system

Abstract

We provide the system. [Solution] To provide a personalized educational experience, A means for generating learning content based on an individual's profile information using an electronic computer, A means of providing the generated learning content via a display device, A means of evaluating the user's learning results and recording progress, A means of presenting recorded progress information to the administrator, A system that includes this.

Description

Technical Field

[0005]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 conventional education system, there is a problem that it is difficult to provide a personalized educational experience according to the interests, concerns, personalities, and learning styles of individual learners. In particular, when applying a uniform curriculum to children with diverse learning abilities, problems such as a decline in learning motivation and insufficient understanding occur. In addition, since the mechanism for appropriately grasping the progress of learning and providing individual support is insufficient, there is a problem that effective learning support cannot be provided.

Means for Solving the Problems

[0005] This invention proposes a system that provides a personalized educational experience tailored to individual learners by generating learning content based on individual profile information using an electronic computing device. The generated learning content is delivered via a display device, allowing users to progress through learning through stories and virtual experiments based on their personal interests. Furthermore, the user's learning results are automatically evaluated, and progress is recorded. This enables administrators to accurately grasp the progress of each learner and provide appropriate support. In this way, the talents and potential of each individual learner can be maximized.

[0006] A "personalized educational experience" is an educational experience that is customized based on each learner's personal information, interests, and learning style.

[0007] "Electronic computing device" refers to all computer devices used to process data and generate or display information.

[0008] "Profile information" refers to attribute data about an individual, such as the learner's age, interests, and learning style.

[0009] "Learning content" refers to educational materials and tasks provided to enhance learners' knowledge.

[0010] A "display device" refers to a device that visually presents information from an electronic computer to a user.

[0011] "Learning results" refer to the performance or output that a learner achieves in a specific learning task.

[0012] "Progress" is an indicator that shows how much learning a learner has achieved within a specific period of time.

[0013] "Administrator" refers to an individual or organization responsible for operating a learning system and providing support to learners. [Brief explanation of the drawing]

[0014] [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 an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for providing personalized educational experiences tailored to the individual characteristics of each learner. This system primarily functions through the interaction of three parties: a server, a terminal, and a user.

[0036] The server first collects learner profile information, including data such as age, interests, and learning style. Next, the server uses this data to run an algorithm that generates learning content best suited to the learner. This content generation includes stories and virtual experiments related to the learner's interests, creating an engaging and enjoyable learning experience.

[0037] The device then presents the generated content obtained from the server to the learner. The learner can then engage with the stories and tasks presented through this device. For example, if the user selects a story with the theme of "space exploration," they are expected to tackle the mathematical problems presented within it.

[0038] As the learning process progresses, the user inputs their learning results through their device. The device sends these results to a server, which automatically evaluates them. Based on the correct / incorrect judgments, the user's learning progress is then recorded.

[0039] Furthermore, the server can manage learning progress and provide information to administrators in real time as needed. This facilitates the tracking of user progress and the provision of individualized instruction.

[0040] This system can maximize learner growth. Furthermore, providing information to parents and educators ensures timely and appropriate support.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects user profile information. Users access the educational platform and enter basic information such as age, interests, and learning style on the initial setup screen. The server stores this data in a database.

[0044] Step 2:

[0045] The server generates personalized learning content based on the collected profile information. The server uses algorithms to create highly customized learning materials and select themes and topics relevant to the user's interests.

[0046] Step 3:

[0047] The device displays learning content received from the server to the user. Through the device's interface, the user accesses the presented stories and virtual tasks and prepares to engage in learning.

[0048] Step 4:

[0049] Users engage in learning tasks through their devices. They complete tasks instructed according to a story, such as solving math problems or virtually conducting science experiments.

[0050] Step 5:

[0051] The device sends the user's learning results to the server. When the user completes an assignment, the device automatically transmits the results to the server.

[0052] Step 6:

[0053] The server evaluates the submitted learning results. The server automatically determines the results and evaluates whether they are correct or incorrect. This information is immediately updated as a user progress record.

[0054] Step 7:

[0055] The device provides the user with feedback on evaluation results and progress. The user checks their progress on the device and obtains information to proceed to the next step.

[0056] Step 8:

[0057] The server provides progress information to administrators and parents. The server displays data via a dashboard, allowing administrators to monitor each user's learning progress in real time.

[0058] (Example 1)

[0059] 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."

[0060] When providing individualized educational experiences to individual learners, it is necessary to effectively generate learning content that reflects the learner's interests and characteristics, and to efficiently evaluate and manage their learning progress. However, conventional systems struggle to provide such sophisticated individualized support and often rely on general content and evaluation methods, making it difficult to optimally draw out the learner's understanding and interest.

[0061] 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.

[0062] In this invention, the server includes means for collecting personal attribute information using an information processing device, means for generating educational information using prompt sentences in a generative artificial intelligence model based on the collected information, and means for presenting the generated educational information via a visual device. This enables the generation of content and progress evaluation tailored to learners, as well as efficient information provision to administrators.

[0063] "Information processing equipment" is a general term for electronic devices used for collecting, processing, and storing data, and includes devices such as computers and servers.

[0064] "Attribute information" refers to information such as an individual's characteristics, interests, and learning style, and is data collected to understand the individual characteristics of learners.

[0065] A "generative artificial intelligence model" refers to a machine learning model that has the ability to automatically generate information and content based on input text and data.

[0066] A "prompt" is input text used to give specific generation instructions to an artificial intelligence model, and it is a factor that determines the direction of the generated content.

[0067] "Educational information" refers to all content created for educational purposes for learners, and includes a variety of formats such as text, images, videos, and interactive elements.

[0068] "Visual devices" include devices such as displays and screens, and are equipment used to present information visually.

[0069] This invention is an information system that generates and presents educational content tailored to the characteristics of learners in order to provide an individualized educational experience. This system uses an information processing device and a visual device to design the optimal educational experience for the learner.

[0070] The server first collects user attribute information. This information includes information entered by the learner and learning history automatically retrieved from logs. Based on this information, the server uses a generative artificial intelligence model to generate prompt sentences. These prompt sentences function as instructions to the generative AI model to generate content that is suited to the learner's interests and learning style. The server analyzes the collected data, generates prompt sentences using natural language processing software, and sends them to the generative AI model.

[0071] For example, a prompt could be: "Generate math learning content with a space exploration story for a 10-year-old child. The mathematical concepts to be included in the story are basic arithmetic operations and unit conversions." Based on this prompt, a generative AI model (e.g., a large language model) would generate educational information, including a story and virtual experiments tailored to the learner's interests.

[0072] The generated educational information is presented to learners via visual devices. The terminal has the capability to provide this educational information to users in text, images, videos, and interactive formats. Users work on tasks and quizzes presented through the terminal and report their results to the server.

[0073] On the other hand, the evaluation method is executed on the server, and evaluations are automatically performed based on the user's responses and actions. This allows administrators to monitor learners' progress in real time and provide guidance and support as needed. This system not only delivers content, but also dynamically and individually optimizes the educational process through the use of collected data and generative AI.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server collects user attribute information. This information includes data entered by the user via the terminal and data obtained from previous learning history. The attribute information obtained as input includes age, interests, and learning style, and by storing this data in a database, it provides the basic data for the next processing step.

[0077] Step 2:

[0078] The server analyzes the collected attribute information and uses a generative artificial intelligence model to create prompt sentences for generating educational content. At this stage, natural language processing technology is utilized to generate prompt sentences tailored to the user's interests and learning style. Based on the attribute information as input, prompt sentences are generated and sent to the generative AI model, which then produces instructions that form the basis of the educational content as output.

[0079] Step 3:

[0080] The server passes prompts to a generative AI model, which then generates educational content. In this process, the generative AI model creates stories, virtual experiments, and other elements based on the provided prompts, generating educational information tailored to each individual learner. The output consists of specific learning content, optimized for the learner's characteristics.

[0081] Step 4:

[0082] The device receives the generated educational content and presents it to the user via a visual device. The user interface on the device is interactive and designed to allow learners to actively engage with the content. Output includes visually presented content in the form of text, images, videos, and quizzes.

[0083] Step 5:

[0084] Users engage in tasks and answer quizzes based on the presented content. Information entered by users through these activities is transmitted to the server in real time and collected as data. This user activity data is aggregated and used in the next step.

[0085] Step 6:

[0086] The server receives activity results from users and evaluates them using an automated evaluation algorithm. Based on the collected activity data, it calculates whether the answers are correct and the progress status, and generates a progress report. The output records the evaluated results and progress information, which is used as feedback for generating the next learning content.

[0087] Step 7:

[0088] The server manages recorded progress information and provides it to administrators and educators as needed. This allows administrators to gain a comprehensive understanding of learners' progress and provide appropriate guidance. As output, progress reports are generated and presented to administrators in a visualized format.

[0089] (Application Example 1)

[0090] 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."

[0091] To provide each learner with an appropriate learning experience, it is necessary to deliver content that is individually optimized based on the learner's interests and goals. However, with conventional technology, it has been difficult to provide content that matches the individual needs of learners in a timely manner. Furthermore, there was a need to accurately grasp progress based on learning responses and to provide feedback and improve content based on that.

[0092] 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.

[0093] In this invention, the server includes means for selecting learning content based on individual interests, means for providing the selected learning content via an electronic device, and means for analyzing the user's learning response and recording progress. This makes it possible to provide highly personalized educational content that is tailored to each learner's interests and goals.

[0094] "Personal interests" refer to the learner's interest in or preference for a particular theme or field, and these are the criteria used to select content.

[0095] "Learning content" refers to educational materials and experiences provided to learners, and may include videos and interactive elements.

[0096] "Electronic devices" refer to all digital devices used for displaying content or acquiring user learning responses.

[0097] "Selection" refers to the act of choosing the most suitable option from among several choices according to specific criteria.

[0098] "Learning response" refers to the responses and actions that learners show to the content they are given, and is data used to measure their level of understanding and interest.

[0099] "Progress" is an indicator that shows how far a learner has progressed in their learning toward their goal, and it indicates the degree of progress in the learning process.

[0100] "Analysis" refers to a series of processes that involve interpreting collected data and deriving specific insights or conclusions.

[0101] The system for realizing this invention consists of three components: a server, a terminal, and a user. The server collects profile information for each learner and selects learning content based on their individual interests. In this selection process, the server uses a generative AI model to generate content, including stories and virtual experiences that reflect the learner's interests, and provides it to the terminal.

[0102] The terminal receives learning content transmitted from the server and provides it to the user via electronic devices. For example, learning materials, including videos and interactive elements, can be displayed using a smartphone or smart glasses. The user progresses through the learning process using this content. The learner's responses and level of understanding are fed back to the server via the terminal and used for further progress tracking.

[0103] Data processing and calculations are performed based on learner information collected by the server, and are implemented using programming languages ​​such as Python and data analysis libraries. The server can record learner progress based on the collected data, and can represent it graphically or present it as management information.

[0104] For example, if a high school student is interested in science, they can receive and watch videos via their device, such as "virtual experiments to learn about chemical reactions" or "simulated experiences of space exploration."

[0105] An example of a prompt might be, "Please suggest learning content suitable for high school students interested in chemistry." Following this prompt, the system can select and provide the most appropriate content.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server collects learner profile information. Input is user-provided data such as age, interests, and learning style. The server stores this data in a database for analysis. The output is a set of learner profile data.

[0109] Step 2:

[0110] The server uses a generative AI model based on profile information to generate learning content tailored to the learner. The input is the profile data saved in step 1. The server inputs this data into the generative AI model and creates content including interest-based stories and virtual experiences. The output is the generated learning content.

[0111] Step 3:

[0112] The server sends the generated learning content to the device. The input is the generated learning content, which is the output of step 2. The server converts the content into a data format (e.g., JSON or XML) and sends it to the device over the network. The output is the content in the data format received by the device.

[0113] Step 4:

[0114] The device displays the received learning content to the user. The input is content in the data format sent in step 3. The device uses video playback software or an interactive content viewer to convert and display it in a format that the user can understand. The output is content that the user can view or interact with.

