system

Abstract

We provide the system. [Solution] A system that individually optimizes children's educational curricula, A database that stores personal information received from users, A generative model means for analyzing collected personal information and generating a curriculum based on learning progress and interests, A means of delivering learning materials that delivers interactive learning materials based on a generated curriculum, A monitoring and evaluation system that monitors learning progress and provides real-time feedback, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Currently, in programming education for children, there is a problem that it is difficult to provide a curriculum optimized for each individual's interests and learning pace. In conventional educational methods, uniform teaching materials are often used, and children may not be able to maintain sufficient interest, which may lead to a decline in learning efficiency. In addition, since there is a lack of a mechanism for sufficiently providing learning support at home, parent-child joint learning cannot be assisted. There is a need to solve such problems and provide an environment in which children can learn while having fun.

Means for Solving the Problems

[0005] This invention provides a system that uses personal information collected from users to generate an educational curriculum optimized for each child. The system monitors and evaluates learning progress by delivering interactive learning materials based on the generated curriculum and providing real-time feedback. It also supports workshop-style lessons that parents and children can participate in together, promoting home learning. Furthermore, it dynamically updates the learning plan in response to changes in the learner's interests, enabling continuous learning support. Through this series of means, it can provide each child with an optimized, enjoyable learning environment and opportunities for growth.

[0006] A "database" is a system for accumulating information used to store and utilize personal information received from users.

[0007] "Generative modeling means" refers to technical means for analyzing collected personal information and generating individually optimized curricula based on learning progress and interests.

[0008] "Educational material distribution method" refers to a function that provides users with interactive learning materials based on a generated curriculum.

[0009] "Monitoring and evaluation means" are technical means for evaluating learning status by monitoring learning progress and providing real-time feedback.

[0010] "Support measures" refer to a system that promotes home learning by regularly holding workshop-style classes that parents and children can participate in together.

[0011] "A means of dynamically updating" refers to the function by which the generative model means appropriately modifies the learning plan in response to changes in the learner's interests. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system for individually optimizing educational programs for children, and is specifically implemented as follows.

[0034] First, the user accesses the educational program and uses an interface to provide information about their child. This information includes the child's age, existing learning experience, and areas of interest. Once the user has finished entering the information, it is sent to the server and stored in the database.

[0035] The server uses a generative model to analyze the information collected in the database. The generative model generates a personalized learning curriculum based on the input data. For example, if a particular child is interested in "game development," the curriculum will be structured to include many tasks and projects related to game development.

[0036] The generated curriculum is delivered to the user's device via a learning material distribution system. The device displays the received curriculum in a format that children can intuitively use. Learning is made enjoyable for children through the inclusion of visual programming tools and interactive quizzes.

[0037] Subsequently, the server uses monitoring and evaluation tools to monitor the child's learning progress in real time. User progress data is collected and analyzed within the server. Feedback is generated based on the progress and sent to the user's device. For example, if a user is taking too long on a particular task, the server provides additional hints or guidance messages.

[0038] Furthermore, through various support measures, workshops that parents and children can participate in together will be held regularly. These workshops will provide opportunities for parents and children to work together on programming projects, creating a system to support learning at home.

[0039] Furthermore, the server has the ability to dynamically update the generative model, continuously optimizing the learning plan in response to changes in the child's interests and learning speed. In this way, the present invention can improve the quality and effectiveness of programming education by providing a learning experience optimized for each child.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] User: Access the educational program's web interface or app and enter the child's basic information (name, age, learning experience, areas of interest, etc.). This information entry screen is designed for user-friendliness.

[0043] Step 2:

[0044] Terminal: Receives information entered by the user and checks for errors in format and content. After confirming that the input information is accurate, it is sent to the server.

[0045] Step 3:

[0046] Server: Stores received user information in a database. This information is analyzed by a generative model and serves as foundational data for creating individually optimized curricula.

[0047] Step 4:

[0048] Server: Analyzes information stored in the database using a generative model. This analysis generates a programming curriculum optimized for each child's interests and learning pace. For example, for a child interested in "robots," a curriculum including relevant programming tasks will be designed.

[0049] Step 5:

[0050] Server: Sends the generated curriculum to the terminal via the course material distribution system. This transmission is communicated to the user using a notification function.

[0051] Step 6:

[0052] Terminal: Displays the received curriculum to the user. The interface incorporates many interactive and visual elements to create an environment where children can learn with interest.

[0053] Step 7:

[0054] Server: Monitors children's learning progress in real time using monitoring and evaluation tools. Progress data is collected and analyzed.

[0055] Step 8:

[0056] Server: Generates and sends feedback based on progress to the terminal. The feedback includes areas for improvement in learning and advice for the next steps.

[0057] Step 9:

[0058] User: Sign up for a parent-child workshop as needed. The workshop will offer practical programming projects where parents and children can learn together.

[0059] Step 10:

[0060] Server: Dynamically updates learning plans using generative models in response to changes in learners' interests and new learning needs. This provides a constantly optimized educational environment.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In providing individualized educational programs, traditional systems have faced challenges in flexibly responding to each learner's interests and progress, and in adequately providing opportunities for collaborative learning between parents and children. Therefore, there is a need to provide optimal learning experiences tailored to individual needs and to more effectively support learning at home.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes an information storage device for storing personal information received from users, a generation model device for analyzing the collected personal information and generating a curriculum based on learning progress and interests, and a material provision device for providing interactive learning materials based on the generated curriculum. This enables the provision of an optimal curriculum tailored to the user's interests and learning progress, as well as real-time progress evaluation and feedback.

[0066] An "information storage device" is a data management system that securely and efficiently stores personal information received from users and allows for quick access to it as needed.

[0067] A "generative model device" is a device equipped with an algorithm that automatically generates appropriate curricula tailored to each learner's interests and progress, based on collected personal information.

[0068] A "material provision device" is a system that delivers learning materials to users in an interactive format, based on a generated curriculum, to facilitate learning.

[0069] A "monitoring and evaluation device" is a device that monitors learners' progress in real time and provides appropriate evaluation and feedback.

[0070] A "display device" is a device that visually displays learning information and feedback in a format optimized for the user's terminal.

[0071] A "generative information device" is a device that generates prompt sentences suitable for specific learning tasks and inputs them into a generative model to improve the suitability of the curriculum.

[0072] A "support device" is a device that provides opportunities for parents and children to learn together and offers means to support learning within the home.

[0073] Embodiments of the present invention are configured as a system for providing educational programs tailored to individual learners. Specific embodiments are described below.

[0074] Users access the educational program and provide their child's personal information, including age, previous learning experience, and areas of interest. This information is transmitted via the user's device to an information storage device and then sent to a server via a secure network connection. The server uses the information storage device to reliably store this information in a database.

[0075] The server uses a generative modeling device to generate an optimal educational curriculum for learners based on the stored information. A general machine learning platform is used as the generative AI model to generate prompts and input them into the AI. A concrete example of a prompt is, "Create music-related programming materials for an 8-year-old child."

[0076] The curriculum generated by the generative modeling device is delivered from the server to the user's terminal via a data delivery device. The terminal uses a display device to show the received curriculum in a format that is easy for children to understand. In this process, general educational software can be used as a visual programming tool.

[0077] During learning, the server tracks the child's learning progress in real time using a monitoring and evaluation device. It collects progress data sent from the user's terminal, generates feedback as needed, and immediately sends evaluation information to the terminal. This ensures that learners receive appropriate support at each stage.

[0078] Furthermore, the server is equipped with support devices and will regularly host workshops that parents and children can participate in together. These workshops will be conducted online or offline in cooperation with educators, creating an environment that promotes learning at home.

[0079] In this way, the present invention provides a system that offers an individually adapted curriculum and effectively supports the learning of each child.

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

[0081] Step 1:

[0082] The user accesses the educational program's interface and enters the child's personal information. This information includes the child's age, previous learning experience, and areas of interest. Once the information is entered into the interface, the device sends it to the server using a secure protocol such as HTTPS.

[0083] Step 2:

[0084] The server stores personal information received from the terminal in a database using an information storage device. In this storage process, the input data undergoes format conversion and validation checks to ensure data integrity and preservation. The output of this step is that the received information is accurately stored in the database.

[0085] Step 3:

[0086] The server operates a generative modeling device to analyze the information stored in the database. In this analysis procedure, the collected data is processed through machine learning algorithms to build a learning curriculum tailored to each individual. A specific prompt used is "Create programming materials on robotics for a 10-year-old child." As output, personalized curriculum data is generated.

[0087] Step 4:

[0088] The server delivers the generated curriculum to the user's terminal via a material delivery device. This delivery utilizes protocols such as WebSocket and HTTP / 2 to ensure the efficiency and reliability of data transactions. The curriculum data received on the terminal is output in a format that children can intuitively understand. For example, an interface for a visual programming environment using Scratch is automatically provided.

[0089] Step 5:

[0090] The server uses monitoring and evaluation equipment to monitor children's learning progress in real time. The terminal continuously sends activity logs of the user's learning to the server. This data is analyzed to generate appropriate feedback and support messages if the user is struggling with a particular task, and these are sent to the user's terminal. This allows learners to receive necessary advice immediately.

[0091] Step 6:

[0092] The server is equipped with support devices that regularly plan workshops that parents and children can participate in together and notify users' terminals. In this step, it is possible to hold workshops that can be attended remotely using a video conferencing system. The output is that participating users can deepen their learning through concrete projects.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In recent years, automation using robots has advanced in factories and other facilities, but there is a problem of a lack of individualized training programs to efficiently acquire the operating techniques and programming skills of workers. Furthermore, there is a need to flexibly optimize the learning content according to the skill level and learning progress of workers. For this reason, new means are needed to smoothly improve the skills of workers within factories.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes a storage device for storing personal information and skills information received from users, a generative AI model means for analyzing the collected personal information and skills information and generating a program based on learning skills or interests, and a distribution means for distributing interactive learning materials or operation guides based on the generated program. This makes it possible for factory workers to receive an optimal training program tailored to their individual skill levels.

[0098] "Personal information" refers to data provided by users that indicates the learner's characteristics, such as age, experience, and areas of interest.

[0099] "Skill information" refers to information about the learner's current skill level and the skills they wish to acquire.

[0100] A "storage device" is a database or medium used to store personal information and skills information, and to retrieve it as needed.

[0101] "Generative AI modeling means" refers to AI technology that automatically generates optimized learning programs based on collected information.

[0102] "Distribution method" refers to a means of communication used to provide users with generated learning programs and materials.

[0103] "Interactive learning materials" are educational content that users can directly interact with and use in a two-way manner.

[0104] An "operation guide" is a document or program that provides instructions on how to use a robot or device.

[0105] To implement this invention, the user first accesses the system using smart glasses or a mobile device. The user provides personal and skill information of the worker, and this information is stored in a storage device.

[0106] The server utilizes a generative AI model to analyze stored information and generate personalized learning programs aimed at improving workers' skills. This AI model uses machine learning libraries such as TENSORFLOW® to automatically generate optimal instructional content based on the worker's skill level and learning progress.

[0107] The generated program is delivered to the user's smart glasses as a visual and interactive learning tool. This allows workers to learn robot operation techniques through visual guidance and simulations. For example, when learning how to operate a new device, workers can practice while checking the operation procedure in real time via their smart glasses.

[0108] Furthermore, the server continuously monitors progress data and provides real-time feedback to the user. This means that if a worker encounters a difficult operation, the server immediately provides additional instructions or hints. In this way, an optimal learning environment tailored to each individual worker is created.

