system

Abstract

We provide the system. [Solution] A device that receives educational information entered by the user, A device for identifying the subject of education based on the aforementioned educational information, A device for generating content based on the aforementioned subject, Based on the above, a device that automatically generates viewable motion images, A device for transmitting the generated motion image to the user, 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] In modern society, moral education of children is very important in their growth, but it is often difficult to conduct specialized education at home. Also, there is a need for an educational method that can keep especially young children interested. However, current educational resources have a problem that there is not enough appropriate tool for parents to teach morals to children in an easily understandable form. As a result, there is a problem that children have limited opportunities to receive moral education in the home environment.

Means for Solving the Problems

[0005] This invention is a system that automatically generates moral education animations tailored to the content or behavior of children, based on user input. The system includes a receiving means for receiving educational content or behaviors input by the user, an identification means for identifying appropriate educational themes based on the received content, a scenario generation means for generating scenarios based on the identified themes, a generation means for automatically generating animations based on the scenarios, and a distribution means for delivering the generated animations to the user. This system provides easy access to professional moral education even in a home environment. As a result, children can maintain their interest and learn morality effectively.

[0006] A "receiving mechanism" is a function that allows the system to receive educational content and actions entered by the user.

[0007] "Specific means" refers to the function of selecting appropriate themes for moral education based on the educational content and behaviors received.

[0008] A "scenario generation method" is a function for designing the narrative structure and development of an animation based on a specific theme.

[0009] "Generation means" refers to a function for creating specific animations according to the story structure created by the scenario generation means.

[0010] "Distribution means" refers to a function for transmitting or providing the generated animation in a format that allows users to view it. [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, and the like.

[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), and the like.

[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] This invention is an animation generation system for supporting moral education in the home. This system is implemented as a web or mobile application and serves as a tool for parents and educators to share with children.

[0033] Main process

[0034] 1. User input

[0035] Users access the application using their device and enter specific details about what they want to teach their child and their recent behavior.

[0036] 2. Data reception and analysis

[0037] The server receives information sent by the user. The received information is stored on the server as text data.

[0038] The server utilizes natural language processing technology to analyze the input text and extract keywords and educational themes.

[0039] 3. Identifying educational themes

[0040] Using specific methods, the server identifies appropriate moral education themes from the analysis results.

[0041] For example, if the input is "I don't clean up," the theme will be selected to be about tidying up and rules.

[0042] 4. Scenario Generation

[0043] The server generates scenarios based on the selected educational theme. These scenarios include character actions, dialogues, and learning points.

[0044] Scenarios are dynamically generated using AI-generated models.

[0045] 5. Animation generation

[0046] The server generates animations based on the scenario. This includes character design, scene setting, and the addition of audio data.

[0047] We will use AI technology to construct a continuous story as an animation.

[0048] 6. Streaming and Viewing

[0049] The generated animation is converted to the appropriate format and delivered from the server to the user's terminal.

[0050] Users can download or stream animations on their devices, making it possible to watch them with children.

[0051] Specific example

[0052] For example, if a child inputs the action description "I had a fight with a friend," the system will generate a scenario themed around "communication" and "empathy." The animation will include scenes of understanding and reconciliation between friends, structured so that the child can learn from it.

[0053] This invention is configured in this way and is a system that assists parents and educators in easily conducting moral education at home.

[0054] The following describes the processing flow.

[0055] Step 1:

[0056] Users access the application using their device's interface and input educational content and behaviors related to their children. The input is in text format and includes specific situations and episodes.

[0057] Step 2:

[0058] The terminal sends the entered information to the server. A communication protocol is used to securely transfer the information and ensure that the data reaches the server accurately.

[0059] Step 3:

[0060] The server receives input data via a receiving device and stores it in the database.

[0061] Step 4:

[0062] The server uses specific methods and natural language processing techniques to extract specific keywords and themes from the input text. This allows for the development of concrete directions for moral education.

[0063] Step 5:

[0064] The server consults a database of educational themes and determines an appropriate moral theme based on extracted keywords. This process refers to a predefined set of themes.

[0065] Step 6:

[0066] Using a scenario generation mechanism, the server generates scenarios based on a specified theme. These scenarios include story development, character dialogue, and learning focus points.

[0067] Step 7:

[0068] Using the generation method, the server generates animations based on the scenario. This process includes character design, animation sequence setup, and the addition of voice and sound effects.

[0069] Step 8:

[0070] The server renders the generated animation into the appropriate video format and prepares it as a viewable file.

[0071] Step 9:

[0072] Using a distribution method, the server sends the completed animation file to the user's terminal.

[0073] Step 10:

[0074] The user plays the animation received on their device and watches it together with their child. The viewing experience supports the child in gaining moral lessons.

[0075] (Example 1)

[0076] 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."

[0077] In traditional home education, it was often burdensome for parents and educators to directly provide moral education to children, and selecting appropriate educational content was difficult. Furthermore, there were limited means of conveying educational content in a format that children could easily engage with, thus creating a need for a system that would promote efficient learning.

[0078] 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.

[0079] In this invention, the server includes means for receiving input information, means for analyzing the input information to identify educational themes, means for generating scenarios, means for automatically generating visual representations, and means for transferring the generated visual representations to the user. This makes it possible for users to provide appropriate moral education to children and promote effective learning through engaging visual materials simply by providing input information.

[0080] "Means for receiving input information" refers to a system in which a server acquires information about educational content and actions provided by users using their devices via a network.

[0081] "Methods for analyzing input information to identify educational themes" refers to the process of analyzing received information using language analysis technology and selecting themes suitable for moral education from that analysis.

[0082] "Means for generating scenarios" refers to a function that uses AI technology to create the structure of an educational story based on a specific educational theme.

[0083] "Means for automatically generating visual representations" refer to tools and processes that enable the construction of visual elements such as character movements and backgrounds based on generated scenarios.

[0084] "Means for transferring generated visual representations to users" refers to technologies for encoding completed visual teaching materials in an appropriate format and transmitting them in a way that users can access.

[0085] This invention is a system for supporting moral education at home, and in particular, provides educational content to children by generating animations. Specific embodiments are shown below.

[0086] Users access this system using a device. First, users input specific text about the educational content they want to teach their child and their child's recent behavior through a particular application. This input information is sent to the server via the device.

[0087] The server stores the received text data and performs analysis using natural language processing techniques. This analysis utilizes libraries such as Python's NLTK and spaCy. Keywords are extracted from the analysis results and compared with predefined libraries within the system to identify the most suitable educational themes.

