system

Abstract

We provide the system. [Solution] A means of generating individualized curricula based on learners' basic information, A means of recording learners' responses and progress data and storing them in a large-capacity storage device, Based on the analysis results, we will adjust the curriculum and provide feedback. A means of visually displaying information through a display device and providing explanations in an interactive manner, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the increase in international marriages, there is a need to efficiently teach Japanese compulsory education and culture to children growing up in different national education systems. However, current educational tools and tutoring services do not provide an adequate mechanism to offer an individualized curriculum according to the progress and characteristics of individual learners and to give them immediate feedback. Therefore, an effective means is needed to smoothly acquire Japanese education while meeting the needs of international marriage families.

Means for Solving the Problems

[0005] This invention solves the above problems by automatically generating individualized curricula using learners' basic information and providing educational plans tailored to each learner. Furthermore, it provides a system that improves learning efficiency by recording and accumulating learners' responses and progress data, and providing real-time feedback and curriculum adjustments based on the analysis results. This system provides learners with learning materials and periodic tests using a terminal, allowing for constant monitoring of the learners' progress.

[0006] "Learner" refers to the person who receives education using this system.

[0007] "Basic information" refers to data necessary for creating individualized curricula, such as the learner's grade level, abilities, and interests.

[0008] An "individualized curriculum" refers to a set of learning plans and materials tailored specifically to the learner's basic information.

[0009] "Analysis results" refer to the results of analysis and evaluation based on learner progress data.

[0010] "Feedback" refers to instruction and suggestions for improvement provided according to the learner's progress and level of understanding.

[0011] An "educational plan" refers to a comprehensive learning objective and progress management plan provided to learners.

[0012] "Progress data" refers to data that shows the learner's progress in their learning.

[0013] "Real-time" refers to processing that takes place almost simultaneously with the occurrence of an event.

[0014] "System" refers to a series of functions and configurations for learning support according to the present invention.

[0015] A "terminal" refers to a device that learners use to interact with the system.

[0016] "Teaching materials" refer to the information and content used by learners for learning.

[0017] "Regular test" refers to a test conducted to regularly evaluate the learning achievements of learners.

Brief Description of Drawings

[0018] [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 Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] The invention described herein is an AI-powered learning support system, which in particular assists learners belonging to different national education systems in efficiently acquiring Japanese compulsory education and culture. This system consists of three main components: a server, a terminal, and a user.

[0040] 1. Server Functions

[0041] The server functions as a primary data processing center, maintaining a rich database of information on compulsory education in Japan. Based on the initial information provided by learners, the server uses AI to generate personalized curricula. It also manages all assignment evaluations and progress data, adjusting the curriculum and providing feedback accordingly. For example, if a learner is found to be weak in kanji, the server regenerates a plan incorporating kanji learning, providing a more appropriate curriculum.

[0042] 2. Device functions

[0043] The terminal is a device for visualizing the curriculum and feedback sent from the server. Learners can access daily learning tasks and materials through the terminal. Furthermore, the terminal also receives learner input in real time and sends data on answers and learning progress to the server. For example, when learners review lesson content or take tests, the terminal provides on-the-spot guidance, ensuring smooth learning progress.

[0044] 3. User Roles

[0045] Users are expected to engage independently with the curriculum and assignments provided within the system from a learner's perspective. Based on the curriculum, they record their progress by reviewing daily learning content and completing assignments. A notable feature is that users can flexibly adjust their learning strategy by utilizing feedback from the server. For example, if a user sees their results on a periodic test and realizes they are weak in a particular area, they can request additional practice from the server and receive new learning materials accordingly.

[0046] This system will meet the need for international marriage families to effectively teach Japanese education and culture, and will provide a flexible educational environment tailored to the characteristics of each learner.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server stores basic information obtained from the user (such as age, grade level, and subjects of interest) in a database. Based on this information, it performs the initial setup of the individual curriculum.

[0050] Step 2:

[0051] The server uses an AI model based on stored foundational information to generate personalized curricula for learners. These curricula include detailed information on the specific content to be studied and the progression of each subject.

[0052] Step 3:

[0053] The server sends the generated individual curriculum to the terminal, allowing learners to proceed with their daily studies based on it.

[0054] Step 4:

[0055] The terminal visually presents the curriculum received from the server to the user and provides an interface that shows daily learning tasks. Here, learners select learning materials and begin their studies.

[0056] Step 5:

[0057] Users work on assignments in the learning curriculum through their devices. They solve problems or view learning content and input their progress into their devices.

[0058] Step 6:

[0059] The terminal records user actions and answers in real time and sends progress data and answer data to the server.

[0060] Step 7:

[0061] The server analyzes the learner's progress data sent from the terminal and generates feedback based on the analysis results. This feedback includes the learner's strengths and areas for improvement, and provides advice to help them in their next learning session.

[0062] Step 8:

[0063] The server sends the generated feedback to the terminal, which then displays it to the user. Based on this, the learner can identify the next learning steps and areas for improvement.

[0064] Step 9:

[0065] Users can adjust their learning methods based on feedback and request additional learning support from the server as needed. This process is repeated until the learner's understanding deepens.

[0066] (Example 1)

[0067] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0068] To enable learners with diverse educational backgrounds to efficiently acquire the Japanese education system and culture, it is necessary to provide individualized learning plans tailored to each learner. However, traditional systems have challenges in providing flexible curricula that meet individual learners' needs and in offering immediate feedback.

[0069] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0070] In this invention, the server includes means for inputting learner's basic information and processing that information to generate an individualized learning plan; means for dynamically generating a curriculum using a generation AI model and optimizing the curriculum through prompt messages; and means for collecting learner's response data and progress data, and analyzing that data to identify areas for improvement. This enables the provision of a learning plan optimized for each learner and effective learning support through real-time feedback.

[0071] A "learner" refers to a person whose purpose is to receive education, and who is the entity that uses this system to implement an individualized educational plan.

[0072] "Basic information" refers to fundamental data about learners, including information such as name, grade level, and learning objectives.

[0073] An "individualized learning plan" is an educational program customized to efficiently achieve specific learning objectives based on the learner's basic information.

[0074] A "generative AI model" is a model that uses artificial intelligence technology to optimize the learning curriculum through data analysis and generation processes.

[0075] A "prompt sentence" is a type of input information for an AI model, serving as an introductory sentence that instructs the model on the content and purpose of the generated curriculum.

[0076] A "curriculum" refers to a set of specific learning content and activities that learners are expected to undertake.

[0077] "Feedback" refers to providing information based on learners' progress and achievements to encourage further improvement and the resolution of new challenges.

[0078] A "terminal" refers to an electronic device used by learners to input basic information, check the curriculum, and receive feedback.

[0079] This invention is a system designed to support learning, and it operates through the cooperation of a server, a terminal, and a user. The server functions as the main data processing center, analyzing the learner's basic information to generate an individualized learning plan. The terminal is an interface for providing information from the server to the learner, and the user uses this information to proceed with their learning.

[0080] Specifically, the server uses generative AI models such as Python and TENSORFLOW® to generate a learning curriculum based on the learner's basic information. For example, it can input a prompt such as "Generate the optimal curriculum for this user" into the AI ​​model to formulate a learning plan tailored to each individual learner. The generated curriculum can be adjusted in real time according to the user's learning pace and goals.

[0081] The terminal is an electronic device used by users for their daily learning activities. It features a user interface using HTML and JavaScript (registered trademark), designed for intuitive operation by learners. The terminal displays the curriculum and feedback sent from the server, providing users with a means to check their progress and access necessary learning materials.

[0082] Users independently work on assignments based on the curriculum displayed on their devices. As they complete daily learning tasks, user input and progress data are constantly transmitted from their devices to the server. This system allows users to flexibly adjust their learning methods and effectively learn about Japanese compulsory education and culture.

[0083] For example, when a user is learning Japanese grammar, the server prompts the AI ​​model with a message such as "Generate practice problems suitable for improving Japanese grammar skills" based on the user's progress, and proposes new learning tasks. Through this process, learners can achieve their goals at their own pace.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] Users log in to the system via their device and enter basic information (name, grade level, learning objectives). The device then sends this information to the server. This information is used as basic data to create individual learning plans.

[0087] Step 2:

[0088] The server analyzes the basic information of the user it receives and generates an individualized learning plan by inputting prompts into the AI ​​model. Specifically, a Python program processes the information and instructs the AI ​​model using the prompt "Generate the optimal curriculum for this user," dynamically creating the most suitable curriculum for the learner. As output, curriculum data tailored to the learner is generated.

[0089] Step 3:

[0090] The server sends the generated curriculum to the terminal. The terminal displays this curriculum through the learner's interface. Users can operate the terminal to access curriculum details and daily learning tasks, and obtain necessary materials and guides in real time.

[0091] Step 4:

[0092] Users begin learning activities using their devices, based on the provided curriculum. As they solve assignments and understand learning materials, users input their results into their devices to record their progress. This input becomes important data that will be used to inform future learning plans.

[0093] Step 5:

[0094] The device sends user-entered progress data to the server. The server analyzes this data and uses a generative AI model to evaluate the user's learning performance. For example, if the user's performance on a particular task is low, the AI ​​model can generate new practice problems and suggest more effective learning methods. The output includes improvement guidelines and new tasks for the user.

[0095] Step 6:

[0096] Based on the analysis results, the server sends feedback and a tailored curriculum to the device. The device displays this to the user, providing information to change their learning strategy or tackle new challenges as needed. Users can use this feedback to optimize their learning methods and work towards achieving their goals.

[0097] (Application Example 1)

[0098] 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."

[0099] When international marriages or learners residing abroad receive compulsory education, they face challenges in adapting to the Japanese education system and culture. Against this backdrop, there is a need for support to help learners efficiently and effectively understand and adapt to Japanese compulsory education and culture.

[0100] 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.

[0101] In this invention, the server includes means for generating individualized curricula based on learners' basic information, means for recording learners' responses and progress data and storing them in a large-capacity storage device, means for adjusting the curriculum based on analysis results and providing feedback, and means for visually displaying information through a display device and providing interactive explanations. This enables learners to efficiently learn the content and culture of compulsory education.

[0102] "Basic learner information" refers to initial data about the learner, such as age, grade level, and learning history.

[0103] An "individualized curriculum" is an educational program optimized for the learner, generated by AI based on the learner's basic information.

[0104] A "large-capacity storage device" is a storage medium that can store and manage learners' answers and progress data over the long term.

[0105] "Analysis results" refer to information obtained as a result of the AI ​​analyzing the collected learner data.

[0106] "Feedback" refers to information provided to learners regarding their learning outcomes and areas for improvement, including suggestions and advice.

[0107] A "display device" is hardware used to provide information to learners visually.

[0108] "A means of providing explanations through dialogue" refers to a function for explaining content through two-way communication with learners.

[0109] "Educational materials based on the compulsory education guidelines" refer to teaching materials that include standard learning content within the compulsory education system.

[0110] A "periodic assessment" is a test designed to periodically measure and evaluate learners' learning outcomes.

[0111] To implement this invention, it is necessary to construct a learning support system and coordinate three main components: a server, a terminal, and a user.

[0112] First, the server functions as the primary data processing center. This server maintains a comprehensive database of compulsory education in Japan. Based on learner background information, AI generates individualized curricula. This process primarily utilizes AI models such as TensorFlow and PyTorch. Progress data and learner responses are analyzed, and the curriculum is adjusted and feedback provided based on this analysis.

[0113] Next, the terminal functions as an access point for learners. Learners can use this terminal to access daily learning tasks and materials. The terminal accepts learner input in real time and sends answers and progress to the server. Furthermore, it displays information visually and provides interactive explanations to the user. Pepper and educational robots in general are particularly used for this purpose.

[0114] Finally, users play a vital role in actively advancing their learning by participating in the system. They can efficiently learn compulsory education content by checking their current learning status and utilizing the feedback received from the server.

[0115] As a concrete example, suppose a learner starts a course using "Momotaro" (Peach Boy) as its subject. The server generates appropriate materials according to the learner's level and starts a visual lecture on the terminal. If a question arises regarding "Momotaro," the terminal uses an AI model to provide a detailed explanation.