[0109] As a concrete example, when learning how to efficiently operate a newly introduced robotic arm in a factory, workers use smart glasses to visually learn the optimal movements for each step. An example of a prompt message to the generated AI model in this case would be: "The worker's current skill level is beginner; please generate an optimized learning program to acquire the skills to operate the new robotic arm."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] Users access the system using smart glasses or mobile devices and input their personal and skills information. The entered information is stored in a storage device. In this step, the server receives the entered text data, formats it in the appropriate format, and saves it to the database.

[0113] Step 2:

[0114] The server retrieves personal and skills information from storage devices and inputs it into a generating AI model. Based on this data, the generating AI model creates an optimized learning program. In this process, the AI ​​model analyzes the input data and generates customized learning materials based on the worker's skill level and areas of interest.

[0115] Step 3:

[0116] The server transmits the generated learning program to the terminal via a distribution method. Based on the received data, the terminal creates visual and interactive learning materials and displays them on smart glasses. Through the provided materials, users can perform simulations of actual robot operation, etc. The learning materials are displayed as 3D images and videos, and information is provided to the user using an intuitive interface.

[0117] Step 4:

[0118] The server monitors the user's learning progress in real time. It analyzes feedback information obtained from dedicated sensors and input devices to identify the worker's challenges and questions. Based on this information, the server provides additional instructions and hints in real time as needed to support the user's learning.

[0119] Step 5:

[0120] Users utilize the feedback they receive from the system to continue learning. The server further collects user input and feedback data, dynamically updating the generated AI model. As a result, it becomes possible to generate continuously improved learning programs.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention is an educational system equipped with an emotion engine, providing a specific method for individually optimizing children's learning experiences. This system can analyze information collected from users using the emotion engine and provide an optimal curriculum tailored to their emotional state during learning.

[0123] First, the user accesses the educational program and registers the child's basic information. This information is sent to the server and stored in a database. Next, when the child begins learning, sensor devices such as cameras and microphones installed on the device analyze the user's facial expressions and voice using an emotion engine. The emotion engine identifies the user's emotional state in real time and sends that data to the server.

[0124] The server uses the collected emotion data to dynamically adjust the curriculum provided by the generative model. For example, if a user shows frustration or excitement with a task, the server can adjust the content of the learning materials and provide the user with appropriate feedback or guidance on new learning steps.

[0125] Furthermore, emotional data is monitored and evaluated by the server and managed along with learning progress. This prevents users from becoming discouraged by tasks that are too difficult, and conversely, maintains a learning environment that is not too easy and therefore not boring.

[0126] Furthermore, the server collects emotional data during parent-child workshops, providing support to optimize the content and flow of the lessons. In this way, home learning is also made more efficient.

[0127] The system of this invention provides a sustained and individually optimized learning experience by taking into account the user's emotional state. This system is expected to draw children into learning in a more natural way.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] User: Access the educational program's web interface or app and enter the child's basic information (name, age, learning experience, areas of interest, etc.). This information is used to identify learning needs.

[0131] Step 2:

[0132] Terminal: Sends the entered information to the server. The server securely stores that information in its database.

[0133] Step 3:

[0134] Server: Analyzes user information stored in the database. Generates individually optimized learning curricula using a generative model.

[0135] Step 4:

[0136] Device: Once learning begins, sensors (such as cameras and microphones) on the device analyze the user's facial expressions and voice using an emotion engine, and send the emotion data to the server in real time.

[0137] Step 5:

[0138] Server: Analyzes collected emotional data to understand the user's current emotional state. Based on this data, it dynamically adjusts the curriculum content and difficulty level using generative models.

[0139] Step 6:

[0140] Terminal: Displays a pre-configured curriculum sent from the server to the user. This includes interactive learning materials and feedback tailored to the user's emotions.

[0141] Step 7:

[0142] Server: Monitors the user's learning progress and evaluates the progress data in real time along with sentiment data. Provides the user with advice and suggestions for improvement for the next learning step.

[0143] Step 8:

[0144] User: Sign up for parent-child workshops as needed. During the workshop, the emotion engine collects user emotion data to optimize the lesson flow.

[0145] Step 9:

[0146] Server: Continuously monitors changes in learners' emotional states and makes improvements and adjustments to long-term learning plans. This continuously optimizes the user's learning experience.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] In today's educational environment, optimizing the learning curriculum for individual learners is essential, but traditional systems have made it difficult to consider each learner's individual emotional state and progress. Furthermore, insufficient learning support at home makes it difficult to achieve sustained learning effectiveness.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes information management means for storing personal information received from users, analysis means for analyzing representation data and identifying data states, and adaptation means for dynamically adjusting teaching materials based on the recognized data states. This enables the provision of individually optimized curricula that take into account the emotional state of learners, and allows for continuous learning support.

[0152] "Information management means" refers to a system that has the function of securely recording personal information entered by users and managing it so that it can be easily retrieved when necessary.

[0153] "Analysis methods" refer to technologies that analyze user expression data and identify emotions and states from it.

[0154] An "adaptive means" is a function that automatically adjusts the learning materials and content to be optimal for the learner based on the data state obtained by the analysis means.

[0155] This system is designed to provide each learner with an individually optimized learning experience based on their emotional state. The following explains how this system is implemented.

[0156] Users first access the educational program and register their child's basic profile information (e.g., name, age, subjects of interest). This information is transmitted to the server via the device and stored in a database by an information management system. This forms the foundation for providing appropriate curricula for each learner.

[0157] When learning begins, sensor devices such as the camera and microphone built into the device activate to capture the user's facial expressions and voice. This data is processed through an analysis system for analysis. This analysis system identifies emotions in real time using facial recognition algorithms and voice tone analysis.

[0158] For example, if the device detects "excitement" from the user's facial expressions, the server dynamically adjusts the difficulty level of the curriculum through adaptive mechanisms. Generative AI models are used to select and provide new learning materials to the user. This enables a flexible learning experience tailored to the learner's emotional state.

[0159] As a concrete example, if the server indicates "fatigue" during an assignment, it provides feedback recommending that the user take a break from learning. This enables education that takes learner fatigue into consideration.

[0160] An example of a prompt to input into a generative AI model is, "An 8-year-old child is excited while learning math. How should the new challenge be adjusted?" By using such prompts, the system can dynamically generate the most suitable learning materials for the learner.

[0161] As described above, the present invention has embodiments that provide a sustainable learning environment tailored to learners by utilizing emotional data.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The user accesses the educational program through a terminal and enters the child's basic information (name, age, subjects of interest). This information becomes the input data. The terminal converts this information into a data format and sends it to the server. The server stores the received data in a database using information management tools and produces an output that creates an individual profile.

[0165] Step 2:

[0166] When a learning session begins, the device activates its built-in sensor devices (camera, microphone) to capture the user's facial expressions and voice. This becomes the input data. The device sends this data to an analysis system, which processes it to identify the emotional state in real time. By sending the analyzed emotional data to a server, the output, representing the emotional state, is obtained.

[0167] Step 3:

[0168] The server receives emotional state data identified by the analysis tools and uses it as a prompt for the generating AI model. For example, information such as "the child is excited" might be input data. Based on this data, the server dynamically adjusts the curriculum content using adaptive tools and selects materials and tasks suitable for the learner. As a result of this process, it generates an output in the form of an adjusted curriculum.

[0169] Step 4:

[0170] The server sends a customized curriculum to the terminal and presents it to the user. The terminal displays the received curriculum in an interactive format, and the user responds to it. This response becomes input data again, and the server generates feedback based on this data. The output here is feedback on learning progress and the next steps.

[0171] Step 5:

[0172] As learning progresses, the server continuously monitors and evaluates emotional data and learning progress. The server uses the accumulated data to generate a report at the end of each learning session. The input data consists of all emotional states and progress obtained during learning, and this is used to create output in the form of notifications for families and educators.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] In today's service environment, there is a demand for providing services in a way that is tailored to the individual customer's emotions and interests. However, current technology makes it difficult to analyze emotions in real time and provide individually optimized services, which hinders improvements in customer satisfaction.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes an information management unit for storing information, an analysis model unit for analyzing the collected information and generating a plan based on the state, a distribution unit for distributing interactive materials based on the generated plan, an emotion recognition unit for detecting emotional states and adjusting responses, and a suggestion unit for making suggestions according to the state of the target. This enables individual optimization that takes into account the dynamic emotional states of each individual.

[0178] The "Information Management Department" is the section responsible for storing and managing the various types of information that have been collected.

[0179] The "analysis model unit" is the part of the system that analyzes collected information and generates a plan based on the state of the subject.

[0180] The "distribution unit" is the part of the system that is responsible for appropriately distributing interactive materials and information based on the generated plan.

[0181] The "evaluation unit" is the part of the system that monitors progress and provides appropriate responses in real time.

[0182] The "emotion recognition unit" is the part of the brain that detects the emotional state of an object and adjusts its response accordingly.

[0183] The "proposal section" is a part of the system that has the function of making the most appropriate proposal based on the subject's condition.

[0184] The system for implementing this invention aims to improve the customer experience by analyzing the emotional state of customers in real time at customer service locations and making optimal suggestions accordingly.

[0185] The server has an information management unit for storing information, where collected customer-related information is stored. The terminal uses an analysis model unit to analyze this information and generate interactive suggestions based on the customer's current state. The generated suggestions are displayed on the terminal held by the store employee via the distribution unit. At this time, the emotion recognition unit uses the terminal's camera and microphone to sense the customer's facial expressions and voice and determines their emotions in real time. Specifically, it uses software such as the Facial Emotion Recognition API to determine emotions.

[0186] The terminal first transmits data acquired from the camera and microphone to the emotion recognition unit. Based on the results, the suggestion unit proposes the most suitable products and services to the customer. For example, if the customer shows interest in a product, other related products and services are recommended. Conversely, if the customer expresses dissatisfaction, the system prompts them to resolve the problem quickly.

[0187] This system can generate and respond to various situations using, for example, the following prompt statements.

[0188] "How can I suggest related products when a customer is smiling in front of a product?"

[0189] "Generate scenarios for how to resolve customer issues when a customer is unhappy."

[0190] In this way, the server and terminal work together to provide individually optimized customer service in physical stores.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The device activates its camera and microphone to capture the customer's facial expressions and voice in real time. The input is raw data from the camera and microphone, and the output is this data being sent to the emotion recognition unit. Specifically, the camera captures facial features, and the microphone generates digital data for recording voice.

[0194] Step 2:

[0195] The device analyzes data acquired using its emotion recognition unit to determine the customer's emotional state. The input consists of facial features and voice data obtained in step 1. The output is an emotion tag (e.g., joy, sadness, interest) as a result of the analysis. The emotion recognition unit uses the Facial Emotion Recognition API to estimate the emotional state based on the acquired data using principal component analysis and neural networks.

[0196] Step 3:

[0197] The server generates suggestions tailored to the target customer based on their emotional state. The input is the emotional tags from step 2, and the output is a list of suggested products and services. The server uses the analysis model to refer to past data and current emotional states, and then uses the suggestion unit to select the most suitable products and services. Specifically, this involves searching the database and executing the generation AI model.

[0198] Step 4:

[0199] The generated suggestions are sent to the terminal via the distribution unit and presented to the store clerk. The input is the suggestion list from step 3, and the output is the list of products and services displayed on the terminal. At this stage, the terminal's UI components are used to display the suggested content.

[0200] Step 5:

[0201] Based on the suggestions presented, the user recommends services and products to the customer. The input is the information presented in step 4, and the output is the actual customer service action. Specifically, the user explains the suggested content to the customer, observes the customer's reaction, and decides on the next action.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] This invention provides a system for individually optimizing educational programs for children, and is specifically implemented as follows.

