system

Abstract

We provide the system. [Solution] A generative artificial intelligence model that generates learning materials based on user information, A communication means for acquiring user information and distributing corresponding learning materials to an information processing device, An analysis means that analyzes the results of user participation in learning materials and generates feedback to show learning progress based on the analysis results, A presentation means that transmits the feedback to an information processing device and presents it to the parent and child visually or audibly, A means of providing safety tests and situational experiences based on a user's past online interaction history, 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, and includes 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] In recent years, the use of social media platforms has increased rapidly among children, and there is an urgent need for education on appropriate use knowledge and digital security therein. However, in conventional methods, uniform education is often provided, and it is difficult to provide personalized guidance according to the usage situation and knowledge level of individual users. Therefore, there is a need for a system that can provide individualized education so that parents and children can use SNS safely.

Means for Solving the Problems

[0005] In this invention, a system was constructed that uses a generative artificial intelligence model to generate learning content based on user information, in order to provide a learning program in which both parents and children can participate. A communication means for providing individualized and interactive educational content tailored to the user's SNS usage patterns and knowledge level ensures the delivery of appropriate learning content. Furthermore, the system evaluates the user's learning participation results using an analysis means and presents the generated feedback to the user via a terminal, thereby promoting continuous knowledge enhancement and optimization of learning. This enables effective and personalized education on safe SNS use for each user.

[0006] A "learning program that both parents and children can participate in" refers to educational content that parents and children can work on together, and aims to enhance learning effectiveness through collaboration and cooperation between the two.

[0007] A "generative artificial intelligence model" refers to an algorithm or platform that uses artificial intelligence to dynamically generate content, providing optimal training materials based on user data.

[0008] "User information" refers to a dataset containing personal data about users, usage history, behavioral patterns, etc., which is used to customize the learning content.

[0009] "Communication methods" refer to the technologies and infrastructure used to transmit information to users and enable two-way communication.

[0010] "Analysis means" refers to the technology or process used to analyze user learning data and evaluate learning effectiveness and comprehension.

[0011] "Presentation methods" refer to methods and techniques for visually or audibly showing analysis results and feedback to the user. [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 labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled 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 labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the labeled 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), etc.

[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] In order to implement the present invention, it is necessary to construct a system in which a server, a terminal, and a user each play their respective roles and work together. Specific embodiments of each component are shown below.

[0034] server:

[0035] The server plays a central role in the platform, dynamically generating learning content using generative artificial intelligence models. Based on information provided by users and past behavioral data, it constructs learning scenarios about SNS risks and safety measures, and prepares educational content optimized for each individual user.

[0036] Terminal:

[0037] The device serves to display and provide learning content to the user and their child. The screen accessible to the user presents scenario-based learning materials and interactive quizzes prepared on the server side. For example, the device may recreate social media trouble scenarios that children are likely to encounter and display content that allows them to choose how to respond.

[0038] User:

[0039] Users, both parents and children, utilize the content and participate in the learning activities presented by the system. Through their devices, users can answer quizzes and learn safe social media usage through scenario-based simulations. This provides an educational environment that enhances decision-making skills through discussions between parents and children.

[0040] Generating and presenting feedback:

[0041] The server analyzes the user's learning progress and quiz results, generating feedback on their level of understanding and areas for improvement. This feedback is sent to the device and presented to the user in an appropriate format. For example, it might point out that "awareness regarding the protection of personal information needs to be further increased." This provides parents and children with information to help them decide on the next steps in their learning.

[0042] Thus, this invention is designed to enable parents and children to learn together in real time by providing personalized educational content utilizing AI.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server initializes the generative artificial intelligence model and prepares to collect user information. It interacts with a database that holds data such as the user's age, social media usage history, and language settings.

[0046] Step 2:

[0047] The device displays a personalized initial setup screen to the user based on their registered information and prompts them to enter any necessary additional information. For example, it might present a question form regarding the type of social networking service (SNS) they use and how often they use it.

[0048] Step 3:

[0049] Users answer the presented questions and enter necessary information through their device. For example, they can also enter household rules such as the amount of time parents allow for social media use.

[0050] Step 4:

[0051] The server uses a live artificial intelligence model to generate appropriate learning scenarios and quizzes based on information provided by the user. This generation process incorporates the latest risk information and protection guidelines regarding SNS usage.

[0052] Step 5:

[0053] The device presents the user with generated learning content. For example, it might display a simulated experience game based on risk scenarios or a quiz about ethical social media use.

[0054] Step 6:

[0055] Users participate in learning content presented on their devices and answer quizzes. Their answers are transmitted to the server in real time.

[0056] Step 7:

[0057] The server analyzes user responses and participation history to evaluate each user's level of understanding and learning progress. It also generates next learning content and supplementary information based on the generated feedback.

[0058] Step 8:

[0059] The device presents the user with analyzed feedback. Specifically, it displays reports on learning progress and the next tasks to be completed, encouraging the user to continue learning.

[0060] Through this series of processes, the system can provide users with a personalized learning 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 today's information environment, there is a need for educational methods that enable parents and children to safely handle online information together. However, traditional learning methods have the challenge of not being able to provide efficient education that incorporates individual user characteristics and real-time feedback.

[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 inference means that generates prompt sentences based on personal information collected from the user and constructs learning content; a display means that delivers the generated learning content to a terminal and presents it to the user; and an evaluation means that analyzes the user's learning progress and input results and generates personalized feedback based on the results. This makes it possible for parents and children to learn efficiently in real time through personalized educational content.

[0066] "Personal information collected from users" refers to profiles and past behavioral data obtained from users in order to personalize learning content.

[0067] A "prompt" refers to an input sentence used as an instruction or guideline to generate training content in a generative AI model.

[0068] "Learning content" refers to educational materials and scenarios provided to users, including educational content designed to foster safety measures and problem-solving skills.

[0069] "Inference tools" refer to AI models and algorithms that use personal information to generate appropriate learning content.

[0070] "Display means" refers to the interface or device function on a terminal that provides the generated learning content to the user visually or audibly.

[0071] "Evaluation means" refers to server functions and software that analyze the user's learning progress and quiz results, and generate feedback based on those results.

[0072] "Personalized feedback" refers to personalized information provided to indicate the next learning steps or areas for improvement based on the user's learning outcomes and level of understanding.

[0073] Implementing this system requires an integrated environment where servers, terminals, and users work together in coordination.

[0074] The server plays a central role in data processing, generating learning content using generative AI models. This begins with generating optimized prompts using AI based on personal information collected from users. Based on this, the server constructs educational scenarios regarding internet safety measures, including those on social media. Specifically, the AI ​​model analyzes the user's past behavior data and dynamically generates appropriate educational content. For example, depending on the user's age group and interests, a prompt such as "Create a safety education scenario for teenagers regarding social media use" might be generated.

[0075] The device displays learning content delivered from the server. Users can receive educational content in real time through the device. The displayed content includes interactive quizzes and multiple-choice scenarios, designed to encourage active user participation. The device also includes features that provide visual and auditory feedback to support effective learning.

[0076] Users, both parents and children, can use this system together to learn safe ways to use social media through the presented content. They are provided with opportunities to deepen their learning by answering quizzes using their devices and participating in simulated experiences through scenarios. The server analyzes the user's learning progress and generates and presents appropriate feedback.

[0077] This invention will enable parents and children to learn personalized educational content collaboratively in real time. The platform for this will require AI-powered content generation and results-based feedback functions as key components.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server collects personal information and past behavioral data entered by the user. Based on this information, the generative AI model performs preprocessing to generate prompt sentences. The input information includes the user's basic data, age, and internet usage history, and this data is processed to identify the user's characteristics. The output is the basic information needed to generate prompt sentences.

[0081] Step 2:

[0082] Based on the basic information obtained in Step 1, the server generates prompt messages using a generative AI model. These generated prompt messages function as instructions for the AI ​​to construct a specific safety education scenario. Data calculations involve user profiling using personal information. The output is a prompt message customized for the relevant user.

[0083] Step 3:

[0084] The server inputs the generated prompt sentences into the AI ​​model, which then generates learning content optimized for the user. In this process, the AI ​​model utilizes a large amount of existing data to create scenario-based learning materials that take trends and risks into account. The input is prompt sentences, and the output is learning content tailored to each individual user.

[0085] Step 4:

[0086] The server sends the generated learning content to the device. The device prepares to display the received content to the user. Specifically, the device downloads the content and displays an interactive screen. The input is the learning content sent from the server, and the output is the display screen accessible to the user.

[0087] Step 5:

[0088] Users participate in learning content presented via their devices, deepening their knowledge through answering quizzes and experiencing scenarios. User input consists of quiz answers and selection of answer choices, and this information is used in the next analysis step. Output consists of user behavior data and response information.

[0089] Step 6:

[0090] The server analyzes user behavior data and quiz answers to evaluate understanding and areas for improvement. Analysis tools process this data and generate feedback. Data processing includes evaluation scores and suggestions for the next learning steps. The output is personalized feedback for the user.

[0091] Step 7:

[0092] The server sends the generated feedback to the terminal. The terminal presents the feedback to the user visually or audibly. The user reflects on their learning based on this feedback and decides on their next action. The input is the feedback information, and the output is the feedback content displayed on the screen.

[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 today's digital society, there is a lack of learning environments that enable both parents and children to understand and appropriately deal with the risks of online interactions. In particular, there is a need for education that helps children recognize potential problems they may encounter on online platforms such as social media and to cultivate sound judgment.

[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 means for generating learning materials based on user information, communication means for acquiring the user information and distributing the corresponding learning materials to an information processing device, and means for providing safety tests and situational experiences based on the user's past online interaction history. This makes it possible for parents and children to work together to learn safe online interaction methods and understand risks through real-world experience.

[0098] "User information" refers to data about the attributes and behavioral history of users, including parents and children, and is used to personalize and optimize the learning materials that are generated.

[0099] "Learning materials" refer to educational content provided through information processing devices, intended to teach about risk awareness and safety measures for online interactions.

[0100] A "generative artificial intelligence model" is a form of artificial intelligence equipped with an algorithm that analyzes user information and past training data to dynamically generate the necessary training materials.

[0101] An "information processing device" is an electronic device that can display learning materials and accept input from users, and serves as a medium for parents and children to access.

[0102] "Communication means" refers to technical means for sending and receiving data between a server and an information processing device, and utilizes network infrastructure.

[0103] "Analysis means" refers to software functions and processes for evaluating user participation results and providing feedback based on that data.

[0104] "Feedback" refers to information provided to point out a user's learning progress and weaknesses, and to instruct or suggest the next course of action.

[0105] "Situational simulation experiences" refer to educational scenarios and situations that are simulated and recreated for users to learn safety measures, fostering practical judgment skills regarding risks.

[0106] The system implementing this invention aims to enable both parents and children to learn about safe and meaningful online interactions, and utilizes a generative AI model to provide learning materials. The specific roles and operations of each component are shown below.

[0107] server

[0108] The server forms the core of the system, using generative AI models to analyze user information and dynamically generate training materials. The server is built using the Flask framework in Python and uses MongoDB for its database. The AI ​​utilizes generative AI models such as OpenAI® and GPT-3® to generate individually optimized educational content based on the user's behavior history. Specifically, the server provides simulated experiences of scenarios and countermeasures for SNS risks that users are likely to face, based on prompt messages.

[0109] terminal

[0110] The device functions as an information processing unit, providing an interface for parent and child users to access. Developed for smartphones using React Native, it displays learning materials in a user-friendly format. The device receives feedback from the server and supports learning progress by presenting it visually or audibly.

[0111] User

[0112] Users consist of parent and child members and participate in educational programs using learning materials. Users select answers through presented quizzes and scenarios, and receive feedback on their results from their devices, which helps them to continue learning. User responses and selections are analyzed on the server side and contribute to the generation of new learning materials.

[0113] A concrete example would be a scenario where a child receives a message from an unknown person on social media and has to choose how to respond. Through such scenarios, children can develop the judgment skills necessary for safe online activities.

[0114] Example of a prompt:

[0115] "Please generate appropriate safety scenarios based on social media problems that users have experienced in the past."

[0116] In this way, we provide a system that allows users to learn how to interact in a safe online environment while strengthening their ability to cope with risks.

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

[0118] Step 1:

[0119] The server collects user information for both parents and children. This includes past online interaction history and behavioral patterns. This information is retrieved from a database and used to input into a generative AI model. The server performs real-time data processing and prepares the data necessary for learning.

[0120] Step 2:

[0121] The generating AI model creates social media risk scenarios based on collected user information. It utilizes prompts to prepare optimal learning materials tailored to the user's past usage history. This process outputs individually optimized educational content for each user.

