system

Abstract

We provide the system. [Solution] A means for inputting information based on the user's interests, A means for generating customized learning content based on the user's interest information and selected learning content, A means of delivering the generated learning content to the user's device as a gamified application, A means of recording user progress data and providing new learning materials and rewards according to learning progress, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, there has been a problem that the initial learning hurdle for learners in newly learned content or difficult fields is high, and it is difficult to maintain continuous learning motivation. Also, in standardized learning content, the interests of individual users are not reflected, and there are limitations in individual optimization. Therefore, a mechanism that allows learning to be continuously advanced while enjoying learning has been demanded.

Means for Solving the Problems

[0005] This invention provides a means for collecting information based on user interests and using that information to customize learning content. This makes it possible to generate unique story-based content that connects interests and learning, and to provide it as a gamified application. This system records learner progress data in real time and promotes continuous learning by providing feedback, new learning materials, and rewards according to learning progress.

[0006] "User interest-based information" refers to information that reflects a user's personal interests and preferences, and this information is used to customize learning content.

[0007] "Customized learning content" refers to educational materials that are individually designed and generated to match the user's interests and choices.

[0008] A "gamified application" refers to an application that transforms learning content into a game format, designed to allow users to learn while having fun.

[0009] "User progress data" refers to data that records the results, achievements, and learning history of a user during their learning process.

[0010] "Feedback" refers to information such as evaluations, comments, and advice provided based on the user's learning status and progress. [Brief explanation of the drawing]

[0011] [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]

[0012] 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.

[0013] First, let's explain the terminology used in the following explanation.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The system of this invention provides a customized learning experience by combining the user's personal interests and the content they wish to learn. This system is primarily realized through a process in which information is appropriately exchanged between the server, terminal, and user, and content is generated and provided according to the user's preferences.

[0033] First, the user accesses the system and enters information such as their personal interests, favorite characters, and themes. In addition, the user selects the topic they want to learn about (e.g., English, mathematics, programming, etc.). This information is then sent from the user's terminal to the server.

[0034] Next, the server designs personalized learning content based on the received user information. The server generates customized stories and quizzes that combine the user's interests with the learning material. This generated content is designed to make learning enjoyable in a game format, aiming to keep the user engaged.

[0035] The generated learning content is sent from the server to the user's device. The device receives this content and makes it accessible to the user in the form of an application. This application has various functions to allow the user to learn interactively. For example, it promotes learning while maintaining motivation by providing feedback and rewards based on the user's results each time they answer a quiz.

[0036] During a learning session, the device records the user's progress and sends this data to the server. The server analyzes this progress data and determines the next steps appropriate for the user. Based on the evaluation data, the server prepares new learning tasks and rewards and resends them to the device.

[0037] For example, if a user expresses a desire to learn English using anime characters, the server will generate English quizzes related to the anime's storyline and characters. These quizzes are designed to engage the user, and are structured to entertain them in various ways, such as allowing them to learn English phrases in scenarios featuring anime characters, and having the characters perform special actions when they answer correctly.

[0038] Through these interactions, users can naturally connect their interests with learning and continue learning effectively. This mechanism allows the present invention to provide users with a personalized learning experience and effectively promote continued learning.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users log in to the platform, enter themes and topics of interest, and select the fields they wish to study. This information is sent from the device to the server.

[0042] Step 2:

[0043] The server analyzes the user information it receives and generates customized learning content based on the user's interests and what they want to learn. Specifically, it creates stories related to the user's interests and incorporates learning content within those stories.

[0044] Step 3:

[0045] This process involves creating data for distributing customized learning content generated by the server, arranging it into a user-specific game or application format.

[0046] Step 4:

[0047] The device receives and installs the learning application data sent from the server, preparing it for use by the user.

[0048] Step 5:

[0049] The user opens the learning application on their device and begins learning. Here, the user progresses through content designed based on their interests.

[0050] Step 6:

[0051] The device records the user's progress and answer results, and sends this information to the server in real time.

[0052] Step 7:

[0053] The server analyzes the received progress data and determines appropriate feedback, new learning tasks, and rewards for the user. This information is then sent back to the device.

[0054] Step 8:

[0055] The device notifies the user of feedback from the server and new learning information, preparing them for the next learning session.

[0056] (Example 1)

[0057] 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."

[0058] Traditional education systems have struggled to customize learning experiences to meet users' specific interests and preferences, limiting the provision of personalized education. Furthermore, they have not effectively provided immediate feedback or new learning materials based on learning progress.

[0059] 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.

[0060] In this invention, the server includes a device for inputting the user's specific interests and the content they wish to be taught; a device for generating individually optimized educational digital materials based on the interest information and selected educational content obtained from the user; and a device for converting the generated educational digital materials into a digital entertainment format and providing it to the user's information and communication device. This enables the provision of a personalized learning experience, as well as immediate feedback and effective provision of new learning materials according to the learning progress.

[0061] A "user" refers to an individual who uses the system to acquire educational content and enjoy a learning experience.

[0062] "Interest information" refers to information that indicates a user's personal interests and preferences, and is used to customize the educational experience.

[0063] "Educational digital materials" refer to digital content generated based on the user's interests and educational content, designed to support learning.

[0064] "Digital entertainment format" refers to a format in which educational digital materials are transformed into games or interactive forms, allowing users to learn while having fun.

[0065] "Information and communication equipment" refers to devices that users use to receive and interactively utilize educational digital materials, and examples include smartphones and computers.

[0066] "Progress status" refers to data that shows the results and achievements of a user's learning activities using educational digital materials.

[0067] "Evaluation information" is feedback generated based on the user's progress, and is provided to help improve learning and guide them to the next step.

[0068] The embodiments for carrying out the present invention will now be described. The system of the present invention operates primarily around a server, a terminal, and a user in order to provide a learning experience based on the user's individualized interests.

[0069] First, users access the system using devices such as smartphones or personal computers and input information about their interests and the topics they want to learn about. This helps to define the user's learning needs.

[0070] Next, the terminal structures this information into data packets and sends them to the server using a secure communication protocol. The terminal also allows users to easily input information through a user interface.

[0071] Based on the received information, the server uses a generative AI model (for example, the representative AI model GPT-4®) to generate customized educational digital materials tailored to the user's interests and learning content. Specifically, the server inputs a prompt sentence into the generative AI model. An example of such a prompt sentence is, "Create an English learning quiz using anime characters as the subject." The AI ​​model generates educational content based on this prompt.

[0072] The generated content is organized by the server, converted into a digital entertainment format, and sent to the user's device. The device receives it and presents it to the user as an interactive learning application. This application is designed to allow users to learn while having fun, providing educational digital materials in the form of stories, quizzes, and other formats.

[0073] Furthermore, as the user progresses through the learning process, the device records its progress and reports it to the server in real time. This allows the server to evaluate the user's learning progress and determine the next appropriate learning step. Based on the progress data, the server generates new learning materials and rewards, which are then redistributed to the device, thereby continuously supporting the user's learning.

[0074] In this way, the system of the present invention provides a personalized learning experience based on the user's interests, enabling efficient learning while maintaining the user's motivation.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user inputs their areas of interest, such as themes, characters, and topics they wish to learn about, through the device's user interface. Based on this input, the user's learning requirements are refined. The input is in text format. The device then prepares to send this information to the server as structured data.

[0078] Step 2:

[0079] The terminal organizes the information entered by the user into data packets and sends them to the server using a secure protocol (e.g., HTTPS). Here, the input is user information, and the output is the data packets sent to the server. These data packets are sent in a structured format such as JSON.

[0080] Step 3:

[0081] The server analyzes the received user information and generates a prompt for use with the generative AI model. This prompt might be something like, "Create an English learning quiz using anime characters as the subject." The input is user information, and the output is the generated prompt sentence. The server inputs the prompt sentence into the generative AI model and creates digital materials.

[0082] Step 4:

[0083] The generative AI model generates customized educational digital materials based on prompts. The input is a prompt sentence, and the output is digital material. This material contains content based on the user's interests and is presented in quiz or story format.

[0084] Step 5:

[0085] The server organizes the generated digital materials and converts them into digital entertainment formats. The input here is materials generated by an AI model, and the output is content in digital entertainment format. This is then sent to the terminal.

[0086] Step 6:

[0087] The device displays received digital entertainment content to the user as an interactive learning application. The input is the content received from the server, and the output is the interface viewed by the user. The user progresses through this application.

[0088] Step 7:

[0089] During learning, the device records the user's progress and sends this data to the server at the end of the session or at regular intervals. The input is the user's interaction data, and the output is the progress data sent to the server.

[0090] Step 8:

[0091] The server analyzes the received progress data and evaluates the user's learning steps. This determines appropriate new learning materials and rewards for the user. The input is progress data, and the output is newly generated learning materials and rewards. This is then resent to the terminal.

[0092] (Application Example 1)

[0093] 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."

[0094] To sustain individual users' motivation to learn, it is necessary to customize learning content based on their individual interests and provide it in diverse media formats. Furthermore, it is essential to improve learning efficiency by providing appropriate feedback in real time according to their progress.

[0095] 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.

[0096] In this invention, the server includes a function to receive information based on the user's interests, a function to generate learning content as stories and problems based on the user's interest information and selected learning content, and a function to provide the generated learning content in a format playable on an electronic device. This makes it possible to provide a customized learning experience that is tailored to the user's individual interests and to enhance their motivation to learn.

[0097] "User interest-based information" refers to information related to areas, themes, or characters that users are personally interested in.

[0098] "Learning content" refers to information related to the knowledge and skills that the user intends to acquire.

[0099] The "ability to generate stories and questions" refers to the capacity of a computer program to automatically create relevant stories or quiz-style questions based on themes that the user is interested in.

[0100] "A format playable on electronic devices" refers to a format provided digitally so that users can access it on electronic devices such as smartphones, tablets, or computers.

[0101] "Learning progress" refers to information that shows the level of understanding and achievements a user has attained during the learning process.

[0102] "Providing information in real time" means providing information that is immediately relevant to the user's actions and responses.

[0103] This invention is implemented using a system that provides customized learning content based on themes of interest to the user. The system consists of a server, a terminal, and the user.

[0104] The server uses a generative AI model to create customized learning content in the form of stories and quizzes, based on information about the user's personal interests and learning goals. This process utilizes programs written in Python and Flask, and uses MongoDB to manage user information and progress. The server can generate natural-sounding stories and questions based on user input using OpenAI® models.

[0105] The device is an electronic device such as a smartphone or tablet that receives generated content sent from the server and presents it to the user. The device is implemented using React Native, and the user interface is designed to provide an intuitive and interactive learning experience. Users receive real-time feedback through the device, and their progress is recorded instantly.

[0106] For example, if a user requests to "learn mathematics using a fantasy adventure story as a theme," the server will generate mathematical problems based on the fantasy story. In this process, the following prompt statements are provided to the AI ​​model to create the content:

[0107] "Create a fantasy-themed narrative for a learning quiz focused on mathematics, incorporating elements of adventure and problem-solving."

[0108] In this way, users can enjoy learning tailored to their individual interests, and it is expected that this will help maintain their motivation to learn.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] Users input their personal interests and topics they want to learn about using their device and send this information to the server. The input includes preferred themes, characters, and topics they want to study. The device packages this information in JSON format and sends it to the server as an HTTP request.

[0112] Step 2:

[0113] The server analyzes the received user information and generates prompt text for input into the AI ​​model. Here, a natural language prompt text is created that combines the user's interests and learned content. Specifically, a Python script takes in the user information, assembles the prompt text based on a template, and sends it to the OpenAI API.

[0114] Step 3:

[0115] The server receives the content returned from the generating AI model and converts it into an application format viewable on the user's device. Here, the server retrieves the text of the generated content and converts it into a format suitable for React Native components. The generated stories and issues are stored in JSON format and sent to the device.

[0116] Step 4:

[0117] The device receives learning content sent from the server and displays it on the user interface. The device uses React Native to generate interactive components that allow the user to take actions such as reading through a story or answering quizzes.