[0219] First, the user accesses the educational program and uses an interface to provide information about their child. This information includes the child's age, existing learning experience, and areas of interest. Once the user has finished entering the information, it is sent to the server and stored in the database.

[0220] The server uses a generative model to analyze the information collected in the database. The generative model generates a personalized learning curriculum based on the input data. For example, if a particular child is interested in "game development," the curriculum will be structured to include many tasks and projects related to game development.

[0221] The generated curriculum is delivered to the user's device via a learning material distribution system. The device displays the received curriculum in a format that children can intuitively use. Learning is made enjoyable for children through the inclusion of visual programming tools and interactive quizzes.

[0222] Subsequently, the server uses monitoring and evaluation tools to monitor the child's learning progress in real time. User progress data is collected and analyzed within the server. Feedback is generated based on the progress and sent to the user's device. For example, if a user is taking too long on a particular task, the server provides additional hints or guidance messages.

[0223] Furthermore, through various support measures, workshops that parents and children can participate in together will be held regularly. These workshops will provide opportunities for parents and children to work together on programming projects, creating a system to support learning at home.

[0224] Furthermore, the server has the ability to dynamically update the generative model, continuously optimizing the learning plan in response to changes in the child's interests and learning speed. In this way, the present invention can improve the quality and effectiveness of programming education by providing a learning experience optimized for each child.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] User: Access the educational program's web interface or app and enter the child's basic information (name, age, learning experience, areas of interest, etc.). This information entry screen is designed for user-friendliness.

[0228] Step 2:

[0229] Terminal: Receives information entered by the user and checks for errors in format and content. After confirming that the input information is accurate, it is sent to the server.

[0230] Step 3:

[0231] Server: Stores received user information in a database. This information is analyzed by a generative model and serves as foundational data for creating individually optimized curricula.

[0232] Step 4:

[0233] Server: Analyzes information stored in the database using a generative model. This analysis generates a programming curriculum optimized for each child's interests and learning pace. For example, for a child interested in "robots," a curriculum including relevant programming tasks will be designed.

[0234] Step 5:

[0235] Server: Sends the generated curriculum to the terminal via the course material distribution system. This transmission is communicated to the user using a notification function.

[0236] Step 6:

[0237] Terminal: Displays the received curriculum to the user. The interface incorporates many interactive and visual elements to create an environment where children can learn with interest.

[0238] Step 7:

[0239] Server: Monitors children's learning progress in real time using monitoring and evaluation tools. Progress data is collected and analyzed.

[0240] Step 8:

[0241] Server: Generates and sends feedback based on progress to the terminal. The feedback includes areas for improvement in learning and advice for the next steps.

[0242] Step 9:

[0243] User: Sign up for a parent-child workshop as needed. The workshop will offer practical programming projects where parents and children can learn together.

[0244] Step 10:

[0245] Server: Dynamically updates learning plans using generative models in response to changes in learners' interests and new learning needs. This provides a constantly optimized educational environment.

[0246] (Example 1)

[0247] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0248] In providing individualized educational programs, traditional systems have faced challenges in flexibly responding to each learner's interests and progress, and in adequately providing opportunities for collaborative learning between parents and children. Therefore, there is a need to provide optimal learning experiences tailored to individual needs and to more effectively support learning at home.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes an information storage device for storing personal information received from users, a generation model device for analyzing the collected personal information and generating a curriculum based on learning progress and interests, and a material provision device for providing interactive learning materials based on the generated curriculum. This enables the provision of an optimal curriculum tailored to the user's interests and learning progress, as well as real-time progress evaluation and feedback.

[0251] An "information storage device" is a data management system that securely and efficiently stores personal information received from users and allows for quick access to it as needed.

[0252] A "generative model device" is a device equipped with an algorithm that automatically generates appropriate curricula tailored to each learner's interests and progress, based on collected personal information.

[0253] A "material provision device" is a system that delivers learning materials to users in an interactive format, based on a generated curriculum, to facilitate learning.

[0254] A "monitoring and evaluation device" is a device that monitors learners' progress in real time and provides appropriate evaluation and feedback.

[0255] A "display device" is a device that visually displays learning information and feedback in a format optimized for the user's terminal.

[0256] A "generative information device" is a device that generates prompt sentences suitable for specific learning tasks and inputs them into a generative model to improve the suitability of the curriculum.

[0257] A "support device" is a device that provides opportunities for parents and children to learn together and offers means to support learning within the home.

[0258] Embodiments of the present invention are configured as a system for providing educational programs tailored to individual learners. Specific embodiments are described below.

[0259] Users access the educational program and provide their child's personal information, including age, previous learning experience, and areas of interest. This information is transmitted via the user's device to an information storage device and then sent to a server via a secure network connection. The server uses the information storage device to reliably store this information in a database.

[0260] The server uses a generative modeling device to generate an optimal educational curriculum for learners based on the stored information. A general machine learning platform is used as the generative AI model to generate prompts and input them into the AI. A concrete example of a prompt is, "Create music-related programming materials for an 8-year-old child."

[0261] The curriculum generated by the generative modeling device is delivered from the server to the user's terminal via a data delivery device. The terminal uses a display device to show the received curriculum in a format that is easy for children to understand. In this process, general educational software can be used as a visual programming tool.

[0262] During learning, the server tracks the child's learning progress in real time using a monitoring and evaluation device. It collects progress data sent from the user's terminal, generates feedback as needed, and immediately sends evaluation information to the terminal. This ensures that learners receive appropriate support at each stage.

[0263] Furthermore, the server is equipped with support devices and will regularly host workshops that parents and children can participate in together. These workshops will be conducted online or offline in cooperation with educators, creating an environment that promotes learning at home.

[0264] In this way, the present invention provides a system that offers an individually adapted curriculum and effectively supports the learning of each child.

[0265] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0266] Step 1:

[0267] The user accesses the educational program's interface and enters the child's personal information. This information includes the child's age, previous learning experience, and areas of interest. Once the information is entered into the interface, the device sends it to the server using a secure protocol such as HTTPS.

[0268] Step 2:

[0269] The server stores personal information received from the terminal in a database using an information storage device. In this storage process, the input data undergoes format conversion and validation checks to ensure data integrity and preservation. The output of this step is that the received information is accurately stored in the database.

[0270] Step 3:

[0271] The server operates a generative modeling device to analyze the information stored in the database. In this analysis procedure, the collected data is processed through machine learning algorithms to build a learning curriculum tailored to each individual. A specific prompt used is "Create programming materials on robotics for a 10-year-old child." As output, personalized curriculum data is generated.

[0272] Step 4:

[0273] The server delivers the generated curriculum to the user's terminal via a material delivery device. This delivery utilizes protocols such as WebSocket and HTTP / 2 to ensure the efficiency and reliability of data transactions. The curriculum data received on the terminal is output in a format that children can intuitively understand. For example, an interface for a visual programming environment using Scratch is automatically provided.

[0274] Step 5:

[0275] The server uses monitoring and evaluation equipment to monitor children's learning progress in real time. The terminal continuously sends activity logs of the user's learning to the server. This data is analyzed to generate appropriate feedback and support messages if the user is struggling with a particular task, and these are sent to the user's terminal. This allows learners to receive necessary advice immediately.

[0276] Step 6:

[0277] The server is equipped with support devices that regularly plan workshops that parents and children can participate in together and notify users' terminals. In this step, it is possible to hold workshops that can be attended remotely using a video conferencing system. The output is that participating users can deepen their learning through concrete projects.

[0278] (Application Example 1)

[0279] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0280] In recent years, automation using robots has advanced in factories and the like, but there is a problem that there is a lack of individualized educational programs for workers to efficiently acquire their operation techniques and programming skills. In addition, it is required to flexibly optimize the learning content according to the skill level and learning progress of the workers. For this reason, a new means is needed to smoothly improve the skills of workers in the factory.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0282] In this invention, the server includes a storage device that stores personal information and skill information received from a user, a generation AI model means that analyzes the collected personal information and skill information and generates a program based on learning techniques or interests, and a distribution means that distributes an interactive learning material or operation guide based on the generated program. As a result, it becomes possible for factory workers to receive an optimal educational program according to their individual skill levels.

[0283] "Personal information" is data indicating the characteristics of a learner, such as age, experience, and field of interest provided by the user.

[0284] "Skill information" is information regarding the current skill level of the learner and the techniques to be acquired.

[0285] "Storage device" is a database or medium for storing personal information and skill information and retrieving them as needed.

[0286] "Generation AI model means" is an AI technology for automatically generating an optimized learning program based on the collected information.

[0287] "Distribution means" is a communication means for providing the generated learning program and teaching materials to the user.

[0288] "Interactive learning materials" are educational content that users can directly interact with and use in a two-way manner.

[0289] An "operation guide" is a document or program that provides instructions on how to use a robot or device.

[0290] To implement this invention, the user first accesses the system using smart glasses or a mobile device. The user provides personal and skill information of the worker, and this information is stored in a storage device.

[0291] The server utilizes a generative AI model to analyze the stored information and generate personalized learning programs aimed at improving workers' skills. This AI model uses machine learning libraries such as TensorFlow to automatically generate optimal instruction based on the worker's skill level and learning progress.

[0292] The generated program is delivered to the user's smart glasses as a visual and interactive learning tool. This allows workers to learn robot operation techniques through visual guidance and simulations. For example, when learning how to operate a new device, workers can practice while checking the operation procedure in real time via their smart glasses.

[0293] Furthermore, the server continuously monitors progress data and provides real-time feedback to the user. This means that if a worker encounters a difficult operation, the server immediately provides additional instructions or hints. In this way, an optimal learning environment tailored to each individual worker is created.

[0294] As a concrete example, when learning how to efficiently operate a newly introduced robotic arm in a factory, workers use smart glasses to visually learn the optimal movements for each step. An example of a prompt message to the generated AI model in this case would be: "The worker's current skill level is beginner; please generate an optimized learning program to acquire the skills to operate the new robotic arm."

[0295] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0296] Step 1:

[0297] Users access the system using smart glasses or mobile devices and input their personal and skills information. The entered information is stored in a storage device. In this step, the server receives the entered text data, formats it in the appropriate format, and saves it to the database.

[0298] Step 2:

[0299] The server retrieves personal and skills information from storage devices and inputs it into a generating AI model. Based on this data, the generating AI model creates an optimized learning program. In this process, the AI ​​model analyzes the input data and generates customized learning materials based on the worker's skill level and areas of interest.

[0300] Step 3:

[0301] The server transmits the generated learning program to the terminal via a distribution method. Based on the received data, the terminal creates visual and interactive learning materials and displays them on smart glasses. Through the provided materials, users can perform simulations of actual robot operation, etc. The learning materials are displayed as 3D images and videos, and information is provided to the user using an intuitive interface.

[0302] Step 4:

[0303] The server monitors the user's learning progress in real time. It analyzes the feedback information obtained from dedicated sensors and input devices to identify the operator's problems and doubts. Based on this information, the server provides additional instructions and hints in real time as needed to support the user's learning.

[0304] Step 5:

[0305] The user utilizes the feedback obtained from the system to continue learning. The server further collects the input information and feedback data from the user and dynamically updates the generated AI model. As a result, it becomes possible to generate a continuously improved learning program.

[0306] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0307] The present invention is an educational system equipped with an emotion engine and provides a specific method for individually optimizing a child's learning experience. This system can analyze the information collected from the user with the emotion engine and provide an optimal curriculum according to the emotional state during learning.