[0115] Step 5:

[0116] The user reacts to the learning content. The input is the content displayed in step 4. The user works on the presented tasks, answering quizzes or advancing the story. The output is the user's action data and reaction data.

[0117] Step 6:

[0118] The terminal records user responses and sends them to the server. The input is the user's operation data and response data, which are the output of step 5. The terminal collects this data and sends it to the database. The output is the user feedback stored on the server.

[0119] Step 7:

[0120] The server analyzes user feedback to evaluate learning progress. The input is the user feedback data aggregated in step 6. The server uses an algorithm to analyze the data and record the progress. The output is learning progress data.

[0121] Step 8:

[0122] The server displays and reports learning progress data as management information. The input is the progress data, which is the output of step 7. The server converts this into a dashboard or report format so that administrators can view it. The output is a progress report for administrators.

[0123] 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.

[0124] This invention is an advanced educational system that combines an emotion engine to provide a personalized educational experience for individuals. This system mainly consists of three parties: a server, a terminal, and a user. The emotion engine recognizes the user's emotional state and adjusts the learning experience based on that state.

[0125] The server first collects user profile information and generates learning content optimized for the learner. An emotion engine is installed on the user's device and recognizes emotions from biosensor data and camera footage. For example, the camera analyzes the user's facial expressions, and the emotion engine uses that data to determine states such as "stress" or "concentration."

[0126] As users access learning content through their devices, an emotion engine monitors their emotional state in real time. The server receives this emotion data and adjusts the content according to the user's learning progress and current mental state. For example, if a user is feeling stressed, the device will suggest learning tasks that are more relaxing.

[0127] The user's learning results are sent from the device to the server, and their progress is automatically recorded. The server comprehensively analyzes existing data and emotional data to evaluate the user's learning performance. This enables detailed educational support tailored to the user's emotional state.

[0128] Finally, the device provides users and guardians with feedback based on progress and emotional state. For example, the dashboard may display information such as "User has been concentrating for a long time" or "Stress has persisted for 30 minutes," enabling effective learning support. This system offers a new educational approach that takes learners' interests and emotions into account.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The server collects user profile information. Users log into the system and enter their age, interests, and learning style. The server records this information in a database and uses it to generate learning content.

[0132] Step 2:

[0133] The emotion engine acquires data in real time from the device's camera and biosensors to analyze the user's emotional state. For example, it analyzes facial expressions and heart rate to detect states such as "focused" or "relaxed."

[0134] Step 3:

[0135] The server generates learning content based on analyzed emotion data and profile information. It adjusts the difficulty and content of the tasks according to the user's emotional state, providing optimal content.

[0136] Step 4:

[0137] The device presents the user with generated learning content. The user works on tasks presented in the form of stories or virtual experiments. For example, if the user is tired, a "relaxation mode" story is provided.

[0138] Step 5:

[0139] Users work on the presented tasks and enter their answers into their devices. User input is sent to the server in real time, and results are checked immediately.

[0140] Step 6:

[0141] The server evaluates the user's learning results and automatically records progress. Feedback is generated that takes sentiment data into account, and advice is provided to improve the quality of learning.

[0142] Step 7:

[0143] The device displays feedback to the user regarding evaluation results, progress, and emotional state. Users can understand their own learning patterns and develop strategies for improvement for the next step.

[0144] Step 8:

[0145] The server reports the learner's status to the administrator in real time, based on recorded progress information and sentiment data. This allows the administrator to obtain data to effectively support learning.

[0146] (Example 2)

[0147] 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".

[0148] Traditional education systems have the problem of not being able to fully address individual needs because they cannot take into account the individual emotional states of learners in real time. In particular, they lack the function to understand the stress and concentration levels that learners experience during learning and to dynamically adjust the learning content accordingly. As a result, learner performance is not optimized, and improvements in learning efficiency cannot be expected.

[0149] 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.

[0150] In this invention, the server includes means for generating learning content based on an individual's profile information, means for analyzing the user's emotional state using an emotion recognition device, and means for dynamically adjusting the learning content based on the emotional state. This makes it possible to consider the individual emotional state of each learner in real time and provide an optimal learning experience.

[0151] An "electronic computing device" is a device that performs information input, processing, and output, and includes computers and servers.

[0152] "Profile information" refers to data that shows an individual's basic characteristics, including age, past learning history, and interests.

[0153] "Learning content" refers to information resources such as teaching materials, workbooks, and virtual experiments used for educational purposes.

[0154] A "display device" is a device used to visually display electronic information, and includes monitors and screens.

[0155] An "emotion recognition device" is a device used to analyze a user's emotional state, and it uses cameras and biosensors to determine emotions.

[0156] "Emotional state" refers to the psychological state a user experiences at a particular point in time, and can be classified as "stress" or "concentration," among other things.

[0157] "User learning results" refer to information that shows the achievements a user has made through their learning activities, and include data such as whether the questions were answered correctly or incorrectly and the time taken to answer them.

[0158] An "administrator" refers to a person or organization that has the authority to monitor and control the entire system.

[0159] This advanced education system is implemented with an emotional engine to provide a personalized learning experience for each individual. Its main components are servers, terminals, and users, which enable the system to function.

[0160] The server first collects user profile information. This profile information includes age, learning objectives, and past learning history. The server stores this information using a database and generates learning content tailored to individual needs using a generative AI model. The generated learning content is delivered to the user via a device. The device functions as a learning device and visually presents this content to the user.

[0161] Furthermore, the device is equipped with an emotion engine that analyzes the user's emotional state in real time using cameras and biosensors. The emotion engine determines states such as "stress" and "concentration" based on facial recognition technology and data from sensors. The data acquired from the emotion engine is sent to a server, which dynamically adjusts the learning content to be appropriate based on this data.

[0162] As the user progresses through the learning process, the device sends data to the server based on their learning results and emotional state. The server analyzes this data to evaluate progress and learning performance, and then provides feedback to the user and their guardian. This feedback is presented in a dashboard format and includes information such as "focused learning time exceeded a certain limit" or "stress persisted for an extended period."

[0163] For example, for a student studying mathematics, this system provides a customized set of problems based on their past performance and current learning goals. If the emotion engine detects that the user is stressed, the system will temporarily suggest easier problems to improve the user's mood. An example of such a prompt might be, "Please suggest some easy math problems to help me relax."

[0164] In this way, the system can take into account the user's emotional state and optimize the learning experience.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The server collects user profile information. It obtains the user's age, learning objectives, and past learning history as input, and stores this data in the server's database. This stored data is used as the basis for future learning content generation. Specifically, this process begins when the user enters the necessary information into an input form from their device.

[0168] Step 2:

[0169] The server uses a generative AI model based on the collected profile information to generate learning content tailored to the user. The profile information obtained in step 1 is passed to the AI ​​model as input, and content matching the individual learning needs is generated as output. For example, if a student wants to learn mathematics, math problem sets and learning materials will be generated.

[0170] Step 3:

[0171] The device presents the generated learning content to the user. It receives the learning content, which is output from the server, and displays it on the user's screen. The user can visually check the learning materials and questions on the device and begin learning. Specifically, learning begins when the user starts up the device and opens the designated learning application.

[0172] Step 4:

[0173] The emotion engine built into the device analyzes the user's emotional state. It receives camera footage and biosensor data as input and outputs emotional states such as "stress" and "concentration" through facial recognition and heart rate analysis. Specifically, changes in the user's facial expressions are captured by the camera, and this data is analyzed in real time.

[0174] Step 5:

[0175] The server receives emotional data sent from the terminal and dynamically adjusts the learning content. Using the output emotional state data as input, it adjusts the difficulty and progress of the learning content before returning it to the user. For example, if the server determines that the user is experiencing high levels of stress, it sends prompt messages to an AI model to provide more relaxing content and generates new learning materials.

[0176] Step 6:

[0177] The terminal presents the user with the adjusted learning content received from the server. It receives the adjusted content, which is the output of the server, and displays it again on the user's display. Specifically, after the user finishes the current learning material, a new learning material is automatically displayed as the next step.

[0178] Step 7:

[0179] The device sends learning results and emotional data to the server. It outputs a summary of performance, response time, and emotional state data obtained during the learning process, and inputs it into the server. The server receives this data and evaluates the user's learning progress. Specifically, data is automatically sent from the device to the server when the user completes a learning session.

[0180] Step 8:

[0181] The server provides feedback to users and guardians based on the analyzed data. It adds evaluation results to the input data and outputs feedback on progress and mental health status. Specifically, the server generates visual feedback in a dashboard format and sends it to the device. The device displays this feedback on its screen, making it viewable by users and guardians.

[0182] (Application Example 2)

[0183] 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".

[0184] Traditional learning systems typically provide learning content based on individual profile information, but they lack sufficient mechanisms for presenting content that takes the user's emotional state into account. This has resulted in learning effectiveness being reduced depending on the user's emotional state, leading to an inefficient learning process.

[0185] 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.

[0186] In this invention, the server includes means for generating learning information based on an individual's profile information using an electronic computing device, means for providing the generated learning information via an output device, means for evaluating the user's learning results and recording progress, and means for analyzing the user's emotional state using an emotion recognition device and dynamically adjusting the learning information based on that state. This makes it possible to provide an optimal learning experience according to the user's emotional state and maximize the learning effect.

[0187] An "electronic computing device" is a device that processes information and provides the processing results via an output device, and includes computers.

[0188] "Profile information" refers to data such as attributes, interests, and past learning history unique to each individual user, and serves as the foundation for customizing the individual learning experience.

[0189] "Learning information" refers to the totality of information provided to facilitate user learning, including educational content and learning programs.

[0190] An "output device" is a device used to present information visually or audibly, and includes displays and speakers.

[0191] An "emotion recognition device" is a device equipped with sensors and analysis functions to analyze a user's facial expressions, voice, physical reactions, etc., and determine their emotional state.

[0192] "Dynamic adjustment" refers to changing the content or procedures in real time or immediately in response to specific situations or conditions.

[0193] The system implementing this invention consists of three parties: a server, a terminal, and a user. The server uses a computer to generate appropriate learning information based on the user's profile information. The generated learning information is provided to the user through an output device on the terminal.

[0194] The device is equipped with an emotion recognition device that analyzes the user's facial expressions, voice, and physical reactions, and uses a generative AI model to determine their emotional state in real time. This data is sent to a server, which dynamically adjusts the learning information based on it. For example, if the user is feeling stressed, relaxing content will be selected.

[0195] The user's learning results are sent from the device to the server, where progress is recorded and feedback is provided to both the user and the administrator. This allows the user to visualize their progress and understand what they should focus on next, along with their emotional state.

[0196] For example, when a user is learning about science, they might find the initial content difficult. In this case, the device senses the user's emotions and suggests simpler quizzes or visual videos to make learning easier. This kind of response increases learning efficiency and allows users to continue learning.

[0197] An example of a prompt message for the generative AI model would be: "We have detected that the student's current emotional state is stressed. To reduce stress, please suggest a relaxing science video, followed by a short quiz. If the student maintains their concentration, please continue with a detailed lecture." This instruction would be incorporated based on the content from the server.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The server receives user profile information and generates learning information based on that data. Input includes the user's interests, past learning history, and goals, which are used to generate customized learning programs and content. The generated data is then sent to the device as learning information.

[0201] Step 2:

[0202] The terminal receives learning information sent from the server and displays it on an output device for the user. The input is customized learning information, which is output to an interface that presents it to the user in formats such as video, text, and quizzes.

[0203] Step 3:

[0204] The device analyzes the user's real-time emotional state using an emotion recognition device. It receives the user's facial expressions and voice as input, analyzes them using a generative AI model, and determines the emotional state (e.g., relaxed, focused, stressed). The determination results are sent to a server to obtain feedback for optimizing the user's learning experience.

[0205] Step 4:

[0206] The server analyzes the emotional state data and learning progress obtained from the terminal and dynamically adjusts the learning information. The inputs are the user's emotional state and learning progress, and the difficulty and format of the learning information are changed based on these. The updated learning information is then sent back to the terminal as output.