[0088] The server then dynamically generates scenarios based on the identified theme using a generative AI model (for example, a general natural language generation model). By inputting prompt sentences into the AI ​​model, an educational story is constructed. Examples of prompt sentences used at this time include "My child has been fighting with friends a lot lately. I want to create an animation that teaches about this," or "My child doesn't clean up, so I want to teach them about tidiness."

[0089] Subsequently, based on the scenario, the server generates a visual representation. This process involves combining character designs, backgrounds, and audio data using animation production tools such as Blender. Once the visual representation is complete, it is encoded into the appropriate video format and delivered from the server to the user's terminal.

[0090] Users can watch this animation with their children through an application on their device. This system allows parents and educators to provide effective and engaging moral education to children.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] Users log in to the application via their device and enter information about the educational content they wish to teach and their child's behavior. This input includes text data describing specific situations and educational objectives. The entered data is sent to the server when the submit button is pressed.

[0094] Step 2:

[0095] The server receives text data sent from the terminal and stores it in a database. Next, the server analyzes the text using natural language processing techniques. In this analysis process, libraries such as NLTK and spaCy are used to extract keywords and theme-related features from the text. As a result, educational themes are identified.

[0096] Step 3:

[0097] The server generates scenarios using AI technology based on identified educational themes. The generative AI model used here automatically constructs a story based on prompts for the aforementioned keywords. The scenarios include character actions, dialogues, and educational points, which form the basis for subsequent visual representation generation.

[0098] Step 4:

[0099] The server creates animations based on the generated scenarios. This process uses animation tools such as Blender to model characters, set up backgrounds, and add audio data. Finally, the animation as a visual representation is completed, which is then encoded into a format for the user.

[0100] Step 5:

[0101] The server delivers the completed animation to the user's device. Using technologies such as HTTP streaming, users can instantly view the animation on their device. This allows users to share educational content with their children in real time and facilitate learning.

[0102] (Application Example 1)

[0103] 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."

[0104] Moral education within the home is a time-consuming task for parents and educators, and it presents challenges in maintaining children's interest. Furthermore, there is a need for effective educational methods that respond to children's specific behaviors. Therefore, there is a demand for a system that easily supports moral education within the home.

[0105] 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.

[0106] In this invention, the server includes a device for receiving educational information input by a user, a device for identifying a subject to be taught based on the educational information, a device for generating content based on the identified subject, and a device for automatically generating viewable motion images. This makes it possible for parents and educators to easily conduct moral education tailored to children's behavior within the home.

[0107] A "user" is the entity that uses the system to input educational information and receive motion images.

[0108] "Educational information" refers to the fundamental information that users provide to the system for conducting education.

[0109] A "server" is a computer device that receives input information from users and performs analysis processing and generates motion images.

[0110] "Device" refers to hardware or software used to perform various processes within a server.

[0111] A "subject" is a specific subject or theme of moral education, identified based on educational information.

[0112] "Content" refers to educational scenarios or stories generated based on a specific theme.

[0113] "Viewable motion images" refer to animations and video expressions that are automatically created based on the generated scenario.

[0114] To implement this invention, a system combining a smartphone and a server is used. The user inputs educational information via the smartphone, and this information is transferred to the server. The server receives the educational information and uses natural language processing technology to identify the topics to be covered based on its content.

[0115] The server can use Python and TENSORFLOW® to extract important keywords from received information and select appropriate educational themes. Based on the selected themes, the server generates scenarios and then generates animated images based on those scenarios. In generating the animated images, elements including character design, background, and sound are automatically integrated using AI technology to form viewable animations.

[0116] The generated animations are converted to the appropriate format and delivered from the server to the user's smartphone. This allows parents and educators to utilize video content to support children's moral education from the comfort of their homes.

[0117] For example, if a user enters the information "My child doesn't clean up," the server identifies the theme "tidying up" and generates a scenario. Then, a corresponding animation is created and sent to the user's smartphone. An example of a prompt is as follows: "My child says they want to do XX. Based on this, please select a relevant moral theme and generate a scenario and animation that will allow the child to learn in a fun way."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] Users input educational information using a terminal. The information entered includes text data such as specific behaviors of children and educational content. This data is sent from the terminal to the server and used as basic data to initiate processing.

[0121] Step 2:

[0122] The server analyzes the received educational information. Here, natural language processing techniques are used to extract important keywords from the input text data. The input is text data, and the output is a set of keywords for theme selection. Specifically, a machine learning model using Python performs the text analysis.

[0123] Step 3:

[0124] The server identifies appropriate educational themes based on the extracted keywords. The selected themes are then used for scenario and animation generation. The input is the keywords extracted in step 2, and the output is the selected educational themes. In operation, a theme selection algorithm evaluates the set of keywords.

[0125] Step 4:

[0126] The server generates scenarios based on specified educational themes. These scenarios include a story, character actions, and dialogues, all based on the educational themes. The input is a selected educational theme, and the output is specific scenario data. Natural language generation technology is used for this operation.

[0127] Step 5:

[0128] The server generates action images based on the generated scenario. Here, AI technology is used for character design, background placement, and voice addition. The input is scenario data, and the output is viewable animation data. Specifically, a model using TensorFlow performs the animation generation.

[0129] Step 6:

[0130] The server converts the generated animation into the appropriate format and delivers it to the user's device. At this point, the user is ready to view the animation on their device. The input is the animation data, and the output is delivery to the user. The operation involves file format conversion and data transmission over the network.

[0131] 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.

[0132] This invention is a moral education animation generation system that takes user emotions into consideration. Designed to support children's moral education in the home environment, this system incorporates emotion recognition based on user input to provide a more personalized educational experience.

[0133] Main process

[0134] 1. User input and sentiment recognition

[0135] Users input educational content and behaviors related to their children through an application on their device. The text entered here also serves as data for emotion recognition.

[0136] Once input is complete on the device, the data is sent to the server, where an emotion engine runs to perform sentiment analysis on the input.

[0137] 2. Data Analysis and Theme Identification

[0138] The server analyzes the received data to extract emotional keywords and identify the emotions the user is feeling.

[0139] This emotional information assists in the selection process of educational themes for specific methods and serves as material for determining the most appropriate moral education themes.

[0140] 3. Scenario generation and animation settings

[0141] The server constructs a detailed story using scenario generation tools, based on educational themes and recognized emotions. In this process, recognized emotions are reflected in the characters' actions and dialogue, resulting in content that resonates more with the audience.