[0116] An example of a prompt for a generative AI model is: "Generate a simple summary of the Japanese fairy tale 'Momotaro' for children, using easy-to-understand language so that an educational robot can teach it. In particular, replace complex words with simpler expressions so that it is easy for beginners to understand." Using this prompt, the AI ​​automatically generates an explanation suitable for the teaching material.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server receives basic learner information as input, including age, grade level, and past learning history. Using this data, an AI model generates an individualized curriculum. This process extracts relevant learning material information from a database and uses TensorFlow to output a curriculum tailored to the learner.

[0120] Step 2:

[0121] The terminal receives the curriculum sent from the server. The display device visually shows learning tasks and materials, allowing the user to access their daily learning. During this process, input data is collected when the user interacts with the learning materials.

[0122] Step 3:

[0123] Users engage with learning materials provided via their devices. Specifically, they solve problems and enter answers to questions. The results of these user actions are sent to the server via the device.

[0124] Step 4:

[0125] The server receives user responses and progress data as input. This data is stored in a database and analyzed by an AI model. Based on the analysis results, the curriculum is adjusted and new feedback is generated.

[0126] Step 5:

[0127] The device receives the generated feedback and displays it to the user in real time. For example, it provides explanations for incorrect answers to help learners correct their mistakes.

[0128] Step 6:

[0129] The server generates new lesson explanations and assignments by providing the AI ​​model with generated prompt sentences as input. For example, it automatically creates a detailed explanation of "Momotaro." The results are then sent back to the terminal to prepare for the start of the next learning cycle.

[0130] 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.

[0131] The invention described herein incorporates an emotion engine into a learner-oriented learning support system to recognize the learner's emotional state and provide a more personalized educational experience. This system is implemented with three main components: a server, a terminal, and a user.

[0132] 1. Server Functions

[0133] The server is the central hub for managing and analyzing learners' basic and emotional information. The server integrates an AI model and an emotion engine, analyzing accumulated emotional data and learning progress information to generate a curriculum optimized for each learner. For example, if emotional data indicates that a learner is fatigued, the server adjusts the curriculum to prioritize relatively lighter content and engaging topics.

[0134] 2. Device functions

[0135] The terminal functions as an interface between the learner and the system. This terminal utilizes data from the emotion engine to provide appropriate learning progress and feedback in real time, tailored to the learner's current emotions. It also monitors changes in emotional state and sends updates to the server as needed. For example, if a learner expresses anxiety, the terminal suggests relaxation content to alleviate that emotion.

[0136] 3. User Roles

[0137] Users progress through their daily learning via their devices, providing learning and emotional data as they input their answers. The device recognizes the learner's facial expressions, tone of voice, input speed, etc., and an emotional engine analyzes this information. For example, if a learner shows difficulty with a challenging problem, the server can appropriately sense the user's emotional state and adjust the difficulty level of the content accordingly to support continued learning.

[0138] This system aims to create a more flexible and effective learning environment by adjusting educational content based on the learner's emotional state. It enables real-time feedback tailored to the learner's emotions, promoting motivation and efficient learning.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The server prepares a database to store learners' basic information and emotional states. At the start of learning, the server generates an initial curriculum based on the basic information.

[0142] Step 2:

[0143] The device accepts emotional information input from the learner. This information is collected through a facial recognition camera and a voice analysis microphone.

[0144] Step 3:

[0145] The device analyzes the collected emotional data in real time and sends the results to the server. The analysis includes the learner's facial expressions and tone of voice.

[0146] Step 4:

[0147] The server uses an AI model to adapt and adjust the curriculum based on emotional data and learning progress received from the terminals.

[0148] Step 5:

[0149] The server sends the adjusted curriculum and appropriate learning content to the device. These adjustments include adjusting the difficulty level of tasks to be emotionally sensitive and providing content to boost motivation.

[0150] Step 6:

[0151] The device displays learning content optimized for the learner and provides visual or audible feedback as needed. The feedback is adjusted according to the learner's emotional state.

[0152] Step 7:

[0153] Users progress through the learning process and answer assignments via their devices. The answer data and associated emotional information are recorded by the device and sent to the server.

[0154] Step 8:

[0155] The server analyzes answer data and the latest sentiment data to identify challenges learners face and incorporate them into the next learning cycle. Based on the new data, further feedback and curriculum adjustments are made.

[0156] By repeating this process, efficient learning support that flexibly responds to learners' emotions can be achieved.

[0157] (Example 2)

[0158] 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".

[0159] Conventional learning support systems lack flexibility to meet the individual needs of learners, and face challenges in providing appropriate feedback and adjusting curricula based on learners' emotional states. This results in decreased learning efficiency and difficulty in maintaining motivation.

[0160] 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.

[0161] In this invention, the server includes means for generating individualized curricula based on learner's basic information, means for recording and accumulating learner's responses and progress information, and means for analyzing the accumulated emotional state data. This enables the implementation of a learning experience optimized for each learner, and allows for real-time feedback and curriculum adjustments according to each individual's emotional state.

[0162] "Basic information" refers to fundamental data about learners, such as age, grade level, and learning history.

[0163] An "individualized curriculum" refers to an educational plan that is customized according to the characteristics and needs of the learner.

[0164] "Progress information" refers to data that shows the learner's current learning status, including which learning materials have been completed and which problems remain unresolved.

[0165] "Emotional state data" refers to information about emotions that is analyzed based on the learner's facial expressions, tone of voice, input speed, etc.

[0166] "Analysis results" refer to the results of the analysis performed by the server based on the collected data, and the information used for curriculum adjustments and providing feedback.

[0167] "Feedback" refers to advice and evaluations provided to learners regarding their learning content.

[0168] This invention integrates an emotion engine into a learning support system for learners, providing a personalized educational experience. The system consists of three main components: a server, a terminal, and a user.

[0169] Server Role

[0170] The server is the central hub of the system, responsible for retrieving and storing learner basic and progress information from the database. It also integrates an emotion engine and generative AI models to analyze this data, understand learners' emotional states, and generate optimized curricula. Servers are located in cloud environments or dedicated data centers, utilizing high-performance processors and large amounts of memory to enable large-scale data processing.

[0171] For example, if emotional data detects that a learner is fatigued, the server will recommend easier content and generate a message encouraging them to rest.

[0172] Terminal role

[0173] The device functions as an interface between the learner and the system. Equipped with a camera, microphone, and sensors, the device collects the learner's emotional state in real time. This allows the device to provide immediate feedback to the user. For example, if the user shows signs of anxiety, it can play relaxation music.

[0174] Furthermore, the device sends learner response data to the server, dynamically adjusting the learning experience.

[0175] User roles

[0176] As users progress through their learning process via their devices, they naturally provide emotional data such as facial expressions, voice, and reaction speed. This data is incorporated into the system's overall feedback loop and used to adjust the learning curriculum to best suit the learner.

[0177] Example of a prompt

[0178] "Design a prompt that generates appropriate feedback and curriculum adjustment suggestions when the facial recognition camera detects anxiety while a learner is solving a math problem."

[0179] In this way, the system utilizes the user's emotional state to provide flexible learning support that enhances learning efficiency.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The server retrieves learner's basic information and past progress data from the database upon system startup. This information is used to set a baseline for individual curricula. The input is the learner's ID, and the output is initial curriculum data tailored to each individual learner. Specifically, SQL queries are used to retrieve the necessary information from the database.

[0183] Step 2:

[0184] The device collects the learner's real-time facial expressions, voice tone, input speed, etc., using sensors, cameras, and microphones. This data is preprocessed on the device as input and sent to the server for analysis in the next step. The output is standardized emotion data. Specifically, the data is evaluated using facial recognition APIs and voice analysis libraries.

[0185] Step 3:

[0186] The server inputs emotional data received from the terminal into an emotion engine to analyze the learner's current emotional state. Inputs include emotional data and past progress information. The output is personalized feedback and suggested curriculum adjustments. Here, a generative AI model is used; for example, if the system infers that the learner is "not focused," it adjusts the learning content to reduce its intensity.

[0187] Step 4:

[0188] The terminal presents learners with feedback received from the server and adjustments to the curriculum. Inputs are feedback information from the server, while outputs are specific instructions or content created for the learners. Specific actions include dynamic UI changes and voice guidance.

[0189] Step 5:

[0190] Users review feedback and adjusted curricula provided via their devices and continue learning. User responses and selections are collected as input and output to a database for improving future feedback. Specific actions include logging completed materials and selected options.

[0191] (Application Example 2)

[0192] 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".

[0193] Conventional learning support systems have difficulty taking into account the individual emotional states of learners, resulting in insufficient real-time support during the learning process. As a result, learners may experience stress and decreased motivation.

[0194] 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.

[0195] In this invention, the server includes means for generating an individualized learning plan based on the learner's basic information, means for recording the learner's responses and progress information and storing it in an information base, means for adjusting the learning plan and providing evaluation based on the analysis results, means for recognizing the learner's emotional state and dynamically adjusting the learning content based on that information, and means for presenting learning content and relaxation content that respond to the learner's emotions in real time. This enables personalized learning support that responds to the learner's emotional state.

[0196] A "learner" is an individual who seeks education and training with the aim of improving their own knowledge and skills.

[0197] "Basic information" refers to data about the learner's profile and initial state, including age, interests, and past learning history.

[0198] An "individualized learning plan" refers to an educational program customized to the individual needs and abilities of each learner.

[0199] "Answer" refers to the solution or response that a learner gives to a given problem or question.

[0200] "Progress information" refers to data that records the progress and achievement level of learners' learning activities.

[0201] An "information base" refers to a digital space or system that aggregates and stores data and knowledge.

[0202] "Analysis results" refer to the conclusions and insights derived from analyzing the collected data.

[0203] "Evaluation" refers to the measurement of a learner's performance and understanding, and the feedback and scores provided based on that measurement.

[0204] "Emotional state" refers to the learner's psychological state, which includes feelings such as joy, anxiety, and fatigue.

[0205] "Dynamic adjustment" refers to immediately changing the content or situation in accordance with the current situation and requirements.

[0206] "Relaxation content" refers to content and activities provided to alleviate the mental and physical tension of learners and to refresh them.

[0207] This learning support system integrates multiple components to deliver an educational experience tailored to the individual needs of learners. The server has the function of generating individualized learning plans based on the learner's basic information and stores the learner's responses and progress information in a database. The server utilizes AI models and an emotion engine to analyze the data, dynamically adjusts the learning plan based on the analysis results, and provides the learner with the optimal assessment. For example, it uses TensorFlow with Python to build an AI model and OpenCV to recognize the learner's emotional state.

[0208] The device functions as an interface connecting the learner and the system, and can present learning content and relaxation materials in real time that respond to the learner's emotions. This allows for situation-appropriate feedback, such as reducing the intensity of learning content or suggesting a break, if the learner shows signs of stress or fatigue.

[0209] Users interact with the device and engage with educational content through their daily learning activities. The device monitors the learner's facial expressions and tone of voice, and an emotion engine analyzes this information to adjust the learning content accordingly. This system can use a generative AI model to create prompts such as, "What is the optimal learning content based on the learner's emotions today?" This enables more personalized learning support tailored to the learner's emotional state.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The server receives basic information about the learner and generates an individualized learning plan. The input includes data such as the learner's age, interests, and past learning history. Based on this, the server selects the most suitable learning content from the curriculum information stored in the database. The output is a learning plan optimized for the learner.

[0213] Step 2:

[0214] The terminal monitors learners' responses and progress information and sends it to the server. Input consists of learners' answers and operation logs. This data is analyzed to store learners' progress in a database, and the educational content is updated in real time. Output is a learner's learning progress report.

[0215] Step 3:

[0216] The device senses the learner's facial expressions and tone of voice and evaluates their emotional state. Input consists of video and audio data obtained through the camera and microphone. An emotion engine analyzes this data using OpenCV and speech analysis techniques. Output is a tag indicating the current emotional state.

[0217] Step 4:

[0218] The server dynamically adjusts the learning plan based on information about the user's emotional state. The input is emotional state data obtained from the emotion engine. The AI ​​model uses this data to switch the learning content to relaxation content or adjust the difficulty level. The output is the adjusted learning content.