[0122] Step 3:

[0123] The server sends the generated training material to the terminal. Communication methods are used to ensure that this training material reaches the user's terminal quickly. The server transfers data packets as efficiently as possible, minimizing latency.

[0124] Step 4:

[0125] The device displays received learning materials visually or audibly. The interface, built with React Native, optimizes the layout to ensure a comfortable learning experience for both parents and children. For example, it uses graphics and audio that are likely to capture a child's interest.

[0126] Step 5:

[0127] Users participate in quizzes and simulated experiences using the provided learning materials. This helps them develop skills to deal with real-world risks. Users input information via their devices and send the results to the server.

[0128] Step 6:

[0129] The server receives user participation results and analyzes the data using analytical tools. It identifies the user's level of understanding and areas for improvement, and generates feedback to reflect these in the next stage of learning materials. As output, information indicating appropriate improvement measures is generated.

[0130] Step 7:

[0131] The server sends feedback information to the device, providing the user with the information necessary to move on to the next step in their learning. The device then presents this information to the parent and child, creating a learning cycle. This entire process allows parents and children to continuously reinforce their awareness of and countermeasures against the dangers of online interaction.

[0132] 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.

[0133] This invention is a system that combines an interactive learning platform, where parents and children can learn safe ways to use social media together, with an emotion engine that recognizes user emotions. An embodiment of the system is shown below.

[0134] server:

[0135] The server is equipped with a generative artificial intelligence model, its main function, to generate learning content based on user information. Furthermore, the server has an emotion engine that integrates with it, providing processing capabilities to recognize emotions from the user's facial expressions, voice, and input. The emotion engine evaluates the user's emotional state and dynamically adjusts the learning content accordingly. For example, if a user expresses frustration with difficult content, the system updates the difficulty level and adjusts the program accordingly.

[0136] Terminal:

[0137] The device is an interface for displaying learning content for parents and children, providing users with content delivered from a server. The learning scenario changes according to pre-set content, allowing users to visually experience the risks associated with using social media. The device also captures the user's facial expressions and voice through its camera and microphone, and sends emotional data to an emotion engine.

[0138] User:

[0139] The users are parent and child, and they actively participate in the learning content provided by the system. They work through scenarios at their own pace and respond to prompts displayed on their devices. Users receive real-time feedback on their actions in quizzes and simulated experiences, and receive adaptive education based on emotion recognition. For example, if a user smiles, content that reinforces their sense of accomplishment is presented.

[0140] The role of the emotional engine:

[0141] The emotion engine analyzes acquired emotional information and provides feedback according to the user's learning progress. This creates a learning experience that makes it easier for users to maintain their motivation. In particular, when the user is showing positive emotions, it promotes further learning by presenting challenging problems.

[0142] This invention combines AI-based content generation with emotion recognition technology to enable personalized learning for parents and children and provide effective digital literacy education.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The server retrieves user information from a database and uses a generative artificial intelligence model to generate learning content optimized for each individual user. The generated content has a flexible structure that takes into account emotional states.

[0146] Step 2:

[0147] The device presents learning content received from the server to the parent and child users. The device uses its camera and microphone to continuously acquire emotional data from the users' facial expressions and voice.

[0148] Step 3:

[0149] Users participate in learning scenarios and quizzes displayed on their devices, and their responses are sent to the server in real time.

[0150] Step 4:

[0151] The emotion engine analyzes the emotion data transmitted from the device and evaluates the user's current emotional state. The evaluation results are then fed back to the server.

[0152] Step 5:

[0153] Based on the received emotion data, the server dynamically adjusts the learning content to suit the user's emotions. Specifically, it lowers the difficulty level of the content when frustration is high, and provides challenging content when positive emotions are high.

[0154] Step 6:

[0155] The device presents users with tailored learning content and emotion-based feedback. The UI may also be modified to maintain a user-friendly learning environment.

[0156] Step 7:

[0157] Users can revisit the scenario and experience improved content, leading to a smoother learning process. This allows for learning aligned with their motivation.

[0158] Step 8:

[0159] The server compiles the final training data, generates performance reports for each user, and performs continuous analysis to improve future training.

[0160] This series of steps makes it possible to continuously provide personalized learning content while taking into account the user's emotional state.

[0161] (Example 2)

[0162] 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".

[0163] In today's digital society, there is a need for personalized education systems that allow parents and children to learn together safely in an online environment. However, conventional systems struggle to provide dynamic feedback that takes user emotions into account, making it difficult to maintain learner motivation. Furthermore, there is a challenge in that standardized learning content fails to meet the individual needs of parents and children.

[0164] 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.

[0165] In this invention, the server includes a computing device for creating learning materials based on user information, a telecommunications device for receiving the user information and providing the corresponding learning materials to the user's device, and an analysis device for analyzing the user's emotions and adjusting the learning materials based on the analysis results. This enables the dynamic adjustment of the learning experience based on the user's emotions, and allows for the provision of individualized and effective education for both parents and children.

[0166] "User information" refers to basic data such as the user's age and experience level, collected for the purpose of personalizing and optimizing learning content within the system.

[0167] "Learning materials" are educational content for parents and children, generated based on user information and sentiment analysis, and serve as teaching materials for acquiring safe digital literacy.

[0168] A "computational device" is a device equipped with mechanical elements for creating appropriate learning materials based on user information.

[0169] A "telecommunications device" is a device that has a means of communication for transmitting learning materials between a server and a user device.

[0170] An "analysis device" is a device that analyzes the user's emotional data and adjusts the learning materials based on the results.

[0171] An "emotion engine" is a software or hardware component that analyzes a user's emotional information, generates feedback based on the analysis results, and dynamically adjusts the learning experience.

[0172] "Dynamic feedback" refers to educational or instructional content that is instantly adjusted in response to the user's real-time emotional state and learning progress.

[0173] This system is a personalized platform for parents and children to learn together and understand safe online environments. The system is primarily composed of three subjects: server, terminal, and user.

[0174] The server is the core of the system and uses a generative AI model to create training material based on user information. The generative AI model processes the input user information (e.g., age, SNS usage experience, etc.) and builds training content optimized for the user. Furthermore, the server has an emotion engine that analyzes the user's real-time emotion data (facial expressions, voice, input content, etc.) to dynamically adjust the training material.

[0175] The device displays learning materials provided by the server to both parent and child users and has a built-in camera and microphone. This allows it to capture the user's physical reactions and send the data to the emotion engine. The device displays interactive and visually easy-to-understand content and provides a convenient interface to encourage user participation.

[0176] The user base consists of parents and children. In this system, users actively participate in learning materials and learn at their own pace. Specifically, they receive immediate feedback in response to quizzes and simulated experiences provided through their devices, promoting emotionally-driven adaptive learning. For example, when a user answers a quiz, they are presented with a prompt such as, "Think about how your child would handle a message from a stranger on social media," and the next learning step is determined based on the user's response.

[0177] This configuration allows the system to provide education that enables parents and children to have safe and beneficial online experiences, and to support the improvement of their digital literacy.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] When logging into the system, users enter basic information (age, SNS usage experience, etc.) into their device. This information becomes input data, which the device then sends to the server.

[0181] Step 2:

[0182] The server uses the received user information to run a generation AI model and generate optimal training materials. Based on the input user information, the AI ​​model performs data analysis and creates content with themes and difficulty levels suitable for both parent and child. The generated training materials are stored on the server as intermediate data.

[0183] Step 3:

[0184] The server delivers the generated learning materials to the terminal. The terminal receives this information and displays the learning materials visually to the user. The learning materials include quizzes, videos, simulated experiences, and prompts to encourage user participation.

[0185] Step 4:

[0186] As users engage with learning materials displayed on their device, they respond to quizzes and simulated experiences provided. Based on the user's answers and choices, the device collects data in real time to display feedback.

[0187] Step 5:

[0188] The device captures the user's facial expressions and vocal responses through its camera and microphone, and sends this emotional data to a server. This data becomes the input and is used for emotion analysis.

[0189] Step 6:

[0190] The server activates an emotion engine and analyzes the received emotion data. It evaluates the user's emotional state and dynamically adjusts the content and difficulty level of the learning materials. For example, if the user indicates difficulty, the server decides to lower the learning difficulty level.

[0191] Step 7:

[0192] The server sends updated learning materials and feedback to the device based on the analysis results. The device redisplays them, and the user continues with the next step. This feedback loop ensures a dynamic learning experience that adapts to the user's progress.

[0193] (Application Example 2)

[0194] 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".

[0195] Modern parents and children face challenges in using social media due to safety concerns and a lack of digital literacy. However, traditional educational methods struggle to maintain the interest of both parents and children, making effective learning difficult. Furthermore, the lack of educational methods that consider individual emotional states makes it difficult to maintain the learning motivation of both parents and children. To address these challenges, an interactive educational system that incorporates emotional recognition is needed.

[0196] 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.

[0197] In this invention, the server includes means equipped with a generative artificial intelligence model that generates learning content based on user information in order to generate a learning program in which both parent and child can participate; communication means for acquiring user information and delivering the corresponding learning content to the user terminal; analysis means for analyzing the results of the user's participation in the learning content and generating feedback based on the analysis results; emotion recognition means for recognizing the user's emotional state and dynamically adjusting the learning content; and a humanoid machine device for enabling parent and child to learn in a home environment. This makes it easier for both parent and child to participate and to effectively learn how to use social networking services safely through an individually tailored learning experience.

[0198] "Parent and child" refers to a parent and their child, and they are the group that learns together within the family.

[0199] A "learning program" is a structured process that provides content and scenarios designed in accordance with educational objectives, with the aim of improving the user's knowledge.

[0200] "User information" refers to data related to a user, including learning history, emotional state, and individual characteristics.

[0201] "Learning content" refers to knowledge, experiences, or training materials and activities provided for educational purposes.

[0202] A "generative artificial intelligence model" is a collection of algorithms that use AI technology to generate optimal content based on user information.

[0203] "Communication means" refers to the technology or device used to send and receive information between a server and a user terminal.

[0204] "Analysis means" refers to a process or function that analyzes user participation results and generates information to effectively guide the next step of education.

[0205] "Presentation means" refers to a device or method for providing feedback or information to a user visually or audibly.

[0206] "Emotion recognition methods" refer to technologies and processes for analyzing emotions from a user's facial expressions, voice, and other similar data.

[0207] A "humanoid mechanical device" is a device that mimics the shape of a human being for educational activities within the home, providing an interactive learning experience.

[0208] The system that realizes this invention consists of a set of software and hardware to work in conjunction with a home educational robot designed for parents and children. The server first generates learning content based on user information using a generative artificial intelligence model. This model has the ability to dynamically generate user-optimized content based on past learning history and sentiment information. This uses a machine learning platform such as TENSORFLOW®.

[0209] On the terminal side, the user (i.e., parent and child) learns through a humanoid robotic device. The terminal is equipped with a camera and microphone, and uses OpenCV and TensorFlow Lite to analyze facial and voice data in real time, functioning as a means of emotion recognition. The emotion data obtained in this way is sent to a server and reflected in the learning content.

[0210] For example, when a user is experiencing a social media usage scenario with a robot, if their facial expressions show something interesting, the server will use that feedback to adjust the learning content to be more challenging. Conversely, if they appear confused, the server will provide guidance with a lower difficulty level to help them continue learning. Specifically, prompts like the following might be used: "What action should you take in this social media situation? Evaluate the user's facial expressions and statements, and generate appropriate educational content for that situation."

[0211] This system allows parents and children to learn how to use social media safely in an interactive and personalized way.

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The server receives user information, including past learning history and current emotional state. Using this user information as input, the generative AI model performs an optimization process to generate learning content. As a result, learning content tailored to the user is output.

[0215] Step 2:

[0216] The generated learning content is transmitted to the terminal via a communication device. The terminal receives this data and displays the learning content to the parent and child on a humanoid robotic device, encouraging participation. At this stage, the terminal begins monitoring the user's voice and facial expressions using a camera and microphone.

[0217] Step 3:

[0218] The user reacts to the presented learning content. The device acquires facial and voice data in real time and analyzes it using emotion recognition technology. Based on the input data, the user's emotional state is output as numerical data.

[0219] Step 4:

[0220] The server receives emotional data from the terminal and incorporates it as feedback into the generating AI model. Based on this data, the learning content is dynamically adjusted. For example, if the output is determined to be causing the user to lose interest, the difficulty level of the content is reduced.

[0221] Step 5:

[0222] The adjusted learning content is sent back to the device. The device presents the updated content to the user and encourages them to participate in the learning process again. The learning experience continues as the user continues to respond to the new scenarios.

[0223] Step 6:

[0224] After a user completes their training, the server collects all the training data and uses it to optimize the next training session. This data collection allows the generative AI model to create a spiral that improves the quality of future training content.