[0142] Through a generation process, elements such as characters, backgrounds, and sounds within the animation are set, and emotional elements are included.

[0143] 4. Animation generation and distribution

[0144] The final animation is generated on the server and rendered in a viewable format. This video is then delivered from the server to the user's device, making it accessible to children.

[0145] Specific example

[0146] For example, if a child expresses sadness in response to having a fight with a friend, the system will select the theme of "communication" and generate an animation that reflects the feeling of sadness. Specifically, the animation will depict a scene where the character reflects on the fight and tries to reconcile with their friend, providing viewers with empathy and moral lessons.

[0147] Thus, the present invention enables the generation of animations that reflect the user's emotions, providing more effective moral education for children.

[0148] The following describes the processing flow.

[0149] Step 1:

[0150] The user opens the application on their device and enters information about educational themes and recent activities related to their child. They describe specific actions or situations in the text box and press the submit button.

[0151] Step 2:

[0152] The terminal sends the input content to the server via a secure communication protocol. During this process, a transmission completion message is displayed to the user to confirm that the data has been sent correctly.

[0153] Step 3:

[0154] The server receives the input data through a receiving mechanism. The received data is temporarily stored in a database.

[0155] Step 4:

[0156] The emotion engine on the server analyzes the received data to recognize the user's emotions. Using natural language processing algorithms, it extracts emotion keywords from words and phrases and identifies emotion categories.

[0157] Step 5:

[0158] The server uses specific methods to determine educational themes based on recognized emotions. In this process, emotional information is used as a reference for theme selection, and more appropriate moral themes are identified.

[0159] Step 6:

[0160] Using a scenario generation method, the server generates scenarios that align with the theme and emotions. Dialogues and character actions that reflect these emotions are meticulously designed.

[0161] Step 7:

[0162] The server uses a generation mechanism to automatically generate animations from the scenario. Here, character designs, backgrounds, and voices are set, and emotional elements are reflected in the animation.

[0163] Step 8:

[0164] The server checks the quality of the completed animation and renders it into a viewable file format. This file is then optimized for smooth playback.

[0165] Step 9:

[0166] The server sends the animation file to the user's device via the distribution method. The device saves the received video in a format that allows it to be streamed or downloaded.

[0167] Step 10:

[0168] Users use their devices to play the received animations. They watch them with their children and use them as an opportunity to gain moral learning.

[0169] (Example 2)

[0170] 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".

[0171] In moral education for children within the home environment, it is difficult to provide educational content that is tailored to the individual feelings and circumstances of each child. Traditional educational methods primarily consist of standardized content and fail to provide educational experiences that fully reflect children's emotions. Furthermore, there were challenges in understanding children's emotions and generating animations that respond accordingly.

[0172] 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.

[0173] In this invention, the server includes means for receiving educational information or activities entered by a user, means for analysis using natural language processing technology to perform sentiment analysis, means for identifying themes for moral education based on the sentiment analysis results, means for generating a story, means for automatically generating video, and means for distributing it to the receiving side. This makes it possible to generate moral education animations that correspond to individual emotions and provide children with a personalized educational experience.

[0174] "Means for receiving educational information or activities entered by users" refers to a mechanism for a server to receive education-related data and activity content provided by users through their devices.

[0175] "An analysis method using natural language processing technology for sentiment analysis" refers to a function that has the ability to analyze the user's emotional state from received text data and extract necessary emotional information.

[0176] "A method for identifying moral education themes based on emotion analysis results" refers to the process of selecting appropriate moral education themes based on analyzed emotional information.

[0177] "A means of generating a story" is a function that combines selected themes and emotional information to construct a narrative that includes the actions and dialogues of the characters.

[0178] "Methods for automatically generating video" refers to the process of creating animations consisting of characters, backgrounds, sound, etc., based on a constructed storyline.

[0179] "Means of distribution to the receiving end" refers to a distribution mechanism that delivers the generated video to the user's device and makes it viewable.

[0180] This system aims to generate moral education animations that respond to the user's emotions. The system is primarily implemented using the following hardware and software.

[0181] Users input educational information and behavioral data about their children as text via their devices. A user interface implemented on the device is used for this input. The entered text data is sent to a server via the internet. Common digital communication technologies such as the HTTP protocol are typically used for communication.

[0182] The server analyzes the received user text data using an emotion recognition engine that implements natural language processing (NLP) technology. This engine is built using commonly used NLP libraries and frameworks. Through this analysis, emotion keywords are extracted from the text entered by the user.

[0183] The extracted emotional keywords are stored in the server's database. Based on this emotional information, the server selects appropriate moral education themes. This process utilizes a generative AI model related to the educational content.

[0184] The server generates a detailed story based on the selected educational theme and emotional information. The story generation utilizes a generation AI model with prompts. A specific example of a prompt is: "A child is feeling sad because they had a fight with a friend. Please generate an animation with a communication theme."

[0185] Once the story is generated, the server runs a program to create the animation. This uses 3DCG software and a speech synthesis engine, among other things. The generated animation is rendered on the server and finally delivered to the user's terminal via a digital streaming protocol.

[0186] In this way, users can provide children with personalized moral education animations tailored to their emotions. This system promotes the personalization of education and deepens children's understanding and empathy.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] Users use their devices to input and submit educational information and data about their children's behavior. The information entered by the user is received as text data by the device's application. This data serves as input for sentiment analysis. The device uses the HTTP protocol to send this input data to the server over the internet.

[0190] Step 2:

[0191] The server receives the text data and activates an emotion recognition engine using natural language processing technology. The engine analyzes the text content and extracts emotion keywords. Based on this analysis, emotions such as "sadness" or "joy" are output from the user's input text. The analysis results are recorded in the server's database.

[0192] Step 3:

[0193] The server initiates a process to identify moral education themes based on emotional keywords recorded in the database. In this process, the aforementioned emotional keywords serve as input, and an appropriate theme is selected as output. The selected educational theme is then passed on to the story generation process.

[0194] Step 4:

[0195] The server uses a generative AI model to construct a story based on the selected theme and emotional information. Here, the AI ​​model outputs a specific story in response to a prompt. For example, a prompt such as, "A child is feeling sad because they had a fight with a friend. Please generate an animation with a communication theme," will generate a detailed story.

[0196] Step 5:

[0197] The server performs a process to automatically generate animation based on the generated story. Elements such as character movements and backgrounds are set. In this process, the story is the input and visual animation data is output. 3DCG software and speech synthesis tools are used to capture character designs and voices.