[0219] Step 5:

[0220] The user receives feedback through the device and takes the next learning action. The input consists of learning content and relaxation suggestions presented by the device. Based on this, the user chooses to continue learning or take a break and inputs their response into the device. The output is the result of the user's selection of the next action.

[0221] Step 6:

[0222] The server generates prompt sentences using a generative AI model. The input consists of past learning progress and sentiment data. Based on this data, the AI ​​model generates prompt sentences and suggests and adjusts appropriate learning content. The output is prompt sentences for improving learning.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] The invention described herein is an AI-powered learning support system, which in particular assists learners belonging to different national education systems in efficiently acquiring Japanese compulsory education and culture. This system consists of three main components: a server, a terminal, and a user.

[0240] 1. Server Functions

[0241] The server functions as a primary data processing center, maintaining a rich database of information on compulsory education in Japan. Based on the initial information provided by learners, the server uses AI to generate personalized curricula. It also manages all assignment evaluations and progress data, adjusting the curriculum and providing feedback accordingly. For example, if a learner is found to be weak in kanji, the server regenerates a plan incorporating kanji learning, providing a more appropriate curriculum.

[0242] 2. Device functions

[0243] The terminal is a device for visualizing the curriculum and feedback sent from the server. Learners can access daily learning tasks and materials through the terminal. Furthermore, the terminal also receives learner input in real time and sends data on answers and learning progress to the server. For example, when learners review lesson content or take tests, the terminal provides on-the-spot guidance, ensuring smooth learning progress.

[0244] 3. User Roles

[0245] Users are expected to engage independently with the curriculum and assignments provided within the system from a learner's perspective. Based on the curriculum, they record their progress by reviewing daily learning content and completing assignments. A notable feature is that users can flexibly adjust their learning strategy by utilizing feedback from the server. For example, if a user sees their results on a periodic test and realizes they are weak in a particular area, they can request additional practice from the server and receive new learning materials accordingly.

[0246] This system will meet the need for international marriage families to effectively teach Japanese education and culture, and will provide a flexible educational environment tailored to the characteristics of each learner.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The server stores basic information obtained from the user (such as age, grade level, and subjects of interest) in a database. Based on this information, it performs the initial setup of the individual curriculum.

[0250] Step 2:

[0251] The server uses an AI model based on stored foundational information to generate personalized curricula for learners. These curricula include detailed information on the specific content to be studied and the progression of each subject.

[0252] Step 3:

[0253] The server sends the generated individual curriculum to the terminal, allowing learners to proceed with their daily studies based on it.

[0254] Step 4:

[0255] The terminal visually presents the curriculum received from the server to the user and provides an interface that shows daily learning tasks. Here, learners select learning materials and begin their studies.

[0256] Step 5:

[0257] Users work on assignments in the learning curriculum through their devices. They solve problems or view learning content and input their progress into their devices.

[0258] Step 6:

[0259] The terminal records user actions and answers in real time and sends progress data and answer data to the server.

[0260] Step 7:

[0261] The server analyzes the learner's progress data sent from the terminal and generates feedback based on the analysis results. This feedback includes the learner's strengths and areas for improvement, and provides advice to help them in their next learning session.

[0262] Step 8:

[0263] The server sends the generated feedback to the terminal, which then displays it to the user. Based on this, the learner can identify the next learning steps and areas for improvement.

[0264] Step 9:

[0265] Users can adjust their learning methods based on feedback and request additional learning support from the server as needed. This process is repeated until the learner's understanding deepens.

[0266] (Example 1)

[0267] 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."

[0268] To enable learners with diverse educational backgrounds to efficiently acquire the Japanese education system and culture, it is necessary to provide individualized learning plans tailored to each learner. However, traditional systems have challenges in providing flexible curricula that meet individual learners' needs and in offering immediate feedback.

[0269] 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.

[0270] In this invention, the server includes means for inputting learner's basic information and processing that information to generate an individualized learning plan; means for dynamically generating a curriculum using a generation AI model and optimizing the curriculum through prompt messages; and means for collecting learner's response data and progress data, and analyzing that data to identify areas for improvement. This enables the provision of a learning plan optimized for each learner and effective learning support through real-time feedback.

[0271] A "learner" refers to a person whose purpose is to receive education, and who is the entity that uses this system to implement an individualized educational plan.

[0272] "Basic information" refers to fundamental data about learners, including information such as name, grade level, and learning objectives.

[0273] An "individualized learning plan" is an educational program customized to efficiently achieve specific learning objectives based on the learner's basic information.

[0274] A "generative AI model" is a model that uses artificial intelligence technology to optimize the learning curriculum through data analysis and generation processes.

[0275] A "prompt sentence" is a type of input information for an AI model, serving as an introductory sentence that instructs the model on the content and purpose of the generated curriculum.

[0276] A "curriculum" refers to a set of specific learning content and activities that learners are expected to undertake.

[0277] "Feedback" refers to providing information based on learners' progress and achievements to encourage further improvement and the resolution of new challenges.

[0278] A "terminal" refers to an electronic device used by learners to input basic information, check the curriculum, and receive feedback.

[0279] This invention is a system designed to support learning, and it operates through the cooperation of a server, a terminal, and a user. The server functions as the main data processing center, analyzing the learner's basic information to generate an individualized learning plan. The terminal is an interface for providing information from the server to the learner, and the user uses this information to proceed with their learning.

[0280] Specifically, the server uses generative AI models such as Python and TensorFlow to generate a learning curriculum based on the learner's basic information. For example, it can input a prompt such as "Generate the optimal curriculum for this user" into the AI ​​model, which then formulates a learning plan tailored to each individual learner. The generated curriculum can be adjusted in real time according to the user's learning pace and goals.

[0281] The terminal is an electronic device used by users for their daily learning activities. It features a user interface using HTML and Javascript, designed for intuitive operation. The terminal displays the curriculum and feedback sent from the server, providing users with a means to check their progress and access necessary learning materials.

[0282] Users independently work on assignments based on the curriculum displayed on their devices. As they complete daily learning tasks, user input and progress data are constantly transmitted from their devices to the server. This system allows users to flexibly adjust their learning methods and effectively learn about Japanese compulsory education and culture.

[0283] For example, when a user is learning Japanese grammar, the server prompts the AI ​​model with a message such as "Generate practice problems suitable for improving Japanese grammar skills" based on the user's progress, and proposes new learning tasks. Through this process, learners can achieve their goals at their own pace.

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

[0285] Step 1:

[0286] The user logs in to the system through the terminal and enters basic information (name, academic year, learning purpose). The terminal sends this information to the server. This information is used as basic data for creating an individual learning plan.

[0287] Step 2:

[0288] The server analyzes the received basic information of the user and generates an individual learning plan by inputting a prompt sentence into the generated AI model. Specifically, a Python program processes the information, gives instructions to the AI model using a prompt sentence such as "Generate the most suitable curriculum for this user", and dynamically creates the most suitable curriculum for the learner. As output, curriculum data tailored to the learner is generated.

[0289] Step 3:

[0290] The server sends the generated curriculum to the terminal. The terminal displays this curriculum through the learner's interface. The user can operate the terminal to access the details of the curriculum and daily learning tasks, and obtain the necessary teaching materials and guides in real time.

[0291] Step 4:

[0292] The user uses the terminal to start learning activities based on the provided curriculum. While solving problems and understanding teaching materials, the user inputs the obtained results into the terminal to record the progress. This input becomes important data for reflecting in future learning plans.

[0293] Step 5:

[0294] The device sends user-entered progress data to the server. The server analyzes this data and uses a generative AI model to evaluate the user's learning performance. For example, if the user's performance on a particular task is low, the AI ​​model can generate new practice problems and suggest more effective learning methods. The output includes improvement guidelines and new tasks for the user.

[0295] Step 6:

[0296] Based on the analysis results, the server sends feedback and a tailored curriculum to the device. The device displays this to the user, providing information to change their learning strategy or tackle new challenges as needed. Users can use this feedback to optimize their learning methods and work towards achieving their goals.

[0297] (Application Example 1)

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

[0299] When international marriages or learners residing abroad receive compulsory education, they face challenges in adapting to the Japanese education system and culture. Against this backdrop, there is a need for support to help learners efficiently and effectively understand and adapt to Japanese compulsory education and culture.

[0300] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0301] In this invention, the server includes means for generating an individual curriculum based on the basic information of the learner, means for recording the learner's answers and progress data and storing them in a mass storage device, means for adjusting the curriculum based on the analysis results and providing feedback, and means for visually displaying information through a display device and providing explanations in an interactive manner. As a result, it becomes possible for the learner to efficiently learn the content and culture of compulsory education.

[0302] The "basic information of the learner" refers to information indicating initial data such as the age, grade, and learning history of the learner.

[0303] The "individual curriculum" is an educational program optimized for the learner, generated by AI based on the basic information of the learner.

[0304] The "mass storage device" is a storage medium that can long-term store and manage the learner's answers and progress data.

[0305] The "analysis results" are information obtained as a result of the AI analyzing the collected learner data.

[0306] The "feedback" is information for providing the learner with pointers and advice regarding learning achievements and areas for improvement.

[0307] The "display device" is hardware for visually providing information to the learner.

[0308] The "means for providing explanations in an interactive manner" is a function for explaining the content through two-way communication with the learner.

[0309] The "educational materials based on the outline of compulsory education" are teaching materials containing standard learning content in the compulsory education system.

[0310] The "regular inspection" is a test for periodically measuring and evaluating the learning achievements of the learner.

[0311] To implement this invention, it is necessary to construct a learning support system and coordinate three main components: a server, a terminal, and a user.

[0312] First, the server functions as the primary data processing center. This server maintains a comprehensive database of compulsory education in Japan. Based on learner background information, AI generates individualized curricula. This process primarily utilizes AI models such as TensorFlow and PyTorch. Progress data and learner responses are analyzed, and the curriculum is adjusted and feedback provided based on this analysis.

[0313] Next, the terminal functions as an access point for learners. Learners can use this terminal to access daily learning tasks and materials. The terminal accepts learner input in real time and sends answers and progress to the server. Furthermore, it displays information visually and provides interactive explanations to the user. Pepper and educational robots in general are particularly used for this purpose.

[0314] Finally, users play a vital role in actively advancing their learning by participating in the system. They can efficiently learn compulsory education content by checking their current learning status and utilizing the feedback received from the server.

[0315] As a concrete example, suppose a learner starts a course using "Momotaro" (Peach Boy) as its subject. The server generates appropriate materials according to the learner's level and starts a visual lecture on the terminal. If a question arises regarding "Momotaro," the terminal uses an AI model to provide a detailed explanation.

[0316] An example of a prompt for a generative AI model is: "Generate a simple summary of the Japanese fairy tale 'Momotaro' for children, using easy-to-understand language so that an educational robot can teach it. In particular, replace complex words with simpler expressions so that it is easy for beginners to understand." Using this prompt, the AI ​​automatically generates an explanation suitable for the teaching material.

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The server receives basic learner information as input, including age, grade level, and past learning history. Using this data, an AI model generates an individualized curriculum. This process extracts relevant learning material information from a database and uses TensorFlow to output a curriculum tailored to the learner.

[0320] Step 2:

[0321] The terminal receives the curriculum sent from the server. The display device visually shows learning tasks and materials, allowing the user to access their daily learning. During this process, input data is collected when the user interacts with the learning materials.

[0322] Step 3:

[0323] Users engage with learning materials provided via their devices. Specifically, they solve problems and enter answers to questions. The results of these user actions are sent to the server via the device.

[0324] Step 4:

[0325] The server receives user responses and progress data as input. This data is stored in a database and analyzed by an AI model. Based on the analysis results, the curriculum is adjusted and new feedback is generated.

[0326] Step 5:

[0327] The device receives the generated feedback and displays it to the user in real time. For example, it provides explanations for incorrect answers to help learners correct their mistakes.

[0328] Step 6:

[0329] The server generates new lesson explanations and assignments by providing the AI ​​model with generated prompt sentences as input. For example, it automatically creates a detailed explanation of "Momotaro." The results are then sent back to the terminal to prepare for the start of the next learning cycle.

