System

Abstract

A system is provided.SOLUTION: The system includes an input means for inputting a learning need of a preschooler, an analysis means for performing data analysis based on the input learning need, a generation means for generating learning content based on an analysis result of the analysis means, and a transmission means for transmitting the learning content generated by the generation means to a user terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In the past, it was difficult to provide learning content tailored to individual learning needs for preschool children preparing for elementary school entrance exams. In particular, there was a lack of a system that automatically generated and provided effective learning plans that balanced the development of strong and weak areas. This required parents and educators to manually create learning plans, which not only took time and effort but often resulted in insufficient learning outcomes. The present invention solves this problem by providing a system that automatically generates and efficiently provides appropriate learning content tailored to the learning progress of preschool children. [Means for solving the problem]

[0005] The present invention provides a system including an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, and a transmission means for transmitting the learning content generated by the generation means to a user terminal. Furthermore, the generated learning content may include at least one of an interactive game, a workbook, or a dictation drill. The system also includes a feedback means for collecting learning progress data of preschool children and providing it to the analysis means. This automatically generates and provides effective learning plans tailored to individual learning needs, thereby reducing the burden on parents and educators and improving the learning outcomes of preschool children.

[0006] "Input means" refers to an interface that allows a user to input information such as the learning needs, current learning progress, strengths and weaknesses of a preschool child.

[0007] "Analysis means" refers to a function that performs data analysis to create an optimal learning plan for preschool children based on the data input by the input means.

[0008] The "generation means" refers to the function of generating specific study content to be provided to the user based on the analysis results of the analysis means.

[0009] The "transmission means" refers to the function for transmitting the study content generated by the generation means to the user terminal.

[0010] "Learning content" means learning materials for use by preschool children, which may consist of interactive games, workbooks, or dictation exercises.

[0011] "Feedback means" refers to the function of collecting progress data and learning results after preschool children have used learning content and providing them again to the analysis means.

[0012] "User devices" refer to devices such as computers, tablets, and smartphones used by parents and educators.

[0013] "Preschooler" refers to a child who is not yet formally attending primary school or an equivalent educational institution.

[0014] "Learning needs" refers to the areas of skills and knowledge that preschoolers need to master, as well as their particular strengths and weaknesses. [Brief explanation of the drawings]

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

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

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

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

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

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

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention relates to a system for automatically generating learning content that meets the learning needs of preschool children, and a specific embodiment thereof will be described.

[0037] System Configuration

[0038] The system is primarily comprised of three elements: users (parents and educators), devices (computers, tablets, smartphones, etc.), and a server. Users interact with the system using their devices, while the server is responsible for data analysis and generation of learning content.

[0039] Enter user information

[0040] First, the user accesses the learning application using a device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, current learning progress, strengths and weaknesses, and goals.

[0041] example:

[0042] The user enters information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0043] Data analysis

[0044] The device sends the input information to a server. The server receives the received information and analyzes the data using an analysis means. The analysis means refers to specific algorithms and past databases to create an optimal learning plan for the preschool child.

[0045] example:

[0046] The server determines that "hiragana study is necessary" and, based on the analysis results, generates a study plan that is a balanced combination of "hiragana writing drills" and "math games."

[0047] Generating learning content

[0048] Based on the analysis results, the server activates a generation means to generate specific learning content, which may be an interactive game, a workbook, or a dictation drill, appropriately selected based on the user's input.

[0049] example:

[0050] The generator creates "an interactive math game to reinforce number concepts" and "hiragana writing practice drills."

[0051] Sending content to users

[0052] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[0053] example:

[0054] The submitted math games and hiragana drills will then appear within the app, allowing users to start using them to learn alongside their children.

[0055] Learning implementation

[0056] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data obtained during this process is sent from the device back to the server and provided to the analysis means as feedback.

[0057] example:

[0058] Users can play number matching puzzles with their children and dictate hiragana to check their progress.

[0059] feedback

[0060] Progress data after each lesson is analyzed on the server and used to adjust new lesson plans via analytical means. This feedback loop allows the system to continually provide optimal lesson plans tailored to each preschool child's learning progress.

[0061] example:

[0062] Based on the information that "the child has cleared math level 1," the server suggests the next level of math game.

[0063] The above is a specific embodiment of the present invention, and this system makes it possible to realize effective learning that meets the individual needs of preschool children.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The user launches the learning application using a device. After launching the application, an interface for entering information about the preschool child is displayed. The user enters information such as age, strengths and weaknesses, current learning progress, and goals.

[0067] Step 2:

[0068] The device sends the entered information to the server, where the data is packaged in an appropriate format, such as JSON.

[0069] Step 3:

[0070] The server passes the data received from the device to the analysis means, which analyzes the data based on the received information and creates the optimal learning plan for the preschool child, referencing algorithms and past databases.

[0071] Step 4:

[0072] Upon receiving the analysis results from the analysis means, the server activates the generation means, which generates appropriate learning content (such as an interactive game, a workbook, or a dictation drill) based on the analysis results.

[0073] Step 5:

[0074] The generated learning content is packaged in an appropriate format and sent from the server to the terminal, and the sent data is provided in a format that can be easily used by the user.

[0075] Step 6:

[0076] The terminal displays the received learning content to the user, who then studies together with the preschool child using the provided learning content.

[0077] Step 7:

[0078] After completing the learning, progress data and results are sent from the device to the server, and the learning application automatically records the learning progress.

[0079] Step 8:

[0080] The server receives the progress data and passes it to the feedback means, which then requests a new analysis from the analysis means based on the data and adjusts the next learning plan.

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] Preschoolers have individual learning needs, making it difficult for them to learn effectively with a uniform learning plan. Furthermore, the lack of feedback based on their learning progress makes it difficult to continuously improve their learning. This makes it difficult to provide an optimal learning experience for preschoolers.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, and a feedback means for providing learning progress data collected from the user terminal back to the analysis means. This makes it possible to generate optimal learning content tailored to the individual learning needs of preschool children, reflect feedback based on the progress data, and continuously optimize learning.

[0086] "Preschool children" refers to young children or infants before the start of compulsory education.

[0087] "Learning needs" refers to the skills and knowledge that learners require when learning, and the requirements for achieving these.

[0088] "Input means" refers to a device or interface that allows a user to input required information into the system.

[0089] "Analysis means" refers to a device or function that analyzes input data using a specific algorithm or model and derives the results.

[0090] "Generation means" refers to a device or function that creates new learning content or plans based on the results of the analysis means.

[0091] "Transmission means" refers to a device or function for sending the generated learning content to a user terminal.

[0092] "Feedback means" refers to a device or function for collecting learning progress data and providing it back to the analysis means to improve the analysis results.

[0093] "User device" refers to an electronic device used by a user, such as a computer, tablet, or smartphone.

[0094] "Learning Content" refers to the educational materials and activities (e.g., interactive games, workbooks, dictation drills) provided to achieve learning objectives.

[0095] "Learning Progress Data" means data that shows the progress or achievements of a learner as they engage in a learning activity.

[0096] The present invention is a system for automatically generating learning content tailored to the learning needs of preschool children. This system is composed of three elements: users (parents and educators), terminals (computers, tablets, smartphones, etc.), and a server. Specific embodiments of this system are described below.

[0097] System Configuration

[0098] Users interact with the system through a terminal, specifically through a learning application, by entering information about their preschool child, including their age, current learning progress, strengths and weaknesses, and learning goals.

[0099] Enter user information

[0100] The user uses the device to access the learning application and input information about the preschool child. For example, the user might input "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0101] Data transmission and analysis

[0102] The device sends the input information to the server. The hardware used in this process can be a smartphone, tablet, or PC. The sent data is received by the server. The server uses analytical means to analyze the received information. This analysis uses specific algorithms and past databases. The server refers to past learning data and AI models to create the optimal learning plan for the preschool child.

[0103] Generating learning content

[0104] Based on the analysis results, the server activates a generation tool to generate specific learning content. This generated content ranges from interactive games, workbooks, and dictation drills. The generation tool uses machine learning algorithms written in Python and libraries such as Scikit-learn and TENSORFLOW (registered trademark).

[0105] Submit content and learn

[0106] The generated learning content is sent to the device and provided in a format that is easily accessible to the user. It is displayed within the learning application, allowing the user to begin learning together with the preschool child. Once the sent learning content is displayed on the device, the user can proceed with the learning together with the preschool child.

[0107] Collection of learning progress data and feedback

[0108] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data obtained during this process is sent from the device back to the server. The server uses this progress data through analytical means to adjust a new learning plan. This feedback loop allows the system to continuously provide the optimal learning plan according to the preschool child's learning progress.

[0109] Prompt Sentence Examples

[0110] For example, the following prompt might be possible:

[0111] "Please suggest some math games and hiragana practice drills suitable for a 5-year-old."

[0112] This system can provide effective learning plans tailored to the individual learning needs of preschool children, improving their learning efficiency and interest, and enabling them to continue learning.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1: Enter your user information

[0115] The user launches a learning application on the device and enters information about the preschool child. For example, the user might enter "6-year-old child," "good at math," "poor reading and writing skills," and "goal is to practice hiragana." The entered data is temporarily saved on the device.

[0116] Input: Preschooler's age, learning progress, strengths, weaknesses, learning goals

[0117] Output: Preschool child information data (format: JSON)

[0118] Step 2: Sending data

[0119] The terminal transmits the information data of the preschool child entered by the user to the server. This data is encrypted before transmission.

[0120] Input: Preschool child information data (format: JSON)

[0121] Output: Data sent to the server

[0122] Step 3: Data analysis

[0123] The server analyzes the received information data of the preschool child using analytical means. In this process, specific algorithms and past databases are used. The server refers to the database and creates an optimal learning plan.

[0124] Input: Preschooler information data, past learning data, AI model

[0125] Output: Learning plan (format: JSON)

[0126] Step 4: Generate learning content

[0127] The server uses libraries such as Scikit-learn and TensorFlow to generate specific learning content based on the analysis results. The generated content ranges from interactive games, workbooks, and dictation drills.

[0128] Input: Learning plan (format: JSON)

[0129] Output: Learning content (format: JSON)

[0130] Step 5: Submit your learning content

[0131] The server transmits the generated learning content to the terminal, where it is provided to the user in an easily accessible format.

[0132] Input: Learning content (format: JSON)

[0133] Output: Learning content sent to the user's device

[0134] Step 6: Learning

[0135] Users use the device to access the provided learning content together with their preschool children, and progress is tracked through exercises and problem-solving. Progress data is also automatically recorded.

[0136] Input: Learning content (format: JSON)

[0137] Output: Learning progress data

[0138] Step 7: Sending progress data

[0139] The device automatically sends learning progress data to the server, which is encrypted and transmitted securely.

[0140] Input: Learning progress data (format: JSON)

[0141] Output: Progress data sent to the server

[0142] Step 8: Feedback analysis

[0143] The server analyzes the received learning progress data and uses it as a basis for generating a new learning plan. The analysis means again uses algorithms and databases to formulate the next optimal plan.

[0144] Input: Learning progress data, past learning data, AI model

[0145] Output: New learning plan (format: JSON)

[0146] These are the processing steps of the system, each of which provides a learning experience that is optimally tailored to the individual needs of preschoolers.

[0147] (Application example 1)

[0148] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0149] Traditional systems that provide learning content tailored to the learning needs of preschoolers are often ineffective. In particular, the learning content can be static or difficult to keep preschoolers interested. Therefore, it is necessary to provide methods for preschoolers to be more actively involved in learning. It is also important for parents and educators to easily track learning progress and receive feedback. There is a need to solve this problem.

[0150] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0151] In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, and a means for allowing preschool children and their parents to experience the educational content in a virtual environment. This not only allows preschool children to continue learning while maintaining their interest, but also makes it easy to provide feedback on their learning progress.

[0152] "Input means" refers to devices and software that input the learning needs of preschool children.

[0153] "Analysis means" refers to devices and software for analyzing data based on input learning needs.

[0154] "Generation means" refers to the device or software that generates learning content based on the analysis results of the analysis means.

[0155] "Transmission means" refers to the device or software used to transmit the study content generated by the generation means to the user terminal.

[0156] "Feedback tools" refer to devices and software that collect and provide preschool children's learning progress data to analytical tools.

[0157] "Virtual environment" refers to a virtual space or interface that allows preschool children and their parents to experience educational content.

[0158] System Configuration

[0159] The system that embodies this invention mainly consists of three elements: a user (parent or educator), a device (such as a smartphone or head-mounted display), and a server. The user interacts with the system using the device, and the server is responsible for data analysis and generation of learning content.

[0160] Enter user information

[0161] The user accesses the application using a device and enters information about the preschool child, specifically, the following information:

[0162] age

[0163] Current learning progress

[0164] Strengths and weaknesses

[0165] the goal

[0166] Data analysis

[0167] The device sends the input information to a server. The server analyzes the received information using analytical means (for example, data analysis tools such as TensorFlow or PyTorch). The analytical means refers to a past database and creates an optimal learning plan for the preschool child.

[0168] Generating learning content

[0169] Based on the analysis results, the server activates a generating means to generate specific learning content, which may be an interactive game, a workbook, or a dictation drill, appropriately selected based on the user's input.

[0170] Sending content to users

[0171] The generated learning content is sent to the device via a transmission means, allowing preschool children and their parents to experience learning in a virtual environment, which is built using front-end development tools such as Unity and Flutter (registered trademark).

[0172] Learning implementation

[0173] Users can use the provided learning content on their devices to study together with their preschool children. For example, they can use smartphones and head-mounted displays to experience interactive learning in a virtual store.

[0174] feedback

[0175] Progress data after the lesson is sent from the device to the server and provided to the analysis means via the feedback means. This feedback loop allows the system to continuously provide optimal lesson plans according to the learning progress of preschool children.

[0176] Specific examples

[0177] For example, if a 6-year-old preschooler is good at math but has difficulty learning hiragana, parents can enter this information into the app. The system analyzes the input data and generates "hiragana writing practice" and "math puzzle games." This content is provided via smartphones or head-mounted displays.

[0178] Prompt Sentence Examples

[0179] Enter the following prompts into the generative AI model:

[0180] Prompt: "6-year-old child, good at math, needs to learn hiragana. Generate the best learning content."

[0181] Based on these prompts, the AI ​​model performs data analysis to generate learning content appropriate for the child.

[0182] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0183] Step 1:

[0184] The user accesses the application using a smartphone or head-mounted display. The user enters information such as the preschool child's age, current learning progress, strengths and weaknesses, and goals into the application's input form. This input data becomes the basic information for the system.

[0185] Input: Age, learning progress, strengths, weaknesses, goals

[0186] Output: Input learning needs data

[0187] Step 2:

[0188] The device sends the input learning needs data to the server. The server passes the received data to an analysis means for data analysis. Here, specific algorithms and past databases are referenced to create an optimal learning plan for the preschool child.

[0189] Input: Learning needs data

[0190] Output: Analysis results (e.g. Hiragana learning required)

[0191] Step 3:

[0192] The server generates specific learning content using a generating means based on the analysis results of the analyzing means. The generating means uses a generative AI model to create appropriate learning content (interactive games, workbooks, dictation drills, etc.) in response to user input.

[0193] Input: Analysis results

[0194] Output: Generated learning content (e.g., hiragana writing practice, math puzzle game)

[0195] Step 4:

[0196] The server transmits the generated learning content to the user's terminal via a transmission means, allowing the user to use the learning content together with the preschool child. This content is provided in a virtual environment, making learning interactive.

[0197] Input: Generated learning content

[0198] Output: Learning content sent to the user's device

[0199] Step 5:

[0200] Users use the device to carry out the learning content provided, and preschoolers use smartphones and head-mounted displays to engage in an interactive learning experience within a virtual store. As their learning progresses, progress data is recorded on the device.

[0201] Input: Submitted learning content

[0202] Output: Learning progress data

[0203] Step 6:

[0204] The learning progress data collected from the device is sent back to the server, which receives the progress data and re-analyzes it using feedback tools. Based on the results of this data analysis, a new learning plan or adjustments are made.

[0205] Input: Learning progress data

[0206] Output: New study plan suggestions or adjustments

[0207] Prompt Sentence Examples

[0208] Enter the following prompts into the generative AI model:

[0209] Prompt: "6-year-old child, good at math, needs to learn hiragana. Generate the best learning content."

[0210] This allows the AI ​​model to analyze the data to generate learning content appropriate for children.

[0211] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0212] The present invention relates to a system that automatically generates learning content according to the learning needs of preschool children and further optimizes learning plans based on emotional data of users and preschool children, and a specific embodiment thereof will be described.

[0213] System Configuration

[0214] This system is primarily composed of three elements: users (parents and educators), devices (computers, tablets, smartphones, etc.), and a server. Users interact with the system using their devices, the server analyzes data and generates learning content, and the emotion engine is responsible for recognizing and analyzing emotion data.

[0215] Enter user information

[0216] First, the user launches the learning application on their device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, strengths and weaknesses, current learning progress, and goals.

[0217] example:

[0218] The user enters information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0219] Data analysis

[0220] The device sends the input information to the server. The server passes the received information to the analysis means. The analysis means performs data analysis based on the received information and creates the optimal learning plan for the preschool child. Algorithms and past databases are referenced.

[0221] example:

[0222] The server determines that "hiragana study is necessary" and, based on the analysis results, generates a study plan that is a balanced combination of "hiragana writing drills" and "math games."

[0223] Emotional Data Recognition and Analysis

[0224] The system incorporates an emotion engine to collect emotional data from preschool children while they are learning. The emotion engine recognizes emotional data from their facial expressions and voices in real time through sensors such as cameras and microphones.

[0225] example:

[0226] While preschoolers are learning, a camera captures their facial expressions and a microphone analyzes their vocal tones to recognize their emotional states, such as "concentrated," "happy," or "tired."