[0118] Step 5:

[0119] As the user progresses through the story or answers quizzes, the device collects progress information and sends the collected data to the server. Specifically, the answer results and learning history are organized in JSON format and sent back to the server as an HTTP request.

[0120] Step 6:

[0121] The server analyzes the user's learning progress based on the received progress data and determines the next content and rewards to present. The server saves the progress data to MongoDB, performs analysis, and then generates a new prompt message considering the next required learning items and rewards.

[0122] Step 7:

[0123] The newly generated learning content and reward information are sent back from the server to the device and provided to the user. This process is dynamically repeated, allowing the user to gain a continuous learning experience based on their interests.

[0124] 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.

[0125] The system of this invention provides a customized learning experience by combining the user's personal interests and learning content, and by recognizing and adjusting the user's emotions. This system is realized by appropriately exchanging information among four parties: the server, the terminal, the emotion engine, and the user, and by adjusting and optimizing content based on the user's emotional state.

[0126] First, the user accesses the system and inputs their interests (e.g., favorite themes or characters) and what they want to learn. The user uses a camera and microphone to record their facial expressions and voice, and the emotion engine analyzes this as emotion data. This information is then transmitted to the server via the terminal.

[0127] Next, the server designs customized learning content based on the user's personal interests and learning goals. This content incorporates variable elements that respond to the user's anticipated emotions and includes a story that blends the learning topic with the user's interests. An emotion engine monitors the user's emotions in real time, adjusting the story's progression and quiz difficulty based on the user's level of excitement and concentration.

[0128] The generated learning content is sent from the server to the device, where it becomes accessible and usable by the user in the form of a game or application. The device monitors the user's interaction with the learning application and optimizes the content based on the data acquired by the emotion engine.

[0129] During learning, the emotion engine detects changes in the user's facial expressions and voice and sends this information to the server. The server then analyzes this emotion data in conjunction with the learning progress to design new learning tasks and rewards that match the user's emotional state. For example, if the system determines that the user is bored, it may speed up the story or offer new challenges to rekindle their interest.

[0130] For example, if a user sets their learning objective to "learn English using anime scenes," the server uses data input from the emotion engine to adjust the generated content so that it closely synchronizes with the user's emotions. A scene is created where characters from the anime speak in English, and the character's next action changes based on the user's response.

[0131] This allows users to enjoy a personalized learning experience aligned with their emotions, leading to deeper and more sustained learning. The present invention makes it possible to provide an even more effective learning experience by dynamically adapting the user's learning environment according to their emotional state.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] Users log in to the platform and enter topics and content they are interested in. They also select fields they want to learn about and send this information to the server via their device.

[0135] Step 2:

[0136] The device collects the user's facial expressions and voice data using its built-in camera and microphone. This information is then sent in real time to an emotion engine to recognize the user's current emotional state.

[0137] Step 3:

[0138] The emotion engine detects emotions from the user's facial expressions and voice data, and sends the results to the server. This allows the server to understand the user's emotional state.

[0139] Step 4:

[0140] The server designs customized learning content based on the user's interests and emotional data received from the emotion engine. This content includes variable elements that adapt to the user's emotional state.

[0141] Step 5:

[0142] The server generates customized learning content, which is then sent to the device in a gamified application format, preparing it for user delivery.

[0143] Step 6:

[0144] The user launches the learning application through their device and begins learning with the provided content. All interactions and progress are recorded in real time.

[0145] Step 7:

[0146] The device continuously sends the user's facial expressions and voice to the emotion engine, tracking changes in the user's emotions. The emotion engine analyzes this data and sends it to the server to be reflected in the learning content.

[0147] Step 8:

[0148] The server considers data from the emotion engine and the user's learning progress to design new learning tasks and rewards. This information is then sent back to the device to provide feedback to the user.

[0149] (Example 2)

[0150] 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".

[0151] Traditional educational systems have difficulty taking into account users' learning interests and emotional states, resulting in an inability to adequately address individual learning processes. As a result, users are more likely to lose interest in activities, potentially leading to decreased learning effectiveness. Furthermore, the inability to make real-time adjustments based on emotions hinders user satisfaction.

[0152] 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.

[0153] In this invention, the server includes means for inputting the user's interests and educational objectives, means for determining the user's emotional state and acquiring emotional data, and means for generating customized learning content based on the user's emotional state and selected educational objectives using a generative AI model. This enables the provision of an individualized learning experience and dynamic content adjustment in response to the user's emotions.

[0154] "User" refers to an individual or entity that uses this system for learning.

[0155] "Interest" refers to the level of interest or curiosity a user shows regarding a particular theme or activity.

[0156] "Educational objectives" refer to the learning goals and content that the user wants to achieve.

[0157] "Emotional state" refers to data that represents the user's psychological response and emotional state.

[0158] "Emotional data" refers to emotional information analyzed from the user's facial expressions and voice.

[0159] A "generative AI model" refers to artificial intelligence technology that generates content based on user input and emotional state.

[0160] "Customized learning content" refers to learning materials that are adapted to the individual interests and emotions of the user.

[0161] "Electronic devices" refer to devices that users use to access this system, and include computers, smartphones, tablets, and other similar devices.

[0162] "Real-time" refers to a system that performs operations and data analysis instantly, and immediately reflects and presents the results.

[0163] "Progress data" refers to information that records the user's learning process and achievements.

[0164] This invention is a system that provides an effective learning experience that takes into account the user's interests and emotions. This system utilizes a server, terminals, a generative AI model, and an emotion engine to deliver learning content tailored to the individual needs of the user.

[0165] First, users access the system using their devices and input their interests and what they want to learn. They also use a camera and microphone to record their facial expressions and voice, providing this data to the system as emotional information. This allows the system to understand the user's level of interest in specific topics or activities.

[0166] Next, the server uses a generative AI model to generate customized learning content based on the received interest and emotion data. This content includes stories and educational materials that combine the user's interests and learning objectives, and is adjusted in real time according to the user's emotional state.

[0167] The generated learning content is delivered from the server to the user's device and becomes available as an application. The device monitors the user's learning progress in real time and sends progress data to the server. Based on this data, the server designs new learning tasks and rewards.

[0168] For example, if a user sets their learning objective to "learn modern history through an interactive story themed around historical events," the server uses a generative AI model to adjust the story's progression and quiz content based on the user's emotional data. It can also insert scenes where characters speak to the user, altering the story's progression based on the user's responses.

[0169] An example of a prompt statement might be, "Build a system that dynamically generates learning content and stories based on themes the user has shown interest in."

[0170] In this way, by dynamically adapting to the user's interests and emotions, a comfortable and effective learning environment can be provided.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] Users log in to their device and input their interests and what they want to learn. The input data provided by the user includes specific themes and characters they are interested in. This input is sent to the server and becomes the foundational data for generating personalized learning content.

[0174] Step 2:

[0175] The device records the user's facial expressions and voice through its camera and microphone, acquiring them as emotion data. This acquired emotion data is analyzed in real time by an emotion engine. This input data quantifies the user's psychological state and is sent to a server, where it is output as basic information used to adjust learning content.

[0176] Step 3:

[0177] The server receives user interest and emotion data as input and uses a generative AI model to build customized learning content. The generated learning content includes stories and quizzes that reflect the user's interests. This output includes elements that can vary depending on the user's emotional state and is sent to the device.

[0178] Step 4:

[0179] The terminal executes the learning content received from the server in application format, making it interactive for the user. At this stage, the user's learning actions are recorded and sent to the server as progress data. This progress data is output as information used to optimize future learning content.

[0180] Step 5:

[0181] The emotion engine continuously monitors changes in the user's emotional state during learning. This allows for real-time adjustments to maintain the user's interest and concentration. Based on the emotion data, the server dynamically adjusts the speed and difficulty of the learning content and designs and outputs new learning tasks.

[0182] Step 6:

[0183] The server comprehensively analyzes progress and sentiment data to provide users with rewards and new challenges. This output is designed to give users a sense of accomplishment and improve learning sustainability. This reward system plays a role in increasing user motivation.

[0184] (Application Example 2)

[0185] 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".

[0186] Traditional learning systems have suffered from reduced learning efficiency and user engagement because they do not provide content that takes into account the user's emotional state. Furthermore, particularly in the entertainment field, there is a demand for content that resonates with users' emotions, but achieving this has been difficult.

[0187] 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.

[0188] In this invention, the server includes means for inputting information based on the user's interests, means for generating customized learning content based on the user's interest information and selected learning content, and means for analyzing the user's visual and auditory information with an emotion analysis device and providing entertainment tailored to the user's emotional state. This makes it possible to provide an optimal learning experience and entertainment that is in line with the user's emotional state.

[0189] A "user" is an entity that interacts with a system through an information processing device.

[0190] "Interest information" refers to data about the themes and fields that users prefer in their learning or entertainment activities.

[0191] "Learning content" refers to information related to specific knowledge or skills that the user wants to acquire or deepen.

[0192] "Customized learning content" refers to educational or entertainment materials tailored to a user's individual interests and learning goals.

[0193] An "information processing device" is a digital device used by users to receive learning content and feedback.

[0194] "Progress data" refers to records that show how far a user has progressed in learning or entertainment.

[0195] "Visual and auditory information" refers to data related to facial expressions and speech provided by the user through the camera and microphone of the information processing device.

[0196] An "emotion analysis device" is a device or software that analyzes a user's visual and auditory information to identify their emotional state at any given time.

[0197] "Providing entertainment" refers to providing content that is structured to evoke enjoyment and excitement based on the user's interests and emotional state.

[0198] In this invention, the user first inputs their interests and learning content through an information processing device. The user's information processing device is equipped with a camera and a microphone, which are used to collect the user's visual and auditory information. Subsequently, this data is detected in real time using an emotion analysis device, and the visual and auditory information is converted into the user's emotional state.

[0199] The server generates customized learning content based on collected interest information, learning content, and user emotional state data. During this process, OpenCV analyzes the user's facial expressions, and Google's speech recognition API analyzes their voice tone. The analysis results are sent to the server via Node.js and processed integrally using Python.

[0200] The generated learning content is delivered to the information processing device in real time. Here, stories and scenes appropriate to the user's emotional state are selected, optimizing the user experience. As a result, users can enjoy a deeper learning experience and greater entertainment.

[0201] For example, when a user is tired, the system can keep them interested by providing relaxing scenery or music content.

[0202] Furthermore, an example of a prompt for the generative AI model is: "This user is currently in a state of ○○ (emotion). Please suggest the most suitable content scene for this state." This allows the generative AI to suggest content that matches the user's emotions.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The user inputs their interests and learning content through the interface of the information processing device. This input is received by the terminal and used for subsequent processing. At this point, the input data consists of text information about the user's preferences and learning goals.

[0206] Step 2:

[0207] The device uses a camera and microphone to collect visual and auditory information from the user. This information is transmitted in real time to an emotion analysis device. The input data consists of video and audio signals, and based on this, the emotion analysis device analyzes the user's emotional state from changes in facial expressions and voice tone. The result of the analysis is numerical data representing the user's emotions.

[0208] Step 3:

[0209] The server receives interest information, learning content, and emotional state data sent from the terminal and generates customized learning content. Specifically, it uses Python to construct stories and scenes that take the user's emotions into account, and utilizes a generative AI model to generate data based on the prompt message, "This user is currently in the state of ○○ (emotion). Please suggest the most suitable content scene for this state." This results in content optimized for each user being output.

[0210] Step 4:

[0211] The generated learning content is delivered from the server to the device in real time. The device adjusts this content to match the user's emotional state and displays it at the optimal time. Specific actions include the playback of scenes and audio displayed on the screen, which dynamically switch in response to the user's reactions. The output data provides visual and auditory feedback as a user interface.

[0212] Step 5:

[0213] User interaction and progress data are recorded on the device and sent to the server. The server analyzes this data and dynamically generates new learning materials and rewards based on the user's progress. This process creates content that promotes continued user engagement.