[0330] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0331] The invention described herein incorporates an emotion engine into a learner-oriented learning support system to recognize the learner's emotional state and provide a more personalized educational experience. This system is implemented with three main components: a server, a terminal, and a user.

[0332] 1. Server Functions

[0333] The server is the central hub for managing and analyzing learners' basic and emotional information. The server integrates an AI model and an emotion engine, analyzing accumulated emotional data and learning progress information to generate a curriculum optimized for each learner. For example, if emotional data indicates that a learner is fatigued, the server adjusts the curriculum to prioritize relatively lighter content and engaging topics.

[0334] 2. Device functions

[0335] The terminal functions as an interface between the learner and the system. This terminal utilizes data from the emotion engine to provide appropriate learning progress and feedback in real time, tailored to the learner's current emotions. It also monitors changes in emotional state and sends updates to the server as needed. For example, if a learner expresses anxiety, the terminal suggests relaxation content to alleviate that emotion.

[0336] 3. User Roles

[0337] Users progress through their daily learning via their devices, providing learning and emotional data as they input their answers. The device recognizes the learner's facial expressions, tone of voice, input speed, etc., and an emotional engine analyzes this information. For example, if a learner shows difficulty with a challenging problem, the server can appropriately sense the user's emotional state and adjust the difficulty level of the content accordingly to support continued learning.

[0338] This system aims to create a more flexible and effective learning environment by adjusting educational content based on the learner's emotional state. It enables real-time feedback tailored to the learner's emotions, promoting motivation and efficient learning.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] The server prepares a database to store learners' basic information and emotional states. At the start of learning, the server generates an initial curriculum based on the basic information.

[0342] Step 2:

[0343] The device accepts emotional information input from the learner. This information is collected through a facial recognition camera and a voice analysis microphone.

[0344] Step 3:

[0345] The device analyzes the collected emotional data in real time and sends the results to the server. The analysis includes the learner's facial expressions and tone of voice.

[0346] Step 4:

[0347] The server uses an AI model to adapt and adjust the curriculum based on emotional data and learning progress received from the terminals.

[0348] Step 5:

[0349] The server sends the adjusted curriculum and appropriate learning content to the device. These adjustments include adjusting the difficulty level of tasks to be emotionally sensitive and providing content to boost motivation.

[0350] Step 6:

[0351] The device displays learning content optimized for the learner and provides visual or audible feedback as needed. The feedback is adjusted according to the learner's emotional state.

[0352] Step 7:

[0353] Users progress through the learning process and answer assignments via their devices. The answer data and associated emotional information are recorded by the device and sent to the server.

[0354] Step 8:

[0355] The server analyzes answer data and the latest sentiment data to identify challenges learners face and incorporate them into the next learning cycle. Based on the new data, further feedback and curriculum adjustments are made.

[0356] By repeating this process, efficient learning support that flexibly responds to learners' emotions can be achieved.

[0357] (Example 2)

[0358] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0359] Conventional learning support systems lack flexibility to meet the individual needs of learners, and face challenges in providing appropriate feedback and adjusting curricula based on learners' emotional states. This results in decreased learning efficiency and difficulty in maintaining motivation.

[0360] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0361] In this invention, the server includes means for generating individualized curricula based on learner's basic information, means for recording and accumulating learner's responses and progress information, and means for analyzing the accumulated emotional state data. This enables the implementation of a learning experience optimized for each learner, and allows for real-time feedback and curriculum adjustments according to each individual's emotional state.

[0362] "Basic information" refers to fundamental data about learners, such as age, grade level, and learning history.

[0363] An "individualized curriculum" refers to an educational plan that is customized according to the characteristics and needs of the learner.

[0364] "Progress information" refers to data that shows the learner's current learning status, including which learning materials have been completed and which problems remain unresolved.

[0365] "Emotional state data" refers to information about emotions that is analyzed based on the learner's facial expressions, tone of voice, input speed, etc.

[0366] "Analysis results" refer to the results of the analysis performed by the server based on the collected data, and the information used for curriculum adjustments and providing feedback.

[0367] "Feedback" refers to advice and evaluations provided to learners regarding their learning content.

[0368] This invention integrates an emotion engine into a learning support system for learners, providing a personalized educational experience. The system consists of three main components: a server, a terminal, and a user.

[0369] Server Role

[0370] The server is the central hub of the system, responsible for retrieving and storing learner basic and progress information from the database. It also integrates an emotion engine and generative AI models to analyze this data, understand learners' emotional states, and generate optimized curricula. Servers are located in cloud environments or dedicated data centers, utilizing high-performance processors and large amounts of memory to enable large-scale data processing.

[0371] For example, if emotional data detects that a learner is fatigued, the server will recommend easier content and generate a message encouraging them to rest.

[0372] Terminal role

[0373] The device functions as an interface between the learner and the system. Equipped with a camera, microphone, and sensors, the device collects the learner's emotional state in real time. This allows the device to provide immediate feedback to the user. For example, if the user shows signs of anxiety, it can play relaxation music.

[0374] Furthermore, the device sends learner response data to the server, dynamically adjusting the learning experience.

[0375] User roles

[0376] As users progress through their learning process via their devices, they naturally provide emotional data such as facial expressions, voice, and reaction speed. This data is incorporated into the system's overall feedback loop and used to adjust the learning curriculum to best suit the learner.

[0377] Example of a prompt

[0378] "Design a prompt that generates appropriate feedback and curriculum adjustment suggestions when the facial recognition camera detects anxiety while a learner is solving a math problem."

[0379] In this way, the system utilizes the user's emotional state to provide flexible learning support that enhances learning efficiency.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] The server retrieves learner's basic information and past progress data from the database upon system startup. This information is used to set a baseline for individual curricula. The input is the learner's ID, and the output is initial curriculum data tailored to each individual learner. Specifically, SQL queries are used to retrieve the necessary information from the database.

[0383] Step 2:

[0384] The device collects the learner's real-time facial expressions, voice tone, input speed, etc., using sensors, cameras, and microphones. This data is preprocessed on the device as input and sent to the server for analysis in the next step. The output is standardized emotion data. Specifically, the data is evaluated using facial recognition APIs and voice analysis libraries.

[0385] Step 3:

[0386] The server inputs emotional data received from the terminal into an emotion engine to analyze the learner's current emotional state. Inputs include emotional data and past progress information. The output is personalized feedback and suggested curriculum adjustments. Here, a generative AI model is used; for example, if the system infers that the learner is "not focused," it adjusts the learning content to reduce its intensity.

[0387] Step 4:

[0388] The terminal presents learners with feedback received from the server and adjustments to the curriculum. Inputs are feedback information from the server, while outputs are specific instructions or content created for the learners. Specific actions include dynamic UI changes and voice guidance.

[0389] Step 5:

[0390] Users review feedback and adjusted curricula provided via their devices and continue learning. User responses and selections are collected as input and output to a database for improving future feedback. Specific actions include logging completed materials and selected options.

[0391] (Application Example 2)

[0392] 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."

[0393] Conventional learning support systems have difficulty taking into account the individual emotional states of learners, resulting in insufficient real-time support during the learning process. As a result, learners may experience stress and decreased motivation.

[0394] 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.

[0395] In this invention, the server includes means for generating an individualized learning plan based on the learner's basic information, means for recording the learner's responses and progress information and storing it in an information base, means for adjusting the learning plan and providing evaluation based on the analysis results, means for recognizing the learner's emotional state and dynamically adjusting the learning content based on that information, and means for presenting learning content and relaxation content that respond to the learner's emotions in real time. This enables personalized learning support that responds to the learner's emotional state.

[0396] A "learner" is an individual who seeks education and training with the aim of improving their own knowledge and skills.

[0397] "Basic information" refers to data about the learner's profile and initial state, including age, interests, and past learning history.

[0398] An "individualized learning plan" refers to an educational program customized to the individual needs and abilities of each learner.

[0399] "Answer" refers to the solution or response that a learner gives to a given problem or question.

[0400] "Progress information" refers to data that records the progress and achievement level of learners' learning activities.

[0401] An "information base" refers to a digital space or system that aggregates and stores data and knowledge.

[0402] "Analysis results" refer to the conclusions and insights derived from analyzing the collected data.

[0403] "Evaluation" refers to the measurement of a learner's performance and understanding, and the feedback and scores provided based on that measurement.

[0404] "Emotional state" refers to the learner's psychological state, which includes feelings such as joy, anxiety, and fatigue.

[0405] "Dynamic adjustment" refers to immediately changing the content or situation in accordance with the current situation and requirements.

[0406] "Relaxation content" refers to content and activities provided to alleviate the mental and physical tension of learners and to refresh them.

[0407] This learning support system integrates multiple components to deliver an educational experience tailored to the individual needs of learners. The server has the function of generating individualized learning plans based on the learner's basic information and stores the learner's responses and progress information in a database. The server utilizes AI models and an emotion engine to analyze the data, dynamically adjusts the learning plan based on the analysis results, and provides the learner with the optimal assessment. For example, it uses TensorFlow with Python to build an AI model and OpenCV to recognize the learner's emotional state.

[0408] The device functions as an interface connecting the learner and the system, and can present learning content and relaxation materials in real time that respond to the learner's emotions. This allows for situation-appropriate feedback, such as reducing the intensity of learning content or suggesting a break, if the learner shows signs of stress or fatigue.

[0409] Users interact with the device and engage with educational content through their daily learning activities. The device monitors the learner's facial expressions and tone of voice, and an emotion engine analyzes this information to adjust the learning content accordingly. This system can use a generative AI model to create prompts such as, "What is the optimal learning content based on the learner's emotions today?" This enables more personalized learning support tailored to the learner's emotional state.

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] The server receives basic information about the learner and generates an individualized learning plan. The input includes data such as the learner's age, interests, and past learning history. Based on this, the server selects the most suitable learning content from the curriculum information stored in the database. The output is a learning plan optimized for the learner.

[0413] Step 2:

[0414] The terminal monitors learners' responses and progress information and sends it to the server. Input consists of learners' answers and operation logs. This data is analyzed to store learners' progress in a database, and the educational content is updated in real time. Output is a learner's learning progress report.

[0415] Step 3:

[0416] The device senses the learner's facial expressions and tone of voice and evaluates their emotional state. Input consists of video and audio data obtained through the camera and microphone. An emotion engine analyzes this data using OpenCV and speech analysis techniques. Output is a tag indicating the current emotional state.

[0417] Step 4:

[0418] The server dynamically adjusts the learning plan based on information about the user's emotional state. The input is emotional state data obtained from the emotion engine. The AI ​​model uses this data to switch the learning content to relaxation content or adjust the difficulty level. The output is the adjusted learning content.

[0419] Step 5:

[0420] The user receives feedback through the device and takes the next learning action. The input consists of learning content and relaxation suggestions presented by the device. Based on this, the user chooses to continue learning or take a break and inputs their response into the device. The output is the result of the user's selection of the next action.

[0421] Step 6:

[0422] The server generates prompt sentences using a generative AI model. The input consists of past learning progress and sentiment data. Based on this data, the AI ​​model generates prompt sentences and suggests and adjusts appropriate learning content. The output is prompt sentences for improving learning.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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".

[0439] The invention described herein is an AI-powered learning support system, which in particular assists learners belonging to different national education systems in efficiently acquiring Japanese compulsory education and culture. This system consists of three main components: a server, a terminal, and a user.

[0440] 1. Server Functions

[0441] The server functions as a primary data processing center, maintaining a rich database of information on compulsory education in Japan. Based on the initial information provided by learners, the server uses AI to generate personalized curricula. It also manages all assignment evaluations and progress data, adjusting the curriculum and providing feedback accordingly. For example, if a learner is found to be weak in kanji, the server regenerates a plan incorporating kanji learning, providing a more appropriate curriculum.

[0442] 2. Device functions

[0443] The terminal is a device for visualizing the curriculum and feedback sent from the server. Learners can access daily learning tasks and materials through the terminal. Furthermore, the terminal also receives learner input in real time and sends data on answers and learning progress to the server. For example, when learners review lesson content or take tests, the terminal provides on-the-spot guidance, ensuring smooth learning progress.