[0227] Learning content generation and adaptation

[0228] Based on the analysis results, the server activates the generation means to generate specific learning content, which can be selected appropriately based on the user's input, such as interactive games, workbooks, or dictation drills. Furthermore, the generated learning content can be adjusted in real time based on the emotional data analyzed by the emotion engine.

[0229] example:

[0230] The generator creates "interactive math games to reinforce number concepts" and "hiragana writing practice drills," and based on the analysis results of the emotion engine, adjustments are made, such as lowering the difficulty of the game if concentration is declining.

[0231] Sending content to users

[0232] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[0233] example:

[0234] The math games and hiragana drills sent to the app are displayed within the app, allowing users to start learning together with their children. The app also adjusts to their child's emotions in real time.

[0235] Learning implementation and feedback

[0236] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data and emotion data obtained during this process are sent from the device to the server again and provided to the analysis means as feedback.

[0237] example:

[0238] Users can play number matching puzzles and dictate hiragana with their children to track their progress, while also sending emotional data along the way.

[0239] Feedback and learning plan adjustments

[0240] Progress and emotional data is analyzed on a server and used to adjust new learning plans, creating a feedback loop that continually provides the best possible learning plan for each preschooler based on their learning progress.

[0241] example:

[0242] The server analyzes the information that "the child has cleared math level 1" and emotional data that "the child felt tired halfway through," and suggests the next level of math games while also adjusting the timing to encourage breaks.

[0243] The above is a specific embodiment of the present invention, and this system enables effective learning that meets the individual needs of preschool children. Furthermore, real-time adjustment of learning plans based on emotional data is expected to improve preschool children's learning effectiveness and motivation.

[0244] The processing flow will be explained below.

[0245] Step 1:

[0246] The user launches the learning application using a device. After launching the application, an interface for entering information about the preschool child is displayed. The user enters information such as age, strengths and weaknesses, current learning progress, and goals.

[0247] Step 2:

[0248] The device sends the entered information to the server, where the data is packaged in an appropriate format, such as JSON.

[0249] Step 3:

[0250] The server passes the data received from the device to the analysis means, which analyzes the data based on the input information and creates an optimal learning plan, referencing algorithms and past databases.

[0251] Step 4:

[0252] The server activates the generating means based on the results of the analyzing means, and the generating means generates learning content based on the analysis results, such as an interactive game, a workbook, or a dictation drill, in accordance with the user's needs.

[0253] Step 5:

[0254] The emotion engine collects real-time emotional data from users or preschoolers while they are learning, recognizing their emotional state from facial expressions and voice tones through sensors such as cameras and microphones.

[0255] Step 6:

[0256] The server analyzes the emotional data obtained from the emotion engine and adjusts the generated learning content in real time based on the emotional data. For example, if the user's concentration is declining, the difficulty of the game may be lowered.

[0257] Step 7:

[0258] The learning content that has been adjusted by the generating means is packaged in an appropriate format and transmitted from the server to the terminal, which then displays the received learning content to the user.

[0259] Step 8:

[0260] Users use the provided learning content on their devices to study together with preschool children. Progress data and learning results are recorded in real time and analyzed along with emotional data.

[0261] Step 9:

[0262] After completing the learning process, the device sends progress data and emotional data to the server, which then receives the data and passes it back to the analysis means to create a new learning plan.

[0263] Step 10:

[0264] The server generates the next learning content based on the newly created learning plan, allowing preschoolers to continue learning according to their individual needs.

[0265] The above are the specific processing steps of the present invention. This system is expected to improve learning effectiveness by providing an effective learning plan that is adapted to the learning needs and emotional state of preschool children.

[0266] Example 2

[0267] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0268] In today's educational environment, there is a demand for learning plans that meet the individual needs of preschool children. However, conventional learning systems face the challenge of identifying each preschool child's learning progress, strengths and weaknesses, and even their emotional state during learning in real time, and providing optimal learning content. As a result, preschool children can lose interest in learning or not fully realize the benefits of learning.

[0269] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, an emotion recognition means for recognizing emotional data of the preschool children in real time while they are studying, an adjustment means for adjusting the learning content based on the emotional data recognized by the emotion recognition means, and a feedback means for collecting learning progress data and emotional data of the preschool children and providing the data to the analysis means. This allows optimal learning content to be provided in real time according to the preschool children's learning progress and emotional state, thereby improving learning effectiveness and motivation.

[0270] An "input means" is a device or software that provides an interface for a user to input information about the learning needs of a preschool child.

[0271] "Analysis means" refers to a device or software that includes an algorithm or program for analyzing input learning needs data and formulating an optimal learning plan.

[0272] The "generation means" refers to a device or software that generates specific study content based on the analysis results of the analysis means.

[0273] The "transmission means" refers to a device or software for transmitting the study content generated by the generation means to the user terminal.

[0274] The "emotion recognition means" refers to sensors such as cameras and microphones that recognize the emotional data of preschool children in real time while they are learning, as well as software that analyzes this input.

[0275] The "adjustment means" is a device or software for adjusting the learning content in real time based on the emotion data recognized by the emotion recognition means.

[0276] A "feedback means" is a device or software that collects learning progress data and emotional data of a preschool child and provides that data to an analysis means.

[0277] "Learning content" refers to educational materials such as interactive games, workbooks, or dictation drills that are generated based on the results of the analytical means.

[0278] A "user device" is an electronic device such as a computer, tablet, or smartphone that displays learning content and allows users to operate it.

[0279] The present invention is a system that responds to the learning needs of preschool children and provides optimized learning content in real time. This system is composed of users, terminals, and a server.

[0280] First, the user launches the learning application using a device (computer, tablet, smartphone, etc.). The application displays an interface for entering information about the preschool child. The information entered includes age, strengths and weaknesses, current learning progress, and goals. For example, the user might enter specific information such as "6 years old," "good at math," "poor reading and writing skills," and "goal is to learn hiragana."

[0281] The device then sends the input information to a server, where it is analyzed and an optimal learning plan is created. The server contains a generative AI model and an analytical means for analyzing data based on past learning data, thereby generating optimal learning content for preschoolers.

[0282] The server generates specific learning content using a generation means based on the analysis results. The generated learning content may include interactive games, workbooks, or dictation drills. For example, a "hiragana dictation drill" or an "arithmetic game" may be generated.

[0283] The generated learning content is transmitted to the terminal using a transmission means. The terminal displays the received learning content in an application so that the user can use it with their preschool child. For example, the transmitted arithmetic game and hiragana drill are displayed in the application, and the user can use them to start learning with their child.

[0284] While the device is learning, it uses a camera and microphone to capture emotional data from the preschool child in real time. The emotion recognition means recognizes emotional data from the preschool child's facial expressions and voice, and determines their emotional state, such as "concentrated," "happy," or "tired." For example, while the child is learning, the camera captures their facial expressions, and the microphone analyzes their voice tone to collect emotional data.

[0285] The collected emotional data is sent from the device to a server, which analyzes it and adjusts the learning content in real time as needed. For example, if the user's concentration is declining, the difficulty of the game will be lowered.

[0286] Finally, the learning progress data and emotional data are analyzed again by the server through the feedback means and used to create the next learning plan, thereby continuously providing the optimal learning plan according to the learning progress and emotional state of the preschool child.

[0287] For example, the prompt text might look like this:

[0288] example:

[0289] "6-year-old child" "Good at math" "Difficult to read and write" "Goal is to practice hiragana"

[0290] This system enables effective learning that meets the individual needs of preschool children, and by adjusting learning plans in real time based on emotional data, it is expected to improve preschool children's learning effectiveness and motivation.

[0291] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0292] Step 1:

[0293] The user launches the learning application using their device. The application displays an interface for entering information about their preschool child. The user enters information such as the child's age, strengths and weaknesses, current learning progress, and goals. For example, the user might enter information such as "6 years old," "good at math," "poor reading and writing skills," and "goal is to learn hiragana." The input data is structured in XML or JSON format and passed to the next step.

[0294] Step 2:

[0295] The device sends the information entered by the user to the server. A secure communication protocol such as HTTPS is used for transmission. By sending the input data to the server, the data becomes available for the next analysis process. For example, data such as "Age: 6," "Strengths: Arithmetic," "Weaknesses: Reading and writing," and "Goal: Mastering Hiragana" are sent in JSON format.

[0296] Step 3:

[0297] The server receives the input data and passes it to a dedicated analysis means. The analysis means analyzes the data by referring to the generative AI model and past learning database. As a result of the analysis, an optimal learning plan for preschool children is generated. For example, the server may create a learning plan that combines "hiragana writing drills" and "arithmetic games." The analyzed data is passed to the next content generation step.

[0298] Step 4:

[0299] The server activates a generation means based on the analysis results to generate specific learning content. The generated learning content can include interactive games, workbooks, or dictation drills. For example, the generation means creates an "interactive math game to reinforce number concepts" or a "hiragana dictation practice drill." The generated learning content is structured in JSON or XML format and passed to the next sending step.

[0300] Step 5:

[0301] The server transmits the generated learning content to the device using a secure communication protocol such as HTTPS. For example, the generated interactive math game or dictation exercise is transmitted and made available on the device. The transmitted data is used in the next display step.

[0302] Step 6:

[0303] The device displays the received learning content within the learning application. The user then uses the device to utilize the displayed learning content together with their preschool child. For example, the transmitted math game and hiragana drill are displayed within the app, and the user can use them to begin learning together with their child. The displayed data is then used directly to implement the learning.

[0304] Step 7:

[0305] During learning, the device uses a camera and microphone to acquire emotional data from the preschooler in real time. The emotion recognition means recognizes emotional data from the preschooler's facial expressions and voice, and determines their emotional state, such as "concentrated," "happy," or "tired." For example, the camera captures facial expressions, and the microphone analyzes the tone of the voice. The acquired emotional data is passed to the next transmission step.

[0306] Step 8:

[0307] The device sends the collected emotional data and learning progress data to the server. The emotional data and learning progress data are structured in JSON or XML format and sent via a secure communication protocol. For example, information such as "concentrating" or "feeling tired during the learning process" is sent. The sent data is used in the next analysis step.

[0308] Step 9:

[0309] The server analyzes the received emotional data and learning progress data and generates feedback as needed. Based on the analysis results, the server creates a new learning plan. For example, it might suggest the next level of math games and adjust the timing of breaks. The analysis results are returned as the following information:

[0310] Step 10:

[0311] The device displays new learning plans and feedback information within the learning application. New learning plans are displayed, allowing users to continue learning with their preschoolers. This improves learning effectiveness and motivation for preschoolers. For example, "next level math games" and "break suggestions" are displayed within the app.

[0312] As described above, the entire system works together to provide an optimized learning experience for preschoolers.

[0313] (Application example 2)

[0314] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0315] To ensure that preschoolers learn efficiently and enjoyably, it is necessary to provide content that is adapted to each child's learning needs and emotional state. However, existing learning systems currently lack the ability to recognize emotional data in real time and adjust content accordingly, making it difficult to maintain children's motivation to learn. Furthermore, there are limited means to help preschoolers learn while having fun, which often results in lower learning outcomes than expected.

[0316] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0317] In this invention, the server includes input means for inputting the learning needs of preschool children, analysis means for performing data analysis based on the input learning needs, generation means for generating learning content based on the analysis results of the analysis means, transmission means for transmitting the learning content generated by the generation means to a user terminal, emotion recognition means for recognizing and analyzing emotional data of the preschool children while they are using the learning content, and adjustment means for adjusting the learning content in real time based on the emotional data obtained by the emotion recognition means. This makes it possible to provide an optimal learning plan in real time that is tailored to each child's learning situation and emotions.

[0318] "Preschooler" is a term used to refer to children who have not yet enrolled in school.

[0319] "Learning needs" refers to information such as the knowledge and skills that preschoolers need to progress in their studies, their learning goals, and their strengths and weaknesses.

[0320] "Input means" refers to an interface or device that allows a user to input information about a preschool child into the system.

[0321] "Analysis means" refers to a system or program that analyzes the input information about preschool children and derives the optimal learning plan.

[0322] "Generation means" refers to a system or program that generates appropriate learning content based on the results of the analysis means.

[0323] "Transmission means" refers to a system or program for transmitting the generated study content to a user terminal.

[0324] "Emotion recognition means" refers to a system or program that recognizes and analyzes emotional data in real time from facial expressions and voices of preschool children while they are using learning content.

[0325] "Adjustment means" refers to a system or program for adjusting learning content in real time based on emotional data from the emotion recognition means.

[0326] "Feedback means" refers to a system or program that has the function of collecting learning progress data and emotional data of preschool children and providing it to an analysis means.

[0327] "User terminal" refers to the device or equipment used by a user to receive and use learning content.

[0328] This invention relates to a system that automatically generates learning content tailored to the learning needs of preschool children and optimizes learning plans based on emotional data of the user and the preschool children. The system's components include users (parents and educators), terminals (computers, tablets, smartphones, etc.), and a server. A specific embodiment of this system is described below.

[0329] System Configuration

[0330] The system allows users to interact with the system using their devices, while the server analyzes data and generates learning content. It also includes emotion recognition capabilities, allowing it to adjust learning plans in real time based on emotional data.

[0331] Enter user information

[0332] First, the user launches the learning application on their device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, strengths and weaknesses, current learning progress, and goals.

[0333] As a specific example, the user inputs information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0334] Data analysis

[0335] The device sends the input information to a server, which then passes the received information to an analysis tool to create the optimal learning plan for the preschool child, referencing algorithms and past databases.

[0336] For example, the server may determine that "hiragana study is necessary" and, based on the analysis results, generate a study plan that combines a balanced combination of "hiragana writing drills" and "arithmetic games."

[0337] Emotional Data Recognition and Analysis

[0338] To obtain emotional data from preschoolers while they are learning, the system incorporates an emotion engine that recognizes emotional data from their facial expressions and voices in real time through sensors such as cameras and microphones.

[0339] For example, while a preschooler is studying, a camera captures their facial expressions and a microphone analyzes their voice tone to recognize their emotional state, such as "concentrated," "happy," or "tired."

[0340] Learning content generation and adaptation

[0341] Based on the analysis results, the server activates the generation means to generate specific learning content, which is appropriately selected based on the user's input, such as interactive games, workbooks, or dictation drills. Furthermore, the learning content is adjusted in real time based on the emotional data analyzed by the emotion engine.

[0342] As a specific example, the generation means creates "an interactive math game to reinforce the concept of numbers" and "a hiragana writing practice drill," and based on the analysis results of the emotion engine, adjustments are made, such as lowering the difficulty of the game if concentration is declining.

[0343] Sending content to users

[0344] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[0345] For example, math games and hiragana drills are displayed in the app, allowing users to start learning together with their children. Emotional adjustments are also made in real time.

[0346] Learning implementation and feedback

[0347] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data and emotion data obtained during this process are sent from the device to the server again and provided to the analysis means as feedback.

[0348] For example, users can play number matching puzzles with their children and check their progress by dictating hiragana characters, while emotional data is also sent along the way.

[0349] Feedback and learning plan adjustments

[0350] Progress and emotional data is analyzed on a server and used to adjust new learning plans, creating a feedback loop that continually provides the best possible learning plan for each preschooler based on their learning progress.

[0351] For example, the server can analyze information such as "a child has completed math level 1" and emotional data such as "the child felt tired midway through," and then suggest the next level of math game while also adjusting the timing to encourage breaks.

[0352] Example prompts include:

[0353] "My 6-year-old child is good at math, but has trouble reading and writing. His goal is to practice hiragana."

[0354] The above is a specific embodiment for carrying out the invention. This system enables effective learning that meets the individual needs of preschool children, and is expected to improve the learning effectiveness and motivation of preschool children by adjusting learning plans in real time based on emotional data.

[0355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0356] Step 1:

[0357] The user launches the learning application using a terminal. An interface for entering information about the preschool child is displayed, and the user enters information such as age, strengths and weaknesses, current learning progress, and goals. This input information becomes the initial data for the system.

[0358] Input: Information about your preschool child (e.g., age, strengths, weaknesses, learning progress, goals)

[0359] Output: Initial data is generated

[0360] Step 2:

[0361] The device sends the information entered by the user to the server, which then passes the received information to an analysis tool, which performs data analysis to derive the optimal learning plan. Past databases and algorithms are used.

[0362] Input: User-entered information about preschool children

[0363] Output: The best lesson plan for preschoolers

[0364] Step 3:

[0365] The server activates a generating means to generate optimal learning content based on the analysis results. The generating means uses the results of the analyzing means to generate educational content such as interactive games, workbooks, and dictation drills.

[0366] Input: Learning plan with analytical tools

[0367] Output: Learning content

[0368] Step 4:

[0369] The server transmits the generated learning content to the user's terminal, and the user can access the provided learning content through the terminal.

[0370] Input: Generated learning content

[0371] Output: Learning content displayed on the device

[0372] Step 5:

[0373] While using the learning content, the emotion recognition means recognizes the facial expressions and voice of preschool children in real time through a camera and microphone, generating emotion data that can be used to acquire emotion data such as concentration, enjoyment, and fatigue.

[0374] Input: Preschooler's facial expressions and voice

[0375] Output: Emotion data

[0376] Step 6:

[0377] Based on the emotion data acquired by the emotion recognition means, the server uses the adjustment means to adjust the learning content in real time, such as changing the difficulty level of the learning content or suggesting breaks.

[0378] Input: Emotion data

[0379] Output: Tailored learning content

[0380] Step 7:

[0381] The user uses the tailored learning content to study together with the preschool child, and the learning progress data and emotion data obtained during this process are sent back to the server from the device.

[0382] Input: Learning progress data and emotion data

[0383] Output: Progress and emotion data sent

[0384] Step 8:

[0385] The server analyzes the received learning progress data and emotion data and reflects them in a new learning plan. Based on this feedback, the next learning plan for the preschooler is optimized and provided to the user.