[0207] Step 5:

[0208] The device receives updated learning information from the server and presents it to the user again. It displays content adjusted based on the user's emotional state to help the user continue learning.

[0209] Step 6:

[0210] When a user completes their learning, the device sends the learning results to the server. The input includes the user's answers, responses, and emotional history. Based on this, the server evaluates the effectiveness of the learning and outputs data to record the progress. The server then uses this data to present the progress status to the user and administrator.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention is a system for providing personalized educational experiences tailored to the individual characteristics of each learner. This system primarily functions through the interaction of three parties: a server, a terminal, and a user.

[0228] The server first collects learner profile information, including data such as age, interests, and learning style. Next, the server uses this data to run an algorithm that generates learning content best suited to the learner. This content generation includes stories and virtual experiments related to the learner's interests, creating an engaging and enjoyable learning experience.

[0229] The device then presents the generated content obtained from the server to the learner. The learner can then engage with the stories and tasks presented through this device. For example, if the user selects a story with the theme of "space exploration," they are expected to tackle the mathematical problems presented within it.

[0230] As the learning process progresses, the user inputs their learning results through their device. The device sends these results to a server, which automatically evaluates them. Based on the correct / incorrect judgments, the user's learning progress is then recorded.

[0231] Furthermore, the server can manage learning progress and provide information to administrators in real time as needed. This facilitates the tracking of user progress and the provision of individualized instruction.

[0232] This system can maximize learner growth. Furthermore, providing information to parents and educators ensures timely and appropriate support.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server collects user profile information. Users access the educational platform and enter basic information such as age, interests, and learning style on the initial setup screen. The server stores this data in a database.

[0236] Step 2:

[0237] The server generates personalized learning content based on the collected profile information. The server uses algorithms to create highly customized learning materials and select themes and topics relevant to the user's interests.

[0238] Step 3:

[0239] The device displays learning content received from the server to the user. Through the device's interface, the user accesses the presented stories and virtual tasks and prepares to engage in learning.

[0240] Step 4:

[0241] Users engage in learning tasks through their devices. They complete tasks instructed according to a story, such as solving math problems or virtually conducting science experiments.

[0242] Step 5:

[0243] The device sends the user's learning results to the server. When the user completes an assignment, the device automatically transmits the results to the server.

[0244] Step 6:

[0245] The server evaluates the submitted learning results. The server automatically determines the results and evaluates whether they are correct or incorrect. This information is immediately updated as a user progress record.

[0246] Step 7:

[0247] The device provides the user with feedback on evaluation results and progress. The user checks their progress on the device and obtains information to proceed to the next step.

[0248] Step 8:

[0249] The server provides progress information to administrators and parents. The server displays data via a dashboard, allowing administrators to monitor each user's learning progress in real time.

[0250] (Example 1)

[0251] 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."

[0252] When providing individualized educational experiences to individual learners, it is necessary to effectively generate learning content that reflects the learner's interests and characteristics, and to efficiently evaluate and manage their learning progress. However, conventional systems struggle to provide such sophisticated individualized support and often rely on general content and evaluation methods, making it difficult to optimally draw out the learner's understanding and interest.

[0253] 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.

[0254] In this invention, the server includes means for collecting personal attribute information using an information processing device, means for generating educational information using prompt sentences in a generative artificial intelligence model based on the collected information, and means for presenting the generated educational information via a visual device. This enables the generation of content and progress evaluation tailored to learners, as well as efficient information provision to administrators.

[0255] "Information processing equipment" is a general term for electronic devices used for collecting, processing, and storing data, and includes devices such as computers and servers.

[0256] "Attribute information" refers to information such as an individual's characteristics, interests, and learning style, and is data collected to understand the individual characteristics of learners.

[0257] A "generative artificial intelligence model" refers to a machine learning model that has the ability to automatically generate information and content based on input text and data.

[0258] A "prompt" is input text used to give specific generation instructions to an artificial intelligence model, and it is a factor that determines the direction of the generated content.

[0259] "Educational information" refers to all content created for educational purposes for learners, and includes a variety of formats such as text, images, videos, and interactive elements.

[0260] "Visual devices" include devices such as displays and screens, and are equipment used to present information visually.

[0261] This invention is an information system that generates and presents educational content tailored to the characteristics of learners in order to provide an individualized educational experience. This system uses an information processing device and a visual device to design the optimal educational experience for the learner.

[0262] The server first collects user attribute information. This information includes information entered by the learner and learning history automatically retrieved from logs. Based on this information, the server uses a generative artificial intelligence model to generate prompt sentences. These prompt sentences function as instructions to the generative AI model to generate content that is suited to the learner's interests and learning style. The server analyzes the collected data, generates prompt sentences using natural language processing software, and sends them to the generative AI model.

[0263] For example, a prompt could be: "Generate math learning content with a space exploration story for a 10-year-old child. The mathematical concepts to be included in the story are basic arithmetic operations and unit conversions." Based on this prompt, a generative AI model (e.g., a large language model) would generate educational information, including a story and virtual experiments tailored to the learner's interests.

[0264] The generated educational information is presented to learners via visual devices. The terminal has the capability to provide this educational information to users in text, images, videos, and interactive formats. Users work on tasks and quizzes presented through the terminal and report their results to the server.

[0265] On the other hand, the evaluation method is executed on the server, and evaluations are automatically performed based on the user's responses and actions. This allows administrators to monitor learners' progress in real time and provide guidance and support as needed. This system not only delivers content, but also dynamically and individually optimizes the educational process through the use of collected data and generative AI.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server collects user attribute information. This information includes data entered by the user via the terminal and data obtained from previous learning history. The attribute information obtained as input includes age, interests, and learning style, and by storing this data in a database, it provides the basic data for the next processing step.

[0269] Step 2:

[0270] The server analyzes the collected attribute information and uses a generative artificial intelligence model to create prompt sentences for generating educational content. At this stage, natural language processing technology is utilized to generate prompt sentences tailored to the user's interests and learning style. Based on the attribute information as input, prompt sentences are generated and sent to the generative AI model, which then produces instructions that form the basis of the educational content as output.

[0271] Step 3:

[0272] The server passes prompts to a generative AI model, which then generates educational content. In this process, the generative AI model creates stories, virtual experiments, and other elements based on the provided prompts, generating educational information tailored to each individual learner. The output consists of specific learning content, optimized for the learner's characteristics.

[0273] Step 4:

[0274] The device receives the generated educational content and presents it to the user via a visual device. The user interface on the device is interactive and designed to allow learners to actively engage with the content. Output includes visually presented content in the form of text, images, videos, and quizzes.

[0275] Step 5:

[0276] Users engage in tasks and answer quizzes based on the presented content. Information entered by users through these activities is transmitted to the server in real time and collected as data. This user activity data is aggregated and used in the next step.

[0277] Step 6:

[0278] The server receives activity results from users and evaluates them using an automated evaluation algorithm. Based on the collected activity data, it calculates whether the answers are correct and the progress status, and generates a progress report. The output records the evaluated results and progress information, which is used as feedback for generating the next learning content.

[0279] Step 7:

[0280] The server manages recorded progress information and provides it to administrators and educators as needed. This allows administrators to gain a comprehensive understanding of learners' progress and provide appropriate guidance. As output, progress reports are generated and presented to administrators in a visualized format.

[0281] (Application Example 1)

[0282] 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."

[0283] In order to provide an appropriate learning experience for each learner, it is necessary to deliver content that is individually optimized based on the learner's interests and goals. However, with conventional technologies, it has been difficult to timely provide content that matches the individual needs of learners. Also, it has been required to accurately grasp progress based on learning reactions and to improve feedback and content based on that.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0285] In this invention, the server includes means for selecting learning content based on an individual's interests, means for providing the selected learning content via an electronic device, and means for analyzing the user's learning reaction and recording progress. Thereby, it becomes possible to provide highly personalized educational content according to the interests and goals of each learner.

[0286] "Individual's interests" refers to the interests and preferences that a learner has regarding a specific theme or field, and is the criterion based on which content is selected.

[0287] "Learning content" means educational materials and experiences provided to learners, and may include videos and interactive elements.

[0288] "Electronic device" refers to all digital devices used for displaying content and acquiring the user's learning reaction.

[0289] "Selection" refers to the act of selecting the optimal one from a plurality of options according to a specific criterion.

[0290] "Learning reaction" refers to the responses and actions shown by a learner to the provided content, and is data for measuring the degree of understanding and interest.

[0291] "Progress" is an indicator that shows how far a learner has progressed in their learning toward their goal, and it indicates the degree of progress in the learning process.

[0292] "Analysis" refers to a series of processes that involve interpreting collected data and deriving specific insights or conclusions.

[0293] The system for realizing this invention consists of three components: a server, a terminal, and a user. The server collects profile information for each learner and selects learning content based on their individual interests. In this selection process, the server uses a generative AI model to generate content, including stories and virtual experiences that reflect the learner's interests, and provides it to the terminal.

[0294] The terminal receives learning content transmitted from the server and provides it to the user via electronic devices. For example, learning materials, including videos and interactive elements, can be displayed using a smartphone or smart glasses. The user progresses through the learning process using this content. The learner's responses and level of understanding are fed back to the server via the terminal and used for further progress tracking.

[0295] Data processing and calculations are performed based on learner information collected by the server, and are implemented using programming languages ​​such as Python and data analysis libraries. The server can record learner progress based on the collected data, and can represent it graphically or present it as management information.

[0296] For example, if a high school student is interested in science, they can receive and watch videos via their device, such as "virtual experiments to learn about chemical reactions" or "simulated experiences of space exploration."

[0297] An example of a prompt might be, "Please suggest learning content suitable for high school students interested in chemistry." Following this prompt, the system can select and provide the most appropriate content.

[0298] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0299] Step 1:

[0300] The server collects learner profile information. The input is data such as age, interests, and learning style provided by the user. The server saves this data in a database in preparation for analysis. The output is a set of learner profile data.

[0301] Step 2:

[0302] The server uses the AI model generated based on the profile information to generate learning content suitable for the learner. The input is the profile data saved in Step 1. The server inputs this data into the generation AI model and creates content including stories and virtual experiences based on interests. The output is the generated learning content.

[0303] Step 3:

[0304] The server transmits the generated learning content to the terminal. The input is the generated learning content, which is the output of Step 2. The server converts the content into a data format (e.g., JSON or XML) and transmits it to the terminal via the network. The output is the content in the data format received by the terminal.

[0305] Step 4: [[ID=3l]]

[0306] The terminal displays the received learning content to the user. The input is the content in the data format transmitted in Step 3. The terminal uses video playback software or an interactive content viewer to convert it into a form understandable by the user and display it. The output is the content that the user views or operates on.

[0307] Step 5:

[0308] The user reacts to the learning content. The input is the content displayed in step 4. The user works on the presented tasks, answering quizzes or advancing the story. The output is the user's action data and reaction data.

[0309] Step 6:

[0310] The terminal records user responses and sends them to the server. The input is the user's operation data and response data, which are the output of step 5. The terminal collects this data and sends it to the database. The output is the user feedback stored on the server.

[0311] Step 7:

[0312] The server analyzes user feedback to evaluate learning progress. The input is the user feedback data aggregated in step 6. The server uses an algorithm to analyze the data and record the progress. The output is learning progress data.

[0313] Step 8:

[0314] The server displays and reports learning progress data as management information. The input is the progress data, which is the output of step 7. The server converts this into a dashboard or report format so that administrators can view it. The output is a progress report for administrators.

[0315] 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.

[0316] This invention is an advanced educational system that combines an emotion engine to provide a personalized educational experience for individuals. This system mainly consists of three parties: a server, a terminal, and a user. The emotion engine recognizes the user's emotional state and adjusts the learning experience based on that state.

[0317] The server first collects user profile information and generates learning content optimized for the learner. An emotion engine is installed on the user's device and recognizes emotions from biosensor data and camera footage. For example, the camera analyzes the user's facial expressions, and the emotion engine uses that data to determine states such as "stress" or "concentration."