[0198] Step 6:

[0199] The server renders the final animation and converts it into a viewable video format. The rendered video is delivered from the server to the device, allowing the user to receive moral education through the video. The video is transmitted and played on the device using digital streaming technology.

[0200] (Application Example 2)

[0201] 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".

[0202] A challenge in providing moral education within the home is ensuring a personalized educational experience that appropriately considers children's emotions. Traditional educational methods often fail to adequately incorporate emotionally-based personalization, limiting the educational effectiveness for children.

[0203] 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.

[0204] In this invention, the server includes receiving means for receiving educational content or actions input by the user, identifying means for recognizing emotions and identifying themes for moral education, and scenario generating means for generating scenarios based on the themes and emotional information. This makes it possible to generate personalized moral education animations that respond to the child's emotions.

[0205] A "receiving means" is a device or system that receives input data from a user and incorporates educational content and actions into the server as an initial step in processing.

[0206] "Specific means" refers to a method or process for selecting appropriate themes for moral education by using natural language processing technology to recognize emotions and keywords based on received educational content and actions.

[0207] A "scenario generation method" is a system or program for creating detailed educational stories based on a specified theme and recognized emotional information.

[0208] "Generation means" refers to the process of setting animation elements, including character designs, backgrounds, and voice acting, according to the story created by the scenario generation means, and automatically generating animation.

[0209] "Distribution means" refers to communication technologies and software that transmit the generated animation to the user's device and make it viewable or playable.

[0210] This invention relates to a system that generates personalized educational animations based on a child's emotions in order to support moral education within the home. The system consists of input performed on the user's terminal, processing performed on a server, and distribution means for the child to view.

[0211] The user first inputs information about their child's educational content and behavior through their device. This input information is sent to the server as text data. The server receives this information, uses natural language processing techniques to recognize emotions, and extracts keywords. Specifically, the Python libraries NLTK and Transformers can be used for this purpose. Based on the recognized emotions and keywords, educational themes are identified.

[0212] Next, the server generates a detailed story based on the recognized emotional information and themes. This process uses a generative AI model, specifically AI-powered scenario generation. Subsequently, the server generates story-based animations using animation software such as Unity or Blender. The generated animations are converted to the appropriate format and delivered to the terminal.

[0213] For example, if a child expresses frustration because they are struggling with their homework, the server recognizes this as a theme of "overcoming difficulties" and generates an animation that reflects this emotion. The animation depicts a scene where the character faces a challenge and ultimately succeeds. This allows children to learn emotionally through a relatable story.

[0214] An example of a prompt given to a generative AI model might be, "If a child is feeling frustrated, how should the animation depict how to overcome difficulties?" In this way, the system can consider the user's emotions and provide more effective moral education animations.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The terminal receives user input. The user enters information about educational content and the child's behavior and emotions into the terminal in text format. The entered data becomes the initial data to be sent to the server via the terminal's input device.

[0218] Step 2:

[0219] The server receives data transmitted from the terminal using a receiving device. The received data is used as raw material for analyzing user emotions and educational content.

[0220] Step 3:

[0221] The server uses specific methods to perform natural language processing on the received data. Specifically, it uses the "Transformers" library in "Python" to analyze the structure of sentences and extract sentiment keywords. The input is the user's text data, and the output is the extracted sentiments and keywords.

[0222] Step 4:

[0223] The server selects a theme using specific means based on the emotions and keywords obtained. The theme selected here forms the basis for the next scenario generation. The input is the extracted results, and the output is a theme suitable for moral education.

[0224] Step 5:

[0225] The server generates scenarios using a generative AI model. Taking a theme selected by specific means and recognized emotional information as input, the generative AI model forms the framework of the story. The output is a specific story plot.

[0226] Step 6:

[0227] The server uses a generation mechanism to create animations based on the generated story plot. Unity and Blender are used to design characters and backgrounds and render the animations. The input is the story plot idea, and the output is a digital file of the animation.

[0228] Step 7:

[0229] The server sends the final generated animation to the terminal via the distribution method. At this point, the animation is ready to be displayed on the terminal. The input is the generated animation data, and the output is the display output for the child to view.

[0230] 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.

[0231] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0232] 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.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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".

[0246] This invention is an animation generation system for supporting moral education in the home. This system is implemented as a web or mobile application and serves as a tool for parents and educators to share with children.

[0247] Main process

[0248] 1. User input

[0249] Users access the application using their device and enter specific details about what they want to teach their child and their recent behavior.

[0250] 2. Data reception and analysis

[0251] The server receives information sent by the user. The received information is stored on the server as text data.

[0252] The server utilizes natural language processing technology to analyze the input text and extract keywords and educational themes.

[0253] 3. Identifying educational themes

[0254] Using specific methods, the server identifies appropriate moral education themes from the analysis results.

[0255] For example, if the input is "I don't clean up," the theme will be selected to be about tidying up and rules.

[0256] 4. Scenario Generation

[0257] The server generates scenarios based on the selected educational theme. These scenarios include character actions, dialogues, and learning points.

[0258] Scenarios are dynamically generated using AI-generated models.

[0259] 5. Animation generation

[0260] The server generates animations based on the scenario. This includes character design, scene setting, and the addition of audio data.

[0261] We will use AI technology to construct a continuous story as an animation.

[0262] 6. Streaming and Viewing

[0263] The generated animation is converted to the appropriate format and delivered from the server to the user's terminal.

[0264] Users can download or stream animations on their devices, making it possible to watch them with children.

[0265] Specific example

[0266] For example, if a child inputs the action description "I had a fight with a friend," the system will generate a scenario themed around "communication" and "empathy." The animation will include scenes of understanding and reconciliation between friends, structured so that the child can learn from it.

[0267] This invention is configured in this way and is a system that assists parents and educators in easily conducting moral education at home.

[0268] The following describes the processing flow.

[0269] Step 1:

[0270] Users access the application using their device's interface and input educational content and behaviors related to their children. The input is in text format and includes specific situations and episodes.

[0271] Step 2:

[0272] The terminal sends the entered information to the server. A communication protocol is used to securely transfer the information and ensure that the data reaches the server accurately.

[0273] Step 3:

[0274] The server receives input data via a receiving device and stores it in the database.

[0275] Step 4:

[0276] The server uses specific means to utilize natural language processing technology and extract specific keywords or themes from the input text. Thereby, the direction of specific moral education is derived.