[0225] 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.

[0226] Data generation model 58 is a type of 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] In order to implement the present invention, it is necessary to construct a system in which a server, a terminal, and a user each play their respective roles and work together. Specific embodiments of each component are shown below.

[0242] server:

[0243] The server plays a central role in the platform, dynamically generating learning content using generative artificial intelligence models. Based on information provided by users and past behavioral data, it constructs learning scenarios about SNS risks and safety measures, and prepares educational content optimized for each individual user.

[0244] Terminal:

[0245] The device serves to display and provide learning content to the user and their child. The screen accessible to the user presents scenario-based learning materials and interactive quizzes prepared on the server side. For example, the device may recreate social media trouble scenarios that children are likely to encounter and display content that allows them to choose how to respond.

[0246] User:

[0247] Users, both parents and children, utilize the content and participate in the learning activities presented by the system. Through their devices, users can answer quizzes and learn safe social media usage through scenario-based simulations. This provides an educational environment that enhances decision-making skills through discussions between parents and children.

[0248] Generating and presenting feedback:

[0249] The server analyzes the user's learning progress and quiz results, generating feedback on their level of understanding and areas for improvement. This feedback is sent to the device and presented to the user in an appropriate format. For example, it might point out that "awareness regarding the protection of personal information needs to be further increased." This provides parents and children with information to help them decide on the next steps in their learning.

[0250] Thus, this invention is designed to enable parents and children to learn together in real time by providing personalized educational content utilizing AI.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server initializes the generative artificial intelligence model and prepares to collect user information. It interacts with a database that holds data such as the user's age, social media usage history, and language settings.

[0254] Step 2:

[0255] The device displays a personalized initial setup screen to the user based on their registered information and prompts them to enter any necessary additional information. For example, it might present a question form regarding the type of social networking service (SNS) they use and how often they use it.

[0256] Step 3:

[0257] Users answer the presented questions and enter necessary information through their device. For example, they can also enter household rules such as the amount of time parents allow for social media use.

[0258] Step 4:

[0259] The server uses a live artificial intelligence model to generate appropriate learning scenarios and quizzes based on information provided by the user. This generation process incorporates the latest risk information and protection guidelines regarding SNS usage.

[0260] Step 5:

[0261] The device presents the user with generated learning content. For example, it might display a simulated experience game based on risk scenarios or a quiz about ethical social media use.

[0262] Step 6:

[0263] Users participate in learning content presented on their devices and answer quizzes. Their answers are transmitted to the server in real time.

[0264] Step 7:

[0265] The server analyzes user responses and participation history to evaluate each user's level of understanding and learning progress. It also generates next learning content and supplementary information based on the generated feedback.

[0266] Step 8:

[0267] The device presents the user with analyzed feedback. Specifically, it displays reports on learning progress and the next tasks to be completed, encouraging the user to continue learning.

[0268] Through this series of processes, the system can provide users with a personalized learning environment.

[0269] (Example 1)

[0270] 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."

[0271] In today's information environment, there is a need for educational methods that enable parents and children to safely handle online information together. However, traditional learning methods have the challenge of not being able to provide efficient education that incorporates individual user characteristics and real-time feedback.

[0272] 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.

[0273] In this invention, the server includes an inference means that generates prompt sentences based on personal information collected from the user and constructs learning content; a display means that delivers the generated learning content to a terminal and presents it to the user; and an evaluation means that analyzes the user's learning progress and input results and generates personalized feedback based on the results. This makes it possible for parents and children to learn efficiently in real time through personalized educational content.

[0274] "Personal information collected from users" refers to profiles and past behavioral data obtained from users in order to personalize learning content.

[0275] A "prompt" refers to an input sentence used as an instruction or guideline to generate training content in a generative AI model.

[0276] "Learning content" refers to educational materials and scenarios provided to users, including educational content designed to foster safety measures and problem-solving skills.

[0277] "Inference tools" refer to AI models and algorithms that use personal information to generate appropriate learning content.

[0278] "Display means" refers to the interface or device function on a terminal that provides the generated learning content to the user visually or audibly.

[0279] "Evaluation means" refers to server functions and software that analyze the user's learning progress and quiz results, and generate feedback based on those results.

[0280] "Personalized feedback" refers to personalized information provided to indicate the next learning steps or areas for improvement based on the user's learning outcomes and level of understanding.

[0281] Implementing this system requires an integrated environment where servers, terminals, and users work together in coordination.

[0282] The server is responsible for the core of data processing and uses a generative AI model to generate learning content. This begins with generating an optimized prompt text using AI based on the personal information collected from users. Based on this, the server constructs an educational scenario regarding security measures on the Internet, including SNS. Specifically, the AI model has the function of analyzing the user's past behavior data and dynamically generating educational content that matches it. For example, a prompt text such as "Please create an educational scenario for SNS use for teenagers" is generated according to the user's age group and interests.

[0283] The terminal plays the role of displaying the learning content distributed from the server. The user can receive educational content in real time via the terminal. The displayed content includes interactive quizzes and options for scenarios, and is designed so that the user can actively participate. The terminal also includes a function to provide visual and auditory feedback to support effective learning.

[0284] Users can use this system together with their parents and learn safe SNS usage methods through the presented content. By answering quizzes using the terminal or conducting simulated experiences through scenarios, opportunities are provided to deepen learning. The learning progress of the user is analyzed by the server, and appropriate feedback is generated and presented.

[0285] With this invention, it becomes possible for parents and children to cooperate and learn individualized educational content in real time. Important components of the platform for this are the content generation function utilizing AI technology and the feedback presentation function based on results.

[0286] The flow of specific processing in Example 1 will be described using FIG. 11.

[0287] Step 1:

[0288] The server collects personal information and past behavioral data entered by the user. Based on this information, the generative AI model performs preprocessing to generate prompt sentences. The input information includes the user's basic data, age, and internet usage history, and this data is processed to identify the user's characteristics. The output is the basic information needed to generate prompt sentences.

[0289] Step 2:

[0290] Based on the basic information obtained in Step 1, the server generates prompt messages using a generative AI model. These generated prompt messages function as instructions for the AI ​​to construct a specific safety education scenario. Data calculations involve user profiling using personal information. The output is a prompt message customized for the relevant user.

[0291] Step 3:

[0292] The server inputs the generated prompt sentences into the AI ​​model, which then generates learning content optimized for the user. In this process, the AI ​​model utilizes a large amount of existing data to create scenario-based learning materials that take trends and risks into account. The input is prompt sentences, and the output is learning content tailored to each individual user.

[0293] Step 4:

[0294] The server sends the generated learning content to the device. The device prepares to display the received content to the user. Specifically, the device downloads the content and displays an interactive screen. The input is the learning content sent from the server, and the output is the display screen accessible to the user.

[0295] Step 5:

[0296] Users participate in learning content presented via their devices, deepening their knowledge through answering quizzes and experiencing scenarios. User input consists of quiz answers and selection of answer choices, and this information is used in the next analysis step. Output consists of user behavior data and response information.

[0297] Step 6:

[0298] The server analyzes user behavior data and quiz answers to evaluate understanding and areas for improvement. Analysis tools process this data and generate feedback. Data processing includes evaluation scores and suggestions for the next learning steps. The output is personalized feedback for the user.

[0299] Step 7:

[0300] The server sends the generated feedback to the terminal. The terminal presents the feedback to the user visually or audibly. The user reflects on their learning based on this feedback and decides on their next action. The input is the feedback information, and the output is the feedback content displayed on the screen.

[0301] (Application Example 1)

[0302] 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."

[0303] In today's digital society, there is a lack of learning environments that enable both parents and children to understand and appropriately deal with the risks of online interactions. In particular, there is a need for education that helps children recognize potential problems they may encounter on online platforms such as social media and to cultivate sound judgment.

[0304] 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.

[0305] In this invention, the server includes means for generating learning materials based on user information, communication means for acquiring the user information and distributing the corresponding learning materials to an information processing device, and means for providing safety-related tests and situation-setting experiences based on the user's past online communication history. As a result, parents and children can cooperate to learn a safe online communication method and understand risks through hands-on experience.

[0306] "User information" refers to data related to the attributes and behavior histories of users including parents and children, and is information used for individualizing and optimizing the generated learning materials.

[0307] "Learning materials" refer to educational content provided through an information processing device and are for learning about the risks of online communication and safety measures.

[0308] "Generated artificial intelligence model" refers to a form of artificial intelligence equipped with an algorithm that analyzes user information and past learning data and dynamically generates necessary learning materials.

[0309] "Information processing device" refers to an electronic device that can display learning materials and receive inputs from users, and is a medium for parents and children to access.

[0310] "Communication means" refers to the technical means for transmitting and receiving data between the server and the information processing device, and utilizes the network infrastructure.

[0311] "Analysis means" refers to software functions and processes for evaluating the user's participation results and providing feedback based on that data.

[0312] "Feedback" refers to information provided to point out the user's learning progress and weaknesses and to instruct or suggest the next actions.

[0313] "Situational simulation experiences" refer to educational scenarios and situations that are simulated and recreated for users to learn safety measures, fostering practical judgment skills regarding risks.

[0314] The system implementing this invention aims to enable both parents and children to learn about safe and meaningful online interactions, and utilizes a generative AI model to provide learning materials. The specific roles and operations of each component are shown below.

[0315] server

[0316] The server forms the core of the system, using generative AI models to analyze user information and dynamically generate training materials. The server is built using the Flask framework in Python and uses MongoDB for its database. The AI ​​leverages generative AI models such as OpenAI GPT-3 to generate individually optimized educational content based on the user's behavior history. Specifically, the server uses prompt messages to provide simulated experiences of scenarios and countermeasures for SNS risks that users are likely to face.

[0317] terminal

[0318] The device functions as an information processing unit, providing an interface for parent and child users to access. Developed for smartphones using React Native, it displays learning materials in a user-friendly format. The device receives feedback from the server and supports learning progress by presenting it visually or audibly.

[0319] User

[0320] Users consist of parent and child members and participate in educational programs using learning materials. Users select answers through presented quizzes and scenarios, and receive feedback on their results from their devices, which helps them to continue learning. User responses and selections are analyzed on the server side and contribute to the generation of new learning materials.

[0321] A concrete example would be a scenario where a child receives a message from an unknown person on social media and has to choose how to respond. Through such scenarios, children can develop the judgment skills necessary for safe online activities.

[0322] Example of a prompt:

[0323] "Please generate appropriate safety scenarios based on social media problems that users have experienced in the past."

[0324] In this way, we provide a system that allows users to learn how to interact in a safe online environment while strengthening their ability to cope with risks.

[0325] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0326] Step 1:

[0327] The server collects user information for both parents and children. This includes past online interaction history and behavioral patterns. This information is retrieved from a database and used to input into a generative AI model. The server performs real-time data processing and prepares the data necessary for learning.

[0328] Step 2:

[0329] The generating AI model creates social media risk scenarios based on collected user information. It utilizes prompts to prepare optimal learning materials tailored to the user's past usage history. This process outputs individually optimized educational content for each user.

[0330] Step 3:

[0331] The server sends the generated training material to the terminal. Communication methods are used to ensure that this training material reaches the user's terminal quickly. The server transfers data packets as efficiently as possible, minimizing latency.

[0332] Step 4:

[0333] The device displays received learning materials visually or audibly. The interface, built with React Native, optimizes the layout to ensure a comfortable learning experience for both parents and children. For example, it uses graphics and audio that are likely to capture a child's interest.

[0334] Step 5:

[0335] Users participate in quizzes and simulated experiences using the provided learning materials. This helps them develop skills to deal with real-world risks. Users input information via their devices and send the results to the server.

[0336] Step 6:

[0337] The server receives user participation results and analyzes the data using analytical tools. It identifies the user's level of understanding and areas for improvement, and generates feedback to reflect these in the next stage of learning materials. As output, information indicating appropriate improvement measures is generated.

[0338] Step 7:

[0339] The server sends feedback information to the device, providing the user with the information necessary to move on to the next step in their learning. The device then presents this information to the parent and child, creating a learning cycle. This entire process allows parents and children to continuously reinforce their awareness of and countermeasures against the dangers of online interaction.

[0340] 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.

[0341] This invention is a system that combines an interactive learning platform, where parents and children can learn safe ways to use social media together, with an emotion engine that recognizes user emotions. An embodiment of the system is shown below.

[0342] server:

[0343] The server is equipped with a generative artificial intelligence model, its main function, to generate learning content based on user information. Furthermore, the server has an emotion engine that integrates with it, providing processing capabilities to recognize emotions from the user's facial expressions, voice, and input. The emotion engine evaluates the user's emotional state and dynamically adjusts the learning content accordingly. For example, if a user expresses frustration with difficult content, the system updates the difficulty level and adjusts the program accordingly.