[0444] 3. User Roles

[0445] Users are expected to engage independently with the curriculum and assignments provided within the system from a learner's perspective. Based on the curriculum, they record their progress by reviewing daily learning content and completing assignments. A notable feature is that users can flexibly adjust their learning strategy by utilizing feedback from the server. For example, if a user sees their results on a periodic test and realizes they are weak in a particular area, they can request additional practice from the server and receive new learning materials accordingly.

[0446] This system will meet the need for international marriage families to effectively teach Japanese education and culture, and will provide a flexible educational environment tailored to the characteristics of each learner.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] The server stores basic information obtained from the user (such as age, grade level, and subjects of interest) in a database. Based on this information, it performs the initial setup of the individual curriculum.

[0450] Step 2:

[0451] The server uses an AI model based on stored foundational information to generate personalized curricula for learners. These curricula include detailed information on the specific content to be studied and the progression of each subject.

[0452] Step 3:

[0453] The server sends the generated individual curriculum to the terminal, allowing learners to proceed with their daily studies based on it.

[0454] Step 4:

[0455] The terminal visually presents the curriculum received from the server to the user and provides an interface that shows daily learning tasks. Here, learners select learning materials and begin their studies.

[0456] Step 5:

[0457] Users work on assignments in the learning curriculum through their devices. They solve problems or view learning content and input their progress into their devices.

[0458] Step 6:

[0459] The terminal records user actions and answers in real time and sends progress data and answer data to the server.

[0460] Step 7:

[0461] The server analyzes the learner's progress data sent from the terminal and generates feedback based on the analysis results. This feedback includes the learner's strengths and areas for improvement, and provides advice to help them in their next learning session.

[0462] Step 8:

[0463] The server sends the generated feedback to the terminal, which then displays it to the user. Based on this, the learner can identify the next learning steps and areas for improvement.

[0464] Step 9:

[0465] Users can adjust their learning methods based on feedback and request additional learning support from the server as needed. This process is repeated until the learner's understanding deepens.

[0466] (Example 1)

[0467] 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."

[0468] To enable learners with diverse educational backgrounds to efficiently acquire the Japanese education system and culture, it is necessary to provide individualized learning plans tailored to each learner. However, traditional systems have challenges in providing flexible curricula that meet individual learners' needs and in offering immediate feedback.

[0469] 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.

[0470] In this invention, the server includes means for inputting learner's basic information and processing that information to generate an individualized learning plan; means for dynamically generating a curriculum using a generation AI model and optimizing the curriculum through prompt messages; and means for collecting learner's response data and progress data, and analyzing that data to identify areas for improvement. This enables the provision of a learning plan optimized for each learner and effective learning support through real-time feedback.

[0471] A "learner" refers to a person whose purpose is to receive education, and who is the entity that uses this system to implement an individualized educational plan.

[0472] "Basic information" refers to fundamental data about learners, including information such as name, grade level, and learning objectives.

[0473] An "individualized learning plan" is an educational program customized to efficiently achieve specific learning objectives based on the learner's basic information.

[0474] A "generative AI model" is a model that uses artificial intelligence technology to optimize the learning curriculum through data analysis and generation processes.

[0475] A "prompt sentence" is a type of input information for an AI model, serving as an introductory sentence that instructs the model on the content and purpose of the generated curriculum.

[0476] A "curriculum" refers to a set of specific learning content and activities that learners are expected to undertake.

[0477] "Feedback" refers to providing information based on learners' progress and achievements to encourage further improvement and the resolution of new challenges.

[0478] A "terminal" refers to an electronic device used by learners to input basic information, check the curriculum, and receive feedback.

[0479] This invention is a system designed to support learning, and it operates through the cooperation of a server, a terminal, and a user. The server functions as the main data processing center, analyzing the learner's basic information to generate an individualized learning plan. The terminal is an interface for providing information from the server to the learner, and the user uses this information to proceed with their learning.

[0480] Specifically, the server uses generative AI models such as Python and TensorFlow to generate a learning curriculum based on the learner's basic information. For example, it can input a prompt such as "Generate the optimal curriculum for this user" into the AI ​​model, which then formulates a learning plan tailored to each individual learner. The generated curriculum can be adjusted in real time according to the user's learning pace and goals.

[0481] The terminal is an electronic device used by users for their daily learning activities. It features a user interface using HTML and Javascript, designed for intuitive operation. The terminal displays the curriculum and feedback sent from the server, providing users with a means to check their progress and access necessary learning materials.

[0482] Users independently work on assignments based on the curriculum displayed on their devices. As they complete daily learning tasks, user input and progress data are constantly transmitted from their devices to the server. This system allows users to flexibly adjust their learning methods and effectively learn about Japanese compulsory education and culture.

[0483] For example, when a user is learning Japanese grammar, the server prompts the AI ​​model with a message such as "Generate practice problems suitable for improving Japanese grammar skills" based on the user's progress, and proposes new learning tasks. Through this process, learners can achieve their goals at their own pace.

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1:

[0486] Users log in to the system via their device and enter basic information (name, grade level, learning objectives). The device then sends this information to the server. This information is used as basic data to create individual learning plans.

[0487] Step 2:

[0488] The server analyzes the basic information of the user it receives and generates an individualized learning plan by inputting prompts into the AI ​​model. Specifically, a Python program processes the information and instructs the AI ​​model using the prompt "Generate the optimal curriculum for this user," dynamically creating the most suitable curriculum for the learner. As output, curriculum data tailored to the learner is generated.

[0489] Step 3:

[0490] The server sends the generated curriculum to the terminal. The terminal displays this curriculum through the learner's interface. Users can operate the terminal to access curriculum details and daily learning tasks, and obtain necessary materials and guides in real time.

[0491] Step 4:

[0492] Users begin learning activities using their devices, based on the provided curriculum. As they solve assignments and understand learning materials, users input their results into their devices to record their progress. This input becomes important data that will be used to inform future learning plans.

[0493] Step 5:

[0494] The device sends user-entered progress data to the server. The server analyzes this data and uses a generative AI model to evaluate the user's learning performance. For example, if the user's performance on a particular task is low, the AI ​​model can generate new practice problems and suggest more effective learning methods. The output includes improvement guidelines and new tasks for the user.

[0495] Step 6:

[0496] Based on the analysis results, the server sends feedback and a tailored curriculum to the device. The device displays this to the user, providing information to change their learning strategy or tackle new challenges as needed. Users can use this feedback to optimize their learning methods and work towards achieving their goals.

[0497] (Application Example 1)

[0498] 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."

[0499] When international marriages or learners residing abroad receive compulsory education, they face challenges in adapting to the Japanese education system and culture. Against this backdrop, there is a need for support to help learners efficiently and effectively understand and adapt to Japanese compulsory education and culture.

[0500] 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.

[0501] In this invention, the server includes means for generating individualized curricula based on learners' basic information, means for recording learners' responses and progress data and storing them in a large-capacity storage device, means for adjusting the curriculum based on analysis results and providing feedback, and means for visually displaying information through a display device and providing interactive explanations. This enables learners to efficiently learn the content and culture of compulsory education.

[0502] "Basic learner information" refers to initial data about the learner, such as age, grade level, and learning history.

[0503] An "individualized curriculum" is an educational program optimized for the learner, generated by AI based on the learner's basic information.

[0504] A "large-capacity storage device" is a storage medium that can store and manage learners' answers and progress data over the long term.

[0505] "Analysis results" refer to information obtained as a result of the AI ​​analyzing the collected learner data.

[0506] "Feedback" refers to information provided to learners regarding their learning outcomes and areas for improvement, including suggestions and advice.

[0507] A "display device" is hardware used to provide information to learners visually.

[0508] "A means of providing explanations through dialogue" refers to a function for explaining content through two-way communication with learners.

[0509] "Educational materials based on the compulsory education guidelines" refer to teaching materials that include standard learning content within the compulsory education system.

[0510] A "periodic assessment" is a test designed to periodically measure and evaluate learners' learning outcomes.

[0511] To implement this invention, it is necessary to construct a learning support system and coordinate three main components: a server, a terminal, and a user.

[0512] First, the server functions as the primary data processing center. This server maintains a comprehensive database of compulsory education in Japan. Based on learner background information, AI generates individualized curricula. This process primarily utilizes AI models such as TensorFlow and PyTorch. Progress data and learner responses are analyzed, and the curriculum is adjusted and feedback provided based on this analysis.

[0513] Next, the terminal functions as an access point for learners. Learners can use this terminal to access daily learning tasks and materials. The terminal accepts learner input in real time and sends answers and progress to the server. Furthermore, it displays information visually and provides interactive explanations to the user. Pepper and educational robots in general are particularly used for this purpose.

[0514] Finally, users play a vital role in actively advancing their learning by participating in the system. They can efficiently learn compulsory education content by checking their current learning status and utilizing the feedback received from the server.

[0515] As a concrete example, suppose a learner starts a course using "Momotaro" (Peach Boy) as its subject. The server generates appropriate materials according to the learner's level and starts a visual lecture on the terminal. If a question arises regarding "Momotaro," the terminal uses an AI model to provide a detailed explanation.

[0516] An example of a prompt for a generative AI model is: "Generate a simple summary of the Japanese fairy tale 'Momotaro' for children, using easy-to-understand language so that an educational robot can teach it. In particular, replace complex words with simpler expressions so that it is easy for beginners to understand." Using this prompt, the AI ​​automatically generates an explanation suitable for the teaching material.

[0517] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0518] Step 1:

[0519] The server receives basic learner information as input, including age, grade level, and past learning history. Using this data, an AI model generates an individualized curriculum. This process extracts relevant learning material information from a database and uses TensorFlow to output a curriculum tailored to the learner.

[0520] Step 2:

[0521] The terminal receives the curriculum sent from the server. The display device visually shows learning tasks and materials, allowing the user to access their daily learning. During this process, input data is collected when the user interacts with the learning materials.

[0522] Step 3:

[0523] Users engage with learning materials provided via their devices. Specifically, they solve problems and enter answers to questions. The results of these user actions are sent to the server via the device.

[0524] Step 4:

[0525] The server receives user responses and progress data as input. This data is stored in a database and analyzed by an AI model. Based on the analysis results, the curriculum is adjusted and new feedback is generated.

[0526] Step 5:

[0527] The device receives the generated feedback and displays it to the user in real time. For example, it provides explanations for incorrect answers to help learners correct their mistakes.

[0528] Step 6:

[0529] The server generates new lesson explanations and assignments by providing the AI ​​model with generated prompt sentences as input. For example, it automatically creates a detailed explanation of "Momotaro." The results are then sent back to the terminal to prepare for the start of the next learning cycle.

[0530] 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.

[0531] The invention described herein incorporates an emotion engine into a learner-oriented learning support system to recognize the learner's emotional state and provide a more personalized educational experience. This system is implemented with three main components: a server, a terminal, and a user.

[0532] 1. Server Functions

[0533] The server is the central hub for managing and analyzing learners' basic and emotional information. The server integrates an AI model and an emotion engine, analyzing accumulated emotional data and learning progress information to generate a curriculum optimized for each learner. For example, if emotional data indicates that a learner is fatigued, the server adjusts the curriculum to prioritize relatively lighter content and engaging topics.

[0534] 2. Device functions

[0535] The terminal functions as an interface between the learner and the system. This terminal utilizes data from the emotion engine to provide appropriate learning progress and feedback in real time, tailored to the learner's current emotions. It also monitors changes in emotional state and sends updates to the server as needed. For example, if a learner expresses anxiety, the terminal suggests relaxation content to alleviate that emotion.

[0536] 3. User Roles

[0537] Users progress through their daily learning via their devices, providing learning and emotional data as they input their answers. The device recognizes the learner's facial expressions, tone of voice, input speed, etc., and an emotional engine analyzes this information. For example, if a learner shows difficulty with a challenging problem, the server can appropriately sense the user's emotional state and adjust the difficulty level of the content accordingly to support continued learning.

[0538] This system aims to create a more flexible and effective learning environment by adjusting educational content based on the learner's emotional state. It enables real-time feedback tailored to the learner's emotions, promoting motivation and efficient learning.

[0539] The following describes the processing flow.

[0540] Step 1:

[0541] The server prepares a database to store learners' basic information and emotional states. At the start of learning, the server generates an initial curriculum based on the basic information.