[0214] 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.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] The system of this invention provides a customized learning experience by combining the user's personal interests and the content they wish to learn. This system is primarily realized through a process in which information is appropriately exchanged between the server, terminal, and user, and content is generated and provided according to the user's preferences.

[0231] First, the user accesses the system and enters information such as their personal interests, favorite characters, and themes. In addition, the user selects the topic they want to learn about (e.g., English, mathematics, programming, etc.). This information is then sent from the user's terminal to the server.

[0232] Next, the server designs personalized learning content based on the received user information. The server generates customized stories and quizzes that combine the user's interests with the learning material. This generated content is designed to make learning enjoyable in a game format, aiming to keep the user engaged.

[0233] The generated learning content is sent from the server to the user's device. The device receives this content and makes it accessible to the user in the form of an application. This application has various functions to allow the user to learn interactively. For example, it promotes learning while maintaining motivation by providing feedback and rewards based on the user's results each time they answer a quiz.

[0234] During a learning session, the device records the user's progress and sends this data to the server. The server analyzes this progress data and determines the next steps appropriate for the user. Based on the evaluation data, the server prepares new learning tasks and rewards and resends them to the device.

[0235] For example, if a user expresses a desire to learn English using anime characters, the server will generate English quizzes related to the anime's storyline and characters. These quizzes are designed to engage the user, and are structured to entertain them in various ways, such as allowing them to learn English phrases in scenarios featuring anime characters, and having the characters perform special actions when they answer correctly.

[0236] Through these interactions, users can naturally connect their interests with learning and continue learning effectively. This mechanism allows the present invention to provide users with a personalized learning experience and effectively promote continued learning.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] Users log in to the platform, enter themes and topics of interest, and select the fields they wish to study. This information is sent from the device to the server.

[0240] Step 2:

[0241] The server analyzes the user information it receives and generates customized learning content based on the user's interests and what they want to learn. Specifically, it creates stories related to the user's interests and incorporates learning content within those stories.

[0242] Step 3:

[0243] This process involves creating data for distributing customized learning content generated by the server, arranging it into a user-specific game or application format.

[0244] Step 4:

[0245] The device receives and installs the learning application data sent from the server, preparing it for use by the user.

[0246] Step 5:

[0247] The user opens the learning application on their device and begins learning. Here, the user progresses through content designed based on their interests.

[0248] Step 6:

[0249] The device records the user's progress and answer results, and sends this information to the server in real time.

[0250] Step 7:

[0251] The server analyzes the received progress data and determines appropriate feedback, new learning tasks, and rewards for the user. This information is then sent back to the device.

[0252] Step 8:

[0253] The device notifies the user of feedback from the server and new learning information, preparing them for the next learning session.

[0254] (Example 1)

[0255] 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."

[0256] Traditional education systems have struggled to customize learning experiences to meet users' specific interests and preferences, limiting the provision of personalized education. Furthermore, they have not effectively provided immediate feedback or new learning materials based on learning progress.

[0257] 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.

[0258] In this invention, the server includes a device for inputting the user's specific interests and the content they wish to be taught; a device for generating individually optimized educational digital materials based on the interest information and selected educational content obtained from the user; and a device for converting the generated educational digital materials into a digital entertainment format and providing it to the user's information and communication device. This enables the provision of a personalized learning experience, as well as immediate feedback and effective provision of new learning materials according to the learning progress.

[0259] A "user" refers to an individual who uses the system to acquire educational content and enjoy a learning experience.

[0260] "Interest information" refers to information that indicates a user's personal interests and preferences, and is used to customize the educational experience.

[0261] "Educational digital materials" refer to digital content generated based on the user's interests and educational content, designed to support learning.

[0262] "Digital entertainment format" refers to a format in which educational digital materials are transformed into games or interactive forms, allowing users to learn while having fun.

[0263] "Information and communication equipment" refers to devices that users use to receive and interactively utilize educational digital materials, and examples include smartphones and computers.

[0264] "Progress status" refers to data that shows the results and achievements of a user's learning activities using educational digital materials.

[0265] "Evaluation information" is feedback generated based on the user's progress, and is provided to help improve learning and guide them to the next step.

[0266] The embodiments for carrying out the present invention will now be described. The system of the present invention operates primarily around a server, a terminal, and a user in order to provide a learning experience based on the user's individualized interests.

[0267] First, users access the system using devices such as smartphones or personal computers and input information about their interests and the topics they want to learn about. This helps to define the user's learning needs.

[0268] Next, the terminal structures this information into data packets and sends them to the server using a secure communication protocol. The terminal also allows users to easily input information through a user interface.

[0269] Based on the received information, the server uses a generative AI model (for example, the representative AI model GPT-4) to generate customized educational digital materials tailored to the user's interests and learning content. Specifically, the server inputs a prompt sentence into the generative AI model. An example of such a prompt sentence would be, "Create an English learning quiz using anime characters as the subject." The AI ​​model then generates educational content based on this prompt.

[0270] The generated content is organized by the server, converted into a digital entertainment format, and sent to the user's device. The device receives it and presents it to the user as an interactive learning application. This application is designed to allow users to learn while having fun, providing educational digital materials in the form of stories, quizzes, and other formats.

[0271] Furthermore, as the user progresses through the learning process, the device records its progress and reports it to the server in real time. This allows the server to evaluate the user's learning progress and determine the next appropriate learning step. Based on the progress data, the server generates new learning materials and rewards, which are then redistributed to the device, thereby continuously supporting the user's learning.

[0272] In this way, the system of the present invention provides a personalized learning experience based on the user's interests, enabling efficient learning while maintaining the user's motivation.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The user inputs their areas of interest, such as themes, characters, and topics they wish to learn about, through the device's user interface. Based on this input, the user's learning requirements are refined. The input is in text format. The device then prepares to send this information to the server as structured data.

[0276] Step 2:

[0277] The terminal organizes the information entered by the user into data packets and sends them to the server using a secure protocol (e.g., HTTPS). Here, the input is user information, and the output is the data packets sent to the server. These data packets are sent in a structured format such as JSON.

[0278] Step 3:

[0279] The server analyzes the received user information and generates a prompt for use with the generative AI model. This prompt might be something like, "Create an English learning quiz using anime characters as the subject." The input is user information, and the output is the generated prompt sentence. The server inputs the prompt sentence into the generative AI model and creates digital materials.

[0280] Step 4:

[0281] The generative AI model generates customized educational digital materials based on prompts. The input is a prompt sentence, and the output is digital material. This material contains content based on the user's interests and is presented in quiz or story format.

[0282] Step 5:

[0283] The server organizes the generated digital materials and converts them into digital entertainment formats. The input here is materials generated by an AI model, and the output is content in digital entertainment format. This is then sent to the terminal.

[0284] Step 6:

[0285] The terminal displays the received digital entertainment - format content to the user as an interactive learning application. The input is the content received from the server, and the output is the interface that the user browses. The user progresses in learning through this application.

[0286] Step 7:

[0287] During learning, the terminal records the user's progress and transmits this data to the server at the end of the session or at regular intervals. The input is the user's interaction data, and the output is the progress data transmitted to the server.

[0288] Step 8:

[0289] The server analyzes the received progress data and evaluates the user's learning steps. Based on this, new learning materials and rewards suitable for the user are determined. The input is the progress data, and the output is the newly generated learning materials and rewards. These are resent to the terminal.

[0290] (Application Example 1)

[0291] Next, Application Example 1 will be described. In the following description, the data - processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] In order to sustain each user's learning motivation, it is required to customize learning content based on each user's individual interests and provide it in various media formats. Also, it is necessary to improve the learning efficiency by providing appropriate feedback according to the progress in real - time.

[0293] The specific processing by the specific processing unit 290 of the data - processing device 12 in Application Example 1 is realized by the following means.

[0294] In this invention, the server includes a function to receive information based on the user's interests, a function to generate learning content as stories and problems based on the user's interest information and selected learning content, and a function to provide the generated learning content in a format playable on an electronic device. This makes it possible to provide a customized learning experience that is tailored to the user's individual interests and to enhance their motivation to learn.

[0295] "User interest-based information" refers to information related to areas, themes, or characters that users are personally interested in.

[0296] "Learning content" refers to information related to the knowledge and skills that the user intends to acquire.

[0297] The "ability to generate stories and questions" refers to the capacity of a computer program to automatically create relevant stories or quiz-style questions based on themes that the user is interested in.

[0298] "A format playable on electronic devices" refers to a format provided digitally so that users can access it on electronic devices such as smartphones, tablets, or computers.

[0299] "Learning progress" refers to information that shows the level of understanding and achievements a user has attained during the learning process.

[0300] "Providing information in real time" means providing information that is immediately relevant to the user's actions and responses.

[0301] This invention is implemented using a system that provides customized learning content based on themes of interest to the user. The system consists of a server, a terminal, and the user.

[0302] The server creates customized story and quiz-style learning content by leveraging a generative AI model based on the information about the user's personal interests and learning content received from the user. In this process, a program using Python and Flask is employed, and MongoDB is used as the database to manage user information and progress. The server can generate natural stories and questions based on the information input by the user by utilizing the model of OpenAI.

[0303] The terminal is an electronic device such as a smartphone or a tablet, which receives the generated content sent from the server and presents it to the user. The terminal is implemented using React Native, and the user interface is designed to provide an intuitive and interactive learning experience. The user can receive real-time feedback through the terminal, and the progress is recorded immediately.

[0304] As a specific example, when the user wishes to "learn mathematics with a fantasy adventure story as the theme", the server generates math problems based on a fantasy story. At this time, the following prompt sentence is provided to the generative AI model to create the content:

[0305] "Create a fantasy-themed narrative for a learning quiz focused on mathematics, incorporating elements of adventure and problem-solving."

[0306] In this way, the user can enjoy learning tailored to their individual interests and can expect the effect of sustaining their learning motivation.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] Users input their personal interests and topics they want to learn about using their device and send this information to the server. The input includes preferred themes, characters, and topics they want to study. The device packages this information in JSON format and sends it to the server as an HTTP request.

[0310] Step 2:

[0311] The server analyzes the received user information and generates prompt text for input into the AI ​​model. Here, a natural language prompt text is created that combines the user's interests and learned content. Specifically, a Python script takes in the user information, assembles the prompt text based on a template, and sends it to the OpenAI API.

[0312] Step 3:

[0313] The server receives the content returned from the generating AI model and converts it into an application format viewable on the user's device. Here, the server retrieves the text of the generated content and converts it into a format suitable for React Native components. The generated stories and issues are stored in JSON format and sent to the device.

[0314] Step 4:

[0315] The device receives learning content sent from the server and displays it on the user interface. The device uses React Native to generate interactive components that allow the user to take actions such as reading through a story or answering quizzes.

[0316] Step 5:

[0317] As the user progresses through the story or answers quizzes, the device collects progress information and sends the collected data to the server. Specifically, the answer results and learning history are organized in JSON format and sent back to the server as an HTTP request.

[0318] Step 6:

[0319] The server analyzes the user's learning progress based on the received progress data and determines the next content and rewards to present. The server saves the progress data to MongoDB, performs analysis, and then generates a new prompt message considering the next required learning items and rewards.

[0320] Step 7:

[0321] The newly generated learning content and reward information are sent back from the server to the device and provided to the user. This process is dynamically repeated, allowing the user to gain a continuous learning experience based on their interests.

[0322] 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.

[0323] The system of this invention provides a customized learning experience by combining the user's personal interests and learning content, and by recognizing and adjusting the user's emotions. This system is realized by appropriately exchanging information among four parties: the server, the terminal, the emotion engine, and the user, and by adjusting and optimizing content based on the user's emotional state.

[0324] First, the user accesses the system and inputs their interests (e.g., favorite themes or characters) and what they want to learn. The user uses a camera and microphone to record their facial expressions and voice, and the emotion engine analyzes this as emotion data. This information is then transmitted to the server via the terminal.

