system

Abstract

Provide a system. 【Solution means】 Means for analyzing the user's learning history to estimate the area of interest, Means for selecting and displaying appropriate learning materials based on the estimated area of interest, Means for analyzing voice commands to operate learning content, Means for promoting learning in a game-like manner by means of a point system and a reward system, Means for managing the learning schedule and giving reminders at set times, Means for providing the learning progress data to the guardian, Means for providing feedback to the user using visual information and auditory information to achieve two-way learning, Means for providing customized quizzes and information based on the user's interests, Means for providing virtual rewards when a specific point is reached, Means for generating prompts based on the learning theme A system including.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in the home learning environment of children, there has been a demand for providing flexible and effective teaching materials according to individual interests and learning paces. However, at present, generally fixed teaching materials and uniform learning approaches are central, and there is a problem that the learning motivation cannot be fully elicited. In addition, there is a problem that there is a lack of information for guardians to appropriately grasp the learning progress of children and provide appropriate support.

Means for Solving the Problems

[0005] This invention provides a system that selects and presents learning materials suitable for each child by analyzing the user's learning history and estimating their areas of interest based on that history. Furthermore, by introducing voice command operation and a point-based reward system, it promotes learning in a game-like manner and enhances motivation to learn. In addition, by providing means for managing learning schedules and providing learning progress data to parents, the system aims to improve the quality of home learning by enabling parents to understand their child's learning situation and provide support at the appropriate time.

[0006] "User learning history" refers to records of learning activities undertaken by individual users in the past, including the type of learning activity, frequency, and results.

[0007] "Areas of interest" refers to categories that represent the direction of a user's interest in specific themes or topics.

[0008] "Learning materials" are educational resources consisting of content and several activities proposed for learning specific knowledge or skills.

[0009] A "voice command" is an operation request that a user makes to the system through voice input.

[0010] A "point system" is a method of assigning a certain numerical value to users based on their learning activities and achievements, and visualizing their level of accomplishment based on the accumulation of these points.

[0011] A "reward system" is a mechanism that shows users the rewards they can earn when they meet certain conditions, in order to increase their motivation to learn.

[0012] "Learning schedule management" is the process of creating a plan and scheduling activities to encourage users to study regularly.

[0013] "Learning progress data" refers to data that quantitatively shows the results and process of a user's learning activities.

[0014] A "guardian" is an adult supervisor who is involved in supporting the user's learning activities and monitoring their educational progress. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Implementing the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a processor with a reference numeral (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), etc.

[0019] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a storage with a reference numeral 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, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention realizes a home learning support system that provides users with an appropriate learning experience. The operation of the system will be described in detail below.

[0037] The server first collects the user's learning history data and analyzes it to estimate the user's areas of interest. This analysis is performed using machine learning algorithms to identify areas of interest. Based on the results, the server selects learning materials related to the user's areas of interest and sends them to the device.

[0038] The device displays learning materials tailored to the user based on information received from the server. These materials include interactive content and are designed to engage the user. Devices equipped with voice recognition capabilities allow users to give voice commands, enabling hands-free changes to learning content and activities.

[0039] For example, if a user wants to learn about dinosaurs, the system provides quizzes and puzzles about the history and types of dinosaurs. As the user solves the quizzes and gets correct answers, points are added, and once a certain amount is reached, the user receives a reward. This makes learning enjoyable and creates a sustainable learning experience.

[0040] Furthermore, the server manages the learning schedule and sends regular reminders to the user. When the user-set learning time arrives, the device provides visual and audible notifications to encourage the user to begin learning. This system allows users to plan efficiently and continue learning consistently.

[0041] Finally, the server periodically generates reports summarizing the user's learning progress and provides them to parents. Through these reports, parents can understand their child's learning status and provide appropriate support and feedback.

[0042] Thus, the present invention effectively supports home learning by maximizing user interest and providing an efficient and enjoyable learning environment.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server retrieves the user's learning history data from the database. This includes past learning activities, areas of interest, and learning outcomes.

[0046] Step 2:

[0047] The server runs machine learning algorithms based on the acquired data to analyze areas of particular interest to the user. Based on these analysis results, it identifies topics of interest to the user.

[0048] Step 3:

[0049] The server selects appropriate learning materials from the database that are related to the user's area of ​​interest. The selected materials are designed to engage the user's interest.

[0050] Step 4:

[0051] The server sends the selected learning materials to the device, which receives and displays them to the user. The materials include interactive elements and visual content.

[0052] Step 5:

[0053] The user begins learning using the learning materials displayed on the device. The device interactively receives the user's selections and answers and records their activity.

[0054] Step 6:

[0055] Users control the learning process using voice commands. For example, they can give voice commands such as "move on to the next question."

[0056] Step 7:

[0057] The device analyzes the user's voice commands using speech recognition technology and executes processing according to the commands. It can present new quizzes or switch learning items.

[0058] Step 8:

[0059] To award points based on the user's learning progress, the device sends the user's results to the server. The server compiles this data and awards rewards once a certain number of points are reached.

[0060] Step 9:

[0061] The server monitors the user's study schedule and sends a reminder to the device as the scheduled study time approaches.

[0062] Step 10:

[0063] The device notifies the user of a reminder and encourages them to start learning with visual and auditory cues. This notification prompts the user to begin learning at the scheduled time.

[0064] Step 11:

[0065] The server periodically collects user learning activity data and generates progress reports. The server provides these reports to parents and users, making learning outcomes visible.

[0066] Step 12:

[0067] Parents can view progress reports through their devices to check their child's learning status. Based on this information, they can provide effective educational support.

[0068] (Example 1)

[0069] 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."

[0070] In today's educational environment, there is a need for adaptive learning support tailored to each user's learning style and interests. However, it is not easy to analyze individual learning history in detail and provide appropriate learning materials based on that analysis, nor is it easy to maintain an interactive and continuous learning experience. This invention aims to provide effective learning support based on the user's interests and to enhance their motivation to learn.

[0071] 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.

[0072] In this invention, the server includes means for analyzing the user's history information to estimate areas of interest, means for selecting and displaying appropriate learning materials based on the estimated areas of interest, and means for analyzing voice instructions to control the learning content. This makes it possible to realize an adaptive learning environment tailored to each individual user.

[0073] "User" refers to anyone who uses the system to engage in learning activities.

[0074] "History information" refers to a record of the learning content and operations that a user has performed in the past.

[0075] "Areas of interest" refers to the learning fields or topics that a user is interested in or has shown interest in.

[0076] "Educational materials" refer to content provided for learning, including in the form of text, images, and videos.

[0077] "Voice instructions" refers to operational requests made by users to the system using their voice.

[0078] A "scoring system" refers to a mechanism that quantifies the results of learning activities and links them to accumulation or rewards.

[0079] A "reward system" refers to a mechanism that provides incentives based on the user's learning achievements.

[0080] A "study plan" refers to the schedule or timetable set by the user for their studies.

[0081] "Notifications" refers to features that send reminders and announcements to users.

[0082] "Learning progress information" refers to data that shows the user's learning progress and results.

[0083] A "guardian" refers to a person who can supervise and guide the learning activities of a user.

[0084] This invention aims to realize a system that provides optimal educational support tailored to the individual learning needs of users. The system mainly consists of a server, terminals, and users.

[0085] The server is installed in a cloud computing environment and collects and stores user history information. The server implements a machine learning algorithm, which is used to analyze the collected history information and identify the user's areas of interest. This algorithm is often implemented using Python and the scikit-learn library. Once the areas of interest are identified, the server selects corresponding learning materials and sends them to the user's device via the internet.

[0086] The terminal is an electronic device equipped with a user interface that displays educational materials sent from the server. The terminal uses a web application based on HTML5 and CSS3 to visually display the materials. Interactive materials include drag-and-drop puzzles and multiple-choice quizzes. Furthermore, the terminal has voice recognition capabilities, allowing operation based on the user's voice commands. This typically utilizes a voice recognition API.

[0087] Users can learn using materials displayed based on their interests. For example, a user who wants to learn about "dinosaurs" will be presented with interactive quizzes and puzzles about their overview, types, and history. Furthermore, users earn points for correctly answering quizzes, and receive rewards based on their accumulated points.

[0088] Learning progress is regularly collected and analyzed on the server to understand the user's situation and generate feedback. The generated feedback is provided to the user's guardian and can be used to support their learning. The system manages the learning plan and encourages users to study regularly by sending notifications according to a pre-set schedule.

[0089] As an example of a prompt, by inputting the instruction "Analyze the user's learning history this week and suggest learning for next week" into the generating AI model, the learning content for the next step can be appropriately selected.

[0090] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0091] Step 1:

[0092] The server collects user history information from a database. Input includes the user's ID and past activity logs. Based on this data, the server uses machine learning algorithms to analyze it and identify the user's areas of interest. This analysis yields a list of learning areas of interest to the user.

[0093] Step 2:

[0094] The server selects appropriate learning materials based on identified areas of interest. The input is a list of the user's areas of interest. The server searches the learning material database for materials related to these areas of interest and selects them. As a result of this selection process, a set of learning materials suitable for the user is generated as output.

[0095] Step 3:

[0096] The server sends the selected learning materials to the terminal. The inputs include the set of learning materials and user information. The server transmits this data to the terminal via the network. The terminal displays the received learning materials in its user interface, and the output presents the user with visual learning content.

[0097] Step 4:

[0098] Users learn using learning materials displayed on their device screen. The input is the learning materials displayed on the device. Users interact with interactive content included in the materials (e.g., quizzes and puzzles). As a result of this interaction, learning progress and scores are recorded as output.

[0099] Step 5:

[0100] The device receives voice commands from the user and converts them to text via a speech recognition API. The input is the user's voice. The device analyzes this, extracts the command content, and performs screen operations based on it. The output is content controlled according to the user's instructions.

[0101] Step 6:

[0102] The server aggregates and analyzes the user's learning progress and generates feedback. Input includes the user's learning history and score data. Using a generative AI model, this data is analyzed to assess learning and suggest the next steps. The output is a feedback report, which is provided to parents.

[0103] Step 7:

[0104] The server sends reminders to the device based on the configured learning plan. The input is learning schedule information. The server executes a procedure to send a notification at the specified time, and the output is that the device displays a visual and audible notification prompting the user to start learning.

[0105] (Application Example 1)

[0106] 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."

[0107] In today's educational environment, providing personalized learning experiences tailored to learners' interests is a challenging task. Furthermore, the importance of motivating learners to continue learning proactively and regularly checking their progress has been highlighted. Traditional systems lack sufficient flexible responses and feedback based on learners' interests and progress, creating a need for a system that balances efficient learning with enjoyment.

[0108] 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.

[0109] In this invention, the server includes means for analyzing the user's learning history to estimate their areas of interest, means for selecting and displaying appropriate learning materials based on the estimated areas of interest, and means for providing customized quizzes and information based on the user's interests. This enables learners to engage in effective learning tailored to their interests. Furthermore, a point system and reward system can be used to create a fun, game-like learning environment, supporting continued learning.

[0110] "User learning history" refers to a collection of information such as the educational activities a learner has participated in in the past, the materials they have used, and their progress.

[0111] An "area of ​​interest" refers to a specific field or topic that a learner is personally interested in or concerned with.

[0112] "Learning materials" refer to educational materials and resources provided to expand learners' knowledge, and may take the form of electronic or physical materials.

[0113] "Voice commands" are a means of communicating operations or requests to a system via voice.

[0114] A "point system" is a type of evaluation system where points are added according to the progress of learning or activities, and rewards or benefits are given when a certain standard is reached.

[0115] A "reward system" is a set of incentives provided to improve motivation for learning or engaging in activities.

[0116] A "study schedule" is a planned arrangement of time and plans aimed at achieving a specific goal.

[0117] A "reminder" is a notification that prompts learners to reconfirm pre-scheduled plans or activities and encourages them to take action.

[0118] "Learning progress data" refers to information that shows the results and degree of progress that learners have achieved through learning activities.

[0119] A "guardian" is a person involved in the protection and management of learners, and is typically responsible for minors.

[0120] "Visual information" refers to information perceived through vision, and includes images, videos, charts, and diagrams.

[0121] "Auditory information" refers to information perceived through hearing, and includes speech, music, sound effects, etc.

[0122] "Feedback" refers to evaluations and responses to activities performed by learners, and is information used to facilitate further improvement and enhance learning effectiveness.

[0123] "Interactive learning" refers to educational activities in which learners and systems interact with each other, and responses are returned in real time.

[0124] A "customized quiz" is a set of questions specifically designed to suit the learner's interests and level.

[0125] "Information provision" is the act of conveying new knowledge or content to learners, and it can take various forms.

[0126] A "virtual reward" is a digital form of incentive that does not physically exist in the real world but is provided to give learners a sense of satisfaction or accomplishment.

[0127] A "learning theme" is a subject or task that learners are expected to focus on intensively over a specific period of time.

[0128] A "prompt" is a formalized input statement used to provide information to a model or system, and an instruction to produce an output result.

[0129] The system for implementing this invention consists of both a server and a terminal. The server first collects and analyzes the user's learning history. The collected data is used to estimate areas of interest, and machine learning algorithms are used to identify the user's areas of interest. This is achieved using data analysis and machine learning libraries such as Python, Pandas, and Scikit-learn.

[0130] Next, the server selects appropriate learning materials based on the estimated areas of interest. These materials are retrieved from a database and configured to provide quizzes and information used during the learning process. On the user's device, an interactive UI is built using web frameworks such as Django or Flask, providing visual and auditory feedback.

[0131] When users give voice commands, a speech recognition library (such as Google® Speech-to-Text API) is used to analyze the voice commands and enable interaction with the learning content. This feature allows users to have a hands-free, interactive learning experience.

[0132] Furthermore, this system incorporates a point system and a reward system. Points are added based on the number of questions answered correctly, and virtual rewards are given when a certain number of points are reached. This incentive increases motivation to learn.

[0133] Furthermore, the server manages the learning schedule and sends reminders according to the learning time. The device alerts the user through visual and auditory notifications to promote learning. Visual information can include pop-up notifications and colorful animations, while auditory information can include alert sounds and voice messages.

[0134] For example, if a user expresses interest in "history," the server will select and provide quizzes and puzzles related to history. Furthermore, by using prompts to instruct the generative AI model, "If the user is interested in 'history,' what kind of quizzes and learning materials should I provide?", it becomes possible to generate even more personalized content. This will give the user's learning experience greater depth and breadth.