[0542] Step 2:

[0543] The device accepts emotional information input from the learner. This information is collected through a facial recognition camera and a voice analysis microphone.

[0544] Step 3:

[0545] The device analyzes the collected emotional data in real time and sends the results to the server. The analysis includes the learner's facial expressions and tone of voice.

[0546] Step 4:

[0547] The server uses an AI model to adapt and adjust the curriculum based on emotional data and learning progress received from the terminals.

[0548] Step 5:

[0549] The server sends the adjusted curriculum and appropriate learning content to the device. These adjustments include adjusting the difficulty level of tasks to be emotionally sensitive and providing content to boost motivation.

[0550] Step 6:

[0551] The device displays learning content optimized for the learner and provides visual or audible feedback as needed. The feedback is adjusted according to the learner's emotional state.

[0552] Step 7:

[0553] Users progress through the learning process and answer assignments via their devices. The answer data and associated emotional information are recorded by the device and sent to the server.

[0554] Step 8:

[0555] The server analyzes answer data and the latest sentiment data to identify challenges learners face and incorporate them into the next learning cycle. Based on the new data, further feedback and curriculum adjustments are made.

[0556] By repeating this process, efficient learning support that flexibly responds to learners' emotions can be achieved.

[0557] (Example 2)

[0558] 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."

[0559] Conventional learning support systems lack flexibility to meet the individual needs of learners, and face challenges in providing appropriate feedback and adjusting curricula based on learners' emotional states. This results in decreased learning efficiency and difficulty in maintaining motivation.

[0560] 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.

[0561] In this invention, the server includes means for generating individualized curricula based on learner's basic information, means for recording and accumulating learner's responses and progress information, and means for analyzing the accumulated emotional state data. This enables the implementation of a learning experience optimized for each learner, and allows for real-time feedback and curriculum adjustments according to each individual's emotional state.

[0562] "Basic information" refers to fundamental data about learners, such as age, grade level, and learning history.

[0563] An "individualized curriculum" refers to an educational plan that is customized according to the characteristics and needs of the learner.

[0564] "Progress information" refers to data that shows the learner's current learning status, including which learning materials have been completed and which problems remain unresolved.

[0565] "Emotional state data" refers to information about emotions that is analyzed based on the learner's facial expressions, tone of voice, input speed, etc.

[0566] "Analysis results" refer to the results of the analysis performed by the server based on the collected data, and the information used for curriculum adjustments and providing feedback.

[0567] "Feedback" refers to advice and evaluations provided to learners regarding their learning content.

[0568] This invention integrates an emotion engine into a learning support system for learners, providing a personalized educational experience. The system consists of three main components: a server, a terminal, and a user.

[0569] Server Role

[0570] The server is the central hub of the system, responsible for retrieving and storing learner basic and progress information from the database. It also integrates an emotion engine and generative AI models to analyze this data, understand learners' emotional states, and generate optimized curricula. Servers are located in cloud environments or dedicated data centers, utilizing high-performance processors and large amounts of memory to enable large-scale data processing.

[0571] For example, if emotional data detects that a learner is fatigued, the server will recommend easier content and generate a message encouraging them to rest.

[0572] Terminal role

[0573] The device functions as an interface between the learner and the system. Equipped with a camera, microphone, and sensors, the device collects the learner's emotional state in real time. This allows the device to provide immediate feedback to the user. For example, if the user shows signs of anxiety, it can play relaxation music.

[0574] Furthermore, the device sends learner response data to the server, dynamically adjusting the learning experience.

[0575] User roles

[0576] As users progress through their learning process via their devices, they naturally provide emotional data such as facial expressions, voice, and reaction speed. This data is incorporated into the system's overall feedback loop and used to adjust the learning curriculum to best suit the learner.

[0577] Example of a prompt

[0578] "Design a prompt that generates appropriate feedback and curriculum adjustment suggestions when the facial recognition camera detects anxiety while a learner is solving a math problem."

[0579] In this way, the system utilizes the user's emotional state to provide flexible learning support that enhances learning efficiency.

[0580] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0581] Step 1:

[0582] The server retrieves learner's basic information and past progress data from the database upon system startup. This information is used to set a baseline for individual curricula. The input is the learner's ID, and the output is initial curriculum data tailored to each individual learner. Specifically, SQL queries are used to retrieve the necessary information from the database.

[0583] Step 2:

[0584] The device collects the learner's real-time facial expressions, voice tone, input speed, etc., using sensors, cameras, and microphones. This data is preprocessed on the device as input and sent to the server for analysis in the next step. The output is standardized emotion data. Specifically, the data is evaluated using facial recognition APIs and voice analysis libraries.

[0585] Step 3:

[0586] The server inputs emotional data received from the terminal into an emotion engine to analyze the learner's current emotional state. Inputs include emotional data and past progress information. The output is personalized feedback and suggested curriculum adjustments. Here, a generative AI model is used; for example, if the system infers that the learner is "not focused," it adjusts the learning content to reduce its intensity.

[0587] Step 4:

[0588] The terminal presents learners with feedback received from the server and adjustments to the curriculum. Inputs are feedback information from the server, while outputs are specific instructions or content created for the learners. Specific actions include dynamic UI changes and voice guidance.

[0589] Step 5:

[0590] Users review feedback and adjusted curricula provided via their devices and continue learning. User responses and selections are collected as input and output to a database for improving future feedback. Specific actions include logging completed materials and selected options.

[0591] (Application Example 2)

[0592] 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."

[0593] Conventional learning support systems have difficulty taking into account the individual emotional states of learners, resulting in insufficient real-time support during the learning process. As a result, learners may experience stress and decreased motivation.

[0594] 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.

[0595] In this invention, the server includes means for generating an individualized learning plan based on the learner's basic information, means for recording the learner's responses and progress information and storing it in an information base, means for adjusting the learning plan and providing evaluation based on the analysis results, means for recognizing the learner's emotional state and dynamically adjusting the learning content based on that information, and means for presenting learning content and relaxation content that respond to the learner's emotions in real time. This enables personalized learning support that responds to the learner's emotional state.

[0596] A "learner" is an individual who seeks education and training with the aim of improving their own knowledge and skills.

[0597] "Basic information" refers to data about the learner's profile and initial state, including age, interests, and past learning history.

[0598] An "individualized learning plan" refers to an educational program customized to the individual needs and abilities of each learner.

[0599] "Answer" refers to the solution or response that a learner gives to a given problem or question.

[0600] "Progress information" refers to data that records the progress and achievement level of learners' learning activities.

[0601] An "information base" refers to a digital space or system that aggregates and stores data and knowledge.

[0602] "Analysis results" refer to the conclusions and insights derived from analyzing the collected data.

[0603] "Evaluation" refers to the measurement of a learner's performance and understanding, and the feedback and scores provided based on that measurement.

[0604] "Emotional state" refers to the learner's psychological state, which includes feelings such as joy, anxiety, and fatigue.

[0605] "Dynamic adjustment" refers to immediately changing the content or situation in accordance with the current situation and requirements.

[0606] "Relaxation content" refers to content and activities provided to alleviate the mental and physical tension of learners and to refresh them.

[0607] This learning support system integrates multiple components to deliver an educational experience tailored to the individual needs of learners. The server has the function of generating individualized learning plans based on the learner's basic information and stores the learner's responses and progress information in a database. The server utilizes AI models and an emotion engine to analyze the data, dynamically adjusts the learning plan based on the analysis results, and provides the learner with the optimal assessment. For example, it uses TensorFlow with Python to build an AI model and OpenCV to recognize the learner's emotional state.

[0608] The device functions as an interface connecting the learner and the system, and can present learning content and relaxation materials in real time that respond to the learner's emotions. This allows for situation-appropriate feedback, such as reducing the intensity of learning content or suggesting a break, if the learner shows signs of stress or fatigue.

[0609] Users interact with the device and engage with educational content through their daily learning activities. The device monitors the learner's facial expressions and tone of voice, and an emotion engine analyzes this information to adjust the learning content accordingly. This system can use a generative AI model to create prompts such as, "What is the optimal learning content based on the learner's emotions today?" This enables more personalized learning support tailored to the learner's emotional state.

[0610] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0611] Step 1:

[0612] The server receives basic information about the learner and generates an individualized learning plan. The input includes data such as the learner's age, interests, and past learning history. Based on this, the server selects the most suitable learning content from the curriculum information stored in the database. The output is a learning plan optimized for the learner.

[0613] Step 2:

[0614] The terminal monitors learners' responses and progress information and sends it to the server. Input consists of learners' answers and operation logs. This data is analyzed to store learners' progress in a database, and the educational content is updated in real time. Output is a learner's learning progress report.

[0615] Step 3:

[0616] The device senses the learner's facial expressions and tone of voice and evaluates their emotional state. Input consists of video and audio data obtained through the camera and microphone. An emotion engine analyzes this data using OpenCV and speech analysis techniques. Output is a tag indicating the current emotional state.

[0617] Step 4:

[0618] The server dynamically adjusts the learning plan based on information about the user's emotional state. The input is emotional state data obtained from the emotion engine. The AI ​​model uses this data to switch the learning content to relaxation content or adjust the difficulty level. The output is the adjusted learning content.

[0619] Step 5:

[0620] The user receives feedback through the device and takes the next learning action. The input consists of learning content and relaxation suggestions presented by the device. Based on this, the user chooses to continue learning or take a break and inputs their response into the device. The output is the result of the user's selection of the next action.

[0621] Step 6:

[0622] The server generates prompt sentences using a generative AI model. The input consists of past learning progress and sentiment data. Based on this data, the AI ​​model generates prompt sentences and suggests and adjusts appropriate learning content. The output is prompt sentences for improving learning.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] [Fourth Embodiment]

[0627] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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".

[0640] The invention described herein is an AI-powered learning support system, which in particular assists learners belonging to different national education systems in efficiently acquiring Japanese compulsory education and culture. This system consists of three main components: a server, a terminal, and a user.

[0641] 1. Server Functions

[0642] The server functions as a primary data processing center, maintaining a rich database of information on compulsory education in Japan. Based on the initial information provided by learners, the server uses AI to generate personalized curricula. It also manages all assignment evaluations and progress data, adjusting the curriculum and providing feedback accordingly. For example, if a learner is found to be weak in kanji, the server regenerates a plan incorporating kanji learning, providing a more appropriate curriculum.

[0643] 2. Device functions

[0644] The terminal is a device for visualizing the curriculum and feedback sent from the server. Learners can access daily learning tasks and materials through the terminal. Furthermore, the terminal also receives learner input in real time and sends data on answers and learning progress to the server. For example, when learners review lesson content or take tests, the terminal provides on-the-spot guidance, ensuring smooth learning progress.

[0645] 3. User Roles

[0646] Users are expected to engage independently with the curriculum and assignments provided within the system from a learner's perspective. Based on the curriculum, they record their progress by reviewing daily learning content and completing assignments. A notable feature is that users can flexibly adjust their learning strategy by utilizing feedback from the server. For example, if a user sees their results on a periodic test and realizes they are weak in a particular area, they can request additional practice from the server and receive new learning materials accordingly.

[0647] This system will meet the need for international marriage families to effectively teach Japanese education and culture, and will provide a flexible educational environment tailored to the characteristics of each learner.

[0648] The following describes the processing flow.

[0649] Step 1:

[0650] The server stores basic information obtained from the user (such as age, grade level, and subjects of interest) in a database. Based on this information, it performs the initial setup of the individual curriculum.

[0651] Step 2:

[0652] The server uses an AI model based on stored foundational information to generate personalized curricula for learners. These curricula include detailed information on the specific content to be studied and the progression of each subject.

[0653] Step 3:

[0654] The server sends the generated individual curriculum to the terminal, allowing learners to proceed with their daily studies based on it.

[0655] Step 4:

[0656] The terminal visually presents the curriculum received from the server to the user and provides an interface that shows daily learning tasks. Here, learners select learning materials and begin their studies.

[0657] Step 5:

[0658] Users work on assignments in the learning curriculum through their devices. They solve problems or view learning content and input their progress into their devices.

[0659] Step 6:

[0660] The terminal records user actions and answers in real time and sends progress data and answer data to the server.