[0325] Next, the server designs customized learning content based on the user's personal interests and learning goals. This content incorporates variable elements that respond to the user's anticipated emotions and includes a story that blends the learning topic with the user's interests. An emotion engine monitors the user's emotions in real time, adjusting the story's progression and quiz difficulty based on the user's level of excitement and concentration.

[0326] The generated learning content is sent from the server to the device, where it becomes accessible and usable by the user in the form of a game or application. The device monitors the user's interaction with the learning application and optimizes the content based on the data acquired by the emotion engine.

[0327] During learning, the emotion engine detects changes in the user's facial expressions and voice and sends this information to the server. The server then analyzes this emotion data in conjunction with the learning progress to design new learning tasks and rewards that match the user's emotional state. For example, if the system determines that the user is bored, it may speed up the story or offer new challenges to rekindle their interest.

[0328] For example, if a user sets their learning objective to "learn English using anime scenes," the server uses data input from the emotion engine to adjust the generated content so that it closely synchronizes with the user's emotions. A scene is created where characters from the anime speak in English, and the character's next action changes based on the user's response.

[0329] This allows users to enjoy a personalized learning experience aligned with their emotions, leading to deeper and more sustained learning. The present invention makes it possible to provide an even more effective learning experience by dynamically adapting the user's learning environment according to their emotional state.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] Users log in to the platform and enter topics and content they are interested in. They also select fields they want to learn about and send this information to the server via their device.

[0333] Step 2:

[0334] The device collects the user's facial expressions and voice data using its built-in camera and microphone. This information is then sent in real time to an emotion engine to recognize the user's current emotional state.

[0335] Step 3:

[0336] The emotion engine detects emotions from the user's facial expressions and voice data, and sends the results to the server. This allows the server to understand the user's emotional state.

[0337] Step 4:

[0338] The server designs customized learning content based on the user's interests and emotional data received from the emotion engine. This content includes variable elements that adapt to the user's emotional state.

[0339] Step 5:

[0340] The server generates customized learning content, which is then sent to the device in a gamified application format, preparing it for user delivery.

[0341] Step 6:

[0342] The user launches the learning application through their device and begins learning with the provided content. All interactions and progress are recorded in real time.

[0343] Step 7:

[0344] The device continuously sends the user's facial expressions and voice to the emotion engine, tracking changes in the user's emotions. The emotion engine analyzes this data and sends it to the server to be reflected in the learning content.

[0345] Step 8:

[0346] The server considers data from the emotion engine and the user's learning progress to design new learning tasks and rewards. This information is then sent back to the device to provide feedback to the user.

[0347] (Example 2)

[0348] 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".

[0349] Traditional educational systems have difficulty taking into account users' learning interests and emotional states, resulting in an inability to adequately address individual learning processes. As a result, users are more likely to lose interest in activities, potentially leading to decreased learning effectiveness. Furthermore, the inability to make real-time adjustments based on emotions hinders user satisfaction.

[0350] 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.

[0351] In this invention, the server includes means for inputting the user's interests and educational objectives, means for determining the user's emotional state and acquiring emotional data, and means for generating customized learning content based on the user's emotional state and selected educational objectives using a generative AI model. This enables the provision of an individualized learning experience and dynamic content adjustment in response to the user's emotions.

[0352] "User" refers to an individual or entity that uses this system for learning.

[0353] "Interest" refers to the level of interest or curiosity a user shows regarding a particular theme or activity.

[0354] "Educational objectives" refer to the learning goals and content that the user wants to achieve.

[0355] "Emotional state" refers to data that represents the user's psychological response and emotional state.

[0356] "Emotional data" refers to emotional information analyzed from the user's facial expressions and voice.

[0357] A "generative AI model" refers to artificial intelligence technology that generates content based on user input and emotional state.

[0358] "Customized learning content" refers to learning materials that are adapted to the individual interests and emotions of the user.

[0359] "Electronic devices" refer to devices that users use to access this system, and include computers, smartphones, tablets, and other similar devices.

[0360] "Real-time" refers to a system that performs operations and data analysis instantly, and immediately reflects and presents the results.

[0361] "Progress data" refers to information that records the user's learning process and achievements.

[0362] This invention is a system that provides an effective learning experience that takes into account the user's interests and emotions. This system utilizes a server, terminals, a generative AI model, and an emotion engine to deliver learning content tailored to the individual needs of the user.

[0363] First, users access the system using their devices and input their interests and what they want to learn. They also use a camera and microphone to record their facial expressions and voice, providing this data to the system as emotional information. This allows the system to understand the user's level of interest in specific topics or activities.

[0364] Next, the server uses a generative AI model to generate customized learning content based on the received interest and emotion data. This content includes stories and educational materials that combine the user's interests and learning objectives, and is adjusted in real time according to the user's emotional state.

[0365] The generated learning content is delivered from the server to the user's device and becomes available as an application. The device monitors the user's learning progress in real time and sends progress data to the server. Based on this data, the server designs new learning tasks and rewards.

[0366] For example, if a user sets their learning objective to "learn modern history through an interactive story themed around historical events," the server uses a generative AI model to adjust the story's progression and quiz content based on the user's emotional data. It can also insert scenes where characters speak to the user, altering the story's progression based on the user's responses.

[0367] An example of a prompt statement might be, "Build a system that dynamically generates learning content and stories based on themes the user has shown interest in."

[0368] In this way, by dynamically adapting to the user's interests and emotions, a comfortable and effective learning environment can be provided.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] Users log in to their device and input their interests and what they want to learn. The input data provided by the user includes specific themes and characters they are interested in. This input is sent to the server and becomes the foundational data for generating personalized learning content.

[0372] Step 2:

[0373] The device records the user's facial expressions and voice through its camera and microphone, acquiring them as emotion data. This acquired emotion data is analyzed in real time by an emotion engine. This input data quantifies the user's psychological state and is sent to a server, where it is output as basic information used to adjust learning content.

[0374] Step 3:

[0375] The server receives user interest and emotion data as input and uses a generative AI model to build customized learning content. The generated learning content includes stories and quizzes that reflect the user's interests. This output includes elements that can vary depending on the user's emotional state and is sent to the device.

[0376] Step 4:

[0377] The terminal executes the learning content received from the server in application format, making it interactive for the user. At this stage, the user's learning actions are recorded and sent to the server as progress data. This progress data is output as information used to optimize future learning content.

[0378] Step 5:

[0379] The emotion engine continuously monitors changes in the user's emotional state during learning. This allows for real-time adjustments to maintain the user's interest and concentration. Based on the emotion data, the server dynamically adjusts the speed and difficulty of the learning content and designs and outputs new learning tasks.

[0380] Step 6:

[0381] The server comprehensively analyzes progress and sentiment data to provide users with rewards and new challenges. This output is designed to give users a sense of accomplishment and improve learning sustainability. This reward system plays a role in increasing user motivation.

[0382] (Application Example 2)

[0383] 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."

[0384] Traditional learning systems have suffered from reduced learning efficiency and user engagement because they do not provide content that takes into account the user's emotional state. Furthermore, particularly in the entertainment field, there is a demand for content that resonates with users' emotions, but achieving this has been difficult.

[0385] 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.

[0386] In this invention, the server includes means for inputting information based on the user's interests, means for generating customized learning content based on the user's interest information and selected learning content, and means for analyzing the user's visual and auditory information with an emotion analysis device and providing entertainment tailored to the user's emotional state. This makes it possible to provide an optimal learning experience and entertainment that is in line with the user's emotional state.

[0387] A "user" is an entity that interacts with a system through an information processing device.

[0388] "Interest information" refers to data about the themes and fields that users prefer in their learning or entertainment activities.

[0389] "Learning content" refers to information related to specific knowledge or skills that the user wants to acquire or deepen.

[0390] "Customized learning content" refers to educational or entertainment materials tailored to a user's individual interests and learning goals.

[0391] An "information processing device" is a digital device used by users to receive learning content and feedback.

[0392] "Progress data" refers to records that show how far a user has progressed in learning or entertainment.

[0393] "Visual and auditory information" refers to data related to facial expressions and speech provided by the user through the camera and microphone of the information processing device.

[0394] An "emotion analysis device" is a device or software that analyzes a user's visual and auditory information to identify their emotional state at any given time.

[0395] "Providing entertainment" refers to providing content that is structured to evoke enjoyment and excitement based on the user's interests and emotional state.

[0396] In this invention, the user first inputs their interests and learning content through an information processing device. The user's information processing device is equipped with a camera and a microphone, which are used to collect the user's visual and auditory information. Subsequently, this data is detected in real time using an emotion analysis device, and the visual and auditory information is converted into the user's emotional state.

[0397] The server generates customized learning content based on collected interest information, learning content, and user emotional state data. During this process, OpenCV analyzes the user's facial expressions, and Google's speech recognition API analyzes their voice tone. The analysis results are sent to the server via Node.js and processed integrally using Python.

[0398] The generated learning content is delivered to the information processing device in real time. Here, stories and scenes appropriate to the user's emotional state are selected, optimizing the user experience. As a result, users can enjoy a deeper learning experience and greater entertainment.

[0399] For example, when a user is tired, the system can keep them interested by providing relaxing scenery or music content.

[0400] Furthermore, an example of a prompt for the generative AI model is: "This user is currently in a state of ○○ (emotion). Please suggest the most suitable content scene for this state." This allows the generative AI to suggest content that matches the user's emotions.

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The user inputs their interests and learning content through the interface of the information processing device. This input is received by the terminal and used for subsequent processing. At this point, the input data consists of text information about the user's preferences and learning goals.

[0404] Step 2:

[0405] The device uses a camera and microphone to collect visual and auditory information from the user. This information is transmitted in real time to an emotion analysis device. The input data consists of video and audio signals, and based on this, the emotion analysis device analyzes the user's emotional state from changes in facial expressions and voice tone. The result of the analysis is numerical data representing the user's emotions.

[0406] Step 3:

[0407] The server receives interest information, learning content, and emotional state data sent from the terminal and generates customized learning content. Specifically, it uses Python to construct stories and scenes that take the user's emotions into account, and utilizes a generative AI model to generate data based on the prompt message, "This user is currently in the state of ○○ (emotion). Please suggest the most suitable content scene for this state." This results in content optimized for each user being output.

[0408] Step 4:

[0409] The generated learning content is delivered from the server to the device in real time. The device adjusts this content to match the user's emotional state and displays it at the optimal time. Specific actions include the playback of scenes and audio displayed on the screen, which dynamically switch in response to the user's reactions. The output data provides visual and auditory feedback as a user interface.

[0410] Step 5:

[0411] User interaction and progress data are recorded on the device and sent to the server. The server analyzes this data and dynamically generates new learning materials and rewards based on the user's progress. This process creates content that promotes continued user engagement.

[0412] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0413] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0414] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0415] [Third Embodiment]

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

[0417] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0418] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0419] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0420] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0422] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0423] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0424] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0425] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0426] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0427] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0428] The system of this invention provides a customized learning experience by combining the user's personal interests and the content they wish to learn. This system is primarily realized through a process in which information is appropriately exchanged between the server, terminal, and user, and content is generated and provided according to the user's preferences.

[0429] First, the user accesses the system and enters information such as their personal interests, favorite characters, and themes. In addition, the user selects the topic they want to learn about (e.g., English, mathematics, programming, etc.). This information is then sent from the user's terminal to the server.

[0430] Next, the server designs personalized learning content based on the received user information. The server generates customized stories and quizzes that combine the user's interests with the learning material. This generated content is designed to make learning enjoyable in a game format, aiming to keep the user engaged.

[0431] The generated learning content is sent from the server to the user's device. The device receives this content and makes it accessible to the user in the form of an application. This application has various functions to allow the user to learn interactively. For example, it promotes learning while maintaining motivation by providing feedback and rewards based on the user's results each time they answer a quiz.

[0432] During a learning session, the device records the user's progress and sends this data to the server. The server analyzes this progress data and determines the next steps appropriate for the user. Based on the evaluation data, the server prepares new learning tasks and rewards and resends them to the device.