[0386] Input: Learning progress data and emotion data

[0387] Output: Updated learning plan

[0388] These are the specific processing steps of the system, which provide the optimal learning environment for preschool children and enable real-time adjustments.

[0389] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0390] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0391] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0392] [Second embodiment]

[0393] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0394] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0395] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0396] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0397] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0398] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0399] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0400] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0401] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0402] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0403] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0404] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0405] The present invention relates to a system for automatically generating learning content that meets the learning needs of preschool children, and a specific embodiment thereof will be described.

[0406] System Configuration

[0407] The system is primarily comprised of three elements: users (parents and educators), devices (computers, tablets, smartphones, etc.), and a server. Users interact with the system using their devices, while the server is responsible for data analysis and generation of learning content.

[0408] Enter user information

[0409] First, the user accesses the learning application using a device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, current learning progress, strengths and weaknesses, and goals.

[0410] example:

[0411] The user enters information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0412] Data analysis

[0413] The device sends the input information to a server. The server receives the received information and analyzes the data using an analysis means. The analysis means refers to specific algorithms and past databases to create an optimal learning plan for the preschool child.

[0414] example:

[0415] The server determines that "hiragana study is necessary" and, based on the analysis results, generates a study plan that is a balanced combination of "hiragana writing drills" and "math games."

[0416] Generating learning content

[0417] Based on the analysis results, the server activates a generation means to generate specific learning content, which may be an interactive game, a workbook, or a dictation drill, appropriately selected based on the user's input.

[0418] example:

[0419] The generator creates "an interactive math game to reinforce number concepts" and "hiragana writing practice drills."

[0420] Sending content to users

[0421] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[0422] example:

[0423] The submitted math games and hiragana drills will then appear within the app, allowing users to start using them to learn alongside their children.

[0424] Learning implementation

[0425] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data obtained during this process is sent from the device back to the server and provided to the analysis means as feedback.

[0426] example:

[0427] Users can play number matching puzzles with their children and dictate hiragana to check their progress.

[0428] feedback

[0429] Progress data after each lesson is analyzed on the server and used to adjust new lesson plans via analytical means. This feedback loop allows the system to continually provide optimal lesson plans tailored to each preschool child's learning progress.

[0430] example:

[0431] Based on the information that "the child has cleared math level 1," the server suggests the next level of math game.

[0432] The above is a specific embodiment of the present invention, and this system makes it possible to realize effective learning that meets the individual needs of preschool children.

[0433] The processing flow will be explained below.

[0434] Step 1:

[0435] The user launches the learning application using a device. After launching the application, an interface for entering information about the preschool child is displayed. The user enters information such as age, strengths and weaknesses, current learning progress, and goals.

[0436] Step 2:

[0437] The device sends the entered information to the server, where the data is packaged in an appropriate format, such as JSON.

[0438] Step 3:

[0439] The server passes the data received from the device to the analysis means, which analyzes the data based on the received information and creates the optimal learning plan for the preschool child, referencing algorithms and past databases.

[0440] Step 4:

[0441] Upon receiving the analysis results from the analysis means, the server activates the generation means, which generates appropriate learning content (such as an interactive game, a workbook, or a dictation drill) based on the analysis results.

[0442] Step 5:

[0443] The generated learning content is packaged in an appropriate format and sent from the server to the terminal, and the sent data is provided in a format that can be easily used by the user.

[0444] Step 6:

[0445] The terminal displays the received learning content to the user, who then studies together with the preschool child using the provided learning content.

[0446] Step 7:

[0447] After completing the learning, progress data and results are sent from the device to the server, and the learning application automatically records the learning progress.

[0448] Step 8:

[0449] The server receives the progress data and passes it to the feedback means, which then requests a new analysis from the analysis means based on the data and adjusts the next learning plan.

[0450] Example 1

[0451] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0452] Preschoolers have individual learning needs, making it difficult for them to learn effectively with a uniform learning plan. Furthermore, the lack of feedback based on their learning progress makes it difficult to continuously improve their learning. This makes it difficult to provide an optimal learning experience for preschoolers.

[0453] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0454] In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, and a feedback means for providing learning progress data collected from the user terminal back to the analysis means. This makes it possible to generate optimal learning content tailored to the individual learning needs of preschool children, reflect feedback based on the progress data, and continuously optimize learning.

[0455] "Preschool children" refers to young children or infants before the start of compulsory education.

[0456] "Learning needs" refers to the skills and knowledge that learners require when learning, and the requirements for achieving these.

[0457] "Input means" refers to a device or interface that allows a user to input required information into the system.

[0458] "Analysis means" refers to a device or function that analyzes input data using a specific algorithm or model and derives the results.

[0459] "Generation means" refers to a device or function that creates new learning content or plans based on the results of the analysis means.

[0460] "Transmission means" refers to a device or function for sending the generated learning content to a user terminal.

[0461] "Feedback means" refers to a device or function for collecting learning progress data and providing it back to the analysis means to improve the analysis results.

[0462] "User device" refers to an electronic device used by a user, such as a computer, tablet, or smartphone.

[0463] "Learning Content" refers to the educational materials and activities (e.g., interactive games, workbooks, dictation drills) provided to achieve learning objectives.

[0464] "Learning Progress Data" means data that shows the progress or achievements of a learner as they engage in a learning activity.

[0465] The present invention is a system for automatically generating learning content tailored to the learning needs of preschool children. This system is composed of three elements: users (parents and educators), terminals (computers, tablets, smartphones, etc.), and a server. Specific embodiments of this system are described below.

[0466] System Configuration

[0467] Users interact with the system through a terminal, specifically through a learning application, by entering information about their preschool child, including their age, current learning progress, strengths and weaknesses, and learning goals.

[0468] Enter user information

[0469] The user uses the device to access the learning application and input information about the preschool child. For example, the user might input "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0470] Data transmission and analysis

[0471] The device sends the input information to the server. The hardware used in this process can be a smartphone, tablet, or PC. The sent data is received by the server. The server uses analytical means to analyze the received information. This analysis uses specific algorithms and past databases. The server refers to past learning data and AI models to create the optimal learning plan for the preschool child.

[0472] Generating learning content

[0473] Based on the analysis results, the server activates a generation tool to generate specific learning content. This generated content ranges from interactive games, workbooks, and dictation drills. The generation tool uses machine learning algorithms written in Python and libraries such as Scikit-learn and TensorFlow.

[0474] Submit content and learn

[0475] The generated learning content is sent to the device and provided in a format that is easily accessible to the user. It is displayed within the learning application, allowing the user to begin learning together with the preschool child. Once the sent learning content is displayed on the device, the user can proceed with the learning together with the preschool child.

[0476] Collection of learning progress data and feedback

[0477] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data obtained during this process is sent from the device back to the server. The server uses this progress data through analytical means to adjust a new learning plan. This feedback loop allows the system to continuously provide the optimal learning plan according to the preschool child's learning progress.

[0478] Prompt Sentence Examples

[0479] For example, the following prompt might be possible:

[0480] "Please suggest some math games and hiragana practice drills suitable for a 5-year-old."

[0481] This system can provide effective learning plans tailored to the individual learning needs of preschool children, improving their learning efficiency and interest, and enabling them to continue learning.

[0482] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0483] Step 1: Enter your user information

[0484] The user launches a learning application on the device and enters information about the preschool child. For example, the user might enter "6-year-old child," "good at math," "poor reading and writing skills," and "goal is to practice hiragana." The entered data is temporarily saved on the device.

[0485] Input: Preschooler's age, learning progress, strengths, weaknesses, learning goals

[0486] Output: Preschool child information data (format: JSON)

[0487] Step 2: Sending data

[0488] The terminal transmits the information data of the preschool child entered by the user to the server. This data is encrypted before transmission.

[0489] Input: Preschool child information data (format: JSON)

[0490] Output: Data sent to the server

[0491] Step 3: Data analysis

[0492] The server analyzes the received information data of the preschool child using analytical means. In this process, specific algorithms and past databases are used. The server refers to the database and creates an optimal learning plan.

[0493] Input: Preschooler information data, past learning data, AI model

[0494] Output: Learning plan (format: JSON)

[0495] Step 4: Generate learning content

[0496] The server uses libraries such as Scikit-learn and TensorFlow to generate specific learning content based on the analysis results. The generated content ranges from interactive games, workbooks, and dictation drills.

[0497] Input: Learning plan (format: JSON)

[0498] Output: Learning content (format: JSON)

[0499] Step 5: Submit your learning content

[0500] The server transmits the generated learning content to the terminal, where it is provided to the user in an easily accessible format.

[0501] Input: Learning content (format: JSON)

[0502] Output: Learning content sent to the user's device

[0503] Step 6: Learning

[0504] Users use the device to access the provided learning content together with their preschool children, and progress is tracked through exercises and problem-solving. Progress data is also automatically recorded.

[0505] Input: Learning content (format: JSON)

[0506] Output: Learning progress data

[0507] Step 7: Sending progress data

[0508] The device automatically sends learning progress data to the server, which is encrypted and transmitted securely.

[0509] Input: Learning progress data (format: JSON)

[0510] Output: Progress data sent to the server

[0511] Step 8: Feedback analysis

[0512] The server analyzes the received learning progress data and uses it as a basis for generating a new learning plan. The analysis means again uses algorithms and databases to formulate the next optimal plan.

[0513] Input: Learning progress data, past learning data, AI model

[0514] Output: New learning plan (format: JSON)

[0515] These are the processing steps of the system, each of which provides a learning experience that is optimally tailored to the individual needs of preschoolers.

[0516] (Application example 1)

[0517] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0518] Traditional systems that provide learning content tailored to the learning needs of preschoolers are often ineffective. In particular, the learning content can be static or difficult to keep preschoolers interested. Therefore, it is necessary to provide methods for preschoolers to be more actively involved in learning. It is also important for parents and educators to easily track learning progress and receive feedback. There is a need to solve this problem.

[0519] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0520] In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, and a means for allowing preschool children and their parents to experience the educational content in a virtual environment. This not only allows preschool children to continue learning while maintaining their interest, but also makes it easy to provide feedback on their learning progress.

[0521] "Input means" refers to devices and software that input the learning needs of preschool children.

[0522] "Analysis means" refers to devices and software for analyzing data based on input learning needs.

[0523] "Generation means" refers to the device or software that generates learning content based on the analysis results of the analysis means.

[0524] "Transmission means" refers to the device or software used to transmit the study content generated by the generation means to the user terminal.

[0525] "Feedback tools" refer to devices and software that collect and provide preschool children's learning progress data to analytical tools.

[0526] "Virtual environment" refers to a virtual space or interface that allows preschool children and their parents to experience educational content.

[0527] System Configuration

[0528] The system that embodies this invention mainly consists of three elements: a user (parent or educator), a device (such as a smartphone or head-mounted display), and a server. The user interacts with the system using the device, and the server is responsible for data analysis and generation of learning content.

[0529] Enter user information

[0530] The user accesses the application using a device and enters information about the preschool child, specifically, the following information:

[0531] age

[0532] Current learning progress

[0533] Strengths and weaknesses

[0534] the goal

[0535] Data analysis

[0536] The device sends the input information to a server. The server analyzes the received information using analytical means (for example, data analysis tools such as TensorFlow or PyTorch). The analytical means refers to a past database and creates an optimal learning plan for the preschool child.

[0537] Generating learning content

[0538] Based on the analysis results, the server activates a generating means to generate specific learning content, which may be an interactive game, a workbook, or a dictation drill, appropriately selected based on the user's input.

[0539] Sending content to users

[0540] The generated learning content is sent to devices via a transmission means, allowing preschoolers and their parents to experience learning in a virtual environment, which is built using front-end development tools such as Unity and Flutter.

[0541] Learning implementation

[0542] Users can use the provided learning content on their devices to study together with their preschool children. For example, they can use smartphones and head-mounted displays to experience interactive learning in a virtual store.

[0543] feedback

[0544] Progress data after the lesson is sent from the device to the server and provided to the analysis means via the feedback means. This feedback loop allows the system to continuously provide optimal lesson plans according to the learning progress of preschool children.

[0545] Specific examples

[0546] For example, if a 6-year-old preschooler is good at math but has difficulty learning hiragana, parents can enter this information into the app. The system analyzes the input data and generates "hiragana writing practice" and "math puzzle games." This content is provided via smartphones or head-mounted displays.

[0547] Prompt Sentence Examples

[0548] Enter the following prompts into the generative AI model:

[0549] Prompt: "6-year-old child, good at math, needs to learn hiragana. Generate the best learning content."

[0550] Based on these prompts, the AI ​​model performs data analysis to generate learning content appropriate for the child.

[0551] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0552] Step 1:

[0553] The user accesses the application using a smartphone or head-mounted display. The user enters information such as the preschool child's age, current learning progress, strengths and weaknesses, and goals into the application's input form. This input data becomes the basic information for the system.

[0554] Input: Age, learning progress, strengths, weaknesses, goals

[0555] Output: Input learning needs data

[0556] Step 2:

[0557] The device sends the input learning needs data to the server. The server passes the received data to an analysis means for data analysis. Here, specific algorithms and past databases are referenced to create an optimal learning plan for the preschool child.

[0558] Input: Learning needs data

[0559] Output: Analysis results (e.g. Hiragana learning required)

[0560] Step 3:

[0561] The server generates specific learning content using a generating means based on the analysis results of the analyzing means. The generating means uses a generative AI model to create appropriate learning content (interactive games, workbooks, dictation drills, etc.) in response to user input.

[0562] Input: Analysis results

[0563] Output: Generated learning content (e.g., hiragana writing practice, math puzzle game)

[0564] Step 4:

[0565] The server transmits the generated learning content to the user's terminal via a transmission means, allowing the user to use the learning content together with the preschool child. This content is provided in a virtual environment, making learning interactive.

[0566] Input: Generated learning content

[0567] Output: Learning content sent to the user's device

[0568] Step 5:

[0569] Users use the device to carry out the learning content provided, and preschoolers use smartphones and head-mounted displays to engage in an interactive learning experience within a virtual store. As their learning progresses, progress data is recorded on the device.

[0570] Input: Submitted learning content

[0571] Output: Learning progress data

[0572] Step 6:

[0573] The learning progress data collected from the device is sent back to the server, which receives the progress data and re-analyzes it using feedback tools. Based on the results of this data analysis, a new learning plan or adjustments are made.

[0574] Input: Learning progress data

[0575] Output: New study plan suggestions or adjustments

[0576] Prompt Sentence Examples

[0577] Enter the following prompts into the generative AI model:

[0578] Prompt: "6-year-old child, good at math, needs to learn hiragana. Generate the best learning content."

[0579] This allows the AI ​​model to analyze the data to generate learning content appropriate for children.

[0580] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0581] The present invention relates to a system that automatically generates learning content according to the learning needs of preschool children and further optimizes learning plans based on emotional data of users and preschool children, and a specific embodiment thereof will be described.

[0582] System Configuration

[0583] This system is primarily composed of three elements: users (parents and educators), devices (computers, tablets, smartphones, etc.), and a server. Users interact with the system using their devices, the server analyzes data and generates learning content, and the emotion engine is responsible for recognizing and analyzing emotion data.

[0584] Enter user information

[0585] First, the user launches the learning application on their device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, strengths and weaknesses, current learning progress, and goals.

[0586] example:

[0587] The user enters information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0588] Data analysis

[0589] The device sends the input information to the server. The server passes the received information to the analysis means. The analysis means performs data analysis based on the received information and creates the optimal learning plan for the preschool child. Algorithms and past databases are referenced.

[0590] example:

[0591] The server determines that "hiragana study is necessary" and, based on the analysis results, generates a study plan that is a balanced combination of "hiragana writing drills" and "math games."

[0592] Emotional Data Recognition and Analysis

[0593] The system incorporates an emotion engine to collect emotional data from preschool children while they are learning. The emotion engine recognizes emotional data from their facial expressions and voices in real time through sensors such as cameras and microphones.

[0594] example:

[0595] While preschoolers are learning, a camera captures their facial expressions and a microphone analyzes their vocal tones to recognize their emotional states, such as "concentrated," "happy," or "tired."

[0596] Learning content generation and adaptation

[0597] Based on the analysis results, the server activates the generation means to generate specific learning content, which can be selected appropriately based on the user's input, such as interactive games, workbooks, or dictation drills. Furthermore, the generated learning content can be adjusted in real time based on the emotional data analyzed by the emotion engine.

[0598] example:

[0599] The generator creates "interactive math games to reinforce number concepts" and "hiragana writing practice drills," and based on the analysis results of the emotion engine, adjustments are made, such as lowering the difficulty of the game if concentration is declining.

[0600] Sending content to users

[0601] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[0602] example:

[0603] The math games and hiragana drills sent to the app are displayed within the app, allowing users to start learning together with their children. The app also adjusts to their child's emotions in real time.

[0604] Learning implementation and feedback

[0605] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data and emotion data obtained during this process are sent from the device to the server again and provided to the analysis means as feedback.

[0606] example:

[0607] Users can play number matching puzzles and dictate hiragana with their children to track their progress, while also sending emotional data along the way.

[0608] Feedback and learning plan adjustments

[0609] Progress and emotional data is analyzed on a server and used to adjust new learning plans, creating a feedback loop that continually provides the best possible learning plan for each preschooler based on their learning progress.

[0610] example:

[0611] The server analyzes the information that "the child has cleared math level 1" and emotional data that "the child felt tired halfway through," and suggests the next level of math games while also adjusting the timing to encourage breaks.

[0612] The above is a specific embodiment of the present invention, and this system enables effective learning that meets the individual needs of preschool children. Furthermore, real-time adjustment of learning plans based on emotional data is expected to improve preschool children's learning effectiveness and motivation.

[0613] The processing flow will be explained below.

[0614] Step 1:

[0615] The user launches the learning application using a device. After launching the application, an interface for entering information about the preschool child is displayed. The user enters information such as age, strengths and weaknesses, current learning progress, and goals.