[0135] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0136] Step 1:

[0137] The server collects user learning history data and stores it in a database. This process receives information about past learning activities, achievements, and materials used as input, organizes it, and stores it in the database. This prepares the foundational data needed for subsequent processing.

[0138] Step 2:

[0139] The server analyzes the collected data and uses machine learning algorithms to estimate the user's areas of interest. The input is the user's learning history data, which is then processed using Pandas and Scikit-learn to output estimated areas of interest. This analysis enables the provision of a learning experience tailored to the user's individual needs.

[0140] Step 3:

[0141] The server selects appropriate learning materials from the database based on estimated areas of interest. Using the estimated areas of interest as input, it retrieves relevant material information via database queries and outputs the selected material information. This ensures that content tailored to a specific user is prepared.

[0142] Step 4:

[0143] The terminal displays learning materials sent from the server to the user. The input is the selected learning materials, and the output is the display of the materials with visual or auditory feedback. This operation creates an environment in which the user can intuitively utilize the content.

[0144] Step 5:

[0145] When a user issues a voice command, the device receives it and performs speech recognition. The input is the user's voice command, and the output is the parsed operation command. The speech recognition process is implemented using the Google Speech-to-Text API, enabling hands-free operation.

[0146] Step 6:

[0147] The server sends reminders at predetermined times based on the learning schedule. It takes schedule data as input and outputs periodic notification messages. This supports users in developing a planned learning habit.

[0148] Step 7:

[0149] The server monitors learning progress using a point system and reward system. It evaluates the results of learning activities, inputs the points added, and outputs the status of virtual rewards. This process serves as an incentive to increase the user's motivation to learn.

[0150] Step 8:

[0151] The server periodically compiles learning progress data and generates reports. It takes progress data as input and outputs visualized reports. This makes it easy for parents and educators to understand the learning progress.

[0152] 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.

[0153] This invention realizes a home learning support system that utilizes an emotion engine in the user's learning environment to provide optimal learning for each individual user. The operation of the system will be described in detail below.

[0154] The server acquires and analyzes the user's learning history, and processes voice and facial expression data using an emotion engine. This process evaluates the user's current emotional state, and uses this data to estimate the user's areas of interest. Based on the obtained areas of interest and the emotion evaluation results, the server selects the most suitable learning materials.

[0155] The selected learning materials are sent to the device. The device not only displays the materials in an interactive format and provides learning opportunities, but also continuously monitors the user's facial expressions and voice to capture emotional changes in real time. This allows the device to provide immediate feedback and adjust the difficulty level according to the user's level of concentration and understanding.

[0156] For example, if a user is feeling frustrated with a difficult problem, the emotion engine will sense this and the device will change its settings to make the problem a little easier or provide hints. Also, if the user appears to be enjoying themselves, the device will maintain their current progress or offer further challenges.

[0157] The server also manages learning schedules, taking the user's emotional state into account. Reminders are adjusted to ensure the user can comfortably begin learning. For example, learning might be scheduled to start during a comfortable time after the user has refreshed themselves.

[0158] Parents are provided with learning progress reports generated by the server. These reports include information on the user's learning outcomes, as well as changes in their emotions and learning attitudes. This allows parents to gain a deeper understanding of their child's learning experience and provide support with an appropriate approach.

[0159] Thus, the present invention utilizes an emotion engine to provide a learning environment that is attuned to the user's emotions, thereby realizing appropriate and effective support for home learning that is tailored to individual learning needs.

[0160] The following describes the processing flow.

[0161] Step 1:

[0162] The server retrieves the user's past learning history data from the database. This data includes information such as the content of the learning, frequency, and success rate.

[0163] Step 2:

[0164] The server passes the user's voice data and facial expression data, collected via voice or webcam, to the emotion engine. This engine then analyzes the user's current emotional state.

[0165] Step 3:

[0166] The server estimates the user's areas of interest based on analyzed emotional state and learning history data. This estimation is then used to select appropriate learning materials. The difficulty level and content of the learning materials are considered during the selection process.

[0167] Step 4:

[0168] The server sends the selected learning material information to the terminal. The terminal receives this information and displays the learning materials in a format suitable for the user. Interactive content may also be included.

[0169] Step 5:

[0170] The user begins learning using the learning materials provided on the device. The device continues to monitor the user's voice and facial expressions, and its emotion engine captures changes in emotion in real time.

[0171] Step 6:

[0172] The device dynamically adapts learning content when it detects changes in emotions. For example, if frustration is detected, it will make adjustments such as displaying hints or lowering the difficulty level of the questions.

[0173] Step 7:

[0174] As the user's learning progresses, points are added and the data is sent to the server. The server compiles the points and updates the reward system as needed.

[0175] Step 8:

[0176] The server monitors the learning schedule and issues reminders at times that take into account the user's emotional state. The device notifies the user of these reminders visually and audibly, encouraging them to start learning at the appropriate time.

[0177] Step 9:

[0178] The server combines the user's learning data and sentiment data to generate a progress report. This report summarizes the learning outcomes and trends in sentiment changes.

[0179] Step 10:

[0180] Parents can view progress reports through their devices and obtain detailed information about their child's learning process. This information can then be used to provide effective learning support.

[0181] (Example 2)

[0182] 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".

[0183] Providing an optimal learning experience tailored to each learner's individual emotional state is difficult with conventional technologies, and a particular challenge is the lack of immediate adjustment of educational resources based on emotional changes. Furthermore, it is necessary to improve parents' understanding of and support for their child's learning by providing them with reports that integrate learning progress and emotional data.

[0184] 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.

[0185] In this invention, the server includes means for analyzing the user's learning history to acquire information and estimating relevant areas based on that information; means for processing audio and video data to evaluate the user's emotional state; and means for adjusting the difficulty level of learning resources based on the evaluated emotional state. This enables personalized education that responds to the learner's emotional state and efficient progress management.

[0186] "Analyzing a user's learning history" refers to the act of thoroughly investigating past learning activity data to clarify learning trends and progress.

[0187] "Estimating relevant fields" is the process of identifying subjects or themes that learners will find interesting and that are likely to lead to effective learning.

[0188] "Selecting and presenting educational resources" means choosing learning materials and content that are suitable for learners and making them available for them to use.

[0189] "Processing audio and video data" refers to analyzing audio and visual information obtained from learners to derive meaningful conclusions.

[0190] "Assessing emotional state" means measuring a learner's mental and emotional state and judging it according to specific indicators.

[0191] "Adjusting the difficulty level" means changing the complexity and pace of learning content to match the learner's level of understanding and emotional state.

[0192] "Effective progress management" refers to understanding the learner's progress and setting appropriate learning plans and goals based on that understanding.

[0193] To implement this invention, it is necessary to build a system in which a server and a terminal work together. The server collects and analyzes the user's learning history using a database and analysis software running on the server. This could involve utilizing data analysis tools and machine learning algorithms (e.g., random forest). By analyzing the learning history, the user's interests and preferences are estimated, and appropriate educational resources are selected. The selected educational resources are presented to the user via the terminal.

[0194] The device interactively presents educational resources through a user interface and captures the user's voice and facial expressions using a camera and microphone. This data is processed by emotion recognition tools (e.g., OpenCV, speech analysis tools) to evaluate the user's emotional state in real time. Based on this evaluation data, the server adjusts the difficulty level of the learning materials. For example, if frustration is detected while the user is working on a challenging problem, the problem is simplified or hints are displayed.

[0195] For example, if a user is showing enjoyment while practicing English listening, the device can continue its progress and add more challenging tasks. In addition, the server generates a report that aggregates learning progress and emotional data, making it accessible to parents. This allows parents to understand not only their child's learning progress but also trends in their emotional changes.

[0196] An example of a prompt for a generative AI model might be a question like, "Please explain in detail the specific methods for selecting learning materials based on user sentiment data."

[0197] In this way, the present invention makes it possible to utilize emotional data to provide learners with a personalized educational experience.

[0198] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0199] Step 1:

[0200] The server retrieves the user's learning history data from the database. The user ID is used as input, and the output provides data such as the user's past learning content, time spent, and performance. This data is used to prepare for analysis using machine learning algorithms. Specifically, the server sends queries to the database via an API to retrieve the necessary information.

[0201] Step 2:

[0202] The server receives user voice and facial expression data and processes it using an emotion engine. The input is real-time voice and video data sent from the terminal, and the output is an evaluation of the user's emotional state (e.g., positive, negative, neutral). This emotion evaluation uses facial expression recognition tools and voice analysis software. Specifically, the server sends this data to a dedicated analysis module for evaluation using statistical methods.

[0203] Step 3:

[0204] The server selects the most suitable educational resources based on analyzed sentiment data and learning history. The previously obtained sentiment state evaluation and learning history data are used as input. The output is a selection of educational resources that match the user's current interests and learning needs. Specifically, the server uses a machine learning model to predict appropriate learning materials and determines their priority.

[0205] Step 4:

[0206] The terminal receives educational resources sent from the server and displays them through a user interface. The input is the selected educational resources from the server, and the output is their display in a format visible to the user. Specifically, the terminal configures display settings and prepares to provide an interactive learning experience.

[0207] Step 5:

[0208] The device continuously monitors the user's voice and facial expressions and transmits data to the server in real time. Input is the user's voice and visual information, and output is continuous data for analysis. The device acquires information using a camera and microphone, packages that data appropriately, and sends it to the server.

[0209] Step 6:

[0210] The server integrates learning progress data and emotional states, generates a report, and provides it to parents. The inputs used are user emotional ratings and statistical data from learning history, and the output is an aggregated report. The server aggregates the data and generates the report using visualization tools, allowing parents to understand learning progress and emotional trends.

[0211] (Application Example 2)

[0212] 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".

[0213] A major challenge in modern home learning environments is the lack of nuanced learning materials and support tailored to learners' interests and emotions. Traditional learning support systems struggle to grasp learners' emotional states in real time and provide appropriate adjustments to material difficulty and feedback. As a result, learners may lose motivation or struggle to understand the material. Providing a suitable learning environment is particularly difficult for children with complex emotional states.

[0214] 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.

[0215] In this invention, the server includes means for evaluating the user's emotional state and adjusting the difficulty level of the learning materials based on the obtained data, and means for monitoring the user's emotions in real time during learning and providing feedback. This enables optimal learning support that is attentive to the learner's emotions.

[0216] "Analyzing a user's learning history" means acquiring data on the learning content and progress that learners have undertaken in the past, and then analyzing that data to understand the learner's interests and tendencies.

[0217] "Estimating areas of interest" means predicting the areas and themes that learners are interested in, based on their acquired learning history and sentiment data.

[0218] "Selecting and displaying appropriate learning materials" means choosing the most effective and appropriate materials based on the estimated areas of interest and the learner's level, and presenting them on the learner's viewing device.

[0219] "Analyzing voice commands" means that the system analyzes the voice commands uttered by the learner and operates or manages the learning content according to the content of those commands.

[0220] A "point system and reward system" is a mechanism that promotes learning in a fun, game-like way by awarding points to learners based on the goals and progress they achieve, and providing rewards based on those points.

[0221] "Managing the learning schedule" means sending learning reminders and notifications based on a pre-set schedule in order to efficiently plan and secure the learner's study time.

[0222] "Providing learning progress data to household members" means recording the learner's achievements and progress in learning content as data, and informing other members of the household in an appropriate format.

[0223] "Assessing the user's emotional state" involves analyzing the learner's current emotions and psychological state based on data such as facial expressions and voice, and then evaluating the results.

[0224] "Adjusting the difficulty level of the learning materials" means appropriately changing the content of the materials and the difficulty level of the problems presented, according to the learner's level of understanding and emotional state.

[0225] "Monitoring emotions in real time and providing feedback" means continuously observing learners' emotions during the learning process and immediately providing advice and improvement measures in response to any changes.

[0226] This invention aims to build a system that provides a learning environment tailored to the learner's emotions within the home. The details are described below.

[0227] The server first retrieves the learner's learning history from a database and analyzes it to estimate the learner's areas of interest and tendencies. The server is equipped with an emotion analysis engine that analyzes the learner's voice and facial expression data in real time. The analyzed emotion data is used to evaluate the learner's current emotional state.

[0228] The device is equipped with a camera and microphone, which capture the user's facial expressions and voice. The acquired data is transmitted to a server in real time for emotion analysis. Based on the analysis results, the server selects appropriate learning materials and sends them to the device. The device displays the materials to the user in an interactive format, supporting the user's learning process.

[0229] Furthermore, the device monitors the user's tone of voice and changes in facial expressions, analyzing the user's emotions in real time based on a pre-configured algorithm. This allows it to adjust the difficulty of a problem or provide hints if the user is feeling frustrated. Conversely, if the user is enjoying learning, it can challenge them with more difficult tasks.

[0230] As a concrete example, let's assume a primary school student is learning mathematics online and is facing a difficult problem. The device detects the user's distressed expression and receives instructions from the server to display a hint. As a result, the user is motivated to continue learning. Such real-time emotional responses improve the user's learning experience and maximize their learning outcomes.

[0231] An example of a prompt when using a generative AI model is: "Think of a way to optimize learning content by reading the user's emotions from their facial expressions and voice while they are working on a math problem. Design a robot application that analyzes the user's emotions and provides appropriate reactions." This prompt forms the basis for the AI ​​model to generate appropriate emotional responses in a home learning support system.

[0232] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0233] Step 1:

[0234] The server retrieves the user's past learning history data from the database. Based on this historical data, it uses data mining techniques to analyze the user's areas of particular interest and past learning trends. As a result of the analysis, it identifies the user's areas of interest and passes that information to the next processing step.

[0235] Step 2:

[0236] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This input data is sent to a server for emotion analysis. The server uses facial recognition software and voice analysis algorithms to evaluate the user's emotional state in real time. The results of this analysis are used in the next step as data indicating the user's emotional state.

[0237] Step 3:

[0238] The server selects appropriate learning materials based on the areas of interest obtained in Step 1 and the sentiment evaluation results in Step 2. Using a material selection algorithm, it determines the material that best matches the user's learning needs and sends the material data to the terminal.

[0239] Step 4:

[0240] The device displays received learning materials in an interactive format. The displayed materials are designed for easy user interaction and can be operated via touchscreens or other means. Users begin learning through the materials, and their progress is recorded on the device.