[0433] For example, if a user expresses a desire to learn English using anime characters, the server will generate English quizzes related to the anime's storyline and characters. These quizzes are designed to engage the user, and are structured to entertain them in various ways, such as allowing them to learn English phrases in scenarios featuring anime characters, and having the characters perform special actions when they answer correctly.

[0434] Through these interactions, users can naturally connect their interests with learning and continue learning effectively. This mechanism allows the present invention to provide users with a personalized learning experience and effectively promote continued learning.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] Users log in to the platform, enter themes and topics of interest, and select the fields they wish to study. This information is sent from the device to the server.

[0438] Step 2:

[0439] The server analyzes the user information it receives and generates customized learning content based on the user's interests and what they want to learn. Specifically, it creates stories related to the user's interests and incorporates learning content within those stories.

[0440] Step 3:

[0441] This process involves creating data for distributing customized learning content generated by the server, arranging it into a user-specific game or application format.

[0442] Step 4:

[0443] The device receives and installs the learning application data sent from the server, preparing it for use by the user.

[0444] Step 5:

[0445] The user opens the learning application on their device and begins learning. Here, the user progresses through content designed based on their interests.

[0446] Step 6:

[0447] The device records the user's progress and answer results, and sends this information to the server in real time.

[0448] Step 7:

[0449] The server analyzes the received progress data and determines appropriate feedback, new learning tasks, and rewards for the user. This information is then sent back to the device.

[0450] Step 8:

[0451] The device notifies the user of feedback from the server and new learning information, preparing them for the next learning session.

[0452] (Example 1)

[0453] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0454] Traditional education systems have struggled to customize learning experiences to meet users' specific interests and preferences, limiting the provision of personalized education. Furthermore, they have not effectively provided immediate feedback or new learning materials based on learning progress.

[0455] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0456] In this invention, the server includes a device for inputting the user's specific interests and the content they wish to be taught; a device for generating individually optimized educational digital materials based on the interest information and selected educational content obtained from the user; and a device for converting the generated educational digital materials into a digital entertainment format and providing it to the user's information and communication device. This enables the provision of a personalized learning experience, as well as immediate feedback and effective provision of new learning materials according to the learning progress.

[0457] A "user" refers to an individual who uses the system to acquire educational content and enjoy a learning experience.

[0458] "Interest information" refers to information that indicates a user's personal interests and preferences, and is used to customize the educational experience.

[0459] "Educational digital materials" refer to digital content generated based on the user's interests and educational content, designed to support learning.

[0460] "Digital entertainment format" refers to a format in which educational digital materials are transformed into games or interactive forms, allowing users to learn while having fun.

[0461] "Information and communication equipment" refers to devices that users use to receive and interactively utilize educational digital materials, and examples include smartphones and computers.

[0462] "Progress status" refers to data that shows the results and achievements of a user's learning activities using educational digital materials.

[0463] "Evaluation information" is feedback generated based on the user's progress, and is provided to help improve learning and guide them to the next step.

[0464] The embodiments for carrying out the present invention will now be described. The system of the present invention operates primarily around a server, a terminal, and a user in order to provide a learning experience based on the user's individualized interests.

[0465] First, users access the system using devices such as smartphones or personal computers and input information about their interests and the topics they want to learn about. This helps to define the user's learning needs.

[0466] Next, the terminal structures this information into data packets and sends them to the server using a secure communication protocol. The terminal also allows users to easily input information through a user interface.

[0467] Based on the received information, the server uses a generative AI model (for example, the representative AI model GPT-4) to generate customized educational digital materials tailored to the user's interests and learning content. Specifically, the server inputs a prompt sentence into the generative AI model. An example of such a prompt sentence would be, "Create an English learning quiz using anime characters as the subject." The AI ​​model then generates educational content based on this prompt.

[0468] The generated content is organized by the server, converted into a digital entertainment format, and sent to the user's device. The device receives it and presents it to the user as an interactive learning application. This application is designed to allow users to learn while having fun, providing educational digital materials in the form of stories, quizzes, and other formats.

[0469] Furthermore, as the user progresses through the learning process, the device records its progress and reports it to the server in real time. This allows the server to evaluate the user's learning progress and determine the next appropriate learning step. Based on the progress data, the server generates new learning materials and rewards, which are then redistributed to the device, thereby continuously supporting the user's learning.

[0470] In this way, the system of the present invention provides a personalized learning experience based on the user's interests, enabling efficient learning while maintaining the user's motivation.

[0471] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0472] Step 1:

[0473] The user inputs their areas of interest, such as themes, characters, and topics they wish to learn about, through the device's user interface. Based on this input, the user's learning requirements are refined. The input is in text format. The device then prepares to send this information to the server as structured data.

[0474] Step 2:

[0475] The terminal organizes the information entered by the user into data packets and sends them to the server using a secure protocol (e.g., HTTPS). Here, the input is user information, and the output is the data packets sent to the server. These data packets are sent in a structured format such as JSON.

[0476] Step 3:

[0477] The server analyzes the received user information and generates a prompt for use with the generative AI model. This prompt might be something like, "Create an English learning quiz using anime characters as the subject." The input is user information, and the output is the generated prompt sentence. The server inputs the prompt sentence into the generative AI model and creates digital materials.

[0478] Step 4:

[0479] The generative AI model generates customized educational digital materials based on prompts. The input is a prompt sentence, and the output is digital material. This material contains content based on the user's interests and is presented in quiz or story format.

[0480] Step 5:

[0481] The server organizes the generated digital materials and converts them into digital entertainment formats. The input here is materials generated by an AI model, and the output is content in digital entertainment format. This is then sent to the terminal.

[0482] Step 6:

[0483] The device displays received digital entertainment content to the user as an interactive learning application. The input is the content received from the server, and the output is the interface viewed by the user. The user progresses through this application.

[0484] Step 7:

[0485] During learning, the device records the user's progress and sends this data to the server at the end of the session or at regular intervals. The input is the user's interaction data, and the output is the progress data sent to the server.

[0486] Step 8:

[0487] The server analyzes the received progress data and evaluates the user's learning steps. This determines appropriate new learning materials and rewards for the user. The input is progress data, and the output is newly generated learning materials and rewards. This is then resent to the terminal.

[0488] (Application Example 1)

[0489] 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."

[0490] To sustain individual users' motivation to learn, it is necessary to customize learning content based on their individual interests and provide it in diverse media formats. Furthermore, it is essential to improve learning efficiency by providing appropriate feedback in real time according to their progress.

[0491] 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.

[0492] In this invention, the server includes a function to receive information based on the user's interests, a function to generate learning content as stories and problems based on the user's interest information and selected learning content, and a function to provide the generated learning content in a format playable on an electronic device. This makes it possible to provide a customized learning experience that is tailored to the user's individual interests and to enhance their motivation to learn.

[0493] "User interest-based information" refers to information related to areas, themes, or characters that users are personally interested in.

[0494] "Learning content" refers to information related to the knowledge and skills that the user intends to acquire.

[0495] The "ability to generate stories and questions" refers to the capacity of a computer program to automatically create relevant stories or quiz-style questions based on themes that the user is interested in.

[0496] "A format playable on electronic devices" refers to a format provided digitally so that users can access it on electronic devices such as smartphones, tablets, or computers.

[0497] "Learning progress" refers to information that shows the level of understanding and achievements a user has attained during the learning process.

[0498] "Providing information in real time" means providing information that is immediately relevant to the user's actions and responses.

[0499] This invention is implemented using a system that provides customized learning content based on themes of interest to the user. The system consists of a server, a terminal, and the user.

[0500] The server uses a generative AI model to create customized learning content in the form of stories and quizzes, based on information about the user's personal interests and learning goals. This process utilizes programs written in Python and Flask, and a MongoDB database is used to manage user information and progress. The server can use OpenAI models to generate natural-sounding stories and questions based on the information entered by the user.

[0501] The device is an electronic device such as a smartphone or tablet that receives generated content sent from the server and presents it to the user. The device is implemented using React Native, and the user interface is designed to provide an intuitive and interactive learning experience. Users receive real-time feedback through the device, and their progress is recorded instantly.

[0502] For example, if a user requests to "learn mathematics using a fantasy adventure story as a theme," the server will generate mathematical problems based on the fantasy story. In this process, the following prompt statements are provided to the AI ​​model to create the content:

[0503] "Create a fantasy-themed narrative for a learning quiz focused on mathematics, incorporating elements of adventure and problem-solving."

[0504] In this way, users can enjoy learning tailored to their individual interests, and it is expected that this will help maintain their motivation to learn.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] Users input their personal interests and topics they want to learn about using their device and send this information to the server. The input includes preferred themes, characters, and topics they want to study. The device packages this information in JSON format and sends it to the server as an HTTP request.

[0508] Step 2:

[0509] The server analyzes the received user information and generates prompt text for input into the AI ​​model. Here, a natural language prompt text is created that combines the user's interests and learned content. Specifically, a Python script takes in the user information, assembles the prompt text based on a template, and sends it to the OpenAI API.

[0510] Step 3:

[0511] The server receives the content returned from the generating AI model and converts it into an application format viewable on the user's device. Here, the server retrieves the text of the generated content and converts it into a format suitable for React Native components. The generated stories and issues are stored in JSON format and sent to the device.

[0512] Step 4:

[0513] The device receives learning content sent from the server and displays it on the user interface. The device uses React Native to generate interactive components that allow the user to take actions such as reading through a story or answering quizzes.

[0514] Step 5:

[0515] As the user progresses through the story or answers quizzes, the device collects progress information and sends the collected data to the server. Specifically, the answer results and learning history are organized in JSON format and sent back to the server as an HTTP request.

[0516] Step 6:

[0517] The server analyzes the user's learning progress based on the received progress data and determines the next content and rewards to present. The server saves the progress data to MongoDB, performs analysis, and then generates a new prompt message considering the next required learning items and rewards.

[0518] Step 7:

[0519] The newly generated learning content and reward information are sent back from the server to the device and provided to the user. This process is dynamically repeated, allowing the user to gain a continuous learning experience based on their interests.

[0520] 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.

[0521] The system of this invention provides a customized learning experience by combining the user's personal interests and learning content, and by recognizing and adjusting the user's emotions. This system is realized by appropriately exchanging information among four parties: the server, the terminal, the emotion engine, and the user, and by adjusting and optimizing content based on the user's emotional state.

[0522] First, the user accesses the system and inputs their interests (e.g., favorite themes or characters) and what they want to learn. The user uses a camera and microphone to record their facial expressions and voice, and the emotion engine analyzes this as emotion data. This information is then transmitted to the server via the terminal.

[0523] Next, the server designs customized learning content based on the user's personal interests and learning goals. This content incorporates variable elements that respond to the user's anticipated emotions and includes a story that blends the learning topic with the user's interests. An emotion engine monitors the user's emotions in real time, adjusting the story's progression and quiz difficulty based on the user's level of excitement and concentration.

[0524] The generated learning content is sent from the server to the device, where it becomes accessible and usable by the user in the form of a game or application. The device monitors the user's interaction with the learning application and optimizes the content based on the data acquired by the emotion engine.

[0525] During learning, the emotion engine detects changes in the user's facial expressions and voice and sends this information to the server. The server then analyzes this emotion data in conjunction with the learning progress to design new learning tasks and rewards that match the user's emotional state. For example, if the system determines that the user is bored, it may speed up the story or offer new challenges to rekindle their interest.

[0526] For example, if a user sets their learning objective to "learn English using anime scenes," the server uses data input from the emotion engine to adjust the generated content so that it closely synchronizes with the user's emotions. A scene is created where characters from the anime speak in English, and the character's next action changes based on the user's response.

[0527] This allows users to enjoy a personalized learning experience aligned with their emotions, leading to deeper and more sustained learning. The present invention makes it possible to provide an even more effective learning experience by dynamically adapting the user's learning environment according to their emotional state.

[0528] The following describes the processing flow.

[0529] Step 1:

[0530] Users log in to the platform and enter topics and content they are interested in. They also select fields they want to learn about and send this information to the server via their device.