[0308] First, the user accesses the educational program and registers the child's basic information. This information is sent to the server and stored in the database. Next, when the child starts learning, sensor devices such as cameras and microphones installed on the terminal analyze the user's expression and voice, etc., by the emotion engine. The emotion engine identifies the user's emotional state in real time and sends the data to the server.

[0309] The server uses the collected emotion data to dynamically adjust the curriculum provided by the generative model. For example, if a user shows frustration or excitement with a task, the server can adjust the content of the learning materials and provide the user with appropriate feedback or guidance on new learning steps.

[0310] Furthermore, emotional data is monitored and evaluated by the server and managed along with learning progress. This prevents users from becoming discouraged by tasks that are too difficult, and conversely, maintains a learning environment that is not too easy and therefore not boring.

[0311] Furthermore, the server collects emotional data during parent-child workshops, providing support to optimize the content and flow of the lessons. In this way, home learning is also made more efficient.

[0312] The system of this invention provides a sustained and individually optimized learning experience by taking into account the user's emotional state. This system is expected to draw children into learning in a more natural way.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] User: Access the educational program's web interface or app and enter the child's basic information (name, age, learning experience, areas of interest, etc.). This information is used to identify learning needs.

[0316] Step 2:

[0317] Terminal: Sends the entered information to the server. The server securely stores that information in its database.

[0318] Step 3:

[0319] Server: Analyzes user information stored in the database. Generates individually optimized learning curricula using a generative model.

[0320] Step 4:

[0321] Device: Once learning begins, sensors (such as cameras and microphones) on the device analyze the user's facial expressions and voice using an emotion engine, and send the emotion data to the server in real time.

[0322] Step 5:

[0323] Server: Analyzes collected emotional data to understand the user's current emotional state. Based on this data, it dynamically adjusts the curriculum content and difficulty level using generative models.

[0324] Step 6:

[0325] Terminal: Displays a pre-configured curriculum sent from the server to the user. This includes interactive learning materials and feedback tailored to the user's emotions.

[0326] Step 7:

[0327] Server: Monitors the user's learning progress and evaluates the progress data in real time along with sentiment data. Provides the user with advice and suggestions for improvement for the next learning step.

[0328] Step 8:

[0329] User: Sign up for parent-child workshops as needed. During the workshop, the emotion engine collects user emotion data to optimize the lesson flow.

[0330] Step 9:

[0331] Server: Continuously monitors changes in learners' emotional states and makes improvements and adjustments to long-term learning plans. This continuously optimizes the user's learning experience.

[0332] (Example 2)

[0333] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0334] In today's educational environment, optimizing the learning curriculum for individual learners is essential, but traditional systems have made it difficult to consider each learner's individual emotional state and progress. Furthermore, insufficient learning support at home makes it difficult to achieve sustained learning effectiveness.

[0335] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0336] In this invention, the server includes information management means for storing personal information received from users, analysis means for analyzing representation data and identifying data states, and adaptation means for dynamically adjusting teaching materials based on the recognized data states. This enables the provision of individually optimized curricula that take into account the emotional state of learners, and allows for continuous learning support.

[0337] "Information management means" refers to a system that has the function of securely recording personal information entered by users and managing it so that it can be easily retrieved when necessary.

[0338] "Analysis methods" refer to technologies that analyze user expression data and identify emotions and states from it.

[0339] An "adaptive means" is a function that automatically adjusts the learning materials and content to be optimal for the learner based on the data state obtained by the analysis means.

[0340] This system is designed to provide each learner with an individually optimized learning experience based on their emotional state. The following explains how this system is implemented.

[0341] Users first access the educational program and register their child's basic profile information (e.g., name, age, subjects of interest). This information is transmitted to the server via the device and stored in a database by an information management system. This forms the foundation for providing appropriate curricula for each learner.

[0342] When learning begins, sensor devices such as the camera and microphone built into the device activate to capture the user's facial expressions and voice. This data is processed through an analysis system for analysis. This analysis system identifies emotions in real time using facial recognition algorithms and voice tone analysis.

[0343] For example, if the device detects "excitement" from the user's facial expressions, the server dynamically adjusts the difficulty level of the curriculum through adaptive mechanisms. Generative AI models are used to select and provide new learning materials to the user. This enables a flexible learning experience tailored to the learner's emotional state.

[0344] As a concrete example, if the server indicates "fatigue" during an assignment, it provides feedback recommending that the user take a break from learning. This enables education that takes learner fatigue into consideration.

[0345] An example of a prompt to input into a generative AI model is, "An 8-year-old child is excited while learning math. How should the new challenge be adjusted?" By using such prompts, the system can dynamically generate the most suitable learning materials for the learner.

[0346] As described above, the present invention has embodiments that provide a sustainable learning environment tailored to learners by utilizing emotional data.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] The user accesses the educational program through a terminal and enters the child's basic information (name, age, subjects of interest). This information becomes the input data. The terminal converts this information into a data format and sends it to the server. The server stores the received data in a database using information management tools and produces an output that creates an individual profile.

[0350] Step 2:

[0351] When a learning session begins, the device activates its built-in sensor devices (camera, microphone) to capture the user's facial expressions and voice. This becomes the input data. The device sends this data to an analysis system, which processes it to identify the emotional state in real time. By sending the analyzed emotional data to a server, the output, representing the emotional state, is obtained.

[0352] Step 3:

[0353] The server receives emotional state data identified by the analysis tools and uses it as a prompt for the generating AI model. For example, information such as "the child is excited" might be input data. Based on this data, the server dynamically adjusts the curriculum content using adaptive tools and selects materials and tasks suitable for the learner. As a result of this process, it generates an output in the form of an adjusted curriculum.

[0354] Step 4:

[0355] The server sends a customized curriculum to the terminal and presents it to the user. The terminal displays the received curriculum in an interactive format, and the user responds to it. This response becomes input data again, and the server generates feedback based on this data. The output here is feedback on learning progress and the next steps.

[0356] Step 5:

[0357] As learning progresses, the server continuously monitors and evaluates emotional data and learning progress. The server uses the accumulated data to generate a report at the end of each learning session. The input data consists of all emotional states and progress obtained during learning, and this is used to create output in the form of notifications for families and educators.

[0358] (Application Example 2)

[0359] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0360] In today's service environment, there is a demand for providing services in a way that is tailored to the individual customer's emotions and interests. However, current technology makes it difficult to analyze emotions in real time and provide individually optimized services, which hinders improvements in customer satisfaction.

[0361] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0362] In this invention, the server includes an information management unit for storing information, an analysis model unit for analyzing the collected information and generating a plan based on the state, a distribution unit for distributing interactive materials based on the generated plan, an emotion recognition unit for detecting emotional states and adjusting responses, and a suggestion unit for making suggestions according to the state of the target. This enables individual optimization that takes into account the dynamic emotional states of each individual.

[0363] The "Information Management Department" is the section responsible for storing and managing the various types of information that have been collected.

[0364] The "analysis model unit" is the part of the system that analyzes collected information and generates a plan based on the state of the subject.

[0365] The "distribution unit" is the part of the system that is responsible for appropriately distributing interactive materials and information based on the generated plan.

[0366] The "evaluation unit" is the part of the system that monitors progress and provides appropriate responses in real time.

[0367] The "emotion recognition unit" is the part of the brain that detects the emotional state of an object and adjusts its response accordingly.

[0368] The "proposal section" is a part of the system that has the function of making the most appropriate proposal based on the subject's condition.

[0369] The system for implementing this invention aims to improve the customer experience by analyzing the emotional state of customers in real time at customer service locations and making optimal suggestions accordingly.

[0370] The server has an information management unit for storing information, where collected customer-related information is stored. The terminal uses an analysis model unit to analyze this information and generate interactive suggestions based on the customer's current state. The generated suggestions are displayed on the terminal held by the store employee via the distribution unit. At this time, the emotion recognition unit uses the terminal's camera and microphone to sense the customer's facial expressions and voice and determines their emotions in real time. Specifically, it uses software such as the Facial Emotion Recognition API to determine emotions.

[0371] The terminal first transmits data acquired from the camera and microphone to the emotion recognition unit. Based on the results, the suggestion unit proposes the most suitable products and services to the customer. For example, if the customer shows interest in a product, other related products and services are recommended. Conversely, if the customer expresses dissatisfaction, the system prompts them to resolve the problem quickly.

[0372] This system can generate and respond to various situations using, for example, the following prompt statements.

[0373] "How can I suggest related products when a customer is smiling in front of a product?"

[0374] "Generate scenarios for how to resolve customer issues when a customer is unhappy."

[0375] In this way, the server and terminal work together to provide individually optimized customer service in physical stores.

[0376] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0377] Step 1:

[0378] The device activates its camera and microphone to capture the customer's facial expressions and voice in real time. The input is raw data from the camera and microphone, and the output is this data being sent to the emotion recognition unit. Specifically, the camera captures facial features, and the microphone generates digital data for recording voice.

[0379] Step 2:

[0380] The device analyzes data acquired using its emotion recognition unit to determine the customer's emotional state. The input consists of facial features and voice data obtained in step 1. The output is an emotion tag (e.g., joy, sadness, interest) as a result of the analysis. The emotion recognition unit uses the Facial Emotion Recognition API to estimate the emotional state based on the acquired data using principal component analysis and neural networks.

[0381] Step 3:

[0382] The server generates suggestions tailored to the target customer based on their emotional state. The input is the emotional tags from step 2, and the output is a list of suggested products and services. The server uses the analysis model to refer to past data and current emotional states, and then uses the suggestion unit to select the most suitable products and services. Specifically, this involves searching the database and executing the generation AI model.

[0383] Step 4:

[0384] The generated suggestions are sent to the terminal via the distribution unit and presented to the store clerk. The input is the suggestion list from step 3, and the output is the list of products and services displayed on the terminal. At this stage, the terminal's UI components are used to display the suggested content.

[0385] Step 5:

[0386] Based on the suggestions presented, the user recommends services and products to the customer. The input is the information presented in step 4, and the output is the actual customer service action. Specifically, the user explains the suggested content to the customer, observes the customer's reaction, and decides on the next action.

[0387] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0388] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0393] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0394] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0395] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0396] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0397] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0398] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0399] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0400] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0401] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0402] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0403] This invention provides a system for individually optimizing educational programs for children, and is specifically implemented as follows.

[0404] First, the user accesses the educational program and uses an interface to provide information about their child. This information includes the child's age, existing learning experience, and areas of interest. Once the user has finished entering the information, it is sent to the server and stored in the database.

[0405] The server uses a generative model to analyze the information collected in the database. The generative model generates a personalized learning curriculum based on the input data. For example, if a particular child is interested in "game development," the curriculum will be structured to include many tasks and projects related to game development.

[0406] The generated curriculum is delivered to the user's device via a learning material distribution system. The device displays the received curriculum in a format that children can intuitively use. Learning is made enjoyable for children through the inclusion of visual programming tools and interactive quizzes.

[0407] Subsequently, the server uses monitoring and evaluation tools to monitor the child's learning progress in real time. User progress data is collected and analyzed within the server. Feedback is generated based on the progress and sent to the user's device. For example, if a user is taking too long on a particular task, the server provides additional hints or guidance messages.

[0408] Furthermore, through various support measures, workshops that parents and children can participate in together will be held regularly. These workshops will provide opportunities for parents and children to work together on programming projects, creating a system to support learning at home.

[0409] Furthermore, the server has the ability to dynamically update the generative model, continuously optimizing the learning plan in response to changes in the child's interests and learning speed. In this way, the present invention can improve the quality and effectiveness of programming education by providing a learning experience optimized for each child.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] User: Access the educational program's web interface or app and enter the child's basic information (name, age, learning experience, areas of interest, etc.). This information entry screen is designed for user-friendliness.