[0318] As users access learning content through their devices, an emotion engine monitors their emotional state in real time. The server receives this emotion data and adjusts the content according to the user's learning progress and current mental state. For example, if a user is feeling stressed, the device will suggest learning tasks that are more relaxing.

[0319] The user's learning results are sent from the device to the server, and their progress is automatically recorded. The server comprehensively analyzes existing data and emotional data to evaluate the user's learning performance. This enables detailed educational support tailored to the user's emotional state.

[0320] Finally, the device provides users and guardians with feedback based on progress and emotional state. For example, the dashboard may display information such as "User has been concentrating for a long time" or "Stress has persisted for 30 minutes," enabling effective learning support. This system offers a new educational approach that takes learners' interests and emotions into account.

[0321] The following describes the processing flow.

[0322] Step 1:

[0323] The server collects user profile information. Users log into the system and enter their age, interests, and learning style. The server records this information in a database and uses it to generate learning content.

[0324] Step 2:

[0325] The emotion engine acquires data in real time from the device's camera and biosensors to analyze the user's emotional state. For example, it analyzes facial expressions and heart rate to detect states such as "focused" or "relaxed."

[0326] Step 3:

[0327] The server generates learning content based on analyzed emotion data and profile information. It adjusts the difficulty and content of the tasks according to the user's emotional state, providing optimal content.

[0328] Step 4:

[0329] The device presents the user with generated learning content. The user works on tasks presented in the form of stories or virtual experiments. For example, if the user is tired, a "relaxation mode" story is provided.

[0330] Step 5:

[0331] Users work on the presented tasks and enter their answers into their devices. User input is sent to the server in real time, and results are checked immediately.

[0332] Step 6:

[0333] The server evaluates the user's learning results and automatically records progress. Feedback is generated that takes sentiment data into account, and advice is provided to improve the quality of learning.

[0334] Step 7:

[0335] The device displays feedback to the user regarding evaluation results, progress, and emotional state. Users can understand their own learning patterns and develop strategies for improvement for the next step.

[0336] Step 8:

[0337] The server reports the learner's status to the administrator in real time, based on recorded progress information and sentiment data. This allows the administrator to obtain data to effectively support learning.

[0338] (Example 2)

[0339] 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".

[0340] Traditional education systems have the problem of not being able to fully address individual needs because they cannot take into account the individual emotional states of learners in real time. In particular, they lack the function to understand the stress and concentration levels that learners experience during learning and to dynamically adjust the learning content accordingly. As a result, learner performance is not optimized, and improvements in learning efficiency cannot be expected.

[0341] 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.

[0342] In this invention, the server includes means for generating learning content based on an individual's profile information, means for analyzing the user's emotional state using an emotion recognition device, and means for dynamically adjusting the learning content based on the emotional state. This makes it possible to consider the individual emotional state of each learner in real time and provide an optimal learning experience.

[0343] An "electronic computing device" is a device that performs information input, processing, and output, and includes computers and servers.

[0344] "Profile information" refers to data that shows an individual's basic characteristics, including age, past learning history, and interests.

[0345] "Learning content" refers to information resources such as teaching materials, workbooks, and virtual experiments used for educational purposes.

[0346] A "display device" is a device used to visually display electronic information, and includes monitors and screens.

[0347] An "emotion recognition device" is a device used to analyze a user's emotional state, and it uses cameras and biosensors to determine emotions.

[0348] "Emotional state" refers to the psychological state a user experiences at a particular point in time, and can be classified as "stress" or "concentration," among other things.

[0349] "User learning results" refer to information that shows the achievements a user has made through their learning activities, and include data such as whether the questions were answered correctly or incorrectly and the time taken to answer them.

[0350] An "administrator" refers to a person or organization that has the authority to monitor and control the entire system.

[0351] This advanced education system is implemented with an emotional engine to provide a personalized learning experience for each individual. Its main components are servers, terminals, and users, which enable the system to function.

[0352] The server first collects user profile information. This profile information includes age, learning objectives, and past learning history. The server stores this information using a database and generates learning content tailored to individual needs using a generative AI model. The generated learning content is delivered to the user via a device. The device functions as a learning device and visually presents this content to the user.

[0353] Furthermore, the device is equipped with an emotion engine that analyzes the user's emotional state in real time using cameras and biosensors. The emotion engine determines states such as "stress" and "concentration" based on facial recognition technology and data from sensors. The data acquired from the emotion engine is sent to a server, which dynamically adjusts the learning content to be appropriate based on this data.

[0354] As the user progresses through the learning process, the device sends data to the server based on their learning results and emotional state. The server analyzes this data to evaluate progress and learning performance, and then provides feedback to the user and their guardian. This feedback is presented in a dashboard format and includes information such as "focused learning time exceeded a certain limit" or "stress persisted for an extended period."

[0355] For example, for a student studying mathematics, this system provides a customized set of problems based on their past performance and current learning goals. If the emotion engine detects that the user is stressed, the system will temporarily suggest easier problems to improve the user's mood. An example of such a prompt might be, "Please suggest some easy math problems to help me relax."

[0356] In this way, the system can take into account the user's emotional state and optimize the learning experience.

[0357] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0358] Step 1:

[0359] The server collects user profile information. It obtains the user's age, learning objectives, and past learning history as input, and stores this data in the server's database. This stored data is used as the basis for future learning content generation. Specifically, this process begins when the user enters the necessary information into an input form from their device.

[0360] Step 2:

[0361] The server uses a generative AI model based on the collected profile information to generate learning content tailored to the user. The profile information obtained in step 1 is passed to the AI ​​model as input, and content matching the individual learning needs is generated as output. For example, if a student wants to learn mathematics, math problem sets and learning materials will be generated.

[0362] Step 3:

[0363] The device presents the generated learning content to the user. It receives the learning content, which is output from the server, and displays it on the user's screen. The user can visually check the learning materials and questions on the device and begin learning. Specifically, learning begins when the user starts up the device and opens the designated learning application.

[0364] Step 4:

[0365] The emotion engine built into the device analyzes the user's emotional state. It receives camera footage and biosensor data as input and outputs emotional states such as "stress" and "concentration" through facial recognition and heart rate analysis. Specifically, changes in the user's facial expressions are captured by the camera, and this data is analyzed in real time.

[0366] Step 5:

[0367] The server receives emotional data sent from the terminal and dynamically adjusts the learning content. Using the output emotional state data as input, it adjusts the difficulty and progress of the learning content before returning it to the user. For example, if the server determines that the user is experiencing high levels of stress, it sends prompt messages to an AI model to provide more relaxing content and generates new learning materials.

[0368] Step 6:

[0369] The terminal presents the user with the adjusted learning content received from the server. It receives the adjusted content, which is the output of the server, and displays it again on the user's display. Specifically, after the user finishes the current learning material, a new learning material is automatically displayed as the next step.

[0370] Step 7:

[0371] The device sends learning results and emotional data to the server. It outputs a summary of performance, response time, and emotional state data obtained during the learning process, and inputs it into the server. The server receives this data and evaluates the user's learning progress. Specifically, data is automatically sent from the device to the server when the user completes a learning session.

[0372] Step 8:

[0373] The server provides feedback to users and guardians based on the analyzed data. It adds evaluation results to the input data and outputs feedback on progress and mental health status. Specifically, the server generates visual feedback in a dashboard format and sends it to the device. The device displays this feedback on its screen, making it viewable by users and guardians.

[0374] (Application Example 2)

[0375] 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."

[0376] Traditional learning systems typically provide learning content based on individual profile information, but they lack sufficient mechanisms for presenting content that takes the user's emotional state into account. This has resulted in learning effectiveness being reduced depending on the user's emotional state, leading to an inefficient learning process.

[0377] 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.

[0378] In this invention, the server includes means for generating learning information based on an individual's profile information using an electronic computing device, means for providing the generated learning information via an output device, means for evaluating the user's learning results and recording progress, and means for analyzing the user's emotional state using an emotion recognition device and dynamically adjusting the learning information based on that state. This makes it possible to provide an optimal learning experience according to the user's emotional state and maximize the learning effect.

[0379] An "electronic computing device" is a device that processes information and provides the processing results via an output device, and includes computers.

[0380] "Profile information" refers to data such as attributes, interests, and past learning history unique to each individual user, and serves as the foundation for customizing the individual learning experience.

[0381] "Learning information" refers to the totality of information provided to facilitate user learning, including educational content and learning programs.

[0382] An "output device" is a device used to present information visually or audibly, and includes displays and speakers.

[0383] An "emotion recognition device" is a device equipped with sensors and analysis functions to analyze a user's facial expressions, voice, physical reactions, etc., and determine their emotional state.

[0384] "Dynamic adjustment" refers to changing the content or procedures in real time or immediately in response to specific situations or conditions.

[0385] The system implementing this invention consists of three parties: a server, a terminal, and a user. The server uses a computer to generate appropriate learning information based on the user's profile information. The generated learning information is provided to the user through an output device on the terminal.

[0386] The device is equipped with an emotion recognition device that analyzes the user's facial expressions, voice, and physical reactions, and uses a generative AI model to determine their emotional state in real time. This data is sent to a server, which dynamically adjusts the learning information based on it. For example, if the user is feeling stressed, relaxing content will be selected.

[0387] The user's learning results are sent from the device to the server, where progress is recorded and feedback is provided to both the user and the administrator. This allows the user to visualize their progress and understand what they should focus on next, along with their emotional state.

[0388] For example, when a user is learning about science, they might find the initial content difficult. In this case, the device senses the user's emotions and suggests simpler quizzes or visual videos to make learning easier. This kind of response increases learning efficiency and allows users to continue learning.

[0389] An example of a prompt message for the generative AI model would be: "We have detected that the student's current emotional state is stressed. To reduce stress, please suggest a relaxing science video, followed by a short quiz. If the student maintains their concentration, please continue with a detailed lecture." This instruction would be incorporated based on the content from the server.

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The server receives user profile information and generates learning information based on that data. Input includes the user's interests, past learning history, and goals, which are used to generate customized learning programs and content. The generated data is then sent to the device as learning information.

[0393] Step 2:

[0394] The terminal receives learning information sent from the server and displays it on an output device for the user. The input is customized learning information, which is output to an interface that presents it to the user in formats such as video, text, and quizzes.

[0395] Step 3:

[0396] The device analyzes the user's real-time emotional state using an emotion recognition device. It receives the user's facial expressions and voice as input, analyzes them using a generative AI model, and determines the emotional state (e.g., relaxed, focused, stressed). The determination results are sent to a server to obtain feedback for optimizing the user's learning experience.

[0397] Step 4:

[0398] The server analyzes the emotional state data and learning progress obtained from the terminal and dynamically adjusts the learning information. The inputs are the user's emotional state and learning progress, and the difficulty and format of the learning information are changed based on these. The updated learning information is then sent back to the terminal as output.

[0399] Step 5:

[0400] The device receives updated learning information from the server and presents it to the user again. It displays content adjusted based on the user's emotional state to help the user continue learning.

[0401] Step 6:

[0402] When a user completes their learning, the device sends the learning results to the server. The input includes the user's answers, responses, and emotional history. Based on this, the server evaluates the effectiveness of the learning and outputs data to record the progress. The server then uses this data to present the progress status to the user and administrator.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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".

[0419] This invention is a system for providing personalized educational experiences tailored to the individual characteristics of each learner. This system primarily functions through the interaction of three parties: a server, a terminal, and a user.

[0420] The server first collects learner profile information, including data such as age, interests, and learning style. Next, the server uses this data to run an algorithm that generates learning content best suited to the learner. This content generation includes stories and virtual experiments related to the learner's interests, creating an engaging and enjoyable learning experience.

[0421] The device then presents the generated content obtained from the server to the learner. The learner can then engage with the stories and tasks presented through this device. For example, if the user selects a story with the theme of "space exploration," they are expected to tackle the mathematical problems presented within it.

[0422] As the learning process progresses, the user inputs their learning results through their device. The device sends these results to a server, which automatically evaluates them. Based on the correct / incorrect judgments, the user's learning progress is then recorded.

