system

Abstract

We provide the system. [Solution] A means by which users can set learning content and usage time via a terminal, A method for capturing children's reactions with a camera and analyzing their emotions, A means of providing the generated learning content on the server, A means of analyzing learning results and providing reports to parents, A means of suggesting additional teaching materials and services, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 a conventional educational support system, it is difficult to respond to children's learning situations and interests in real time, and there is a problem that guardians lack information for effectively supporting children's learning. As a result, it has been difficult to provide an optimal learning experience for children.

Means for Solving the Problems

[0005] This invention provides a system that allows users to set learning content and usage time via a terminal, captures children's reactions with a camera, and analyzes their emotions. Furthermore, it provides a system that includes means for a server to provide generated learning content, analyze learning results, provide reports to parents, and suggest additional learning materials and services, thereby realizing a customized learning experience for each child and enhancing the information provided to parents.

[0006] A "device" is an electronic device that a user operates and can use to set learning content and usage time.

[0007] A "user" is someone who sets the learning content and schedule for a child through the system, and generally refers to a parent or guardian.

[0008] A "camera" is a video input device used to capture children's facial expressions and movements.

[0009] "Emotional analysis" is an algorithm used to infer a child's emotional state from acquired video data.

[0010] A "server" is a central control unit that analyzes data sent from terminals and generates learning content.

[0011] "Generated learning content" refers to educational materials that are automatically provided based on the child's interests and learning progress.

[0012] A "report" is a compilation of information provided to parents regarding their child's learning progress.

[0013] "Suggesting teaching materials and services" means notifying parents of the most suitable additional learning resources and programs based on their child's learning history.

[0014] A "system" is an integrated application for educational support that operates by combining all of the elements described above. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0020] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[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 provides a system in which terminals and servers work together to improve children's learning experiences. The embodiments thereof are described in detail below.

[0037] First, the user (parent / guardian) uses their device to set the child's learning content and desired study time. This information is entered through the device's application and sent to the server. This allows the system to create a personalized learning plan for each child.

[0038] The device then uses its camera to capture the child's facial expressions and movements in real time. This video data is sent to an emotion analysis algorithm to estimate the child's level of interest and concentration. The resulting analysis data is sent directly to a server for further processing.

[0039] The server integrates the received child's emotional data with user-defined settings and uses a generative AI model to generate or select the most suitable learning content for that child. The generated content is based on the child's current level of understanding and interests, and aims to enhance learning effectiveness. The content thus identified is then sent back to the device and presented to the child.

[0040] As learning progresses, the device records the student's activity log. This includes how the student interacted with the content, whether their answers were correct or incorrect, and the time taken. This data is sent to a server, and a comprehensive learning report is generated.

[0041] Ultimately, the server provides the user with a generated report that clearly shows the child's learning progress. It also supports continuous learning by suggesting appropriate additional learning materials and services based on the child's learning history.

[0042] For example, when a child is learning basic math problems and their concentration on the material increases, the server will create more difficult problems on the same topic, providing challenging content. In this way, learning can be effectively advanced while stimulating the child's motivation to learn.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user operates the device to set the child's learning content and desired learning time. The set information is sent to the server through the application on the device.

[0046] Step 2:

[0047] The device activates its camera to capture the child's facial expressions and movements in real time. This collects video data for analyzing the child's emotions and level of concentration.

[0048] Step 3:

[0049] The device inputs the collected video data into an emotion analysis algorithm to estimate the children's interest and level of concentration. The analysis results are then sent from the device to the server.

[0050] Step 4:

[0051] Based on the emotion data and user settings information received by the server, it selects or generates the most suitable learning content. It may also use a generative AI model to create new learning content tailored to the child.

[0052] Step 5:

[0053] The server generates or selects learning content and sends it to the device, presenting the content to the student. This allows the student to learn with content tailored to their individual needs.

[0054] Step 6:

[0055] The device monitors the child's learning progress and records operation logs. This includes correct / incorrect answers, response time, and reactions to the learning content.

[0056] Step 7:

[0057] The device sends the recorded learning data to the server, which analyzes the data and generates a learning outcome report.

[0058] Step 8:

[0059] The server generates reports and provides users with detailed information about the children's learning progress. Furthermore, it suggests additional learning materials and services based on the children's learning history.

[0060] (Example 1)

[0061] 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."

[0062] Conventional learning systems face the challenge of providing personalized learning that appropriately reflects the learning needs and interests of each individual child. Furthermore, they have not adequately achieved the visualization of learning effectiveness and the provision of accurate feedback to parents. This invention aims to overcome these challenges and provide a system that improves children's learning experience.

[0063] 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.

[0064] In this invention, the server includes means for users to set learning information via a terminal, means for capturing the child's response with a display device and analyzing the state, and means for generating prompt sentences using the analyzed state data and creating learning materials utilizing a generation AI model. This enables the provision of personalized learning content that meets the individual learning needs of each child and provides detailed feedback on the child's learning status to parents.

[0065] A "terminal" is a device that a user operates to input information or view learning content.

[0066] A "representation device" is a device that captures children's reactions and actions and uses them for analysis.

[0067] An "information processing device" is a device that analyzes received data and generates or provides appropriate learning content.

[0068] "Learning information" refers to information about a child's learning, such as the content of their studies and their desired study time, which is set by the user.

[0069] "Means for analyzing the state" refer to methods and devices for inferring a child's interests, level of concentration, etc., from captured information.

[0070] An "action log" is data that records how children interacted with learning content.

[0071] A "prompt statement" is an instruction statement used to create appropriate training material using a generative AI model.

[0072] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate optimal learning content for children.

[0073] "Learning materials" is a general term for the information and assignments provided to children for learning.

[0074] A description of embodiments for carrying out this invention will be given.

[0075] Users configure children's learning information via their devices, promoting learning tailored to individual needs. Specifically, users input learning content and desired learning time using a dedicated application on their devices. This information is transmitted to the information processing device using a secure communication protocol.

[0076] The device captures children's facial expressions and movements using its built-in display device. This data is used for state analysis, evaluating children's interest and concentration levels in real time. The analyzed data is transmitted to an information processing device and becomes basic information for optimizing children's learning experience.

[0077] The server uses a generative AI model based on the received data to generate or select the most suitable learning materials for the child. This process generates personalized learning content by converting state data into prompts and providing them to the AI ​​model. For example, a prompt might say, "Based on the child's current understanding and interests, suggest the optimal next learning step."

[0078] The generated learning content is presented to the children via their devices. During learning, the devices meticulously record the children's activity logs and transmit them to the information processing device.

[0079] Ultimately, the server analyzes the student's learning progress and provides parents with a comprehensive learning report. It also suggests additional learning materials and services based on past learning achievements, supporting the child's continued learning. This allows the system to provide an effective and efficient learning experience tailored to each child's individual learning needs.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users input student learning information via a terminal. This input includes subjects to be studied, target time, and priority for specific topics. The terminal aggregates this information, converts it into a digital format, and sends it to an information processing device. This enables the creation of personalized learning plans for each student.

[0083] Step 2:

[0084] The device uses its built-in display device to capture children's facial expressions and movements in real time. The captured data is input into an emotion analysis algorithm, which quantifies the children's interest and concentration levels. This analysis result is then sent to an information processing device for use in subsequent processing.

[0085] Step 3:

[0086] The server integrates the child's emotional data with the user's set learning information to generate prompt messages. These prompt messages include instructions such as "Suggest the most suitable learning content based on the child's current state," and a generative AI model is used to select or generate the most suitable learning materials.

[0087] Step 4:

[0088] The generative AI model dynamically generates training material based on prompt messages. This process involves calculations based on past learning history and current learning status to generate optimal content. This generated content is then sent back from the server to the terminal.

[0089] Step 5:

[0090] The device displays learning content received from the server to the child. During the presentation, the device monitors how the child interacts with the content and records it as an operation log. The log includes screen transitions, selected answers, and solution time.

[0091] Step 6:

[0092] The device sends recorded operation logs to the server. The server analyzes the learning progress based on this log information and generates a learning report. This report clearly indicates the student's level of understanding and any necessary follow-up.

[0093] Step 7:

[0094] The server provides the generated learning report to the user (parent / guardian). Based on the data obtained, it also suggests appropriate additional learning materials and services, ensuring continuous learning support.

[0095] (Application Example 1)

[0096] 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."

[0097] In today's learning environment, it is difficult for children to receive education tailored to their individual needs, and general learning methods struggle to provide effective learning experiences. Furthermore, there is a lack of systems to efficiently deliver learning content optimized for individual children.

[0098] 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.

[0099] In this invention, the server includes means for the user to set learning content and usage time via a terminal, means for capturing the user's reactions with an image acquisition device and analyzing their emotions, and means for providing the learning information generated by the server. This enables the provision of personalized learning and dynamic optimization of content according to the child's learning progress.

[0100] A "terminal" is an information processing device that users operate, and it is possible to set learning content and usage time.

[0101] An "image acquisition device" is a device that captures images of a target and stores them as electronic information, and can capture the learner's reactions.

[0102] "Emotional analysis" is a process that evaluates the emotional state of learners based on acquired image data, and uses the results to optimize learning.

[0103] A "server" is a centralized computing resource used to process and provide information via a network, and it is responsible for generating and providing learning information.

[0104] "Learning information" refers to educational resources and materials provided during a child's learning process, with the aim of enhancing the effectiveness of the child's learning.

[0105] "Individualized learning" is a method for providing optimal educational content tailored to each child's learning progress and level of understanding.

[0106] A "virtual space" is a virtual environment created using digital technology, where users can receive education.

[0107] The system implementing this invention consists of a terminal and a server working together. The terminal is an information processing device for which the user sets learning content and desired learning time, and can take the form of a smartphone or tablet. The information entered by the user is transmitted to the server through the terminal.

[0108] The terminal is equipped with an image acquisition device (e.g., a camera) to capture the user's face and facial expressions in real time. The acquired video data is processed by an emotion analysis algorithm to evaluate the learner's emotional state. This analysis uses an image processing library (e.g., OpenCV) and an emotion analysis tool (e.g., Dlib).

[0109] Analysis results and user settings are sent to the server, which generates personalized learning information using a generative AI model. This generative AI model can utilize a data analysis platform (e.g., Google Cloud AI). The server sends the generated learning information to the device, providing the user with appropriate learning content. As the user progresses through the learning process, the device records reaction data and operation logs and sends them to the server.

[0110] As a concrete example, here is a prompt message for a child to receive learning content best suited to history: "Generate learning content related to topics this child is interested in." Such a prompt allows the server to effectively generate personalized educational content. This system makes it possible to provide an effective learning experience tailored to each child's learning needs.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The terminal receives input from the user regarding learning content and desired learning time. This information is formatted within the terminal and prepared as a data package to be sent to the server. It receives user configuration information as input and generates an information package to send to the server as output.

[0114] Step 2:

[0115] The device uses image acquisition equipment to capture the child's face and expressions in real time. The obtained video data is processed through an internal emotion analysis algorithm to estimate the child's emotional state as numerical data. The input here is video data from the camera, and the output is the analyzed emotion data.

[0116] Step 3:

[0117] The server combines the configuration information received in Step 1 with the sentiment data obtained in Step 2. Based on this data, it uses a generative AI model to generate learning content tailored to the learner. This generation process utilizes the prompt "Generate learning content related to topics this child is interested in." The input is user settings and sentiment data, and the output is personalized learning information.

[0118] Step 4:

[0119] The server sends the generated learning information to the terminal. The terminal displays the received information to the user, enabling the child to begin learning. The input for this step is the generated learning information, and the output is the content displayed on the learning screen.

[0120] Step 5:

[0121] The terminal continuously monitors the actions and responses of students during learning and records the data as logs. The recorded data, including learning speed and accuracy rate, is sent to the server at regular intervals. Here, the input is the user's action data, and the output is the log data for analysis.