[0344] Terminal:

[0345] The device is an interface for displaying learning content for parents and children, providing users with content delivered from a server. The learning scenario changes according to pre-set content, allowing users to visually experience the risks associated with using social media. The device also captures the user's facial expressions and voice through its camera and microphone, and sends emotional data to an emotion engine.

[0346] User:

[0347] The users are parent and child, and they actively participate in the learning content provided by the system. They work through scenarios at their own pace and respond to prompts displayed on their devices. Users receive real-time feedback on their actions in quizzes and simulated experiences, and receive adaptive education based on emotion recognition. For example, if a user smiles, content that reinforces their sense of accomplishment is presented.

[0348] The role of the emotional engine:

[0349] The emotion engine analyzes acquired emotional information and provides feedback according to the user's learning progress. This creates a learning experience that makes it easier for users to maintain their motivation. In particular, when the user is showing positive emotions, it promotes further learning by presenting challenging problems.

[0350] This invention combines AI-based content generation with emotion recognition technology to enable personalized learning for parents and children and provide effective digital literacy education.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The server retrieves user information from a database and uses a generative artificial intelligence model to generate learning content optimized for each individual user. The generated content has a flexible structure that takes into account emotional states.

[0354] Step 2:

[0355] The device presents learning content received from the server to the parent and child users. The device uses its camera and microphone to continuously acquire emotional data from the users' facial expressions and voice.

[0356] Step 3:

[0357] Users participate in learning scenarios and quizzes displayed on their devices, and their responses are sent to the server in real time.

[0358] Step 4:

[0359] The emotion engine analyzes the emotion data transmitted from the device and evaluates the user's current emotional state. The evaluation results are then fed back to the server.

[0360] Step 5:

[0361] Based on the received emotion data, the server dynamically adjusts the learning content to suit the user's emotions. Specifically, it lowers the difficulty level of the content when frustration is high, and provides challenging content when positive emotions are high.

[0362] Step 6:

[0363] The device presents users with tailored learning content and emotion-based feedback. The UI may also be modified to maintain a user-friendly learning environment.

[0364] Step 7:

[0365] Users can revisit the scenario and experience improved content, leading to a smoother learning process. This allows for learning aligned with their motivation.

[0366] Step 8:

[0367] The server compiles the final training data, generates performance reports for each user, and performs continuous analysis to improve future training.

[0368] This series of steps makes it possible to continuously provide personalized learning content while taking into account the user's emotional state.

[0369] (Example 2)

[0370] 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".

[0371] In today's digital society, there is a need for personalized education systems that allow parents and children to learn together safely in an online environment. However, conventional systems struggle to provide dynamic feedback that takes user emotions into account, making it difficult to maintain learner motivation. Furthermore, there is a challenge in that standardized learning content fails to meet the individual needs of parents and children.

[0372] 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.

[0373] In this invention, the server includes a computing device for creating learning materials based on user information, a telecommunications device for receiving the user information and providing the corresponding learning materials to the user's device, and an analysis device for analyzing the user's emotions and adjusting the learning materials based on the analysis results. This enables the dynamic adjustment of the learning experience based on the user's emotions, and allows for the provision of individualized and effective education for both parents and children.

[0374] "User information" refers to basic data such as the user's age and experience level, collected for the purpose of personalizing and optimizing learning content within the system.

[0375] "Learning materials" are educational content for parents and children, generated based on user information and sentiment analysis, and serve as teaching materials for acquiring safe digital literacy.

[0376] A "computational device" is a device equipped with mechanical elements for creating appropriate learning materials based on user information.

[0377] A "telecommunications device" is a device that has a means of communication for transmitting learning materials between a server and a user device.

[0378] An "analysis device" is a device that analyzes the user's emotional data and adjusts the learning materials based on the results.

[0379] An "emotion engine" is a software or hardware component that analyzes a user's emotional information, generates feedback based on the analysis results, and dynamically adjusts the learning experience.

[0380] "Dynamic feedback" refers to educational or instructional content that is instantly adjusted in response to the user's real-time emotional state and learning progress.

[0381] This system is a personalized platform for parents and children to learn together and understand safe online environments. The system is primarily composed of three subjects: server, terminal, and user.

[0382] The server is the core of the system and uses a generative AI model to create training material based on user information. The generative AI model processes the input user information (e.g., age, SNS usage experience, etc.) and builds training content optimized for the user. Furthermore, the server has an emotion engine that analyzes the user's real-time emotion data (facial expressions, voice, input content, etc.) to dynamically adjust the training material.

[0383] The device displays learning materials provided by the server to both parent and child users and has a built-in camera and microphone. This allows it to capture the user's physical reactions and send the data to the emotion engine. The device displays interactive and visually easy-to-understand content and provides a convenient interface to encourage user participation.

[0384] The user base consists of parents and children. In this system, users actively participate in learning materials and learn at their own pace. Specifically, they receive immediate feedback in response to quizzes and simulated experiences provided through their devices, promoting emotionally-driven adaptive learning. For example, when a user answers a quiz, they are presented with a prompt such as, "Think about how your child would handle a message from a stranger on social media," and the next learning step is determined based on the user's response.

[0385] This configuration allows the system to provide education that enables parents and children to have safe and beneficial online experiences, and to support the improvement of their digital literacy.

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] When logging into the system, users enter basic information (age, SNS usage experience, etc.) into their device. This information becomes input data, which the device then sends to the server.

[0389] Step 2:

[0390] The server uses the received user information to run a generation AI model and generate optimal training materials. Based on the input user information, the AI ​​model performs data analysis and creates content with themes and difficulty levels suitable for both parent and child. The generated training materials are stored on the server as intermediate data.

[0391] Step 3:

[0392] The server delivers the generated learning materials to the terminal. The terminal receives this information and displays the learning materials visually to the user. The learning materials include quizzes, videos, simulated experiences, and prompts to encourage user participation.

[0393] Step 4:

[0394] As users engage with learning materials displayed on their device, they respond to quizzes and simulated experiences provided. Based on the user's answers and choices, the device collects data in real time to display feedback.

[0395] Step 5:

[0396] The device captures the user's facial expressions and vocal responses through its camera and microphone, and sends this emotional data to a server. This data becomes the input and is used for emotion analysis.

[0397] Step 6:

[0398] The server activates an emotion engine and analyzes the received emotion data. It evaluates the user's emotional state and dynamically adjusts the content and difficulty level of the learning materials. For example, if the user indicates difficulty, the server decides to lower the learning difficulty level.

[0399] Step 7:

[0400] The server sends updated learning materials and feedback to the device based on the analysis results. The device redisplays them, and the user continues with the next step. This feedback loop ensures a dynamic learning experience that adapts to the user's progress.

[0401] (Application Example 2)

[0402] 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."

[0403] Modern parents and children face challenges in using social media due to safety concerns and a lack of digital literacy. However, traditional educational methods struggle to maintain the interest of both parents and children, making effective learning difficult. Furthermore, the lack of educational methods that consider individual emotional states makes it difficult to maintain the learning motivation of both parents and children. To address these challenges, an interactive educational system that incorporates emotional recognition is needed.

[0404] 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.

[0405] In this invention, the server includes means equipped with a generative artificial intelligence model that generates learning content based on user information in order to generate a learning program in which both parent and child can participate; communication means for acquiring user information and delivering the corresponding learning content to the user terminal; analysis means for analyzing the results of the user's participation in the learning content and generating feedback based on the analysis results; emotion recognition means for recognizing the user's emotional state and dynamically adjusting the learning content; and a humanoid machine device for enabling parent and child to learn in a home environment. This makes it easier for both parent and child to participate and to effectively learn how to use social networking services safely through an individually tailored learning experience.

[0406] "Parent and child" refers to a parent and their child, and they are the group that learns together within the family.

[0407] A "learning program" is a structured process that provides content and scenarios designed in accordance with educational objectives, with the aim of improving the user's knowledge.

[0408] "User information" refers to data related to a user, including learning history, emotional state, and individual characteristics.

[0409] "Learning content" refers to knowledge, experiences, or training materials and activities provided for educational purposes.

[0410] A "generative artificial intelligence model" is a collection of algorithms that use AI technology to generate optimal content based on user information.

[0411] "Communication means" refers to the technology or device used to send and receive information between a server and a user terminal.

[0412] "Analysis means" refers to a process or function that analyzes user participation results and generates information to effectively guide the next step of education.

[0413] "Presentation means" refers to a device or method for providing feedback or information to a user visually or audibly.

[0414] "Emotion recognition methods" refer to technologies and processes for analyzing emotions from a user's facial expressions, voice, and other similar data.

[0415] A "humanoid mechanical device" is a device that mimics the shape of a human being for educational activities within the home, providing an interactive learning experience.

[0416] The system that realizes this invention consists of a set of software and hardware to work in conjunction with a home educational robot designed for parents and children. The server first generates learning content based on user information using a generative artificial intelligence model. This model has the ability to dynamically generate user-optimized content based on past learning history and sentiment information. This uses a machine learning platform such as TensorFlow.

[0417] On the terminal side, the user (i.e., parent and child) learns through a humanoid robotic device. The terminal is equipped with a camera and microphone, and uses OpenCV and TensorFlow Lite to analyze facial and voice data in real time, functioning as a means of emotion recognition. The emotion data obtained in this way is sent to a server and reflected in the learning content.

[0418] For example, when a user is experiencing a social media usage scenario with a robot, if their facial expressions show something interesting, the server will use that feedback to adjust the learning content to be more challenging. Conversely, if they appear confused, the server will provide guidance with a lower difficulty level to help them continue learning. Specifically, prompts like the following might be used: "What action should you take in this social media situation? Evaluate the user's facial expressions and statements, and generate appropriate educational content for that situation."

[0419] This system allows parents and children to learn how to use social media safely in an interactive and personalized way.

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] The server receives user information, including past learning history and current emotional state. Using this user information as input, the generative AI model performs an optimization process to generate learning content. As a result, learning content tailored to the user is output.

[0423] Step 2:

[0424] The generated learning content is transmitted to the terminal via a communication device. The terminal receives this data and displays the learning content to the parent and child on a humanoid robotic device, encouraging participation. At this stage, the terminal begins monitoring the user's voice and facial expressions using a camera and microphone.

[0425] Step 3:

[0426] The user reacts to the presented learning content. The device acquires facial and voice data in real time and analyzes it using emotion recognition technology. Based on the input data, the user's emotional state is output as numerical data.

[0427] Step 4:

[0428] The server receives emotional data from the terminal and incorporates it as feedback into the generating AI model. Based on this data, the learning content is dynamically adjusted. For example, if the output is determined to be causing the user to lose interest, the difficulty level of the content is reduced.

[0429] Step 5:

[0430] The adjusted learning content is sent back to the device. The device presents the updated content to the user and encourages them to participate in the learning process again. The learning experience continues as the user continues to respond to the new scenarios.

[0431] Step 6:

[0432] After a user completes their training, the server collects all the training data and uses it to optimize the next training session. This data collection allows the generative AI model to create a spiral that improves the quality of future training content.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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".

[0449] In order to implement the present invention, it is necessary to construct a system in which a server, a terminal, and a user each play their respective roles and work together. Specific embodiments of each component are shown below.

[0450] server:

[0451] The server plays a central role in the platform, dynamically generating learning content using generative artificial intelligence models. Based on information provided by users and past behavioral data, it constructs learning scenarios about SNS risks and safety measures, and prepares educational content optimized for each individual user.

[0452] Terminal:

[0453] The device serves to display and provide learning content to the user and their child. The screen accessible to the user presents scenario-based learning materials and interactive quizzes prepared on the server side. For example, the device may recreate social media trouble scenarios that children are likely to encounter and display content that allows them to choose how to respond.

[0454] User:

[0455] Users, both parents and children, utilize the content and participate in the learning activities presented by the system. Through their devices, users can answer quizzes and learn safe social media usage through scenario-based simulations. This provides an educational environment that enhances decision-making skills through discussions between parents and children.

[0456] Generating and presenting feedback:

[0457] The server analyzes the user's learning progress and quiz results, generating feedback on their level of understanding and areas for improvement. This feedback is sent to the device and presented to the user in an appropriate format. For example, it might point out that "awareness regarding the protection of personal information needs to be further increased." This provides parents and children with information to help them decide on the next steps in their learning.

[0458] Thus, this invention is designed to enable parents and children to learn together in real time by providing personalized educational content utilizing AI.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] The server initializes the generative artificial intelligence model and prepares to collect user information. It interacts with a database that holds data such as the user's age, social media usage history, and language settings.

