized service content for each individual user based on the analysis results. The generated content is adaptively adjusted according to the user's emotional state and presented via a display device. In this case, the display device is often a smartphone or tablet.

[0797] Considering a scenario where a user orders food delivery, one application would be for the delivery person to analyze the user's facial expressions using their smartphone camera upon arrival at their home to understand their emotional state for the day. Based on these results, the server would offer special service offers or suggest customized menus. For example, if the system determines that the user is tired, it might suggest a complimentary refreshing drink.

[0798] An example of a prompt message to an actual generation AI model is, "Please suggest a refreshing service to provide if the user of this application is determined to be tired." In this way, the present invention makes it possible to provide optimal services in real time, taking into account the individual emotional state of the user.

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The device acquires user behavior data, facial expression data, and voice data. This acquisition uses the smartphone's camera and microphone. Input is image data from the camera and voice data from the microphone, and output is formalized behavior data, facial expression data, and voice data. This data is then transmitted to the server in real time.

[0802] Step 2:

[0803] The server integrates the received behavioral data, facial expression data, and voice data, and performs analysis using a cloud-based emotion analysis engine. The input is data acquired from the terminal, and the output is the analyzed emotional state data. As part of the data processing, features of the user's face are extracted from the image data, and tone and speed are extracted from the voice data.

[0804] Step 3:

[0805] The server generates service content tailored to individual users based on the analysis results. The input is emotional state data, and the output is specialized service content or special offers. Specifically, it uses an AI model to suggest optimal menus and calculate benefits based on emotional state.

[0806] Step 4:

[0807] The server sends the generated service content to the terminal. The input is the service content, and the output is the content of the offer proposed to the user. Specifically, it sends a notification to the user's smartphone and prepares to display the content in the appropriate format.

[0808] Step 5:

[0809] Users react to service content presented through their devices, making selections and placing orders as needed. The input is the displayed service content, and the output is the user's selections and feedback. Specifically, if the user accepts an offer, a coupon applicable to their next order is activated.

[0810] 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.

[0811] 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.

[0812] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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."

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] The following is further disclosed regarding the embodiments described above.

[0832] (Claim 1)

[0833] A device means for acquiring behavioral information, facial expression information, and voice information,

[0834] A computing device means that integrates and analyzes the acquired information in real time,

[0835] A generation device means that generates educational content suitable for individual learners based on the analysis results,

[0836] A system including a display device for presenting generated educational content to learners.

[0837] (Claim 2)

[0838] The system according to claim 1, wherein the processes of information acquisition, integration, analysis, generation, and presentation are performed on the cloud.

[0839] (Claim 3)

[0840] The system according to claim 1, which continuously monitors the behavioral information, facial expression information, and voice information of the student, and thereby adaptively adjusts the learning progress.

[0841] "Example 1"

[0842] (Claim 1)

[0843] Information acquisition means for acquiring various types of information,

[0844] A processing means that preprocesses the acquired information and transmits the data,

[0845] An analysis method that integrates and analyzes acquired information in real time,

[0846] A content generation means that generates educational materials suitable for individual learners based on the analysis results,

[0847] A system including a display means for presenting generated educational materials to learners.

[0848] (Claim 2)

[0849] The system according to claim 1, in which the processes of information acquisition, preprocessing, transmission, analysis, generation, and presentation are performed on an information processing platform.

[0850] (Claim 3)

[0851] The system according to claim 1, which continuously monitors various information about learners and thereby adaptively adjusts the learning experience.

[0852] "Application Example 1"

[0853] (Claim 1)

[0854] A device means for acquiring gaze information, facial expression information, and voice information,

[0855] A computing device means that integrates and analyzes the acquired information in real time,

[0856] A generation device means that generates information content suitable for individual users based on the analysis results,

[0857] A system including a display device that presents generated information content to the user.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the processes of information acquisition, integration, analysis, generation, and presentation are performed on the cloud.

[0860] (Claim 3)

[0861] The system according to claim 1, which continuously monitors the user's eye gaze, facial expressions, and voice information, and adaptively adjusts the information presented accordingly.

[0862] "Example 2 of combining an emotion engine"

[0863] (Claim 1)

[0864] A means for collecting biological information using a sensing device,

[0865] Information processing device means for integrating and analyzing collected data,

[0866] A generation apparatus means for creating educational materials optimized for the learner's mental state based on the analysis results,

[0867] A system including a display device that provides generated educational materials to learners.

[0868] (Claim 2)

[0869] The system according to claim 1, which performs data collection, integration, analysis, and provision of generated materials in a distributed information processing environment.

[0870] (Claim 3)

[0871] The system according to claim 1, which sequentially observes the learner's biological information and dynamically adjusts the learning pace based on the analysis.

[0872] "Application example 2 when combining with an emotional engine"

[0873] (Claim 1)

[0874] A device and means for acquiring behavioral data, facial expression data, and voice data,

[0875] A computing device means for integrating and analyzing acquired data in real time,

[0876] A generation device means that generates service content suitable for individual users based on the analysis results,

[0877] A system including a display device that presents generated service content to the user.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the processes of data acquisition, integration, analysis, generation, and presentation are performed on the cloud.

[0880] (Claim 3)

[0881] The system according to claim 1, which continuously monitors user behavior data, facial expression data, and voice data, and adaptively adjusts the service content accordingly. [Explanation of Symbols]

[0882] 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 device means for acquiring behavioral information, facial expression information, and voice information, A computing device means that integrates and analyzes the acquired information in real time, A generation device means that generates educational content suitable for individual learners based on the analysis results, A system including a display device for presenting generated educational content to learners.

2. The system according to claim 1, wherein the processes of information acquisition, integration, analysis, generation, and presentation are performed on the cloud.

3. The system according to claim 1, which continuously monitors the behavioral information, facial expression information, and voice information of the student, and thereby adaptively adjusts the learning progress.

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