[0661] Step 7:

[0662] The server analyzes the learner's progress data sent from the terminal and generates feedback based on the analysis results. This feedback includes the learner's strengths and areas for improvement, and provides advice to help them in their next learning session.

[0663] Step 8:

[0664] The server sends the generated feedback to the terminal, which then displays it to the user. Based on this, the learner can identify the next learning steps and areas for improvement.

[0665] Step 9:

[0666] Users can adjust their learning methods based on feedback and request additional learning support from the server as needed. This process is repeated until the learner's understanding deepens.

[0667] (Example 1)

[0668] 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".

[0669] To enable learners with diverse educational backgrounds to efficiently acquire the Japanese education system and culture, it is necessary to provide individualized learning plans tailored to each learner. However, traditional systems have challenges in providing flexible curricula that meet individual learners' needs and in offering immediate feedback.

[0670] 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.

[0671] In this invention, the server includes means for inputting learner's basic information and processing that information to generate an individualized learning plan; means for dynamically generating a curriculum using a generation AI model and optimizing the curriculum through prompt messages; and means for collecting learner's response data and progress data, and analyzing that data to identify areas for improvement. This enables the provision of a learning plan optimized for each learner and effective learning support through real-time feedback.

[0672] A "learner" refers to a person whose purpose is to receive education, and who is the entity that uses this system to implement an individualized educational plan.

[0673] "Basic information" refers to fundamental data about learners, including information such as name, grade level, and learning objectives.

[0674] An "individualized learning plan" is an educational program customized to efficiently achieve specific learning objectives based on the learner's basic information.

[0675] A "generative AI model" is a model that uses artificial intelligence technology to optimize the learning curriculum through data analysis and generation processes.

[0676] A "prompt sentence" is a type of input information for an AI model, serving as an introductory sentence that instructs the model on the content and purpose of the generated curriculum.

[0677] A "curriculum" refers to a set of specific learning content and activities that learners are expected to undertake.

[0678] "Feedback" refers to providing information based on learners' progress and achievements to encourage further improvement and the resolution of new challenges.

[0679] A "terminal" refers to an electronic device used by learners to input basic information, check the curriculum, and receive feedback.

[0680] This invention is a system designed to support learning, and it operates through the cooperation of a server, a terminal, and a user. The server functions as the main data processing center, analyzing the learner's basic information to generate an individualized learning plan. The terminal is an interface for providing information from the server to the learner, and the user uses this information to proceed with their learning.

[0681] Specifically, the server uses generative AI models such as Python and TensorFlow to generate a learning curriculum based on the learner's basic information. For example, it can input a prompt such as "Generate the optimal curriculum for this user" into the AI ​​model, which then formulates a learning plan tailored to each individual learner. The generated curriculum can be adjusted in real time according to the user's learning pace and goals.

[0682] The terminal is an electronic device used by users for their daily learning activities. It features a user interface using HTML and Javascript, designed for intuitive operation. The terminal displays the curriculum and feedback sent from the server, providing users with a means to check their progress and access necessary learning materials.

[0683] Users independently work on assignments based on the curriculum displayed on their devices. As they complete daily learning tasks, user input and progress data are constantly transmitted from their devices to the server. This system allows users to flexibly adjust their learning methods and effectively learn about Japanese compulsory education and culture.

[0684] For example, when a user is learning Japanese grammar, the server prompts the AI ​​model with a message such as "Generate practice problems suitable for improving Japanese grammar skills" based on the user's progress, and proposes new learning tasks. Through this process, learners can achieve their goals at their own pace.

[0685] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0686] Step 1:

[0687] Users log in to the system via their device and enter basic information (name, grade level, learning objectives). The device then sends this information to the server. This information is used as basic data to create individual learning plans.

[0688] Step 2:

[0689] The server analyzes the basic information of the user it receives and generates an individualized learning plan by inputting prompts into the AI ​​model. Specifically, a Python program processes the information and instructs the AI ​​model using the prompt "Generate the optimal curriculum for this user," dynamically creating the most suitable curriculum for the learner. As output, curriculum data tailored to the learner is generated.

[0690] Step 3:

[0691] The server sends the generated curriculum to the terminal. The terminal displays this curriculum through the learner's interface. Users can operate the terminal to access curriculum details and daily learning tasks, and obtain necessary materials and guides in real time.

[0692] Step 4:

[0693] Users begin learning activities using their devices, based on the provided curriculum. As they solve assignments and understand learning materials, users input their results into their devices to record their progress. This input becomes important data that will be used to inform future learning plans.

[0694] Step 5:

[0695] The device sends user-entered progress data to the server. The server analyzes this data and uses a generative AI model to evaluate the user's learning performance. For example, if the user's performance on a particular task is low, the AI ​​model can generate new practice problems and suggest more effective learning methods. The output includes improvement guidelines and new tasks for the user.

[0696] Step 6:

[0697] Based on the analysis results, the server sends feedback and a tailored curriculum to the device. The device displays this to the user, providing information to change their learning strategy or tackle new challenges as needed. Users can use this feedback to optimize their learning methods and work towards achieving their goals.

[0698] (Application Example 1)

[0699] 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".

[0700] When international marriages or learners residing abroad receive compulsory education, they face challenges in adapting to the Japanese education system and culture. Against this backdrop, there is a need for support to help learners efficiently and effectively understand and adapt to Japanese compulsory education and culture.

[0701] 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.

[0702] In this invention, the server includes means for generating individualized curricula based on learners' basic information, means for recording learners' responses and progress data and storing them in a large-capacity storage device, means for adjusting the curriculum based on analysis results and providing feedback, and means for visually displaying information through a display device and providing interactive explanations. This enables learners to efficiently learn the content and culture of compulsory education.

[0703] "Basic learner information" refers to initial data about the learner, such as age, grade level, and learning history.

[0704] An "individualized curriculum" is an educational program optimized for the learner, generated by AI based on the learner's basic information.

[0705] A "large-capacity storage device" is a storage medium that can store and manage learners' answers and progress data over the long term.

[0706] "Analysis results" refer to information obtained as a result of the AI ​​analyzing the collected learner data.

[0707] "Feedback" refers to information provided to learners regarding their learning outcomes and areas for improvement, including suggestions and advice.

[0708] A "display device" is hardware used to provide information to learners visually.

[0709] "A means of providing explanations through dialogue" refers to a function for explaining content through two-way communication with learners.

[0710] "Educational materials based on the compulsory education guidelines" refer to teaching materials that include standard learning content within the compulsory education system.

[0711] A "periodic assessment" is a test designed to periodically measure and evaluate learners' learning outcomes.

[0712] To implement this invention, it is necessary to construct a learning support system and coordinate three main components: a server, a terminal, and a user.

[0713] First, the server functions as the primary data processing center. This server maintains a comprehensive database of compulsory education in Japan. Based on learner background information, AI generates individualized curricula. This process primarily utilizes AI models such as TensorFlow and PyTorch. Progress data and learner responses are analyzed, and the curriculum is adjusted and feedback provided based on this analysis.

[0714] Next, the terminal functions as an access point for learners. Learners can use this terminal to access daily learning tasks and materials. The terminal accepts learner input in real time and sends answers and progress to the server. Furthermore, it displays information visually and provides interactive explanations to the user. Pepper and educational robots in general are particularly used for this purpose.

[0715] Finally, users play a vital role in actively advancing their learning by participating in the system. They can efficiently learn compulsory education content by checking their current learning status and utilizing the feedback received from the server.

[0716] As a concrete example, suppose a learner starts a course using "Momotaro" (Peach Boy) as its subject. The server generates appropriate materials according to the learner's level and starts a visual lecture on the terminal. If a question arises regarding "Momotaro," the terminal uses an AI model to provide a detailed explanation.

[0717] An example of a prompt for a generative AI model is: "Generate a simple summary of the Japanese fairy tale 'Momotaro' for children, using easy-to-understand language so that an educational robot can teach it. In particular, replace complex words with simpler expressions so that it is easy for beginners to understand." Using this prompt, the AI ​​automatically generates an explanation suitable for the teaching material.

[0718] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0719] Step 1:

[0720] The server receives basic learner information as input, including age, grade level, and past learning history. Using this data, an AI model generates an individualized curriculum. This process extracts relevant learning material information from a database and uses TensorFlow to output a curriculum tailored to the learner.

[0721] Step 2:

[0722] The terminal receives the curriculum sent from the server. The display device visually shows learning tasks and materials, allowing the user to access their daily learning. During this process, input data is collected when the user interacts with the learning materials.

[0723] Step 3:

[0724] Users engage with learning materials provided via their devices. Specifically, they solve problems and enter answers to questions. The results of these user actions are sent to the server via the device.

[0725] Step 4:

[0726] The server receives user responses and progress data as input. This data is stored in a database and analyzed by an AI model. Based on the analysis results, the curriculum is adjusted and new feedback is generated.

[0727] Step 5:

[0728] The device receives the generated feedback and displays it to the user in real time. For example, it provides explanations for incorrect answers to help learners correct their mistakes.

[0729] Step 6:

[0730] The server generates new lesson explanations and assignments by providing the AI ​​model with generated prompt sentences as input. For example, it automatically creates a detailed explanation of "Momotaro." The results are then sent back to the terminal to prepare for the start of the next learning cycle.

[0731] 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.

[0732] The invention described herein incorporates an emotion engine into a learner-oriented learning support system to recognize the learner's emotional state and provide a more personalized educational experience. This system is implemented with three main components: a server, a terminal, and a user.

[0733] 1. Server Functions

[0734] The server is the central hub for managing and analyzing learners' basic and emotional information. The server integrates an AI model and an emotion engine, analyzing accumulated emotional data and learning progress information to generate a curriculum optimized for each learner. For example, if emotional data indicates that a learner is fatigued, the server adjusts the curriculum to prioritize relatively lighter content and engaging topics.

[0735] 2. Device functions

[0736] The terminal functions as an interface between the learner and the system. This terminal utilizes data from the emotion engine to provide appropriate learning progress and feedback in real time, tailored to the learner's current emotions. It also monitors changes in emotional state and sends updates to the server as needed. For example, if a learner expresses anxiety, the terminal suggests relaxation content to alleviate that emotion.

[0737] 3. User Roles

[0738] Users progress through their daily learning via their devices, providing learning and emotional data as they input their answers. The device recognizes the learner's facial expressions, tone of voice, input speed, etc., and an emotional engine analyzes this information. For example, if a learner shows difficulty with a challenging problem, the server can appropriately sense the user's emotional state and adjust the difficulty level of the content accordingly to support continued learning.

[0739] This system aims to create a more flexible and effective learning environment by adjusting educational content based on the learner's emotional state. It enables real-time feedback tailored to the learner's emotions, promoting motivation and efficient learning.

[0740] The following describes the processing flow.

[0741] Step 1:

[0742] The server prepares a database to store learners' basic information and emotional states. At the start of learning, the server generates an initial curriculum based on the basic information.

[0743] Step 2:

[0744] The device accepts emotional information input from the learner. This information is collected through a facial recognition camera and a voice analysis microphone.

[0745] Step 3:

[0746] The device analyzes the collected emotional data in real time and sends the results to the server. The analysis includes the learner's facial expressions and tone of voice.

[0747] Step 4:

[0748] The server uses an AI model to adapt and adjust the curriculum based on emotional data and learning progress received from the terminals.

[0749] Step 5:

[0750] The server sends the adjusted curriculum and appropriate learning content to the device. These adjustments include adjusting the difficulty level of tasks to be emotionally sensitive and providing content to boost motivation.

[0751] Step 6:

[0752] The device displays learning content optimized for the learner and provides visual or audible feedback as needed. The feedback is adjusted according to the learner's emotional state.

[0753] Step 7:

[0754] Users progress through the learning process and answer assignments via their devices. The answer data and associated emotional information are recorded by the device and sent to the server.

[0755] Step 8:

[0756] The server analyzes answer data and the latest sentiment data to identify challenges learners face and incorporate them into the next learning cycle. Based on the new data, further feedback and curriculum adjustments are made.

[0757] By repeating this process, efficient learning support that flexibly responds to learners' emotions can be achieved.