[0462] Step 2:

[0463] The device displays a personalized initial setup screen to the user based on their registered information and prompts them to enter any necessary additional information. For example, it might present a question form regarding the type of social networking service (SNS) they use and how often they use it.

[0464] Step 3:

[0465] Users answer the presented questions and enter necessary information through their device. For example, they can also enter household rules such as the amount of time parents allow for social media use.

[0466] Step 4:

[0467] The server uses a live artificial intelligence model to generate appropriate learning scenarios and quizzes based on information provided by the user. This generation process incorporates the latest risk information and protection guidelines regarding SNS usage.

[0468] Step 5:

[0469] The device presents the user with generated learning content. For example, it might display a simulated experience game based on risk scenarios or a quiz about ethical social media use.

[0470] Step 6:

[0471] Users participate in learning content presented on their devices and answer quizzes. Their answers are transmitted to the server in real time.

[0472] Step 7:

[0473] The server analyzes user responses and participation history to evaluate each user's level of understanding and learning progress. It also generates next learning content and supplementary information based on the generated feedback.

[0474] Step 8:

[0475] The device presents the user with analyzed feedback. Specifically, it displays reports on learning progress and the next tasks to be completed, encouraging the user to continue learning.

[0476] Through this series of processes, the system can provide users with a personalized learning environment.

[0477] (Example 1)

[0478] 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."

[0479] In today's information environment, there is a need for educational methods that enable parents and children to safely handle online information together. However, traditional learning methods have the challenge of not being able to provide efficient education that incorporates individual user characteristics and real-time feedback.

[0480] 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.

[0481] In this invention, the server includes an inference means that generates prompt sentences based on personal information collected from the user and constructs learning content; a display means that delivers the generated learning content to a terminal and presents it to the user; and an evaluation means that analyzes the user's learning progress and input results and generates personalized feedback based on the results. This makes it possible for parents and children to learn efficiently in real time through personalized educational content.

[0482] "Personal information collected from users" refers to profiles and past behavioral data obtained from users in order to personalize learning content.

[0483] A "prompt" refers to an input sentence used as an instruction or guideline to generate training content in a generative AI model.

[0484] "Learning content" refers to educational materials and scenarios provided to users, including educational content designed to foster safety measures and problem-solving skills.

[0485] "Inference tools" refer to AI models and algorithms that use personal information to generate appropriate learning content.

[0486] "Display means" refers to the interface or device function on a terminal that provides the generated learning content to the user visually or audibly.

[0487] "Evaluation means" refers to server functions and software that analyze the user's learning progress and quiz results, and generate feedback based on those results.

[0488] "Personalized feedback" refers to personalized information provided to indicate the next learning steps or areas for improvement based on the user's learning outcomes and level of understanding.

[0489] Implementing this system requires an integrated environment where servers, terminals, and users work together in coordination.

[0490] The server plays a central role in data processing, generating learning content using generative AI models. This begins with generating optimized prompts using AI based on personal information collected from users. Based on this, the server constructs educational scenarios regarding internet safety measures, including those on social media. Specifically, the AI ​​model analyzes the user's past behavior data and dynamically generates appropriate educational content. For example, depending on the user's age group and interests, a prompt such as "Create a safety education scenario for teenagers regarding social media use" might be generated.

[0491] The device displays learning content delivered from the server. Users can receive educational content in real time through the device. The displayed content includes interactive quizzes and multiple-choice scenarios, designed to encourage active user participation. The device also includes features that provide visual and auditory feedback to support effective learning.

[0492] Users, both parents and children, can use this system together to learn safe ways to use social media through the presented content. They are provided with opportunities to deepen their learning by answering quizzes using their devices and participating in simulated experiences through scenarios. The server analyzes the user's learning progress and generates and presents appropriate feedback.

[0493] This invention will enable parents and children to learn personalized educational content collaboratively in real time. The platform for this will require AI-powered content generation and results-based feedback functions as key components.

[0494] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0495] Step 1:

[0496] The server collects personal information and past behavioral data entered by the user. Based on this information, the generative AI model performs preprocessing to generate prompt sentences. The input information includes the user's basic data, age, and internet usage history, and this data is processed to identify the user's characteristics. The output is the basic information needed to generate prompt sentences.

[0497] Step 2:

[0498] Based on the basic information obtained in Step 1, the server generates prompt messages using a generative AI model. These generated prompt messages function as instructions for the AI ​​to construct a specific safety education scenario. Data calculations involve user profiling using personal information. The output is a prompt message customized for the relevant user.

[0499] Step 3:

[0500] The server inputs the generated prompt sentences into the AI ​​model, which then generates learning content optimized for the user. In this process, the AI ​​model utilizes a large amount of existing data to create scenario-based learning materials that take trends and risks into account. The input is prompt sentences, and the output is learning content tailored to each individual user.

[0501] Step 4:

[0502] The server sends the generated learning content to the device. The device prepares to display the received content to the user. Specifically, the device downloads the content and displays an interactive screen. The input is the learning content sent from the server, and the output is the display screen accessible to the user.

[0503] Step 5:

[0504] Users participate in learning content presented via their devices, deepening their knowledge through answering quizzes and experiencing scenarios. User input consists of quiz answers and selection of answer choices, and this information is used in the next analysis step. Output consists of user behavior data and response information.

[0505] Step 6:

[0506] The server analyzes user behavior data and quiz answers to evaluate understanding and areas for improvement. Analysis tools process this data and generate feedback. Data processing includes evaluation scores and suggestions for the next learning steps. The output is personalized feedback for the user.

[0507] Step 7:

[0508] The server sends the generated feedback to the terminal. The terminal presents the feedback to the user visually or audibly. The user reflects on their learning based on this feedback and decides on their next action. The input is the feedback information, and the output is the feedback content displayed on the screen.

[0509] (Application Example 1)

[0510] 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."

[0511] In today's digital society, there is a lack of learning environments that enable both parents and children to understand and appropriately deal with the risks of online interactions. In particular, there is a need for education that helps children recognize potential problems they may encounter on online platforms such as social media and to cultivate sound judgment.

[0512] 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.

[0513] In this invention, the server includes means for generating learning materials based on user information, communication means for acquiring the user information and distributing the corresponding learning materials to an information processing device, and means for providing safety tests and situational experiences based on the user's past online interaction history. This makes it possible for parents and children to work together to learn safe online interaction methods and understand risks through real-world experience.

[0514] "User information" refers to data about the attributes and behavioral history of users, including parents and children, and is used to personalize and optimize the learning materials that are generated.

[0515] "Learning materials" refer to educational content provided through information processing devices, intended to teach about risk awareness and safety measures for online interactions.

[0516] A "generative artificial intelligence model" is a form of artificial intelligence equipped with an algorithm that analyzes user information and past training data to dynamically generate the necessary training materials.

[0517] An "information processing device" is an electronic device that can display learning materials and accept input from users, and serves as a medium for parents and children to access.

[0518] "Communication means" refers to technical means for sending and receiving data between a server and an information processing device, and utilizes network infrastructure.

[0519] "Analysis means" refers to software functions and processes for evaluating user participation results and providing feedback based on that data.

[0520] "Feedback" refers to information provided to point out a user's learning progress and weaknesses, and to instruct or suggest the next course of action.

[0521] "Situational simulation experiences" refer to educational scenarios and situations that are simulated and recreated for users to learn safety measures, fostering practical judgment skills regarding risks.

[0522] The system implementing this invention aims to enable both parents and children to learn about safe and meaningful online interactions, and utilizes a generative AI model to provide learning materials. The specific roles and operations of each component are shown below.

[0523] server

[0524] The server forms the core of the system, using generative AI models to analyze user information and dynamically generate training materials. The server is built using the Flask framework in Python and uses MongoDB for its database. The AI ​​leverages generative AI models such as OpenAI GPT-3 to generate individually optimized educational content based on the user's behavior history. Specifically, the server uses prompt messages to provide simulated experiences of scenarios and countermeasures for SNS risks that users are likely to face.

[0525] terminal

[0526] The device functions as an information processing unit, providing an interface for parent and child users to access. Developed for smartphones using React Native, it displays learning materials in a user-friendly format. The device receives feedback from the server and supports learning progress by presenting it visually or audibly.

[0527] User

[0528] Users consist of parent and child members and participate in educational programs using learning materials. Users select answers through presented quizzes and scenarios, and receive feedback on their results from their devices, which helps them to continue learning. User responses and selections are analyzed on the server side and contribute to the generation of new learning materials.

[0529] A concrete example would be a scenario where a child receives a message from an unknown person on social media and has to choose how to respond. Through such scenarios, children can develop the judgment skills necessary for safe online activities.

[0530] Example of a prompt:

[0531] "Please generate appropriate safety scenarios based on social media problems that users have experienced in the past."

[0532] In this way, we provide a system that allows users to learn how to interact in a safe online environment while strengthening their ability to cope with risks.

[0533] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0534] Step 1:

[0535] The server collects user information for both parents and children. This includes past online interaction history and behavioral patterns. This information is retrieved from a database and used to input into a generative AI model. The server performs real-time data processing and prepares the data necessary for learning.

[0536] Step 2:

[0537] The generating AI model creates social media risk scenarios based on collected user information. It utilizes prompts to prepare optimal learning materials tailored to the user's past usage history. This process outputs individually optimized educational content for each user.

[0538] Step 3:

[0539] The server sends the generated training material to the terminal. Communication methods are used to ensure that this training material reaches the user's terminal quickly. The server transfers data packets as efficiently as possible, minimizing latency.

[0540] Step 4:

[0541] The device displays received learning materials visually or audibly. The interface, built with React Native, optimizes the layout to ensure a comfortable learning experience for both parents and children. For example, it uses graphics and audio that are likely to capture a child's interest.

[0542] Step 5:

[0543] Users participate in quizzes and simulated experiences using the provided learning materials. This helps them develop skills to deal with real-world risks. Users input information via their devices and send the results to the server.

[0544] Step 6:

[0545] The server receives user participation results and analyzes the data using analytical tools. It identifies the user's level of understanding and areas for improvement, and generates feedback to reflect these in the next stage of learning materials. As output, information indicating appropriate improvement measures is generated.

[0546] Step 7:

[0547] The server sends feedback information to the device, providing the user with the information necessary to move on to the next step in their learning. The device then presents this information to the parent and child, creating a learning cycle. This entire process allows parents and children to continuously reinforce their awareness of and countermeasures against the dangers of online interaction.

[0548] 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.

[0549] This invention is a system that combines an interactive learning platform, where parents and children can learn safe ways to use social media together, with an emotion engine that recognizes user emotions. An embodiment of the system is shown below.

[0550] server:

[0551] The server is equipped with a generative artificial intelligence model, its main function, to generate learning content based on user information. Furthermore, the server has an emotion engine that integrates with it, providing processing capabilities to recognize emotions from the user's facial expressions, voice, and input. The emotion engine evaluates the user's emotional state and dynamically adjusts the learning content accordingly. For example, if a user expresses frustration with difficult content, the system updates the difficulty level and adjusts the program accordingly.

[0552] Terminal:

[0553] The device is an interface for displaying learning content for parents and children, providing users with content delivered from a server. The learning scenario changes according to pre-set content, allowing users to visually experience the risks associated with using social media. The device also captures the user's facial expressions and voice through its camera and microphone, and sends emotional data to an emotion engine.

[0554] User:

[0555] The users are parent and child, and they actively participate in the learning content provided by the system. They work through scenarios at their own pace and respond to prompts displayed on their devices. Users receive real-time feedback on their actions in quizzes and simulated experiences, and receive adaptive education based on emotion recognition. For example, if a user smiles, content that reinforces their sense of accomplishment is presented.

[0556] The role of the emotional engine:

[0557] The emotion engine analyzes acquired emotional information and provides feedback according to the user's learning progress. This creates a learning experience that makes it easier for users to maintain their motivation. In particular, when the user is showing positive emotions, it promotes further learning by presenting challenging problems.

[0558] This invention combines AI-based content generation with emotion recognition technology to enable personalized learning for parents and children and provide effective digital literacy education.

[0559] The following describes the processing flow.

[0560] Step 1:

[0561] The server retrieves user information from a database and uses a generative artificial intelligence model to generate learning content optimized for each individual user. The generated content has a flexible structure that takes into account emotional states.

[0562] Step 2:

[0563] The device presents learning content received from the server to the parent and child users. The device uses its camera and microphone to continuously acquire emotional data from the users' facial expressions and voice.

[0564] Step 3:

[0565] Users participate in learning scenarios and quizzes displayed on their devices, and their responses are sent to the server in real time.

[0566] Step 4:

[0567] The emotion engine analyzes the emotion data transmitted from the device and evaluates the user's current emotional state. The evaluation results are then fed back to the server.