[0122] 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.

[0123] This invention aims to realize an individually optimized learning experience and enhance feedback to parents by combining an emotion engine with a system that supports children's learning. The embodiments are described in detail below.

[0124] The user (parent / guardian) first uses their device to set the child's learning content and desired learning time. This clarifies the child's learning goals, and the settings are instantly sent to the server. Based on this created learning plan, the entire system is then adjusted.

[0125] The device integrates a camera and an emotion engine to capture children's facial expressions and movements in real time. The emotion engine analyzes the children's emotions based on the collected video data and evaluates their level of interest and engagement. As soon as this data is acquired by the device, it is sent to a server and used to adjust the learning content.

[0126] The server integrates received emotional data and learning progress information from the children, and utilizes a generative AI model to generate or select the most suitable learning content for each child. Furthermore, based on the analysis results obtained by the emotional engine, the presentation order and difficulty level of the content can be dynamically adjusted. The generated or adjusted content is immediately sent to the device and provided to the child.

[0127] The device records detailed activity logs when children use learning content. This data, including emotional states recognized by the emotion engine, is sent to a server. The server analyzes the received data and generates a comprehensive learning outcome report.

[0128] The reports generated by the server include not only the children's learning progress but also insights gained through sentiment analysis. Users can use this information to further optimize the children's learning environment. In addition, the server suggests additional learning materials and services that may be effective based on the children's emotional state.

[0129] For example, when a child is learning English vocabulary, if the emotional engine recognizes that the child is showing high interest, the server will generate content for the child as a further challenge, such as reading comprehension or pronunciation practice. In this way, it becomes possible to provide an advanced learning experience that utilizes the child's emotional state.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user operates the device to set the child's learning content and desired learning time. This information is sent from the device to the server and stored in the database as a basic learning plan.

[0133] Step 2:

[0134] The device activates its camera and uses an emotion engine to capture the child's facial expressions and movements in real time. The video data is analyzed by the emotion engine to identify the child's interests and emotional state.

[0135] Step 3:

[0136] The device sends the emotional data it has processed to the server. Based on the received data, the server uses a generation AI model to dynamically generate or select learning content tailored to that child.

[0137] Step 4:

[0138] The server sends the generated or selected learning content to the device. The content is configured with appropriate difficulty levels and topics, taking into account the analysis results obtained from the emotion engine.

[0139] Step 5:

[0140] The device presents learning content to the children, and logs their interactions with the content. The logs include the children's response patterns and reaction times.

[0141] Step 6:

[0142] The device sends the recorded log data back to the server. The server integrates the operation logs and sentiment data to generate a learning outcome report.

[0143] Step 7:

[0144] The server generates reports for the user, providing detailed data on the child's learning progress. These reports also include advice for the next learning steps, along with insights gained from sentiment analysis.

[0145] Step 8:

[0146] Based on emotional data, the server suggests additional learning materials and services deemed most suitable for each child. This improves the continuity and depth of learning.

[0147] (Example 2)

[0148] 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".

[0149] In today's educational environment, a challenge is providing an optimal learning experience tailored to each child's emotional state and learning progress. Traditional systems have struggled to grasp children's reactions and emotions in real time and dynamically adjust learning content based on that information. This has resulted in limitations in maximizing children's learning effectiveness and in providing appropriate feedback to parents.

[0150] 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.

[0151] In this invention, the server includes means for dynamically adjusting educational content to suit the child using a generation AI system, means for transmitting the child's response data acquired by the information processing device to the information processing server in real time, and means for analyzing the results of educational activities and providing a report to the parents. This makes it possible to provide an individually optimized learning experience that is tailored to the child's emotional state and learning progress.

[0152] An "information processing device" is a terminal used by users to set learning content and usage time, and is a device that handles data input and transmission.

[0153] A "filming device" is a device such as a camera used to capture a child's facial expressions and actions, and has the function of acquiring video data.

[0154] "Means for analyzing emotions" refers to technologies and processes that use acquired video data to evaluate the emotional state of children, including an emotion engine.

[0155] An "information processing server" is a central system that generates and provides learning content, and has the function of integrating and analyzing data and issuing appropriate instructions.

[0156] "Educational content" refers to teaching materials and learning programs provided to support children's learning, which are generated or adjusted by a generative AI system.

[0157] A "generative AI system" refers to an algorithm and system that uses AI technology to automatically generate or adjust learning content that is optimal for children.

[0158] "Educational resources and services" refer to additional teaching materials and support services proposed to effectively advance children's learning.

[0159] This invention is a system that individually optimizes children's learning and provides effective feedback to parents. Specific embodiments are described below.

[0160] The user begins by setting the child's learning content and usage time using an information processing device. This information is transmitted in real time from the user's terminal to the information processing server. Based on the received data, the information processing server uses a generative AI model to generate or select the optimal educational content. The generative AI model utilizes AI technology and has the ability to dynamically adjust the content according to the child's learning progress and emotional data.

[0161] The terminal, in conjunction with a camera, captures the children's facial expressions and movements in real time. This video data is processed by an emotion analysis system to evaluate the children's emotional state, such as their level of interest and concentration. The evaluation results are then sent back to the information processing server and used to adjust the difficulty level and presentation order of the next content presented.

[0162] For example, if a child is learning English vocabulary and sentiment analysis indicates a high level of interest, the information processing server will provide content such as more challenging reading comprehension passages or pronunciation practice. As a result, children can learn at their own pace.

[0163] An example of a prompt message would be, "Please generate appropriate reading comprehension content for children who have shown a high level of interest in English vocabulary."

[0164] This system can improve the quality of education not only by individually optimizing children's learning experiences, but also by analyzing learning outcomes and providing detailed reports to parents.

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

[0166] Step 1:

[0167] The user operates the information processing device to input the child's learning content and desired learning time. The entered information is sent to the information processing server in real time. This process allows the server to receive basic data for creating the child's learning plan. Specifically, the user opens the application and sets items such as "math drill" and "30 minutes."

[0168] Step 2:

[0169] The information processing server generates or selects educational content using a generative AI model based on the received learning content and time setting data. The server inputs this data into the generative AI model and outputs learning content optimized for the child. In this process, the prompt message "Please select a math practice problem suitable for this child" may be used.

[0170] Step 3:

[0171] The device uses a camera to capture the child's facial expressions and movements in real time. The acquired video data is processed by an emotion analysis system operated on the device. The video data is analyzed as input, and the child's emotional state is output. This analysis includes measurements of interest and concentration. As a specific example, the camera captures the child's smile, which is evaluated as "high interest."

[0172] Step 4:

[0173] The emotional data analyzed by the device is immediately sent to the information processing server. The server receives this data and uses it to adjust the presentation order and difficulty level of educational content. The server then re-inputs it into a generating AI model and outputs appropriate content suggestions based on the child's state. At this time, the server provides instructions such as, "Increase the difficulty level when the child is highly focused."

[0174] Step 5:

[0175] The server sends the adjusted educational content to the device and presents it to the student. The device receives this content and displays it to the student in the specified manner. In this step, the student can work on learning tasks optimized for them. Specifically, a newly added, high-difficulty math problem is displayed on the device screen.

[0176] Step 6:

[0177] The device records the student's actions and emotional state during learning. This data is sent to a server and used as analysis data for learning progress. The server processes the input data and outputs a learning outcome report for parents. Specifically, the student's accuracy rate and response time are recorded and periodically uploaded to the server.

[0178] Step 7:

[0179] Based on the data analyzed by the server, a report on the child's learning progress is provided to parents. This report also includes insights from emotional analysis obtained during learning, resulting in parents receiving information to better support their child's learning. Specifically, the report email may include a statement such as, "Your child is progressing with a very high level of interest in their learning."

[0180] (Application Example 2)

[0181] 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".

[0182] In children's learning, there is a need to provide learning experiences that are adapted to each child's emotional state and interests. However, conventional learning systems cannot adequately analyze emotions and concentration levels in real time, making it difficult to provide optimal learning content for children. Furthermore, providing parents with detailed and timely feedback on learning progress is also crucial.

[0183] 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.

[0184] In this invention, the server includes means for dynamically adjusting educational content based on emotion analysis, means for automatically generating new educational content using a generative AI model and reflecting the results of emotion analysis, and means for analyzing educational results and providing reports to parents. This enables the provision of an optimal learning experience tailored to the emotional state of children and appropriate feedback to parents.

[0185] A "terminal" is an electronic device that allows users to input and configure educational content and usage time.

[0186] "Children" refers to children who are eligible to receive education.

[0187] A "recording device" is a device used to capture a child's reactions, and usually includes a camera.

[0188] "Emotional analysis" is a process of evaluating a child's emotional state based on video data captured by a camera.

[0189] "Dynamic adjustment" means flexibly changing the content and presentation methods of educational materials in response to the results of analyzing children's emotions.

[0190] A "server" is a device that processes data via a network and generates and provides educational content.

[0191] A "generative AI model" is a computational model that uses artificial intelligence technology to automatically generate new content.

[0192] "Educational content" refers to a collection of information and teaching materials intended for children's learning.

[0193] "Learning resources" refer to teaching materials and tools used to improve children's knowledge and skills.

[0194] A "report" is a document that summarizes information about a child's educational outcomes and progress, and is provided to the parents / guardians.

[0195] The system that realizes this application example integrates multiple technological elements to individually optimize children's learning activities. The terminal provides an interface for users to input children's educational content and study time. This terminal is also equipped with a camera that can capture children's movements and facial expressions in real time.

[0196] Emotion analysis uses image processing libraries such as OpenCV to process video data obtained from the device's camera and evaluate the child's emotions. This makes it possible to estimate the child's interest and level of concentration. The analyzed data is immediately sent to a server, which dynamically adjusts the educational content based on that information. At this stage, generative AI models using TENSORFLOW® are utilized to automatically generate new content and adjust the presentation method.

[0197] The server also sends the generated educational content to the device and presents it to the child. This educational content includes information and materials that are most appropriate to the child's current emotional state. The server further analyzes learning progress and results and provides a report to the parents. Through this, parents can understand their child's learning performance and emotional fluctuations and gain guidance to provide a better learning environment.

[0198] As a concrete example, if a child shows high interest while learning English vocabulary, the server generates pronunciation practice materials based on that response. A generative AI model is applied to this generation. An example of a prompt sentence would be: "When the child's expression brightens, what kind of learning content should be provided to further pique their interest?" This makes it possible to provide the child with the most optimal learning experience.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The terminal receives input from users regarding the child's educational content and study time. This input is saved to a database and processed into a format that can be managed within the terminal.

[0202] Step 2:

[0203] During a child's learning session, the device uses its camera to capture the child's facial expressions and movements in real time. The input is video data from the camera, and the output is facial feature data processed using OpenCV. This provides the basis for sentiment analysis.

[0204] Step 3:

[0205] The server receives facial feature data sent from the terminal and analyzes the emotional state using TensorFlow. The input is feature data, and the output is an estimate of the child's emotional state and level of interest and concentration. This allows for an evaluation of each child's response to learning.

[0206] Step 4:

[0207] The server uses a generative AI model based on emotion analysis results to generate or select appropriate educational content. The input is emotional state data, and the output is educational content optimized for the child. The generated content is dynamically adjusted to match the child's interest and level of concentration.

[0208] Step 5:

[0209] The server generates educational content and sends it to the terminal, which then presents it to the child. The input is appropriately selected educational content, and the output is a display of the content for the child to use. The child gains a learning experience through this content.

[0210] Step 6:

[0211] During the learning process, the device records the child's operation logs and additional facial expression data, and sends them to the server. The input is user operation data and facial expression data, and the output is detailed learning and emotion tracking data.

[0212] Step 7:

[0213] The server analyzes the submitted learning and emotional data and generates a report for parents. The input is integrated learning and emotional data, and the output is a comprehensive learning outcome report. This report is used to understand the child's learning progress and emotional changes.