[0758] (Example 2)

[0759] 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".

[0760] Conventional learning support systems lack flexibility to meet the individual needs of learners, and face challenges in providing appropriate feedback and adjusting curricula based on learners' emotional states. This results in decreased learning efficiency and difficulty in maintaining motivation.

[0761] 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.

[0762] In this invention, the server includes means for generating individualized curricula based on learner's basic information, means for recording and accumulating learner's responses and progress information, and means for analyzing the accumulated emotional state data. This enables the implementation of a learning experience optimized for each learner, and allows for real-time feedback and curriculum adjustments according to each individual's emotional state.

[0763] "Basic information" refers to fundamental data about learners, such as age, grade level, and learning history.

[0764] An "individualized curriculum" refers to an educational plan that is customized according to the characteristics and needs of the learner.

[0765] "Progress information" refers to data that shows the learner's current learning status, including which learning materials have been completed and which problems remain unresolved.

[0766] "Emotional state data" refers to information about emotions that is analyzed based on the learner's facial expressions, tone of voice, input speed, etc.

[0767] "Analysis results" refer to the results of the analysis performed by the server based on the collected data, and the information used for curriculum adjustments and providing feedback.

[0768] "Feedback" refers to advice and evaluations provided to learners regarding their learning content.

[0769] This invention integrates an emotion engine into a learning support system for learners, providing a personalized educational experience. The system consists of three main components: a server, a terminal, and a user.

[0770] Server Role

[0771] The server is the central hub of the system, responsible for retrieving and storing learner basic and progress information from the database. It also integrates an emotion engine and generative AI models to analyze this data, understand learners' emotional states, and generate optimized curricula. Servers are located in cloud environments or dedicated data centers, utilizing high-performance processors and large amounts of memory to enable large-scale data processing.

[0772] For example, if emotional data detects that a learner is fatigued, the server will recommend easier content and generate a message encouraging them to rest.

[0773] Terminal role

[0774] The device functions as an interface between the learner and the system. Equipped with a camera, microphone, and sensors, the device collects the learner's emotional state in real time. This allows the device to provide immediate feedback to the user. For example, if the user shows signs of anxiety, it can play relaxation music.

[0775] Furthermore, the device sends learner response data to the server, dynamically adjusting the learning experience.

[0776] User roles

[0777] As users progress through their learning process via their devices, they naturally provide emotional data such as facial expressions, voice, and reaction speed. This data is incorporated into the system's overall feedback loop and used to adjust the learning curriculum to best suit the learner.

[0778] Example of a prompt

[0779] "Design a prompt that generates appropriate feedback and curriculum adjustment suggestions when the facial recognition camera detects anxiety while a learner is solving a math problem."

[0780] In this way, the system utilizes the user's emotional state to provide flexible learning support that enhances learning efficiency.

[0781] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0782] Step 1:

[0783] The server retrieves learner's basic information and past progress data from the database upon system startup. This information is used to set a baseline for individual curricula. The input is the learner's ID, and the output is initial curriculum data tailored to each individual learner. Specifically, SQL queries are used to retrieve the necessary information from the database.

[0784] Step 2:

[0785] The device collects the learner's real-time facial expressions, voice tone, input speed, etc., using sensors, cameras, and microphones. This data is preprocessed on the device as input and sent to the server for analysis in the next step. The output is standardized emotion data. Specifically, the data is evaluated using facial recognition APIs and voice analysis libraries.

[0786] Step 3:

[0787] The server inputs emotional data received from the terminal into an emotion engine to analyze the learner's current emotional state. Inputs include emotional data and past progress information. The output is personalized feedback and suggested curriculum adjustments. Here, a generative AI model is used; for example, if the system infers that the learner is "not focused," it adjusts the learning content to reduce its intensity.

[0788] Step 4:

[0789] The terminal presents learners with feedback received from the server and adjustments to the curriculum. Inputs are feedback information from the server, while outputs are specific instructions or content created for the learners. Specific actions include dynamic UI changes and voice guidance.

[0790] Step 5:

[0791] Users review feedback and adjusted curricula provided via their devices and continue learning. User responses and selections are collected as input and output to a database for improving future feedback. Specific actions include logging completed materials and selected options.

[0792] (Application Example 2)

[0793] 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".

[0794] Conventional learning support systems have difficulty taking into account the individual emotional states of learners, resulting in insufficient real-time support during the learning process. As a result, learners may experience stress and decreased motivation.

[0795] 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.

[0796] In this invention, the server includes means for generating an individualized learning plan based on the learner's basic information, means for recording the learner's responses and progress information and storing it in an information base, means for adjusting the learning plan and providing evaluation based on the analysis results, means for recognizing the learner's emotional state and dynamically adjusting the learning content based on that information, and means for presenting learning content and relaxation content that respond to the learner's emotions in real time. This enables personalized learning support that responds to the learner's emotional state.

[0797] A "learner" is an individual who seeks education and training with the aim of improving their own knowledge and skills.

[0798] "Basic information" refers to data about the learner's profile and initial state, including age, interests, and past learning history.

[0799] An "individualized learning plan" refers to an educational program customized to the individual needs and abilities of each learner.

[0800] "Answer" refers to the solution or response that a learner gives to a given problem or question.

[0801] "Progress information" refers to data that records the progress and achievement level of learners' learning activities.

[0802] An "information base" refers to a digital space or system that aggregates and stores data and knowledge.

[0803] "Analysis results" refer to the conclusions and insights derived from analyzing the collected data.

[0804] "Evaluation" refers to the measurement of a learner's performance and understanding, and the feedback and scores provided based on that measurement.

[0805] "Emotional state" refers to the learner's psychological state, which includes feelings such as joy, anxiety, and fatigue.

[0806] "Dynamic adjustment" refers to immediately changing the content or situation in accordance with the current situation and requirements.

[0807] "Relaxation content" refers to content and activities provided to alleviate the mental and physical tension of learners and to refresh them.

[0808] This learning support system integrates multiple components to deliver an educational experience tailored to the individual needs of learners. The server has the function of generating individualized learning plans based on the learner's basic information and stores the learner's responses and progress information in a database. The server utilizes AI models and an emotion engine to analyze the data, dynamically adjusts the learning plan based on the analysis results, and provides the learner with the optimal assessment. For example, it uses TensorFlow with Python to build an AI model and OpenCV to recognize the learner's emotional state.

[0809] The device functions as an interface connecting the learner and the system, and can present learning content and relaxation materials in real time that respond to the learner's emotions. This allows for situation-appropriate feedback, such as reducing the intensity of learning content or suggesting a break, if the learner shows signs of stress or fatigue.

[0810] Users interact with the device and engage with educational content through their daily learning activities. The device monitors the learner's facial expressions and tone of voice, and an emotion engine analyzes this information to adjust the learning content accordingly. This system can use a generative AI model to create prompts such as, "What is the optimal learning content based on the learner's emotions today?" This enables more personalized learning support tailored to the learner's emotional state.

[0811] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0812] Step 1:

[0813] The server receives basic information about the learner and generates an individualized learning plan. The input includes data such as the learner's age, interests, and past learning history. Based on this, the server selects the most suitable learning content from the curriculum information stored in the database. The output is a learning plan optimized for the learner.

[0814] Step 2:

[0815] The terminal monitors learners' responses and progress information and sends it to the server. Input consists of learners' answers and operation logs. This data is analyzed to store learners' progress in a database, and the educational content is updated in real time. Output is a learner's learning progress report.

[0816] Step 3:

[0817] The device senses the learner's facial expressions and tone of voice and evaluates their emotional state. Input consists of video and audio data obtained through the camera and microphone. An emotion engine analyzes this data using OpenCV and speech analysis techniques. Output is a tag indicating the current emotional state.

[0818] Step 4:

[0819] The server dynamically adjusts the learning plan based on information about the user's emotional state. The input is emotional state data obtained from the emotion engine. The AI ​​model uses this data to switch the learning content to relaxation content or adjust the difficulty level. The output is the adjusted learning content.

[0820] Step 5:

[0821] The user receives feedback through the device and takes the next learning action. The input consists of learning content and relaxation suggestions presented by the device. Based on this, the user chooses to continue learning or take a break and inputs their response into the device. The output is the result of the user's selection of the next action.

[0822] Step 6:

[0823] The server generates prompt sentences using a generative AI model. The input consists of past learning progress and sentiment data. Based on this data, the AI ​​model generates prompt sentences and suggests and adjusts appropriate learning content. The output is prompt sentences for improving learning.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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."

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] The following is further disclosed regarding the embodiments described above.

[0846] (Claim 1)

[0847] A means of generating individualized curricula based on learners' basic information,

[0848] A means of recording learners' responses and progress data and storing them in a database,

[0849] Based on the analysis results, we will adjust the curriculum and provide feedback.

[0850] A learning support system that includes this.

[0851] (Claim 2)

[0852] A learning support system according to claim 1, comprising means for providing learners with teaching materials based on the Japanese compulsory education guidelines and for generating periodic tests.

[0853] (Claim 3)

[0854] The learning support system according to claim 1, comprising means for displaying real-time feedback to learners via a terminal and providing guidance for correcting mistakes.

[0855] "Example 1"

[0856] (Claim 1)

[0857] A means of inputting learner's basic information, processing that information, and generating an individualized learning plan,

[0858] A method for dynamically generating a curriculum using a generative AI model and optimizing the curriculum through prompt statements,

[0859] A means of collecting learner response data and progress data, and analyzing that data to identify areas for improvement,

[0860] A means of adjusting the curriculum based on the analysis results and providing feedback,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, comprising means for providing learning materials suitable for learners and conducting periodic evaluations.

[0864] (Claim 3)

[0865] The system according to claim 1, comprising means for presenting real-time responses to learners via a terminal and providing guidance for immediate correction of errors.

[0866] "Application Example 1"

[0867] (Claim 1)

[0868] A means of generating individualized curricula based on learners' basic information,

[0869] A means of recording learners' responses and progress data and storing them in a large-capacity storage device,

[0870] Based on the analysis results, we will adjust the curriculum and provide feedback.

[0871] A means of visually displaying information through a display device and providing explanations in an interactive manner,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, comprising means for providing learners with educational materials based on the compulsory education guidelines and for generating periodic examinations.

[0875] (Claim 3)

[0876] The system according to claim 1, comprising means for displaying real-time feedback to learners via a terminal and providing instruction to correct errors.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] A means of generating individualized curricula based on learners' basic information,

[0880] A means of recording and accumulating learners' responses and progress information,

[0881] A means of analyzing accumulated emotional state data,

[0882] A means of adjusting the curriculum and providing real-time feedback based on the analysis results,

[0883] A means of adjusting the learning experience using learners' emotional information,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, comprising means for providing learners with learning materials based on a prescribed learning plan and for conducting evaluations.

[0887] (Claim 3)

[0888] The system according to claim 1, comprising means for displaying the learner's progress via an information terminal and providing instruction to correct errors.

[0889] "Application example 2 when combining with an emotional engine"

[0890] (Claim 1)

[0891] A means of generating an individualized learning plan based on the learner's basic information,

[0892] A means of recording learners' responses and progress information and accumulating it in an information base,

[0893] A means to adjust the learning plan based on the analysis results and provide evaluation,

[0894] A means of recognizing the learner's emotional state and dynamically adjusting the learning content based on that information,

[0895] A means of presenting learning content and relaxation content that responds to emotions in real time,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, comprising means for providing learners with educational materials based on an educational curriculum and for generating evaluation tests.

[0899] (Claim 3)

[0900] The system according to claim 1, comprising means for displaying evaluations to learners in real time via a terminal and providing instruction to correct errors. [Explanation of Symbols]

[0901] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of generating individualized curricula based on learners' basic information, A means of recording learners' responses and progress data and storing them in a large-capacity storage device, Based on the analysis results, we will adjust the curriculum and provide feedback. A means of visually displaying information through a display device and providing explanations in an interactive manner, A system that includes this.

2. The system according to claim 1, comprising means for providing learners with educational materials based on the compulsory education guidelines and for generating periodic examinations.

3. The system according to claim 1, comprising means for displaying real-time feedback to learners via a terminal and providing instruction to correct errors.

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