[0277] Step 5:

[0278] The server refers to the database of educational themes and determines an appropriate moral theme based on the extracted keywords. In this process, a predefined set of themes is referred to.

[0279] Step 6:

[0280] The server uses scenario generation means to generate a scenario based on the identified theme. The scenario includes story development, character dialogue, and learning focus points.

[0281] Step 7:

[0282] Using generation means, the server generates an animation based on the scenario. In this process, character design, setting of animation sequences, and addition of voices and sound effects are performed.

[0283] Step 8: <​​​​​​​​​​​​​​​​​​​

[0289] (Example 1)

[0290] 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."

[0291] In traditional home education, it was often burdensome for parents and educators to directly provide moral education to children, and selecting appropriate educational content was difficult. Furthermore, there were limited means of conveying educational content in a format that children could easily engage with, thus creating a need for a system that would promote efficient learning.

[0292] 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.

[0293] In this invention, the server includes means for receiving input information, means for analyzing the input information to identify educational themes, means for generating scenarios, means for automatically generating visual representations, and means for transferring the generated visual representations to the user. This makes it possible for users to provide appropriate moral education to children and promote effective learning through engaging visual materials simply by providing input information.

[0294] "Means for receiving input information" refers to a system in which a server acquires information about educational content and actions provided by users using their devices via a network.

[0295] "Methods for analyzing input information to identify educational themes" refers to the process of analyzing received information using language analysis technology and selecting themes suitable for moral education from that analysis.

[0296] "Means for generating scenarios" refers to a function that uses AI technology to create the structure of an educational story based on a specific educational theme.

[0297] "Means for automatically generating visual representations" refer to tools and processes that enable the construction of visual elements such as character movements and backgrounds based on generated scenarios.

[0298] "Means for transferring generated visual representations to users" refers to technologies for encoding completed visual teaching materials in an appropriate format and transmitting them in a way that users can access.

[0299] This invention is a system for supporting moral education at home, and in particular, provides educational content to children by generating animations. Specific embodiments are shown below.

[0300] Users access this system using a device. First, users input specific text about the educational content they want to teach their child and their child's recent behavior through a particular application. This input information is sent to the server via the device.

[0301] The server stores the received text data and performs analysis using natural language processing techniques. This analysis utilizes libraries such as Python's NLTK and spaCy. Keywords are extracted from the analysis results and compared with predefined libraries within the system to identify the most suitable educational themes.

[0302] The server then dynamically generates scenarios based on the identified theme using a generative AI model (for example, a general natural language generation model). By inputting prompt sentences into the AI ​​model, an educational story is constructed. Examples of prompt sentences used at this time include "My child has been fighting with friends a lot lately. I want to create an animation that teaches about this," or "My child doesn't clean up, so I want to teach them about tidiness."

[0303] After that, based on the scenario, the server generates a visual representation. In this process, animation production tools such as Blender are used to combine character design, backgrounds, and voice data. After the visual representation is completed, it is encoded into an appropriate video format and distributed from the server to the user's terminal.

[0304] The user can watch this animation with their children through an application on the terminal. By using this system, parents and educators can implement effective and engaging moral education for children.

[0305] The flow of the specific process in Example 1 will be described using FIG. 11.

[0306] Step 1:

[0307] The user logs in to the application via the terminal and enters information regarding the content they want to teach and the behavior of the child. This input includes text data describing specific situations and educational objectives. The input data is sent to the server by pressing the send button.

[0308] Step 2:

[0309] The server receives the text data sent from the terminal and stores it in the database. Next, the server analyzes the text using natural language processing technology. In this analysis process, libraries such as NLTK and spaCy are used to extract features related to keywords and themes from the text. As a result, the theme for education is identified.

[0310] Step 3:

[0311] The server generates scenarios using AI technology based on identified educational themes. The generative AI model used here automatically constructs a story based on prompts for the aforementioned keywords. The scenarios include character actions, dialogues, and educational points, which form the basis for subsequent visual representation generation.

[0312] Step 4:

[0313] The server creates animations based on the generated scenarios. This process uses animation tools such as Blender to model characters, set up backgrounds, and add audio data. Finally, the animation as a visual representation is completed, which is then encoded into a format for the user.

[0314] Step 5:

[0315] The server delivers the completed animation to the user's device. Using technologies such as HTTP streaming, users can instantly view the animation on their device. This allows users to share educational content with their children in real time and facilitate learning.

[0316] (Application Example 1)

[0317] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0318] Moral education within the home is a time-consuming task for parents and educators, and it presents challenges in maintaining children's interest. Furthermore, there is a need for effective educational methods that respond to children's specific behaviors. Therefore, there is a demand for a system that easily supports moral education within the home.

[0319] 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.

[0320] In this invention, the server includes a device for receiving educational information input by a user, a device for identifying a subject to be taught based on the educational information, a device for generating content based on the identified subject, and a device for automatically generating viewable motion images. This makes it possible for parents and educators to easily conduct moral education tailored to children's behavior within the home.

[0321] A "user" is the entity that uses the system to input educational information and receive motion images.

[0322] "Educational information" refers to the fundamental information that users provide to the system for conducting education.

[0323] A "server" is a computer device that receives input information from users and performs analysis processing and generates motion images.

[0324] "Device" refers to hardware or software used to perform various processes within a server.

[0325] A "subject" is a specific subject or theme of moral education, identified based on educational information.

[0326] "Content" refers to educational scenarios or stories generated based on a specific theme.

[0327] "Viewable motion images" refer to animations and video expressions that are automatically created based on the generated scenario.

[0328] To implement this invention, a system combining a smartphone and a server is used. The user inputs educational information via the smartphone, and this information is transferred to the server. The server receives the educational information and uses natural language processing technology to identify the topics to be covered based on its content.

[0329] The server can use Python and TensorFlow to extract important keywords from received information and select appropriate educational topics. Based on the selected topics, the server generates scenarios and then generates animated images based on those scenarios. In generating the animated images, elements such as character design, background, and sound are automatically integrated using AI technology to form viewable animations.

[0330] The generated animations are converted to the appropriate format and delivered from the server to the user's smartphone. This allows parents and educators to utilize video content to support children's moral education from the comfort of their homes.

[0331] For example, if a user enters the information "My child doesn't clean up," the server identifies the theme "tidying up" and generates a scenario. Then, a corresponding animation is created and sent to the user's smartphone. An example of a prompt is as follows: "My child says they want to do XX. Based on this, please select a relevant moral theme and generate a scenario and animation that will allow the child to learn in a fun way."