[0214] Step 8:

[0215] The server suggests additional learning resources and services to the user based on the child's learning record and emotional state. Input is learning and emotional reports, and output is recommended additional learning resources. The suggested content aims to further improve the learning experience.

[0216] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

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

[0221] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0223] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0225] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0227] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] This invention provides a system in which terminals and servers work together to improve children's learning experiences. The embodiments thereof are described in detail below.

[0233] The user (parent / guardian) first uses their device to set the child's learning content and desired study time. This information is entered through the device's application and sent to the server. This allows the system to create a personalized learning plan for each child.

[0234] The device then uses its camera to capture the child's facial expressions and movements in real time. This video data is sent to an emotion analysis algorithm to estimate the child's level of interest and concentration. The resulting analysis data is sent directly to a server for further processing.

[0235] The server integrates the received child's emotional data with user-defined settings and uses a generative AI model to generate or select the most suitable learning content for that child. The generated content is based on the child's current level of understanding and interests, and aims to enhance learning effectiveness. The content thus identified is then sent back to the device and presented to the child.

[0236] As learning progresses, the device records the student's activity log. This includes how the student interacted with the content, whether their answers were correct or incorrect, and the time taken. This data is sent to a server, and a comprehensive learning report is generated.

[0237] Ultimately, the server provides the user with a generated report that clearly shows the child's learning progress. It also supports continuous learning by suggesting appropriate additional learning materials and services based on the child's learning history.

[0238] For example, when a child is learning basic math problems and their concentration on the material increases, the server will create more difficult problems on the same topic, providing challenging content. In this way, learning can be effectively advanced while stimulating the child's motivation to learn.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The user operates the device to set the child's learning content and desired learning time. The set information is sent to the server through the application on the device.

[0242] Step 2:

[0243] The device activates its camera to capture the child's facial expressions and movements in real time. This collects video data for analyzing the child's emotions and level of concentration.

[0244] Step 3:

[0245] The device inputs the collected video data into an emotion analysis algorithm to estimate the children's interest and level of concentration. The analysis results are then sent from the device to the server.

[0246] Step 4:

[0247] Based on the emotion data and user settings information received by the server, it selects or generates the most suitable learning content. It may also use a generative AI model to create new learning content tailored to the child.

[0248] Step 5:

[0249] The server generates or selects learning content and sends it to the device, presenting the content to the student. This allows the student to learn with content tailored to their individual needs.

[0250] Step 6:

[0251] The device monitors the child's learning progress and records operation logs. This includes correct / incorrect answers, response time, and reactions to the learning content.

[0252] Step 7:

[0253] The device sends the recorded learning data to the server, which analyzes the data and generates a learning outcome report.

[0254] Step 8:

[0255] The server generates reports and provides users with detailed information about the children's learning progress. Furthermore, it suggests additional learning materials and services based on the children's learning history.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0258] Conventional learning systems face the challenge of providing personalized learning that appropriately reflects the learning needs and interests of each individual child. Furthermore, they have not adequately achieved the visualization of learning effectiveness and the provision of accurate feedback to parents. This invention aims to overcome these challenges and provide a system that improves children's learning experience.

[0259] 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.

[0260] In this invention, the server includes means for users to set learning information via a terminal, means for capturing the child's response with a display device and analyzing the state, and means for generating prompt sentences using the analyzed state data and creating learning materials utilizing a generation AI model. This enables the provision of personalized learning content that meets the individual learning needs of each child and provides detailed feedback on the child's learning status to parents.

[0261] A "terminal" is a device that a user operates to input information or view learning content.

[0262] A "representation device" is a device that captures children's reactions and actions and uses them for analysis.

[0263] An "information processing device" is a device that analyzes received data and generates or provides appropriate learning content.

[0264] "Learning information" refers to information about a child's learning, such as the content of their studies and their desired study time, which is set by the user.

[0265] "Means for analyzing the state" refer to methods and devices for inferring a child's interests, level of concentration, etc., from captured information.

[0266] An "action log" is data that records how children interacted with learning content.

[0267] A "prompt statement" is an instruction statement used to create appropriate training material using a generative AI model.

[0268] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate optimal learning content for children.

[0269] "Learning materials" is a general term for the information and assignments provided to children for learning.

[0270] A description of embodiments for carrying out this invention will be given.

[0271] Users configure children's learning information via their devices, promoting learning tailored to individual needs. Specifically, users input learning content and desired learning time using a dedicated application on their devices. This information is transmitted to the information processing device using a secure communication protocol.

[0272] The device captures children's facial expressions and movements using its built-in display device. This data is used for state analysis, evaluating children's interest and concentration levels in real time. The analyzed data is transmitted to an information processing device and becomes basic information for optimizing children's learning experience.

[0273] The server uses a generative AI model based on the received data to generate or select the most suitable learning materials for the child. This process generates personalized learning content by converting state data into prompts and providing them to the AI ​​model. For example, a prompt might say, "Based on the child's current understanding and interests, suggest the optimal next learning step."

[0274] The generated learning content is presented to the children via their devices. During learning, the devices meticulously record the children's activity logs and transmit them to the information processing device.

[0275] Ultimately, the server analyzes the student's learning progress and provides parents with a comprehensive learning report. It also suggests additional learning materials and services based on past learning achievements, supporting the child's continued learning. This allows the system to provide an effective and efficient learning experience tailored to each child's individual learning needs.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] Users input student learning information via a terminal. This input includes subjects to be studied, target time, and priority for specific topics. The terminal aggregates this information, converts it into a digital format, and sends it to an information processing device. This enables the creation of personalized learning plans for each student.

[0279] Step 2:

[0280] The device uses its built-in display device to capture children's facial expressions and movements in real time. The captured data is input into an emotion analysis algorithm, which quantifies the children's interest and concentration levels. This analysis result is then sent to an information processing device for use in subsequent processing.

[0281] Step 3:

[0282] The server integrates the child's emotional data and the learning information set by the user to generate a prompt sentence. This prompt sentence contains instructions in the form of "Please propose the optimal learning content based on the child's current state", and is used as the basis for the generative AI model to select or generate the optimal learning materials.

[0283] Step 4:

[0284] The generative AI model dynamically generates learning materials based on the prompt sentence. In this process, computational processing based on past learning histories and current learning situations is performed to generate optimal content. This generated content is sent from the server to the terminal again.

[0285] Step 5:

[0286] The terminal presents the learning content received from the server to the child. During the presentation, the terminal monitors how the child interacts with the content and records it as an operation log. The log includes screen transitions, selected answers, solution times, etc.

[0287] Step 6:

[0288] The terminal sends the recorded operation log to the server. The server analyzes the progress of learning based on this log information and generates a learning report. This report indicates the child's understanding level and necessary follow-up.

[0289] Step 7:

[0290] The server provides the generated learning report to the user (guardian). Also, based on the obtained data, appropriate additional teaching materials and learning services are proposed to realize continuous learning support.

[0291] (Application Example 1)

[0292] 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."

[0293] In today's learning environment, it is difficult for children to receive education tailored to their individual needs, and general learning methods struggle to provide effective learning experiences. Furthermore, there is a lack of systems to efficiently deliver learning content optimized for individual children.

[0294] 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.

[0295] In this invention, the server includes means for the user to set learning content and usage time via a terminal, means for capturing the user's reactions with an image acquisition device and analyzing their emotions, and means for providing the learning information generated by the server. This enables the provision of personalized learning and dynamic optimization of content according to the child's learning progress.

[0296] A "terminal" is an information processing device that users operate, and it is possible to set learning content and usage time.

[0297] An "image acquisition device" is a device that captures images of a target and stores them as electronic information, and can capture the learner's reactions.

[0298] "Emotional analysis" is a process that evaluates the emotional state of learners based on acquired image data, and uses the results to optimize learning.

[0299] A "server" is a centralized computing resource used to process and provide information via a network, and it is responsible for generating and providing learning information.

[0300] "Learning information" refers to educational resources and materials provided during a child's learning process, with the aim of enhancing the effectiveness of the child's learning.

[0301] "Individualized learning" is a method for providing optimal educational content tailored to each child's learning progress and level of understanding.

[0302] A "virtual space" is a virtual environment created using digital technology, where users can receive education.

[0303] The system implementing this invention consists of a terminal and a server working together. The terminal is an information processing device for which the user sets learning content and desired learning time, and can take the form of a smartphone or tablet. The information entered by the user is transmitted to the server through the terminal.

[0304] The terminal is equipped with an image acquisition device (e.g., a camera) to capture the user's face and facial expressions in real time. The acquired video data is processed by an emotion analysis algorithm to evaluate the learner's emotional state. This analysis uses an image processing library (e.g., OpenCV) and an emotion analysis tool (e.g., Dlib).

[0305] Analysis results and user settings are sent to the server, which uses a generative AI model to generate personalized learning information. This generative AI model can utilize a data analysis platform (e.g., Google Cloud AI). The server sends the generated learning information to the device, providing the user with appropriate learning content. As the user progresses through the learning process, the device records reaction data and operation logs and sends them to the server.

[0306] As a concrete example, here is a prompt message for a child to receive learning content best suited to history: "Generate learning content related to topics this child is interested in." Such a prompt allows the server to effectively generate personalized educational content. This system makes it possible to provide an effective learning experience tailored to each child's learning needs.

[0307] The process of the specific processing in Application Example 1 will be described with reference to FIG. 12.

[0308] Step 1:

[0309] The terminal inputs the learning content and the desired learning time set by the user. These pieces of information are formatted in the terminal and prepared as a data package to be sent to the server. The terminal receives the user's setting information as input and generates an information package to be sent to the server as output.

[0310] Step 2:

[0311] The terminal uses an image acquisition device to capture the face and expressions of the child in real time. The obtained video data is processed through an internal emotion analysis algorithm to infer the emotional state of the child as numerical data. The input here is the video data from the camera, and the output is the analyzed emotion data.

[0312] Step 3:

[0313] The server combines the setting information received in Step 1 and the emotion data obtained in Step 2. Based on these data, a learning content suitable for the learner is generated using a generative AI model. In this generation process, the prompt sentence "Please generate learning content related to the topics that this child is interested in." is utilized. The input is the user setting and the emotion data, and the output is the individualized learning information.

[0314] Step 4:

[0315] The server sends the generated learning information to the terminal. The terminal displays the received information to the user to enable the child to start learning. The input of this step is the generated learning information, and the output is the content displayed on the learning screen.

[0316] Step 5:

[0317] The terminal continuously monitors the actions and responses of students during learning and records the data as logs. The recorded data, including learning speed and accuracy rate, is sent to the server at regular intervals. Here, the input is the user's action data, and the output is the log data for analysis.

[0318] 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.

[0319] This invention aims to realize an individually optimized learning experience and enhance feedback to parents by combining an emotion engine with a system that supports children's learning. The embodiments are described in detail below.

[0320] The user (parent / guardian) first uses their device to set the child's learning content and desired learning time. This clarifies the child's learning goals, and the settings are instantly sent to the server. Based on this created learning plan, the entire system is then adjusted.

[0321] The device integrates a camera and an emotion engine to capture children's facial expressions and movements in real time. The emotion engine analyzes the children's emotions based on the collected video data and evaluates their level of interest and engagement. As soon as this data is acquired by the device, it is sent to a server and used to adjust the learning content.

[0322] The server integrates received emotional data and learning progress information from the children, and utilizes a generative AI model to generate or select the most suitable learning content for each child. Furthermore, based on the analysis results obtained by the emotional engine, the presentation order and difficulty level of the content can be dynamically adjusted. The generated or adjusted content is immediately sent to the device and provided to the child.

[0323] The device records detailed activity logs when children use learning content. This data, including emotional states recognized by the emotion engine, is sent to a server. The server analyzes the received data and generates a comprehensive learning outcome report.