[0616] Step 2:

[0617] The device sends the entered information to the server, where the data is packaged in an appropriate format, such as JSON.

[0618] Step 3:

[0619] The server passes the data received from the device to the analysis means, which analyzes the data based on the input information and creates an optimal learning plan, referencing algorithms and past databases.

[0620] Step 4:

[0621] The server activates the generating means based on the results of the analyzing means, and the generating means generates learning content based on the analysis results, such as an interactive game, a workbook, or a dictation drill, in accordance with the user's needs.

[0622] Step 5:

[0623] The emotion engine collects real-time emotional data from users or preschoolers while they are learning, recognizing their emotional state from facial expressions and voice tones through sensors such as cameras and microphones.

[0624] Step 6:

[0625] The server analyzes the emotional data obtained from the emotion engine and adjusts the generated learning content in real time based on the emotional data. For example, if the user's concentration is declining, the difficulty of the game may be lowered.

[0626] Step 7:

[0627] The learning content that has been adjusted by the generating means is packaged in an appropriate format and transmitted from the server to the terminal, which then displays the received learning content to the user.

[0628] Step 8:

[0629] Users use the provided learning content on their devices to study together with preschool children. Progress data and learning results are recorded in real time and analyzed along with emotional data.

[0630] Step 9:

[0631] After completing the learning process, the device sends progress data and emotional data to the server, which then receives the data and passes it back to the analysis means to create a new learning plan.

[0632] Step 10:

[0633] The server generates the next learning content based on the newly created learning plan, allowing preschoolers to continue learning according to their individual needs.

[0634] The above are the specific processing steps of the present invention. This system is expected to improve learning effectiveness by providing an effective learning plan that is adapted to the learning needs and emotional state of preschool children.

[0635] Example 2

[0636] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0637] In today's educational environment, there is a demand for learning plans that meet the individual needs of preschool children. However, conventional learning systems face the challenge of identifying each preschool child's learning progress, strengths and weaknesses, and even their emotional state during learning in real time, and providing optimal learning content. As a result, preschool children can lose interest in learning or not fully realize the benefits of learning.

[0638] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, an emotion recognition means for recognizing emotional data of the preschool children in real time while they are studying, an adjustment means for adjusting the learning content based on the emotional data recognized by the emotion recognition means, and a feedback means for collecting learning progress data and emotional data of the preschool children and providing the data to the analysis means. This allows optimal learning content to be provided in real time according to the preschool children's learning progress and emotional state, thereby improving learning effectiveness and motivation.

[0639] An "input means" is a device or software that provides an interface for a user to input information about the learning needs of a preschool child.

[0640] "Analysis means" refers to a device or software that includes an algorithm or program for analyzing input learning needs data and formulating an optimal learning plan.

[0641] The "generation means" refers to a device or software that generates specific study content based on the analysis results of the analysis means.

[0642] The "transmission means" refers to a device or software for transmitting the study content generated by the generation means to the user terminal.

[0643] The "emotion recognition means" refers to sensors such as cameras and microphones that recognize the emotional data of preschool children in real time while they are learning, as well as software that analyzes this input.

[0644] The "adjustment means" is a device or software for adjusting the learning content in real time based on the emotion data recognized by the emotion recognition means.

[0645] A "feedback means" is a device or software that collects learning progress data and emotional data of a preschool child and provides that data to an analysis means.

[0646] "Learning content" refers to educational materials such as interactive games, workbooks, or dictation drills that are generated based on the results of the analytical means.

[0647] A "user device" is an electronic device such as a computer, tablet, or smartphone that displays learning content and allows users to operate it.

[0648] The present invention is a system that responds to the learning needs of preschool children and provides optimized learning content in real time. This system is composed of users, terminals, and a server.

[0649] First, the user launches the learning application using a device (computer, tablet, smartphone, etc.). The application displays an interface for entering information about the preschool child. The information entered includes age, strengths and weaknesses, current learning progress, and goals. For example, the user might enter specific information such as "6 years old," "good at math," "poor reading and writing skills," and "goal is to learn hiragana."

[0650] The device then sends the input information to a server, where it is analyzed and an optimal learning plan is created. The server contains a generative AI model and an analytical means for analyzing data based on past learning data, thereby generating optimal learning content for preschoolers.

[0651] The server generates specific learning content using a generation means based on the analysis results. The generated learning content may include interactive games, workbooks, or dictation drills. For example, a "hiragana dictation drill" or an "arithmetic game" may be generated.

[0652] The generated learning content is transmitted to the terminal using a transmission means. The terminal displays the received learning content in an application so that the user can use it with their preschool child. For example, the transmitted arithmetic game and hiragana drill are displayed in the application, and the user can use them to start learning with their child.

[0653] While the device is learning, it uses a camera and microphone to capture emotional data from the preschool child in real time. The emotion recognition means recognizes emotional data from the preschool child's facial expressions and voice, and determines their emotional state, such as "concentrated," "happy," or "tired." For example, while the child is learning, the camera captures their facial expressions, and the microphone analyzes their voice tone to collect emotional data.

[0654] The collected emotional data is sent from the device to a server, which analyzes it and adjusts the learning content in real time as needed. For example, if the user's concentration is declining, the difficulty of the game will be lowered.

[0655] Finally, the learning progress data and emotional data are analyzed again by the server through the feedback means and used to create the next learning plan, thereby continuously providing the optimal learning plan according to the learning progress and emotional state of the preschool child.

[0656] For example, the prompt text might look like this:

[0657] example:

[0658] "6-year-old child" "Good at math" "Difficult to read and write" "Goal is to practice hiragana"

[0659] This system enables effective learning that meets the individual needs of preschool children, and by adjusting learning plans in real time based on emotional data, it is expected to improve preschool children's learning effectiveness and motivation.

[0660] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0661] Step 1:

[0662] The user launches the learning application using their device. The application displays an interface for entering information about their preschool child. The user enters information such as the child's age, strengths and weaknesses, current learning progress, and goals. For example, the user might enter information such as "6 years old," "good at math," "poor reading and writing skills," and "goal is to learn hiragana." The input data is structured in XML or JSON format and passed to the next step.

[0663] Step 2:

[0664] The device sends the information entered by the user to the server. A secure communication protocol such as HTTPS is used for transmission. By sending the input data to the server, the data becomes available for the next analysis process. For example, data such as "Age: 6," "Strengths: Arithmetic," "Weaknesses: Reading and writing," and "Goal: Mastering Hiragana" are sent in JSON format.

[0665] Step 3:

[0666] The server receives the input data and passes it to a dedicated analysis means. The analysis means analyzes the data by referring to the generative AI model and past learning database. As a result of the analysis, an optimal learning plan for preschool children is generated. For example, the server may create a learning plan that combines "hiragana writing drills" and "arithmetic games." The analyzed data is passed to the next content generation step.

[0667] Step 4:

[0668] The server activates a generation means based on the analysis results to generate specific learning content. The generated learning content can include interactive games, workbooks, or dictation drills. For example, the generation means creates an "interactive math game to reinforce number concepts" or a "hiragana dictation practice drill." The generated learning content is structured in JSON or XML format and passed to the next sending step.

[0669] Step 5:

[0670] The server transmits the generated learning content to the device using a secure communication protocol such as HTTPS. For example, the generated interactive math game or dictation exercise is transmitted and made available on the device. The transmitted data is used in the next display step.

[0671] Step 6:

[0672] The device displays the received learning content within the learning application. The user then uses the device to utilize the displayed learning content together with their preschool child. For example, the transmitted math game and hiragana drill are displayed within the app, and the user can use them to begin learning together with their child. The displayed data is then used directly to implement the learning.

[0673] Step 7:

[0674] During learning, the device uses a camera and microphone to acquire emotional data from the preschooler in real time. The emotion recognition means recognizes emotional data from the preschooler's facial expressions and voice, and determines their emotional state, such as "concentrated," "happy," or "tired." For example, the camera captures facial expressions, and the microphone analyzes the tone of the voice. The acquired emotional data is passed to the next transmission step.

[0675] Step 8:

[0676] The device sends the collected emotional data and learning progress data to the server. The emotional data and learning progress data are structured in JSON or XML format and sent via a secure communication protocol. For example, information such as "concentrating" or "feeling tired during the learning process" is sent. The sent data is used in the next analysis step.

[0677] Step 9:

[0678] The server analyzes the received emotional data and learning progress data and generates feedback as needed. Based on the analysis results, the server creates a new learning plan. For example, it might suggest the next level of math games and adjust the timing of breaks. The analysis results are returned as the following information:

[0679] Step 10:

[0680] The device displays new learning plans and feedback information within the learning application. New learning plans are displayed, allowing users to continue learning with their preschoolers. This improves learning effectiveness and motivation for preschoolers. For example, "next level math games" and "break suggestions" are displayed within the app.

[0681] As described above, the entire system works together to provide an optimized learning experience for preschoolers.

[0682] (Application example 2)

[0683] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0684] To ensure that preschoolers learn efficiently and enjoyably, it is necessary to provide content that is adapted to each child's learning needs and emotional state. However, existing learning systems currently lack the ability to recognize emotional data in real time and adjust content accordingly, making it difficult to maintain children's motivation to learn. Furthermore, there are limited means to help preschoolers learn while having fun, which often results in lower learning outcomes than expected.

[0685] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0686] In this invention, the server includes input means for inputting the learning needs of preschool children, analysis means for performing data analysis based on the input learning needs, generation means for generating learning content based on the analysis results of the analysis means, transmission means for transmitting the learning content generated by the generation means to a user terminal, emotion recognition means for recognizing and analyzing emotional data of the preschool children while they are using the learning content, and adjustment means for adjusting the learning content in real time based on the emotional data obtained by the emotion recognition means. This makes it possible to provide an optimal learning plan in real time that is tailored to each child's learning situation and emotions.

[0687] "Preschooler" is a term used to refer to children who have not yet enrolled in school.

[0688] "Learning needs" refers to information such as the knowledge and skills that preschoolers need to progress in their studies, their learning goals, and their strengths and weaknesses.

[0689] "Input means" refers to an interface or device that allows a user to input information about a preschool child into the system.

[0690] "Analysis means" refers to a system or program that analyzes the input information about preschool children and derives the optimal learning plan.

[0691] "Generation means" refers to a system or program that generates appropriate learning content based on the results of the analysis means.

[0692] "Transmission means" refers to a system or program for transmitting the generated study content to a user terminal.

[0693] "Emotion recognition means" refers to a system or program that recognizes and analyzes emotional data in real time from facial expressions and voices of preschool children while they are using learning content.

[0694] "Adjustment means" refers to a system or program for adjusting learning content in real time based on emotional data from the emotion recognition means.

[0695] "Feedback means" refers to a system or program that has the function of collecting learning progress data and emotional data of preschool children and providing it to an analysis means.

[0696] "User terminal" refers to the device or equipment used by a user to receive and use learning content.

[0697] This invention relates to a system that automatically generates learning content tailored to the learning needs of preschool children and optimizes learning plans based on emotional data of the user and the preschool children. The system's components include users (parents and educators), terminals (computers, tablets, smartphones, etc.), and a server. A specific embodiment of this system is described below.

[0698] System Configuration

[0699] The system allows users to interact with the system using their devices, while the server analyzes data and generates learning content. It also includes emotion recognition capabilities, allowing it to adjust learning plans in real time based on emotional data.

[0700] Enter user information

[0701] First, the user launches the learning application on their device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, strengths and weaknesses, current learning progress, and goals.

[0702] As a specific example, the user inputs information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0703] Data analysis

[0704] The device sends the input information to a server, which then passes the received information to an analysis tool to create the optimal learning plan for the preschool child, referencing algorithms and past databases.

[0705] For example, the server may determine that "hiragana study is necessary" and, based on the analysis results, generate a study plan that combines a balanced combination of "hiragana writing drills" and "arithmetic games."

[0706] Emotional Data Recognition and Analysis

[0707] To obtain emotional data from preschoolers while they are learning, the system incorporates an emotion engine that recognizes emotional data from their facial expressions and voices in real time through sensors such as cameras and microphones.

[0708] For example, while a preschooler is studying, a camera captures their facial expressions and a microphone analyzes their voice tone to recognize their emotional state, such as "concentrated," "happy," or "tired."

[0709] Learning content generation and adaptation

[0710] Based on the analysis results, the server activates the generation means to generate specific learning content, which is appropriately selected based on the user's input, such as interactive games, workbooks, or dictation drills. Furthermore, the learning content is adjusted in real time based on the emotional data analyzed by the emotion engine.

[0711] As a specific example, the generation means creates "an interactive math game to reinforce the concept of numbers" and "a hiragana writing practice drill," and based on the analysis results of the emotion engine, adjustments are made, such as lowering the difficulty of the game if concentration is declining.

[0712] Sending content to users

[0713] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[0714] For example, math games and hiragana drills are displayed in the app, allowing users to start learning together with their children. Emotional adjustments are also made in real time.

[0715] Learning implementation and feedback

[0716] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data and emotion data obtained during this process are sent from the device to the server again and provided to the analysis means as feedback.

[0717] For example, users can play number matching puzzles with their children and check their progress by dictating hiragana characters, while emotional data is also sent along the way.

[0718] Feedback and learning plan adjustments

[0719] Progress and emotional data is analyzed on a server and used to adjust new learning plans, creating a feedback loop that continually provides the best possible learning plan for each preschooler based on their learning progress.

[0720] For example, the server can analyze information such as "a child has completed math level 1" and emotional data such as "the child felt tired midway through," and then suggest the next level of math game while also adjusting the timing to encourage breaks.

[0721] Example prompts include:

[0722] "My 6-year-old child is good at math, but has trouble reading and writing. His goal is to practice hiragana."

[0723] The above is a specific embodiment for carrying out the invention. This system enables effective learning that meets the individual needs of preschool children, and is expected to improve the learning effectiveness and motivation of preschool children by adjusting learning plans in real time based on emotional data.

[0724] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0725] Step 1:

[0726] The user launches the learning application using a terminal. An interface for entering information about the preschool child is displayed, and the user enters information such as age, strengths and weaknesses, current learning progress, and goals. This input information becomes the initial data for the system.

[0727] Input: Information about your preschool child (e.g., age, strengths, weaknesses, learning progress, goals)

[0728] Output: Initial data is generated

[0729] Step 2:

[0730] The device sends the information entered by the user to the server, which then passes the received information to an analysis tool, which performs data analysis to derive the optimal learning plan. Past databases and algorithms are used.

[0731] Input: User-entered information about preschool children

[0732] Output: The best lesson plan for preschoolers

[0733] Step 3:

[0734] The server activates a generating means to generate optimal learning content based on the analysis results. The generating means uses the results of the analyzing means to generate educational content such as interactive games, workbooks, and dictation drills.

[0735] Input: Learning plan with analytical tools

[0736] Output: Learning content

[0737] Step 4:

[0738] The server transmits the generated learning content to the user's terminal, and the user can access the provided learning content through the terminal.

[0739] Input: Generated learning content

[0740] Output: Learning content displayed on the device

[0741] Step 5:

[0742] While using the learning content, the emotion recognition means recognizes the facial expressions and voice of preschool children in real time through a camera and microphone, generating emotion data that can be used to acquire emotion data such as concentration, enjoyment, and fatigue.

[0743] Input: Preschooler's facial expressions and voice

[0744] Output: Emotion data

[0745] Step 6:

[0746] Based on the emotion data acquired by the emotion recognition means, the server uses the adjustment means to adjust the learning content in real time, such as changing the difficulty level of the learning content or suggesting breaks.

[0747] Input: Emotion data

[0748] Output: Tailored learning content

[0749] Step 7:

[0750] The user uses the tailored learning content to study together with the preschool child, and the learning progress data and emotion data obtained during this process are sent back to the server from the device.

[0751] Input: Learning progress data and emotion data

[0752] Output: Progress and emotion data sent

[0753] Step 8:

[0754] The server analyzes the received learning progress data and emotion data and reflects them in a new learning plan. Based on this feedback, the next learning plan for the preschooler is optimized and provided to the user.

[0755] Input: Learning progress data and emotion data

[0756] Output: Updated learning plan

[0757] These are the specific processing steps of the system, which provide the optimal learning environment for preschool children and enable real-time adjustments.

[0758] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0759] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0760] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0761] [Third embodiment]

[0762] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0763] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0764] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0765] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0766] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0767] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0768] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0769] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0770] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0771] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0772] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0773] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0774] The present invention relates to a system for automatically generating learning content that meets the learning needs of preschool children, and a specific embodiment thereof will be described.

[0775] System Configuration

[0776] The system is primarily comprised of three elements: users (parents and educators), devices (computers, tablets, smartphones, etc.), and a server. Users interact with the system using their devices, while the server is responsible for data analysis and generation of learning content.

[0777] Enter user information

[0778] First, the user accesses the learning application using a device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, current learning progress, strengths and weaknesses, and goals.

[0779] example:

[0780] The user enters information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0781] Data analysis

[0782] The device sends the input information to a server. The server receives the received information and analyzes the data using an analysis means. The analysis means refers to specific algorithms and past databases to create an optimal learning plan for the preschool child.

[0783] example:

[0784] The server determines that "hiragana study is necessary" and, based on the analysis results, generates a study plan that is a balanced combination of "hiragana writing drills" and "math games."

[0785] Generating learning content

[0786] Based on the analysis results, the server activates a generation means to generate specific learning content, which may be an interactive game, a workbook, or a dictation drill, appropriately selected based on the user's input.

[0787] example:

[0788] The generator creates "an interactive math game to reinforce number concepts" and "hiragana writing practice drills."

[0789] Sending content to users

[0790] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[0791] example:

[0792] The submitted math games and hiragana drills will then appear within the app, allowing users to start using them to learn alongside their children.

[0793] Learning implementation

[0794] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data obtained during this process is sent from the device back to the server and provided to the analysis means as feedback.