[0568] Step 5:

[0569] Based on the received emotion data, the server dynamically adjusts the learning content to suit the user's emotions. Specifically, it lowers the difficulty level of the content when frustration is high, and provides challenging content when positive emotions are high.

[0570] Step 6:

[0571] The device presents users with tailored learning content and emotion-based feedback. The UI may also be modified to maintain a user-friendly learning environment.

[0572] Step 7:

[0573] Users can revisit the scenario and experience improved content, leading to a smoother learning process. This allows for learning aligned with their motivation.

[0574] Step 8:

[0575] The server compiles the final training data, generates performance reports for each user, and performs continuous analysis to improve future training.

[0576] This series of steps makes it possible to continuously provide personalized learning content while taking into account the user's emotional state.

[0577] (Example 2)

[0578] 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."

[0579] In today's digital society, there is a need for personalized education systems that allow parents and children to learn together safely in an online environment. However, conventional systems struggle to provide dynamic feedback that takes user emotions into account, making it difficult to maintain learner motivation. Furthermore, there is a challenge in that standardized learning content fails to meet the individual needs of parents and children.

[0580] 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.

[0581] In this invention, the server includes a computing device for creating learning materials based on user information, a telecommunications device for receiving the user information and providing the corresponding learning materials to the user's device, and an analysis device for analyzing the user's emotions and adjusting the learning materials based on the analysis results. This enables the dynamic adjustment of the learning experience based on the user's emotions, and allows for the provision of individualized and effective education for both parents and children.

[0582] "User information" refers to basic data such as the user's age and experience level, collected for the purpose of personalizing and optimizing learning content within the system.

[0583] "Learning materials" are educational content for parents and children, generated based on user information and sentiment analysis, and serve as teaching materials for acquiring safe digital literacy.

[0584] A "computational device" is a device equipped with mechanical elements for creating appropriate learning materials based on user information.

[0585] A "telecommunications device" is a device that has a means of communication for transmitting learning materials between a server and a user device.

[0586] An "analysis device" is a device that analyzes the user's emotional data and adjusts the learning materials based on the results.

[0587] An "emotion engine" is a software or hardware component that analyzes a user's emotional information, generates feedback based on the analysis results, and dynamically adjusts the learning experience.

[0588] "Dynamic feedback" refers to educational or instructional content that is instantly adjusted in response to the user's real-time emotional state and learning progress.

[0589] This system is a personalized platform for parents and children to learn together and understand safe online environments. The system is primarily composed of three subjects: server, terminal, and user.

[0590] The server is the core of the system and uses a generative AI model to create training material based on user information. The generative AI model processes the input user information (e.g., age, SNS usage experience, etc.) and builds training content optimized for the user. Furthermore, the server has an emotion engine that analyzes the user's real-time emotion data (facial expressions, voice, input content, etc.) to dynamically adjust the training material.

[0591] The device displays learning materials provided by the server to both parent and child users and has a built-in camera and microphone. This allows it to capture the user's physical reactions and send the data to the emotion engine. The device displays interactive and visually easy-to-understand content and provides a convenient interface to encourage user participation.

[0592] The user base consists of parents and children. In this system, users actively participate in learning materials and learn at their own pace. Specifically, they receive immediate feedback in response to quizzes and simulated experiences provided through their devices, promoting emotionally-driven adaptive learning. For example, when a user answers a quiz, they are presented with a prompt such as, "Think about how your child would handle a message from a stranger on social media," and the next learning step is determined based on the user's response.

[0593] This configuration allows the system to provide education that enables parents and children to have safe and beneficial online experiences, and to support the improvement of their digital literacy.

[0594] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0595] Step 1:

[0596] When logging into the system, users enter basic information (age, SNS usage experience, etc.) into their device. This information becomes input data, which the device then sends to the server.

[0597] Step 2:

[0598] The server uses the received user information to run a generation AI model and generate optimal training materials. Based on the input user information, the AI ​​model performs data analysis and creates content with themes and difficulty levels suitable for both parent and child. The generated training materials are stored on the server as intermediate data.

[0599] Step 3:

[0600] The server delivers the generated learning materials to the terminal. The terminal receives this information and displays the learning materials visually to the user. The learning materials include quizzes, videos, simulated experiences, and prompts to encourage user participation.

[0601] Step 4:

[0602] As users engage with learning materials displayed on their device, they respond to quizzes and simulated experiences provided. Based on the user's answers and choices, the device collects data in real time to display feedback.

[0603] Step 5:

[0604] The device captures the user's facial expressions and vocal responses through its camera and microphone, and sends this emotional data to a server. This data becomes the input and is used for emotion analysis.

[0605] Step 6:

[0606] The server activates an emotion engine and analyzes the received emotion data. It evaluates the user's emotional state and dynamically adjusts the content and difficulty level of the learning materials. For example, if the user indicates difficulty, the server decides to lower the learning difficulty level.

[0607] Step 7:

[0608] The server sends updated learning materials and feedback to the device based on the analysis results. The device redisplays them, and the user continues with the next step. This feedback loop ensures a dynamic learning experience that adapts to the user's progress.

[0609] (Application Example 2)

[0610] 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."

[0611] Modern parents and children face challenges in using social media due to safety concerns and a lack of digital literacy. However, traditional educational methods struggle to maintain the interest of both parents and children, making effective learning difficult. Furthermore, the lack of educational methods that consider individual emotional states makes it difficult to maintain the learning motivation of both parents and children. To address these challenges, an interactive educational system that incorporates emotional recognition is needed.

[0612] 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.

[0613] In this invention, the server includes means equipped with a generative artificial intelligence model that generates learning content based on user information in order to generate a learning program in which both parent and child can participate; communication means for acquiring user information and delivering the corresponding learning content to the user terminal; analysis means for analyzing the results of the user's participation in the learning content and generating feedback based on the analysis results; emotion recognition means for recognizing the user's emotional state and dynamically adjusting the learning content; and a humanoid machine device for enabling parent and child to learn in a home environment. This makes it easier for both parent and child to participate and to effectively learn how to use social networking services safely through an individually tailored learning experience.

[0614] "Parent and child" refers to a parent and their child, and they are the group that learns together within the family.

[0615] A "learning program" is a structured process that provides content and scenarios designed in accordance with educational objectives, with the aim of improving the user's knowledge.

[0616] "User information" refers to data related to a user, including learning history, emotional state, and individual characteristics.

[0617] "Learning content" refers to knowledge, experiences, or training materials and activities provided for educational purposes.

[0618] A "generative artificial intelligence model" is a collection of algorithms that use AI technology to generate optimal content based on user information.

[0619] "Communication means" refers to the technology or device used to send and receive information between a server and a user terminal.

[0620] "Analysis means" refers to a process or function that analyzes user participation results and generates information to effectively guide the next step of education.

[0621] "Presentation means" refers to a device or method for providing feedback or information to a user visually or audibly.

[0622] "Emotion recognition methods" refer to technologies and processes for analyzing emotions from a user's facial expressions, voice, and other similar data.

[0623] A "humanoid mechanical device" is a device that mimics the shape of a human being for educational activities within the home, providing an interactive learning experience.

[0624] The system that realizes this invention consists of a set of software and hardware to work in conjunction with a home educational robot designed for parents and children. The server first generates learning content based on user information using a generative artificial intelligence model. This model has the ability to dynamically generate user-optimized content based on past learning history and sentiment information. This uses a machine learning platform such as TensorFlow.

[0625] On the terminal side, the user (i.e., parent and child) learns through a humanoid robotic device. The terminal is equipped with a camera and microphone, and uses OpenCV and TensorFlow Lite to analyze facial and voice data in real time, functioning as a means of emotion recognition. The emotion data obtained in this way is sent to a server and reflected in the learning content.

[0626] For example, when a user is experiencing a social media usage scenario with a robot, if their facial expressions show something interesting, the server will use that feedback to adjust the learning content to be more challenging. Conversely, if they appear confused, the server will provide guidance with a lower difficulty level to help them continue learning. Specifically, prompts like the following might be used: "What action should you take in this social media situation? Evaluate the user's facial expressions and statements, and generate appropriate educational content for that situation."

[0627] This system allows parents and children to learn how to use social media safely in an interactive and personalized way.

[0628] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0629] Step 1:

[0630] The server receives user information, including past learning history and current emotional state. Using this user information as input, the generative AI model performs an optimization process to generate learning content. As a result, learning content tailored to the user is output.

[0631] Step 2:

[0632] The generated learning content is transmitted to the terminal via a communication device. The terminal receives this data and displays the learning content to the parent and child on a humanoid robotic device, encouraging participation. At this stage, the terminal begins monitoring the user's voice and facial expressions using a camera and microphone.

[0633] Step 3:

[0634] The user reacts to the presented learning content. The device acquires facial and voice data in real time and analyzes it using emotion recognition technology. Based on the input data, the user's emotional state is output as numerical data.

[0635] Step 4:

[0636] The server receives emotional data from the terminal and incorporates it as feedback into the generating AI model. Based on this data, the learning content is dynamically adjusted. For example, if the output is determined to be causing the user to lose interest, the difficulty level of the content is reduced.

[0637] Step 5:

[0638] The adjusted learning content is sent back to the device. The device presents the updated content to the user and encourages them to participate in the learning process again. The learning experience continues as the user continues to respond to the new scenarios.

[0639] Step 6:

[0640] After a user completes their training, the server collects all the training data and uses it to optimize the next training session. This data collection allows the generative AI model to create a spiral that improves the quality of future training content.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] [Fourth Embodiment]

[0645] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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).

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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".

[0658] In order to implement the present invention, it is necessary to construct a system in which a server, a terminal, and a user each play their respective roles and work together. Specific embodiments of each component are shown below.

[0659] server:

[0660] The server plays a central role in the platform, dynamically generating learning content using generative artificial intelligence models. Based on information provided by users and past behavioral data, it constructs learning scenarios about SNS risks and safety measures, and prepares educational content optimized for each individual user.

[0661] Terminal:

[0662] The device serves to display and provide learning content to the user and their child. The screen accessible to the user presents scenario-based learning materials and interactive quizzes prepared on the server side. For example, the device may recreate social media trouble scenarios that children are likely to encounter and display content that allows them to choose how to respond.

[0663] User:

[0664] Users, both parents and children, utilize the content and participate in the learning activities presented by the system. Through their devices, users can answer quizzes and learn safe social media usage through scenario-based simulations. This provides an educational environment that enhances decision-making skills through discussions between parents and children.

[0665] Generating and presenting feedback:

[0666] The server analyzes the user's learning progress and quiz results, generating feedback on their level of understanding and areas for improvement. This feedback is sent to the device and presented to the user in an appropriate format. For example, it might point out that "awareness regarding the protection of personal information needs to be further increased." This provides parents and children with information to help them decide on the next steps in their learning.

[0667] Thus, this invention is designed to enable parents and children to learn together in real time by providing personalized educational content utilizing AI.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] The server initializes the generative artificial intelligence model and prepares to collect user information. It interacts with a database that holds data such as the user's age, social media usage history, and language settings.

[0671] Step 2:

[0672] The device displays a personalized initial setup screen to the user based on their registered information and prompts them to enter any necessary additional information. For example, it might present a question form regarding the type of social networking service (SNS) they use and how often they use it.

[0673] Step 3:

[0674] Users answer the presented questions and enter necessary information through their device. For example, they can also enter household rules such as the amount of time parents allow for social media use.

[0675] Step 4:

[0676] The server uses a live artificial intelligence model to generate appropriate learning scenarios and quizzes based on information provided by the user. This generation process incorporates the latest risk information and protection guidelines regarding SNS usage.

[0677] Step 5:

[0678] The device presents the user with generated learning content. For example, it might display a simulated experience game based on risk scenarios or a quiz about ethical social media use.

[0679] Step 6:

[0680] Users participate in learning content presented on their devices and answer quizzes. Their answers are transmitted to the server in real time.

[0681] Step 7:

[0682] The server analyzes user responses and participation history to evaluate each user's level of understanding and learning progress. It also generates next learning content and supplementary information based on the generated feedback.

[0683] Step 8:

[0684] The device presents the user with analyzed feedback. Specifically, it displays reports on learning progress and the next tasks to be completed, encouraging the user to continue learning.

[0685] Through this series of processes, the system can provide users with a personalized learning environment.

[0686] (Example 1)

[0687] 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".

[0688] In today's information environment, there is a need for educational methods that enable parents and children to safely handle online information together. However, traditional learning methods have the challenge of not being able to provide efficient education that incorporates individual user characteristics and real-time feedback.

[0689] 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.