[0324] The reports generated by the server include not only the children's learning progress but also insights gained through sentiment analysis. Users can use this information to further optimize the children's learning environment. In addition, the server suggests additional learning materials and services that may be effective based on the children's emotional state.

[0325] For example, when a child is learning English vocabulary, if the emotional engine recognizes that the child is showing high interest, the server will generate content for the child as a further challenge, such as reading comprehension or pronunciation practice. In this way, it becomes possible to provide an advanced learning experience that utilizes the child's emotional state.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user operates the device to set the child's learning content and desired learning time. This information is sent from the device to the server and stored in the database as a basic learning plan.

[0329] Step 2:

[0330] The device activates its camera and uses an emotion engine to capture the child's facial expressions and movements in real time. The video data is analyzed by the emotion engine to identify the child's interests and emotional state.

[0331] Step 3:

[0332] The device sends the emotional data it has processed to the server. Based on the received data, the server uses a generation AI model to dynamically generate or select learning content tailored to that child.

[0333] Step 4:

[0334] The server sends the generated or selected learning content to the device. The content is configured with appropriate difficulty levels and topics, taking into account the analysis results obtained from the emotion engine.

[0335] Step 5:

[0336] The device presents learning content to the children, and logs their interactions with the content. The logs include the children's response patterns and reaction times.

[0337] Step 6:

[0338] The device sends the recorded log data back to the server. The server integrates the operation logs and sentiment data to generate a learning outcome report.

[0339] Step 7:

[0340] The server generates reports for the user, providing detailed data on the child's learning progress. These reports also include advice for the next learning steps, along with insights gained from sentiment analysis.

[0341] Step 8:

[0342] Based on emotional data, the server suggests additional learning materials and services deemed most suitable for each child. This improves the continuity and depth of learning.

[0343] (Example 2)

[0344] 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".

[0345] In today's educational environment, a challenge is providing an optimal learning experience tailored to each child's emotional state and learning progress. Traditional systems have struggled to grasp children's reactions and emotions in real time and dynamically adjust learning content based on that information. This has resulted in limitations in maximizing children's learning effectiveness and in providing appropriate feedback to parents.

[0346] 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.

[0347] In this invention, the server includes means for dynamically adjusting educational content to suit the child using a generation AI system, means for transmitting the child's response data acquired by the information processing device to the information processing server in real time, and means for analyzing the results of educational activities and providing a report to the parents. This makes it possible to provide an individually optimized learning experience that is tailored to the child's emotional state and learning progress.

[0348] An "information processing device" is a terminal used by users to set learning content and usage time, and is a device that handles data input and transmission.

[0349] A "filming device" is a device such as a camera used to capture a child's facial expressions and actions, and has the function of acquiring video data.

[0350] "Means for analyzing emotions" refers to technologies and processes that use acquired video data to evaluate the emotional state of children, including an emotion engine.

[0351] An "information processing server" is a central system that generates and provides learning content, and has the function of integrating and analyzing data and issuing appropriate instructions.

[0352] "Educational content" refers to teaching materials and learning programs provided to support children's learning, which are generated or adjusted by a generative AI system.

[0353] A "generative AI system" refers to an algorithm and system that uses AI technology to automatically generate or adjust learning content that is optimal for children.

[0354] "Educational resources and services" refer to additional teaching materials and support services proposed to effectively advance children's learning.

[0355] This invention is a system that individually optimizes children's learning and provides effective feedback to parents. Specific embodiments are described below.

[0356] The user begins by setting the child's learning content and usage time using an information processing device. This information is transmitted in real time from the user's terminal to the information processing server. Based on the received data, the information processing server uses a generative AI model to generate or select the optimal educational content. The generative AI model utilizes AI technology and has the ability to dynamically adjust the content according to the child's learning progress and emotional data.

[0357] The terminal, in conjunction with a camera, captures the children's facial expressions and movements in real time. This video data is processed by an emotion analysis system to evaluate the children's emotional state, such as their level of interest and concentration. The evaluation results are then sent back to the information processing server and used to adjust the difficulty level and presentation order of the next content presented.

[0358] For example, if a child is learning English vocabulary and sentiment analysis indicates a high level of interest, the information processing server will provide content such as more challenging reading comprehension passages or pronunciation practice. As a result, children can learn at their own pace.

[0359] An example of a prompt message would be, "Please generate appropriate reading comprehension content for children who have shown a high level of interest in English vocabulary."

[0360] This system can improve the quality of education not only by individually optimizing children's learning experiences, but also by analyzing learning outcomes and providing detailed reports to parents.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The user operates the information processing device to input the child's learning content and desired learning time. The entered information is sent to the information processing server in real time. This process allows the server to receive basic data for creating the child's learning plan. Specifically, the user opens the application and sets items such as "math drill" and "30 minutes."

[0364] Step 2:

[0365] The information processing server generates or selects educational content using a generative AI model based on the received learning content and time setting data. The server inputs this data into the generative AI model and outputs learning content optimized for the child. In this process, the prompt message "Please select a math practice problem suitable for this child" may be used.

[0366] Step 3:

[0367] The device uses a camera to capture the child's facial expressions and movements in real time. The acquired video data is processed by an emotion analysis system operated on the device. The video data is analyzed as input, and the child's emotional state is output. This analysis includes measurements of interest and concentration. As a specific example, the camera captures the child's smile, which is evaluated as "high interest."

[0368] Step 4:

[0369] The emotional data analyzed by the device is immediately sent to the information processing server. The server receives this data and uses it to adjust the presentation order and difficulty level of educational content. The server then re-inputs it into a generating AI model and outputs appropriate content suggestions based on the child's state. At this time, the server provides instructions such as, "Increase the difficulty level when the child is highly focused."

[0370] Step 5:

[0371] The server sends the adjusted educational content to the device and presents it to the student. The device receives this content and displays it to the student in the specified manner. In this step, the student can work on learning tasks optimized for them. Specifically, a newly added, high-difficulty math problem is displayed on the device screen.

[0372] Step 6:

[0373] The device records the student's actions and emotional state during learning. This data is sent to a server and used as analysis data for learning progress. The server processes the input data and outputs a learning outcome report for parents. Specifically, the student's accuracy rate and response time are recorded and periodically uploaded to the server.

[0374] Step 7:

[0375] Based on the data analyzed by the server, a report on the child's learning progress is provided to parents. This report also includes insights from emotional analysis obtained during learning, resulting in parents receiving information to better support their child's learning. Specifically, the report email may include a statement such as, "Your child is progressing with a very high level of interest in their learning."

[0376] (Application Example 2)

[0377] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0378] In children's learning, there is a need to provide learning experiences that are adapted to each child's emotional state and interests. However, conventional learning systems cannot adequately analyze emotions and concentration levels in real time, making it difficult to provide optimal learning content for children. Furthermore, providing parents with detailed and timely feedback on learning progress is also crucial.

[0379] 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.

[0380] In this invention, the server includes means for dynamically adjusting educational content based on emotion analysis, means for automatically generating new educational content using a generative AI model and reflecting the results of emotion analysis, and means for analyzing educational results and providing reports to parents. This enables the provision of an optimal learning experience tailored to the emotional state of children and appropriate feedback to parents.

[0381] A "terminal" is an electronic device that allows users to input and configure educational content and usage time.

[0382] "Children" refers to children who are eligible to receive education.

[0383] A "recording device" is a device used to capture a child's reactions, and usually includes a camera.

[0384] "Emotional analysis" is a process of evaluating a child's emotional state based on video data captured by a camera.

[0385] "Dynamic adjustment" means flexibly changing the content and presentation methods of educational materials in response to the results of analyzing children's emotions.

[0386] A "server" is a device that processes data via a network and generates and provides educational content.

[0387] A "generative AI model" is a computational model that uses artificial intelligence technology to automatically generate new content.

[0388] "Educational content" refers to a collection of information and teaching materials intended for children's learning.

[0389] "Learning resources" refer to teaching materials and tools used to improve children's knowledge and skills.

[0390] A "report" is a document that summarizes information about a child's educational outcomes and progress, and is provided to the parents / guardians.

[0391] The system that realizes this application example integrates multiple technological elements to individually optimize children's learning activities. The terminal provides an interface for users to input children's educational content and study time. This terminal is also equipped with a camera that can capture children's movements and facial expressions in real time.

[0392] Emotion analysis uses image processing libraries such as OpenCV to process video data obtained from the device's camera and evaluate the child's emotions. This makes it possible to estimate the child's interest and level of concentration. The analyzed data is immediately sent to a server, which dynamically adjusts the educational content based on that information. At this stage, generative AI models using TensorFlow and other tools are utilized to automatically generate new content and adjust its presentation method.

[0393] The server also sends the generated educational content to the device and presents it to the child. This educational content includes information and materials that are most appropriate to the child's current emotional state. The server further analyzes learning progress and results and provides a report to the parents. Through this, parents can understand their child's learning performance and emotional fluctuations and gain guidance to provide a better learning environment.

[0394] As a concrete example, if a child shows high interest while learning English vocabulary, the server generates pronunciation practice materials based on that response. A generative AI model is applied to this generation. An example of a prompt sentence would be: "When the child's expression brightens, what kind of learning content should be provided to further pique their interest?" This makes it possible to provide the child with the most optimal learning experience.

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The terminal receives input from users regarding the child's educational content and study time. This input is saved to a database and processed into a format that can be managed within the terminal.

[0398] Step 2:

[0399] During a child's learning session, the device uses its camera to capture the child's facial expressions and movements in real time. The input is video data from the camera, and the output is facial feature data processed using OpenCV. This provides the basis for sentiment analysis.

[0400] Step 3:

[0401] The server receives facial feature data sent from the terminal and analyzes the emotional state using TensorFlow. The input is feature data, and the output is an estimate of the child's emotional state and level of interest and concentration. This allows for an evaluation of each child's response to learning.

[0402] Step 4:

[0403] The server uses a generative AI model based on emotion analysis results to generate or select appropriate educational content. The input is emotional state data, and the output is educational content optimized for the child. The generated content is dynamically adjusted to match the child's interest and level of concentration.

[0404] Step 5:

[0405] The server generates educational content and sends it to the terminal, which then presents it to the child. The input is appropriately selected educational content, and the output is a display of the content for the child to use. The child gains a learning experience through this content.

[0406] Step 6:

[0407] During the learning process, the device records the child's operation logs and additional facial expression data, and sends them to the server. The input is user operation data and facial expression data, and the output is detailed learning and emotion tracking data.

[0408] Step 7:

[0409] The server analyzes the submitted learning and emotional data and generates a report for parents. The input is integrated learning and emotional data, and the output is a comprehensive learning outcome report. This report is used to understand the child's learning progress and emotional changes.

[0410] Step 8:

[0411] The server suggests additional learning resources and services to the user based on the child's learning record and emotional state. Input is learning and emotional reports, and output is recommended additional learning resources. The suggested content aims to further improve the learning experience.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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".

[0428] This invention provides a system in which terminals and servers work together to improve children's learning experiences. The embodiments thereof are described in detail below.

[0429] First, the user (parent / guardian) uses their device to set the child's learning content and desired study time. This information is entered through the device's application and sent to the server. This allows the system to create a personalized learning plan for each child.

[0430] The device then uses its camera to capture the child's facial expressions and movements in real time. This video data is sent to an emotion analysis algorithm to estimate the child's level of interest and concentration. The resulting analysis data is sent directly to a server for further processing.

[0431] The server integrates the received child's emotional data with user-defined settings and uses a generative AI model to generate or select the most suitable learning content for that child. The generated content is based on the child's current level of understanding and interests, and aims to enhance learning effectiveness. The content thus identified is then sent back to the device and presented to the child.

[0432] As learning progresses, the device records the student's activity log. This includes how the student interacted with the content, whether their answers were correct or incorrect, and the time taken. This data is sent to a server, and a comprehensive learning report is generated.