[0531] Step 2:

[0532] The device collects the user's facial expressions and voice data using its built-in camera and microphone. This information is then sent in real time to an emotion engine to recognize the user's current emotional state.

[0533] Step 3:

[0534] The emotion engine detects emotions from the user's facial expressions and voice data, and sends the results to the server. This allows the server to understand the user's emotional state.

[0535] Step 4:

[0536] The server designs customized learning content based on the user's interests and emotional data received from the emotion engine. This content includes variable elements that adapt to the user's emotional state.

[0537] Step 5:

[0538] The server generates customized learning content, which is then sent to the device in a gamified application format, preparing it for user delivery.

[0539] Step 6:

[0540] The user launches the learning application through their device and begins learning with the provided content. All interactions and progress are recorded in real time.

[0541] Step 7:

[0542] The device continuously sends the user's facial expressions and voice to the emotion engine, tracking changes in the user's emotions. The emotion engine analyzes this data and sends it to the server to be reflected in the learning content.

[0543] Step 8:

[0544] The server considers data from the emotion engine and the user's learning progress to design new learning tasks and rewards. This information is then sent back to the device to provide feedback to the user.

[0545] (Example 2)

[0546] 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."

[0547] Traditional educational systems have difficulty taking into account users' learning interests and emotional states, resulting in an inability to adequately address individual learning processes. As a result, users are more likely to lose interest in activities, potentially leading to decreased learning effectiveness. Furthermore, the inability to make real-time adjustments based on emotions hinders user satisfaction.

[0548] 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.

[0549] In this invention, the server includes means for inputting the user's interests and educational objectives, means for determining the user's emotional state and acquiring emotional data, and means for generating customized learning content based on the user's emotional state and selected educational objectives using a generative AI model. This enables the provision of an individualized learning experience and dynamic content adjustment in response to the user's emotions.

[0550] "User" refers to an individual or entity that uses this system for learning.

[0551] "Interest" refers to the level of interest or curiosity a user shows regarding a particular theme or activity.

[0552] "Educational objectives" refer to the learning goals and content that the user wants to achieve.

[0553] "Emotional state" refers to data that represents the user's psychological response and emotional state.

[0554] "Emotional data" refers to emotional information analyzed from the user's facial expressions and voice.

[0555] A "generative AI model" refers to artificial intelligence technology that generates content based on user input and emotional state.

[0556] "Customized learning content" refers to learning materials that are adapted to the individual interests and emotions of the user.

[0557] "Electronic devices" refer to devices that users use to access this system, and include computers, smartphones, tablets, and other similar devices.

[0558] "Real-time" refers to a system that performs operations and data analysis instantly, and immediately reflects and presents the results.

[0559] "Progress data" refers to information that records the user's learning process and achievements.

[0560] This invention is a system that provides an effective learning experience that takes into account the user's interests and emotions. This system utilizes a server, terminals, a generative AI model, and an emotion engine to deliver learning content tailored to the individual needs of the user.

[0561] First, users access the system using their devices and input their interests and what they want to learn. They also use a camera and microphone to record their facial expressions and voice, providing this data to the system as emotional information. This allows the system to understand the user's level of interest in specific topics or activities.

[0562] Next, the server uses a generative AI model to generate customized learning content based on the received interest and emotion data. This content includes stories and educational materials that combine the user's interests and learning objectives, and is adjusted in real time according to the user's emotional state.

[0563] The generated learning content is delivered from the server to the user's device and becomes available as an application. The device monitors the user's learning progress in real time and sends progress data to the server. Based on this data, the server designs new learning tasks and rewards.

[0564] For example, if a user sets their learning objective to "learn modern history through an interactive story themed around historical events," the server uses a generative AI model to adjust the story's progression and quiz content based on the user's emotional data. It can also insert scenes where characters speak to the user, altering the story's progression based on the user's responses.

[0565] An example of a prompt statement might be, "Build a system that dynamically generates learning content and stories based on themes the user has shown interest in."

[0566] In this way, by dynamically adapting to the user's interests and emotions, a comfortable and effective learning environment can be provided.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] Users log in to their device and input their interests and what they want to learn. The input data provided by the user includes specific themes and characters they are interested in. This input is sent to the server and becomes the foundational data for generating personalized learning content.

[0570] Step 2:

[0571] The device records the user's facial expressions and voice through its camera and microphone, acquiring them as emotion data. This acquired emotion data is analyzed in real time by an emotion engine. This input data quantifies the user's psychological state and is sent to a server, where it is output as basic information used to adjust learning content.

[0572] Step 3:

[0573] The server receives user interest and emotion data as input and uses a generative AI model to build customized learning content. The generated learning content includes stories and quizzes that reflect the user's interests. This output includes elements that can vary depending on the user's emotional state and is sent to the device.

[0574] Step 4:

[0575] The terminal executes the learning content received from the server in application format, making it interactive for the user. At this stage, the user's learning actions are recorded and sent to the server as progress data. This progress data is output as information used to optimize future learning content.

[0576] Step 5:

[0577] The emotion engine continuously monitors changes in the user's emotional state during learning. This allows for real-time adjustments to maintain the user's interest and concentration. Based on the emotion data, the server dynamically adjusts the speed and difficulty of the learning content and designs and outputs new learning tasks.

[0578] Step 6:

[0579] The server comprehensively analyzes progress and sentiment data to provide users with rewards and new challenges. This output is designed to give users a sense of accomplishment and improve learning sustainability. This reward system plays a role in increasing user motivation.

[0580] (Application Example 2)

[0581] 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."

[0582] Traditional learning systems have suffered from reduced learning efficiency and user engagement because they do not provide content that takes into account the user's emotional state. Furthermore, particularly in the entertainment field, there is a demand for content that resonates with users' emotions, but achieving this has been difficult.

[0583] 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.

[0584] In this invention, the server includes means for inputting information based on the user's interests, means for generating customized learning content based on the user's interest information and selected learning content, and means for analyzing the user's visual and auditory information with an emotion analysis device and providing entertainment tailored to the user's emotional state. This makes it possible to provide an optimal learning experience and entertainment that is in line with the user's emotional state.

[0585] A "user" is an entity that interacts with a system through an information processing device.

[0586] "Interest information" refers to data about the themes and fields that users prefer in their learning or entertainment activities.

[0587] "Learning content" refers to information related to specific knowledge or skills that the user wants to acquire or deepen.

[0588] "Customized learning content" refers to educational or entertainment materials tailored to a user's individual interests and learning goals.

[0589] An "information processing device" is a digital device used by users to receive learning content and feedback.

[0590] "Progress data" refers to records that show how far a user has progressed in learning or entertainment.

[0591] "Visual and auditory information" refers to data related to facial expressions and speech provided by the user through the camera and microphone of the information processing device.

[0592] An "emotion analysis device" is a device or software that analyzes a user's visual and auditory information to identify their emotional state at any given time.

[0593] "Providing entertainment" refers to providing content that is structured to evoke enjoyment and excitement based on the user's interests and emotional state.

[0594] In this invention, the user first inputs their interests and learning content through an information processing device. The user's information processing device is equipped with a camera and a microphone, which are used to collect the user's visual and auditory information. Subsequently, this data is detected in real time using an emotion analysis device, and the visual and auditory information is converted into the user's emotional state.

[0595] The server generates customized learning content based on collected interest information, learning content, and user emotional state data. During this process, OpenCV analyzes the user's facial expressions, and Google's speech recognition API analyzes their voice tone. The analysis results are sent to the server via Node.js and processed integrally using Python.

[0596] The generated learning content is delivered to the information processing device in real time. Here, stories and scenes appropriate to the user's emotional state are selected, optimizing the user experience. As a result, users can enjoy a deeper learning experience and greater entertainment.

[0597] For example, when a user is tired, the system can keep them interested by providing relaxing scenery or music content.

[0598] Furthermore, an example of a prompt for the generative AI model is: "This user is currently in a state of ○○ (emotion). Please suggest the most suitable content scene for this state." This allows the generative AI to suggest content that matches the user's emotions.

[0599] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0600] Step 1:

[0601] The user inputs their interests and learning content through the interface of the information processing device. This input is received by the terminal and used for subsequent processing. At this point, the input data consists of text information about the user's preferences and learning goals.

[0602] Step 2:

[0603] The device uses a camera and microphone to collect visual and auditory information from the user. This information is transmitted in real time to an emotion analysis device. The input data consists of video and audio signals, and based on this, the emotion analysis device analyzes the user's emotional state from changes in facial expressions and voice tone. The result of the analysis is numerical data representing the user's emotions.

[0604] Step 3:

[0605] The server receives interest information, learning content, and emotional state data sent from the terminal and generates customized learning content. Specifically, it uses Python to construct stories and scenes that take the user's emotions into account, and utilizes a generative AI model to generate data based on the prompt message, "This user is currently in the state of ○○ (emotion). Please suggest the most suitable content scene for this state." This results in content optimized for each user being output.

[0606] Step 4:

[0607] The generated learning content is delivered from the server to the device in real time. The device adjusts this content to match the user's emotional state and displays it at the optimal time. Specific actions include the playback of scenes and audio displayed on the screen, which dynamically switch in response to the user's reactions. The output data provides visual and auditory feedback as a user interface.

[0608] Step 5:

[0609] User interaction and progress data are recorded on the device and sent to the server. The server analyzes this data and dynamically generates new learning materials and rewards based on the user's progress. This process creates content that promotes continued user engagement.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] [Fourth Embodiment]

[0614] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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".

[0627] The system of this invention provides a customized learning experience by combining the user's personal interests and the content they wish to learn. This system is primarily realized through a process in which information is appropriately exchanged between the server, terminal, and user, and content is generated and provided according to the user's preferences.

[0628] First, the user accesses the system and enters information such as their personal interests, favorite characters, and themes. In addition, the user selects the topic they want to learn about (e.g., English, mathematics, programming, etc.). This information is then sent from the user's terminal to the server.

[0629] Next, the server designs personalized learning content based on the received user information. The server generates customized stories and quizzes that combine the user's interests with the learning material. This generated content is designed to make learning enjoyable in a game format, aiming to keep the user engaged.

[0630] The generated learning content is sent from the server to the user's device. The device receives this content and makes it accessible to the user in the form of an application. This application has various functions to allow the user to learn interactively. For example, it promotes learning while maintaining motivation by providing feedback and rewards based on the user's results each time they answer a quiz.

[0631] During a learning session, the device records the user's progress and sends this data to the server. The server analyzes this progress data and determines the next steps appropriate for the user. Based on the evaluation data, the server prepares new learning tasks and rewards and resends them to the device.

[0632] For example, if a user expresses a desire to learn English using anime characters, the server will generate English quizzes related to the anime's storyline and characters. These quizzes are designed to engage the user, and are structured to entertain them in various ways, such as allowing them to learn English phrases in scenarios featuring anime characters, and having the characters perform special actions when they answer correctly.

[0633] Through these interactions, users can naturally connect their interests with learning and continue learning effectively. This mechanism allows the present invention to provide users with a personalized learning experience and effectively promote continued learning.

[0634] The following describes the processing flow.

[0635] Step 1:

[0636] Users log in to the platform, enter themes and topics of interest, and select the fields they wish to study. This information is sent from the device to the server.

[0637] Step 2:

[0638] The server analyzes the user information it receives and generates customized learning content based on the user's interests and what they want to learn. Specifically, it creates stories related to the user's interests and incorporates learning content within those stories.

[0639] Step 3:

[0640] This process involves creating data for distributing customized learning content generated by the server, arranging it into a user-specific game or application format.

[0641] Step 4:

[0642] The device receives and installs the learning application data sent from the server, preparing it for use by the user.

[0643] Step 5:

[0644] The user opens the learning application on their device and begins learning. Here, the user progresses through content designed based on their interests.

[0645] Step 6:

[0646] The device records the user's progress and answer results, and sends this information to the server in real time.