[0241] Step 5:

[0242] The device continuously monitors the user's facial expressions and voice during learning and sends real-time emotional changes back to the server. Based on the latest emotional data, the server generates feedback tailored to the user's learning progress. This feedback is displayed on the device in the following steps.

[0243] Step 6:

[0244] The device provides the user with feedback received from the server in step 5. This feedback optimizes the user's learning experience by adjusting the difficulty level of the problems and providing hints. The goal of the feedback is also to maintain the user's motivation to learn and improve their understanding.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] [Second Embodiment]

[0249] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0250] 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.

[0251] 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).

[0252] 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.

[0253] 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.

[0254] 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).

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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".

[0261] This invention realizes a home learning support system that provides users with an appropriate learning experience. The operation of the system will be described in detail below.

[0262] The server first collects the user's learning history data and analyzes it to estimate the user's areas of interest. This analysis is performed using machine learning algorithms to identify areas of interest. Based on the results, the server selects learning materials related to the user's areas of interest and sends them to the device.

[0263] The device displays learning materials tailored to the user based on information received from the server. These materials include interactive content and are designed to engage the user. Devices equipped with voice recognition capabilities allow users to give voice commands, enabling hands-free changes to learning content and activities.

[0264] For example, if a user wants to learn about dinosaurs, the system provides quizzes and puzzles about the history and types of dinosaurs. As the user solves the quizzes and gets correct answers, points are added, and once a certain amount is reached, the user receives a reward. This makes learning enjoyable and creates a sustainable learning experience.

[0265] Furthermore, the server manages the learning schedule and sends regular reminders to the user. When the user-set learning time arrives, the device provides visual and audible notifications to encourage the user to begin learning. This system allows users to plan efficiently and continue learning consistently.

[0266] Finally, the server periodically generates reports summarizing the user's learning progress and provides them to parents. Through these reports, parents can understand their child's learning status and provide appropriate support and feedback.

[0267] Thus, the present invention effectively supports home learning by maximizing user interest and providing an efficient and enjoyable learning environment.

[0268] The following describes the processing flow.

[0269] Step 1:

[0270] The server retrieves the user's learning history data from the database. This includes past learning activities, areas of interest, and learning outcomes.

[0271] Step 2:

[0272] The server runs machine learning algorithms based on the acquired data to analyze areas of particular interest to the user. Based on these analysis results, it identifies topics of interest to the user.

[0273] Step 3:

[0274] The server selects appropriate learning materials from the database that are related to the user's area of ​​interest. The selected materials are designed to engage the user's interest.

[0275] Step 4:

[0276] The server sends the selected learning materials to the device, which receives and displays them to the user. The materials include interactive elements and visual content.

[0277] Step 5:

[0278] The user begins learning using the learning materials displayed on the device. The device interactively receives the user's selections and answers and records their activity.

[0279] Step 6:

[0280] Users control the learning process using voice commands. For example, they can give voice commands such as "move on to the next question."

[0281] Step 7:

[0282] The device analyzes the user's voice commands using speech recognition technology and executes processing according to the commands. It can present new quizzes or switch learning items.

[0283] Step 8:

[0284] To assign points according to the progress of the user's learning activities, the terminal sends the user's achievements to the server. The server aggregates this and awards rewards when a certain number of points are achieved.

[0285] Step 9:

[0286] The server monitors the user's learning schedule and sends a reminder to the terminal when the scheduled learning time approaches.

[0287] Step 10:

[0288] The terminal notifies the user of the reminder and prompts the start of learning visually and audibly. With this notification, the user starts learning at the planned time.

[0289] Step 11:

[0290] The server periodically aggregates the user's learning activity data and generates a progress report. The server provides this report to the guardian and the user to visualize the learning results.

[0291] Step 12:

[0292] The guardian views the progress report through the terminal and checks the user's learning status. Based on this information, effective educational support can be provided.

[0293] (Example 1)

[0294] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0295] In today's educational environment, there is a need for adaptive learning support tailored to each user's learning style and interests. However, it is not easy to analyze individual learning history in detail and provide appropriate learning materials based on that analysis, nor is it easy to maintain an interactive and continuous learning experience. This invention aims to provide effective learning support based on the user's interests and to enhance their motivation to learn.

[0296] 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.

[0297] In this invention, the server includes means for analyzing the user's history information to estimate areas of interest, means for selecting and displaying appropriate learning materials based on the estimated areas of interest, and means for analyzing voice instructions to control the learning content. This makes it possible to realize an adaptive learning environment tailored to each individual user.

[0298] "User" refers to anyone who uses the system to engage in learning activities.

[0299] "History information" refers to a record of the learning content and operations that a user has performed in the past.

[0300] "Areas of interest" refers to the learning fields or topics that a user is interested in or has shown interest in.

[0301] "Educational materials" refer to content provided for learning, including in the form of text, images, and videos.

[0302] "Voice instructions" refers to operational requests made by users to the system using their voice.

[0303] A "scoring system" refers to a mechanism that quantifies the results of learning activities and links them to accumulation or rewards.

[0304] A "reward system" refers to a mechanism that provides incentives based on the user's learning achievements.

[0305] The "learning plan" refers to the schedule or timetable for learning set by the user.

[0306] The "notification" refers to the function of reminding and informing the user.

[0307] The "learning progress information" refers to the data indicating the progress and results of the user's learning.

[0308] The "guardian" refers to a person who can supervise and guide the user's learning activities.

[0309] The present invention realizes a system that provides optimal educational support according to the individual learning needs of users. The system mainly consists of a server, a terminal, and a user.

[0310] The server is installed in a cloud computing environment and collects and stores the user's history information. A machine learning algorithm is implemented on the server, and this algorithm is used to analyze the collected history information to identify the user's area of interest. Python and the scikit-learn library are often used for the implementation of this algorithm. When the area of interest is identified, the server selects the corresponding teaching materials and transmits them to the terminal via Internet communication.

[0311] The terminal is an electronic device equipped with a user interface and displays the teaching materials sent from the server. The terminal performs a visual display of the teaching materials based on a web application using HTML5 and CSS3. Teaching materials containing interactive content include puzzles in the drag & drop format, quizzes in the multiple-choice format, etc. Furthermore, the terminal is equipped with a voice recognition function and can perform operations based on the user's voice instructions. Generally, a voice recognition API is used for this.

[0312] Users can learn using materials displayed based on their interests. For example, a user who wants to learn about "dinosaurs" will be presented with interactive quizzes and puzzles about their overview, types, and history. Furthermore, users earn points for correctly answering quizzes, and receive rewards based on their accumulated points.

[0313] Learning progress is regularly collected and analyzed on the server to understand the user's situation and generate feedback. The generated feedback is provided to the user's guardian and can be used to support their learning. The system manages the learning plan and encourages users to study regularly by sending notifications according to a pre-set schedule.

[0314] As an example of a prompt, by inputting the instruction "Analyze the user's learning history this week and suggest learning for next week" into the generating AI model, the learning content for the next step can be appropriately selected.

[0315] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0316] Step 1:

[0317] The server collects user history information from a database. Input includes the user's ID and past activity logs. Based on this data, the server uses machine learning algorithms to analyze it and identify the user's areas of interest. This analysis yields a list of learning areas of interest to the user.

[0318] Step 2:

[0319] The server selects appropriate learning materials based on identified areas of interest. The input is a list of the user's areas of interest. The server searches the learning material database for materials related to these areas of interest and selects them. As a result of this selection process, a set of learning materials suitable for the user is generated as output.

[0320] Step 3:

[0321] The server sends the selected learning materials to the terminal. The inputs include the set of learning materials and user information. The server transmits this data to the terminal via the network. The terminal displays the received learning materials in its user interface, and the output presents the user with visual learning content.

[0322] Step 4:

[0323] Users learn using learning materials displayed on their device screen. The input is the learning materials displayed on the device. Users interact with interactive content included in the materials (e.g., quizzes and puzzles). As a result of this interaction, learning progress and scores are recorded as output.

[0324] Step 5:

[0325] The device receives voice commands from the user and converts them to text via a speech recognition API. The input is the user's voice. The device analyzes this, extracts the command content, and performs screen operations based on it. The output is content controlled according to the user's instructions.

[0326] Step 6:

[0327] The server aggregates and analyzes the user's learning progress and generates feedback. Input includes the user's learning history and score data. Using a generative AI model, this data is analyzed to assess learning and suggest the next steps. The output is a feedback report, which is provided to parents.

[0328] Step 7:

[0329] The server sends reminders to the device based on the configured learning plan. The input is learning schedule information. The server executes a procedure to send a notification at the specified time, and the output is that the device displays a visual and audible notification prompting the user to start learning.

[0330] (Application Example 1)

[0331] 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."

[0332] In today's educational environment, providing personalized learning experiences tailored to learners' interests is a challenging task. Furthermore, the importance of motivating learners to continue learning proactively and regularly checking their progress has been highlighted. Traditional systems lack sufficient flexible responses and feedback based on learners' interests and progress, creating a need for a system that balances efficient learning with enjoyment.

[0333] 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.

[0334] In this invention, the server includes means for analyzing the user's learning history to estimate their areas of interest, means for selecting and displaying appropriate learning materials based on the estimated areas of interest, and means for providing customized quizzes and information based on the user's interests. This enables learners to engage in effective learning tailored to their interests. Furthermore, a point system and reward system can be used to create a fun, game-like learning environment, supporting continued learning.

[0335] "User learning history" refers to a collection of information such as the educational activities a learner has participated in in the past, the materials they have used, and their progress.

[0336] An "area of ​​interest" refers to a specific field or topic that a learner is personally interested in or concerned with.

[0337] "Learning materials" refer to educational materials and resources provided to expand learners' knowledge, and may take the form of electronic or physical materials.

[0338] "Voice commands" are a means of communicating operations or requests to a system via voice.

[0339] A "point system" is a type of evaluation system where points are added according to the progress of learning or activities, and rewards or benefits are given when a certain standard is reached.

[0340] A "reward system" is a set of incentives provided to improve motivation for learning or engaging in activities.

[0341] A "study schedule" is a planned arrangement of time and plans aimed at achieving a specific goal.

[0342] A "reminder" is a notification that prompts learners to reconfirm pre-scheduled plans or activities and encourages them to take action.

[0343] "Learning progress data" refers to information that shows the results and degree of progress that learners have achieved through learning activities.

[0344] A "guardian" is a person involved in the protection and management of learners, and is typically responsible for minors.

[0345] "Visual information" refers to information perceived through vision, and includes images, videos, charts, and diagrams.

[0346] "Auditory information" refers to information perceived through hearing, and includes speech, music, sound effects, etc.

[0347] "Feedback" refers to evaluations and responses to activities performed by learners, and is information used to facilitate further improvement and enhance learning effectiveness.

[0348] "Interactive learning" refers to educational activities in which learners and systems interact with each other, and responses are returned in real time.

[0349] A "customized quiz" is a set of questions specifically designed to suit the learner's interests and level.

[0350] "Information provision" is the act of conveying new knowledge or content to learners, and it can take various forms.

[0351] A "virtual reward" is a digital form of incentive that does not physically exist in the real world but is provided to give learners a sense of satisfaction or accomplishment.

[0352] A "learning theme" is a subject or task that learners are expected to focus on intensively over a specific period of time.

[0353] A "prompt" is a formalized input statement used to provide information to a model or system, and an instruction to produce an output result.

[0354] The system for implementing this invention consists of both a server and a terminal. The server first collects and analyzes the user's learning history. The collected data is used to estimate areas of interest, and machine learning algorithms are used to identify the user's areas of interest. This is achieved using data analysis and machine learning libraries such as Python, Pandas, and Scikit-learn.

[0355] Next, the server selects appropriate learning materials based on the estimated areas of interest. These materials are retrieved from a database and configured to provide quizzes and information used during the learning process. On the user's device, an interactive UI is built using web frameworks such as Django or Flask, providing visual and auditory feedback.

[0356] When users give voice commands, a speech recognition library (such as the Google Speech-to-Text API) is used to analyze the voice commands and enable interaction with the learning content. This feature allows users to have a hands-free, interactive learning experience.

[0357] Furthermore, this system incorporates a point system and a reward system. Points are added based on the number of questions answered correctly, and virtual rewards are given when a certain number of points are reached. This incentive increases motivation to learn.

[0358] Furthermore, the server manages the learning schedule and sends reminders according to the learning time. The device alerts the user through visual and auditory notifications to promote learning. Visual information can include pop-up notifications and colorful animations, while auditory information can include alert sounds and voice messages.

[0359] For example, if a user expresses interest in "history," the server will select and provide quizzes and puzzles related to history. Furthermore, by using prompts to instruct the generative AI model, "If the user is interested in 'history,' what kind of quizzes and learning materials should I provide?", it becomes possible to generate even more personalized content. This will give the user's learning experience greater depth and breadth.

[0360] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0361] Step 1:

[0362] The server collects user learning history data and stores it in a database. This process receives information about past learning activities, achievements, and materials used as input, organizes it, and stores it in the database. This prepares the foundational data needed for subsequent processing.

[0363] Step 2:

[0364] The server analyzes the collected data and uses machine learning algorithms to estimate the user's areas of interest. The input is the user's learning history data, which is then processed using Pandas and Scikit-learn to output estimated areas of interest. This analysis enables the provision of a learning experience tailored to the user's individual needs.

[0365] Step 3:

[0366] The server selects appropriate learning materials from the database based on estimated areas of interest. Using the estimated areas of interest as input, it retrieves relevant material information via database queries and outputs the selected material information. This ensures that content tailored to a specific user is prepared.

[0367] Step 4:

[0368] The terminal displays learning materials sent from the server to the user. The input is the selected learning materials, and the output is the display of the materials with visual or auditory feedback. This operation creates an environment in which the user can intuitively utilize the content.

[0369] Step 5:

[0370] When a user issues a voice command, the device receives it and performs speech recognition. The input is the user's voice command, and the output is the parsed operation command. The speech recognition process is implemented using the Google Speech-to-Text API, enabling hands-free operation.

[0371] Step 6:

[0372] The server sends reminders at predetermined times based on the learning schedule. It takes schedule data as input and outputs periodic notification messages. This supports users in developing a planned learning habit.

[0373] Step 7:

[0374] The server monitors learning progress using a point system and reward system. It evaluates the results of learning activities, inputs the points added, and outputs the status of virtual rewards. This process serves as an incentive to increase the user's motivation to learn.