[0433] Ultimately, the server provides the user with a generated report that clearly shows the child's learning progress. It also supports continuous learning by suggesting appropriate additional learning materials and services based on the child's learning history.

[0434] For example, when a child is learning basic math problems and their concentration on the material increases, the server will create more difficult problems on the same topic, providing challenging content. In this way, learning can be effectively advanced while stimulating the child's motivation to learn.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] The user operates the device to set the child's learning content and desired learning time. The set information is sent to the server through the application on the device.

[0438] Step 2:

[0439] The device activates its camera to capture the child's facial expressions and movements in real time. This collects video data for analyzing the child's emotions and level of concentration.

[0440] Step 3:

[0441] The device inputs the collected video data into an emotion analysis algorithm to estimate the children's interest and level of concentration. The analysis results are then sent from the device to the server.

[0442] Step 4:

[0443] Based on the emotion data and user settings information received by the server, it selects or generates the most suitable learning content. It may also use a generative AI model to create new learning content tailored to the child.

[0444] Step 5:

[0445] The server generates or selects learning content and sends it to the device, presenting the content to the student. This allows the student to learn with content tailored to their individual needs.

[0446] Step 6:

[0447] The device monitors the child's learning progress and records operation logs. This includes correct / incorrect answers, response time, and reactions to the learning content.

[0448] Step 7:

[0449] The device sends the recorded learning data to the server, which analyzes the data and generates a learning outcome report.

[0450] Step 8:

[0451] The server generates reports and provides users with detailed information about the children's learning progress. Furthermore, it suggests additional learning materials and services based on the children's learning history.

[0452] (Example 1)

[0453] 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."

[0454] Conventional learning systems face the challenge of providing personalized learning that appropriately reflects the learning needs and interests of each individual child. Furthermore, they have not adequately achieved the visualization of learning effectiveness and the provision of accurate feedback to parents. This invention aims to overcome these challenges and provide a system that improves children's learning experience.

[0455] 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.

[0456] In this invention, the server includes means for users to set learning information via a terminal, means for capturing the child's response with a display device and analyzing the state, and means for generating prompt sentences using the analyzed state data and creating learning materials utilizing a generation AI model. This enables the provision of personalized learning content that meets the individual learning needs of each child and provides detailed feedback on the child's learning status to parents.

[0457] A "terminal" is a device that a user operates to input information or view learning content.

[0458] A "representation device" is a device that captures children's reactions and actions and uses them for analysis.

[0459] An "information processing device" is a device that analyzes received data and generates or provides appropriate learning content.

[0460] "Learning information" refers to information about a child's learning, such as the content of their studies and their desired study time, which is set by the user.

[0461] "Means for analyzing the state" refers to methods and devices for inferring a child's interests, level of concentration, etc., from captured information.

[0462] An "action log" is data that records how children interacted with learning content.

[0463] A "prompt statement" is an instruction statement used to create appropriate training material using a generative AI model.

[0464] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate optimal learning content for children.

[0465] "Learning materials" is a general term for the information and assignments provided to children for learning.

[0466] A description of embodiments for carrying out this invention will be given.

[0467] Users configure children's learning information via their devices, promoting learning tailored to individual needs. Specifically, users input learning content and desired learning time using a dedicated application on their devices. This information is transmitted to the information processing device using a secure communication protocol.

[0468] The device captures children's facial expressions and movements using its built-in display device. This data is used for state analysis, evaluating children's interest and concentration levels in real time. The analyzed data is transmitted to an information processing device and becomes basic information for optimizing children's learning experience.

[0469] The server uses a generative AI model based on the received data to generate or select the most suitable learning materials for the child. This process generates personalized learning content by converting state data into prompts and providing them to the AI ​​model. For example, a prompt might say, "Based on the child's current understanding and interests, suggest the optimal next learning step."

[0470] The generated learning content is presented to the children via their devices. During learning, the devices meticulously record the children's activity logs and transmit them to the information processing device.

[0471] Ultimately, the server analyzes the student's learning progress and provides parents with a comprehensive learning report. It also suggests additional learning materials and services based on past learning achievements, supporting the child's continued learning. This allows the system to provide an effective and efficient learning experience tailored to each child's individual learning needs.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] Users input student learning information via a terminal. This input includes subjects to be studied, target time, and priority for specific topics. The terminal aggregates this information, converts it into a digital format, and sends it to an information processing device. This enables the creation of personalized learning plans for each student.

[0475] Step 2:

[0476] The device uses its built-in display device to capture children's facial expressions and movements in real time. The captured data is input into an emotion analysis algorithm, which quantifies the children's interest and concentration levels. This analysis result is then sent to an information processing device for use in subsequent processing.

[0477] Step 3:

[0478] The server integrates the child's emotional data with the user's set learning information to generate prompt messages. These prompt messages include instructions such as "Suggest the most suitable learning content based on the child's current state," and a generative AI model is used to select or generate the most suitable learning materials.

[0479] Step 4:

[0480] The generative AI model dynamically generates training material based on prompt messages. This process involves calculations based on past learning history and current learning status to generate optimal content. This generated content is then sent back from the server to the terminal.

[0481] Step 5:

[0482] The device displays learning content received from the server to the child. During the presentation, the device monitors how the child interacts with the content and records it as an operation log. The log includes screen transitions, selected answers, and solution time.

[0483] Step 6:

[0484] The device sends recorded operation logs to the server. The server analyzes the learning progress based on this log information and generates a learning report. This report clearly indicates the student's level of understanding and any necessary follow-up.

[0485] Step 7:

[0486] The server provides the generated learning report to the user (parent / guardian). Based on the data obtained, it also suggests appropriate additional learning materials and services, ensuring continuous learning support.

[0487] (Application Example 1)

[0488] 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."

[0489] In today's learning environment, it is difficult for children to receive education tailored to their individual needs, and general learning methods struggle to provide effective learning experiences. Furthermore, there is a lack of systems to efficiently deliver learning content optimized for individual children.

[0490] 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.

[0491] In this invention, the server includes means for the user to set learning content and usage time via a terminal, means for capturing the user's reactions with an image acquisition device and analyzing their emotions, and means for providing the learning information generated by the server. This enables the provision of personalized learning and dynamic optimization of content according to the child's learning progress.

[0492] A "terminal" is an information processing device that users operate, and it is possible to set learning content and usage time.

[0493] An "image acquisition device" is a device that captures images of a target and stores them as electronic information, and can capture the learner's reactions.

[0494] "Emotional analysis" is a process that evaluates the emotional state of learners based on acquired image data, and uses the results to optimize learning.

[0495] A "server" is a centralized computing resource used to process and provide information via a network, and it is responsible for generating and providing learning information.

[0496] "Learning information" refers to educational resources and materials provided during a child's learning process, with the aim of enhancing the effectiveness of the child's learning.

[0497] "Individualized learning" is a method for providing optimal educational content tailored to each child's learning progress and level of understanding.

[0498] A "virtual space" is a virtual environment created using digital technology, where users can receive education.

[0499] The system implementing this invention consists of a terminal and a server working together. The terminal is an information processing device for which the user sets learning content and desired learning time, and can take the form of a smartphone or tablet. The information entered by the user is transmitted to the server through the terminal.

[0500] The terminal is equipped with an image acquisition device (e.g., a camera) to capture the user's face and facial expressions in real time. The acquired video data is processed by an emotion analysis algorithm to evaluate the learner's emotional state. This analysis uses an image processing library (e.g., OpenCV) and an emotion analysis tool (e.g., Dlib).

[0501] Analysis results and user settings are sent to the server, which uses a generative AI model to generate personalized learning information. This generative AI model can utilize a data analysis platform (e.g., Google Cloud AI). The server sends the generated learning information to the device, providing the user with appropriate learning content. As the user progresses through the learning process, the device records reaction data and operation logs and sends them to the server.

[0502] As a concrete example, here is a prompt message for a child to receive learning content best suited to history: "Generate learning content related to topics this child is interested in." Such a prompt allows the server to effectively generate personalized educational content. This system makes it possible to provide an effective learning experience tailored to each child's learning needs.

[0503] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0504] Step 1:

[0505] The terminal receives input from the user regarding learning content and desired learning time. This information is formatted within the terminal and prepared as a data package to be sent to the server. It receives user configuration information as input and generates an information package to send to the server as output.

[0506] Step 2:

[0507] The device uses image acquisition equipment to capture the child's face and expressions in real time. The obtained video data is processed through an internal emotion analysis algorithm to estimate the child's emotional state as numerical data. The input here is video data from the camera, and the output is the analyzed emotion data.

[0508] Step 3:

[0509] The server combines the configuration information received in Step 1 with the sentiment data obtained in Step 2. Based on this data, it uses a generative AI model to generate learning content tailored to the learner. This generation process utilizes the prompt "Generate learning content related to topics this child is interested in." The input is user settings and sentiment data, and the output is personalized learning information.

[0510] Step 4:

[0511] The server sends the generated learning information to the terminal. The terminal displays the received information to the user, enabling the child to begin learning. The input for this step is the generated learning information, and the output is the content displayed on the learning screen.

[0512] Step 5:

[0513] The terminal continuously monitors the actions and responses of students during learning and records the data as logs. The recorded data, including learning speed and accuracy rate, is sent to the server at regular intervals. Here, the input is the user's action data, and the output is the log data for analysis.

[0514] 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.

[0515] This invention aims to realize an individually optimized learning experience and enhance feedback to parents by combining an emotion engine with a system that supports children's learning. The embodiments are described in detail below.

[0516] The user (parent / guardian) first uses their device to set the child's learning content and desired learning time. This clarifies the child's learning goals, and the settings are instantly sent to the server. Based on this created learning plan, the entire system is then adjusted.

[0517] The device integrates a camera and an emotion engine to capture children's facial expressions and movements in real time. The emotion engine analyzes the children's emotions based on the collected video data and evaluates their level of interest and engagement. As soon as this data is acquired by the device, it is sent to a server and used to adjust the learning content.

[0518] The server integrates received emotional data and learning progress information from the children, and utilizes a generative AI model to generate or select the most suitable learning content for each child. Furthermore, based on the analysis results obtained by the emotional engine, the presentation order and difficulty level of the content can be dynamically adjusted. The generated or adjusted content is immediately sent to the device and provided to the child.

[0519] The device records detailed activity logs when children use learning content. This data, including emotional states recognized by the emotion engine, is sent to a server. The server analyzes the received data and generates a comprehensive learning outcome report.

[0520] The reports generated by the server include not only the children's learning progress but also insights gained through sentiment analysis. Users can use this information to further optimize the children's learning environment. In addition, the server suggests additional learning materials and services that may be effective based on the children's emotional state.

[0521] For example, when a child is learning English vocabulary, if the emotional engine recognizes that the child is showing high interest, the server will generate content for the child as a further challenge, such as reading comprehension or pronunciation practice. In this way, it becomes possible to provide an advanced learning experience that utilizes the child's emotional state.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The user operates the device to set the child's learning content and desired learning time. This information is sent from the device to the server and stored in the database as a basic learning plan.

[0525] Step 2:

[0526] The device activates its camera and uses an emotion engine to capture the child's facial expressions and movements in real time. The video data is analyzed by the emotion engine to identify the child's interests and emotional state.

[0527] Step 3:

[0528] The device sends the emotional data it has processed to the server. Based on the received data, the server uses a generation AI model to dynamically generate or select learning content tailored to that child.

[0529] Step 4:

[0530] The server sends the generated or selected learning content to the device. The content is configured with appropriate difficulty levels and topics, taking into account the analysis results obtained from the emotion engine.

[0531] Step 5:

[0532] The device presents learning content to the children, and logs their interactions with the content. The logs include the children's response patterns and reaction times.

[0533] Step 6:

[0534] The device sends the recorded log data back to the server. The server integrates the operation logs and sentiment data to generate a learning outcome report.

[0535] Step 7:

[0536] The server generates reports for the user, providing detailed data on the child's learning progress. These reports also include advice for the next learning steps, along with insights gained from sentiment analysis.