[0332] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0333] Step 1:

[0334] Users input educational information using a terminal. The information entered includes text data such as specific behaviors of children and educational content. This data is sent from the terminal to the server and used as basic data to initiate processing.

[0335] Step 2:

[0336] The server analyzes the received educational information. Here, natural language processing techniques are used to extract important keywords from the input text data. The input is text data, and the output is a set of keywords for theme selection. Specifically, a machine learning model using Python performs the text analysis.

[0337] Step 3:

[0338] The server identifies appropriate educational themes based on the extracted keywords. The selected themes are then used for scenario and animation generation. The input is the keywords extracted in step 2, and the output is the selected educational themes. In operation, a theme selection algorithm evaluates the set of keywords.

[0339] Step 4:

[0340] The server generates scenarios based on specified educational themes. These scenarios include a story, character actions, and dialogues, all based on the educational themes. The input is a selected educational theme, and the output is specific scenario data. Natural language generation technology is used for this operation.

[0341] Step 5:

[0342] The server generates action images based on the generated scenario. Here, AI technology is used for character design, background placement, and voice addition. The input is scenario data, and the output is viewable animation data. Specifically, a model using TensorFlow performs the animation generation.

[0343] Step 6:

[0344] The server converts the generated animation into the appropriate format and delivers it to the user's device. At this point, the user is ready to view the animation on their device. The input is the animation data, and the output is delivery to the user. The operation involves file format conversion and data transmission over the network.

[0345] 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.

[0346] This invention is a moral education animation generation system that takes user emotions into consideration. Designed to support children's moral education in the home environment, this system incorporates emotion recognition based on user input to provide a more personalized educational experience.

[0347] Main process

[0348] 1. User input and sentiment recognition

[0349] Users input educational content and behaviors related to their children through an application on their device. The text entered here also serves as data for emotion recognition.

[0350] Once input is complete on the device, the data is sent to the server, where an emotion engine runs to perform sentiment analysis on the input.

[0351] 2. Data Analysis and Theme Identification

[0352] The server analyzes the received data to extract emotional keywords and identify the emotions the user is feeling.

[0353] This emotional information assists in the selection process of educational themes for specific methods and serves as material for determining the most appropriate moral education themes.

[0354] 3. Scenario generation and animation settings

[0355] The server constructs a detailed story using scenario generation tools, based on educational themes and recognized emotions. In this process, recognized emotions are reflected in the characters' actions and dialogue, resulting in content that resonates more with the audience.

[0356] Through a generation process, elements such as characters, backgrounds, and sounds within the animation are set, and emotional elements are included.

[0357] 4. Animation generation and distribution

[0358] The final animation is generated on the server and rendered in a viewable format. This video is then delivered from the server to the user's device, making it accessible to children.

[0359] Specific example

[0360] For example, if a child expresses sadness in response to having a fight with a friend, the system will select the theme of "communication" and generate an animation that reflects the feeling of sadness. Specifically, the animation will depict a scene where the character reflects on the fight and tries to reconcile with their friend, providing viewers with empathy and moral lessons.

[0361] Thus, the present invention enables the generation of animations that reflect the user's emotions, providing more effective moral education for children.

[0362] The following describes the processing flow.

[0363] Step 1:

[0364] The user opens the application on their device and enters information about educational themes and recent activities related to their child. They describe specific actions or situations in the text box and press the submit button.

[0365] Step 2:

[0366] The terminal sends the input content to the server via a secure communication protocol. During this process, a transmission completion message is displayed to the user to confirm that the data has been sent correctly.

[0367] Step 3:

[0368] The server receives the input data through a receiving mechanism. The received data is temporarily stored in a database.

[0369] Step 4:

[0370] The emotion engine on the server analyzes the received data to recognize the user's emotions. Using natural language processing algorithms, it extracts emotion keywords from words and phrases and identifies emotion categories.

[0371] Step 5:

[0372] The server uses specific methods to determine educational themes based on recognized emotions. In this process, emotional information is used as a reference for theme selection, and more appropriate moral themes are identified.

[0373] Step 6:

[0374] Using a scenario generation method, the server generates scenarios that align with the theme and emotions. Dialogues and character actions that reflect these emotions are meticulously designed.

[0375] Step 7:

[0376] The server uses a generation mechanism to automatically generate animations from the scenario. Here, character designs, backgrounds, and voices are set, and emotional elements are reflected in the animation.

[0377] Step 8:

[0378] The server checks the quality of the completed animation and renders it into a viewable file format. This file is then optimized for smooth playback.

[0379] Step 9:

[0380] The server sends the animation file to the user's device via the distribution method. The device saves the received video in a format that allows it to be streamed or downloaded.

[0381] Step 10:

[0382] Users use their devices to play the received animations. They watch them with their children and use them as an opportunity to gain moral learning.

[0383] (Example 2)

[0384] 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".

[0385] In moral education for children within the home environment, it is difficult to provide educational content that is tailored to the individual feelings and circumstances of each child. Traditional educational methods primarily consist of standardized content and fail to provide educational experiences that fully reflect children's emotions. Furthermore, there were challenges in understanding children's emotions and generating animations that respond accordingly.

[0386] 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.

[0387] In this invention, the server includes means for receiving educational information or activities entered by a user, means for analysis using natural language processing technology to perform sentiment analysis, means for identifying themes for moral education based on the sentiment analysis results, means for generating a story, means for automatically generating video, and means for distributing it to the receiving side. This makes it possible to generate moral education animations that correspond to individual emotions and provide children with a personalized educational experience.

[0388] "Means for receiving educational information or activities entered by users" refers to a mechanism for a server to receive education-related data and activity content provided by users through their devices.

[0389] "An analysis method using natural language processing technology for sentiment analysis" refers to a function that has the ability to analyze the user's emotional state from received text data and extract necessary emotional information.

[0390] "A method for identifying moral education themes based on emotion analysis results" refers to the process of selecting appropriate moral education themes based on analyzed emotional information.

[0391] "A means of generating a story" is a function that combines selected themes and emotional information to construct a narrative that includes the actions and dialogues of the characters.

[0392] "Methods for automatically generating video" refers to the process of creating animations consisting of characters, backgrounds, sound, etc., based on a constructed storyline.

[0393] "Means of distribution to the receiving end" refers to a distribution mechanism that delivers the generated video to the user's device and makes it viewable.

[0394] This system aims to generate moral education animations that respond to the user's emotions. The system is primarily implemented using the following hardware and software.