[0375] Step 8:

[0376] The server periodically compiles learning progress data and generates reports. It takes progress data as input and outputs visualized reports. This makes it easy for parents and educators to understand the learning progress.

[0377] 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.

[0378] This invention realizes a home learning support system that utilizes an emotion engine in the user's learning environment to provide optimal learning for each individual user. The operation of the system will be described in detail below.

[0379] The server acquires and analyzes the user's learning history, and processes voice and facial expression data using an emotion engine. This process evaluates the user's current emotional state, and uses this data to estimate the user's areas of interest. Based on the obtained areas of interest and the emotion evaluation results, the server selects the most suitable learning materials.

[0380] The selected learning materials are sent to the device. The device not only displays the materials in an interactive format and provides learning opportunities, but also continuously monitors the user's facial expressions and voice to capture emotional changes in real time. This allows the device to provide immediate feedback and adjust the difficulty level according to the user's level of concentration and understanding.

[0381] For example, if a user is feeling frustrated with a difficult problem, the emotion engine will sense this and the device will change its settings to make the problem a little easier or provide hints. Also, if the user appears to be enjoying themselves, the device will maintain their current progress or offer further challenges.

[0382] The server also manages learning schedules, taking the user's emotional state into account. Reminders are adjusted to ensure the user can comfortably begin learning. For example, learning might be scheduled to start during a comfortable time after the user has refreshed themselves.

[0383] Parents are provided with learning progress reports generated by the server. These reports include information on the user's learning outcomes, as well as changes in their emotions and learning attitudes. This allows parents to gain a deeper understanding of their child's learning experience and provide support with an appropriate approach.

[0384] Thus, the present invention utilizes an emotion engine to provide a learning environment that is attuned to the user's emotions, thereby realizing appropriate and effective support for home learning that is tailored to individual learning needs.

[0385] The following describes the processing flow.

[0386] Step 1:

[0387] The server retrieves the user's past learning history data from the database. This data includes information such as the content of the learning, frequency, and success rate.

[0388] Step 2:

[0389] The server passes the user's voice data and facial expression data, collected via voice or webcam, to the emotion engine. This engine then analyzes the user's current emotional state.

[0390] Step 3:

[0391] The server estimates the user's areas of interest based on analyzed emotional state and learning history data. This estimation is then used to select appropriate learning materials. The difficulty level and content of the learning materials are considered during the selection process.

[0392] Step 4:

[0393] The server sends the selected learning material information to the terminal. The terminal receives this information and displays the learning materials in a format suitable for the user. Interactive content may also be included.

[0394] Step 5:

[0395] The user begins learning using the learning materials provided on the device. The device continues to monitor the user's voice and facial expressions, and its emotion engine captures changes in emotion in real time.

[0396] Step 6:

[0397] The device dynamically adapts learning content when it detects changes in emotions. For example, if frustration is detected, it will make adjustments such as displaying hints or lowering the difficulty level of the questions.

[0398] Step 7:

[0399] As the user's learning progresses, points are added and the data is sent to the server. The server compiles the points and updates the reward system as needed.

[0400] Step 8:

[0401] The server monitors the learning schedule and issues reminders at times that take into account the user's emotional state. The device notifies the user of these reminders visually and audibly, encouraging them to start learning at the appropriate time.

[0402] Step 9:

[0403] The server combines the user's learning data and sentiment data to generate a progress report. This report summarizes the learning outcomes and trends in sentiment changes.

[0404] Step 10:

[0405] Parents can view progress reports through their devices and obtain detailed information about their child's learning process. This information can then be used to provide effective learning support.

[0406] (Example 2)

[0407] 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".

[0408] Providing an optimal learning experience tailored to each learner's individual emotional state is difficult with conventional technologies, and a particular challenge is the lack of immediate adjustment of educational resources based on emotional changes. Furthermore, it is necessary to improve parents' understanding of and support for their child's learning by providing them with reports that integrate learning progress and emotional data.

[0409] 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.

[0410] In this invention, the server includes means for analyzing the user's learning history to acquire information and estimating relevant areas based on that information; means for processing audio and video data to evaluate the user's emotional state; and means for adjusting the difficulty level of learning resources based on the evaluated emotional state. This enables personalized education that responds to the learner's emotional state and efficient progress management.

[0411] "Analyzing a user's learning history" refers to the act of thoroughly investigating past learning activity data to clarify learning trends and progress.

[0412] "Estimating relevant fields" is the process of identifying subjects or themes that learners will find interesting and that are likely to lead to effective learning.

[0413] "Selecting and presenting educational resources" means choosing learning materials and content that are suitable for learners and making them available for them to use.

[0414] "Processing audio and video data" refers to analyzing audio and visual information obtained from learners to derive meaningful conclusions.

[0415] "Assessing emotional state" means measuring a learner's mental and emotional state and judging it according to specific indicators.

[0416] "Adjusting the difficulty level" means changing the complexity and pace of learning content to match the learner's level of understanding and emotional state.

[0417] "Effective progress management" refers to understanding the learner's progress and setting appropriate learning plans and goals based on that understanding.

[0418] To implement this invention, it is necessary to build a system in which a server and a terminal work together. The server collects and analyzes the user's learning history using a database and analysis software running on the server. This could involve utilizing data analysis tools and machine learning algorithms (e.g., random forest). By analyzing the learning history, the user's interests and preferences are estimated, and appropriate educational resources are selected. The selected educational resources are presented to the user via the terminal.

[0419] The device interactively presents educational resources through a user interface and captures the user's voice and facial expressions using a camera and microphone. This data is processed by emotion recognition tools (e.g., OpenCV, speech analysis tools) to evaluate the user's emotional state in real time. Based on this evaluation data, the server adjusts the difficulty level of the learning materials. For example, if frustration is detected while the user is working on a challenging problem, the problem is simplified or hints are displayed.

[0420] For example, if a user is showing enjoyment while practicing English listening, the device can continue its progress and add more challenging tasks. In addition, the server generates a report that aggregates learning progress and emotional data, making it accessible to parents. This allows parents to understand not only their child's learning progress but also trends in their emotional changes.

[0421] An example of a prompt for a generative AI model might be a question like, "Please explain in detail the specific methods for selecting learning materials based on user sentiment data."

[0422] In this way, the present invention makes it possible to utilize emotional data to provide learners with a personalized educational experience.

[0423] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0424] Step 1:

[0425] The server retrieves the user's learning history data from the database. The user ID is used as input, and the output provides data such as the user's past learning content, time spent, and performance. This data is used to prepare for analysis using machine learning algorithms. Specifically, the server sends queries to the database via an API to retrieve the necessary information.

[0426] Step 2:

[0427] The server receives user voice and facial expression data and processes it using an emotion engine. The input is real-time voice and video data sent from the terminal, and the output is an evaluation of the user's emotional state (e.g., positive, negative, neutral). This emotion evaluation uses facial expression recognition tools and voice analysis software. Specifically, the server sends this data to a dedicated analysis module for evaluation using statistical methods.

[0428] Step 3:

[0429] The server selects the most suitable educational resources based on analyzed sentiment data and learning history. The previously obtained sentiment state evaluation and learning history data are used as input. The output is a selection of educational resources that match the user's current interests and learning needs. Specifically, the server uses a machine learning model to predict appropriate learning materials and determines their priority.

[0430] Step 4:

[0431] The terminal receives educational resources sent from the server and displays them through a user interface. The input is the selected educational resources from the server, and the output is their display in a format visible to the user. Specifically, the terminal configures display settings and prepares to provide an interactive learning experience.

[0432] Step 5:

[0433] The device continuously monitors the user's voice and facial expressions and transmits data to the server in real time. Input is the user's voice and visual information, and output is continuous data for analysis. The device acquires information using a camera and microphone, packages that data appropriately, and sends it to the server.

[0434] Step 6:

[0435] The server integrates learning progress data and emotional states, generates a report, and provides it to parents. The inputs used are user emotional ratings and statistical data from learning history, and the output is an aggregated report. The server aggregates the data and generates the report using visualization tools, allowing parents to understand learning progress and emotional trends.

[0436] (Application Example 2)

[0437] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0438] A major challenge in modern home learning environments is the lack of nuanced learning materials and support tailored to learners' interests and emotions. Traditional learning support systems struggle to grasp learners' emotional states in real time and provide appropriate adjustments to material difficulty and feedback. As a result, learners may lose motivation or struggle to understand the material. Providing a suitable learning environment is particularly difficult for children with complex emotional states.

[0439] 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.

[0440] In this invention, the server includes means for evaluating the user's emotional state and adjusting the difficulty level of the learning materials based on the obtained data, and means for monitoring the user's emotions in real time during learning and providing feedback. This enables optimal learning support that is attentive to the learner's emotions.

[0441] "Analyzing a user's learning history" means acquiring data on the learning content and progress that learners have undertaken in the past, and then analyzing that data to understand the learner's interests and tendencies.

[0442] "Estimating areas of interest" means predicting the areas and themes that learners are interested in, based on their acquired learning history and sentiment data.

[0443] "Selecting and displaying appropriate learning materials" means choosing the most effective and appropriate materials based on the estimated areas of interest and the learner's level, and presenting them on the learner's viewing device.

[0444] "Analyzing voice commands" means that the system analyzes the voice commands uttered by the learner and operates or manages the learning content according to the content of those commands.

[0445] A "point system and reward system" is a mechanism that promotes learning in a fun, game-like way by awarding points to learners based on the goals and progress they achieve, and providing rewards based on those points.

[0446] "Managing the learning schedule" means sending learning reminders and notifications based on a pre-set schedule in order to efficiently plan and secure the learner's study time.

[0447] "Providing learning progress data to household members" means recording the learner's achievements and progress in learning content as data, and informing other members of the household in an appropriate format.

[0448] "Assessing the user's emotional state" involves analyzing the learner's current emotions and psychological state based on data such as facial expressions and voice, and then evaluating the results.

[0449] "Adjusting the difficulty level of the learning materials" means appropriately changing the content of the materials and the difficulty level of the problems presented, according to the learner's level of understanding and emotional state.

[0450] "Monitoring emotions in real time and providing feedback" means continuously observing learners' emotions during the learning process and immediately providing advice and improvement measures in response to any changes.

[0451] This invention aims to build a system that provides a learning environment tailored to the learner's emotions within the home. The details are described below.

[0452] The server first retrieves the learner's learning history from a database and analyzes it to estimate the learner's areas of interest and tendencies. The server is equipped with an emotion analysis engine that analyzes the learner's voice and facial expression data in real time. The analyzed emotion data is used to evaluate the learner's current emotional state.

[0453] The device is equipped with a camera and microphone, which capture the user's facial expressions and voice. The acquired data is transmitted to a server in real time for emotion analysis. Based on the analysis results, the server selects appropriate learning materials and sends them to the device. The device displays the materials to the user in an interactive format, supporting the user's learning process.

[0454] Furthermore, the device monitors the user's tone of voice and changes in facial expressions, analyzing the user's emotions in real time based on a pre-configured algorithm. This allows it to adjust the difficulty of a problem or provide hints if the user is feeling frustrated. Conversely, if the user is enjoying learning, it can challenge them with more difficult tasks.

[0455] As a concrete example, let's assume a primary school student is learning mathematics online and is facing a difficult problem. The device detects the user's distressed expression and receives instructions from the server to display a hint. As a result, the user is motivated to continue learning. Such real-time emotional responses improve the user's learning experience and maximize their learning outcomes.

[0456] An example of a prompt when using a generative AI model is: "Think of a way to optimize learning content by reading the user's emotions from their facial expressions and voice while they are working on a math problem. Design a robot application that analyzes the user's emotions and provides appropriate reactions." This prompt forms the basis for the AI ​​model to generate appropriate emotional responses in a home learning support system.

[0457] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0458] Step 1:

[0459] The server retrieves the user's past learning history data from the database. Based on this historical data, it uses data mining techniques to analyze the user's areas of particular interest and past learning trends. As a result of the analysis, it identifies the user's areas of interest and passes that information to the next processing step.

[0460] Step 2:

[0461] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This input data is sent to a server for emotion analysis. The server uses facial recognition software and voice analysis algorithms to evaluate the user's emotional state in real time. The results of this analysis are used in the next step as data indicating the user's emotional state.

[0462] Step 3:

[0463] The server selects appropriate learning materials based on the areas of interest obtained in Step 1 and the sentiment evaluation results in Step 2. Using a material selection algorithm, it determines the material that best matches the user's learning needs and sends the material data to the terminal.

[0464] Step 4:

[0465] The device displays received learning materials in an interactive format. The displayed materials are designed for easy user interaction and can be operated via touchscreens or other means. Users begin learning through the materials, and their progress is recorded on the device.

[0466] Step 5:

[0467] The device continuously monitors the user's facial expressions and voice during learning and sends real-time emotional changes back to the server. Based on the latest emotional data, the server generates feedback tailored to the user's learning progress. This feedback is displayed on the device in the following steps.

[0468] Step 6:

[0469] The device provides the user with feedback received from the server in step 5. This feedback optimizes the user's learning experience by adjusting the difficulty level of the problems and providing hints. The goal of the feedback is also to maintain the user's motivation to learn and improve their understanding.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] [Third Embodiment]

[0474] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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".

[0486] This invention realizes a home learning support system that provides users with an appropriate learning experience. The operation of the system will be described in detail below.

[0487] The server first collects the user's learning history data and analyzes it to estimate the user's areas of interest. This analysis is performed using machine learning algorithms to identify areas of interest. Based on the results, the server selects learning materials related to the user's areas of interest and sends them to the device.

[0488] The device displays learning materials tailored to the user based on information received from the server. These materials include interactive content and are designed to engage the user. Devices equipped with voice recognition capabilities allow users to give voice commands, enabling hands-free changes to learning content and activities.

[0489] For example, if a user wants to learn about dinosaurs, the system provides quizzes and puzzles about the history and types of dinosaurs. As the user solves the quizzes and gets correct answers, points are added, and once a certain amount is reached, the user receives a reward. This makes learning enjoyable and creates a sustainable learning experience.

[0490] Furthermore, the server manages the learning schedule and sends regular reminders to the user. When the user-set learning time arrives, the device provides visual and audible notifications to encourage the user to begin learning. This system allows users to plan efficiently and continue learning consistently.

[0491] Finally, the server periodically generates reports summarizing the user's learning progress and provides them to parents. Through these reports, parents can understand their child's learning status and provide appropriate support and feedback.