[0537] Step 8:

[0538] Based on emotional data, the server suggests additional learning materials and services deemed most suitable for each child. This improves the continuity and depth of learning.

[0539] (Example 2)

[0540] 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."

[0541] In today's educational environment, a challenge is providing an optimal learning experience tailored to each child's emotional state and learning progress. Traditional systems have struggled to grasp children's reactions and emotions in real time and dynamically adjust learning content based on that information. This has resulted in limitations in maximizing children's learning effectiveness and in providing appropriate feedback to parents.

[0542] 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.

[0543] In this invention, the server includes means for dynamically adjusting educational content to suit the child using a generation AI system, means for transmitting the child's response data acquired by the information processing device to the information processing server in real time, and means for analyzing the results of educational activities and providing a report to the parents. This makes it possible to provide an individually optimized learning experience that is tailored to the child's emotional state and learning progress.

[0544] An "information processing device" is a terminal used by users to set learning content and usage time, and is a device that handles data input and transmission.

[0545] A "filming device" is a device such as a camera used to capture a child's facial expressions and actions, and has the function of acquiring video data.

[0546] "Means for analyzing emotions" refers to technologies and processes that use acquired video data to evaluate the emotional state of children, including an emotion engine.

[0547] An "information processing server" is a central system that generates and provides learning content, and has the function of integrating and analyzing data and issuing appropriate instructions.

[0548] "Educational content" refers to teaching materials and learning programs provided to support children's learning, which are generated or adjusted by a generative AI system.

[0549] A "generative AI system" refers to an algorithm and system that uses AI technology to automatically generate or adjust learning content that is optimal for children.

[0550] "Educational resources and services" refer to additional teaching materials and support services proposed to effectively advance children's learning.

[0551] This invention is a system that individually optimizes children's learning and provides effective feedback to parents. Specific embodiments are described below.

[0552] The user begins by setting the child's learning content and usage time using an information processing device. This information is transmitted in real time from the user's terminal to the information processing server. Based on the received data, the information processing server uses a generative AI model to generate or select the optimal educational content. The generative AI model utilizes AI technology and has the ability to dynamically adjust the content according to the child's learning progress and emotional data.

[0553] The terminal, in conjunction with a camera, captures the children's facial expressions and movements in real time. This video data is processed by an emotion analysis system to evaluate the children's emotional state, such as their level of interest and concentration. The evaluation results are then sent back to the information processing server and used to adjust the difficulty level and presentation order of the next content presented.

[0554] For example, if a child is learning English vocabulary and sentiment analysis indicates a high level of interest, the information processing server will provide content such as more challenging reading comprehension passages or pronunciation practice. As a result, children can learn at their own pace.

[0555] An example of a prompt message would be, "Please generate appropriate reading comprehension content for children who have shown a high level of interest in English vocabulary."

[0556] This system can improve the quality of education not only by individually optimizing children's learning experiences, but also by analyzing learning outcomes and providing detailed reports to parents.

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The user operates the information processing device to input the child's learning content and desired learning time. The entered information is sent to the information processing server in real time. This process allows the server to receive basic data for creating the child's learning plan. Specifically, the user opens the application and sets items such as "math drill" and "30 minutes."

[0560] Step 2:

[0561] The information processing server generates or selects educational content using a generative AI model based on the received learning content and time setting data. The server inputs this data into the generative AI model and outputs learning content optimized for the child. In this process, the prompt message "Please select a math practice problem suitable for this child" may be used.

[0562] Step 3:

[0563] The device uses a camera to capture the child's facial expressions and movements in real time. The acquired video data is processed by an emotion analysis system operated on the device. The video data is analyzed as input, and the child's emotional state is output. This analysis includes measurements of interest and concentration. As a specific example, the camera captures the child's smile, which is evaluated as "high interest."

[0564] Step 4:

[0565] The emotional data analyzed by the device is immediately sent to the information processing server. The server receives this data and uses it to adjust the presentation order and difficulty level of educational content. The server then re-inputs it into a generating AI model and outputs appropriate content suggestions based on the child's state. At this time, the server provides instructions such as, "Increase the difficulty level when the child is highly focused."

[0566] Step 5:

[0567] The server sends the adjusted educational content to the device and presents it to the student. The device receives this content and displays it to the student in the specified manner. In this step, the student can work on learning tasks optimized for them. Specifically, a newly added, high-difficulty math problem is displayed on the device screen.

[0568] Step 6:

[0569] The device records the student's actions and emotional state during learning. This data is sent to a server and used as analysis data for learning progress. The server processes the input data and outputs a learning outcome report for parents. Specifically, the student's accuracy rate and response time are recorded and periodically uploaded to the server.

[0570] Step 7:

[0571] Based on the data analyzed by the server, a report on the child's learning progress is provided to parents. This report also includes insights from emotional analysis obtained during learning, resulting in parents receiving information to better support their child's learning. Specifically, the report email may include a statement such as, "Your child is progressing with a very high level of interest in their learning."

[0572] (Application Example 2)

[0573] 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."

[0574] In children's learning, there is a need to provide learning experiences that are adapted to each child's emotional state and interests. However, conventional learning systems cannot adequately analyze emotions and concentration levels in real time, making it difficult to provide optimal learning content for children. Furthermore, providing parents with detailed and timely feedback on learning progress is also crucial.

[0575] 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.

[0576] In this invention, the server includes means for dynamically adjusting educational content based on emotion analysis, means for automatically generating new educational content using a generative AI model and reflecting the results of emotion analysis, and means for analyzing educational results and providing reports to parents. This enables the provision of an optimal learning experience tailored to the emotional state of children and appropriate feedback to parents.

[0577] A "terminal" is an electronic device that allows users to input and configure educational content and usage time.

[0578] "Children" refers to children who are eligible to receive education.

[0579] A "recording device" is a device used to capture a child's reactions, and usually includes a camera.

[0580] "Emotional analysis" is a process of evaluating a child's emotional state based on video data captured by a camera.

[0581] "Dynamic adjustment" means flexibly changing the content and presentation methods of educational materials in response to the results of analyzing children's emotions.

[0582] A "server" is a device that processes data via a network and generates and provides educational content.

[0583] A "generative AI model" is a computational model that uses artificial intelligence technology to automatically generate new content.

[0584] "Educational content" refers to a collection of information and teaching materials intended for children's learning.

[0585] "Learning resources" refer to teaching materials and tools used to improve children's knowledge and skills.

[0586] A "report" is a document that summarizes information about a child's educational outcomes and progress, and is provided to the parents / guardians.

[0587] The system that realizes this application example integrates multiple technological elements to individually optimize children's learning activities. The terminal provides an interface for users to input children's educational content and study time. This terminal is also equipped with a camera that can capture children's movements and facial expressions in real time.

[0588] Emotion analysis uses image processing libraries such as OpenCV to process video data obtained from the device's camera and evaluate the child's emotions. This makes it possible to estimate the child's interest and level of concentration. The analyzed data is immediately sent to a server, which dynamically adjusts the educational content based on that information. At this stage, generative AI models using TensorFlow and other tools are utilized to automatically generate new content and adjust its presentation method.

[0589] The server also sends the generated educational content to the device and presents it to the child. This educational content includes information and materials that are most appropriate to the child's current emotional state. The server further analyzes learning progress and results and provides a report to the parents. Through this, parents can understand their child's learning performance and emotional fluctuations and gain guidance to provide a better learning environment.

[0590] As a concrete example, if a child shows high interest while learning English vocabulary, the server generates pronunciation practice materials based on that response. A generative AI model is applied to this generation. An example of a prompt sentence would be: "When the child's expression brightens, what kind of learning content should be provided to further pique their interest?" This makes it possible to provide the child with the most optimal learning experience.

[0591] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0592] Step 1:

[0593] The terminal receives input from users regarding the child's educational content and study time. This input is saved to a database and processed into a format that can be managed within the terminal.

[0594] Step 2:

[0595] During a child's learning session, the device uses its camera to capture the child's facial expressions and movements in real time. The input is video data from the camera, and the output is facial feature data processed using OpenCV. This provides the basis for sentiment analysis.

[0596] Step 3:

[0597] The server receives facial feature data sent from the terminal and analyzes the emotional state using TensorFlow. The input is feature data, and the output is an estimate of the child's emotional state and level of interest and concentration. This allows for an evaluation of each child's response to learning.

[0598] Step 4:

[0599] The server uses a generative AI model based on emotion analysis results to generate or select appropriate educational content. The input is emotional state data, and the output is educational content optimized for the child. The generated content is dynamically adjusted to match the child's interest and level of concentration.

[0600] Step 5:

[0601] The server generates educational content and sends it to the terminal, which then presents it to the child. The input is appropriately selected educational content, and the output is a display of the content for the child to use. The child gains a learning experience through this content.

[0602] Step 6:

[0603] During the learning process, the device records the child's operation logs and additional facial expression data, and sends them to the server. The input is user operation data and facial expression data, and the output is detailed learning and emotion tracking data.

[0604] Step 7:

[0605] The server analyzes the submitted learning and emotional data and generates a report for parents. The input is integrated learning and emotional data, and the output is a comprehensive learning outcome report. This report is used to understand the child's learning progress and emotional changes.

[0606] Step 8:

[0607] The server suggests additional learning resources and services to the user based on the child's learning record and emotional state. Input is learning and emotional reports, and output is recommended additional learning resources. The suggested content aims to further improve the learning experience.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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".

[0625] This invention provides a system in which terminals and servers work together to improve children's learning experiences. The embodiments thereof are described in detail below.

[0626] First, the user (parent / guardian) uses their device to set the child's learning content and desired study time. This information is entered through the device's application and sent to the server. This allows the system to create a personalized learning plan for each child.

[0627] The device then uses its camera to capture the child's facial expressions and movements in real time. This video data is sent to an emotion analysis algorithm to estimate the child's level of interest and concentration. The resulting analysis data is sent directly to a server for further processing.

[0628] The server integrates the received child's emotional data with user-defined settings and uses a generative AI model to generate or select the most suitable learning content for that child. The generated content is based on the child's current level of understanding and interests, and aims to enhance learning effectiveness. The content thus identified is then sent back to the device and presented to the child.

[0629] As learning progresses, the device records the student's activity log. This includes how the student interacted with the content, whether their answers were correct or incorrect, and the time taken. This data is sent to a server, and a comprehensive learning report is generated.

[0630] Ultimately, the server provides the user with a generated report that clearly shows the child's learning progress. It also supports continuous learning by suggesting appropriate additional learning materials and services based on the child's learning history.

[0631] For example, when a child is learning basic math problems and their concentration on the material increases, the server will create more difficult problems on the same topic, providing challenging content. In this way, learning can be effectively advanced while stimulating the child's motivation to learn.

[0632] The following describes the processing flow.

[0633] Step 1:

[0634] The user operates the device to set the child's learning content and desired learning time. The set information is sent to the server through the application on the device.

[0635] Step 2:

[0636] The device activates its camera to capture the child's facial expressions and movements in real time. This collects video data for analyzing the child's emotions and level of concentration.

[0637] Step 3:

[0638] The device inputs the collected video data into an emotion analysis algorithm to estimate the children's interest and level of concentration. The analysis results are then sent from the device to the server.

[0639] Step 4:

[0640] Based on the emotion data and user settings information received by the server, it selects or generates the most suitable learning content. It may also use a generative AI model to create new learning content tailored to the child.

[0641] Step 5:

[0642] The server generates or selects learning content and sends it to the device, presenting the content to the student. This allows the student to learn with content tailored to their individual needs.

[0643] Step 6:

[0644] The device monitors the child's learning progress and records operation logs. This includes correct / incorrect answers, response time, and reactions to the learning content.

[0645] Step 7:

[0646] The device sends the recorded learning data to the server, which analyzes the data and generates a learning outcome report.