[0690] In this invention, the server includes an inference means that generates prompt sentences based on personal information collected from the user and constructs learning content; a display means that delivers the generated learning content to a terminal and presents it to the user; and an evaluation means that analyzes the user's learning progress and input results and generates personalized feedback based on the results. This makes it possible for parents and children to learn efficiently in real time through personalized educational content.

[0691] "Personal information collected from users" refers to profiles and past behavioral data obtained from users in order to personalize learning content.

[0692] A "prompt" refers to an input sentence used as an instruction or guideline to generate training content in a generative AI model.

[0693] "Learning content" refers to educational materials and scenarios provided to users, including educational content designed to foster safety measures and problem-solving skills.

[0694] "Inference tools" refer to AI models and algorithms that use personal information to generate appropriate learning content.

[0695] "Display means" refers to the interface or device function on a terminal that provides the generated learning content to the user visually or audibly.

[0696] "Evaluation means" refers to server functions and software that analyze the user's learning progress and quiz results, and generate feedback based on those results.

[0697] "Personalized feedback" refers to personalized information provided to indicate the next learning steps or areas for improvement based on the user's learning outcomes and level of understanding.

[0698] Implementing this system requires an integrated environment where servers, terminals, and users work together in coordination.

[0699] The server plays a central role in data processing, generating learning content using generative AI models. This begins with generating optimized prompts using AI based on personal information collected from users. Based on this, the server constructs educational scenarios regarding internet safety measures, including those on social media. Specifically, the AI ​​model analyzes the user's past behavior data and dynamically generates appropriate educational content. For example, depending on the user's age group and interests, a prompt such as "Create a safety education scenario for teenagers regarding social media use" might be generated.

[0700] The device displays learning content delivered from the server. Users can receive educational content in real time through the device. The displayed content includes interactive quizzes and multiple-choice scenarios, designed to encourage active user participation. The device also includes features that provide visual and auditory feedback to support effective learning.

[0701] Users, both parents and children, can use this system together to learn safe ways to use social media through the presented content. They are provided with opportunities to deepen their learning by answering quizzes using their devices and participating in simulated experiences through scenarios. The server analyzes the user's learning progress and generates and presents appropriate feedback.

[0702] This invention will enable parents and children to learn personalized educational content collaboratively in real time. The platform for this will require AI-powered content generation and results-based feedback functions as key components.

[0703] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0704] Step 1:

[0705] The server collects personal information and past behavioral data entered by the user. Based on this information, the generative AI model performs preprocessing to generate prompt sentences. The input information includes the user's basic data, age, and internet usage history, and this data is processed to identify the user's characteristics. The output is the basic information needed to generate prompt sentences.

[0706] Step 2:

[0707] Based on the basic information obtained in Step 1, the server generates prompt messages using a generative AI model. These generated prompt messages function as instructions for the AI ​​to construct a specific safety education scenario. Data calculations involve user profiling using personal information. The output is a prompt message customized for the relevant user.

[0708] Step 3:

[0709] The server inputs the generated prompt sentences into the AI ​​model, which then generates learning content optimized for the user. In this process, the AI ​​model utilizes a large amount of existing data to create scenario-based learning materials that take trends and risks into account. The input is prompt sentences, and the output is learning content tailored to each individual user.

[0710] Step 4:

[0711] The server sends the generated learning content to the device. The device prepares to display the received content to the user. Specifically, the device downloads the content and displays an interactive screen. The input is the learning content sent from the server, and the output is the display screen accessible to the user.

[0712] Step 5:

[0713] Users participate in learning content presented via their devices, deepening their knowledge through answering quizzes and experiencing scenarios. User input consists of quiz answers and selection of answer choices, and this information is used in the next analysis step. Output consists of user behavior data and response information.

[0714] Step 6:

[0715] The server analyzes user behavior data and quiz answers to evaluate understanding and areas for improvement. Analysis tools process this data and generate feedback. Data processing includes evaluation scores and suggestions for the next learning steps. The output is personalized feedback for the user.

[0716] Step 7:

[0717] The server sends the generated feedback to the terminal. The terminal presents the feedback to the user visually or audibly. The user reflects on their learning based on this feedback and decides on their next action. The input is the feedback information, and the output is the feedback content displayed on the screen.

[0718] (Application Example 1)

[0719] 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".

[0720] In today's digital society, there is a lack of learning environments that enable both parents and children to understand and appropriately deal with the risks of online interactions. In particular, there is a need for education that helps children recognize potential problems they may encounter on online platforms such as social media and to cultivate sound judgment.

[0721] 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.

[0722] In this invention, the server includes means for generating learning materials based on user information, communication means for acquiring the user information and distributing the corresponding learning materials to an information processing device, and means for providing safety tests and situational experiences based on the user's past online interaction history. This makes it possible for parents and children to work together to learn safe online interaction methods and understand risks through real-world experience.

[0723] "User information" refers to data about the attributes and behavioral history of users, including parents and children, and is used to personalize and optimize the learning materials that are generated.

[0724] "Learning materials" refer to educational content provided through information processing devices, intended to teach about risk awareness and safety measures for online interactions.

[0725] A "generative artificial intelligence model" is a form of artificial intelligence equipped with an algorithm that analyzes user information and past training data to dynamically generate the necessary training materials.

[0726] An "information processing device" is an electronic device that can display learning materials and accept input from users, and serves as a medium for parents and children to access.

[0727] "Communication means" refers to technical means for sending and receiving data between a server and an information processing device, and utilizes network infrastructure.

[0728] "Analysis means" refers to software functions and processes for evaluating user participation results and providing feedback based on that data.

[0729] "Feedback" refers to information provided to point out a user's learning progress and weaknesses, and to instruct or suggest the next course of action.

[0730] "Situational simulation experiences" refer to educational scenarios and situations that are simulated and recreated for users to learn safety measures, fostering practical judgment skills regarding risks.

[0731] The system implementing this invention aims to enable both parents and children to learn about safe and meaningful online interactions, and utilizes a generative AI model to provide learning materials. The specific roles and operations of each component are shown below.

[0732] server

[0733] The server forms the core of the system, using generative AI models to analyze user information and dynamically generate training materials. The server is built using the Flask framework in Python and uses MongoDB for its database. The AI ​​leverages generative AI models such as OpenAI GPT-3 to generate individually optimized educational content based on the user's behavior history. Specifically, the server uses prompt messages to provide simulated experiences of scenarios and countermeasures for SNS risks that users are likely to face.

[0734] terminal

[0735] The device functions as an information processing unit, providing an interface for parent and child users to access. Developed for smartphones using React Native, it displays learning materials in a user-friendly format. The device receives feedback from the server and supports learning progress by presenting it visually or audibly.

[0736] User

[0737] Users consist of parent and child members and participate in educational programs using learning materials. Users select answers through presented quizzes and scenarios, and receive feedback on their results from their devices, which helps them to continue learning. User responses and selections are analyzed on the server side and contribute to the generation of new learning materials.

[0738] A concrete example would be a scenario where a child receives a message from an unknown person on social media and has to choose how to respond. Through such scenarios, children can develop the judgment skills necessary for safe online activities.

[0739] Example of a prompt:

[0740] "Please generate appropriate safety scenarios based on social media problems that users have experienced in the past."

[0741] In this way, we provide a system that allows users to learn how to interact in a safe online environment while strengthening their ability to cope with risks.

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] The server collects user information for both parents and children. This includes past online interaction history and behavioral patterns. This information is retrieved from a database and used to input into a generative AI model. The server performs real-time data processing and prepares the data necessary for learning.

[0745] Step 2:

[0746] The generating AI model creates social media risk scenarios based on collected user information. It utilizes prompts to prepare optimal learning materials tailored to the user's past usage history. This process outputs individually optimized educational content for each user.

[0747] Step 3:

[0748] The server sends the generated training material to the terminal. Communication methods are used to ensure that this training material reaches the user's terminal quickly. The server transfers data packets as efficiently as possible, minimizing latency.

[0749] Step 4:

[0750] The device displays received learning materials visually or audibly. The interface, built with React Native, optimizes the layout to ensure a comfortable learning experience for both parents and children. For example, it uses graphics and audio that are likely to capture a child's interest.

[0751] Step 5:

[0752] Users participate in quizzes and simulated experiences using the provided learning materials. This helps them develop skills to deal with real-world risks. Users input information via their devices and send the results to the server.

[0753] Step 6:

[0754] The server receives user participation results and analyzes the data using analytical tools. It identifies the user's level of understanding and areas for improvement, and generates feedback to reflect these in the next stage of learning materials. As output, information indicating appropriate improvement measures is generated.

[0755] Step 7:

[0756] The server sends feedback information to the device, providing the user with the information necessary to move on to the next step in their learning. The device then presents this information to the parent and child, creating a learning cycle. This entire process allows parents and children to continuously reinforce their awareness of and countermeasures against the dangers of online interaction.

[0757] 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.

[0758] This invention is a system that combines an interactive learning platform, where parents and children can learn safe ways to use social media together, with an emotion engine that recognizes user emotions. An embodiment of the system is shown below.

[0759] server:

[0760] The server is equipped with a generative artificial intelligence model, its main function, to generate learning content based on user information. Furthermore, the server has an emotion engine that integrates with it, providing processing capabilities to recognize emotions from the user's facial expressions, voice, and input. The emotion engine evaluates the user's emotional state and dynamically adjusts the learning content accordingly. For example, if a user expresses frustration with difficult content, the system updates the difficulty level and adjusts the program accordingly.

[0761] Terminal:

[0762] The device is an interface for displaying learning content for parents and children, providing users with content delivered from a server. The learning scenario changes according to pre-set content, allowing users to visually experience the risks associated with using social media. The device also captures the user's facial expressions and voice through its camera and microphone, and sends emotional data to an emotion engine.

[0763] User:

[0764] The users are parent and child, and they actively participate in the learning content provided by the system. They work through scenarios at their own pace and respond to prompts displayed on their devices. Users receive real-time feedback on their actions in quizzes and simulated experiences, and receive adaptive education based on emotion recognition. For example, if a user smiles, content that reinforces their sense of accomplishment is presented.

[0765] The role of the emotional engine:

[0766] The emotion engine analyzes acquired emotional information and provides feedback according to the user's learning progress. This creates a learning experience that makes it easier for users to maintain their motivation. In particular, when the user is showing positive emotions, it promotes further learning by presenting challenging problems.

[0767] This invention combines AI-based content generation with emotion recognition technology to enable personalized learning for parents and children and provide effective digital literacy education.

[0768] The following describes the processing flow.

[0769] Step 1:

[0770] The server retrieves user information from a database and uses a generative artificial intelligence model to generate learning content optimized for each individual user. The generated content has a flexible structure that takes into account emotional states.

[0771] Step 2:

[0772] The device presents learning content received from the server to the parent and child users. The device uses its camera and microphone to continuously acquire emotional data from the users' facial expressions and voice.

[0773] Step 3:

[0774] Users participate in learning scenarios and quizzes displayed on their devices, and their responses are sent to the server in real time.

[0775] Step 4:

[0776] The emotion engine analyzes the emotion data transmitted from the device and evaluates the user's current emotional state. The evaluation results are then fed back to the server.

[0777] Step 5:

[0778] Based on the received emotion data, the server dynamically adjusts the learning content to suit the user's emotions. Specifically, it lowers the difficulty level of the content when frustration is high, and provides challenging content when positive emotions are high.

[0779] Step 6:

[0780] The device presents users with tailored learning content and emotion-based feedback. The UI may also be modified to maintain a user-friendly learning environment.

[0781] Step 7:

[0782] Users can revisit the scenario and experience improved content, leading to a smoother learning process. This allows for learning aligned with their motivation.

[0783] Step 8:

[0784] The server compiles the final training data, generates performance reports for each user, and performs continuous analysis to improve future training.

[0785] This series of steps makes it possible to continuously provide personalized learning content while taking into account the user's emotional state.

[0786] (Example 2)

[0787] 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".

[0788] In today's digital society, there is a need for personalized education systems that allow parents and children to learn together safely in an online environment. However, conventional systems struggle to provide dynamic feedback that takes user emotions into account, making it difficult to maintain learner motivation. Furthermore, there is a challenge in that standardized learning content fails to meet the individual needs of parents and children.

[0789] 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.

[0790] In this invention, the server includes a computing device for creating learning materials based on user information, a telecommunications device for receiving the user information and providing the corresponding learning materials to the user's device, and an analysis device for analyzing the user's emotions and adjusting the learning materials based on the analysis results. This enables the dynamic adjustment of the learning experience based on the user's emotions, and allows for the provision of individualized and effective education for both parents and children.

[0791] "User information" refers to basic data such as the user's age and experience level, collected for the purpose of personalizing and optimizing learning content within the system.