[0492] Thus, the present invention effectively supports home learning by maximizing user interest and providing an efficient and enjoyable learning environment.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The server retrieves the user's learning history data from the database. This includes past learning activities, areas of interest, and learning outcomes.

[0496] Step 2:

[0497] The server runs machine learning algorithms based on the acquired data to analyze areas of particular interest to the user. Based on these analysis results, it identifies topics of interest to the user.

[0498] Step 3:

[0499] The server selects appropriate learning materials from the database that are related to the user's area of ​​interest. The selected materials are designed to engage the user's interest.

[0500] Step 4:

[0501] The server sends the selected learning materials to the device, which receives and displays them to the user. The materials include interactive elements and visual content.

[0502] Step 5:

[0503] The user begins learning using the learning materials displayed on the device. The device interactively receives the user's selections and answers and records their activity.

[0504] Step 6:

[0505] Users control the learning process using voice commands. For example, they can give voice commands such as "move on to the next question."

[0506] Step 7:

[0507] The device analyzes the user's voice commands using speech recognition technology and executes processing according to the commands. It can present new quizzes or switch learning items.

[0508] Step 8:

[0509] To award points based on the user's learning progress, the device sends the user's results to the server. The server compiles this data and awards rewards once a certain number of points are reached.

[0510] Step 9:

[0511] The server monitors the user's study schedule and sends a reminder to the device as the scheduled study time approaches.

[0512] Step 10:

[0513] The device notifies the user of a reminder and encourages them to start learning with visual and auditory cues. This notification prompts the user to begin learning at the scheduled time.

[0514] Step 11:

[0515] The server periodically collects user learning activity data and generates progress reports. The server provides these reports to parents and users, making learning outcomes visible.

[0516] Step 12:

[0517] Parents can view progress reports through their devices to check their child's learning status. Based on this information, they can provide effective educational support.

[0518] (Example 1)

[0519] 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."

[0520] In today's educational environment, there is a need for adaptive learning support tailored to each user's learning style and interests. However, it is not easy to analyze individual learning history in detail and provide appropriate learning materials based on that analysis, nor is it easy to maintain an interactive and continuous learning experience. This invention aims to provide effective learning support based on the user's interests and to enhance their motivation to learn.

[0521] 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.

[0522] In this invention, the server includes means for analyzing the user's history information to estimate areas of interest, means for selecting and displaying appropriate learning materials based on the estimated areas of interest, and means for analyzing voice instructions to control the learning content. This makes it possible to realize an adaptive learning environment tailored to each individual user.

[0523] "User" refers to anyone who uses the system to engage in learning activities.

[0524] "History information" refers to a record of the learning content and operations that a user has performed in the past.

[0525] "Areas of interest" refers to the learning fields or topics that a user is interested in or has shown interest in.

[0526] "Educational materials" refer to content provided for learning, including in the form of text, images, and videos.

[0527] "Voice instructions" refers to operational requests made by users to the system using their voice.

[0528] A "scoring system" refers to a mechanism that quantifies the results of learning activities and links them to accumulation or rewards.

[0529] A "reward system" refers to a mechanism that provides incentives based on the user's learning achievements.

[0530] A "study plan" refers to the schedule or timetable set by the user for their studies.

[0531] "Notifications" refers to features that send reminders and announcements to users.

[0532] "Learning progress information" refers to data that shows the user's learning progress and results.

[0533] A "guardian" refers to a person who can supervise and guide the learning activities of a user.

[0534] This invention aims to realize a system that provides optimal educational support tailored to the individual learning needs of users. The system mainly consists of a server, terminals, and users.

[0535] The server is installed in a cloud computing environment and collects and stores user history information. The server implements a machine learning algorithm, which is used to analyze the collected history information and identify the user's areas of interest. This algorithm is often implemented using Python and the scikit-learn library. Once the areas of interest are identified, the server selects corresponding learning materials and sends them to the user's device via the internet.

[0536] The terminal is an electronic device equipped with a user interface that displays educational materials sent from the server. The terminal uses a web application based on HTML5 and CSS3 to visually display the materials. Interactive materials include drag-and-drop puzzles and multiple-choice quizzes. Furthermore, the terminal has voice recognition capabilities, allowing operation based on the user's voice commands. This typically utilizes a voice recognition API.

[0537] Users can learn using materials displayed based on their interests. For example, a user who wants to learn about "dinosaurs" will be presented with interactive quizzes and puzzles about their overview, types, and history. Furthermore, users earn points for correctly answering quizzes, and receive rewards based on their accumulated points.

[0538] Learning progress is regularly collected and analyzed on the server to understand the user's situation and generate feedback. The generated feedback is provided to the user's guardian and can be used to support their learning. The system manages the learning plan and encourages users to study regularly by sending notifications according to a pre-set schedule.

[0539] As an example of a prompt, by inputting the instruction "Analyze the user's learning history this week and suggest learning for next week" into the generating AI model, the learning content for the next step can be appropriately selected.

[0540] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0541] Step 1:

[0542] The server collects user history information from a database. Input includes the user's ID and past activity logs. Based on this data, the server uses machine learning algorithms to analyze it and identify the user's areas of interest. This analysis yields a list of learning areas of interest to the user.

[0543] Step 2:

[0544] The server selects appropriate learning materials based on identified areas of interest. The input is a list of the user's areas of interest. The server searches the learning material database for materials related to these areas of interest and selects them. As a result of this selection process, a set of learning materials suitable for the user is generated as output.

[0545] Step 3:

[0546] The server sends the selected learning materials to the terminal. The inputs include the set of learning materials and user information. The server transmits this data to the terminal via the network. The terminal displays the received learning materials in its user interface, and the output presents the user with visual learning content.

[0547] Step 4:

[0548] Users learn using learning materials displayed on their device screen. The input is the learning materials displayed on the device. Users interact with interactive content included in the materials (e.g., quizzes and puzzles). As a result of this interaction, learning progress and scores are recorded as output.

[0549] Step 5:

[0550] The device receives voice commands from the user and converts them to text via a speech recognition API. The input is the user's voice. The device analyzes this, extracts the command content, and performs screen operations based on it. The output is content controlled according to the user's instructions.

[0551] Step 6:

[0552] The server aggregates and analyzes the user's learning progress and generates feedback. Input includes the user's learning history and score data. Using a generative AI model, this data is analyzed to assess learning and suggest the next steps. The output is a feedback report, which is provided to parents.

[0553] Step 7:

[0554] The server sends reminders to the device based on the configured learning plan. The input is learning schedule information. The server executes a procedure to send a notification at the specified time, and the output is that the device displays a visual and audible notification prompting the user to start learning.

[0555] (Application Example 1)

[0556] 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."

[0557] In today's educational environment, providing personalized learning experiences tailored to learners' interests is a challenging task. Furthermore, the importance of motivating learners to continue learning proactively and regularly checking their progress has been highlighted. Traditional systems lack sufficient flexible responses and feedback based on learners' interests and progress, creating a need for a system that balances efficient learning with enjoyment.

[0558] 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.

[0559] In this invention, the server includes means for analyzing the user's learning history to estimate their areas of interest, means for selecting and displaying appropriate learning materials based on the estimated areas of interest, and means for providing customized quizzes and information based on the user's interests. This enables learners to engage in effective learning tailored to their interests. Furthermore, a point system and reward system can be used to create a fun, game-like learning environment, supporting continued learning.

[0560] "User learning history" refers to a collection of information such as the educational activities a learner has participated in in the past, the materials they have used, and their progress.

[0561] An "area of ​​interest" refers to a specific field or topic that a learner is personally interested in or concerned with.

[0562] "Learning materials" refer to educational materials and resources provided to expand learners' knowledge, and may take the form of electronic or physical materials.

[0563] "Voice commands" are a means of communicating operations or requests to a system via voice.

[0564] A "point system" is a type of evaluation system where points are added according to the progress of learning or activities, and rewards or benefits are given when a certain standard is reached.

[0565] A "reward system" is a set of incentives provided to improve motivation for learning or engaging in activities.

[0566] A "study schedule" is a planned arrangement of time and plans aimed at achieving a specific goal.

[0567] A "reminder" is a notification that prompts learners to reconfirm pre-scheduled plans or activities and encourages them to take action.

[0568] "Learning progress data" refers to information that shows the results and degree of progress that learners have achieved through learning activities.

[0569] A "guardian" is a person involved in the protection and management of learners, and is typically responsible for minors.

[0570] "Visual information" refers to information perceived through vision, and includes images, videos, charts, and diagrams.

[0571] "Auditory information" refers to information perceived through hearing, and includes speech, music, sound effects, etc.

[0572] "Feedback" refers to evaluations and responses to activities performed by learners, and is information used to facilitate further improvement and enhance learning effectiveness.

[0573] "Interactive learning" refers to educational activities in which learners and systems interact with each other, and responses are returned in real time.

[0574] A "customized quiz" is a set of questions specifically designed to suit the learner's interests and level.

[0575] "Information provision" is the act of conveying new knowledge or content to learners, and it can take various forms.

[0576] A "virtual reward" is a digital form of incentive that does not physically exist in the real world but is provided to give learners a sense of satisfaction or accomplishment.

[0577] A "learning theme" is a subject or task that learners are expected to focus on intensively over a specific period of time.

[0578] A "prompt" is a formalized input statement used to provide information to a model or system, and an instruction to produce an output result.

[0579] The system for implementing this invention consists of both a server and a terminal. The server first collects and analyzes the user's learning history. The collected data is used to estimate areas of interest, and machine learning algorithms are used to identify the user's areas of interest. This is achieved using data analysis and machine learning libraries such as Python, Pandas, and Scikit-learn.

[0580] Next, the server selects appropriate learning materials based on the estimated areas of interest. These materials are retrieved from a database and configured to provide quizzes and information used during the learning process. On the user's device, an interactive UI is built using web frameworks such as Django or Flask, providing visual and auditory feedback.

[0581] When users give voice commands, a speech recognition library (such as the Google Speech-to-Text API) is used to analyze the voice commands and enable interaction with the learning content. This feature allows users to have a hands-free, interactive learning experience.

[0582] Furthermore, this system incorporates a point system and a reward system. Points are added based on the number of questions answered correctly, and virtual rewards are given when a certain number of points are reached. This incentive increases motivation to learn.

[0583] Furthermore, the server manages the learning schedule and sends reminders according to the learning time. The device alerts the user through visual and auditory notifications to promote learning. Visual information can include pop-up notifications and colorful animations, while auditory information can include alert sounds and voice messages.

[0584] For example, if a user expresses interest in "history," the server will select and provide quizzes and puzzles related to history. Furthermore, by using prompts to instruct the generative AI model, "If the user is interested in 'history,' what kind of quizzes and learning materials should I provide?", it becomes possible to generate even more personalized content. This will give the user's learning experience greater depth and breadth.

[0585] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0586] Step 1:

[0587] The server collects user learning history data and stores it in a database. This process receives information about past learning activities, achievements, and materials used as input, organizes it, and stores it in the database. This prepares the foundational data needed for subsequent processing.

[0588] Step 2:

[0589] The server analyzes the collected data and uses machine learning algorithms to estimate the user's areas of interest. The input is the user's learning history data, which is then processed using Pandas and Scikit-learn to output estimated areas of interest. This analysis enables the provision of a learning experience tailored to the user's individual needs.

[0590] Step 3:

[0591] The server selects appropriate learning materials from the database based on estimated areas of interest. Using the estimated areas of interest as input, it retrieves relevant material information via database queries and outputs the selected material information. This ensures that content tailored to a specific user is prepared.

[0592] Step 4:

[0593] The terminal displays learning materials sent from the server to the user. The input is the selected learning materials, and the output is the display of the materials with visual or auditory feedback. This operation creates an environment in which the user can intuitively utilize the content.

[0594] Step 5:

[0595] When a user issues a voice command, the device receives it and performs speech recognition. The input is the user's voice command, and the output is the parsed operation command. The speech recognition process is implemented using the Google Speech-to-Text API, enabling hands-free operation.

[0596] Step 6:

[0597] The server sends reminders at predetermined times based on the learning schedule. It takes schedule data as input and outputs periodic notification messages. This supports users in developing a planned learning habit.

[0598] Step 7:

[0599] The server monitors learning progress using a point system and reward system. It evaluates the results of learning activities, inputs the points added, and outputs the status of virtual rewards. This process serves as an incentive to increase the user's motivation to learn.

[0600] Step 8:

[0601] The server periodically compiles learning progress data and generates reports. It takes progress data as input and outputs visualized reports. This makes it easy for parents and educators to understand the learning progress.

[0602] 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.

[0603] This invention realizes a home learning support system that utilizes an emotion engine in the user's learning environment to provide optimal learning for each individual user. The operation of the system will be described in detail below.

[0604] The server acquires and analyzes the user's learning history, and processes voice and facial expression data using an emotion engine. This process evaluates the user's current emotional state, and uses this data to estimate the user's areas of interest. Based on the obtained areas of interest and the emotion evaluation results, the server selects the most suitable learning materials.

[0605] The selected learning materials are sent to the device. The device not only displays the materials in an interactive format and provides learning opportunities, but also continuously monitors the user's facial expressions and voice to capture emotional changes in real time. This allows the device to provide immediate feedback and adjust the difficulty level according to the user's level of concentration and understanding.

[0606] For example, if a user is feeling frustrated with a difficult problem, the emotion engine will sense this and the device will change its settings to make the problem a little easier or provide hints. Also, if the user appears to be enjoying themselves, the device will maintain their current progress or offer further challenges.

[0607] The server also manages learning schedules, taking the user's emotional state into account. Reminders are adjusted to ensure the user can comfortably begin learning. For example, learning might be scheduled to start during a comfortable time after the user has refreshed themselves.

[0608] Parents are provided with learning progress reports generated by the server. These reports include information on the user's learning outcomes, as well as changes in their emotions and learning attitudes. This allows parents to gain a deeper understanding of their child's learning experience and provide support with an appropriate approach.

[0609] Thus, the present invention utilizes an emotion engine to provide a learning environment that is attuned to the user's emotions, thereby realizing appropriate and effective support for home learning that is tailored to individual learning needs.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The server retrieves the user's past learning history data from the database. This data includes information such as the content of the learning, frequency, and success rate.

[0613] Step 2:

[0614] The server passes the user's voice data and facial expression data, collected via voice or webcam, to the emotion engine. This engine then analyzes the user's current emotional state.