[0647] Step 8:

[0648] The server generates reports and provides users with detailed information about the children's learning progress. Furthermore, it suggests additional learning materials and services based on the children's learning history.

[0649] (Example 1)

[0650] 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".

[0651] Conventional learning systems face the challenge of providing personalized learning that appropriately reflects the learning needs and interests of each individual child. Furthermore, they have not adequately achieved the visualization of learning effectiveness and the provision of accurate feedback to parents. This invention aims to overcome these challenges and provide a system that improves children's learning experience.

[0652] 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.

[0653] In this invention, the server includes means for users to set learning information via a terminal, means for capturing the child's response with a display device and analyzing the state, and means for generating prompt sentences using the analyzed state data and creating learning materials utilizing a generation AI model. This enables the provision of personalized learning content that meets the individual learning needs of each child and provides detailed feedback on the child's learning status to parents.

[0654] A "terminal" is a device that a user operates to input information or view learning content.

[0655] A "representation device" is a device that captures children's reactions and actions and uses them for analysis.

[0656] An "information processing device" is a device that analyzes received data and generates or provides appropriate learning content.

[0657] "Learning information" refers to information about a child's learning, such as the content of their studies and their desired study time, which is set by the user.

[0658] "Means for analyzing the state" refer to methods and devices for inferring a child's interests, level of concentration, etc., from captured information.

[0659] An "action log" is data that records how children interacted with learning content.

[0660] A "prompt statement" is an instruction statement used to create appropriate training material using a generative AI model.

[0661] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate optimal learning content for children.

[0662] "Learning materials" is a general term for the information and assignments provided to children for learning.

[0663] A description of embodiments for carrying out this invention will be given.

[0664] Users configure children's learning information via their devices, promoting learning tailored to individual needs. Specifically, users input learning content and desired learning time using a dedicated application on their devices. This information is transmitted to the information processing device using a secure communication protocol.

[0665] The device captures children's facial expressions and movements using its built-in display device. This data is used for state analysis, evaluating children's interest and concentration levels in real time. The analyzed data is transmitted to an information processing device and becomes basic information for optimizing children's learning experience.

[0666] The server uses a generative AI model based on the received data to generate or select the most suitable learning materials for the child. This process generates personalized learning content by converting state data into prompts and providing them to the AI ​​model. For example, a prompt might say, "Based on the child's current understanding and interests, suggest the optimal next learning step."

[0667] The generated learning content is presented to the children via their devices. During learning, the devices meticulously record the children's activity logs and transmit them to the information processing device.

[0668] Ultimately, the server analyzes the student's learning progress and provides parents with a comprehensive learning report. It also suggests additional learning materials and services based on past learning achievements, supporting the child's continued learning. This allows the system to provide an effective and efficient learning experience tailored to each child's individual learning needs.

[0669] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0670] Step 1:

[0671] Users input student learning information via a terminal. This input includes subjects to be studied, target time, and priority for specific topics. The terminal aggregates this information, converts it into a digital format, and sends it to an information processing device. This enables the creation of personalized learning plans for each student.

[0672] Step 2:

[0673] The device uses its built-in display device to capture children's facial expressions and movements in real time. The captured data is input into an emotion analysis algorithm, which quantifies the children's interest and concentration levels. This analysis result is then sent to an information processing device for use in subsequent processing.

[0674] Step 3:

[0675] The server integrates the child's emotional data with the user's set learning information to generate prompt messages. These prompt messages include instructions such as "Suggest the most suitable learning content based on the child's current state," and a generative AI model is used to select or generate the most suitable learning materials.

[0676] Step 4:

[0677] The generative AI model dynamically generates training material based on prompt messages. This process involves calculations based on past learning history and current learning status to generate optimal content. This generated content is then sent back from the server to the terminal.

[0678] Step 5:

[0679] The device displays learning content received from the server to the child. During the presentation, the device monitors how the child interacts with the content and records it as an operation log. The log includes screen transitions, selected answers, and solution time.

[0680] Step 6:

[0681] The device sends recorded operation logs to the server. The server analyzes the learning progress based on this log information and generates a learning report. This report clearly indicates the student's level of understanding and any necessary follow-up.

[0682] Step 7:

[0683] The server provides the generated learning report to the user (parent / guardian). Based on the data obtained, it also suggests appropriate additional learning materials and services, ensuring continuous learning support.

[0684] (Application Example 1)

[0685] 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".

[0686] In today's learning environment, it is difficult for children to receive education tailored to their individual needs, and general learning methods struggle to provide effective learning experiences. Furthermore, there is a lack of systems to efficiently deliver learning content optimized for individual children.

[0687] 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.

[0688] In this invention, the server includes means for the user to set learning content and usage time via a terminal, means for capturing the user's reactions with an image acquisition device and analyzing their emotions, and means for providing the learning information generated by the server. This enables the provision of personalized learning and dynamic optimization of content according to the child's learning progress.

[0689] A "terminal" is an information processing device that users operate, and it is possible to set learning content and usage time.

[0690] An "image acquisition device" is a device that captures images of a target and stores them as electronic information, and can capture the learner's reactions.

[0691] "Emotional analysis" is a process that evaluates the emotional state of learners based on acquired image data, and uses the results to optimize learning.

[0692] A "server" is a centralized computing resource used to process and provide information via a network, and it is responsible for generating and providing learning information.

[0693] "Learning information" refers to educational resources and materials provided during a child's learning process, with the aim of enhancing the effectiveness of the child's learning.

[0694] "Individualized learning" is a method for providing optimal educational content tailored to each child's learning progress and level of understanding.

[0695] A "virtual space" is a virtual environment created using digital technology, where users can receive education.

[0696] The system implementing this invention consists of a terminal and a server working together. The terminal is an information processing device for which the user sets learning content and desired learning time, and can take the form of a smartphone or tablet. The information entered by the user is transmitted to the server through the terminal.

[0697] The terminal is equipped with an image acquisition device (e.g., a camera) to capture the user's face and facial expressions in real time. The acquired video data is processed by an emotion analysis algorithm to evaluate the learner's emotional state. This analysis uses an image processing library (e.g., OpenCV) and an emotion analysis tool (e.g., Dlib).

[0698] Analysis results and user settings are sent to the server, which uses a generative AI model to generate personalized learning information. This generative AI model can utilize a data analysis platform (e.g., Google Cloud AI). The server sends the generated learning information to the device, providing the user with appropriate learning content. As the user progresses through the learning process, the device records reaction data and operation logs and sends them to the server.

[0699] As a concrete example, here is a prompt message for a child to receive learning content best suited to history: "Generate learning content related to topics this child is interested in." Such a prompt allows the server to effectively generate personalized educational content. This system makes it possible to provide an effective learning experience tailored to each child's learning needs.

[0700] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0701] Step 1:

[0702] The terminal receives input from the user regarding learning content and desired learning time. This information is formatted within the terminal and prepared as a data package to be sent to the server. It receives user configuration information as input and generates an information package to send to the server as output.

[0703] Step 2:

[0704] The device uses image acquisition equipment to capture the child's face and expressions in real time. The obtained video data is processed through an internal emotion analysis algorithm to estimate the child's emotional state as numerical data. The input here is video data from the camera, and the output is the analyzed emotion data.

[0705] Step 3:

[0706] The server combines the configuration information received in Step 1 with the sentiment data obtained in Step 2. Based on this data, it uses a generative AI model to generate learning content tailored to the learner. This generation process utilizes the prompt "Generate learning content related to topics this child is interested in." The input is user settings and sentiment data, and the output is personalized learning information.

[0707] Step 4:

[0708] The server sends the generated learning information to the terminal. The terminal displays the received information to the user, enabling the child to begin learning. The input for this step is the generated learning information, and the output is the content displayed on the learning screen.

[0709] Step 5:

[0710] The terminal continuously monitors the actions and responses of students during learning and records the data as logs. The recorded data, including learning speed and accuracy rate, is sent to the server at regular intervals. Here, the input is the user's action data, and the output is the log data for analysis.

[0711] 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.

[0712] This invention aims to realize an individually optimized learning experience and enhance feedback to parents by combining an emotion engine with a system that supports children's learning. The embodiments are described in detail below.

[0713] The user (parent / guardian) first uses their device to set the child's learning content and desired learning time. This clarifies the child's learning goals, and the settings are instantly sent to the server. Based on this created learning plan, the entire system is then adjusted.

[0714] The device integrates a camera and an emotion engine to capture children's facial expressions and movements in real time. The emotion engine analyzes the children's emotions based on the collected video data and evaluates their level of interest and engagement. As soon as this data is acquired by the device, it is sent to a server and used to adjust the learning content.

[0715] The server integrates received emotional data and learning progress information from the children, and utilizes a generative AI model to generate or select the most suitable learning content for each child. Furthermore, based on the analysis results obtained by the emotional engine, the presentation order and difficulty level of the content can be dynamically adjusted. The generated or adjusted content is immediately sent to the device and provided to the child.

[0716] The device records detailed activity logs when children use learning content. This data, including emotional states recognized by the emotion engine, is sent to a server. The server analyzes the received data and generates a comprehensive learning outcome report.

[0717] The reports generated by the server include not only the children's learning progress but also insights gained through sentiment analysis. Users can use this information to further optimize the children's learning environment. In addition, the server suggests additional learning materials and services that may be effective based on the children's emotional state.

[0718] For example, when a child is learning English vocabulary, if the emotional engine recognizes that the child is showing high interest, the server will generate content for the child as a further challenge, such as reading comprehension or pronunciation practice. In this way, it becomes possible to provide an advanced learning experience that utilizes the child's emotional state.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The user operates the device to set the child's learning content and desired learning time. This information is sent from the device to the server and stored in the database as a basic learning plan.

[0722] Step 2:

[0723] The device activates its camera and uses an emotion engine to capture the child's facial expressions and movements in real time. The video data is analyzed by the emotion engine to identify the child's interests and emotional state.

[0724] Step 3:

[0725] The device sends the emotional data it has processed to the server. Based on the received data, the server uses a generation AI model to dynamically generate or select learning content tailored to that child.

[0726] Step 4:

[0727] The server sends the generated or selected learning content to the device. The content is configured with appropriate difficulty levels and topics, taking into account the analysis results obtained from the emotion engine.

[0728] Step 5:

[0729] The device presents learning content to the children, and logs their interactions with the content. The logs include the children's response patterns and reaction times.

[0730] Step 6:

[0731] The device sends the recorded log data back to the server. The server integrates the operation logs and sentiment data to generate a learning outcome report.

[0732] Step 7:

[0733] The server generates reports for the user, providing detailed data on the child's learning progress. These reports also include advice for the next learning steps, along with insights gained from sentiment analysis.

[0734] Step 8:

[0735] Based on emotional data, the server suggests additional learning materials and services deemed most suitable for each child. This improves the continuity and depth of learning.

[0736] (Example 2)

[0737] 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".

[0738] In today's educational environment, a challenge is providing an optimal learning experience tailored to each child's emotional state and learning progress. Traditional systems have struggled to grasp children's reactions and emotions in real time and dynamically adjust learning content based on that information. This has resulted in limitations in maximizing children's learning effectiveness and in providing appropriate feedback to parents.

[0739] 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.

[0740] In this invention, the server includes means for dynamically adjusting educational content to suit the child using a generation AI system, means for transmitting the child's response data acquired by the information processing device to the information processing server in real time, and means for analyzing the results of educational activities and providing a report to the parents. This makes it possible to provide an individually optimized learning experience that is tailored to the child's emotional state and learning progress.

[0741] An "information processing device" is a terminal used by users to set learning content and usage time, and is a device that handles data input and transmission.

[0742] A "filming device" is a device such as a camera used to capture a child's facial expressions and actions, and has the function of acquiring video data.

[0743] "Means for analyzing emotions" refers to technologies and processes that use acquired video data to evaluate the emotional state of children, including an emotion engine.