[0647] Step 7:

[0648] The server analyzes the received progress data and determines appropriate feedback, new learning tasks, and rewards for the user. This information is then sent back to the device.

[0649] Step 8:

[0650] The device notifies the user of feedback from the server and new learning information, preparing them for the next learning session.

[0651] (Example 1)

[0652] 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".

[0653] Traditional education systems have struggled to customize learning experiences to meet users' specific interests and preferences, limiting the provision of personalized education. Furthermore, they have not effectively provided immediate feedback or new learning materials based on learning progress.

[0654] 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.

[0655] In this invention, the server includes a device for inputting the user's specific interests and the content they wish to be taught; a device for generating individually optimized educational digital materials based on the interest information and selected educational content obtained from the user; and a device for converting the generated educational digital materials into a digital entertainment format and providing it to the user's information and communication device. This enables the provision of a personalized learning experience, as well as immediate feedback and effective provision of new learning materials according to the learning progress.

[0656] A "user" refers to an individual who uses the system to acquire educational content and enjoy a learning experience.

[0657] "Interest information" refers to information that indicates a user's personal interests and preferences, and is used to customize the educational experience.

[0658] "Educational digital materials" refer to digital content generated based on the user's interests and educational content, designed to support learning.

[0659] "Digital entertainment format" refers to a format in which educational digital materials are transformed into games or interactive forms, allowing users to learn while having fun.

[0660] "Information and communication equipment" refers to devices that users use to receive and interactively utilize educational digital materials, and examples include smartphones and computers.

[0661] "Progress status" refers to data that shows the results and achievements of a user's learning activities using educational digital materials.

[0662] "Evaluation information" is feedback generated based on the user's progress, and is provided to help improve learning and guide them to the next step.

[0663] The embodiments for carrying out the present invention will now be described. The system of the present invention operates primarily around a server, a terminal, and a user in order to provide a learning experience based on the user's individualized interests.

[0664] First, users access the system using devices such as smartphones or personal computers and input information about their interests and the topics they want to learn about. This helps to define the user's learning needs.

[0665] Next, the terminal structures this information into data packets and sends them to the server using a secure communication protocol. The terminal also allows users to easily input information through a user interface.

[0666] Based on the received information, the server uses a generative AI model (for example, the representative AI model GPT-4) to generate customized educational digital materials tailored to the user's interests and learning content. Specifically, the server inputs a prompt sentence into the generative AI model. An example of such a prompt sentence would be, "Create an English learning quiz using anime characters as the subject." The AI ​​model then generates educational content based on this prompt.

[0667] The generated content is organized by the server, converted into a digital entertainment format, and sent to the user's device. The device receives it and presents it to the user as an interactive learning application. This application is designed to allow users to learn while having fun, providing educational digital materials in the form of stories, quizzes, and other formats.

[0668] Furthermore, as the user progresses through the learning process, the device records its progress and reports it to the server in real time. This allows the server to evaluate the user's learning progress and determine the next appropriate learning step. Based on the progress data, the server generates new learning materials and rewards, which are then redistributed to the device, thereby continuously supporting the user's learning.

[0669] In this way, the system of the present invention provides a personalized learning experience based on the user's interests, enabling efficient learning while maintaining the user's motivation.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] The user inputs their areas of interest, such as themes, characters, and topics they wish to learn about, through the device's user interface. Based on this input, the user's learning requirements are refined. The input is in text format. The device then prepares to send this information to the server as structured data.

[0673] Step 2:

[0674] The terminal organizes the information entered by the user into data packets and sends them to the server using a secure protocol (e.g., HTTPS). Here, the input is user information, and the output is the data packets sent to the server. These data packets are sent in a structured format such as JSON.

[0675] Step 3:

[0676] The server analyzes the received user information and generates a prompt for use with the generative AI model. This prompt might be something like, "Create an English learning quiz using anime characters as the subject." The input is user information, and the output is the generated prompt sentence. The server inputs the prompt sentence into the generative AI model and creates digital materials.

[0677] Step 4:

[0678] The generative AI model generates customized educational digital materials based on prompts. The input is a prompt sentence, and the output is digital material. This material contains content based on the user's interests and is presented in quiz or story format.

[0679] Step 5:

[0680] The server organizes the generated digital materials and converts them into digital entertainment formats. The input here is materials generated by an AI model, and the output is content in digital entertainment format. This is then sent to the terminal.

[0681] Step 6:

[0682] The device displays received digital entertainment content to the user as an interactive learning application. The input is the content received from the server, and the output is the interface viewed by the user. The user progresses through this application.

[0683] Step 7:

[0684] During learning, the device records the user's progress and sends this data to the server at the end of the session or at regular intervals. The input is the user's interaction data, and the output is the progress data sent to the server.

[0685] Step 8:

[0686] The server analyzes the received progress data and evaluates the user's learning steps. This determines appropriate new learning materials and rewards for the user. The input is progress data, and the output is newly generated learning materials and rewards. This is then resent to the terminal.

[0687] (Application Example 1)

[0688] 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".

[0689] To sustain individual users' motivation to learn, it is necessary to customize learning content based on their individual interests and provide it in diverse media formats. Furthermore, it is essential to improve learning efficiency by providing appropriate feedback in real time according to their progress.

[0690] 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.

[0691] In this invention, the server includes a function to receive information based on the user's interests, a function to generate learning content as stories and problems based on the user's interest information and selected learning content, and a function to provide the generated learning content in a format playable on an electronic device. This makes it possible to provide a customized learning experience that is tailored to the user's individual interests and to enhance their motivation to learn.

[0692] "User interest-based information" refers to information related to areas, themes, or characters that users are personally interested in.

[0693] "Learning content" refers to information related to the knowledge and skills that the user intends to acquire.

[0694] The "ability to generate stories and questions" refers to the capacity of a computer program to automatically create relevant stories or quiz-style questions based on themes that the user is interested in.

[0695] "A format playable on electronic devices" refers to a format provided digitally so that users can access it on electronic devices such as smartphones, tablets, or computers.

[0696] "Learning progress" refers to information that shows the level of understanding and achievements a user has attained during the learning process.

[0697] "Providing information in real time" means providing information that is immediately relevant to the user's actions and responses.

[0698] This invention is implemented using a system that provides customized learning content based on themes of interest to the user. The system consists of a server, a terminal, and the user.

[0699] The server uses a generative AI model to create customized learning content in the form of stories and quizzes, based on information about the user's personal interests and learning goals. This process utilizes programs written in Python and Flask, and a MongoDB database is used to manage user information and progress. The server can use OpenAI models to generate natural-sounding stories and questions based on the information entered by the user.

[0700] The device is an electronic device such as a smartphone or tablet that receives generated content sent from the server and presents it to the user. The device is implemented using React Native, and the user interface is designed to provide an intuitive and interactive learning experience. Users receive real-time feedback through the device, and their progress is recorded instantly.

[0701] For example, if a user requests to "learn mathematics using a fantasy adventure story as a theme," the server will generate mathematical problems based on the fantasy story. In this process, the following prompt statements are provided to the AI ​​model to create the content:

[0702] "Create a fantasy-themed narrative for a learning quiz focused on mathematics, incorporating elements of adventure and problem-solving."

[0703] In this way, users can enjoy learning tailored to their individual interests, and it is expected that this will help maintain their motivation to learn.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] Users input their personal interests and topics they want to learn about using their device and send this information to the server. The input includes preferred themes, characters, and topics they want to study. The device packages this information in JSON format and sends it to the server as an HTTP request.

[0707] Step 2:

[0708] The server analyzes the received user information and generates prompt text for input into the AI ​​model. Here, a natural language prompt text is created that combines the user's interests and learned content. Specifically, a Python script takes in the user information, assembles the prompt text based on a template, and sends it to the OpenAI API.

[0709] Step 3:

[0710] The server receives the content returned from the generating AI model and converts it into an application format viewable on the user's device. Here, the server retrieves the text of the generated content and converts it into a format suitable for React Native components. The generated stories and issues are stored in JSON format and sent to the device.

[0711] Step 4:

[0712] The device receives learning content sent from the server and displays it on the user interface. The device uses React Native to generate interactive components that allow the user to take actions such as reading through a story or answering quizzes.

[0713] Step 5:

[0714] As the user progresses through the story or answers quizzes, the device collects progress information and sends the collected data to the server. Specifically, the answer results and learning history are organized in JSON format and sent back to the server as an HTTP request.

[0715] Step 6:

[0716] The server analyzes the user's learning progress based on the received progress data and determines the next content and rewards to present. The server saves the progress data to MongoDB, performs analysis, and then generates a new prompt message considering the next required learning items and rewards.

[0717] Step 7:

[0718] The newly generated learning content and reward information are sent back from the server to the device and provided to the user. This process is dynamically repeated, allowing the user to gain a continuous learning experience based on their interests.

[0719] 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.

[0720] The system of this invention provides a customized learning experience by combining the user's personal interests and learning content, and by recognizing and adjusting the user's emotions. This system is realized by appropriately exchanging information among four parties: the server, the terminal, the emotion engine, and the user, and by adjusting and optimizing content based on the user's emotional state.

[0721] First, the user accesses the system and inputs their interests (e.g., favorite themes or characters) and what they want to learn. The user uses a camera and microphone to record their facial expressions and voice, and the emotion engine analyzes this as emotion data. This information is then transmitted to the server via the terminal.

[0722] Next, the server designs customized learning content based on the user's personal interests and learning goals. This content incorporates variable elements that respond to the user's anticipated emotions and includes a story that blends the learning topic with the user's interests. An emotion engine monitors the user's emotions in real time, adjusting the story's progression and quiz difficulty based on the user's level of excitement and concentration.

[0723] The generated learning content is sent from the server to the device, where it becomes accessible and usable by the user in the form of a game or application. The device monitors the user's interaction with the learning application and optimizes the content based on the data acquired by the emotion engine.

[0724] During learning, the emotion engine detects changes in the user's facial expressions and voice and sends this information to the server. The server then analyzes this emotion data in conjunction with the learning progress to design new learning tasks and rewards that match the user's emotional state. For example, if the system determines that the user is bored, it may speed up the story or offer new challenges to rekindle their interest.

[0725] For example, if a user sets their learning objective to "learn English using anime scenes," the server uses data input from the emotion engine to adjust the generated content so that it closely synchronizes with the user's emotions. A scene is created where characters from the anime speak in English, and the character's next action changes based on the user's response.

[0726] This allows users to enjoy a personalized learning experience aligned with their emotions, leading to deeper and more sustained learning. The present invention makes it possible to provide an even more effective learning experience by dynamically adapting the user's learning environment according to their emotional state.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] Users log in to the platform and enter topics and content they are interested in. They also select fields they want to learn about and send this information to the server via their device.

[0730] Step 2:

[0731] The device collects the user's facial expressions and voice data using its built-in camera and microphone. This information is then sent in real time to an emotion engine to recognize the user's current emotional state.

[0732] Step 3:

[0733] The emotion engine detects emotions from the user's facial expressions and voice data, and sends the results to the server. This allows the server to understand the user's emotional state.

[0734] Step 4:

[0735] The server designs customized learning content based on the user's interests and emotional data received from the emotion engine. This content includes variable elements that adapt to the user's emotional state.

[0736] Step 5:

[0737] The server generates customized learning content, which is then sent to the device in a gamified application format, preparing it for user delivery.

[0738] Step 6:

[0739] The user launches the learning application through their device and begins learning with the provided content. All interactions and progress are recorded in real time.

[0740] Step 7:

[0741] The device continuously sends the user's facial expressions and voice to the emotion engine, tracking changes in the user's emotions. The emotion engine analyzes this data and sends it to the server to be reflected in the learning content.

[0742] Step 8:

[0743] The server considers data from the emotion engine and the user's learning progress to design new learning tasks and rewards. This information is then sent back to the device to provide feedback to the user.

[0744] (Example 2)

[0745] 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".