[0795] example:

[0796] Users can play number matching puzzles with their children and dictate hiragana to check their progress.

[0797] feedback

[0798] Progress data after each lesson is analyzed on the server and used to adjust new lesson plans via analytical means. This feedback loop allows the system to continually provide optimal lesson plans tailored to each preschool child's learning progress.

[0799] example:

[0800] Based on the information that "the child has cleared math level 1," the server suggests the next level of math game.

[0801] The above is a specific embodiment of the present invention, and this system makes it possible to realize effective learning that meets the individual needs of preschool children.

[0802] The processing flow will be explained below.

[0803] Step 1:

[0804] The user launches the learning application using a device. After launching the application, an interface for entering information about the preschool child is displayed. The user enters information such as age, strengths and weaknesses, current learning progress, and goals.

[0805] Step 2:

[0806] The device sends the entered information to the server, where the data is packaged in an appropriate format, such as JSON.

[0807] Step 3:

[0808] The server passes the data received from the device to the analysis means, which analyzes the data based on the received information and creates the optimal learning plan for the preschool child, referencing algorithms and past databases.

[0809] Step 4:

[0810] Upon receiving the analysis results from the analysis means, the server activates the generation means, which generates appropriate learning content (such as an interactive game, a workbook, or a dictation drill) based on the analysis results.

[0811] Step 5:

[0812] The generated learning content is packaged in an appropriate format and sent from the server to the terminal, and the sent data is provided in a format that can be easily used by the user.

[0813] Step 6:

[0814] The terminal displays the received learning content to the user, who then studies together with the preschool child using the provided learning content.

[0815] Step 7:

[0816] After completing the learning, progress data and results are sent from the device to the server, and the learning application automatically records the learning progress.

[0817] Step 8:

[0818] The server receives the progress data and passes it to the feedback means, which then requests a new analysis from the analysis means based on the data and adjusts the next learning plan.

[0819] Example 1

[0820] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0821] Preschoolers have individual learning needs, making it difficult for them to learn effectively with a uniform learning plan. Furthermore, the lack of feedback based on their learning progress makes it difficult to continuously improve their learning. This makes it difficult to provide an optimal learning experience for preschoolers.

[0822] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0823] In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, and a feedback means for providing learning progress data collected from the user terminal back to the analysis means. This makes it possible to generate optimal learning content tailored to the individual learning needs of preschool children, reflect feedback based on the progress data, and continuously optimize learning.

[0824] "Preschool children" refers to young children or infants before the start of compulsory education.

[0825] "Learning needs" refers to the skills and knowledge that learners require when learning, and the requirements for achieving these.

[0826] "Input means" refers to a device or interface that allows a user to input required information into the system.

[0827] "Analysis means" refers to a device or function that analyzes input data using a specific algorithm or model and derives the results.

[0828] "Generation means" refers to a device or function that creates new learning content or plans based on the results of the analysis means.

[0829] "Transmission means" refers to a device or function for sending the generated learning content to a user terminal.

[0830] "Feedback means" refers to a device or function for collecting learning progress data and providing it back to the analysis means to improve the analysis results.

[0831] "User device" refers to an electronic device used by a user, such as a computer, tablet, or smartphone.

[0832] "Learning Content" refers to the educational materials and activities (e.g., interactive games, workbooks, dictation drills) provided to achieve learning objectives.

[0833] "Learning Progress Data" means data that shows the progress or achievements of a learner as they engage in a learning activity.

[0834] The present invention is a system for automatically generating learning content tailored to the learning needs of preschool children. This system is composed of three elements: users (parents and educators), terminals (computers, tablets, smartphones, etc.), and a server. Specific embodiments of this system are described below.

[0835] System Configuration

[0836] Users interact with the system through a terminal, specifically through a learning application, by entering information about their preschool child, including their age, current learning progress, strengths and weaknesses, and learning goals.

[0837] Enter user information

[0838] The user uses the device to access the learning application and input information about the preschool child. For example, the user might input "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0839] Data transmission and analysis

[0840] The device sends the input information to the server. The hardware used in this process can be a smartphone, tablet, or PC. The sent data is received by the server. The server uses analytical means to analyze the received information. This analysis uses specific algorithms and past databases. The server refers to past learning data and AI models to create the optimal learning plan for the preschool child.

[0841] Generating learning content

[0842] Based on the analysis results, the server activates a generation tool to generate specific learning content. This generated content ranges from interactive games, workbooks, and dictation drills. The generation tool uses machine learning algorithms written in Python and libraries such as Scikit-learn and TensorFlow.

[0843] Submit content and learn

[0844] The generated learning content is sent to the device and provided in a format that is easily accessible to the user. It is displayed within the learning application, allowing the user to begin learning together with the preschool child. Once the sent learning content is displayed on the device, the user can proceed with the learning together with the preschool child.

[0845] Collection of learning progress data and feedback

[0846] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data obtained during this process is sent from the device back to the server. The server uses this progress data through analytical means to adjust a new learning plan. This feedback loop allows the system to continuously provide the optimal learning plan according to the preschool child's learning progress.

[0847] Prompt Sentence Examples

[0848] For example, the following prompt might be possible:

[0849] "Please suggest some math games and hiragana practice drills suitable for a 5-year-old."

[0850] This system can provide effective learning plans tailored to the individual learning needs of preschool children, improving their learning efficiency and interest, and enabling them to continue learning.

[0851] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0852] Step 1: Enter your user information

[0853] The user launches a learning application on the device and enters information about the preschool child. For example, the user might enter "6-year-old child," "good at math," "poor reading and writing skills," and "goal is to practice hiragana." The entered data is temporarily saved on the device.

[0854] Input: Preschooler's age, learning progress, strengths, weaknesses, learning goals

[0855] Output: Preschool child information data (format: JSON)

[0856] Step 2: Sending data

[0857] The terminal transmits the information data of the preschool child entered by the user to the server. This data is encrypted before transmission.

[0858] Input: Preschool child information data (format: JSON)

[0859] Output: Data sent to the server

[0860] Step 3: Data analysis

[0861] The server analyzes the received information data of the preschool child using analytical means. In this process, specific algorithms and past databases are used. The server refers to the database and creates an optimal learning plan.

[0862] Input: Preschooler information data, past learning data, AI model

[0863] Output: Learning plan (format: JSON)

[0864] Step 4: Generate learning content

[0865] The server uses libraries such as Scikit-learn and TensorFlow to generate specific learning content based on the analysis results. The generated content ranges from interactive games, workbooks, and dictation drills.

[0866] Input: Learning plan (format: JSON)

[0867] Output: Learning content (format: JSON)

[0868] Step 5: Submit your learning content

[0869] The server transmits the generated learning content to the terminal, where it is provided to the user in an easily accessible format.

[0870] Input: Learning content (format: JSON)

[0871] Output: Learning content sent to the user's device

[0872] Step 6: Learning

[0873] Users use the device to access the provided learning content together with their preschool children, and progress is tracked through exercises and problem-solving. Progress data is also automatically recorded.

[0874] Input: Learning content (format: JSON)

[0875] Output: Learning progress data

[0876] Step 7: Sending progress data

[0877] The device automatically sends learning progress data to the server, which is encrypted and transmitted securely.

[0878] Input: Learning progress data (format: JSON)

[0879] Output: Progress data sent to the server

[0880] Step 8: Feedback analysis

[0881] The server analyzes the received learning progress data and uses it as a basis for generating a new learning plan. The analysis means again uses algorithms and databases to formulate the next optimal plan.

[0882] Input: Learning progress data, past learning data, AI model

[0883] Output: New learning plan (format: JSON)

[0884] These are the processing steps of the system, each of which provides a learning experience that is optimally tailored to the individual needs of preschoolers.

[0885] (Application example 1)

[0886] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0887] Traditional systems that provide learning content tailored to the learning needs of preschoolers are often ineffective. In particular, the learning content can be static or difficult to keep preschoolers interested. Therefore, it is necessary to provide methods for preschoolers to be more actively involved in learning. It is also important for parents and educators to easily track learning progress and receive feedback. There is a need to solve this problem.

[0888] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0889] In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, and a means for allowing preschool children and their parents to experience the educational content in a virtual environment. This not only allows preschool children to continue learning while maintaining their interest, but also makes it easy to provide feedback on their learning progress.

[0890] "Input means" refers to devices and software that input the learning needs of preschool children.

[0891] "Analysis means" refers to devices and software for analyzing data based on input learning needs.

[0892] "Generation means" refers to the device or software that generates learning content based on the analysis results of the analysis means.

[0893] "Transmission means" refers to the device or software used to transmit the study content generated by the generation means to the user terminal.

[0894] "Feedback tools" refer to devices and software that collect and provide preschool children's learning progress data to analytical tools.

[0895] "Virtual environment" refers to a virtual space or interface that allows preschool children and their parents to experience educational content.

[0896] System Configuration

[0897] The system that embodies this invention mainly consists of three elements: a user (parent or educator), a device (such as a smartphone or head-mounted display), and a server. The user interacts with the system using the device, and the server is responsible for data analysis and generation of learning content.

[0898] Enter user information

[0899] The user accesses the application using a device and enters information about the preschool child, specifically, the following information:

[0900] age

[0901] Current learning progress

[0902] Strengths and weaknesses

[0903] the goal

[0904] Data analysis

[0905] The device sends the input information to a server. The server analyzes the received information using analytical means (for example, data analysis tools such as TensorFlow or PyTorch). The analytical means refers to a past database and creates an optimal learning plan for the preschool child.

[0906] Generating learning content

[0907] Based on the analysis results, the server activates a generating means to generate specific learning content, which may be an interactive game, a workbook, or a dictation drill, appropriately selected based on the user's input.

[0908] Sending content to users

[0909] The generated learning content is sent to devices via a transmission means, allowing preschoolers and their parents to experience learning in a virtual environment, which is built using front-end development tools such as Unity and Flutter.

[0910] Learning implementation

[0911] Users can use the provided learning content on their devices to study together with their preschool children. For example, they can use smartphones and head-mounted displays to experience interactive learning in a virtual store.

[0912] feedback

[0913] Progress data after the lesson is sent from the device to the server and provided to the analysis means via the feedback means. This feedback loop allows the system to continuously provide optimal lesson plans according to the learning progress of preschool children.

[0914] Specific examples

[0915] For example, if a 6-year-old preschooler is good at math but has difficulty learning hiragana, parents can enter this information into the app. The system analyzes the input data and generates "hiragana writing practice" and "math puzzle games." This content is provided via smartphones or head-mounted displays.

[0916] Prompt Sentence Examples

[0917] Enter the following prompts into the generative AI model:

[0918] Prompt: "6-year-old child, good at math, needs to learn hiragana. Generate the best learning content."

[0919] Based on these prompts, the AI ​​model performs data analysis to generate learning content appropriate for the child.

[0920] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0921] Step 1:

[0922] The user accesses the application using a smartphone or head-mounted display. The user enters information such as the preschool child's age, current learning progress, strengths and weaknesses, and goals into the application's input form. This input data becomes the basic information for the system.

[0923] Input: Age, learning progress, strengths, weaknesses, goals

[0924] Output: Input learning needs data

[0925] Step 2:

[0926] The device sends the input learning needs data to the server. The server passes the received data to an analysis means for data analysis. Here, specific algorithms and past databases are referenced to create an optimal learning plan for the preschool child.

[0927] Input: Learning needs data

[0928] Output: Analysis results (e.g. Hiragana learning required)

[0929] Step 3:

[0930] The server generates specific learning content using a generating means based on the analysis results of the analyzing means. The generating means uses a generative AI model to create appropriate learning content (interactive games, workbooks, dictation drills, etc.) in response to user input.

[0931] Input: Analysis results

[0932] Output: Generated learning content (e.g., hiragana writing practice, math puzzle game)

[0933] Step 4:

[0934] The server transmits the generated learning content to the user's terminal via a transmission means, allowing the user to use the learning content together with the preschool child. This content is provided in a virtual environment, making learning interactive.

[0935] Input: Generated learning content

[0936] Output: Learning content sent to the user's device

[0937] Step 5:

[0938] Users use the device to carry out the learning content provided, and preschoolers use smartphones and head-mounted displays to engage in an interactive learning experience within a virtual store. As their learning progresses, progress data is recorded on the device.

[0939] Input: Submitted learning content

[0940] Output: Learning progress data

[0941] Step 6:

[0942] The learning progress data collected from the device is sent back to the server, which receives the progress data and re-analyzes it using feedback tools. Based on the results of this data analysis, a new learning plan or adjustments are made.

[0943] Input: Learning progress data

[0944] Output: New study plan suggestions or adjustments

[0945] Prompt Sentence Examples

[0946] Enter the following prompts into the generative AI model:

[0947] Prompt: "6-year-old child, good at math, needs to learn hiragana. Generate the best learning content."

[0948] This allows the AI ​​model to analyze the data to generate learning content appropriate for children.

[0949] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0950] The present invention relates to a system that automatically generates learning content according to the learning needs of preschool children and further optimizes learning plans based on emotional data of users and preschool children, and a specific embodiment thereof will be described.

[0951] System Configuration

[0952] This system is primarily composed of three elements: users (parents and educators), devices (computers, tablets, smartphones, etc.), and a server. Users interact with the system using their devices, the server analyzes data and generates learning content, and the emotion engine is responsible for recognizing and analyzing emotion data.

[0953] Enter user information

[0954] First, the user launches the learning application on their device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, strengths and weaknesses, current learning progress, and goals.

[0955] example:

[0956] The user enters information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[0957] Data analysis

[0958] The device sends the input information to the server. The server passes the received information to the analysis means. The analysis means performs data analysis based on the received information and creates the optimal learning plan for the preschool child. Algorithms and past databases are referenced.

[0959] example:

[0960] The server determines that "hiragana study is necessary" and, based on the analysis results, generates a study plan that is a balanced combination of "hiragana writing drills" and "math games."

[0961] Emotional Data Recognition and Analysis

[0962] The system incorporates an emotion engine to collect emotional data from preschool children while they are learning. The emotion engine recognizes emotional data from their facial expressions and voices in real time through sensors such as cameras and microphones.

[0963] example:

[0964] While preschoolers are learning, a camera captures their facial expressions and a microphone analyzes their vocal tones to recognize their emotional states, such as "concentrated," "happy," or "tired."

[0965] Learning content generation and adaptation

[0966] Based on the analysis results, the server activates the generation means to generate specific learning content, which can be selected appropriately based on the user's input, such as interactive games, workbooks, or dictation drills. Furthermore, the generated learning content can be adjusted in real time based on the emotional data analyzed by the emotion engine.

[0967] example:

[0968] The generator creates "interactive math games to reinforce number concepts" and "hiragana writing practice drills," and based on the analysis results of the emotion engine, adjustments are made, such as lowering the difficulty of the game if concentration is declining.

[0969] Sending content to users

[0970] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[0971] example:

[0972] The math games and hiragana drills sent to the app are displayed within the app, allowing users to start learning together with their children. The app also adjusts to their child's emotions in real time.

[0973] Learning implementation and feedback

[0974] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data and emotion data obtained during this process are sent from the device to the server again and provided to the analysis means as feedback.

[0975] example:

[0976] Users can play number matching puzzles and dictate hiragana with their children to track their progress, while also sending emotional data along the way.

[0977] Feedback and learning plan adjustments

[0978] Progress and emotional data is analyzed on a server and used to adjust new learning plans, creating a feedback loop that continually provides the best possible learning plan for each preschooler based on their learning progress.

[0979] example:

[0980] The server analyzes the information that "the child has cleared math level 1" and emotional data that "the child felt tired halfway through," and suggests the next level of math games while also adjusting the timing to encourage breaks.

[0981] The above is a specific embodiment of the present invention, and this system enables effective learning that meets the individual needs of preschool children. Furthermore, real-time adjustment of learning plans based on emotional data is expected to improve preschool children's learning effectiveness and motivation.

[0982] The processing flow will be explained below.

[0983] Step 1:

[0984] The user launches the learning application using a device. After launching the application, an interface for entering information about the preschool child is displayed. The user enters information such as age, strengths and weaknesses, current learning progress, and goals.

[0985] Step 2:

[0986] The device sends the entered information to the server, where the data is packaged in an appropriate format, such as JSON.

[0987] Step 3:

[0988] The server passes the data received from the device to the analysis means, which analyzes the data based on the input information and creates an optimal learning plan, referencing algorithms and past databases.

[0989] Step 4:

[0990] The server activates the generating means based on the results of the analyzing means, and the generating means generates learning content based on the analysis results, such as an interactive game, a workbook, or a dictation drill, in accordance with the user's needs.

[0991] Step 5:

[0992] The emotion engine collects real-time emotional data from users or preschoolers while they are learning, recognizing their emotional state from facial expressions and voice tones through sensors such as cameras and microphones.

[0993] Step 6:

[0994] The server analyzes the emotional data obtained from the emotion engine and adjusts the generated learning content in real time based on the emotional data. For example, if the user's concentration is declining, the difficulty of the game may be lowered.

[0995] Step 7:

[0996] The learning content that has been adjusted by the generating means is packaged in an appropriate format and transmitted from the server to the terminal, which then displays the received learning content to the user.

[0997] Step 8:

[0998] Users use the provided learning content on their devices to study together with preschool children. Progress data and learning results are recorded in real time and analyzed along with emotional data.

[0999] Step 9:

[1000] After completing the learning process, the device sends progress data and emotional data to the server, which then receives the data and passes it back to the analysis means to create a new learning plan.

[1001] Step 10:

[1002] The server generates the next learning content based on the newly created learning plan, allowing preschoolers to continue learning according to their individual needs.

[1003] The above are the specific processing steps of the present invention. This system is expected to improve learning effectiveness by providing an effective learning plan that is adapted to the learning needs and emotional state of preschool children.