[0744] An "information processing server" is a central system that generates and provides learning content, and has the function of integrating and analyzing data and issuing appropriate instructions.

[0745] "Educational content" refers to teaching materials and learning programs provided to support children's learning, which are generated or adjusted by a generative AI system.

[0746] A "generative AI system" refers to an algorithm and system that uses AI technology to automatically generate or adjust learning content that is optimal for children.

[0747] "Educational resources and services" refer to additional teaching materials and support services proposed to effectively advance children's learning.

[0748] This invention is a system that individually optimizes children's learning and provides effective feedback to parents. Specific embodiments are described below.

[0749] The user begins by setting the child's learning content and usage time using an information processing device. This information is transmitted in real time from the user's terminal to the information processing server. Based on the received data, the information processing server uses a generative AI model to generate or select the optimal educational content. The generative AI model utilizes AI technology and has the ability to dynamically adjust the content according to the child's learning progress and emotional data.

[0750] The terminal, in conjunction with a camera, captures the children's facial expressions and movements in real time. This video data is processed by an emotion analysis system to evaluate the children's emotional state, such as their level of interest and concentration. The evaluation results are then sent back to the information processing server and used to adjust the difficulty level and presentation order of the next content presented.

[0751] For example, if a child is learning English vocabulary and sentiment analysis indicates a high level of interest, the information processing server will provide content such as more challenging reading comprehension passages or pronunciation practice. As a result, children can learn at their own pace.

[0752] An example of a prompt message would be, "Please generate appropriate reading comprehension content for children who have shown a high level of interest in English vocabulary."

[0753] This system can improve the quality of education not only by individually optimizing children's learning experiences, but also by analyzing learning outcomes and providing detailed reports to parents.

[0754] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0755] Step 1:

[0756] The user operates the information processing device to input the child's learning content and desired learning time. The entered information is sent to the information processing server in real time. This process allows the server to receive basic data for creating the child's learning plan. Specifically, the user opens the application and sets items such as "math drill" and "30 minutes."

[0757] Step 2:

[0758] The information processing server generates or selects educational content using a generative AI model based on the received learning content and time setting data. The server inputs this data into the generative AI model and outputs learning content optimized for the child. In this process, the prompt message "Please select a math practice problem suitable for this child" may be used.

[0759] Step 3:

[0760] The device uses a camera to capture the child's facial expressions and movements in real time. The acquired video data is processed by an emotion analysis system operated on the device. The video data is analyzed as input, and the child's emotional state is output. This analysis includes measurements of interest and concentration. As a specific example, the camera captures the child's smile, which is evaluated as "high interest."

[0761] Step 4:

[0762] The emotional data analyzed by the device is immediately sent to the information processing server. The server receives this data and uses it to adjust the presentation order and difficulty level of educational content. The server then re-inputs it into a generating AI model and outputs appropriate content suggestions based on the child's state. At this time, the server provides instructions such as, "Increase the difficulty level when the child is highly focused."

[0763] Step 5:

[0764] The server sends the adjusted educational content to the device and presents it to the student. The device receives this content and displays it to the student in the specified manner. In this step, the student can work on learning tasks optimized for them. Specifically, a newly added, high-difficulty math problem is displayed on the device screen.

[0765] Step 6:

[0766] The device records the student's actions and emotional state during learning. This data is sent to a server and used as analysis data for learning progress. The server processes the input data and outputs a learning outcome report for parents. Specifically, the student's accuracy rate and response time are recorded and periodically uploaded to the server.

[0767] Step 7:

[0768] Based on the data analyzed by the server, a report on the child's learning progress is provided to parents. This report also includes insights from emotional analysis obtained during learning, resulting in parents receiving information to better support their child's learning. Specifically, the report email may include a statement such as, "Your child is progressing with a very high level of interest in their learning."

[0769] (Application Example 2)

[0770] 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".

[0771] In children's learning, there is a need to provide learning experiences that are adapted to each child's emotional state and interests. However, conventional learning systems cannot adequately analyze emotions and concentration levels in real time, making it difficult to provide optimal learning content for children. Furthermore, providing parents with detailed and timely feedback on learning progress is also crucial.

[0772] 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.

[0773] In this invention, the server includes means for dynamically adjusting educational content based on emotion analysis, means for automatically generating new educational content using a generative AI model and reflecting the results of emotion analysis, and means for analyzing educational results and providing reports to parents. This enables the provision of an optimal learning experience tailored to the emotional state of children and appropriate feedback to parents.

[0774] A "terminal" is an electronic device that allows users to input and configure educational content and usage time.

[0775] "Children" refers to children who are eligible to receive education.

[0776] A "recording device" is a device used to capture a child's reactions, and usually includes a camera.

[0777] "Emotional analysis" is a process of evaluating a child's emotional state based on video data captured by a camera.

[0778] "Dynamic adjustment" means flexibly changing the content and presentation methods of educational materials in response to the results of analyzing children's emotions.

[0779] A "server" is a device that processes data via a network and generates and provides educational content.

[0780] A "generative AI model" is a computational model that uses artificial intelligence technology to automatically generate new content.

[0781] "Educational content" refers to a collection of information and teaching materials intended for children's learning.

[0782] "Learning resources" refer to teaching materials and tools used to improve children's knowledge and skills.

[0783] A "report" is a document that summarizes information about a child's educational outcomes and progress, and is provided to the parents / guardians.

[0784] The system that realizes this application example integrates multiple technological elements to individually optimize children's learning activities. The terminal provides an interface for users to input children's educational content and study time. This terminal is also equipped with a camera that can capture children's movements and facial expressions in real time.

[0785] Emotion analysis uses image processing libraries such as OpenCV to process video data obtained from the device's camera and evaluate the child's emotions. This makes it possible to estimate the child's interest and level of concentration. The analyzed data is immediately sent to a server, which dynamically adjusts the educational content based on that information. At this stage, generative AI models using TensorFlow and other tools are utilized to automatically generate new content and adjust its presentation method.

[0786] The server also sends the generated educational content to the device and presents it to the child. This educational content includes information and materials that are most appropriate to the child's current emotional state. The server further analyzes learning progress and results and provides a report to the parents. Through this, parents can understand their child's learning performance and emotional fluctuations and gain guidance to provide a better learning environment.

[0787] As a concrete example, if a child shows high interest while learning English vocabulary, the server generates pronunciation practice materials based on that response. A generative AI model is applied to this generation. An example of a prompt sentence would be: "When the child's expression brightens, what kind of learning content should be provided to further pique their interest?" This makes it possible to provide the child with the most optimal learning experience.

[0788] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0789] Step 1:

[0790] The terminal receives input from users regarding the child's educational content and study time. This input is saved to a database and processed into a format that can be managed within the terminal.

[0791] Step 2:

[0792] During a child's learning session, the device uses its camera to capture the child's facial expressions and movements in real time. The input is video data from the camera, and the output is facial feature data processed using OpenCV. This provides the basis for sentiment analysis.

[0793] Step 3:

[0794] The server receives facial feature data sent from the terminal and analyzes the emotional state using TensorFlow. The input is feature data, and the output is an estimate of the child's emotional state and level of interest and concentration. This allows for an evaluation of each child's response to learning.

[0795] Step 4:

[0796] The server uses a generative AI model based on emotion analysis results to generate or select appropriate educational content. The input is emotional state data, and the output is educational content optimized for the child. The generated content is dynamically adjusted to match the child's interest and level of concentration.

[0797] Step 5:

[0798] The server generates educational content and sends it to the terminal, which then presents it to the child. The input is appropriately selected educational content, and the output is a display of the content for the child to use. The child gains a learning experience through this content.

[0799] Step 6:

[0800] During the learning process, the device records the child's operation logs and additional facial expression data, and sends them to the server. The input is user operation data and facial expression data, and the output is detailed learning and emotion tracking data.

[0801] Step 7:

[0802] The server analyzes the submitted learning and emotional data and generates a report for parents. The input is integrated learning and emotional data, and the output is a comprehensive learning outcome report. This report is used to understand the child's learning progress and emotional changes.

[0803] Step 8:

[0804] The server suggests additional learning resources and services to the user based on the child's learning record and emotional state. Input is learning and emotional reports, and output is recommended additional learning resources. The suggested content aims to further improve the learning experience.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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."

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0826] The following is further disclosed regarding the embodiments described above.

[0827] (Claim 1)

[0828] A means by which users can set learning content and usage time via a terminal,

[0829] A method for capturing children's reactions with a camera and analyzing their emotions,

[0830] A means of providing the generated learning content on the server,

[0831] A means of analyzing learning results and providing reports to parents,

[0832] A means of suggesting additional teaching materials and services,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, which transmits child response data acquired by a terminal to a server in real time.

[0836] (Claim 3)

[0837] The system according to claim 1, which automatically generates new learning content using a generative AI model.

[0838] "Example 1"

[0839] (Claim 1)

[0840] A means by which users set learning information via a terminal,

[0841] A means of capturing children's reactions using a representation device and analyzing their state,

[0842] A means for providing learning content generated by an information processing device,

[0843] A means of analyzing learning history and providing reports to third parties,

[0844] Means of proposing additional materials or services,

[0845] A means for recording a child's activity log and transmitting it to an information processing device,

[0846] A means for generating prompt sentences using analyzed state data and creating training material utilizing a generative AI model,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, which transmits child response data acquired by a terminal to an information processing device in real time.

[0850] (Claim 3)

[0851] The system according to claim 1, which uses a generative AI model to automatically generate new learning materials and provide children with an optimal learning experience.

[0852] "Application Example 1"

[0853] (Claim 1)

[0854] A means by which users can set learning content and usage time via a terminal,

[0855] A means of capturing user reactions with image acquisition equipment and analyzing emotions,

[0856] A means for providing the generated learning information to the server,

[0857] A means of analyzing learning results and providing reports to parents,

[0858] Means of proposing additional educational resources and support,

[0859] A means of providing a virtual space where users can receive education in a digital space,

[0860] A means of obtaining real-time feedback and personalizing learning,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, which transmits user response data acquired by a terminal to a server in real time.

[0864] (Claim 3)

[0865] The system according to claim 1, which automatically generates new learning information using a generative AI model.

[0866] "Example 2 of combining an emotion engine"

[0867] (Claim 1)

[0868] A means by which users can set learning content and usage time via an information processing device,

[0869] A means of capturing children's facial expressions and behavior with a camera and analyzing their emotions,

[0870] A means by which an information processing server provides generated educational content,

[0871] A means of analyzing the results of educational activities and providing reports to parents,

[0872] A means of proposing educational resources and services based on the emotional state and learning progress of children,

[0873] A means of dynamically adjusting educational content to suit children using a generative AI system,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, which transmits child response data acquired by an information processing device to an information processing server in real time.

[0877] (Claim 3)

[0878] The system according to claim 1, which automatically generates new educational content using a generation AI system.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] A means by which users can set educational content and usage time via a terminal,

[0882] A means of capturing children's reactions with a camera and analyzing their emotions,

[0883] A means of dynamically adjusting educational content based on sentiment analysis,

[0884] A server provides a means of delivering generated educational content,

[0885] A means of analyzing educational outcomes and providing reports to parents,

[0886] Means of suggesting additional learning resources and services,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, which immediately transmits child response data acquired by a terminal to a server.

[0890] (Claim 3)

[0891] The system according to claim 1, which uses a generative AI model to automatically generate new educational content and reflects the results of sentiment analysis. [Explanation of Symbols]

[0892] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means by which users can set learning content and usage time via a terminal, A method for capturing children's reactions with a camera and analyzing their emotions, A means of providing the generated learning content on the server, A means of analyzing learning results and providing reports to parents, A means of suggesting additional teaching materials and services, A system that includes this.

2. The system according to claim 1, which transmits child response data acquired by a terminal to a server in real time.

3. The system according to claim 1, which automatically generates new learning content using a generative AI model.

Images