[0792] "Learning materials" are educational content for parents and children, generated based on user information and sentiment analysis, and serve as teaching materials for acquiring safe digital literacy.

[0793] A "computational device" is a device equipped with mechanical elements for creating appropriate learning materials based on user information.

[0794] A "telecommunications device" is a device that has a means of communication for transmitting learning materials between a server and a user device.

[0795] An "analysis device" is a device that analyzes the user's emotional data and adjusts the learning materials based on the results.

[0796] An "emotion engine" is a software or hardware component that analyzes a user's emotional information, generates feedback based on the analysis results, and dynamically adjusts the learning experience.

[0797] "Dynamic feedback" refers to educational or instructional content that is instantly adjusted in response to the user's real-time emotional state and learning progress.

[0798] This system is a personalized platform for parents and children to learn together and understand safe online environments. The system is primarily composed of three subjects: server, terminal, and user.

[0799] The server is the core of the system and uses a generative AI model to create training material based on user information. The generative AI model processes the input user information (e.g., age, SNS usage experience, etc.) and builds training content optimized for the user. Furthermore, the server has an emotion engine that analyzes the user's real-time emotion data (facial expressions, voice, input content, etc.) to dynamically adjust the training material.

[0800] The device displays learning materials provided by the server to both parent and child users and has a built-in camera and microphone. This allows it to capture the user's physical reactions and send the data to the emotion engine. The device displays interactive and visually easy-to-understand content and provides a convenient interface to encourage user participation.

[0801] The user base consists of parents and children. In this system, users actively participate in learning materials and learn at their own pace. Specifically, they receive immediate feedback in response to quizzes and simulated experiences provided through their devices, promoting emotionally-driven adaptive learning. For example, when a user answers a quiz, they are presented with a prompt such as, "Think about how your child would handle a message from a stranger on social media," and the next learning step is determined based on the user's response.

[0802] This configuration allows the system to provide education that enables parents and children to have safe and beneficial online experiences, and to support the improvement of their digital literacy.

[0803] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0804] Step 1:

[0805] When logging into the system, users enter basic information (age, SNS usage experience, etc.) into their device. This information becomes input data, which the device then sends to the server.

[0806] Step 2:

[0807] The server uses the received user information to run a generation AI model and generate optimal training materials. Based on the input user information, the AI ​​model performs data analysis and creates content with themes and difficulty levels suitable for both parent and child. The generated training materials are stored on the server as intermediate data.

[0808] Step 3:

[0809] The server delivers the generated learning materials to the terminal. The terminal receives this information and displays the learning materials visually to the user. The learning materials include quizzes, videos, simulated experiences, and prompts to encourage user participation.

[0810] Step 4:

[0811] As users engage with learning materials displayed on their device, they respond to quizzes and simulated experiences provided. Based on the user's answers and choices, the device collects data in real time to display feedback.

[0812] Step 5:

[0813] The device captures the user's facial expressions and vocal responses through its camera and microphone, and sends this emotional data to a server. This data becomes the input and is used for emotion analysis.

[0814] Step 6:

[0815] The server activates an emotion engine and analyzes the received emotion data. It evaluates the user's emotional state and dynamically adjusts the content and difficulty level of the learning materials. For example, if the user indicates difficulty, the server decides to lower the learning difficulty level.

[0816] Step 7:

[0817] The server sends updated learning materials and feedback to the device based on the analysis results. The device redisplays them, and the user continues with the next step. This feedback loop ensures a dynamic learning experience that adapts to the user's progress.

[0818] (Application Example 2)

[0819] 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".

[0820] Modern parents and children face challenges in using social media due to safety concerns and a lack of digital literacy. However, traditional educational methods struggle to maintain the interest of both parents and children, making effective learning difficult. Furthermore, the lack of educational methods that consider individual emotional states makes it difficult to maintain the learning motivation of both parents and children. To address these challenges, an interactive educational system that incorporates emotional recognition is needed.

[0821] 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.

[0822] In this invention, the server includes means equipped with a generative artificial intelligence model that generates learning content based on user information in order to generate a learning program in which both parent and child can participate; communication means for acquiring user information and delivering the corresponding learning content to the user terminal; analysis means for analyzing the results of the user's participation in the learning content and generating feedback based on the analysis results; emotion recognition means for recognizing the user's emotional state and dynamically adjusting the learning content; and a humanoid machine device for enabling parent and child to learn in a home environment. This makes it easier for both parent and child to participate and to effectively learn how to use social networking services safely through an individually tailored learning experience.

[0823] "Parent and child" refers to a parent and their child, and they are the group that learns together within the family.

[0824] A "learning program" is a structured process that provides content and scenarios designed in accordance with educational objectives, with the aim of improving the user's knowledge.

[0825] "User information" refers to data related to a user, including learning history, emotional state, and individual characteristics.

[0826] "Learning content" refers to knowledge, experiences, or training materials and activities provided for educational purposes.

[0827] A "generative artificial intelligence model" is a collection of algorithms that use AI technology to generate optimal content based on user information.

[0828] "Communication means" refers to the technology or device used to send and receive information between a server and a user terminal.

[0829] "Analysis means" refers to a process or function that analyzes user participation results and generates information to effectively guide the next step of education.

[0830] "Presentation means" refers to a device or method for providing feedback or information to a user visually or audibly.

[0831] "Emotion recognition methods" refer to technologies and processes for analyzing emotions from a user's facial expressions, voice, and other similar data.

[0832] A "humanoid mechanical device" is a device that mimics the shape of a human being for educational activities within the home, providing an interactive learning experience.

[0833] The system that realizes this invention consists of a set of software and hardware to work in conjunction with a home educational robot designed for parents and children. The server first generates learning content based on user information using a generative artificial intelligence model. This model has the ability to dynamically generate user-optimized content based on past learning history and sentiment information. This uses a machine learning platform such as TensorFlow.

[0834] On the terminal side, the user (i.e., parent and child) learns through a humanoid robotic device. The terminal is equipped with a camera and microphone, and uses OpenCV and TensorFlow Lite to analyze facial and voice data in real time, functioning as a means of emotion recognition. The emotion data obtained in this way is sent to a server and reflected in the learning content.

[0835] For example, when a user is experiencing a social media usage scenario with a robot, if their facial expressions show something interesting, the server will use that feedback to adjust the learning content to be more challenging. Conversely, if they appear confused, the server will provide guidance with a lower difficulty level to help them continue learning. Specifically, prompts like the following might be used: "What action should you take in this social media situation? Evaluate the user's facial expressions and statements, and generate appropriate educational content for that situation."

[0836] This system allows parents and children to learn how to use social media safely in an interactive and personalized way.

[0837] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0838] Step 1:

[0839] The server receives user information, including past learning history and current emotional state. Using this user information as input, the generative AI model performs an optimization process to generate learning content. As a result, learning content tailored to the user is output.

[0840] Step 2:

[0841] The generated learning content is transmitted to the terminal via a communication device. The terminal receives this data and displays the learning content to the parent and child on a humanoid robotic device, encouraging participation. At this stage, the terminal begins monitoring the user's voice and facial expressions using a camera and microphone.

[0842] Step 3:

[0843] The user reacts to the presented learning content. The device acquires facial and voice data in real time and analyzes it using emotion recognition technology. Based on the input data, the user's emotional state is output as numerical data.

[0844] Step 4:

[0845] The server receives emotional data from the terminal and incorporates it as feedback into the generating AI model. Based on this data, the learning content is dynamically adjusted. For example, if the output is determined to be causing the user to lose interest, the difficulty level of the content is reduced.

[0846] Step 5:

[0847] The adjusted learning content is sent back to the device. The device presents the updated content to the user and encourages them to participate in the learning process again. The learning experience continues as the user continues to respond to the new scenarios.

[0848] Step 6:

[0849] After a user completes their training, the server collects all the training data and uses it to optimize the next training session. This data collection allows the generative AI model to create a spiral that improves the quality of future training content.

[0850] 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.

[0851] 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.

[0852] 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 robot 414.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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."

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] The following is further disclosed regarding the embodiments described above.

[0872] (Claim 1)

[0873] To generate a learning program that both parents and children can participate in, a generative artificial intelligence model generates learning content based on user information,

[0874] A communication means for acquiring user information and delivering corresponding learning content to the user's terminal,

[0875] An analysis means that analyzes the results of user participation in learning content and generates feedback based on the analysis results,

[0876] A presentation means for transmitting the feedback to the user's terminal and presenting it to the user visually or audibly,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the analysis means generates information for suggesting the next learning step to the user based on the analysis results.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein the generative artificial intelligence model has a function for accumulating past learning results from different users and continuously optimizing the generated learning content.

[0882] "Example 1"

[0883] (Claim 1)

[0884] An inference method that generates prompt sentences based on person information collected from users and constructs learning content,

[0885] A display means for delivering generated learning content to a device and presenting it to the user,

[0886] An evaluation means that analyzes the user's learning progress and input results, and generates personalized feedback based on those results,

[0887] A means of communication that sends generated feedback to a terminal and provides information to the user visually,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, wherein the evaluation means generates information for presenting the next learning stage to the user based on the analysis results and the adaptability of the learning content.

[0891] (Claim 3)

[0892] The system according to claim 1, wherein the inference means has a function for aggregating and analyzing past learning data of other users and for improving the quality of continuously generated learning content.

[0893] "Application Example 1"

[0894] (Claim 1)

[0895] A generative artificial intelligence model that generates learning materials based on user information,

[0896] A communication means for acquiring user information and distributing corresponding learning materials to an information processing device,

[0897] An analysis means that analyzes the results of user participation in learning materials and generates feedback to show learning progress based on the analysis results,

[0898] A presentation means that transmits the feedback to an information processing device and presents it to the parent and child visually or audibly,

[0899] A means of providing safety tests and situational experiences based on a user's past online interaction history,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, wherein the analysis means generates information for suggesting the next learning stage to the user based on the analysis results.

[0903] (Claim 3)

[0904] The system according to claim 1, which includes a generative artificial intelligence model that has a function to continuously optimize the generated learning materials by accumulating the past learning results of different users, and provides a program for parents and children to learn together.

[0905] "Example 2 of combining an emotion engine"

[0906] (Claim 1)

[0907] A computing device for creating learning materials based on user information,

[0908] A telecommunications device for receiving user information and providing corresponding learning materials to the user's device,

[0909] An analytical device for analyzing user emotions and adjusting learning materials based on the analysis results,

[0910] An analysis device for analyzing user engagement with learning materials and creating an evaluation based on the analysis results,

[0911] A display device for sending the evaluation to a user device and providing it to the user visually or audibly,

[0912] A device equipped with an emotion engine that dynamically adjusts the learning experience based on user emotions, enabling personalized education for parents and children,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, wherein the analysis device generates information to suggest additional learning steps based on the results of the user's emotion analysis, thereby optimizing the user's motivation in education.

[0916] (Claim 3)

[0917] The system according to claim 1, wherein the generated emotion engine has a function to collect past emotion data from different users and continuously optimize the learning material.

[0918] "Application example 2 when combining with an emotional engine"

[0919] (Claim 1)

[0920] To generate a learning program that both parents and children can participate in, a generative artificial intelligence model is used to generate learning content based on user information,

[0921] A communication means for acquiring user information and delivering corresponding learning content to the user's terminal,

[0922] An analysis means that analyzes the results of user participation in learning content and generates feedback based on the analysis results,

[0923] A presentation means for transmitting the feedback to the user's terminal and presenting it to the user visually or audibly,

[0924] An emotion recognition means for recognizing the user's emotional state and dynamically adjusting the learned content,

[0925] A humanoid mechanical device that enables parents and children to learn together in a home environment,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, wherein the analysis means generates information for suggesting the next learning step to the user based on the analysis results and data obtained by the emotion recognition means.

[0929] (Claim 3)

[0930] The system according to claim 1, wherein the generative artificial intelligence model has a function for continuously optimizing the generated learning content by accumulating past learning results and sentiment data of different users. [Explanation of Symbols]

[0931] 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 generative artificial intelligence model that generates learning materials based on user information, A communication means for acquiring user information and distributing corresponding learning materials to an information processing device, An analysis means that analyzes the results of user participation in learning materials and generates feedback to show learning progress based on the analysis results, A presentation means that transmits the feedback to an information processing device and presents it to the parent and child visually or audibly, A means of providing safety tests and situational experiences based on a user's past online interaction history, A system that includes this.

2. The system according to claim 1, wherein the analysis means generates information for suggesting the next learning stage to the user based on the analysis results.

3. The system according to claim 1, which includes a generative artificial intelligence model that has a function to continuously optimize the generated learning materials by accumulating the past learning results of different users, and provides a program for parents and children to learn together.

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