[1004] Example 2

[1005] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1006] In today's educational environment, there is a demand for learning plans that meet the individual needs of preschool children. However, conventional learning systems face the challenge of identifying each preschool child's learning progress, strengths and weaknesses, and even their emotional state during learning in real time, and providing optimal learning content. As a result, preschool children can lose interest in learning or not fully realize the benefits of learning.

[1007] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, an emotion recognition means for recognizing emotional data of the preschool children in real time while they are studying, an adjustment means for adjusting the learning content based on the emotional data recognized by the emotion recognition means, and a feedback means for collecting learning progress data and emotional data of the preschool children and providing the data to the analysis means. This allows optimal learning content to be provided in real time according to the preschool children's learning progress and emotional state, thereby improving learning effectiveness and motivation.

[1008] An "input means" is a device or software that provides an interface for a user to input information about the learning needs of a preschool child.

[1009] "Analysis means" refers to a device or software that includes an algorithm or program for analyzing input learning needs data and formulating an optimal learning plan.

[1010] The "generation means" refers to a device or software that generates specific study content based on the analysis results of the analysis means.

[1011] The "transmission means" refers to a device or software for transmitting the study content generated by the generation means to the user terminal.

[1012] The "emotion recognition means" refers to sensors such as cameras and microphones that recognize the emotional data of preschool children in real time while they are learning, as well as software that analyzes this input.

[1013] The "adjustment means" is a device or software for adjusting the learning content in real time based on the emotion data recognized by the emotion recognition means.

[1014] A "feedback means" is a device or software that collects learning progress data and emotional data of a preschool child and provides that data to an analysis means.

[1015] "Learning content" refers to educational materials such as interactive games, workbooks, or dictation drills that are generated based on the results of the analytical means.

[1016] A "user device" is an electronic device such as a computer, tablet, or smartphone that displays learning content and allows users to operate it.

[1017] The present invention is a system that responds to the learning needs of preschool children and provides optimized learning content in real time. This system is composed of users, terminals, and a server.

[1018] First, the user launches the learning application using a device (computer, tablet, smartphone, etc.). The application displays an interface for entering information about the preschool child. The information entered includes age, strengths and weaknesses, current learning progress, and goals. For example, the user might enter specific information such as "6 years old," "good at math," "poor reading and writing skills," and "goal is to learn hiragana."

[1019] The device then sends the input information to a server, where it is analyzed and an optimal learning plan is created. The server contains a generative AI model and an analytical means for analyzing data based on past learning data, thereby generating optimal learning content for preschoolers.

[1020] The server generates specific learning content using a generation means based on the analysis results. The generated learning content may include interactive games, workbooks, or dictation drills. For example, a "hiragana dictation drill" or an "arithmetic game" may be generated.

[1021] The generated learning content is transmitted to the terminal using a transmission means. The terminal displays the received learning content in an application so that the user can use it with their preschool child. For example, the transmitted arithmetic game and hiragana drill are displayed in the application, and the user can use them to start learning with their child.

[1022] While the device is learning, it uses a camera and microphone to capture emotional data from the preschool child in real time. The emotion recognition means recognizes emotional data from the preschool child's facial expressions and voice, and determines their emotional state, such as "concentrated," "happy," or "tired." For example, while the child is learning, the camera captures their facial expressions, and the microphone analyzes their voice tone to collect emotional data.

[1023] The collected emotional data is sent from the device to a server, which analyzes it and adjusts the learning content in real time as needed. For example, if the user's concentration is declining, the difficulty of the game will be lowered.

[1024] Finally, the learning progress data and emotional data are analyzed again by the server through the feedback means and used to create the next learning plan, thereby continuously providing the optimal learning plan according to the learning progress and emotional state of the preschool child.

[1025] For example, the prompt text might look like this:

[1026] example:

[1027] "6-year-old child" "Good at math" "Difficult to read and write" "Goal is to practice hiragana"

[1028] This system enables effective learning that meets the individual needs of preschool children, and by adjusting learning plans in real time based on emotional data, it is expected to improve preschool children's learning effectiveness and motivation.

[1029] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1030] Step 1:

[1031] The user launches the learning application using their device. The application displays an interface for entering information about their preschool child. The user enters information such as the child's age, strengths and weaknesses, current learning progress, and goals. For example, the user might enter information such as "6 years old," "good at math," "poor reading and writing skills," and "goal is to learn hiragana." The input data is structured in XML or JSON format and passed to the next step.

[1032] Step 2:

[1033] The device sends the information entered by the user to the server. A secure communication protocol such as HTTPS is used for transmission. By sending the input data to the server, the data becomes available for the next analysis process. For example, data such as "Age: 6," "Strengths: Arithmetic," "Weaknesses: Reading and writing," and "Goal: Mastering Hiragana" are sent in JSON format.

[1034] Step 3:

[1035] The server receives the input data and passes it to a dedicated analysis means. The analysis means analyzes the data by referring to the generative AI model and past learning database. As a result of the analysis, an optimal learning plan for preschool children is generated. For example, the server may create a learning plan that combines "hiragana writing drills" and "arithmetic games." The analyzed data is passed to the next content generation step.

[1036] Step 4:

[1037] The server activates a generation means based on the analysis results to generate specific learning content. The generated learning content can include interactive games, workbooks, or dictation drills. For example, the generation means creates an "interactive math game to reinforce number concepts" or a "hiragana dictation practice drill." The generated learning content is structured in JSON or XML format and passed to the next sending step.

[1038] Step 5:

[1039] The server transmits the generated learning content to the device using a secure communication protocol such as HTTPS. For example, the generated interactive math game or dictation exercise is transmitted and made available on the device. The transmitted data is used in the next display step.

[1040] Step 6:

[1041] The device displays the received learning content within the learning application. The user then uses the device to utilize the displayed learning content together with their preschool child. For example, the transmitted math game and hiragana drill are displayed within the app, and the user can use them to begin learning together with their child. The displayed data is then used directly to implement the learning.

[1042] Step 7:

[1043] During learning, the device uses a camera and microphone to acquire emotional data from the preschooler in real time. The emotion recognition means recognizes emotional data from the preschooler's facial expressions and voice, and determines their emotional state, such as "concentrated," "happy," or "tired." For example, the camera captures facial expressions, and the microphone analyzes the tone of the voice. The acquired emotional data is passed to the next transmission step.

[1044] Step 8:

[1045] The device sends the collected emotional data and learning progress data to the server. The emotional data and learning progress data are structured in JSON or XML format and sent via a secure communication protocol. For example, information such as "concentrating" or "feeling tired during the learning process" is sent. The sent data is used in the next analysis step.

[1046] Step 9:

[1047] The server analyzes the received emotional data and learning progress data and generates feedback as needed. Based on the analysis results, the server creates a new learning plan. For example, it might suggest the next level of math games and adjust the timing of breaks. The analysis results are returned as the following information:

[1048] Step 10:

[1049] The device displays new learning plans and feedback information within the learning application. New learning plans are displayed, allowing users to continue learning with their preschoolers. This improves learning effectiveness and motivation for preschoolers. For example, "next level math games" and "break suggestions" are displayed within the app.

[1050] As described above, the entire system works together to provide an optimized learning experience for preschoolers.

[1051] (Application example 2)

[1052] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1053] To ensure that preschoolers learn efficiently and enjoyably, it is necessary to provide content that is adapted to each child's learning needs and emotional state. However, existing learning systems currently lack the ability to recognize emotional data in real time and adjust content accordingly, making it difficult to maintain children's motivation to learn. Furthermore, there are limited means to help preschoolers learn while having fun, which often results in lower learning outcomes than expected.

[1054] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1055] In this invention, the server includes input means for inputting the learning needs of preschool children, analysis means for performing data analysis based on the input learning needs, generation means for generating learning content based on the analysis results of the analysis means, transmission means for transmitting the learning content generated by the generation means to a user terminal, emotion recognition means for recognizing and analyzing emotional data of the preschool children while they are using the learning content, and adjustment means for adjusting the learning content in real time based on the emotional data obtained by the emotion recognition means. This makes it possible to provide an optimal learning plan in real time that is tailored to each child's learning situation and emotions.

[1056] "Preschooler" is a term used to refer to children who have not yet enrolled in school.

[1057] "Learning needs" refers to information such as the knowledge and skills that preschoolers need to progress in their studies, their learning goals, and their strengths and weaknesses.

[1058] "Input means" refers to an interface or device that allows a user to input information about a preschool child into the system.

[1059] "Analysis means" refers to a system or program that analyzes the input information about preschool children and derives the optimal learning plan.

[1060] "Generation means" refers to a system or program that generates appropriate learning content based on the results of the analysis means.

[1061] "Transmission means" refers to a system or program for transmitting the generated study content to a user terminal.

[1062] "Emotion recognition means" refers to a system or program that recognizes and analyzes emotional data in real time from facial expressions and voices of preschool children while they are using learning content.

[1063] "Adjustment means" refers to a system or program for adjusting learning content in real time based on emotional data from the emotion recognition means.

[1064] "Feedback means" refers to a system or program that has the function of collecting learning progress data and emotional data of preschool children and providing it to an analysis means.

[1065] "User terminal" refers to the device or equipment used by a user to receive and use learning content.

[1066] This invention relates to a system that automatically generates learning content tailored to the learning needs of preschool children and optimizes learning plans based on emotional data of the user and the preschool children. The system's components include users (parents and educators), terminals (computers, tablets, smartphones, etc.), and a server. A specific embodiment of this system is described below.

[1067] System Configuration

[1068] The system allows users to interact with the system using their devices, while the server analyzes data and generates learning content. It also includes emotion recognition capabilities, allowing it to adjust learning plans in real time based on emotional data.

[1069] Enter user information

[1070] First, the user launches the learning application on their device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, strengths and weaknesses, current learning progress, and goals.

[1071] As a specific example, the user inputs information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[1072] Data analysis

[1073] The device sends the input information to a server, which then passes the received information to an analysis tool to create the optimal learning plan for the preschool child, referencing algorithms and past databases.

[1074] For example, the server may determine that "hiragana study is necessary" and, based on the analysis results, generate a study plan that combines a balanced combination of "hiragana writing drills" and "arithmetic games."

[1075] Emotional Data Recognition and Analysis

[1076] To obtain emotional data from preschoolers while they are learning, the system incorporates an emotion engine that recognizes emotional data from their facial expressions and voices in real time through sensors such as cameras and microphones.

[1077] For example, while a preschooler is studying, a camera captures their facial expressions and a microphone analyzes their voice tone to recognize their emotional state, such as "concentrated," "happy," or "tired."

[1078] Learning content generation and adaptation

[1079] Based on the analysis results, the server activates the generation means to generate specific learning content, which is appropriately selected based on the user's input, such as interactive games, workbooks, or dictation drills. Furthermore, the learning content is adjusted in real time based on the emotional data analyzed by the emotion engine.

[1080] As a specific example, the generation means creates "an interactive math game to reinforce the concept of numbers" and "a hiragana writing practice drill," and based on the analysis results of the emotion engine, adjustments are made, such as lowering the difficulty of the game if concentration is declining.

[1081] Sending content to users

[1082] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[1083] For example, math games and hiragana drills are displayed in the app, allowing users to start learning together with their children. Emotional adjustments are also made in real time.

[1084] Learning implementation and feedback

[1085] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data and emotion data obtained during this process are sent from the device to the server again and provided to the analysis means as feedback.

[1086] For example, users can play number matching puzzles with their children and check their progress by dictating hiragana characters, while emotional data is also sent along the way.

[1087] Feedback and learning plan adjustments

[1088] Progress and emotional data is analyzed on a server and used to adjust new learning plans, creating a feedback loop that continually provides the best possible learning plan for each preschooler based on their learning progress.

[1089] For example, the server can analyze information such as "a child has completed math level 1" and emotional data such as "the child felt tired midway through," and then suggest the next level of math game while also adjusting the timing to encourage breaks.

[1090] Example prompts include:

[1091] "My 6-year-old child is good at math, but has trouble reading and writing. His goal is to practice hiragana."

[1092] The above is a specific embodiment for carrying out the invention. This system enables effective learning that meets the individual needs of preschool children, and is expected to improve the learning effectiveness and motivation of preschool children by adjusting learning plans in real time based on emotional data.

[1093] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1094] Step 1:

[1095] The user launches the learning application using a terminal. An interface for entering information about the preschool child is displayed, and the user enters information such as age, strengths and weaknesses, current learning progress, and goals. This input information becomes the initial data for the system.

[1096] Input: Information about your preschool child (e.g., age, strengths, weaknesses, learning progress, goals)

[1097] Output: Initial data is generated

[1098] Step 2:

[1099] The device sends the information entered by the user to the server, which then passes the received information to an analysis tool, which performs data analysis to derive the optimal learning plan. Past databases and algorithms are used.

[1100] Input: User-entered information about preschool children

[1101] Output: The best lesson plan for preschoolers

[1102] Step 3:

[1103] The server activates a generating means to generate optimal learning content based on the analysis results. The generating means uses the results of the analyzing means to generate educational content such as interactive games, workbooks, and dictation drills.

[1104] Input: Learning plan with analytical tools

[1105] Output: Learning content

[1106] Step 4:

[1107] The server transmits the generated learning content to the user's terminal, and the user can access the provided learning content through the terminal.

[1108] Input: Generated learning content

[1109] Output: Learning content displayed on the device

[1110] Step 5:

[1111] While using the learning content, the emotion recognition means recognizes the facial expressions and voice of preschool children in real time through a camera and microphone, generating emotion data that can be used to acquire emotion data such as concentration, enjoyment, and fatigue.

[1112] Input: Preschooler's facial expressions and voice

[1113] Output: Emotion data

[1114] Step 6:

[1115] Based on the emotion data acquired by the emotion recognition means, the server uses the adjustment means to adjust the learning content in real time, such as changing the difficulty level of the learning content or suggesting breaks.

[1116] Input: Emotion data

[1117] Output: Tailored learning content

[1118] Step 7:

[1119] The user uses the tailored learning content to study together with the preschool child, and the learning progress data and emotion data obtained during this process are sent back to the server from the device.

[1120] Input: Learning progress data and emotion data

[1121] Output: Progress and emotion data sent

[1122] Step 8:

[1123] The server analyzes the received learning progress data and emotion data and reflects them in a new learning plan. Based on this feedback, the next learning plan for the preschooler is optimized and provided to the user.

[1124] Input: Learning progress data and emotion data

[1125] Output: Updated learning plan

[1126] These are the specific processing steps of the system, which provide the optimal learning environment for preschool children and enable real-time adjustments.

[1127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1129] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1130] [Fourth embodiment]

[1131] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1132] 7, a 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.

[1133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1135] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1138] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1140] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1142] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1143] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1144] The present invention relates to a system for automatically generating learning content that meets the learning needs of preschool children, and a specific embodiment thereof will be described.

[1145] System Configuration

[1146] The system is primarily comprised of three elements: users (parents and educators), devices (computers, tablets, smartphones, etc.), and a server. Users interact with the system using their devices, while the server is responsible for data analysis and generation of learning content.

[1147] Enter user information

[1148] First, the user accesses the learning application using a device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, current learning progress, strengths and weaknesses, and goals.

[1149] example:

[1150] The user enters information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[1151] Data analysis

[1152] The device sends the input information to a server. The server receives the received information and analyzes the data using an analysis means. The analysis means refers to specific algorithms and past databases to create an optimal learning plan for the preschool child.

[1153] example:

[1154] The server determines that "hiragana study is necessary" and, based on the analysis results, generates a study plan that is a balanced combination of "hiragana writing drills" and "math games."

[1155] Generating learning content

[1156] Based on the analysis results, the server activates a generation means to generate specific learning content, which may be an interactive game, a workbook, or a dictation drill, appropriately selected based on the user's input.

[1157] example:

[1158] The generator creates "an interactive math game to reinforce number concepts" and "hiragana writing practice drills."

[1159] Sending content to users

[1160] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[1161] example:

[1162] The submitted math games and hiragana drills will then appear within the app, allowing users to start using them to learn alongside their children.

[1163] Learning implementation

[1164] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data obtained during this process is sent from the device back to the server and provided to the analysis means as feedback.

[1165] example:

[1166] Users can play number matching puzzles with their children and dictate hiragana to check their progress.

[1167] feedback

[1168] Progress data after each lesson is analyzed on the server and used to adjust new lesson plans via analytical means. This feedback loop allows the system to continually provide optimal lesson plans tailored to each preschool child's learning progress.

[1169] example:

[1170] Based on the information that "the child has cleared math level 1," the server suggests the next level of math game.

[1171] The above is a specific embodiment of the present invention, and this system makes it possible to realize effective learning that meets the individual needs of preschool children.

[1172] The processing flow will be explained below.

[1173] Step 1:

[1174] The user launches the learning application using a device. After launching the application, an interface for entering information about the preschool child is displayed. The user enters information such as age, strengths and weaknesses, current learning progress, and goals.

[1175] Step 2:

[1176] The device sends the entered information to the server, where the data is packaged in an appropriate format, such as JSON.

[1177] Step 3:

[1178] The server passes the data received from the device to the analysis means, which analyzes the data based on the received information and creates the optimal learning plan for the preschool child, referencing algorithms and past databases.

[1179] Step 4:

[1180] Upon receiving the analysis results from the analysis means, the server activates the generation means, which generates appropriate learning content (such as an interactive game, a workbook, or a dictation drill) based on the analysis results.

[1181] Step 5:

[1182] The generated learning content is packaged in an appropriate format and sent from the server to the terminal, and the sent data is provided in a format that can be easily used by the user.

[1183] Step 6:

[1184] The terminal displays the received learning content to the user, who then studies together with the preschool child using the provided learning content.

[1185] Step 7:

[1186] After completing the learning, progress data and results are sent from the device to the server, and the learning application automatically records the learning progress.

[1187] Step 8:

[1188] The server receives the progress data and passes it to the feedback means, which then requests a new analysis from the analysis means based on the data and adjusts the next learning plan.