[0423] Furthermore, the server can manage learning progress and provide information to administrators in real time as needed. This facilitates the tracking of user progress and the provision of individualized instruction.

[0424] This system can maximize learner growth. Furthermore, providing information to parents and educators ensures timely and appropriate support.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] The server collects user profile information. Users access the educational platform and enter basic information such as age, interests, and learning style on the initial setup screen. The server stores this data in a database.

[0428] Step 2:

[0429] The server generates personalized learning content based on the collected profile information. The server uses algorithms to create highly customized learning materials and select themes and topics relevant to the user's interests.

[0430] Step 3:

[0431] The device displays learning content received from the server to the user. Through the device's interface, the user accesses the presented stories and virtual tasks and prepares to engage in learning.

[0432] Step 4:

[0433] Users engage in learning tasks through their devices. They complete tasks instructed according to a story, such as solving math problems or virtually conducting science experiments.

[0434] Step 5:

[0435] The device sends the user's learning results to the server. When the user completes an assignment, the device automatically transmits the results to the server.

[0436] Step 6:

[0437] The server evaluates the submitted learning results. The server automatically determines the results and evaluates whether they are correct or incorrect. This information is immediately updated as a user progress record.

[0438] Step 7:

[0439] The device provides the user with feedback on evaluation results and progress. The user checks their progress on the device and obtains information to proceed to the next step.

[0440] Step 8:

[0441] The server provides progress information to administrators and parents. The server displays data via a dashboard, allowing administrators to monitor each user's learning progress in real time.

[0442] (Example 1)

[0443] 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."

[0444] When providing individualized educational experiences to individual learners, it is necessary to effectively generate learning content that reflects the learner's interests and characteristics, and to efficiently evaluate and manage their learning progress. However, conventional systems struggle to provide such sophisticated individualized support and often rely on general content and evaluation methods, making it difficult to optimally draw out the learner's understanding and interest.

[0445] 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.

[0446] In this invention, the server includes means for collecting personal attribute information using an information processing device, means for generating educational information using prompt sentences in a generative artificial intelligence model based on the collected information, and means for presenting the generated educational information via a visual device. This enables the generation of content and progress evaluation tailored to learners, as well as efficient information provision to administrators.

[0447] "Information processing equipment" is a general term for electronic devices used for collecting, processing, and storing data, and includes devices such as computers and servers.

[0448] "Attribute information" refers to information such as an individual's characteristics, interests, and learning style, and is data collected to understand the individual characteristics of learners.

[0449] A "generative artificial intelligence model" refers to a machine learning model that has the ability to automatically generate information and content based on input text and data.

[0450] A "prompt" is input text used to give specific generation instructions to an artificial intelligence model, and it is a factor that determines the direction of the generated content.

[0451] "Educational information" refers to all content created for educational purposes for learners, and includes a variety of formats such as text, images, videos, and interactive elements.

[0452] "Visual devices" include devices such as displays and screens, and are equipment used to present information visually.

[0453] This invention is an information system that generates and presents educational content tailored to the characteristics of learners in order to provide an individualized educational experience. This system uses an information processing device and a visual device to design the optimal educational experience for the learner.

[0454] The server first collects user attribute information. This information includes information entered by the learner and learning history automatically retrieved from logs. Based on this information, the server uses a generative artificial intelligence model to generate prompt sentences. These prompt sentences function as instructions to the generative AI model to generate content that is suited to the learner's interests and learning style. The server analyzes the collected data, generates prompt sentences using natural language processing software, and sends them to the generative AI model.

[0455] For example, a prompt could be: "Generate math learning content with a space exploration story for a 10-year-old child. The mathematical concepts to be included in the story are basic arithmetic operations and unit conversions." Based on this prompt, a generative AI model (e.g., a large language model) would generate educational information, including a story and virtual experiments tailored to the learner's interests.

[0456] The generated educational information is presented to learners via visual devices. The terminal has the capability to provide this educational information to users in text, images, videos, and interactive formats. Users work on tasks and quizzes presented through the terminal and report their results to the server.

[0457] On the other hand, the evaluation method is executed on the server, and evaluations are automatically performed based on the user's responses and actions. This allows administrators to monitor learners' progress in real time and provide guidance and support as needed. This system not only delivers content, but also dynamically and individually optimizes the educational process through the use of collected data and generative AI.

[0458] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0459] Step 1:

[0460] The server collects user attribute information. This information includes data entered by the user via the terminal and data obtained from previous learning history. The attribute information obtained as input includes age, interests, and learning style, and by storing this data in a database, it provides the basic data for the next processing step.

[0461] Step 2:

[0462] The server analyzes the collected attribute information and uses a generative artificial intelligence model to create prompt sentences for generating educational content. At this stage, natural language processing technology is utilized to generate prompt sentences tailored to the user's interests and learning style. Based on the attribute information as input, prompt sentences are generated and sent to the generative AI model, which then produces instructions that form the basis of the educational content as output.

[0463] Step 3:

[0464] The server passes prompts to a generative AI model, which then generates educational content. In this process, the generative AI model creates stories, virtual experiments, and other elements based on the provided prompts, generating educational information tailored to each individual learner. The output consists of specific learning content, optimized for the learner's characteristics.

[0465] Step 4:

[0466] The device receives the generated educational content and presents it to the user via a visual device. The user interface on the device is interactive and designed to allow learners to actively engage with the content. Output includes visually presented content in the form of text, images, videos, and quizzes.

[0467] Step 5:

[0468] Users engage in tasks and answer quizzes based on the presented content. Information entered by users through these activities is transmitted to the server in real time and collected as data. This user activity data is aggregated and used in the next step.

[0469] Step 6:

[0470] The server receives activity results from users and evaluates them using an automated evaluation algorithm. Based on the collected activity data, it calculates whether the answers are correct and the progress status, and generates a progress report. The output records the evaluated results and progress information, which is used as feedback for generating the next learning content.

[0471] Step 7:

[0472] The server manages recorded progress information and provides it to administrators and educators as needed. This allows administrators to gain a comprehensive understanding of learners' progress and provide appropriate guidance. As output, progress reports are generated and presented to administrators in a visualized format.

[0473] (Application Example 1)

[0474] 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."

[0475] To provide each learner with an appropriate learning experience, it is necessary to deliver content that is individually optimized based on the learner's interests and goals. However, with conventional technology, it has been difficult to provide content that matches the individual needs of learners in a timely manner. Furthermore, there was a need to accurately grasp progress based on learning responses and to provide feedback and improve content based on that.

[0476] 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.

[0477] In this invention, the server includes means for selecting learning content based on individual interests, means for providing the selected learning content via an electronic device, and means for analyzing the user's learning response and recording progress. This makes it possible to provide highly personalized educational content that is tailored to each learner's interests and goals.

[0478] "Personal interests" refer to the learner's interest in or preference for a particular theme or field, and these are the criteria used to select content.

[0479] "Learning content" refers to educational materials and experiences provided to learners, and may include videos and interactive elements.

[0480] "Electronic devices" refer to all digital devices used for displaying content or acquiring user learning responses.

[0481] "Selection" refers to the act of choosing the most suitable option from among several choices according to specific criteria.

[0482] "Learning response" refers to the responses and actions that learners show to the content they are given, and is data used to measure their level of understanding and interest.

[0483] "Progress" is an indicator that shows how far a learner has progressed in their learning toward their goal, and it indicates the degree of progress in the learning process.

[0484] "Analysis" refers to a series of processes that involve interpreting collected data and deriving specific insights or conclusions.

[0485] The system for realizing this invention consists of three components: a server, a terminal, and a user. The server collects profile information for each learner and selects learning content based on their individual interests. In this selection process, the server uses a generative AI model to generate content, including stories and virtual experiences that reflect the learner's interests, and provides it to the terminal.

[0486] The terminal receives learning content transmitted from the server and provides it to the user via electronic devices. For example, learning materials, including videos and interactive elements, can be displayed using a smartphone or smart glasses. The user progresses through the learning process using this content. The learner's responses and level of understanding are fed back to the server via the terminal and used for further progress tracking.

[0487] Data processing and calculations are performed based on learner information collected by the server, and are implemented using programming languages ​​such as Python and data analysis libraries. The server can record learner progress based on the collected data, and can represent it graphically or present it as management information.

[0488] For example, if a high school student is interested in science, they can receive and watch videos via their device, such as "virtual experiments to learn about chemical reactions" or "simulated experiences of space exploration."

[0489] An example of a prompt might be, "Please suggest learning content suitable for high school students interested in chemistry." Following this prompt, the system can select and provide the most appropriate content.

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The server collects learner profile information. Input is user-provided data such as age, interests, and learning style. The server stores this data in a database for analysis. The output is a set of learner profile data.

[0493] Step 2:

[0494] The server uses a generative AI model based on profile information to generate learning content tailored to the learner. The input is the profile data saved in step 1. The server inputs this data into the generative AI model and creates content including interest-based stories and virtual experiences. The output is the generated learning content.

[0495] Step 3:

[0496] The server sends the generated learning content to the device. The input is the generated learning content, which is the output of step 2. The server converts the content into a data format (e.g., JSON or XML) and sends it to the device over the network. The output is the content in the data format received by the device.

[0497] Step 4:

[0498] The device displays the received learning content to the user. The input is content in the data format sent in step 3. The device uses video playback software or an interactive content viewer to convert and display it in a format that the user can understand. The output is content that the user can view or interact with.

[0499] Step 5:

[0500] The user reacts to the learning content. The input is the content displayed in step 4. The user works on the presented tasks, answering quizzes or advancing the story. The output is the user's action data and reaction data.

[0501] Step 6:

[0502] The terminal records user responses and sends them to the server. The input is the user's operation data and response data, which are the output of step 5. The terminal collects this data and sends it to the database. The output is the user feedback stored on the server.

[0503] Step 7:

[0504] The server analyzes user feedback to evaluate learning progress. The input is the user feedback data aggregated in step 6. The server uses an algorithm to analyze the data and record the progress. The output is learning progress data.

[0505] Step 8:

[0506] The server displays and reports learning progress data as management information. The input is the progress data, which is the output of step 7. The server converts this into a dashboard or report format so that administrators can view it. The output is a progress report for administrators.

[0507] 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.

[0508] This invention is an advanced educational system that combines an emotion engine to provide a personalized educational experience for individuals. This system mainly consists of three parties: a server, a terminal, and a user. The emotion engine recognizes the user's emotional state and adjusts the learning experience based on that state.

[0509] The server first collects user profile information and generates learning content optimized for the learner. An emotion engine is installed on the user's device and recognizes emotions from biosensor data and camera footage. For example, the camera analyzes the user's facial expressions, and the emotion engine uses that data to determine states such as "stress" or "concentration."

[0510] As users access learning content through their devices, an emotion engine monitors their emotional state in real time. The server receives this emotion data and adjusts the content according to the user's learning progress and current mental state. For example, if a user is feeling stressed, the device will suggest learning tasks that are more relaxing.

[0511] The user's learning results are sent from the device to the server, and their progress is automatically recorded. The server comprehensively analyzes existing data and emotional data to evaluate the user's learning performance. This enables detailed educational support tailored to the user's emotional state.

[0512] Finally, the device provides users and guardians with feedback based on progress and emotional state. For example, the dashboard may display information such as "User has been concentrating for a long time" or "Stress has persisted for 30 minutes," enabling effective learning support. This system offers a new educational approach that takes learners' interests and emotions into account.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The server collects user profile information. Users log into the system and enter their age, interests, and learning style. The server records this information in a database and uses it to generate learning content.

[0516] Step 2:

[0517] The emotion engine acquires data in real time from the device's camera and biosensors to analyze the user's emotional state. For example, it analyzes facial expressions and heart rate to detect states such as "focused" or "relaxed."

[0518] Step 3:

[0519] The server generates learning content based on analyzed emotion data and profile information. It adjusts the difficulty and content of the tasks according to the user's emotional state, providing optimal content.

[0520] Step 4:

[0521] The device presents the user with generated learning content. The user works on tasks presented in the form of stories or virtual experiments. For example, if the user is tired, a "relaxation mode" story is provided.