[0615] Step 3:

[0616] The server estimates the user's areas of interest based on analyzed emotional state and learning history data. This estimation is then used to select appropriate learning materials. The difficulty level and content of the learning materials are considered during the selection process.

[0617] Step 4:

[0618] The server sends the selected learning material information to the terminal. The terminal receives this information and displays the learning materials in a format suitable for the user. Interactive content may also be included.

[0619] Step 5:

[0620] The user begins learning using the learning materials provided on the device. The device continues to monitor the user's voice and facial expressions, and its emotion engine captures changes in emotion in real time.

[0621] Step 6:

[0622] The device dynamically adapts learning content when it detects changes in emotions. For example, if frustration is detected, it will make adjustments such as displaying hints or lowering the difficulty level of the questions.

[0623] Step 7:

[0624] As the user's learning progresses, points are added and the data is sent to the server. The server compiles the points and updates the reward system as needed.

[0625] Step 8:

[0626] The server monitors the learning schedule and issues reminders at times that take into account the user's emotional state. The device notifies the user of these reminders visually and audibly, encouraging them to start learning at the appropriate time.

[0627] Step 9:

[0628] The server combines the user's learning data and sentiment data to generate a progress report. This report summarizes the learning outcomes and trends in sentiment changes.

[0629] Step 10:

[0630] Parents can view progress reports through their devices and obtain detailed information about their child's learning process. This information can then be used to provide effective learning support.

[0631] (Example 2)

[0632] 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."

[0633] Providing an optimal learning experience tailored to each learner's individual emotional state is difficult with conventional technologies, and a particular challenge is the lack of immediate adjustment of educational resources based on emotional changes. Furthermore, it is necessary to improve parents' understanding of and support for their child's learning by providing them with reports that integrate learning progress and emotional data.

[0634] 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.

[0635] In this invention, the server includes means for analyzing the user's learning history to acquire information and estimating relevant areas based on that information; means for processing audio and video data to evaluate the user's emotional state; and means for adjusting the difficulty level of learning resources based on the evaluated emotional state. This enables personalized education that responds to the learner's emotional state and efficient progress management.

[0636] "Analyzing a user's learning history" refers to the act of thoroughly investigating past learning activity data to clarify learning trends and progress.

[0637] "Estimating relevant fields" is the process of identifying subjects or themes that learners will find interesting and that are likely to lead to effective learning.

[0638] "Selecting and presenting educational resources" means choosing learning materials and content that are suitable for learners and making them available for them to use.

[0639] "Processing audio and video data" refers to analyzing audio and visual information obtained from learners to derive meaningful conclusions.

[0640] "Assessing emotional state" means measuring a learner's mental and emotional state and judging it according to specific indicators.

[0641] "Adjusting the difficulty level" means changing the complexity and pace of learning content to match the learner's level of understanding and emotional state.

[0642] "Effective progress management" refers to understanding the learner's progress and setting appropriate learning plans and goals based on that understanding.

[0643] To implement this invention, it is necessary to build a system in which a server and a terminal work together. The server collects and analyzes the user's learning history using a database and analysis software running on the server. This could involve utilizing data analysis tools and machine learning algorithms (e.g., random forest). By analyzing the learning history, the user's interests and preferences are estimated, and appropriate educational resources are selected. The selected educational resources are presented to the user via the terminal.

[0644] The device interactively presents educational resources through a user interface and captures the user's voice and facial expressions using a camera and microphone. This data is processed by emotion recognition tools (e.g., OpenCV, speech analysis tools) to evaluate the user's emotional state in real time. Based on this evaluation data, the server adjusts the difficulty level of the learning materials. For example, if frustration is detected while the user is working on a challenging problem, the problem is simplified or hints are displayed.

[0645] For example, if a user is showing enjoyment while practicing English listening, the device can continue its progress and add more challenging tasks. In addition, the server generates a report that aggregates learning progress and emotional data, making it accessible to parents. This allows parents to understand not only their child's learning progress but also trends in their emotional changes.

[0646] An example of a prompt for a generative AI model might be a question like, "Please explain in detail the specific methods for selecting learning materials based on user sentiment data."

[0647] In this way, the present invention makes it possible to utilize emotional data to provide learners with a personalized educational experience.

[0648] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0649] Step 1:

[0650] The server retrieves the user's learning history data from the database. The user ID is used as input, and the output provides data such as the user's past learning content, time spent, and performance. This data is used to prepare for analysis using machine learning algorithms. Specifically, the server sends queries to the database via an API to retrieve the necessary information.

[0651] Step 2:

[0652] The server receives user voice and facial expression data and processes it using an emotion engine. The input is real-time voice and video data sent from the terminal, and the output is an evaluation of the user's emotional state (e.g., positive, negative, neutral). This emotion evaluation uses facial expression recognition tools and voice analysis software. Specifically, the server sends this data to a dedicated analysis module for evaluation using statistical methods.

[0653] Step 3:

[0654] The server selects the most suitable educational resources based on analyzed sentiment data and learning history. The previously obtained sentiment state evaluation and learning history data are used as input. The output is a selection of educational resources that match the user's current interests and learning needs. Specifically, the server uses a machine learning model to predict appropriate learning materials and determines their priority.

[0655] Step 4:

[0656] The terminal receives educational resources sent from the server and displays them through a user interface. The input is the selected educational resources from the server, and the output is their display in a format visible to the user. Specifically, the terminal configures display settings and prepares to provide an interactive learning experience.

[0657] Step 5:

[0658] The device continuously monitors the user's voice and facial expressions and transmits data to the server in real time. Input is the user's voice and visual information, and output is continuous data for analysis. The device acquires information using a camera and microphone, packages that data appropriately, and sends it to the server.

[0659] Step 6:

[0660] The server integrates learning progress data and emotional states, generates a report, and provides it to parents. The inputs used are user emotional ratings and statistical data from learning history, and the output is an aggregated report. The server aggregates the data and generates the report using visualization tools, allowing parents to understand learning progress and emotional trends.

[0661] (Application Example 2)

[0662] 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."

[0663] A major challenge in modern home learning environments is the lack of nuanced learning materials and support tailored to learners' interests and emotions. Traditional learning support systems struggle to grasp learners' emotional states in real time and provide appropriate adjustments to material difficulty and feedback. As a result, learners may lose motivation or struggle to understand the material. Providing a suitable learning environment is particularly difficult for children with complex emotional states.

[0664] 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.

[0665] In this invention, the server includes means for evaluating the user's emotional state and adjusting the difficulty level of the learning materials based on the obtained data, and means for monitoring the user's emotions in real time during learning and providing feedback. This enables optimal learning support that is attentive to the learner's emotions.

[0666] "Analyzing a user's learning history" means acquiring data on the learning content and progress that learners have undertaken in the past, and then analyzing that data to understand the learner's interests and tendencies.

[0667] "Estimating areas of interest" means predicting the areas and themes that learners are interested in, based on their acquired learning history and sentiment data.

[0668] "Selecting and displaying appropriate learning materials" means choosing the most effective and appropriate materials based on the estimated areas of interest and the learner's level, and presenting them on the learner's viewing device.

[0669] "Analyzing voice commands" means that the system analyzes the voice commands uttered by the learner and operates or manages the learning content according to the content of those commands.

[0670] A "point system and reward system" is a mechanism that promotes learning in a fun, game-like way by awarding points to learners based on the goals and progress they achieve, and providing rewards based on those points.

[0671] "Managing the learning schedule" means sending learning reminders and notifications based on a pre-set schedule in order to efficiently plan and secure the learner's study time.

[0672] "Providing learning progress data to household members" means recording the learner's achievements and progress in learning content as data, and informing other members of the household in an appropriate format.

[0673] "Assessing the user's emotional state" involves analyzing the learner's current emotions and psychological state based on data such as facial expressions and voice, and then evaluating the results.

[0674] "Adjusting the difficulty level of the learning materials" means appropriately changing the content of the materials and the difficulty level of the problems presented, according to the learner's level of understanding and emotional state.

[0675] "Monitoring emotions in real time and providing feedback" means continuously observing learners' emotions during the learning process and immediately providing advice and improvement measures in response to any changes.

[0676] This invention aims to build a system that provides a learning environment tailored to the learner's emotions within the home. The details are described below.

[0677] The server first retrieves the learner's learning history from a database and analyzes it to estimate the learner's areas of interest and tendencies. The server is equipped with an emotion analysis engine that analyzes the learner's voice and facial expression data in real time. The analyzed emotion data is used to evaluate the learner's current emotional state.

[0678] The device is equipped with a camera and microphone, which capture the user's facial expressions and voice. The acquired data is transmitted to a server in real time for emotion analysis. Based on the analysis results, the server selects appropriate learning materials and sends them to the device. The device displays the materials to the user in an interactive format, supporting the user's learning process.

[0679] Furthermore, the device monitors the user's tone of voice and changes in facial expressions, analyzing the user's emotions in real time based on a pre-configured algorithm. This allows it to adjust the difficulty of a problem or provide hints if the user is feeling frustrated. Conversely, if the user is enjoying learning, it can challenge them with more difficult tasks.

[0680] As a concrete example, let's assume a primary school student is learning mathematics online and is facing a difficult problem. The device detects the user's distressed expression and receives instructions from the server to display a hint. As a result, the user is motivated to continue learning. Such real-time emotional responses improve the user's learning experience and maximize their learning outcomes.

[0681] An example of a prompt when using a generative AI model is: "Think of a way to optimize learning content by reading the user's emotions from their facial expressions and voice while they are working on a math problem. Design a robot application that analyzes the user's emotions and provides appropriate reactions." This prompt forms the basis for the AI ​​model to generate appropriate emotional responses in a home learning support system.

[0682] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0683] Step 1:

[0684] The server retrieves the user's past learning history data from the database. Based on this historical data, it uses data mining techniques to analyze the user's areas of particular interest and past learning trends. As a result of the analysis, it identifies the user's areas of interest and passes that information to the next processing step.

[0685] Step 2:

[0686] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This input data is sent to a server for emotion analysis. The server uses facial recognition software and voice analysis algorithms to evaluate the user's emotional state in real time. The results of this analysis are used in the next step as data indicating the user's emotional state.

[0687] Step 3:

[0688] The server selects appropriate learning materials based on the areas of interest obtained in Step 1 and the sentiment evaluation results in Step 2. Using a material selection algorithm, it determines the material that best matches the user's learning needs and sends the material data to the terminal.

[0689] Step 4:

[0690] The device displays received learning materials in an interactive format. The displayed materials are designed for easy user interaction and can be operated via touchscreens or other means. Users begin learning through the materials, and their progress is recorded on the device.

[0691] Step 5:

[0692] The device continuously monitors the user's facial expressions and voice during learning and sends real-time emotional changes back to the server. Based on the latest emotional data, the server generates feedback tailored to the user's learning progress. This feedback is displayed on the device in the following steps.

[0693] Step 6:

[0694] The device provides the user with feedback received from the server in step 5. This feedback optimizes the user's learning experience by adjusting the difficulty level of the problems and providing hints. The goal of the feedback is also to maintain the user's motivation to learn and improve their understanding.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] [Fourth Embodiment]

[0699] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0700] 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.

[0701] 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).

[0702] 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.

[0703] 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.

[0704] 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).

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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".

[0712] This invention realizes a home learning support system that provides users with an appropriate learning experience. The operation of the system will be described in detail below.

[0713] The server first collects the user's learning history data and analyzes it to estimate the user's areas of interest. This analysis is performed using machine learning algorithms to identify areas of interest. Based on the results, the server selects learning materials related to the user's areas of interest and sends them to the device.

[0714] The device displays learning materials tailored to the user based on information received from the server. These materials include interactive content and are designed to engage the user. Devices equipped with voice recognition capabilities allow users to give voice commands, enabling hands-free changes to learning content and activities.

[0715] For example, if a user wants to learn about dinosaurs, the system provides quizzes and puzzles about the history and types of dinosaurs. As the user solves the quizzes and gets correct answers, points are added, and once a certain amount is reached, the user receives a reward. This makes learning enjoyable and creates a sustainable learning experience.

[0716] Furthermore, the server manages the learning schedule and sends regular reminders to the user. When the user-set learning time arrives, the device provides visual and audible notifications to encourage the user to begin learning. This system allows users to plan efficiently and continue learning consistently.

[0717] Finally, the server periodically generates reports summarizing the user's learning progress and provides them to parents. Through these reports, parents can understand their child's learning status and provide appropriate support and feedback.

[0718] Thus, the present invention effectively supports home learning by maximizing user interest and providing an efficient and enjoyable learning environment.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The server retrieves the user's learning history data from the database. This includes past learning activities, areas of interest, and learning outcomes.

[0722] Step 2:

[0723] The server runs machine learning algorithms based on the acquired data to analyze areas of particular interest to the user. Based on these analysis results, it identifies topics of interest to the user.

[0724] Step 3:

[0725] The server selects appropriate learning materials from the database that are related to the user's area of ​​interest. The selected materials are designed to engage the user's interest.

[0726] Step 4:

[0727] The server sends the selected learning materials to the device, which receives and displays them to the user. The materials include interactive elements and visual content.

[0728] Step 5:

[0729] The user begins learning using the learning materials displayed on the device. The device interactively receives the user's selections and answers and records their activity.

[0730] Step 6:

[0731] Users control the learning process using voice commands. For example, they can give voice commands such as "move on to the next question."

[0732] Step 7:

[0733] The device analyzes the user's voice commands using speech recognition technology and executes processing according to the commands. It can present new quizzes or switch learning items.

[0734] Step 8:

[0735] To award points based on the user's learning progress, the device sends the user's results to the server. The server compiles this data and awards rewards once a certain number of points are reached.

[0736] Step 9:

[0737] The server monitors the user's study schedule and sends a reminder to the device as the scheduled study time approaches.

[0738] Step 10:

[0739] The device notifies the user of a reminder and encourages them to start learning with visual and auditory cues. This notification prompts the user to begin learning at the scheduled time.

[0740] Step 11:

[0741] The server periodically collects user learning activity data and generates progress reports. The server provides these reports to parents and users, making learning outcomes visible.

[0742] Step 12:

[0743] Parents can view progress reports through their devices to check their child's learning status. Based on this information, they can provide effective educational support.

[0744] (Example 1)

[0745] 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".