[0746] Traditional educational systems have difficulty taking into account users' learning interests and emotional states, resulting in an inability to adequately address individual learning processes. As a result, users are more likely to lose interest in activities, potentially leading to decreased learning effectiveness. Furthermore, the inability to make real-time adjustments based on emotions hinders user satisfaction.

[0747] 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.

[0748] In this invention, the server includes means for inputting the user's interests and educational objectives, means for determining the user's emotional state and acquiring emotional data, and means for generating customized learning content based on the user's emotional state and selected educational objectives using a generative AI model. This enables the provision of an individualized learning experience and dynamic content adjustment in response to the user's emotions.

[0749] "User" refers to an individual or entity that uses this system for learning.

[0750] "Interest" refers to the level of interest or curiosity a user shows regarding a particular theme or activity.

[0751] "Educational objectives" refer to the learning goals and content that the user wants to achieve.

[0752] "Emotional state" refers to data that represents the user's psychological response and emotional state.

[0753] "Emotional data" refers to emotional information analyzed from the user's facial expressions and voice.

[0754] A "generative AI model" refers to artificial intelligence technology that generates content based on user input and emotional state.

[0755] "Customized learning content" refers to learning materials that are adapted to the individual interests and emotions of the user.

[0756] "Electronic devices" refer to devices that users use to access this system, and include computers, smartphones, tablets, and other similar devices.

[0757] "Real-time" refers to a system that performs operations and data analysis instantly, and immediately reflects and presents the results.

[0758] "Progress data" refers to information that records the user's learning process and achievements.

[0759] This invention is a system that provides an effective learning experience that takes into account the user's interests and emotions. This system utilizes a server, terminals, a generative AI model, and an emotion engine to deliver learning content tailored to the individual needs of the user.

[0760] First, users access the system using their devices and input their interests and what they want to learn. They also use a camera and microphone to record their facial expressions and voice, providing this data to the system as emotional information. This allows the system to understand the user's level of interest in specific topics or activities.

[0761] Next, the server uses a generative AI model to generate customized learning content based on the received interest and emotion data. This content includes stories and educational materials that combine the user's interests and learning objectives, and is adjusted in real time according to the user's emotional state.

[0762] The generated learning content is delivered from the server to the user's device and becomes available as an application. The device monitors the user's learning progress in real time and sends progress data to the server. Based on this data, the server designs new learning tasks and rewards.

[0763] For example, if a user sets their learning objective to "learn modern history through an interactive story themed around historical events," the server uses a generative AI model to adjust the story's progression and quiz content based on the user's emotional data. It can also insert scenes where characters speak to the user, altering the story's progression based on the user's responses.

[0764] An example of a prompt statement might be, "Build a system that dynamically generates learning content and stories based on themes the user has shown interest in."

[0765] In this way, by dynamically adapting to the user's interests and emotions, a comfortable and effective learning environment can be provided.

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] Users log in to their device and input their interests and what they want to learn. The input data provided by the user includes specific themes and characters they are interested in. This input is sent to the server and becomes the foundational data for generating personalized learning content.

[0769] Step 2:

[0770] The device records the user's facial expressions and voice through its camera and microphone, acquiring them as emotion data. This acquired emotion data is analyzed in real time by an emotion engine. This input data quantifies the user's psychological state and is sent to a server, where it is output as basic information used to adjust learning content.

[0771] Step 3:

[0772] The server receives user interest and emotion data as input and uses a generative AI model to build customized learning content. The generated learning content includes stories and quizzes that reflect the user's interests. This output includes elements that can vary depending on the user's emotional state and is sent to the device.

[0773] Step 4:

[0774] The terminal executes the learning content received from the server in application format, making it interactive for the user. At this stage, the user's learning actions are recorded and sent to the server as progress data. This progress data is output as information used to optimize future learning content.

[0775] Step 5:

[0776] The emotion engine continuously monitors changes in the user's emotional state during learning. This allows for real-time adjustments to maintain the user's interest and concentration. Based on the emotion data, the server dynamically adjusts the speed and difficulty of the learning content and designs and outputs new learning tasks.

[0777] Step 6:

[0778] The server comprehensively analyzes progress and sentiment data to provide users with rewards and new challenges. This output is designed to give users a sense of accomplishment and improve learning sustainability. This reward system plays a role in increasing user motivation.

[0779] (Application Example 2)

[0780] 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".

[0781] Traditional learning systems have suffered from reduced learning efficiency and user engagement because they do not provide content that takes into account the user's emotional state. Furthermore, particularly in the entertainment field, there is a demand for content that resonates with users' emotions, but achieving this has been difficult.

[0782] 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.

[0783] In this invention, the server includes means for inputting information based on the user's interests, means for generating customized learning content based on the user's interest information and selected learning content, and means for analyzing the user's visual and auditory information with an emotion analysis device and providing entertainment tailored to the user's emotional state. This makes it possible to provide an optimal learning experience and entertainment that is in line with the user's emotional state.

[0784] A "user" is an entity that interacts with a system through an information processing device.

[0785] "Interest information" refers to data about the themes and fields that users prefer in their learning or entertainment activities.

[0786] "Learning content" refers to information related to specific knowledge or skills that the user wants to acquire or deepen.

[0787] "Customized learning content" refers to educational or entertainment materials tailored to a user's individual interests and learning goals.

[0788] An "information processing device" is a digital device used by users to receive learning content and feedback.

[0789] "Progress data" refers to records that show how far a user has progressed in learning or entertainment.

[0790] "Visual and auditory information" refers to data related to facial expressions and speech provided by the user through the camera and microphone of the information processing device.

[0791] An "emotion analysis device" is a device or software that analyzes a user's visual and auditory information to identify their emotional state at any given time.

[0792] "Providing entertainment" refers to providing content that is structured to evoke enjoyment and excitement based on the user's interests and emotional state.

[0793] In this invention, the user first inputs their interests and learning content through an information processing device. The user's information processing device is equipped with a camera and a microphone, which are used to collect the user's visual and auditory information. Subsequently, this data is detected in real time using an emotion analysis device, and the visual and auditory information is converted into the user's emotional state.

[0794] The server generates customized learning content based on collected interest information, learning content, and user emotional state data. During this process, OpenCV analyzes the user's facial expressions, and Google's speech recognition API analyzes their voice tone. The analysis results are sent to the server via Node.js and processed integrally using Python.

[0795] The generated learning content is delivered to the information processing device in real time. Here, stories and scenes appropriate to the user's emotional state are selected, optimizing the user experience. As a result, users can enjoy a deeper learning experience and greater entertainment.

[0796] For example, when a user is tired, the system can keep them interested by providing relaxing scenery or music content.

[0797] Furthermore, an example of a prompt for the generative AI model is: "This user is currently in a state of ○○ (emotion). Please suggest the most suitable content scene for this state." This allows the generative AI to suggest content that matches the user's emotions.

[0798] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0799] Step 1:

[0800] The user inputs their interests and learning content through the interface of the information processing device. This input is received by the terminal and used for subsequent processing. At this point, the input data consists of text information about the user's preferences and learning goals.

[0801] Step 2:

[0802] The device uses a camera and microphone to collect visual and auditory information from the user. This information is transmitted in real time to an emotion analysis device. The input data consists of video and audio signals, and based on this, the emotion analysis device analyzes the user's emotional state from changes in facial expressions and voice tone. The result of the analysis is numerical data representing the user's emotions.

[0803] Step 3:

[0804] The server receives interest information, learning content, and emotional state data sent from the terminal and generates customized learning content. Specifically, it uses Python to construct stories and scenes that take the user's emotions into account, and utilizes a generative AI model to generate data based on the prompt message, "This user is currently in the state of ○○ (emotion). Please suggest the most suitable content scene for this state." This results in content optimized for each user being output.

[0805] Step 4:

[0806] The generated learning content is delivered from the server to the device in real time. The device adjusts this content to match the user's emotional state and displays it at the optimal time. Specific actions include the playback of scenes and audio displayed on the screen, which dynamically switch in response to the user's reactions. The output data provides visual and auditory feedback as a user interface.

[0807] Step 5:

[0808] User interaction and progress data are recorded on the device and sent to the server. The server analyzes this data and dynamically generates new learning materials and rewards based on the user's progress. This process creates content that promotes continued user engagement.

[0809] 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.

[0810] 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.

[0811] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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."

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] The following is further disclosed regarding the embodiments described above.

[0831] (Claim 1)

[0832] A means of inputting information based on the user's interests,

[0833] A means for generating customized learning content based on the user's interest information and selected learning content,

[0834] A means of delivering the generated learning content to the user's device as a gamified application,

[0835] A means of recording user progress data and providing new learning materials and rewards according to learning progress,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, characterized in that the customized learning content includes a story that connects the user's interests with the learning topic.

[0839] (Claim 3)

[0840] The system according to claim 1, characterized in that it provides feedback to the user's terminal in real time, based on the user's progress data.

[0841] "Example 1"

[0842] (Claim 1)

[0843] A device for inputting the user's specific interests and the content they wish to be taught,

[0844] A device for generating individually optimized educational digital materials based on interest information obtained from the user and selected educational content,

[0845] A device that converts generated educational digital materials into a digital entertainment format and provides them to the user's information and communication device,

[0846] A device for tracking user progress and supplying new educational materials and rewards in accordance with educational progress,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, characterized in that the individually optimized educational digital materials include stories that combine the user's interests with educational themes.

[0850] (Claim 3)

[0851] The system according to claim 1, characterized in that it immediately provides evaluation information based on the user's progress to the user's information and communication device.

[0852] "Application Example 1"

[0853] (Claim 1)

[0854] Features that allow users to receive information based on their interests,

[0855] Based on the user's interest information and selected learning content, the system generates learning content as stories and problems.

[0856] A function to provide the generated learning content in a format playable on electronic devices,

[0857] A feature that records the user's learning progress and provides the next learning tasks and rewards according to this progress,

[0858] A device that includes this.

[0859] (Claim 2)

[0860] The apparatus according to claim 1, characterized in that the generated learning content includes diverse information media and includes narrative elements that relate the user's interests to the learning theme.

[0861] (Claim 3)

[0862] The apparatus according to claim 1, characterized in that it immediately provides a response on an information terminal based on the user's learning progress data.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] A means for users to input their interests and educational objectives,

[0866] A means for determining the emotional state of the user and acquiring emotional data,

[0867] A means for generating customized learning content based on the user's emotional state and selected educational objectives using the aforementioned generation AI model,

[0868] A means of distributing and making available the generated learning content on electronic devices,

[0869] A means of monitoring the user's emotional state in real time and adjusting the content's progression and difficulty,

[0870] A means of providing new learning tasks and rewards based on the user's progress data and emotional state,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, characterized in that the customized learning content includes a story that combines the user's interests and educational goals.

[0874] (Claim 3)

[0875] The system according to claim 1, characterized in that it provides feedback to the user's electronic device in real time, based on the user's emotional state.

[0876] "Application example 2 when combining with an emotional engine"

[0877] (Claim 1)

[0878] A means of inputting information based on the user's interests,

[0879] A means for generating customized learning content based on the user's interest information and selected learning content,

[0880] A means of delivering the generated learning content to the user's information processing device as a gamified application,

[0881] A means of recording user progress data and providing new learning materials and rewards according to learning progress,

[0882] A means of providing entertainment based on the user's emotional state, by analyzing the user's visual and auditory information with an emotion analysis device,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, characterized in that the customized learning content includes a narrative that connects the user's interests with the field of study.

[0886] (Claim 3)

[0887] The system according to claim 1, characterized in that it provides feedback based on the user's progress data and emotional information to the user's information processing device in real time. [Explanation of Symbols]

[0888] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting information based on the user's interests, A means for generating customized learning content based on the user's interest information and selected learning content, A means of delivering the generated learning content to the user's device as a gamified application, A means of recording user progress data and providing new learning materials and rewards according to learning progress, A system that includes this.

2. The system according to claim 1, characterized in that the customized learning content includes a story that connects the user's interests with the learning topic.

3. The system according to claim 1, characterized in that it provides feedback to the user's terminal in real time, based on the user's progress data.

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