[0522] Step 5:

[0523] Users work on the presented tasks and enter their answers into their devices. User input is sent to the server in real time, and results are checked immediately.

[0524] Step 6:

[0525] The server evaluates the user's learning results and automatically records progress. Feedback is generated that takes sentiment data into account, and advice is provided to improve the quality of learning.

[0526] Step 7:

[0527] The device displays feedback to the user regarding evaluation results, progress, and emotional state. Users can understand their own learning patterns and develop strategies for improvement for the next step.

[0528] Step 8:

[0529] The server reports the learner's status to the administrator in real time, based on recorded progress information and sentiment data. This allows the administrator to obtain data to effectively support learning.

[0530] (Example 2)

[0531] 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."

[0532] Traditional education systems have the problem of not being able to fully address individual needs because they cannot take into account the individual emotional states of learners in real time. In particular, they lack the function to understand the stress and concentration levels that learners experience during learning and to dynamically adjust the learning content accordingly. As a result, learner performance is not optimized, and improvements in learning efficiency cannot be expected.

[0533] 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.

[0534] In this invention, the server includes means for generating learning content based on an individual's profile information, means for analyzing the user's emotional state using an emotion recognition device, and means for dynamically adjusting the learning content based on the emotional state. This makes it possible to consider the individual emotional state of each learner in real time and provide an optimal learning experience.

[0535] An "electronic computing device" is a device that performs information input, processing, and output, and includes computers and servers.

[0536] "Profile information" refers to data that shows an individual's basic characteristics, including age, past learning history, and interests.

[0537] "Learning content" refers to information resources such as teaching materials, workbooks, and virtual experiments used for educational purposes.

[0538] A "display device" is a device used to visually display electronic information, and includes monitors and screens.

[0539] An "emotion recognition device" is a device used to analyze a user's emotional state, and it uses cameras and biosensors to determine emotions.

[0540] "Emotional state" refers to the psychological state a user experiences at a particular point in time, and can be classified as "stress" or "concentration," among other things.

[0541] "User learning results" refer to information that shows the achievements a user has made through their learning activities, and include data such as whether the questions were answered correctly or incorrectly and the time taken to answer them.

[0542] An "administrator" refers to a person or organization that has the authority to monitor and control the entire system.

[0543] This advanced education system is implemented with an emotional engine to provide a personalized learning experience for each individual. Its main components are servers, terminals, and users, which enable the system to function.

[0544] The server first collects user profile information. This profile information includes age, learning objectives, and past learning history. The server stores this information using a database and generates learning content tailored to individual needs using a generative AI model. The generated learning content is delivered to the user via a device. The device functions as a learning device and visually presents this content to the user.

[0545] Furthermore, the device is equipped with an emotion engine that analyzes the user's emotional state in real time using cameras and biosensors. The emotion engine determines states such as "stress" and "concentration" based on facial recognition technology and data from sensors. The data acquired from the emotion engine is sent to a server, which dynamically adjusts the learning content to be appropriate based on this data.

[0546] As the user progresses through the learning process, the device sends data to the server based on their learning results and emotional state. The server analyzes this data to evaluate progress and learning performance, and then provides feedback to the user and their guardian. This feedback is presented in a dashboard format and includes information such as "focused learning time exceeded a certain limit" or "stress persisted for an extended period."

[0547] For example, for a student studying mathematics, this system provides a customized set of problems based on their past performance and current learning goals. If the emotion engine detects that the user is stressed, the system will temporarily suggest easier problems to improve the user's mood. An example of such a prompt might be, "Please suggest some easy math problems to help me relax."

[0548] In this way, the system can take into account the user's emotional state and optimize the learning experience.

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] The server collects user profile information. It obtains the user's age, learning objectives, and past learning history as input, and stores this data in the server's database. This stored data is used as the basis for future learning content generation. Specifically, this process begins when the user enters the necessary information into an input form from their device.

[0552] Step 2:

[0553] The server uses a generative AI model based on the collected profile information to generate learning content tailored to the user. The profile information obtained in step 1 is passed to the AI ​​model as input, and content matching the individual learning needs is generated as output. For example, if a student wants to learn mathematics, math problem sets and learning materials will be generated.

[0554] Step 3:

[0555] The device presents the generated learning content to the user. It receives the learning content, which is output from the server, and displays it on the user's screen. The user can visually check the learning materials and questions on the device and begin learning. Specifically, learning begins when the user starts up the device and opens the designated learning application.

[0556] Step 4:

[0557] The emotion engine built into the device analyzes the user's emotional state. It receives camera footage and biosensor data as input and outputs emotional states such as "stress" and "concentration" through facial recognition and heart rate analysis. Specifically, changes in the user's facial expressions are captured by the camera, and this data is analyzed in real time.

[0558] Step 5:

[0559] The server receives emotional data sent from the terminal and dynamically adjusts the learning content. Using the output emotional state data as input, it adjusts the difficulty and progress of the learning content before returning it to the user. For example, if the server determines that the user is experiencing high levels of stress, it sends prompt messages to an AI model to provide more relaxing content and generates new learning materials.

[0560] Step 6:

[0561] The terminal presents the user with the adjusted learning content received from the server. It receives the adjusted content, which is the output of the server, and displays it again on the user's display. Specifically, after the user finishes the current learning material, a new learning material is automatically displayed as the next step.

[0562] Step 7:

[0563] The device sends learning results and emotional data to the server. It outputs a summary of performance, response time, and emotional state data obtained during the learning process, and inputs it into the server. The server receives this data and evaluates the user's learning progress. Specifically, data is automatically sent from the device to the server when the user completes a learning session.

[0564] Step 8:

[0565] The server provides feedback to users and guardians based on the analyzed data. It adds evaluation results to the input data and outputs feedback on progress and mental health status. Specifically, the server generates visual feedback in a dashboard format and sends it to the device. The device displays this feedback on its screen, making it viewable by users and guardians.

[0566] (Application Example 2)

[0567] 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."

[0568] Traditional learning systems typically provide learning content based on individual profile information, but they lack sufficient mechanisms for presenting content that takes the user's emotional state into account. This has resulted in learning effectiveness being reduced depending on the user's emotional state, leading to an inefficient learning process.

[0569] 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.

[0570] In this invention, the server includes means for generating learning information based on an individual's profile information using an electronic computing device, means for providing the generated learning information via an output device, means for evaluating the user's learning results and recording progress, and means for analyzing the user's emotional state using an emotion recognition device and dynamically adjusting the learning information based on that state. This makes it possible to provide an optimal learning experience according to the user's emotional state and maximize the learning effect.

[0571] An "electronic computing device" is a device that processes information and provides the processing results via an output device, and includes computers.

[0572] "Profile information" refers to data such as attributes, interests, and past learning history unique to each individual user, and serves as the foundation for customizing the individual learning experience.

[0573] "Learning information" refers to the totality of information provided to facilitate user learning, including educational content and learning programs.

[0574] An "output device" is a device used to present information visually or audibly, and includes displays and speakers.

[0575] An "emotion recognition device" is a device equipped with sensors and analysis functions to analyze a user's facial expressions, voice, physical reactions, etc., and determine their emotional state.

[0576] "Dynamic adjustment" refers to changing the content or procedures in real time or immediately in response to specific situations or conditions.

[0577] The system implementing this invention consists of three parties: a server, a terminal, and a user. The server uses a computer to generate appropriate learning information based on the user's profile information. The generated learning information is provided to the user through an output device on the terminal.

[0578] The device is equipped with an emotion recognition device that analyzes the user's facial expressions, voice, and physical reactions, and uses a generative AI model to determine their emotional state in real time. This data is sent to a server, which dynamically adjusts the learning information based on it. For example, if the user is feeling stressed, relaxing content will be selected.

[0579] The user's learning results are sent from the device to the server, where progress is recorded and feedback is provided to both the user and the administrator. This allows the user to visualize their progress and understand what they should focus on next, along with their emotional state.

[0580] For example, when a user is learning about science, they might find the initial content difficult. In this case, the device senses the user's emotions and suggests simpler quizzes or visual videos to make learning easier. This kind of response increases learning efficiency and allows users to continue learning.

[0581] An example of a prompt message for the generative AI model would be: "We have detected that the student's current emotional state is stressed. To reduce stress, please suggest a relaxing science video, followed by a short quiz. If the student maintains their concentration, please continue with a detailed lecture." This instruction would be incorporated based on the content from the server.

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The server receives user profile information and generates learning information based on that data. Input includes the user's interests, past learning history, and goals, which are used to generate customized learning programs and content. The generated data is then sent to the device as learning information.

[0585] Step 2:

[0586] The terminal receives learning information sent from the server and displays it on an output device for the user. The input is customized learning information, which is output to an interface that presents it to the user in formats such as video, text, and quizzes.

[0587] Step 3:

[0588] The device analyzes the user's real-time emotional state using an emotion recognition device. It receives the user's facial expressions and voice as input, analyzes them using a generative AI model, and determines the emotional state (e.g., relaxed, focused, stressed). The determination results are sent to a server to obtain feedback for optimizing the user's learning experience.

[0589] Step 4:

[0590] The server analyzes the emotional state data and learning progress obtained from the terminal and dynamically adjusts the learning information. The inputs are the user's emotional state and learning progress, and the difficulty and format of the learning information are changed based on these. The updated learning information is then sent back to the terminal as output.

[0591] Step 5:

[0592] The device receives updated learning information from the server and presents it to the user again. It displays content adjusted based on the user's emotional state to help the user continue learning.

[0593] Step 6:

[0594] When a user completes their learning, the device sends the learning results to the server. The input includes the user's answers, responses, and emotional history. Based on this, the server evaluates the effectiveness of the learning and outputs data to record the progress. The server then uses this data to present the progress status to the user and administrator.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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".

[0612] This invention is a system for providing personalized educational experiences tailored to the individual characteristics of each learner. This system primarily functions through the interaction of three parties: a server, a terminal, and a user.

[0613] The server first collects learner profile information, including data such as age, interests, and learning style. Next, the server uses this data to run an algorithm that generates learning content best suited to the learner. This content generation includes stories and virtual experiments related to the learner's interests, creating an engaging and enjoyable learning experience.

[0614] The device then presents the generated content obtained from the server to the learner. The learner can then engage with the stories and tasks presented through this device. For example, if the user selects a story with the theme of "space exploration," they are expected to tackle the mathematical problems presented within it.

[0615] As the learning process progresses, the user inputs their learning results through their device. The device sends these results to a server, which automatically evaluates them. Based on the correct / incorrect judgments, the user's learning progress is then recorded.

[0616] Furthermore, the server can manage learning progress and provide information to administrators in real time as needed. This facilitates the tracking of user progress and the provision of individualized instruction.

[0617] This system can maximize learner growth. Furthermore, providing information to parents and educators ensures timely and appropriate support.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The server collects user profile information. Users access the educational platform and enter basic information such as age, interests, and learning style on the initial setup screen. The server stores this data in a database.

[0621] Step 2:

[0622] The server generates personalized learning content based on the collected profile information. The server uses algorithms to create highly customized learning materials and select themes and topics relevant to the user's interests.

[0623] Step 3:

[0624] The device displays learning content received from the server to the user. Through the device's interface, the user accesses the presented stories and virtual tasks and prepares to engage in learning.

[0625] Step 4:

[0626] Users engage in learning tasks through their devices. They complete tasks instructed according to a story, such as solving math problems or virtually conducting science experiments.

[0627] Step 5:

[0628] The device sends the user's learning results to the server. When the user completes an assignment, the device automatically transmits the results to the server.

[0629] Step 6:

[0630] The server evaluates the submitted learning results. The server automatically determines the results and evaluates whether they are correct or incorrect. This information is immediately updated as a user progress record.

[0631] Step 7:

[0632] The device provides the user with feedback on evaluation results and progress. The user checks their progress on the device and obtains information to proceed to the next step.

[0633] Step 8:

[0634] The server provides progress information to administrators and parents. The server displays data via a dashboard, allowing administrators to monitor each user's learning progress in real time.

[0635] (Example 1)

[0636] 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".