[0395] Users input educational information and behavioral data about their children as text via their devices. A user interface implemented on the device is used for this input. The entered text data is sent to a server via the internet. Common digital communication technologies such as the HTTP protocol are typically used for communication.

[0396] The server analyzes the received user text data using an emotion recognition engine that implements natural language processing (NLP) technology. This engine is built using commonly used NLP libraries and frameworks. Through this analysis, emotion keywords are extracted from the text entered by the user.

[0397] The extracted emotional keywords are stored in the server's database. Based on this emotional information, the server selects appropriate moral education themes. This process utilizes a generative AI model related to the educational content.

[0398] The server generates a detailed story based on the selected educational theme and emotional information. The story generation utilizes a generation AI model with prompts. A specific example of a prompt is: "A child is feeling sad because they had a fight with a friend. Please generate an animation with a communication theme."

[0399] Once the story is generated, the server runs a program to create the animation. This uses 3DCG software and a speech synthesis engine, among other things. The generated animation is rendered on the server and finally delivered to the user's terminal via a digital streaming protocol.

[0400] In this way, users can provide children with personalized moral education animations tailored to their emotions. This system promotes the personalization of education and deepens children's understanding and empathy.

[0401] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0402] Step 1:

[0403] Users use their devices to input and submit educational information and data about their children's behavior. The information entered by the user is received as text data by the device's application. This data serves as input for sentiment analysis. The device uses the HTTP protocol to send this input data to the server over the internet.

[0404] Step 2:

[0405] The server receives the text data and activates an emotion recognition engine using natural language processing technology. The engine analyzes the text content and extracts emotion keywords. Based on this analysis, emotions such as "sadness" or "joy" are output from the user's input text. The analysis results are recorded in the server's database.

[0406] Step 3:

[0407] The server initiates a process to identify moral education themes based on emotional keywords recorded in the database. In this process, the aforementioned emotional keywords serve as input, and an appropriate theme is selected as output. The selected educational theme is then passed on to the story generation process.

[0408] Step 4:

[0409] The server uses a generative AI model to construct a story based on the selected theme and emotional information. Here, the AI ​​model outputs a specific story in response to a prompt. For example, a prompt such as, "A child is feeling sad because they had a fight with a friend. Please generate an animation with a communication theme," will generate a detailed story.

[0410] Step 5:

[0411] The server performs a process to automatically generate animation based on the generated story. Elements such as character movements and backgrounds are set. In this process, the story is the input and visual animation data is output. 3DCG software and speech synthesis tools are used to capture character designs and voices.

[0412] Step 6:

[0413] The server renders the final animation and converts it into a viewable video format. The rendered video is delivered from the server to the device, allowing the user to receive moral education through the video. The video is transmitted and played on the device using digital streaming technology.

[0414] (Application Example 2)

[0415] 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."

[0416] A challenge in providing moral education within the home is ensuring a personalized educational experience that appropriately considers children's emotions. Traditional educational methods often fail to adequately incorporate emotionally-based personalization, limiting the educational effectiveness for children.

[0417] 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.

[0418] In this invention, the server includes receiving means for receiving educational content or actions input by the user, identifying means for recognizing emotions and identifying themes for moral education, and scenario generating means for generating scenarios based on the themes and emotional information. This makes it possible to generate personalized moral education animations that respond to the child's emotions.

[0419] A "receiving means" is a device or system that receives input data from a user and incorporates educational content and actions into the server as an initial step in processing.

[0420] "Specific means" refers to a method or process for selecting appropriate themes for moral education by using natural language processing technology to recognize emotions and keywords based on received educational content and actions.

[0421] A "scenario generation method" is a system or program for creating detailed educational stories based on a specified theme and recognized emotional information.

[0422] "Generation means" refers to the process of setting animation elements, including character designs, backgrounds, and voice acting, according to the story created by the scenario generation means, and automatically generating animation.

[0423] "Distribution means" refers to communication technologies and software that transmit the generated animation to the user's device and make it viewable or playable.

[0424] This invention relates to a system that generates personalized educational animations based on a child's emotions in order to support moral education within the home. The system consists of input performed on the user's terminal, processing performed on a server, and distribution means for the child to view.

[0425] The user first inputs information about their child's educational content and behavior through their device. This input information is sent to the server as text data. The server receives this information, uses natural language processing techniques to recognize emotions, and extracts keywords. Specifically, the Python libraries NLTK and Transformers can be used for this purpose. Based on the recognized emotions and keywords, educational themes are identified.

[0426] Next, the server generates a detailed story based on the recognized emotional information and themes. This process uses a generative AI model, specifically AI-powered scenario generation. Subsequently, the server generates story-based animations using animation software such as Unity or Blender. The generated animations are converted to the appropriate format and delivered to the terminal.

[0427] For example, if a child expresses frustration because they are struggling with their homework, the server recognizes this as a theme of "overcoming difficulties" and generates an animation that reflects this emotion. The animation depicts a scene where the character faces a challenge and ultimately succeeds. This allows children to learn emotionally through a relatable story.

[0428] An example of a prompt given to a generative AI model might be, "If a child is feeling frustrated, how should the animation depict how to overcome difficulties?" In this way, the system can consider the user's emotions and provide more effective moral education animations.

[0429] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0430] Step 1:

[0431] The terminal receives user input. The user enters information about educational content and the child's behavior and emotions into the terminal in text format. The entered data becomes the initial data to be sent to the server via the terminal's input device.

[0432] Step 2:

[0433] The server receives data transmitted from the terminal using a receiving device. The received data is used as raw material for analyzing user emotions and educational content.

[0434] Step 3:

[0435] The server uses specific methods to perform natural language processing on the received data. Specifically, it uses the "Transformers" library in "Python" to analyze the structure of sentences and extract sentiment keywords. The input is the user's text data, and the output is the extracted sentiments and keywords.

[0436] Step 4:

[0437] The server selects a theme using specific means based on the emotions and keywords obtained. The theme selected here forms the basis for the next scenario generation. The input is the extracted results, and the output is a theme suitable for moral education.

[0438] Step 5:

[0439] The server generates scenarios using a generative AI model. Taking a theme selected by specific means and recognized emotional information as input, the generative AI model forms the framework of the story. The output is a specific story plot.

[0440] Step 6:

[0441] The server uses a generation mechanism to create animations based on the generated story plot. Unity and Blender are used to design characters and backgrounds and render the animations. The input is the story plot idea, and the output is a digital file of the animation.