[0413] Step 2:

[0414] Terminal: Receives information entered by the user and checks for errors in format and content. After confirming that the input information is accurate, it is sent to the server.

[0415] Step 3:

[0416] Server: Stores received user information in a database. This information is analyzed by a generative model and serves as foundational data for creating individually optimized curricula.

[0417] Step 4:

[0418] Server: Analyzes information stored in the database using a generative model. This analysis generates a programming curriculum optimized for each child's interests and learning pace. For example, for a child interested in "robots," a curriculum including relevant programming tasks will be designed.

[0419] Step 5:

[0420] Server: Sends the generated curriculum to the terminal via the course material distribution system. This transmission is communicated to the user using a notification function.

[0421] Step 6:

[0422] Terminal: Displays the received curriculum to the user. The interface incorporates many interactive and visual elements to create an environment where children can learn with interest.

[0423] Step 7:

[0424] Server: Monitors children's learning progress in real time using monitoring and evaluation tools. Progress data is collected and analyzed.

[0425] Step 8:

[0426] Server: Generates and sends feedback based on progress to the terminal. The feedback includes areas for improvement in learning and advice for the next steps.

[0427] Step 9:

[0428] User: Sign up for a parent-child workshop as needed. The workshop will offer practical programming projects where parents and children can learn together.

[0429] Step 10:

[0430] Server: Dynamically updates learning plans using generative models in response to changes in learners' interests and new learning needs. This provides a constantly optimized educational environment.

[0431] (Example 1)

[0432] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0433] In providing individualized educational programs, traditional systems have faced challenges in flexibly responding to each learner's interests and progress, and in adequately providing opportunities for collaborative learning between parents and children. Therefore, there is a need to provide optimal learning experiences tailored to individual needs and to more effectively support learning at home.

[0434] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0435] In this invention, the server includes an information storage device for storing personal information received from users, a generation model device for analyzing the collected personal information and generating a curriculum based on learning progress and interests, and a material provision device for providing interactive learning materials based on the generated curriculum. This enables the provision of an optimal curriculum tailored to the user's interests and learning progress, as well as real-time progress evaluation and feedback.

[0436] An "information storage device" is a data management system that securely and efficiently stores personal information received from users and allows for quick access to it as needed.

[0437] A "generative model device" is a device equipped with an algorithm that automatically generates appropriate curricula tailored to each learner's interests and progress, based on collected personal information.

[0438] A "material provision device" is a system that delivers learning materials to users in an interactive format, based on a generated curriculum, to facilitate learning.

[0439] A "monitoring and evaluation device" is a device that monitors learners' progress in real time and provides appropriate evaluation and feedback.

[0440] A "display device" is a device that visually displays learning information and feedback in a format optimized for the user's terminal.

[0441] A "generative information device" is a device that generates prompt sentences suitable for specific learning tasks and inputs them into a generative model to improve the suitability of the curriculum.

[0442] A "support device" is a device that provides opportunities for parents and children to learn together and offers means to support learning within the home.

[0443] Embodiments of the present invention are configured as a system for providing educational programs tailored to individual learners. Specific embodiments are described below.

[0444] Users access the educational program and provide their child's personal information, including age, previous learning experience, and areas of interest. This information is transmitted via the user's device to an information storage device and then sent to a server via a secure network connection. The server uses the information storage device to reliably store this information in a database.

[0445] The server uses a generative modeling device to generate an optimal educational curriculum for learners based on the stored information. A general machine learning platform is used as the generative AI model to generate prompts and input them into the AI. A concrete example of a prompt is, "Create music-related programming materials for an 8-year-old child."

[0446] The curriculum generated by the generative modeling device is delivered from the server to the user's terminal via a data delivery device. The terminal uses a display device to show the received curriculum in a format that is easy for children to understand. In this process, general educational software can be used as a visual programming tool.

[0447] During learning, the server tracks the child's learning progress in real time using a monitoring and evaluation device. It collects progress data sent from the user's terminal, generates feedback as needed, and immediately sends evaluation information to the terminal. This ensures that learners receive appropriate support at each stage.

[0448] Furthermore, the server is equipped with support devices and will regularly host workshops that parents and children can participate in together. These workshops will be conducted online or offline in cooperation with educators, creating an environment that promotes learning at home.

[0449] In this way, the present invention provides a system that offers an individually adapted curriculum and effectively supports the learning of each child.

[0450] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0451] Step 1:

[0452] The user accesses the educational program's interface and enters the child's personal information. This information includes the child's age, previous learning experience, and areas of interest. Once the information is entered into the interface, the device sends it to the server using a secure protocol such as HTTPS.

[0453] Step 2:

[0454] The server stores personal information received from the terminal in a database using an information storage device. In this storage process, the input data undergoes format conversion and validation checks to ensure data integrity and preservation. The output of this step is that the received information is accurately stored in the database.

[0455] Step 3:

[0456] The server operates a generative modeling device to analyze the information stored in the database. In this analysis procedure, the collected data is processed through machine learning algorithms to build a learning curriculum tailored to each individual. A specific prompt used is "Create programming materials on robotics for a 10-year-old child." As output, personalized curriculum data is generated.

[0457] Step 4:

[0458] The server delivers the generated curriculum to the user's terminal via a material delivery device. This delivery utilizes protocols such as WebSocket and HTTP / 2 to ensure the efficiency and reliability of data transactions. The curriculum data received on the terminal is output in a format that children can intuitively understand. For example, an interface for a visual programming environment using Scratch is automatically provided.

[0459] Step 5:

[0460] The server uses monitoring and evaluation equipment to monitor children's learning progress in real time. The terminal continuously sends activity logs of the user's learning to the server. This data is analyzed to generate appropriate feedback and support messages if the user is struggling with a particular task, and these are sent to the user's terminal. This allows learners to receive necessary advice immediately.

[0461] Step 6:

[0462] The server is equipped with support devices that regularly plan workshops that parents and children can participate in together and notify users' terminals. In this step, it is possible to hold workshops that can be attended remotely using a video conferencing system. The output is that participating users can deepen their learning through concrete projects.

[0463] (Application Example 1)

[0464] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0465] In recent years, automation using robots has advanced in factories and other facilities, but there is a problem of a lack of individualized training programs to efficiently acquire the operating techniques and programming skills of workers. Furthermore, there is a need to flexibly optimize the learning content according to the skill level and learning progress of workers. For this reason, new means are needed to smoothly improve the skills of workers within factories.

[0466] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0467] In this invention, the server includes a storage device for storing personal information and skills information received from users, a generative AI model means for analyzing the collected personal information and skills information and generating a program based on learning skills or interests, and a distribution means for distributing interactive learning materials or operation guides based on the generated program. This makes it possible for factory workers to receive an optimal training program tailored to their individual skill levels.

[0468] "Personal information" refers to data provided by users that indicates the learner's characteristics, such as age, experience, and areas of interest.

[0469] "Skill information" refers to information about the learner's current skill level and the skills they wish to acquire.

[0470] A "storage device" is a database or medium used to store personal information and skills information, and to retrieve it as needed.

[0471] "Generative AI modeling means" refers to AI technology that automatically generates optimized learning programs based on collected information.

[0472] "Distribution method" refers to a means of communication used to provide users with generated learning programs and materials.

[0473] "Interactive learning materials" are educational content that users can directly interact with and use in a two-way manner.

[0474] An "operation guide" is a document or program that provides instructions on how to use a robot or device.

[0475] To implement this invention, the user first accesses the system using smart glasses or a mobile device. The user provides personal and skill information of the worker, and this information is stored in a storage device.

[0476] The server utilizes a generative AI model to analyze the stored information and generate personalized learning programs aimed at improving workers' skills. This AI model uses machine learning libraries such as TensorFlow to automatically generate optimal instruction based on the worker's skill level and learning progress.

[0477] The generated program is delivered to the user's smart glasses as a visual and interactive learning tool. This allows workers to learn robot operation techniques through visual guidance and simulations. For example, when learning how to operate a new device, workers can practice while checking the operation procedure in real time via their smart glasses.

[0478] Furthermore, the server continuously monitors progress data and provides real-time feedback to the user. This means that if a worker encounters a difficult operation, the server immediately provides additional instructions or hints. In this way, an optimal learning environment tailored to each individual worker is created.

[0479] As a concrete example, when learning how to efficiently operate a newly introduced robotic arm in a factory, workers use smart glasses to visually learn the optimal movements for each step. An example of a prompt message to the generated AI model in this case would be: "The worker's current skill level is beginner; please generate an optimized learning program to acquire the skills to operate the new robotic arm."

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] Users access the system using smart glasses or mobile devices and input their personal and skills information. The entered information is stored in a storage device. In this step, the server receives the entered text data, formats it in the appropriate format, and saves it to the database.

[0483] Step 2:

[0484] The server retrieves personal and skills information from storage devices and inputs it into a generating AI model. Based on this data, the generating AI model creates an optimized learning program. In this process, the AI ​​model analyzes the input data and generates customized learning materials based on the worker's skill level and areas of interest.

[0485] Step 3:

[0486] The server transmits the generated learning program to the terminal via a distribution method. Based on the received data, the terminal creates visual and interactive learning materials and displays them on smart glasses. Through the provided materials, users can perform simulations of actual robot operation, etc. The learning materials are displayed as 3D images and videos, and information is provided to the user using an intuitive interface.

[0487] Step 4:

[0488] The server monitors the user's learning progress in real time. It analyzes feedback information obtained from dedicated sensors and input devices to identify the worker's challenges and questions. Based on this information, the server provides additional instructions and hints in real time as needed to support the user's learning.

[0489] Step 5:

[0490] Users utilize the feedback they receive from the system to continue learning. The server further collects user input and feedback data, dynamically updating the generated AI model. As a result, it becomes possible to generate continuously improved learning programs.

[0491] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0492] This invention is an educational system equipped with an emotion engine, providing a specific method for individually optimizing children's learning experiences. This system can analyze information collected from users using the emotion engine and provide an optimal curriculum tailored to their emotional state during learning.

[0493] First, the user accesses the educational program and registers the child's basic information. This information is sent to the server and stored in a database. Next, when the child begins learning, sensor devices such as cameras and microphones installed on the device analyze the user's facial expressions and voice using an emotion engine. The emotion engine identifies the user's emotional state in real time and sends that data to the server.

[0494] The server uses the collected emotion data to dynamically adjust the curriculum provided by the generative model. For example, if a user shows frustration or excitement with a task, the server can adjust the content of the learning materials and provide the user with appropriate feedback or guidance on new learning steps.

[0495] Furthermore, emotional data is monitored and evaluated by the server and managed along with learning progress. This prevents users from becoming discouraged by tasks that are too difficult, and conversely, maintains a learning environment that is not too easy and therefore not boring.

[0496] Furthermore, the server collects emotional data during parent-child workshops, providing support to optimize the content and flow of the lessons. In this way, home learning is also made more efficient.

[0497] The system of this invention provides a sustained and individually optimized learning experience by taking into account the user's emotional state. This system is expected to draw children into learning in a more natural way.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] User: Access the educational program's web interface or app and enter the child's basic information (name, age, learning experience, areas of interest, etc.). This information is used to identify learning needs.

[0501] Step 2:

[0502] Terminal: Sends the entered information to the server. The server securely stores that information in its database.

[0503] Step 3:

[0504] Server: Analyzes user information stored in the database. Generates individually optimized learning curricula using a generative model.

[0505] Step 4:

[0506] Device: Once learning begins, sensors (such as cameras and microphones) on the device analyze the user's facial expressions and voice using an emotion engine, and send the emotion data to the server in real time.