[0637] When providing individualized educational experiences to individual learners, it is necessary to effectively generate learning content that reflects the learner's interests and characteristics, and to efficiently evaluate and manage their learning progress. However, conventional systems struggle to provide such sophisticated individualized support and often rely on general content and evaluation methods, making it difficult to optimally draw out the learner's understanding and interest.

[0638] 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.

[0639] In this invention, the server includes means for collecting personal attribute information using an information processing device, means for generating educational information using prompt sentences in a generative artificial intelligence model based on the collected information, and means for presenting the generated educational information via a visual device. This enables the generation of content and progress evaluation tailored to learners, as well as efficient information provision to administrators.

[0640] "Information processing equipment" is a general term for electronic devices used for collecting, processing, and storing data, and includes devices such as computers and servers.

[0641] "Attribute information" refers to information such as an individual's characteristics, interests, and learning style, and is data collected to understand the individual characteristics of learners.

[0642] A "generative artificial intelligence model" refers to a machine learning model that has the ability to automatically generate information and content based on input text and data.

[0643] A "prompt" is input text used to give specific generation instructions to an artificial intelligence model, and it is a factor that determines the direction of the generated content.

[0644] "Educational information" refers to all content created for educational purposes for learners, and includes a variety of formats such as text, images, videos, and interactive elements.

[0645] "Visual devices" include devices such as displays and screens, and are equipment used to present information visually.

[0646] This invention is an information system that generates and presents educational content tailored to the characteristics of learners in order to provide an individualized educational experience. This system uses an information processing device and a visual device to design the optimal educational experience for the learner.

[0647] The server first collects user attribute information. This information includes information entered by the learner and learning history automatically retrieved from logs. Based on this information, the server uses a generative artificial intelligence model to generate prompt sentences. These prompt sentences function as instructions to the generative AI model to generate content that is suited to the learner's interests and learning style. The server analyzes the collected data, generates prompt sentences using natural language processing software, and sends them to the generative AI model.

[0648] For example, a prompt could be: "Generate math learning content with a space exploration story for a 10-year-old child. The mathematical concepts to be included in the story are basic arithmetic operations and unit conversions." Based on this prompt, a generative AI model (e.g., a large language model) would generate educational information, including a story and virtual experiments tailored to the learner's interests.

[0649] The generated educational information is presented to learners via visual devices. The terminal has the capability to provide this educational information to users in text, images, videos, and interactive formats. Users work on tasks and quizzes presented through the terminal and report their results to the server.

[0650] On the other hand, the evaluation method is executed on the server, and evaluations are automatically performed based on the user's responses and actions. This allows administrators to monitor learners' progress in real time and provide guidance and support as needed. This system not only delivers content, but also dynamically and individually optimizes the educational process through the use of collected data and generative AI.

[0651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0652] Step 1:

[0653] The server collects user attribute information. This information includes data entered by the user via the terminal and data obtained from previous learning history. The attribute information obtained as input includes age, interests, and learning style, and by storing this data in a database, it provides the basic data for the next processing step.

[0654] Step 2:

[0655] The server analyzes the collected attribute information and uses a generative artificial intelligence model to create prompt sentences for generating educational content. At this stage, natural language processing technology is utilized to generate prompt sentences tailored to the user's interests and learning style. Based on the attribute information as input, prompt sentences are generated and sent to the generative AI model, which then produces instructions that form the basis of the educational content as output.

[0656] Step 3:

[0657] The server passes prompts to a generative AI model, which then generates educational content. In this process, the generative AI model creates stories, virtual experiments, and other elements based on the provided prompts, generating educational information tailored to each individual learner. The output consists of specific learning content, optimized for the learner's characteristics.

[0658] Step 4:

[0659] The device receives the generated educational content and presents it to the user via a visual device. The user interface on the device is interactive and designed to allow learners to actively engage with the content. Output includes visually presented content in the form of text, images, videos, and quizzes.

[0660] Step 5:

[0661] Users engage in tasks and answer quizzes based on the presented content. Information entered by users through these activities is transmitted to the server in real time and collected as data. This user activity data is aggregated and used in the next step.

[0662] Step 6:

[0663] The server receives activity results from users and evaluates them using an automated evaluation algorithm. Based on the collected activity data, it calculates whether the answers are correct and the progress status, and generates a progress report. The output records the evaluated results and progress information, which is used as feedback for generating the next learning content.

[0664] Step 7:

[0665] The server manages recorded progress information and provides it to administrators and educators as needed. This allows administrators to gain a comprehensive understanding of learners' progress and provide appropriate guidance. As output, progress reports are generated and presented to administrators in a visualized format.

[0666] (Application Example 1)

[0667] 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".

[0668] To provide each learner with an appropriate learning experience, it is necessary to deliver content that is individually optimized based on the learner's interests and goals. However, with conventional technology, it has been difficult to provide content that matches the individual needs of learners in a timely manner. Furthermore, there was a need to accurately grasp progress based on learning responses and to provide feedback and improve content based on that.

[0669] 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.

[0670] In this invention, the server includes means for selecting learning content based on individual interests, means for providing the selected learning content via an electronic device, and means for analyzing the user's learning response and recording progress. This makes it possible to provide highly personalized educational content that is tailored to each learner's interests and goals.

[0671] "Personal interests" refer to the learner's interest in or preference for a particular theme or field, and these are the criteria used to select content.

[0672] "Learning content" refers to educational materials and experiences provided to learners, and may include videos and interactive elements.

[0673] "Electronic devices" refer to all digital devices used for displaying content or acquiring user learning responses.

[0674] "Selection" refers to the act of choosing the most suitable option from among several choices according to specific criteria.

[0675] "Learning response" refers to the responses and actions that learners show to the content they are given, and is data used to measure their level of understanding and interest.

[0676] "Progress" is an indicator that shows how far a learner has progressed in their learning toward their goal, and it indicates the degree of progress in the learning process.

[0677] "Analysis" refers to a series of processes that involve interpreting collected data and deriving specific insights or conclusions.

[0678] The system for realizing this invention consists of three components: a server, a terminal, and a user. The server collects profile information for each learner and selects learning content based on their individual interests. In this selection process, the server uses a generative AI model to generate content, including stories and virtual experiences that reflect the learner's interests, and provides it to the terminal.

[0679] The terminal receives learning content transmitted from the server and provides it to the user via electronic devices. For example, learning materials, including videos and interactive elements, can be displayed using a smartphone or smart glasses. The user progresses through the learning process using this content. The learner's responses and level of understanding are fed back to the server via the terminal and used for further progress tracking.

[0680] Data processing and calculations are performed based on learner information collected by the server, and are implemented using programming languages ​​such as Python and data analysis libraries. The server can record learner progress based on the collected data, and can represent it graphically or present it as management information.

[0681] For example, if a high school student is interested in science, they can receive and watch videos via their device, such as "virtual experiments to learn about chemical reactions" or "simulated experiences of space exploration."

[0682] An example of a prompt might be, "Please suggest learning content suitable for high school students interested in chemistry." Following this prompt, the system can select and provide the most appropriate content.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The server collects learner profile information. Input is user-provided data such as age, interests, and learning style. The server stores this data in a database for analysis. The output is a set of learner profile data.

[0686] Step 2:

[0687] The server uses a generative AI model based on profile information to generate learning content tailored to the learner. The input is the profile data saved in step 1. The server inputs this data into the generative AI model and creates content including interest-based stories and virtual experiences. The output is the generated learning content.

[0688] Step 3:

[0689] The server sends the generated learning content to the device. The input is the generated learning content, which is the output of step 2. The server converts the content into a data format (e.g., JSON or XML) and sends it to the device over the network. The output is the content in the data format received by the device.

[0690] Step 4:

[0691] The device displays the received learning content to the user. The input is content in the data format sent in step 3. The device uses video playback software or an interactive content viewer to convert and display it in a format that the user can understand. The output is content that the user can view or interact with.

[0692] Step 5:

[0693] The user reacts to the learning content. The input is the content displayed in step 4. The user works on the presented tasks, answering quizzes or advancing the story. The output is the user's action data and reaction data.

[0694] Step 6:

[0695] The terminal records user responses and sends them to the server. The input is the user's operation data and response data, which are the output of step 5. The terminal collects this data and sends it to the database. The output is the user feedback stored on the server.

[0696] Step 7:

[0697] The server analyzes user feedback to evaluate learning progress. The input is the user feedback data aggregated in step 6. The server uses an algorithm to analyze the data and record the progress. The output is learning progress data.

[0698] Step 8:

[0699] The server displays and reports learning progress data as management information. The input is the progress data, which is the output of step 7. The server converts this into a dashboard or report format so that administrators can view it. The output is a progress report for administrators.

[0700] 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.

[0701] This invention is an advanced educational system that combines an emotion engine to provide a personalized educational experience for individuals. This system mainly consists of three parties: a server, a terminal, and a user. The emotion engine recognizes the user's emotional state and adjusts the learning experience based on that state.

[0702] The server first collects user profile information and generates learning content optimized for the learner. An emotion engine is installed on the user's device and recognizes emotions from biosensor data and camera footage. For example, the camera analyzes the user's facial expressions, and the emotion engine uses that data to determine states such as "stress" or "concentration."

[0703] As users access learning content through their devices, an emotion engine monitors their emotional state in real time. The server receives this emotion data and adjusts the content according to the user's learning progress and current mental state. For example, if a user is feeling stressed, the device will suggest learning tasks that are more relaxing.

[0704] The user's learning results are sent from the device to the server, and their progress is automatically recorded. The server comprehensively analyzes existing data and emotional data to evaluate the user's learning performance. This enables detailed educational support tailored to the user's emotional state.

[0705] Finally, the device provides users and guardians with feedback based on progress and emotional state. For example, the dashboard may display information such as "User has been concentrating for a long time" or "Stress has persisted for 30 minutes," enabling effective learning support. This system offers a new educational approach that takes learners' interests and emotions into account.

[0706] The following describes the processing flow.

[0707] Step 1:

[0708] The server collects user profile information. Users log into the system and enter their age, interests, and learning style. The server records this information in a database and uses it to generate learning content.

[0709] Step 2:

[0710] The emotion engine acquires data in real time from the device's camera and biosensors to analyze the user's emotional state. For example, it analyzes facial expressions and heart rate to detect states such as "focused" or "relaxed."

[0711] Step 3:

[0712] The server generates learning content based on analyzed emotion data and profile information. It adjusts the difficulty and content of the tasks according to the user's emotional state, providing optimal content.

[0713] Step 4:

[0714] The device presents the user with generated learning content. The user works on tasks presented in the form of stories or virtual experiments. For example, if the user is tired, a "relaxation mode" story is provided.

[0715] Step 5:

[0716] Users work on the presented tasks and enter their answers into their devices. User input is sent to the server in real time, and results are checked immediately.

[0717] Step 6:

[0718] The server evaluates the user's learning results and automatically records progress. Feedback is generated that takes sentiment data into account, and advice is provided to improve the quality of learning.

[0719] Step 7:

[0720] The device displays feedback to the user regarding evaluation results, progress, and emotional state. Users can understand their own learning patterns and develop strategies for improvement for the next step.

[0721] Step 8:

[0722] The server reports the learner's status to the administrator in real time, based on recorded progress information and sentiment data. This allows the administrator to obtain data to effectively support learning.

[0723] (Example 2)

[0724] 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".

[0725] Traditional education systems have the problem of not being able to fully address individual needs because they cannot take into account the individual emotional states of learners in real time. In particular, they lack the function to understand the stress and concentration levels that learners experience during learning and to dynamically adjust the learning content accordingly. As a result, learner performance is not optimized, and improvements in learning efficiency cannot be expected.

[0726] 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.

[0727] In this invention, the server includes means for generating learning content based on an individual's profile information, means for analyzing the user's emotional state using an emotion recognition device, and means for dynamically adjusting the learning content based on the emotional state. This makes it possible to consider the individual emotional state of each learner in real time and provide an optimal learning experience.

[0728] An "electronic computing device" is a device that performs information input, processing, and output, and includes computers and servers.