[0746] In today's educational environment, there is a need for adaptive learning support tailored to each user's learning style and interests. However, it is not easy to analyze individual learning history in detail and provide appropriate learning materials based on that analysis, nor is it easy to maintain an interactive and continuous learning experience. This invention aims to provide effective learning support based on the user's interests and to enhance their motivation to learn.

[0747] 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.

[0748] In this invention, the server includes means for analyzing the user's history information to estimate areas of interest, means for selecting and displaying appropriate learning materials based on the estimated areas of interest, and means for analyzing voice instructions to control the learning content. This makes it possible to realize an adaptive learning environment tailored to each individual user.

[0749] "User" refers to anyone who uses the system to engage in learning activities.

[0750] "History information" refers to a record of the learning content and operations that a user has performed in the past.

[0751] "Areas of interest" refers to the learning fields or topics that a user is interested in or has shown interest in.

[0752] "Educational materials" refer to content provided for learning, including in the form of text, images, and videos.

[0753] "Voice instructions" refers to operational requests made by users to the system using their voice.

[0754] A "scoring system" refers to a mechanism that quantifies the results of learning activities and links them to accumulation or rewards.

[0755] A "reward system" refers to a mechanism that provides incentives based on the user's learning achievements.

[0756] A "study plan" refers to the schedule or timetable set by the user for their studies.

[0757] "Notifications" refers to features that send reminders and announcements to users.

[0758] "Learning progress information" refers to data that shows the user's learning progress and results.

[0759] A "guardian" refers to a person who can supervise and guide the learning activities of a user.

[0760] This invention aims to realize a system that provides optimal educational support tailored to the individual learning needs of users. The system mainly consists of a server, terminals, and users.

[0761] The server is installed in a cloud computing environment and collects and stores user history information. The server implements a machine learning algorithm, which is used to analyze the collected history information and identify the user's areas of interest. This algorithm is often implemented using Python and the scikit-learn library. Once the areas of interest are identified, the server selects corresponding learning materials and sends them to the user's device via the internet.

[0762] The terminal is an electronic device equipped with a user interface that displays educational materials sent from the server. The terminal uses a web application based on HTML5 and CSS3 to visually display the materials. Interactive materials include drag-and-drop puzzles and multiple-choice quizzes. Furthermore, the terminal has voice recognition capabilities, allowing operation based on the user's voice commands. This typically utilizes a voice recognition API.

[0763] Users can learn using materials displayed based on their interests. For example, a user who wants to learn about "dinosaurs" will be presented with interactive quizzes and puzzles about their overview, types, and history. Furthermore, users earn points for correctly answering quizzes, and receive rewards based on their accumulated points.

[0764] Learning progress is regularly collected and analyzed on the server to understand the user's situation and generate feedback. The generated feedback is provided to the user's guardian and can be used to support their learning. The system manages the learning plan and encourages users to study regularly by sending notifications according to a pre-set schedule.

[0765] As an example of a prompt, by inputting the instruction "Analyze the user's learning history this week and suggest learning for next week" into the generating AI model, the learning content for the next step can be appropriately selected.

[0766] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0767] Step 1:

[0768] The server collects user history information from a database. Input includes the user's ID and past activity logs. Based on this data, the server uses machine learning algorithms to analyze it and identify the user's areas of interest. This analysis yields a list of learning areas of interest to the user.

[0769] Step 2:

[0770] The server selects appropriate learning materials based on identified areas of interest. The input is a list of the user's areas of interest. The server searches the learning material database for materials related to these areas of interest and selects them. As a result of this selection process, a set of learning materials suitable for the user is generated as output.

[0771] Step 3:

[0772] The server sends the selected learning materials to the terminal. The inputs include the set of learning materials and user information. The server transmits this data to the terminal via the network. The terminal displays the received learning materials in its user interface, and the output presents the user with visual learning content.

[0773] Step 4:

[0774] Users learn using learning materials displayed on their device screen. The input is the learning materials displayed on the device. Users interact with interactive content included in the materials (e.g., quizzes and puzzles). As a result of this interaction, learning progress and scores are recorded as output.

[0775] Step 5:

[0776] The device receives voice commands from the user and converts them to text via a speech recognition API. The input is the user's voice. The device analyzes this, extracts the command content, and performs screen operations based on it. The output is content controlled according to the user's instructions.

[0777] Step 6:

[0778] The server aggregates and analyzes the user's learning progress and generates feedback. Input includes the user's learning history and score data. Using a generative AI model, this data is analyzed to assess learning and suggest the next steps. The output is a feedback report, which is provided to parents.

[0779] Step 7:

[0780] The server sends reminders to the device based on the configured learning plan. The input is learning schedule information. The server executes a procedure to send a notification at the specified time, and the output is that the device displays a visual and audible notification prompting the user to start learning.

[0781] (Application Example 1)

[0782] 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".

[0783] In today's educational environment, providing personalized learning experiences tailored to learners' interests is a challenging task. Furthermore, the importance of motivating learners to continue learning proactively and regularly checking their progress has been highlighted. Traditional systems lack sufficient flexible responses and feedback based on learners' interests and progress, creating a need for a system that balances efficient learning with enjoyment.

[0784] 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.

[0785] In this invention, the server includes means for analyzing the user's learning history to estimate their areas of interest, means for selecting and displaying appropriate learning materials based on the estimated areas of interest, and means for providing customized quizzes and information based on the user's interests. This enables learners to engage in effective learning tailored to their interests. Furthermore, a point system and reward system can be used to create a fun, game-like learning environment, supporting continued learning.

[0786] "User learning history" refers to a collection of information such as the educational activities a learner has participated in in the past, the materials they have used, and their progress.

[0787] An "area of ​​interest" refers to a specific field or topic that a learner is personally interested in or concerned with.

[0788] "Learning materials" refer to educational materials and resources provided to expand learners' knowledge, and may take the form of electronic or physical materials.

[0789] "Voice commands" are a means of communicating operations or requests to a system via voice.

[0790] A "point system" is a type of evaluation system where points are added according to the progress of learning or activities, and rewards or benefits are given when a certain standard is reached.

[0791] A "reward system" is a set of incentives provided to improve motivation for learning or engaging in activities.

[0792] A "study schedule" is a planned arrangement of time and plans aimed at achieving a specific goal.

[0793] A "reminder" is a notification that prompts learners to reconfirm pre-scheduled plans or activities and encourages them to take action.

[0794] "Learning progress data" refers to information that shows the results and degree of progress that learners have achieved through learning activities.

[0795] A "guardian" is a person involved in the protection and management of learners, and is typically responsible for minors.

[0796] "Visual information" refers to information perceived through vision, and includes images, videos, charts, and diagrams.

[0797] "Auditory information" refers to information perceived through hearing, and includes speech, music, sound effects, etc.

[0798] "Feedback" refers to evaluations and responses to activities performed by learners, and is information used to facilitate further improvement and enhance learning effectiveness.

[0799] "Interactive learning" refers to educational activities in which learners and systems interact with each other, and responses are returned in real time.

[0800] A "customized quiz" is a set of questions specifically designed to suit the learner's interests and level.

[0801] "Information provision" is the act of conveying new knowledge or content to learners, and it can take various forms.

[0802] A "virtual reward" is a digital form of incentive that does not physically exist in the real world but is provided to give learners a sense of satisfaction or accomplishment.

[0803] A "learning theme" is a subject or task that learners are expected to focus on intensively over a specific period of time.

[0804] A "prompt" is a formalized input statement used to provide information to a model or system, and an instruction to produce an output result.

[0805] The system for implementing this invention consists of both a server and a terminal. The server first collects and analyzes the user's learning history. The collected data is used to estimate areas of interest, and machine learning algorithms are used to identify the user's areas of interest. This is achieved using data analysis and machine learning libraries such as Python, Pandas, and Scikit-learn.

[0806] Next, the server selects appropriate learning materials based on the estimated areas of interest. These materials are retrieved from a database and configured to provide quizzes and information used during the learning process. On the user's device, an interactive UI is built using web frameworks such as Django or Flask, providing visual and auditory feedback.

[0807] When users give voice commands, a speech recognition library (such as the Google Speech-to-Text API) is used to analyze the voice commands and enable interaction with the learning content. This feature allows users to have a hands-free, interactive learning experience.

[0808] Furthermore, this system incorporates a point system and a reward system. Points are added based on the number of questions answered correctly, and virtual rewards are given when a certain number of points are reached. This incentive increases motivation to learn.

[0809] Furthermore, the server manages the learning schedule and sends reminders according to the learning time. The device alerts the user through visual and auditory notifications to promote learning. Visual information can include pop-up notifications and colorful animations, while auditory information can include alert sounds and voice messages.

[0810] For example, if a user expresses interest in "history," the server will select and provide quizzes and puzzles related to history. Furthermore, by using prompts to instruct the generative AI model, "If the user is interested in 'history,' what kind of quizzes and learning materials should I provide?", it becomes possible to generate even more personalized content. This will give the user's learning experience greater depth and breadth.

[0811] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0812] Step 1:

[0813] The server collects user learning history data and stores it in a database. This process receives information about past learning activities, achievements, and materials used as input, organizes it, and stores it in the database. This prepares the foundational data needed for subsequent processing.

[0814] Step 2:

[0815] The server analyzes the collected data and uses machine learning algorithms to estimate the user's areas of interest. The input is the user's learning history data, which is then processed using Pandas and Scikit-learn to output estimated areas of interest. This analysis enables the provision of a learning experience tailored to the user's individual needs.

[0816] Step 3:

[0817] The server selects appropriate learning materials from the database based on estimated areas of interest. Using the estimated areas of interest as input, it retrieves relevant material information via database queries and outputs the selected material information. This ensures that content tailored to a specific user is prepared.

[0818] Step 4:

[0819] The terminal displays learning materials sent from the server to the user. The input is the selected learning materials, and the output is the display of the materials with visual or auditory feedback. This operation creates an environment in which the user can intuitively utilize the content.

[0820] Step 5:

[0821] When a user issues a voice command, the device receives it and performs speech recognition. The input is the user's voice command, and the output is the parsed operation command. The speech recognition process is implemented using the Google Speech-to-Text API, enabling hands-free operation.

[0822] Step 6:

[0823] The server sends reminders at predetermined times based on the learning schedule. It takes schedule data as input and outputs periodic notification messages. This supports users in developing a planned learning habit.

[0824] Step 7:

[0825] The server monitors learning progress using a point system and reward system. It evaluates the results of learning activities, inputs the points added, and outputs the status of virtual rewards. This process serves as an incentive to increase the user's motivation to learn.

[0826] Step 8:

[0827] The server periodically compiles learning progress data and generates reports. It takes progress data as input and outputs visualized reports. This makes it easy for parents and educators to understand the learning progress.

[0828] 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.

[0829] This invention realizes a home learning support system that utilizes an emotion engine in the user's learning environment to provide optimal learning for each individual user. The operation of the system will be described in detail below.

[0830] The server acquires and analyzes the user's learning history, and processes voice and facial expression data using an emotion engine. This process evaluates the user's current emotional state, and uses this data to estimate the user's areas of interest. Based on the obtained areas of interest and the emotion evaluation results, the server selects the most suitable learning materials.

[0831] The selected learning materials are sent to the device. The device not only displays the materials in an interactive format and provides learning opportunities, but also continuously monitors the user's facial expressions and voice to capture emotional changes in real time. This allows the device to provide immediate feedback and adjust the difficulty level according to the user's level of concentration and understanding.

[0832] For example, if a user is feeling frustrated with a difficult problem, the emotion engine will sense this and the device will change its settings to make the problem a little easier or provide hints. Also, if the user appears to be enjoying themselves, the device will maintain their current progress or offer further challenges.

[0833] The server also manages learning schedules, taking the user's emotional state into account. Reminders are adjusted to ensure the user can comfortably begin learning. For example, learning might be scheduled to start during a comfortable time after the user has refreshed themselves.

[0834] Parents are provided with learning progress reports generated by the server. These reports include information on the user's learning outcomes, as well as changes in their emotions and learning attitudes. This allows parents to gain a deeper understanding of their child's learning experience and provide support with an appropriate approach.

[0835] Thus, the present invention utilizes an emotion engine to provide a learning environment that is attuned to the user's emotions, thereby realizing appropriate and effective support for home learning that is tailored to individual learning needs.

[0836] The following describes the processing flow.

[0837] Step 1:

[0838] The server retrieves the user's past learning history data from the database. This data includes information such as the content of the learning, frequency, and success rate.

[0839] Step 2:

[0840] The server passes the user's voice data and facial expression data, collected via voice or webcam, to the emotion engine. This engine then analyzes the user's current emotional state.

[0841] Step 3:

[0842] The server estimates the user's areas of interest based on analyzed emotional state and learning history data. This estimation is then used to select appropriate learning materials. The difficulty level and content of the learning materials are considered during the selection process.

[0843] Step 4:

[0844] The server sends the selected learning material information to the terminal. The terminal receives this information and displays the learning materials in a format suitable for the user. Interactive content may also be included.

[0845] Step 5:

[0846] The user begins learning using the learning materials provided on the device. The device continues to monitor the user's voice and facial expressions, and its emotion engine captures changes in emotion in real time.

[0847] Step 6:

[0848] The device dynamically adapts learning content when it detects changes in emotions. For example, if frustration is detected, it will make adjustments such as displaying hints or lowering the difficulty level of the questions.

[0849] Step 7:

[0850] As the user's learning progresses, points are added and the data is sent to the server. The server compiles the points and updates the reward system as needed.

[0851] Step 8:

[0852] The server monitors the learning schedule and issues reminders at times that take into account the user's emotional state. The device notifies the user of these reminders visually and audibly, encouraging them to start learning at the appropriate time.

[0853] Step 9:

[0854] The server combines the user's learning data and sentiment data to generate a progress report. This report summarizes the learning outcomes and trends in sentiment changes.

[0855] Step 10:

[0856] Parents can view progress reports through their devices and obtain detailed information about their child's learning process. This information can then be used to provide effective learning support.

[0857] (Example 2)

[0858] 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".

[0859] Providing an optimal learning experience tailored to each learner's individual emotional state is difficult with conventional technologies, and a particular challenge is the lack of immediate adjustment of educational resources based on emotional changes. Furthermore, it is necessary to improve parents' understanding of and support for their child's learning by providing them with reports that integrate learning progress and emotional data.

[0860] 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.