[0507] Step 5:

[0508] Server: Analyzes collected emotional data to understand the user's current emotional state. Based on this data, it dynamically adjusts the curriculum content and difficulty level using generative models.

[0509] Step 6:

[0510] Terminal: Displays a pre-configured curriculum sent from the server to the user. This includes interactive learning materials and feedback tailored to the user's emotions.

[0511] Step 7:

[0512] Server: Monitors the user's learning progress and evaluates the progress data in real time along with sentiment data. Provides the user with advice and suggestions for improvement for the next learning step.

[0513] Step 8:

[0514] User: Sign up for parent-child workshops as needed. During the workshop, the emotion engine collects user emotion data to optimize the lesson flow.

[0515] Step 9:

[0516] Server: Continuously monitors changes in learners' emotional states and makes improvements and adjustments to long-term learning plans. This continuously optimizes the user's learning experience.

[0517] (Example 2)

[0518] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0519] In today's educational environment, optimizing the learning curriculum for individual learners is essential, but traditional systems have made it difficult to consider each learner's individual emotional state and progress. Furthermore, insufficient learning support at home makes it difficult to achieve sustained learning effectiveness.

[0520] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0521] In this invention, the server includes information management means for storing personal information received from users, analysis means for analyzing representation data and identifying data states, and adaptation means for dynamically adjusting teaching materials based on the recognized data states. This enables the provision of individually optimized curricula that take into account the emotional state of learners, and allows for continuous learning support.

[0522] "Information management means" refers to a system that has the function of securely recording personal information entered by users and managing it so that it can be easily retrieved when necessary.

[0523] "Analysis methods" refer to technologies that analyze user expression data and identify emotions and states from it.

[0524] An "adaptive means" is a function that automatically adjusts the learning materials and content to be optimal for the learner based on the data state obtained by the analysis means.

[0525] This system is designed to provide each learner with an individually optimized learning experience based on their emotional state. The following explains how this system is implemented.

[0526] Users first access the educational program and register their child's basic profile information (e.g., name, age, subjects of interest). This information is transmitted to the server via the device and stored in a database by an information management system. This forms the foundation for providing appropriate curricula for each learner.

[0527] When learning begins, sensor devices such as the camera and microphone built into the device activate to capture the user's facial expressions and voice. This data is processed through an analysis system for analysis. This analysis system identifies emotions in real time using facial recognition algorithms and voice tone analysis.

[0528] For example, if the device detects "excitement" from the user's facial expressions, the server dynamically adjusts the difficulty level of the curriculum through adaptive mechanisms. Generative AI models are used to select and provide new learning materials to the user. This enables a flexible learning experience tailored to the learner's emotional state.

[0529] As a concrete example, if the server indicates "fatigue" during an assignment, it provides feedback recommending that the user take a break from learning. This enables education that takes learner fatigue into consideration.

[0530] An example of a prompt to input into a generative AI model is, "An 8-year-old child is excited while learning math. How should the new challenge be adjusted?" By using such prompts, the system can dynamically generate the most suitable learning materials for the learner.

[0531] As described above, the present invention has embodiments that provide a sustainable learning environment tailored to learners by utilizing emotional data.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The user accesses the educational program through a terminal and enters the child's basic information (name, age, subjects of interest). This information becomes the input data. The terminal converts this information into a data format and sends it to the server. The server stores the received data in a database using information management tools and produces an output that creates an individual profile.

[0535] Step 2:

[0536] When a learning session begins, the device activates its built-in sensor devices (camera, microphone) to capture the user's facial expressions and voice. This becomes the input data. The device sends this data to an analysis system, which processes it to identify the emotional state in real time. By sending the analyzed emotional data to a server, the output, representing the emotional state, is obtained.

[0537] Step 3:

[0538] The server receives emotional state data identified by the analysis tools and uses it as a prompt for the generating AI model. For example, information such as "the child is excited" might be input data. Based on this data, the server dynamically adjusts the curriculum content using adaptive tools and selects materials and tasks suitable for the learner. As a result of this process, it generates an output in the form of an adjusted curriculum.

[0539] Step 4:

[0540] The server sends a customized curriculum to the terminal and presents it to the user. The terminal displays the received curriculum in an interactive format, and the user responds to it. This response becomes input data again, and the server generates feedback based on this data. The output here is feedback on learning progress and the next steps.

[0541] Step 5:

[0542] As learning progresses, the server continuously monitors and evaluates emotional data and learning progress. The server uses the accumulated data to generate a report at the end of each learning session. The input data consists of all emotional states and progress obtained during learning, and this is used to create output in the form of notifications for families and educators.

[0543] (Application Example 2)

[0544] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0545] In today's service environment, there is a demand for providing services in a way that is tailored to the individual customer's emotions and interests. However, current technology makes it difficult to analyze emotions in real time and provide individually optimized services, which hinders improvements in customer satisfaction.

[0546] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0547] In this invention, the server includes an information management unit for storing information, an analysis model unit for analyzing the collected information and generating a plan based on the state, a distribution unit for distributing interactive materials based on the generated plan, an emotion recognition unit for detecting emotional states and adjusting responses, and a suggestion unit for making suggestions according to the state of the target. This enables individual optimization that takes into account the dynamic emotional states of each individual.

[0548] The "Information Management Department" is the section responsible for storing and managing the various types of information that have been collected.

[0549] The "analysis model unit" is the part of the system that analyzes collected information and generates a plan based on the state of the subject.

[0550] The "distribution unit" is the part of the system that is responsible for appropriately distributing interactive materials and information based on the generated plan.

[0551] The "evaluation unit" is the part of the system that monitors progress and provides appropriate responses in real time.

[0552] The "emotion recognition unit" is the part of the brain that detects the emotional state of an object and adjusts its response accordingly.

[0553] The "proposal section" is a part of the system that has the function of making the most appropriate proposal based on the subject's condition.

[0554] The system for implementing this invention aims to improve the customer experience by analyzing the emotional state of customers in real time at customer service locations and making optimal suggestions accordingly.

[0555] The server has an information management unit for storing information, where collected customer-related information is stored. The terminal uses an analysis model unit to analyze this information and generate interactive suggestions based on the customer's current state. The generated suggestions are displayed on the terminal held by the store employee via the distribution unit. At this time, the emotion recognition unit uses the terminal's camera and microphone to sense the customer's facial expressions and voice and determines their emotions in real time. Specifically, it uses software such as the Facial Emotion Recognition API to determine emotions.

[0556] The terminal first transmits data acquired from the camera and microphone to the emotion recognition unit. Based on the results, the suggestion unit proposes the most suitable products and services to the customer. For example, if the customer shows interest in a product, other related products and services are recommended. Conversely, if the customer expresses dissatisfaction, the system prompts them to resolve the problem quickly.

[0557] This system can generate and respond to various situations using, for example, the following prompt statements.

[0558] "How can I suggest related products when a customer is smiling in front of a product?"

[0559] "Generate scenarios for how to resolve customer issues when a customer is unhappy."

[0560] In this way, the server and terminal work together to provide individually optimized customer service in physical stores.

[0561] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0562] Step 1:

[0563] The device activates its camera and microphone to capture the customer's facial expressions and voice in real time. The input is raw data from the camera and microphone, and the output is this data being sent to the emotion recognition unit. Specifically, the camera captures facial features, and the microphone generates digital data for recording voice.

[0564] Step 2:

[0565] The device analyzes data acquired using its emotion recognition unit to determine the customer's emotional state. The input consists of facial features and voice data obtained in step 1. The output is an emotion tag (e.g., joy, sadness, interest) as a result of the analysis. The emotion recognition unit uses the Facial Emotion Recognition API to estimate the emotional state based on the acquired data using principal component analysis and neural networks.

[0566] Step 3:

[0567] The server generates suggestions tailored to the target customer based on their emotional state. The input is the emotional tags from step 2, and the output is a list of suggested products and services. The server uses the analysis model to refer to past data and current emotional states, and then uses the suggestion unit to select the most suitable products and services. Specifically, this involves searching the database and executing the generation AI model.

[0568] Step 4:

[0569] The generated suggestions are sent to the terminal via the distribution unit and presented to the store clerk. The input is the suggestion list from step 3, and the output is the list of products and services displayed on the terminal. At this stage, the terminal's UI components are used to display the suggested content.

[0570] Step 5:

[0571] Based on the suggestions presented, the user recommends services and products to the customer. The input is the information presented in step 4, and the output is the actual customer service action. Specifically, the user explains the suggested content to the customer, observes the customer's reaction, and decides on the next action.

[0572] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0573] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0574] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0575] [Fourth Embodiment]

[0576] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0577] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0578] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0579] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0580] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0581] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0582] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0583] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0584] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0585] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0586] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0587] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0588] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0589] This invention provides a system for individually optimizing educational programs for children, and is specifically implemented as follows.

[0590] First, the user accesses the educational program and uses an interface to provide information about their child. This information includes the child's age, existing learning experience, and areas of interest. Once the user has finished entering the information, it is sent to the server and stored in the database.

[0591] The server uses a generative model to analyze the information collected in the database. The generative model generates a personalized learning curriculum based on the input data. For example, if a particular child is interested in "game development," the curriculum will be structured to include many tasks and projects related to game development.

[0592] The generated curriculum is delivered to the user's device via a learning material distribution system. The device displays the received curriculum in a format that children can intuitively use. Learning is made enjoyable for children through the inclusion of visual programming tools and interactive quizzes.

[0593] Subsequently, the server uses monitoring and evaluation tools to monitor the child's learning progress in real time. User progress data is collected and analyzed within the server. Feedback is generated based on the progress and sent to the user's device. For example, if a user is taking too long on a particular task, the server provides additional hints or guidance messages.

[0594] Furthermore, through various support measures, workshops that parents and children can participate in together will be held regularly. These workshops will provide opportunities for parents and children to work together on programming projects, creating a system to support learning at home.

[0595] Furthermore, the server has the ability to dynamically update the generative model, continuously optimizing the learning plan in response to changes in the child's interests and learning speed. In this way, the present invention can improve the quality and effectiveness of programming education by providing a learning experience optimized for each child.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] User: Access the educational program's web interface or app and enter the child's basic information (name, age, learning experience, areas of interest, etc.). This information entry screen is designed for user-friendliness.

[0599] Step 2:

[0600] Terminal: Receives information entered by the user and checks for errors in format and content. After confirming that the input information is accurate, it is sent to the server.

[0601] Step 3:

[0602] Server: Stores received user information in a database. This information is analyzed by a generative model and serves as foundational data for creating individually optimized curricula.

[0603] Step 4:

[0604] Server: Analyzes information stored in the database using a generative model. This analysis generates a programming curriculum optimized for each child's interests and learning pace. For example, for a child interested in "robots," a curriculum including relevant programming tasks will be designed.

[0605] Step 5:

[0606] Server: Sends the generated curriculum to the terminal via the course material distribution system. This transmission is communicated to the user using a notification function.

[0607] Step 6:

[0608] Terminal: Displays the received curriculum to the user. The interface incorporates many interactive and visual elements to create an environment where children can learn with interest.

[0609] Step 7:

[0610] Server: Monitors children's learning progress in real time using monitoring and evaluation tools. Progress data is collected and analyzed.

[0611] Step 8:

[0612] Server: Generates and sends feedback based on progress to the terminal. The feedback includes areas for improvement in learning and advice for the next steps.

[0613] Step 9:

[0614] User: Sign up for a parent-child workshop as needed. The workshop will offer practical programming projects where parents and children can learn together.

[0615] Step 10:

[0616] Server: Dynamically updates learning plans using generative models in response to changes in learners' interests and new learning needs. This provides a constantly optimized educational environment.