[1189] Example 1

[1190] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1191] Preschoolers have individual learning needs, making it difficult for them to learn effectively with a uniform learning plan. Furthermore, the lack of feedback based on their learning progress makes it difficult to continuously improve their learning. This makes it difficult to provide an optimal learning experience for preschoolers.

[1192] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1193] In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, and a feedback means for providing learning progress data collected from the user terminal back to the analysis means. This makes it possible to generate optimal learning content tailored to the individual learning needs of preschool children, reflect feedback based on the progress data, and continuously optimize learning.

[1194] "Preschool children" refers to young children or infants before the start of compulsory education.

[1195] "Learning needs" refers to the skills and knowledge that learners require when learning, and the requirements for achieving these.

[1196] "Input means" refers to a device or interface that allows a user to input required information into the system.

[1197] "Analysis means" refers to a device or function that analyzes input data using a specific algorithm or model and derives the results.

[1198] "Generation means" refers to a device or function that creates new learning content or plans based on the results of the analysis means.

[1199] "Transmission means" refers to a device or function for sending the generated learning content to a user terminal.

[1200] "Feedback means" refers to a device or function for collecting learning progress data and providing it back to the analysis means to improve the analysis results.

[1201] "User device" refers to an electronic device used by a user, such as a computer, tablet, or smartphone.

[1202] "Learning Content" refers to the educational materials and activities (e.g., interactive games, workbooks, dictation drills) provided to achieve learning objectives.

[1203] "Learning Progress Data" means data that shows the progress or achievements of a learner as they engage in a learning activity.

[1204] The present invention is a system for automatically generating learning content tailored to the learning needs of preschool children. This system is composed of three elements: users (parents and educators), terminals (computers, tablets, smartphones, etc.), and a server. Specific embodiments of this system are described below.

[1205] System Configuration

[1206] Users interact with the system through a terminal, specifically through a learning application, by entering information about their preschool child, including their age, current learning progress, strengths and weaknesses, and learning goals.

[1207] Enter user information

[1208] The user uses the device to access the learning application and input information about the preschool child. For example, the user might input "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[1209] Data transmission and analysis

[1210] The device sends the input information to the server. The hardware used in this process can be a smartphone, tablet, or PC. The sent data is received by the server. The server uses analytical means to analyze the received information. This analysis uses specific algorithms and past databases. The server refers to past learning data and AI models to create the optimal learning plan for the preschool child.

[1211] Generating learning content

[1212] Based on the analysis results, the server activates a generation tool to generate specific learning content. This generated content ranges from interactive games, workbooks, and dictation drills. The generation tool uses machine learning algorithms written in Python and libraries such as Scikit-learn and TensorFlow.

[1213] Submit content and learn

[1214] The generated learning content is sent to the device and provided in a format that is easily accessible to the user. It is displayed within the learning application, allowing the user to begin learning together with the preschool child. Once the sent learning content is displayed on the device, the user can proceed with the learning together with the preschool child.

[1215] Collection of learning progress data and feedback

[1216] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data obtained during this process is sent from the device back to the server. The server uses this progress data through analytical means to adjust a new learning plan. This feedback loop allows the system to continuously provide the optimal learning plan according to the preschool child's learning progress.

[1217] Prompt Sentence Examples

[1218] For example, the following prompt might be possible:

[1219] "Please suggest some math games and hiragana practice drills suitable for a 5-year-old."

[1220] This system can provide effective learning plans tailored to the individual learning needs of preschool children, improving their learning efficiency and interest, and enabling them to continue learning.

[1221] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1222] Step 1: Enter your user information

[1223] The user launches a learning application on the device and enters information about the preschool child. For example, the user might enter "6-year-old child," "good at math," "poor reading and writing skills," and "goal is to practice hiragana." The entered data is temporarily saved on the device.

[1224] Input: Preschooler's age, learning progress, strengths, weaknesses, learning goals

[1225] Output: Preschool child information data (format: JSON)

[1226] Step 2: Sending data

[1227] The terminal transmits the information data of the preschool child entered by the user to the server. This data is encrypted before transmission.

[1228] Input: Preschool child information data (format: JSON)

[1229] Output: Data sent to the server

[1230] Step 3: Data analysis

[1231] The server analyzes the received information data of the preschool child using analytical means. In this process, specific algorithms and past databases are used. The server refers to the database and creates an optimal learning plan.

[1232] Input: Preschooler information data, past learning data, AI model

[1233] Output: Learning plan (format: JSON)

[1234] Step 4: Generate learning content

[1235] The server uses libraries such as Scikit-learn and TensorFlow to generate specific learning content based on the analysis results. The generated content ranges from interactive games, workbooks, and dictation drills.

[1236] Input: Learning plan (format: JSON)

[1237] Output: Learning content (format: JSON)

[1238] Step 5: Submit your learning content

[1239] The server transmits the generated learning content to the terminal, where it is provided to the user in an easily accessible format.

[1240] Input: Learning content (format: JSON)

[1241] Output: Learning content sent to the user's device

[1242] Step 6: Learning

[1243] Users use the device to access the provided learning content together with their preschool children, and progress is tracked through exercises and problem-solving. Progress data is also automatically recorded.

[1244] Input: Learning content (format: JSON)

[1245] Output: Learning progress data

[1246] Step 7: Sending progress data

[1247] The device automatically sends learning progress data to the server, which is encrypted and transmitted securely.

[1248] Input: Learning progress data (format: JSON)

[1249] Output: Progress data sent to the server

[1250] Step 8: Feedback analysis

[1251] The server analyzes the received learning progress data and uses it as a basis for generating a new learning plan. The analysis means again uses algorithms and databases to formulate the next optimal plan.

[1252] Input: Learning progress data, past learning data, AI model

[1253] Output: New learning plan (format: JSON)

[1254] These are the processing steps of the system, each of which provides a learning experience that is optimally tailored to the individual needs of preschoolers.

[1255] (Application example 1)

[1256] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1257] Traditional systems that provide learning content tailored to the learning needs of preschoolers are often ineffective. In particular, the learning content can be static or difficult to keep preschoolers interested. Therefore, it is necessary to provide methods for preschoolers to be more actively involved in learning. It is also important for parents and educators to easily track learning progress and receive feedback. There is a need to solve this problem.

[1258] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1259] In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, and a means for allowing preschool children and their parents to experience the educational content in a virtual environment. This not only allows preschool children to continue learning while maintaining their interest, but also makes it easy to provide feedback on their learning progress.

[1260] "Input means" refers to devices and software that input the learning needs of preschool children.

[1261] "Analysis means" refers to devices and software for analyzing data based on input learning needs.

[1262] "Generation means" refers to the device or software that generates learning content based on the analysis results of the analysis means.

[1263] "Transmission means" refers to the device or software used to transmit the study content generated by the generation means to the user terminal.

[1264] "Feedback tools" refer to devices and software that collect and provide preschool children's learning progress data to analytical tools.

[1265] "Virtual environment" refers to a virtual space or interface that allows preschool children and their parents to experience educational content.

[1266] System Configuration

[1267] The system that embodies this invention mainly consists of three elements: a user (parent or educator), a device (such as a smartphone or head-mounted display), and a server. The user interacts with the system using the device, and the server is responsible for data analysis and generation of learning content.

[1268] Enter user information

[1269] The user accesses the application using a device and enters information about the preschool child, specifically, the following information:

[1270] age

[1271] Current learning progress

[1272] Strengths and weaknesses

[1273] the goal

[1274] Data analysis

[1275] The device sends the input information to a server. The server analyzes the received information using analytical means (for example, data analysis tools such as TensorFlow or PyTorch). The analytical means refers to a past database and creates an optimal learning plan for the preschool child.

[1276] Generating learning content

[1277] Based on the analysis results, the server activates a generating means to generate specific learning content, which may be an interactive game, a workbook, or a dictation drill, appropriately selected based on the user's input.

[1278] Sending content to users

[1279] The generated learning content is sent to devices via a transmission means, allowing preschoolers and their parents to experience learning in a virtual environment, which is built using front-end development tools such as Unity and Flutter.

[1280] Learning implementation

[1281] Users can use the provided learning content on their devices to study together with their preschool children. For example, they can use smartphones and head-mounted displays to experience interactive learning in a virtual store.

[1282] feedback

[1283] Progress data after the lesson is sent from the device to the server and provided to the analysis means via the feedback means. This feedback loop allows the system to continuously provide optimal lesson plans according to the learning progress of preschool children.

[1284] Specific examples

[1285] For example, if a 6-year-old preschooler is good at math but has difficulty learning hiragana, parents can enter this information into the app. The system analyzes the input data and generates "hiragana writing practice" and "math puzzle games." This content is provided via smartphones or head-mounted displays.

[1286] Prompt Sentence Examples

[1287] Enter the following prompts into the generative AI model:

[1288] Prompt: "6-year-old child, good at math, needs to learn hiragana. Generate the best learning content."

[1289] Based on these prompts, the AI ​​model performs data analysis to generate learning content appropriate for the child.

[1290] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1291] Step 1:

[1292] The user accesses the application using a smartphone or head-mounted display. The user enters information such as the preschool child's age, current learning progress, strengths and weaknesses, and goals into the application's input form. This input data becomes the basic information for the system.

[1293] Input: Age, learning progress, strengths, weaknesses, goals

[1294] Output: Input learning needs data

[1295] Step 2:

[1296] The device sends the input learning needs data to the server. The server passes the received data to an analysis means for data analysis. Here, specific algorithms and past databases are referenced to create an optimal learning plan for the preschool child.

[1297] Input: Learning needs data

[1298] Output: Analysis results (e.g. Hiragana learning required)

[1299] Step 3:

[1300] The server generates specific learning content using a generating means based on the analysis results of the analyzing means. The generating means uses a generative AI model to create appropriate learning content (interactive games, workbooks, dictation drills, etc.) in response to user input.

[1301] Input: Analysis results

[1302] Output: Generated learning content (e.g., hiragana writing practice, math puzzle game)

[1303] Step 4:

[1304] The server transmits the generated learning content to the user's terminal via a transmission means, allowing the user to use the learning content together with the preschool child. This content is provided in a virtual environment, making learning interactive.

[1305] Input: Generated learning content

[1306] Output: Learning content sent to the user's device

[1307] Step 5:

[1308] Users use the device to carry out the learning content provided, and preschoolers use smartphones and head-mounted displays to engage in an interactive learning experience within a virtual store. As their learning progresses, progress data is recorded on the device.

[1309] Input: Submitted learning content

[1310] Output: Learning progress data

[1311] Step 6:

[1312] The learning progress data collected from the device is sent back to the server, which receives the progress data and re-analyzes it using feedback tools. Based on the results of this data analysis, a new learning plan or adjustments are made.

[1313] Input: Learning progress data

[1314] Output: New study plan suggestions or adjustments

[1315] Prompt Sentence Examples

[1316] Enter the following prompts into the generative AI model:

[1317] Prompt: "6-year-old child, good at math, needs to learn hiragana. Generate the best learning content."

[1318] This allows the AI ​​model to analyze the data to generate learning content appropriate for children.

[1319] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1320] The present invention relates to a system that automatically generates learning content according to the learning needs of preschool children and further optimizes learning plans based on emotional data of users and preschool children, and a specific embodiment thereof will be described.

[1321] System Configuration

[1322] This system is primarily composed of three elements: users (parents and educators), devices (computers, tablets, smartphones, etc.), and a server. Users interact with the system using their devices, the server analyzes data and generates learning content, and the emotion engine is responsible for recognizing and analyzing emotion data.

[1323] Enter user information

[1324] First, the user launches the learning application on their device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, strengths and weaknesses, current learning progress, and goals.

[1325] example:

[1326] The user enters information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[1327] Data analysis

[1328] The device sends the input information to the server. The server passes the received information to the analysis means. The analysis means performs data analysis based on the received information and creates the optimal learning plan for the preschool child. Algorithms and past databases are referenced.

[1329] example:

[1330] The server determines that "hiragana study is necessary" and, based on the analysis results, generates a study plan that is a balanced combination of "hiragana writing drills" and "math games."

[1331] Emotional Data Recognition and Analysis

[1332] The system incorporates an emotion engine to collect emotional data from preschool children while they are learning. The emotion engine recognizes emotional data from their facial expressions and voices in real time through sensors such as cameras and microphones.

[1333] example:

[1334] While preschoolers are learning, a camera captures their facial expressions and a microphone analyzes their vocal tones to recognize their emotional states, such as "concentrated," "happy," or "tired."

[1335] Learning content generation and adaptation

[1336] Based on the analysis results, the server activates the generation means to generate specific learning content, which can be selected appropriately based on the user's input, such as interactive games, workbooks, or dictation drills. Furthermore, the generated learning content can be adjusted in real time based on the emotional data analyzed by the emotion engine.

[1337] example:

[1338] The generator creates "interactive math games to reinforce number concepts" and "hiragana writing practice drills," and based on the analysis results of the emotion engine, adjustments are made, such as lowering the difficulty of the game if concentration is declining.

[1339] Sending content to users

[1340] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[1341] example:

[1342] The math games and hiragana drills sent to the app are displayed within the app, allowing users to start learning together with their children. The app also adjusts to their child's emotions in real time.

[1343] Learning implementation and feedback

[1344] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data and emotion data obtained during this process are sent from the device to the server again and provided to the analysis means as feedback.

[1345] example:

[1346] Users can play number matching puzzles and dictate hiragana with their children to track their progress, while also sending emotional data along the way.

[1347] Feedback and learning plan adjustments

[1348] Progress and emotional data is analyzed on a server and used to adjust new learning plans, creating a feedback loop that continually provides the best possible learning plan for each preschooler based on their learning progress.

[1349] example:

[1350] The server analyzes the information that "the child has cleared math level 1" and emotional data that "the child felt tired halfway through," and suggests the next level of math games while also adjusting the timing to encourage breaks.

[1351] The above is a specific embodiment of the present invention, and this system enables effective learning that meets the individual needs of preschool children. Furthermore, real-time adjustment of learning plans based on emotional data is expected to improve preschool children's learning effectiveness and motivation.

[1352] The processing flow will be explained below.

[1353] Step 1:

[1354] The user launches the learning application using a device. After launching the application, an interface for entering information about the preschool child is displayed. The user enters information such as age, strengths and weaknesses, current learning progress, and goals.

[1355] Step 2:

[1356] The device sends the entered information to the server, where the data is packaged in an appropriate format, such as JSON.

[1357] Step 3:

[1358] The server passes the data received from the device to the analysis means, which analyzes the data based on the input information and creates an optimal learning plan, referencing algorithms and past databases.

[1359] Step 4:

[1360] The server activates the generating means based on the results of the analyzing means, and the generating means generates learning content based on the analysis results, such as an interactive game, a workbook, or a dictation drill, in accordance with the user's needs.

[1361] Step 5:

[1362] The emotion engine collects real-time emotional data from users or preschoolers while they are learning, recognizing their emotional state from facial expressions and voice tones through sensors such as cameras and microphones.

[1363] Step 6:

[1364] The server analyzes the emotional data obtained from the emotion engine and adjusts the generated learning content in real time based on the emotional data. For example, if the user's concentration is declining, the difficulty of the game may be lowered.

[1365] Step 7:

[1366] The learning content that has been adjusted by the generating means is packaged in an appropriate format and transmitted from the server to the terminal, which then displays the received learning content to the user.

[1367] Step 8:

[1368] Users use the provided learning content on their devices to study together with preschool children. Progress data and learning results are recorded in real time and analyzed along with emotional data.

[1369] Step 9:

[1370] After completing the learning process, the device sends progress data and emotional data to the server, which then receives the data and passes it back to the analysis means to create a new learning plan.

[1371] Step 10:

[1372] The server generates the next learning content based on the newly created learning plan, allowing preschoolers to continue learning according to their individual needs.

[1373] The above are the specific processing steps of the present invention. This system is expected to improve learning effectiveness by providing an effective learning plan that is adapted to the learning needs and emotional state of preschool children.

[1374] Example 2

[1375] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1376] In today's educational environment, there is a demand for learning plans that meet the individual needs of preschool children. However, conventional learning systems face the challenge of identifying each preschool child's learning progress, strengths and weaknesses, and even their emotional state during learning in real time, and providing optimal learning content. As a result, preschool children can lose interest in learning or not fully realize the benefits of learning.

[1377] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means for inputting the learning needs of preschool children, an analysis means for performing data analysis based on the input learning needs, a generation means for generating learning content based on the analysis results of the analysis means, a transmission means for transmitting the learning content generated by the generation means to a user terminal, an emotion recognition means for recognizing emotional data of the preschool children in real time while they are studying, an adjustment means for adjusting the learning content based on the emotional data recognized by the emotion recognition means, and a feedback means for collecting learning progress data and emotional data of the preschool children and providing the data to the analysis means. This allows optimal learning content to be provided in real time according to the preschool children's learning progress and emotional state, thereby improving learning effectiveness and motivation.

[1378] An "input means" is a device or software that provides an interface for a user to input information about the learning needs of a preschool child.

[1379] "Analysis means" refers to a device or software that includes an algorithm or program for analyzing input learning needs data and formulating an optimal learning plan.

[1380] The "generation means" refers to a device or software that generates specific study content based on the analysis results of the analysis means.

[1381] The "transmission means" refers to a device or software for transmitting the study content generated by the generation means to the user terminal.

[1382] The "emotion recognition means" refers to sensors such as cameras and microphones that recognize the emotional data of preschool children in real time while they are learning, as well as software that analyzes this input.

[1383] The "adjustment means" is a device or software for adjusting the learning content in real time based on the emotion data recognized by the emotion recognition means.

[1384] A "feedback means" is a device or software that collects learning progress data and emotional data of a preschool child and provides that data to an analysis means.

[1385] "Learning content" refers to educational materials such as interactive games, workbooks, or dictation drills that are generated based on the results of the analytical means.