[0861] In this invention, the server includes means for analyzing the user's learning history to acquire information and estimating relevant areas based on that information; means for processing audio and video data to evaluate the user's emotional state; and means for adjusting the difficulty level of learning resources based on the evaluated emotional state. This enables personalized education that responds to the learner's emotional state and efficient progress management.

[0862] "Analyzing a user's learning history" refers to the act of thoroughly investigating past learning activity data to clarify learning trends and progress.

[0863] "Estimating relevant fields" is the process of identifying subjects or themes that learners will find interesting and that are likely to lead to effective learning.

[0864] "Selecting and presenting educational resources" means choosing learning materials and content that are suitable for learners and making them available for them to use.

[0865] "Processing audio and video data" refers to analyzing audio and visual information obtained from learners to derive meaningful conclusions.

[0866] "Assessing emotional state" means measuring a learner's mental and emotional state and judging it according to specific indicators.

[0867] "Adjusting the difficulty level" means changing the complexity and pace of learning content to match the learner's level of understanding and emotional state.

[0868] "Effective progress management" refers to understanding the learner's progress and setting appropriate learning plans and goals based on that understanding.

[0869] To implement this invention, it is necessary to build a system in which a server and a terminal work together. The server collects and analyzes the user's learning history using a database and analysis software running on the server. This could involve utilizing data analysis tools and machine learning algorithms (e.g., random forest). By analyzing the learning history, the user's interests and preferences are estimated, and appropriate educational resources are selected. The selected educational resources are presented to the user via the terminal.

[0870] The device interactively presents educational resources through a user interface and captures the user's voice and facial expressions using a camera and microphone. This data is processed by emotion recognition tools (e.g., OpenCV, speech analysis tools) to evaluate the user's emotional state in real time. Based on this evaluation data, the server adjusts the difficulty level of the learning materials. For example, if frustration is detected while the user is working on a challenging problem, the problem is simplified or hints are displayed.

[0871] For example, if a user is showing enjoyment while practicing English listening, the device can continue its progress and add more challenging tasks. In addition, the server generates a report that aggregates learning progress and emotional data, making it accessible to parents. This allows parents to understand not only their child's learning progress but also trends in their emotional changes.

[0872] An example of a prompt for a generative AI model might be a question like, "Please explain in detail the specific methods for selecting learning materials based on user sentiment data."

[0873] In this way, the present invention makes it possible to utilize emotional data to provide learners with a personalized educational experience.

[0874] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0875] Step 1:

[0876] The server retrieves the user's learning history data from the database. The user ID is used as input, and the output provides data such as the user's past learning content, time spent, and performance. This data is used to prepare for analysis using machine learning algorithms. Specifically, the server sends queries to the database via an API to retrieve the necessary information.

[0877] Step 2:

[0878] The server receives user voice and facial expression data and processes it using an emotion engine. The input is real-time voice and video data sent from the terminal, and the output is an evaluation of the user's emotional state (e.g., positive, negative, neutral). This emotion evaluation uses facial expression recognition tools and voice analysis software. Specifically, the server sends this data to a dedicated analysis module for evaluation using statistical methods.

[0879] Step 3:

[0880] The server selects the most suitable educational resources based on analyzed sentiment data and learning history. The previously obtained sentiment state evaluation and learning history data are used as input. The output is a selection of educational resources that match the user's current interests and learning needs. Specifically, the server uses a machine learning model to predict appropriate learning materials and determines their priority.

[0881] Step 4:

[0882] The terminal receives educational resources sent from the server and displays them through a user interface. The input is the selected educational resources from the server, and the output is their display in a format visible to the user. Specifically, the terminal configures display settings and prepares to provide an interactive learning experience.

[0883] Step 5:

[0884] The device continuously monitors the user's voice and facial expressions and transmits data to the server in real time. Input is the user's voice and visual information, and output is continuous data for analysis. The device acquires information using a camera and microphone, packages that data appropriately, and sends it to the server.

[0885] Step 6:

[0886] The server integrates learning progress data and emotional states, generates a report, and provides it to parents. The inputs used are user emotional ratings and statistical data from learning history, and the output is an aggregated report. The server aggregates the data and generates the report using visualization tools, allowing parents to understand learning progress and emotional trends.

[0887] (Application Example 2)

[0888] 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".

[0889] A major challenge in modern home learning environments is the lack of nuanced learning materials and support tailored to learners' interests and emotions. Traditional learning support systems struggle to grasp learners' emotional states in real time and provide appropriate adjustments to material difficulty and feedback. As a result, learners may lose motivation or struggle to understand the material. Providing a suitable learning environment is particularly difficult for children with complex emotional states.

[0890] 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.

[0891] In this invention, the server includes means for evaluating the user's emotional state and adjusting the difficulty level of the learning materials based on the obtained data, and means for monitoring the user's emotions in real time during learning and providing feedback. This enables optimal learning support that is attentive to the learner's emotions.

[0892] "Analyzing a user's learning history" means acquiring data on the learning content and progress that learners have undertaken in the past, and then analyzing that data to understand the learner's interests and tendencies.

[0893] "Estimating areas of interest" means predicting the areas and themes that learners are interested in, based on their acquired learning history and sentiment data.

[0894] "Selecting and displaying appropriate learning materials" means choosing the most effective and appropriate materials based on the estimated areas of interest and the learner's level, and presenting them on the learner's viewing device.

[0895] "Analyzing voice commands" means that the system analyzes the voice commands uttered by the learner and operates or manages the learning content according to the content of those commands.

[0896] A "point system and reward system" is a mechanism that promotes learning in a fun, game-like way by awarding points to learners based on the goals and progress they achieve, and providing rewards based on those points.

[0897] "Managing the learning schedule" means sending learning reminders and notifications based on a pre-set schedule in order to efficiently plan and secure the learner's study time.

[0898] "Providing learning progress data to household members" means recording the learner's achievements and progress in learning content as data, and informing other members of the household in an appropriate format.

[0899] "Assessing the user's emotional state" involves analyzing the learner's current emotions and psychological state based on data such as facial expressions and voice, and then evaluating the results.

[0900] "Adjusting the difficulty level of the learning materials" means appropriately changing the content of the materials and the difficulty level of the problems presented, according to the learner's level of understanding and emotional state.

[0901] "Monitoring emotions in real time and providing feedback" means continuously observing learners' emotions during the learning process and immediately providing advice and improvement measures in response to any changes.

[0902] This invention aims to build a system that provides a learning environment tailored to the learner's emotions within the home. The details are described below.

[0903] The server first retrieves the learner's learning history from a database and analyzes it to estimate the learner's areas of interest and tendencies. The server is equipped with an emotion analysis engine that analyzes the learner's voice and facial expression data in real time. The analyzed emotion data is used to evaluate the learner's current emotional state.

[0904] The device is equipped with a camera and microphone, which capture the user's facial expressions and voice. The acquired data is transmitted to a server in real time for emotion analysis. Based on the analysis results, the server selects appropriate learning materials and sends them to the device. The device displays the materials to the user in an interactive format, supporting the user's learning process.

[0905] Furthermore, the device monitors the user's tone of voice and changes in facial expressions, analyzing the user's emotions in real time based on a pre-configured algorithm. This allows it to adjust the difficulty of a problem or provide hints if the user is feeling frustrated. Conversely, if the user is enjoying learning, it can challenge them with more difficult tasks.

[0906] As a concrete example, let's assume a primary school student is learning mathematics online and is facing a difficult problem. The device detects the user's distressed expression and receives instructions from the server to display a hint. As a result, the user is motivated to continue learning. Such real-time emotional responses improve the user's learning experience and maximize their learning outcomes.

[0907] An example of a prompt when using a generative AI model is: "Think of a way to optimize learning content by reading the user's emotions from their facial expressions and voice while they are working on a math problem. Design a robot application that analyzes the user's emotions and provides appropriate reactions." This prompt forms the basis for the AI ​​model to generate appropriate emotional responses in a home learning support system.

[0908] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0909] Step 1:

[0910] The server retrieves the user's past learning history data from the database. Based on this historical data, it uses data mining techniques to analyze the user's areas of particular interest and past learning trends. As a result of the analysis, it identifies the user's areas of interest and passes that information to the next processing step.

[0911] Step 2:

[0912] The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This input data is sent to a server for emotion analysis. The server uses facial recognition software and voice analysis algorithms to evaluate the user's emotional state in real time. The results of this analysis are used in the next step as data indicating the user's emotional state.

[0913] Step 3:

[0914] The server selects appropriate learning materials based on the areas of interest obtained in Step 1 and the sentiment evaluation results in Step 2. Using a material selection algorithm, it determines the material that best matches the user's learning needs and sends the material data to the terminal.

[0915] Step 4:

[0916] The device displays received learning materials in an interactive format. The displayed materials are designed for easy user interaction and can be operated via touchscreens or other means. Users begin learning through the materials, and their progress is recorded on the device.

[0917] Step 5:

[0918] The device continuously monitors the user's facial expressions and voice during learning and sends real-time emotional changes back to the server. Based on the latest emotional data, the server generates feedback tailored to the user's learning progress. This feedback is displayed on the device in the following steps.

[0919] Step 6:

[0920] The device provides the user with feedback received from the server in step 5. This feedback optimizes the user's learning experience by adjusting the difficulty level of the problems and providing hints. The goal of the feedback is also to maintain the user's motivation to learn and improve their understanding.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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."

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0942] The following is further disclosed regarding the embodiments described above.

[0943] (Claim 1)

[0944] A method for analyzing a user's learning history to estimate their areas of interest,

[0945] A means of selecting and displaying appropriate learning materials based on estimated areas of interest,

[0946] A means of manipulating learning content by analyzing voice commands,

[0947] A point system and reward system are used to promote learning in a gamified way.

[0948] A means of managing study schedules and setting reminders at designated times,

[0949] A system that includes means for providing learning progress data to parents.

[0950] (Claim 2)

[0951] The system according to claim 1, comprising means for providing users with visual and auditory feedback and enabling interactive learning.

[0952] (Claim 3)

[0953] The system according to claim 1, comprising means for generating a progress report based on acquired learning data and allowing a parent or guardian to view the report.

[0954] "Example 1"

[0955] (Claim 1)

[0956] A means of estimating areas of interest by analyzing user history information,

[0957] A means for selecting and displaying appropriate teaching materials based on estimated areas of interest,

[0958] A means of controlling learning content by analyzing voice instructions,

[0959] The scoring system and reward system are means of promoting learning through entertainment elements,

[0960] A means of managing the learning plan and providing notifications at designated times,

[0961] Means of providing parents with information on the progress of their child's learning,

[0962] A system that includes means for collecting and analyzing a user's learning history and generating the next learning content.

[0963] (Claim 2)

[0964] The system according to claim 1, comprising means for providing users with visual and auditory feedback and enabling interactive learning.

[0965] (Claim 3)

[0966] The system according to claim 1, comprising means for generating a progress report based on acquired learning information and for parents to review the report.

[0967] "Application Example 1"

[0968] (Claim 1)

[0969] A method for analyzing a user's learning history to estimate their areas of interest,

[0970] A means of selecting and displaying appropriate learning materials based on estimated areas of interest,

[0971] A means of manipulating learning content by analyzing voice commands,

[0972] A point system and reward system are used to promote learning in a gamified way.

[0973] A means of managing study schedules and setting reminders at designated times,

[0974] Means of providing learning progress data to parents,

[0975] A means of providing feedback to users using visual and auditory information to realize interactive learning,

[0976] A means of providing customized quizzes and information based on users' interests,

[0977] A means of providing virtual rewards when a specific point is reached,

[0978] Means for generating prompts based on learning themes

[0979] A system that includes this.

[0980] (Claim 2)

[0981] The system according to claim 1, comprising means for providing an interactive educational experience using visual and auditory feedback.

[0982] (Claim 3)

[0983] The system according to claim 1, comprising means for generating a progress report based on acquired learning data and allowing a designated person to view the report.

[0984] "Example 2 of combining an emotion engine"

[0985] (Claim 1)

[0986] A means of analyzing the user's learning history to obtain information and estimating related fields based on that information,

[0987] A means of selecting and presenting appropriate educational resources based on estimated relevant areas,

[0988] A means for processing audio and video data to evaluate the user's emotional state,

[0989] A means of adjusting the difficulty level of learning resources based on the evaluated emotional state,

[0990] A means to optimize time management and notifications for the start of classes,

[0991] A system that includes means for creating and providing reports that integrate learning progress information and emotional changes.

[0992] (Claim 2)

[0993] The system according to claim 1, which adjusts educational resources based on emotional data of users.

[0994] (Claim 3)

[0995] The system according to claim 1, which generates individual progress reports based on acquired sentiment data and makes them available for viewing.

[0996] "Application example 2 when combining with an emotional engine"

[0997] (Claim 1)

[0998] A method for analyzing a user's learning history to estimate their areas of interest,

[0999] A means of selecting and displaying appropriate learning materials based on estimated areas of interest,

[1000] A means of manipulating learning content by analyzing voice commands,

[1001] A point system and reward system are used to promote learning in a gamified way.

[1002] A means of managing study schedules and setting reminders at designated times,

[1003] A means of providing learning progress data to household members,

[1004] A means of evaluating the emotional state of users and adjusting the difficulty level of educational materials based on the data obtained,

[1005] A means of monitoring the emotions of users during learning in real time and providing feedback,

[1006] A system that includes this.

[1007] (Claim 2)

[1008] The system according to claim 1, comprising means for providing users with visual and auditory feedback and enabling interactive learning.

[1009] (Claim 3)

[1010] The system according to claim 1, comprising means for generating a progress report based on acquired learning data and emotional state data, and allowing household members to view the report. [Explanation of Symbols]

[1011] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method for analyzing a user's learning history to estimate their areas of interest, A means of selecting and displaying appropriate learning materials based on estimated areas of interest, A means of manipulating learning content by analyzing voice commands, A point system and reward system are used to promote learning in a gamified way. A means of managing study schedules and setting reminders at designated times, Means of providing learning progress data to parents, A means of providing feedback to users using visual and auditory information to realize interactive learning, A means of providing customized quizzes and information based on users' interests, A means of providing virtual rewards when a specific point is reached, Means for generating prompts based on learning themes A system that includes this.

2. The system according to claim 1, comprising means for providing an interactive educational experience using visual and auditory feedback.

3. The system according to claim 1, comprising means for generating a progress report based on acquired learning data and allowing a designated person to view the report.

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