[0617] (Example 1)

[0618] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0619] In providing individualized educational programs, traditional systems have faced challenges in flexibly responding to each learner's interests and progress, and in adequately providing opportunities for collaborative learning between parents and children. Therefore, there is a need to provide optimal learning experiences tailored to individual needs and to more effectively support learning at home.

[0620] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0621] In this invention, the server includes an information storage device for storing personal information received from users, a generation model device for analyzing the collected personal information and generating a curriculum based on learning progress and interests, and a material provision device for providing interactive learning materials based on the generated curriculum. This enables the provision of an optimal curriculum tailored to the user's interests and learning progress, as well as real-time progress evaluation and feedback.

[0622] An "information storage device" is a data management system that securely and efficiently stores personal information received from users and allows for quick access to it as needed.

[0623] A "generative model device" is a device equipped with an algorithm that automatically generates appropriate curricula tailored to each learner's interests and progress, based on collected personal information.

[0624] A "material provision device" is a system that delivers learning materials to users in an interactive format, based on a generated curriculum, to facilitate learning.

[0625] A "monitoring and evaluation device" is a device that monitors learners' progress in real time and provides appropriate evaluation and feedback.

[0626] A "display device" is a device that visually displays learning information and feedback in a format optimized for the user's terminal.

[0627] A "generative information device" is a device that generates prompt sentences suitable for specific learning tasks and inputs them into a generative model to improve the suitability of the curriculum.

[0628] A "support device" is a device that provides opportunities for parents and children to learn together and offers means to support learning within the home.

[0629] Embodiments of the present invention are configured as a system for providing educational programs tailored to individual learners. Specific embodiments are described below.

[0630] Users access the educational program and provide their child's personal information, including age, previous learning experience, and areas of interest. This information is transmitted via the user's device to an information storage device and then sent to a server via a secure network connection. The server uses the information storage device to reliably store this information in a database.

[0631] The server uses a generative modeling device to generate an optimal educational curriculum for learners based on the stored information. A general machine learning platform is used as the generative AI model to generate prompts and input them into the AI. A concrete example of a prompt is, "Create music-related programming materials for an 8-year-old child."

[0632] The curriculum generated by the generative modeling device is delivered from the server to the user's terminal via a data delivery device. The terminal uses a display device to show the received curriculum in a format that is easy for children to understand. In this process, general educational software can be used as a visual programming tool.

[0633] During learning, the server tracks the child's learning progress in real time using a monitoring and evaluation device. It collects progress data sent from the user's terminal, generates feedback as needed, and immediately sends evaluation information to the terminal. This ensures that learners receive appropriate support at each stage.

[0634] Furthermore, the server is equipped with support devices and will regularly host workshops that parents and children can participate in together. These workshops will be conducted online or offline in cooperation with educators, creating an environment that promotes learning at home.

[0635] In this way, the present invention provides a system that offers an individually adapted curriculum and effectively supports the learning of each child.

[0636] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0637] Step 1:

[0638] The user accesses the educational program's interface and enters the child's personal information. This information includes the child's age, previous learning experience, and areas of interest. Once the information is entered into the interface, the device sends it to the server using a secure protocol such as HTTPS.

[0639] Step 2:

[0640] The server stores personal information received from the terminal in a database using an information storage device. In this storage process, the input data undergoes format conversion and validation checks to ensure data integrity and preservation. The output of this step is that the received information is accurately stored in the database.

[0641] Step 3:

[0642] The server operates a generative modeling device to analyze the information stored in the database. In this analysis procedure, the collected data is processed through machine learning algorithms to build a learning curriculum tailored to each individual. A specific prompt used is "Create programming materials on robotics for a 10-year-old child." As output, personalized curriculum data is generated.

[0643] Step 4:

[0644] The server delivers the generated curriculum to the user's terminal via a material delivery device. This delivery utilizes protocols such as WebSocket and HTTP / 2 to ensure the efficiency and reliability of data transactions. The curriculum data received on the terminal is output in a format that children can intuitively understand. For example, an interface for a visual programming environment using Scratch is automatically provided.

[0645] Step 5:

[0646] The server uses monitoring and evaluation equipment to monitor children's learning progress in real time. The terminal continuously sends activity logs of the user's learning to the server. This data is analyzed to generate appropriate feedback and support messages if the user is struggling with a particular task, and these are sent to the user's terminal. This allows learners to receive necessary advice immediately.

[0647] Step 6:

[0648] The server is equipped with support devices that regularly plan workshops that parents and children can participate in together and notify users' terminals. In this step, it is possible to hold workshops that can be attended remotely using a video conferencing system. The output is that participating users can deepen their learning through concrete projects.

[0649] (Application Example 1)

[0650] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0651] In recent years, automation using robots has advanced in factories and other facilities, but there is a problem of a lack of individualized training programs to efficiently acquire the operating techniques and programming skills of workers. Furthermore, there is a need to flexibly optimize the learning content according to the skill level and learning progress of workers. For this reason, new means are needed to smoothly improve the skills of workers within factories.

[0652] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0653] In this invention, the server includes a storage device for storing personal information and skills information received from users, a generative AI model means for analyzing the collected personal information and skills information and generating a program based on learning skills or interests, and a distribution means for distributing interactive learning materials or operation guides based on the generated program. This makes it possible for factory workers to receive an optimal training program tailored to their individual skill levels.

[0654] "Personal information" refers to data provided by users that indicates the learner's characteristics, such as age, experience, and areas of interest.

[0655] "Skill information" refers to information about the learner's current skill level and the skills they wish to acquire.

[0656] A "storage device" is a database or medium used to store personal information and skills information, and to retrieve it as needed.

[0657] "Generative AI modeling means" refers to AI technology that automatically generates optimized learning programs based on collected information.

[0658] "Distribution method" refers to a means of communication used to provide users with generated learning programs and materials.

[0659] "Interactive learning materials" are educational content that users can directly interact with and use in a two-way manner.

[0660] An "operation guide" is a document or program that provides instructions on how to use a robot or device.

[0661] To implement this invention, the user first accesses the system using smart glasses or a mobile device. The user provides personal and skill information of the worker, and this information is stored in a storage device.

[0662] The server utilizes a generative AI model to analyze the stored information and generate personalized learning programs aimed at improving workers' skills. This AI model uses machine learning libraries such as TensorFlow to automatically generate optimal instruction based on the worker's skill level and learning progress.

[0663] The generated program is delivered to the user's smart glasses as a visual and interactive learning tool. This allows workers to learn robot operation techniques through visual guidance and simulations. For example, when learning how to operate a new device, workers can practice while checking the operation procedure in real time via their smart glasses.

[0664] Furthermore, the server continuously monitors progress data and provides real-time feedback to the user. This means that if a worker encounters a difficult operation, the server immediately provides additional instructions or hints. In this way, an optimal learning environment tailored to each individual worker is created.

[0665] As a concrete example, when learning how to efficiently operate a newly introduced robotic arm in a factory, workers use smart glasses to visually learn the optimal movements for each step. An example of a prompt message to the generated AI model in this case would be: "The worker's current skill level is beginner; please generate an optimized learning program to acquire the skills to operate the new robotic arm."

[0666] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0667] Step 1:

[0668] Users access the system using smart glasses or mobile devices and input their personal and skills information. The entered information is stored in a storage device. In this step, the server receives the entered text data, formats it in the appropriate format, and saves it to the database.

[0669] Step 2:

[0670] The server retrieves personal and skills information from storage devices and inputs it into a generating AI model. Based on this data, the generating AI model creates an optimized learning program. In this process, the AI ​​model analyzes the input data and generates customized learning materials based on the worker's skill level and areas of interest.

[0671] Step 3:

[0672] The server transmits the generated learning program to the terminal via a distribution method. Based on the received data, the terminal creates visual and interactive learning materials and displays them on smart glasses. Through the provided materials, users can perform simulations of actual robot operation, etc. The learning materials are displayed as 3D images and videos, and information is provided to the user using an intuitive interface.

[0673] Step 4:

[0674] The server monitors the user's learning progress in real time. It analyzes feedback information obtained from dedicated sensors and input devices to identify the worker's challenges and questions. Based on this information, the server provides additional instructions and hints in real time as needed to support the user's learning.

[0675] Step 5:

[0676] Users utilize the feedback they receive from the system to continue learning. The server further collects user input and feedback data, dynamically updating the generated AI model. As a result, it becomes possible to generate continuously improved learning programs.

[0677] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0678] This invention is an educational system equipped with an emotion engine, providing a specific method for individually optimizing children's learning experiences. This system can analyze information collected from users using the emotion engine and provide an optimal curriculum tailored to their emotional state during learning.

[0679] First, the user accesses the educational program and registers the child's basic information. This information is sent to the server and stored in a database. Next, when the child begins learning, sensor devices such as cameras and microphones installed on the device analyze the user's facial expressions and voice using an emotion engine. The emotion engine identifies the user's emotional state in real time and sends that data to the server.

[0680] The server uses the collected emotion data to dynamically adjust the curriculum provided by the generative model. For example, if a user shows frustration or excitement with a task, the server can adjust the content of the learning materials and provide the user with appropriate feedback or guidance on new learning steps.

[0681] Furthermore, emotional data is monitored and evaluated by the server and managed along with learning progress. This prevents users from becoming discouraged by tasks that are too difficult, and conversely, maintains a learning environment that is not too easy and therefore not boring.

[0682] Furthermore, the server collects emotional data during parent-child workshops, providing support to optimize the content and flow of the lessons. In this way, home learning is also made more efficient.

[0683] The system of this invention provides a sustained and individually optimized learning experience by taking into account the user's emotional state. This system is expected to draw children into learning in a more natural way.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] User: Access the educational program's web interface or app and enter the child's basic information (name, age, learning experience, areas of interest, etc.). This information is used to identify learning needs.

[0687] Step 2:

[0688] Terminal: Sends the entered information to the server. The server securely stores that information in its database.

[0689] Step 3:

[0690] Server: Analyzes user information stored in the database. Generates individually optimized learning curricula using a generative model.

[0691] Step 4:

[0692] Device: Once learning begins, sensors (such as cameras and microphones) on the device analyze the user's facial expressions and voice using an emotion engine, and send the emotion data to the server in real time.

[0693] Step 5:

[0694] Server: Analyzes collected emotional data to understand the user's current emotional state. Based on this data, it dynamically adjusts the curriculum content and difficulty level using generative models.

[0695] Step 6:

[0696] Terminal: Displays a pre-configured curriculum sent from the server to the user. This includes interactive learning materials and feedback tailored to the user's emotions.

[0697] Step 7:

[0698] Server: Monitors the user's learning progress and evaluates the progress data in real time along with sentiment data. Provides the user with advice and suggestions for improvement for the next learning step.

[0699] Step 8:

[0700] User: Sign up for parent-child workshops as needed. During the workshop, the emotion engine collects user emotion data to optimize the lesson flow.

[0701] Step 9:

[0702] Server: Continuously monitors changes in learners' emotional states and makes improvements and adjustments to long-term learning plans. This continuously optimizes the user's learning experience.

[0703] (Example 2)

[0704] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0705] In today's educational environment, optimizing the learning curriculum for individual learners is essential, but traditional systems have made it difficult to consider each learner's individual emotional state and progress. Furthermore, insufficient learning support at home makes it difficult to achieve sustained learning effectiveness.

[0706] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0707] In this invention, the server includes information management means for storing personal information received from users, analysis means for analyzing representation data and identifying data states, and adaptation means for dynamically adjusting teaching materials based on the recognized data states. This enables the provision of individually optimized curricula that take into account the emotional state of learners, and allows for continuous learning support.