[1386] A "user device" is an electronic device such as a computer, tablet, or smartphone that displays learning content and allows users to operate it.

[1387] The present invention is a system that responds to the learning needs of preschool children and provides optimized learning content in real time. This system is composed of users, terminals, and a server.

[1388] First, the user launches the learning application using a device (computer, tablet, smartphone, etc.). The application displays an interface for entering information about the preschool child. The information entered includes age, strengths and weaknesses, current learning progress, and goals. For example, the user might enter specific information such as "6 years old," "good at math," "poor reading and writing skills," and "goal is to learn hiragana."

[1389] The device then sends the input information to a server, where it is analyzed and an optimal learning plan is created. The server contains a generative AI model and an analytical means for analyzing data based on past learning data, thereby generating optimal learning content for preschoolers.

[1390] The server generates specific learning content using a generation means based on the analysis results. The generated learning content may include interactive games, workbooks, or dictation drills. For example, a "hiragana dictation drill" or an "arithmetic game" may be generated.

[1391] The generated learning content is transmitted to the terminal using a transmission means. The terminal displays the received learning content in an application so that the user can use it with their preschool child. For example, the transmitted arithmetic game and hiragana drill are displayed in the application, and the user can use them to start learning with their child.

[1392] While the device is learning, it uses a camera and microphone to capture emotional data from the preschool child in real time. The emotion recognition means recognizes emotional data from the preschool child's facial expressions and voice, and determines their emotional state, such as "concentrated," "happy," or "tired." For example, while the child is learning, the camera captures their facial expressions, and the microphone analyzes their voice tone to collect emotional data.

[1393] The collected emotional data is sent from the device to a server, which analyzes it and adjusts the learning content in real time as needed. For example, if the user's concentration is declining, the difficulty of the game will be lowered.

[1394] Finally, the learning progress data and emotional data are analyzed again by the server through the feedback means and used to create the next learning plan, thereby continuously providing the optimal learning plan according to the learning progress and emotional state of the preschool child.

[1395] For example, the prompt text might look like this:

[1396] example:

[1397] "6-year-old child" "Good at math" "Difficult to read and write" "Goal is to practice hiragana"

[1398] This system enables effective learning that meets the individual needs of preschool children, and by adjusting learning plans in real time based on emotional data, it is expected to improve preschool children's learning effectiveness and motivation.

[1399] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1400] Step 1:

[1401] The user launches the learning application using their device. The application displays an interface for entering information about their preschool child. The user enters information such as the child's age, strengths and weaknesses, current learning progress, and goals. For example, the user might enter information such as "6 years old," "good at math," "poor reading and writing skills," and "goal is to learn hiragana." The input data is structured in XML or JSON format and passed to the next step.

[1402] Step 2:

[1403] The device sends the information entered by the user to the server. A secure communication protocol such as HTTPS is used for transmission. By sending the input data to the server, the data becomes available for the next analysis process. For example, data such as "Age: 6," "Strengths: Arithmetic," "Weaknesses: Reading and writing," and "Goal: Mastering Hiragana" are sent in JSON format.

[1404] Step 3:

[1405] The server receives the input data and passes it to a dedicated analysis means. The analysis means analyzes the data by referring to the generative AI model and past learning database. As a result of the analysis, an optimal learning plan for preschool children is generated. For example, the server may create a learning plan that combines "hiragana writing drills" and "arithmetic games." The analyzed data is passed to the next content generation step.

[1406] Step 4:

[1407] The server activates a generation means based on the analysis results to generate specific learning content. The generated learning content can include interactive games, workbooks, or dictation drills. For example, the generation means creates an "interactive math game to reinforce number concepts" or a "hiragana dictation practice drill." The generated learning content is structured in JSON or XML format and passed to the next sending step.

[1408] Step 5:

[1409] The server transmits the generated learning content to the device using a secure communication protocol such as HTTPS. For example, the generated interactive math game or dictation exercise is transmitted and made available on the device. The transmitted data is used in the next display step.

[1410] Step 6:

[1411] The device displays the received learning content within the learning application. The user then uses the device to utilize the displayed learning content together with their preschool child. For example, the transmitted math game and hiragana drill are displayed within the app, and the user can use them to begin learning together with their child. The displayed data is then used directly to implement the learning.

[1412] Step 7:

[1413] During learning, the device uses a camera and microphone to acquire emotional data from the preschooler in real time. The emotion recognition means recognizes emotional data from the preschooler's facial expressions and voice, and determines their emotional state, such as "concentrated," "happy," or "tired." For example, the camera captures facial expressions, and the microphone analyzes the tone of the voice. The acquired emotional data is passed to the next transmission step.

[1414] Step 8:

[1415] The device sends the collected emotional data and learning progress data to the server. The emotional data and learning progress data are structured in JSON or XML format and sent via a secure communication protocol. For example, information such as "concentrating" or "feeling tired during the learning process" is sent. The sent data is used in the next analysis step.

[1416] Step 9:

[1417] The server analyzes the received emotional data and learning progress data and generates feedback as needed. Based on the analysis results, the server creates a new learning plan. For example, it might suggest the next level of math games and adjust the timing of breaks. The analysis results are returned as the following information:

[1418] Step 10:

[1419] The device displays new learning plans and feedback information within the learning application. New learning plans are displayed, allowing users to continue learning with their preschoolers. This improves learning effectiveness and motivation for preschoolers. For example, "next level math games" and "break suggestions" are displayed within the app.

[1420] As described above, the entire system works together to provide an optimized learning experience for preschoolers.

[1421] (Application example 2)

[1422] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1423] To ensure that preschoolers learn efficiently and enjoyably, it is necessary to provide content that is adapted to each child's learning needs and emotional state. However, existing learning systems currently lack the ability to recognize emotional data in real time and adjust content accordingly, making it difficult to maintain children's motivation to learn. Furthermore, there are limited means to help preschoolers learn while having fun, which often results in lower learning outcomes than expected.

[1424] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1425] In this invention, the server includes input means for inputting the learning needs of preschool children, analysis means for performing data analysis based on the input learning needs, generation means for generating learning content based on the analysis results of the analysis means, transmission means for transmitting the learning content generated by the generation means to a user terminal, emotion recognition means for recognizing and analyzing emotional data of the preschool children while they are using the learning content, and adjustment means for adjusting the learning content in real time based on the emotional data obtained by the emotion recognition means. This makes it possible to provide an optimal learning plan in real time that is tailored to each child's learning situation and emotions.

[1426] "Preschooler" is a term used to refer to children who have not yet enrolled in school.

[1427] "Learning needs" refers to information such as the knowledge and skills that preschoolers need to progress in their studies, their learning goals, and their strengths and weaknesses.

[1428] "Input means" refers to an interface or device that allows a user to input information about a preschool child into the system.

[1429] "Analysis means" refers to a system or program that analyzes the input information about preschool children and derives the optimal learning plan.

[1430] "Generation means" refers to a system or program that generates appropriate learning content based on the results of the analysis means.

[1431] "Transmission means" refers to a system or program for transmitting the generated study content to a user terminal.

[1432] "Emotion recognition means" refers to a system or program that recognizes and analyzes emotional data in real time from facial expressions and voices of preschool children while they are using learning content.

[1433] "Adjustment means" refers to a system or program for adjusting learning content in real time based on emotional data from the emotion recognition means.

[1434] "Feedback means" refers to a system or program that has the function of collecting learning progress data and emotional data of preschool children and providing it to an analysis means.

[1435] "User terminal" refers to the device or equipment used by a user to receive and use learning content.

[1436] This invention relates to a system that automatically generates learning content tailored to the learning needs of preschool children and optimizes learning plans based on emotional data of the user and the preschool children. The system's components include users (parents and educators), terminals (computers, tablets, smartphones, etc.), and a server. A specific embodiment of this system is described below.

[1437] System Configuration

[1438] The system allows users to interact with the system using their devices, while the server analyzes data and generates learning content. It also includes emotion recognition capabilities, allowing it to adjust learning plans in real time based on emotional data.

[1439] Enter user information

[1440] First, the user launches the learning application on their device. After launching the application, an interface is displayed for entering information about the preschool child. The information entered here includes the child's age, strengths and weaknesses, current learning progress, and goals.

[1441] As a specific example, the user inputs information such as "6-year-old child," "good at math," "poor at reading and writing," and "goal is to practice hiragana."

[1442] Data analysis

[1443] The device sends the input information to a server, which then passes the received information to an analysis tool to create the optimal learning plan for the preschool child, referencing algorithms and past databases.

[1444] For example, the server may determine that "hiragana study is necessary" and, based on the analysis results, generate a study plan that combines a balanced combination of "hiragana writing drills" and "arithmetic games."

[1445] Emotional Data Recognition and Analysis

[1446] To obtain emotional data from preschoolers while they are learning, the system incorporates an emotion engine that recognizes emotional data from their facial expressions and voices in real time through sensors such as cameras and microphones.

[1447] For example, while a preschooler is studying, a camera captures their facial expressions and a microphone analyzes their voice tone to recognize their emotional state, such as "concentrated," "happy," or "tired."

[1448] Learning content generation and adaptation

[1449] Based on the analysis results, the server activates the generation means to generate specific learning content, which is appropriately selected based on the user's input, such as interactive games, workbooks, or dictation drills. Furthermore, the learning content is adjusted in real time based on the emotional data analyzed by the emotion engine.

[1450] As a specific example, the generation means creates "an interactive math game to reinforce the concept of numbers" and "a hiragana writing practice drill," and based on the analysis results of the emotion engine, adjustments are made, such as lowering the difficulty of the game if concentration is declining.

[1451] Sending content to users

[1452] The generated learning content is sent to the device and provided in an easily accessible format for the user, where it is displayed within a learning application that the user can use with their preschool child.

[1453] For example, math games and hiragana drills are displayed in the app, allowing users to start learning together with their children. Emotional adjustments are also made in real time.

[1454] Learning implementation and feedback

[1455] The user uses the device to access the provided learning content and study together with the preschool child. The learning progress data and emotion data obtained during this process are sent from the device to the server again and provided to the analysis means as feedback.

[1456] For example, users can play number matching puzzles with their children and check their progress by dictating hiragana characters, while emotional data is also sent along the way.

[1457] Feedback and learning plan adjustments

[1458] Progress and emotional data is analyzed on a server and used to adjust new learning plans, creating a feedback loop that continually provides the best possible learning plan for each preschooler based on their learning progress.

[1459] For example, the server can analyze information such as "a child has completed math level 1" and emotional data such as "the child felt tired midway through," and then suggest the next level of math game while also adjusting the timing to encourage breaks.

[1460] Example prompts include:

[1461] "My 6-year-old child is good at math, but has trouble reading and writing. His goal is to practice hiragana."

[1462] The above is a specific embodiment for carrying out the invention. This system enables effective learning that meets the individual needs of preschool children, and is expected to improve the learning effectiveness and motivation of preschool children by adjusting learning plans in real time based on emotional data.

[1463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1464] Step 1:

[1465] The user launches the learning application using a terminal. An interface for entering information about the preschool child is displayed, and the user enters information such as age, strengths and weaknesses, current learning progress, and goals. This input information becomes the initial data for the system.

[1466] Input: Information about your preschool child (e.g., age, strengths, weaknesses, learning progress, goals)

[1467] Output: Initial data is generated

[1468] Step 2:

[1469] The device sends the information entered by the user to the server, which then passes the received information to an analysis tool, which performs data analysis to derive the optimal learning plan. Past databases and algorithms are used.

[1470] Input: User-entered information about preschool children

[1471] Output: The best lesson plan for preschoolers

[1472] Step 3:

[1473] The server activates a generating means to generate optimal learning content based on the analysis results. The generating means uses the results of the analyzing means to generate educational content such as interactive games, workbooks, and dictation drills.

[1474] Input: Learning plan with analytical tools

[1475] Output: Learning content

[1476] Step 4:

[1477] The server transmits the generated learning content to the user's terminal, and the user can access the provided learning content through the terminal.

[1478] Input: Generated learning content

[1479] Output: Learning content displayed on the device

[1480] Step 5:

[1481] While using the learning content, the emotion recognition means recognizes the facial expressions and voice of preschool children in real time through a camera and microphone, generating emotion data that can be used to acquire emotion data such as concentration, enjoyment, and fatigue.

[1482] Input: Preschooler's facial expressions and voice

[1483] Output: Emotion data

[1484] Step 6:

[1485] Based on the emotion data acquired by the emotion recognition means, the server uses the adjustment means to adjust the learning content in real time, such as changing the difficulty level of the learning content or suggesting breaks.

[1486] Input: Emotion data

[1487] Output: Tailored learning content

[1488] Step 7:

[1489] The user uses the tailored learning content to study together with the preschool child, and the learning progress data and emotion data obtained during this process are sent back to the server from the device.

[1490] Input: Learning progress data and emotion data

[1491] Output: Progress and emotion data sent

[1492] Step 8:

[1493] The server analyzes the received learning progress data and emotion data and reflects them in a new learning plan. Based on this feedback, the next learning plan for the preschooler is optimized and provided to the user.

[1494] Input: Learning progress data and emotion data

[1495] Output: Updated learning plan

[1496] These are the specific processing steps of the system, which provide the optimal learning environment for preschool children and enable real-time adjustments.

[1497] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1498] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1499] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1500] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1501] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1502] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1503] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1504] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1505] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1506] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1507] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1508] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1509] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1510] 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.

[1511] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1512] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1513] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1514] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1515] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1516] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1517] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1518] The following is further disclosed regarding the above embodiment.

[1519] (Claim 1)

[1520] an input means for inputting learning needs of a preschool child;

[1521] an analysis means for performing data analysis based on the input learning needs;

[1522] a generation means for generating study content based on the analysis results of the analysis means;

[1523] a transmission means for transmitting the study content generated by the generation means to a user terminal;

[1524] A system including:

[1525] (Claim 2)

[1526] 10. The system of claim 1, wherein the learning content includes at least one of an interactive game, a workbook, or a writing drill.

[1527] (Claim 3)

[1528] 10. The system of claim 1, further comprising a feedback means for collecting and providing learning progress data of the preschool child to the analysis means.

[1529] "Example 1"

[1530] (Claim 1)

[1531] an input means for inputting learning needs of a preschool child;

[1532] an analysis means for performing data analysis based on the input learning needs;

[1533] a generation means for generating study content based on the analysis results of the analysis means;

[1534] a transmission means for transmitting the study content generated by the generation means to a user terminal;

[1535] a feedback means for providing the learning progress data collected from the user terminal to the analysis means again;

[1536] A system including:

[1537] (Claim 2)

[1538] 10. The system of claim 1, wherein the learning content includes at least one of an interactive game, a workbook, or a writing drill.

[1539] (Claim 3)

[1540] 2. The system according to claim 1, wherein the generating means generates a new learning plan based on the learning progress data and provides the new learning plan to the user terminal via the transmitting means.

[1541] "Application Example 1"

[1542] (Claim 1)

[1543] an input means for inputting learning needs of a preschool child;

[1544] an analysis means for performing data analysis based on the input learning needs;

[1545] a generation means for generating study content based on the analysis results of the analysis means;

[1546] a transmission means for transmitting the study content generated by the generation means to a user terminal;

[1547] A means for preschoolers and their parents to experience educational content in a virtual environment;

[1548] A system including:

[1549] (Claim 2)

[1550] 10. The system of claim 1, wherein the learning content includes at least one of an interactive game, a workbook, or a writing drill.

[1551] (Claim 3)

[1552] 10. The system of claim 1, further comprising a feedback means for collecting and providing learning progress data of the preschool child to the analysis means.

[1553] "Example 2: Combining Emotion Engines"

[1554] (Claim 1)

[1555] an input means for inputting learning needs of a preschool child;

[1556] an analysis means for performing data analysis based on the input learning needs;

[1557] a generation means for generating study content based on the analysis results of the analysis means;

[1558] a transmission means for transmitting the study content generated by the generation means to a user terminal;

[1559] an emotion recognition means for recognizing emotion data of a preschool child in real time while the child is learning;

[1560] an adjustment means for adjusting the learning content based on the emotion data recognized by the emotion recognition means;

[1561] a feedback means for collecting learning progress data and emotion data of the preschool child and providing the data to the analysis means;

[1562] A system including:

[1563] (Claim 2)

[1564] 10. The system of claim 1, wherein the learning content includes at least one of an interactive game, a workbook, or a writing drill.

[1565] (Claim 3)

[1566] 10. The system of claim 1, wherein the emotion recognition means uses a camera and a microphone to capture the preschool child's facial expressions and vocal tone.

[1567] "Application example 2 when combining emotion engines"

[1568] (Claim 1)

[1569] an input means for inputting learning needs of a preschool child;

[1570] an analysis means for performing data analysis based on the input learning needs;

[1571] a generation means for generating study content based on the analysis results of the analysis means;

[1572] a transmission means for transmitting the study content generated by the generation means to a user terminal;

[1573] emotion recognition means for recognizing and analyzing emotion data of a preschool child while using the learning content;

[1574] an adjustment means for adjusting the learning content in real time based on the emotion data obtained by the emotion recognition means;

[1575] A system including:

[1576] (Claim 2)

[1577] 10. The system of claim 1, wherein the learning content includes at least one of an interactive game, a workbook, or a writing drill.

[1578] (Claim 3)

[1579] 10. The system of claim 1, further comprising a feedback means for collecting learning progress data and emotional data of the preschool child and providing the data to the analysis means. [Explanation of symbols]

[1580] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting learning needs of a preschool child; an analysis means for performing data analysis based on the input learning needs; a generation means for generating study content based on the analysis results of the analysis means; a transmission means for transmitting the study content generated by the generation means to a user terminal; A system including:

2. 10. The system of claim 1, wherein the learning content includes at least one of an interactive game, a workbook, or a writing drill.

3. 10. The system of claim 1, further comprising a feedback means for collecting and providing learning progress data of the preschool child to the analysis means.

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