[0729] "Profile information" refers to data that shows an individual's basic characteristics, including age, past learning history, and interests.

[0730] "Learning content" refers to information resources such as teaching materials, workbooks, and virtual experiments used for educational purposes.

[0731] A "display device" is a device used to visually display electronic information, and includes monitors and screens.

[0732] An "emotion recognition device" is a device used to analyze a user's emotional state, and it uses cameras and biosensors to determine emotions.

[0733] "Emotional state" refers to the psychological state a user experiences at a particular point in time, and can be classified as "stress" or "concentration," among other things.

[0734] "User learning results" refer to information that shows the achievements a user has made through their learning activities, and include data such as whether the questions were answered correctly or incorrectly and the time taken to answer them.

[0735] An "administrator" refers to a person or organization that has the authority to monitor and control the entire system.

[0736] This advanced education system is implemented with an emotional engine to provide a personalized learning experience for each individual. Its main components are servers, terminals, and users, which enable the system to function.

[0737] The server first collects user profile information. This profile information includes age, learning objectives, and past learning history. The server stores this information using a database and generates learning content tailored to individual needs using a generative AI model. The generated learning content is delivered to the user via a device. The device functions as a learning device and visually presents this content to the user.

[0738] Furthermore, the device is equipped with an emotion engine that analyzes the user's emotional state in real time using cameras and biosensors. The emotion engine determines states such as "stress" and "concentration" based on facial recognition technology and data from sensors. The data acquired from the emotion engine is sent to a server, which dynamically adjusts the learning content to be appropriate based on this data.

[0739] As the user progresses through the learning process, the device sends data to the server based on their learning results and emotional state. The server analyzes this data to evaluate progress and learning performance, and then provides feedback to the user and their guardian. This feedback is presented in a dashboard format and includes information such as "focused learning time exceeded a certain limit" or "stress persisted for an extended period."

[0740] For example, for a student studying mathematics, this system provides a customized set of problems based on their past performance and current learning goals. If the emotion engine detects that the user is stressed, the system will temporarily suggest easier problems to improve the user's mood. An example of such a prompt might be, "Please suggest some easy math problems to help me relax."

[0741] In this way, the system can take into account the user's emotional state and optimize the learning experience.

[0742] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0743] Step 1:

[0744] The server collects user profile information. It obtains the user's age, learning objectives, and past learning history as input, and stores this data in the server's database. This stored data is used as the basis for future learning content generation. Specifically, this process begins when the user enters the necessary information into an input form from their device.

[0745] Step 2:

[0746] The server uses a generative AI model based on the collected profile information to generate learning content tailored to the user. The profile information obtained in step 1 is passed to the AI ​​model as input, and content matching the individual learning needs is generated as output. For example, if a student wants to learn mathematics, math problem sets and learning materials will be generated.

[0747] Step 3:

[0748] The device presents the generated learning content to the user. It receives the learning content, which is output from the server, and displays it on the user's screen. The user can visually check the learning materials and questions on the device and begin learning. Specifically, learning begins when the user starts up the device and opens the designated learning application.

[0749] Step 4:

[0750] The emotion engine built into the device analyzes the user's emotional state. It receives camera footage and biosensor data as input and outputs emotional states such as "stress" and "concentration" through facial recognition and heart rate analysis. Specifically, changes in the user's facial expressions are captured by the camera, and this data is analyzed in real time.

[0751] Step 5:

[0752] The server receives emotional data sent from the terminal and dynamically adjusts the learning content. Using the output emotional state data as input, it adjusts the difficulty and progress of the learning content before returning it to the user. For example, if the server determines that the user is experiencing high levels of stress, it sends prompt messages to an AI model to provide more relaxing content and generates new learning materials.

[0753] Step 6:

[0754] The terminal presents the user with the adjusted learning content received from the server. It receives the adjusted content, which is the output of the server, and displays it again on the user's display. Specifically, after the user finishes the current learning material, a new learning material is automatically displayed as the next step.

[0755] Step 7:

[0756] The device sends learning results and emotional data to the server. It outputs a summary of performance, response time, and emotional state data obtained during the learning process, and inputs it into the server. The server receives this data and evaluates the user's learning progress. Specifically, data is automatically sent from the device to the server when the user completes a learning session.

[0757] Step 8:

[0758] The server provides feedback to users and guardians based on the analyzed data. It adds evaluation results to the input data and outputs feedback on progress and mental health status. Specifically, the server generates visual feedback in a dashboard format and sends it to the device. The device displays this feedback on its screen, making it viewable by users and guardians.

[0759] (Application Example 2)

[0760] 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".

[0761] Traditional learning systems typically provide learning content based on individual profile information, but they lack sufficient mechanisms for presenting content that takes the user's emotional state into account. This has resulted in learning effectiveness being reduced depending on the user's emotional state, leading to an inefficient learning process.

[0762] 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.

[0763] In this invention, the server includes means for generating learning information based on an individual's profile information using an electronic computing device, means for providing the generated learning information via an output device, means for evaluating the user's learning results and recording progress, and means for analyzing the user's emotional state using an emotion recognition device and dynamically adjusting the learning information based on that state. This makes it possible to provide an optimal learning experience according to the user's emotional state and maximize the learning effect.

[0764] An "electronic computing device" is a device that processes information and provides the processing results via an output device, and includes computers.

[0765] "Profile information" refers to data such as attributes, interests, and past learning history unique to each individual user, and serves as the foundation for customizing the individual learning experience.

[0766] "Learning information" refers to the totality of information provided to facilitate user learning, including educational content and learning programs.

[0767] An "output device" is a device used to present information visually or audibly, and includes displays and speakers.

[0768] An "emotion recognition device" is a device equipped with sensors and analysis functions to analyze a user's facial expressions, voice, physical reactions, etc., and determine their emotional state.

[0769] "Dynamic adjustment" refers to changing the content or procedures in real time or immediately in response to specific situations or conditions.

[0770] The system implementing this invention consists of three parties: a server, a terminal, and a user. The server uses a computer to generate appropriate learning information based on the user's profile information. The generated learning information is provided to the user through an output device on the terminal.

[0771] The device is equipped with an emotion recognition device that analyzes the user's facial expressions, voice, and physical reactions, and uses a generative AI model to determine their emotional state in real time. This data is sent to a server, which dynamically adjusts the learning information based on it. For example, if the user is feeling stressed, relaxing content will be selected.

[0772] The user's learning results are sent from the device to the server, where progress is recorded and feedback is provided to both the user and the administrator. This allows the user to visualize their progress and understand what they should focus on next, along with their emotional state.

[0773] For example, when a user is learning about science, they might find the initial content difficult. In this case, the device senses the user's emotions and suggests simpler quizzes or visual videos to make learning easier. This kind of response increases learning efficiency and allows users to continue learning.

[0774] An example of a prompt message for the generative AI model would be: "We have detected that the student's current emotional state is stressed. To reduce stress, please suggest a relaxing science video, followed by a short quiz. If the student maintains their concentration, please continue with a detailed lecture." This instruction would be incorporated based on the content from the server.

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The server receives user profile information and generates learning information based on that data. Input includes the user's interests, past learning history, and goals, which are used to generate customized learning programs and content. The generated data is then sent to the device as learning information.

[0778] Step 2:

[0779] The terminal receives learning information sent from the server and displays it on an output device for the user. The input is customized learning information, which is output to an interface that presents it to the user in formats such as video, text, and quizzes.

[0780] Step 3:

[0781] The device analyzes the user's real-time emotional state using an emotion recognition device. It receives the user's facial expressions and voice as input, analyzes them using a generative AI model, and determines the emotional state (e.g., relaxed, focused, stressed). The determination results are sent to a server to obtain feedback for optimizing the user's learning experience.

[0782] Step 4:

[0783] The server analyzes the emotional state data and learning progress obtained from the terminal and dynamically adjusts the learning information. The inputs are the user's emotional state and learning progress, and the difficulty and format of the learning information are changed based on these. The updated learning information is then sent back to the terminal as output.

[0784] Step 5:

[0785] The device receives updated learning information from the server and presents it to the user again. It displays content adjusted based on the user's emotional state to help the user continue learning.

[0786] Step 6:

[0787] When a user completes their learning, the device sends the learning results to the server. The input includes the user's answers, responses, and emotional history. Based on this, the server evaluates the effectiveness of the learning and outputs data to record the progress. The server then uses this data to present the progress status to the user and administrator.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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."

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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 as being incorporated by reference.

[0809] The following is further disclosed regarding the embodiments described above.

[0810] (Claim 1)

[0811] To provide a personalized educational experience,

[0812] A means for generating learning content based on an individual's profile information using an electronic computer,

[0813] A means of providing the generated learning content via a display device,

[0814] A means of evaluating the user's learning results and recording progress,

[0815] A means of presenting recorded progress information to the administrator,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the generated learning content includes stories and virtual experiments based on the individual's interests.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the evaluation of the learning results is performed using an algorithm that automatically determines whether the user's answer is correct or incorrect.

[0821] "Example 1"

[0822] (Claim 1)

[0823] A means of collecting personal attribute information using an information processing device,

[0824] A means for generating educational information using prompt sentences in a generative artificial intelligence model based on collected information,

[0825] A means of presenting generated educational information via a visual device,

[0826] A means of evaluating user activity results and recording progress,

[0827] A means of providing recorded progress information to the administrator,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, wherein the generated educational information includes stories and virtual experiences based on individual interests.

[0831] (Claim 3)

[0832] The system according to claim 1, wherein the evaluation of the activity results is performed using a calculation method that automatically determines the user's response.

[0833] "Application Example 1"

[0834] (Claim 1)

[0835] A means of selecting learning content based on individual interests,

[0836] A means of providing selected learning content via an electronic device,

[0837] A means of analyzing the user's learning response and recording progress,

[0838] A means of presenting recorded progress information as management information,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, wherein the learning content provides videos and interactive content related to an individual's learning goals.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the analysis of learning responses uses an algorithm that recommends optimal learning content based on the user's interests.

[0844] "Example 2 of combining an emotion engine"

[0845] (Claim 1)

[0846] A means for generating learning content based on an individual's profile information using an electronic computer,

[0847] A means of providing the generated learning content via a display device,

[0848] A means for analyzing a user's emotional state using an emotion recognition device,

[0849] A means of dynamically adjusting learning content based on emotional state,

[0850] A means of evaluating user learning results and sentiment data and recording progress,

[0851] A means of presenting recorded progress information to the administrator,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, wherein the generated learning content includes stories and virtual experiments based on the individual's interests.

[0855] (Claim 3)

[0856] The system according to claim 1, wherein the evaluation of learning results is performed using an algorithm that automatically determines whether the user's answer is correct or incorrect, and further integrated with data obtained from sentiment analysis.

[0857] "Application example 2 when combining with an emotional engine"

[0858] (Claim 1)

[0859] A means for generating learning information based on an individual's profile information using an electronic computer,

[0860] A means for providing the generated learning information via an output device,

[0861] A means of evaluating the user's learning results and recording progress,

[0862] A means of presenting recorded progress information to the administrator,

[0863] A means for analyzing the user's emotional state using an emotion recognition device and dynamically adjusting the learned information based on that state,

[0864] A means of selecting and providing content that corresponds to specific emotional states such as stress or concentration,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, wherein the generated learning information includes stories and virtual experiences based on the individual's interests, and the content format is selected according to the user's emotional state.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein the evaluation of learning results is performed using an algorithm that automatically determines whether the user's answer is correct or incorrect, and further provides feedback of the evaluation results based on the emotional state. [Explanation of Symbols]

[0870] 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. To provide a personalized educational experience, A means for generating learning content based on an individual's profile information using an electronic computer, A means of providing the generated learning content via a display device, A means of evaluating the user's learning results and recording progress, A means of presenting recorded progress information to the administrator, A system that includes this.

2. The system according to claim 1, wherein the generated learning content includes stories and virtual experiments based on the individual's interests.

3. The system according to claim 1, wherein the evaluation of the learning results is performed using an algorithm that automatically determines whether the user's answer is correct or incorrect.

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