[0708] "Information management means" refers to a system that has the function of securely recording personal information entered by users and managing it so that it can be easily retrieved when necessary.

[0709] "Analysis methods" refer to technologies that analyze user expression data and identify emotions and states from it.

[0710] An "adaptive means" is a function that automatically adjusts the learning materials and content to be optimal for the learner based on the data state obtained by the analysis means.

[0711] This system is designed to provide each learner with an individually optimized learning experience based on their emotional state. The following explains how this system is implemented.

[0712] Users first access the educational program and register their child's basic profile information (e.g., name, age, subjects of interest). This information is transmitted to the server via the device and stored in a database by an information management system. This forms the foundation for providing appropriate curricula for each learner.

[0713] When learning begins, sensor devices such as the camera and microphone built into the device activate to capture the user's facial expressions and voice. This data is processed through an analysis system for analysis. This analysis system identifies emotions in real time using facial recognition algorithms and voice tone analysis.

[0714] For example, if the device detects "excitement" from the user's facial expressions, the server dynamically adjusts the difficulty level of the curriculum through adaptive mechanisms. Generative AI models are used to select and provide new learning materials to the user. This enables a flexible learning experience tailored to the learner's emotional state.

[0715] As a concrete example, if the server indicates "fatigue" during an assignment, it provides feedback recommending that the user take a break from learning. This enables education that takes learner fatigue into consideration.

[0716] An example of a prompt to input into a generative AI model is, "An 8-year-old child is excited while learning math. How should the new challenge be adjusted?" By using such prompts, the system can dynamically generate the most suitable learning materials for the learner.

[0717] As described above, the present invention has embodiments that provide a sustainable learning environment tailored to learners by utilizing emotional data.

[0718] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0719] Step 1:

[0720] The user accesses the educational program through a terminal and enters the child's basic information (name, age, subjects of interest). This information becomes the input data. The terminal converts this information into a data format and sends it to the server. The server stores the received data in a database using information management tools and produces an output that creates an individual profile.

[0721] Step 2:

[0722] When a learning session begins, the device activates its built-in sensor devices (camera, microphone) to capture the user's facial expressions and voice. This becomes the input data. The device sends this data to an analysis system, which processes it to identify the emotional state in real time. By sending the analyzed emotional data to a server, the output, representing the emotional state, is obtained.

[0723] Step 3:

[0724] The server receives emotional state data identified by the analysis tools and uses it as a prompt for the generating AI model. For example, information such as "the child is excited" might be input data. Based on this data, the server dynamically adjusts the curriculum content using adaptive tools and selects materials and tasks suitable for the learner. As a result of this process, it generates an output in the form of an adjusted curriculum.

[0725] Step 4:

[0726] The server sends a customized curriculum to the terminal and presents it to the user. The terminal displays the received curriculum in an interactive format, and the user responds to it. This response becomes input data again, and the server generates feedback based on this data. The output here is feedback on learning progress and the next steps.

[0727] Step 5:

[0728] As learning progresses, the server continuously monitors and evaluates emotional data and learning progress. The server uses the accumulated data to generate a report at the end of each learning session. The input data consists of all emotional states and progress obtained during learning, and this is used to create output in the form of notifications for families and educators.

[0729] (Application Example 2)

[0730] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0731] In today's service environment, there is a demand for providing services in a way that is tailored to the individual customer's emotions and interests. However, current technology makes it difficult to analyze emotions in real time and provide individually optimized services, which hinders improvements in customer satisfaction.

[0732] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0733] In this invention, the server includes an information management unit for storing information, an analysis model unit for analyzing the collected information and generating a plan based on the state, a distribution unit for distributing interactive materials based on the generated plan, an emotion recognition unit for detecting emotional states and adjusting responses, and a suggestion unit for making suggestions according to the state of the target. This enables individual optimization that takes into account the dynamic emotional states of each individual.

[0734] The "Information Management Department" is the section responsible for storing and managing the various types of information that have been collected.

[0735] The "analysis model unit" is the part of the system that analyzes collected information and generates a plan based on the state of the subject.

[0736] The "distribution unit" is the part of the system that is responsible for appropriately distributing interactive materials and information based on the generated plan.

[0737] The "evaluation unit" is the part of the system that monitors progress and provides appropriate responses in real time.

[0738] The "emotion recognition unit" is the part of the brain that detects the emotional state of an object and adjusts its response accordingly.

[0739] The "proposal section" is a part of the system that has the function of making the most appropriate proposal based on the subject's condition.

[0740] The system for implementing this invention aims to improve the customer experience by analyzing the emotional state of customers in real time at customer service locations and making optimal suggestions accordingly.

[0741] The server has an information management unit for storing information, where collected customer-related information is stored. The terminal uses an analysis model unit to analyze this information and generate interactive suggestions based on the customer's current state. The generated suggestions are displayed on the terminal held by the store employee via the distribution unit. At this time, the emotion recognition unit uses the terminal's camera and microphone to sense the customer's facial expressions and voice and determines their emotions in real time. Specifically, it uses software such as the Facial Emotion Recognition API to determine emotions.

[0742] The terminal first transmits data acquired from the camera and microphone to the emotion recognition unit. Based on the results, the suggestion unit proposes the most suitable products and services to the customer. For example, if the customer shows interest in a product, other related products and services are recommended. Conversely, if the customer expresses dissatisfaction, the system prompts them to resolve the problem quickly.

[0743] This system can generate and respond to various situations using, for example, the following prompt statements.

[0744] "How can I suggest related products when a customer is smiling in front of a product?"

[0745] "Generate scenarios for how to resolve customer issues when a customer is unhappy."

[0746] In this way, the server and terminal work together to provide individually optimized customer service in physical stores.

[0747] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0748] Step 1:

[0749] The device activates its camera and microphone to capture the customer's facial expressions and voice in real time. The input is raw data from the camera and microphone, and the output is this data being sent to the emotion recognition unit. Specifically, the camera captures facial features, and the microphone generates digital data for recording voice.

[0750] Step 2:

[0751] The device analyzes data acquired using its emotion recognition unit to determine the customer's emotional state. The input consists of facial features and voice data obtained in step 1. The output is an emotion tag (e.g., joy, sadness, interest) as a result of the analysis. The emotion recognition unit uses the Facial Emotion Recognition API to estimate the emotional state based on the acquired data using principal component analysis and neural networks.

[0752] Step 3:

[0753] The server generates suggestions tailored to the target customer based on their emotional state. The input is the emotional tags from step 2, and the output is a list of suggested products and services. The server uses the analysis model to refer to past data and current emotional states, and then uses the suggestion unit to select the most suitable products and services. Specifically, this involves searching the database and executing the generation AI model.

[0754] Step 4:

[0755] The generated suggestions are sent to the terminal via the distribution unit and presented to the store clerk. The input is the suggestion list from step 3, and the output is the list of products and services displayed on the terminal. At this stage, the terminal's UI components are used to display the suggested content.

[0756] Step 5:

[0757] Based on the suggestions presented, the user recommends services and products to the customer. The input is the information presented in step 4, and the output is the actual customer service action. Specifically, the user explains the suggested content to the customer, observes the customer's reaction, and decides on the next action.

[0758] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0759] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0760] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0761] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0762] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0763] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0764] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0765] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0766] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0767] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0768] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0769] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0770] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0771] 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.

[0772] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0773] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0774] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0775] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0776] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0777] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0778] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0779] The following is further disclosed regarding the embodiments described above.

[0780] (Claim 1)

[0781] To individually optimize children's educational curricula,

[0782] A database that stores personal information received from users,

[0783] A generative model means for analyzing collected personal information and generating a curriculum based on learning progress and interests,

[0784] A means of delivering learning materials that delivers interactive learning materials based on a generated curriculum,

[0785] A monitoring and evaluation system that monitors learning progress and provides real-time feedback,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, further comprising means of supporting home learning by regularly holding workshop-style classes that parents and children can participate in together.

[0789] (Claim 3)

[0790] The system according to claim 1, further comprising means for dynamically updating the learning plan in response to changes in the learner's interests using a generative model, thereby providing continuous learning support.

[0791] "Example 1"

[0792] (Claim 1)

[0793] An information storage device that stores personal information received from users,

[0794] A generative model device that analyzes collected personal information and generates a curriculum based on learning progress and interests,

[0795] A material provision device that provides interactive learning materials based on a generated curriculum,

[0796] A monitoring and evaluation device that monitors learning progress and provides evaluation information in real time,

[0797] A display device for providing a learning experience adapted to the user's device,

[0798] A generation information device that generates prompt statements and inputs them into a generation model,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, further comprising a support device for regularly holding classes in a format in which parents and children participate together, and for supporting home learning.

[0802] (Claim 3)

[0803] The system according to claim 1, further comprising a generative modeling device that dynamically updates the plan in response to changes in the learner's interests and provides continuous learning support.

[0804] "Application Example 1"

[0805] (Claim 1)

[0806] To individually optimize children's educational or skills acquisition programs,

[0807] A storage device that stores personal information and skills information received from users,

[0808] A generative AI model means that analyzes collected personal information and skills information and generates a program based on learning techniques or interests,

[0809] A distribution means for delivering interactive learning materials or operation guides based on a generated program,

[0810] A monitoring and evaluation system that monitors learning progress and provides real-time feedback,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, further comprising means of supporting the promotion of skills acquisition by regularly holding workshop-style learning events in which parents and children or users can participate.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising means for dynamically updating the learning plan in response to changes in the learner's interests or progress in skill acquisition using a generative AI model, thereby providing continuous learning and skill acquisition support.

[0816] "Example 2 of combining an emotion engine"

[0817] (Claim 1)

[0818] Information management means for storing personal information received from users,

[0819] An analysis means for analyzing representation data and identifying the data state,

[0820] Adaptive means for dynamically adjusting teaching materials based on recognized data states,

[0821] An educational delivery method that delivers interactive learning materials based on a generated curriculum,

[0822] An evaluation method that provides real-time feedback according to the data state and learning progress,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, further comprising means for coordinating home-participatory workshop-style learning sessions and supporting learning at home.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising means for dynamically updating the learning plan in response to changes in data state using analysis means, and providing continuous learning support.

[0828] "Application example 2 when combining with an emotional engine"

[0829] (Claim 1)

[0830] To dynamically optimize individual experiences,

[0831] The Information Management Department, which provides the information,

[0832] An analysis model unit analyzes the collected information and generates a plan based on the state,

[0833] A distribution unit that delivers interactive materials based on the generated plan,

[0834] An evaluation unit that monitors progress and provides real-time responses,

[0835] An emotion recognition unit that detects the emotional state and adjusts the response,

[0836] The proposal department makes suggestions tailored to the specific situation of the subject,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, further comprising a support unit for regularly holding participatory events and promoting collaborative learning.

[0840] (Claim 3)

[0841] The system according to claim 1, further comprising an analysis model unit that dynamically updates plans in response to changes in interests and provides continuous support. [Explanation of Symbols]

[0842] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A system that individually optimizes children's educational curricula, A database that stores personal information received from users, A generative model means for analyzing collected personal information and generating a curriculum based on learning progress and interests, A means of delivering learning materials that delivers interactive learning materials based on a generated curriculum, A monitoring and evaluation system that monitors learning progress and provides real-time feedback, A system that includes this.

2. The system according to claim 1, further comprising means of supporting home learning by regularly holding workshop-style classes that parents and children can participate in together.

3. The system according to claim 1, further comprising means for dynamically updating the learning plan in response to changes in the learner's interests using a generative model, thereby providing continuous learning support.

Images