[0442] Step 7:

[0443] The server sends the final generated animation to the terminal via the distribution method. At this point, the animation is ready to be displayed on the terminal. The input is the generated animation data, and the output is the display output for the child to view.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] [Third Embodiment]

[0448] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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".

[0460] This invention is an animation generation system for supporting moral education in the home. This system is implemented as a web or mobile application and serves as a tool for parents and educators to share with children.

[0461] Main process

[0462] 1. User input

[0463] Users access the application using their device and enter specific details about what they want to teach their child and their recent behavior.

[0464] 2. Data reception and analysis

[0465] The server receives information sent by the user. The received information is stored on the server as text data.

[0466] The server utilizes natural language processing technology to analyze the input text and extract keywords and educational themes.

[0467] 3. Identifying educational themes

[0468] Using specific methods, the server identifies appropriate moral education themes from the analysis results.

[0469] For example, if the input is "I don't clean up," the theme will be selected to be about tidying up and rules.

[0470] 4. Scenario Generation

[0471] The server generates scenarios based on the selected educational theme. These scenarios include character actions, dialogues, and learning points.

[0472] Scenarios are dynamically generated using AI-generated models.

[0473] 5. Animation generation

[0474] The server generates animations based on the scenario. This includes character design, scene setting, and the addition of audio data.

[0475] We will use AI technology to construct a continuous story as an animation.

[0476] 6. Streaming and Viewing

[0477] The generated animation is converted to the appropriate format and delivered from the server to the user's terminal.

[0478] Users can download or stream animations on their devices, making it possible to watch them with children.

[0479] Specific example

[0480] For example, if a child inputs the action description "I had a fight with a friend," the system will generate a scenario themed around "communication" and "empathy." The animation will include scenes of understanding and reconciliation between friends, structured so that the child can learn from it.

[0481] This invention is configured in this way and is a system that assists parents and educators in easily conducting moral education at home.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] Users access the application using their device's interface and input educational content and behaviors related to their children. The input is in text format and includes specific situations and episodes.

[0485] Step 2:

[0486] The terminal sends the entered information to the server. A communication protocol is used to securely transfer the information and ensure that the data reaches the server accurately.

[0487] Step 3:

[0488] The server receives input data via a receiving device and stores it in the database.

[0489] Step 4:

[0490] The server uses specific methods and natural language processing techniques to extract specific keywords and themes from the input text. This allows for the development of concrete directions for moral education.

[0491] Step 5:

[0492] The server consults a database of educational themes and determines an appropriate moral theme based on extracted keywords. This process refers to a predefined set of themes.

[0493] Step 6:

[0494] Using a scenario generation mechanism, the server generates scenarios based on a specified theme. These scenarios include story development, character dialogue, and learning focus points.

[0495] Step 7:

[0496] Using the generation method, the server generates animations based on the scenario. This process includes character design, animation sequence setup, and the addition of voice and sound effects.

[0497] Step 8:

[0498] The server renders the generated animation into the appropriate video format and prepares it as a viewable file.

[0499] Step 9:

[0500] Using a distribution method, the server sends the completed animation file to the user's terminal.

[0501] Step 10:

[0502] The user plays the animation received on their device and watches it together with their child. The viewing experience supports the child in gaining moral lessons.

[0503] (Example 1)

[0504] 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."

[0505] In traditional home education, it was often burdensome for parents and educators to directly provide moral education to children, and selecting appropriate educational content was difficult. Furthermore, there were limited means of conveying educational content in a format that children could easily engage with, thus creating a need for a system that would promote efficient learning.

[0506] 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.

[0507] In this invention, the server includes means for receiving input information, means for analyzing the input information to identify educational themes, means for generating scenarios, means for automatically generating visual representations, and means for transferring the generated visual representations to the user. This makes it possible for users to provide appropriate moral education to children and promote effective learning through engaging visual materials simply by providing input information.

[0508] "Means for receiving input information" refers to a system in which a server acquires information about educational content and actions provided by users using their devices via a network.

[0509] "Methods for analyzing input information to identify educational themes" refers to the process of analyzing received information using language analysis technology and selecting themes suitable for moral education from that analysis.

[0510] "Means for generating scenarios" refers to a function that uses AI technology to create the structure of an educational story based on a specific educational theme.

[0511] "Means for automatically generating visual representations" refer to tools and processes that enable the construction of visual elements such as character movements and backgrounds based on generated scenarios.

[0512] "Means for transferring generated visual representations to users" refers to technologies for encoding completed visual teaching materials in an appropriate format and transmitting them in a way that users can access.

[0513] This invention is a system for supporting moral education at home, and in particular, provides educational content to children by generating animations. Specific embodiments are shown below.

[0514] Users access this system using a device. First, users input specific text about the educational content they want to teach their child and their child's recent behavior through a particular application. This input information is sent to the server via the device.

[0515] The server stores the received text data and performs analysis using natural language processing techniques. This analysis utilizes libraries such as Python's NLTK and spaCy. Keywords are extracted from the analysis results and compared with predefined libraries within the system to identify the most suitable educational themes.

[0516] The server then dynamically generates scenarios based on the identified theme using a generative AI model (for example, a general natural language generation model). By inputting prompt sentences into the AI ​​model, an educational story is constructed. Examples of prompt sentences used at this time include "My child has been fighting with friends a lot lately. I want to create an animation that teaches about this," or "My child doesn't clean up, so I want to teach them about tidiness."

[0517] Subsequently, based on the scenario, the server generates a visual representation. This process involves combining character designs, backgrounds, and audio data using animation production tools such as Blender. Once the visual representation is complete, it is encoded into the appropriate video format and delivered from the server to the user's terminal.

[0518] Users can watch this animation with their children through an application on their device. This system allows parents and educators to provide effective and engaging moral education to children.

[0519] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0520] Step 1:

[0521] Users log in to the application via their device and enter information about the educational content they wish to teach and their child's behavior. This input includes text data describing specific situations and educational objectives. The entered data is sent to the server when the submit button is pressed.

[0522] Step 2:

[0523] The server receives text data sent from the terminal and stores it in a database. Next, the server analyzes the text using natural language processing techniques. In this analysis process, libraries such as NLTK and spaCy are used to extract keywords and theme-related features from the text. As a result, educational themes are identified.

[0524] Step 3:

[0525] The server generates scenarios using AI technology based on identified educational themes. The generative AI model used here automatically constructs a story based on prompts for the aforementioned keywords. The scenarios include character actions, dialogues, and educational points, which form the basis for subsequent visual representation generation.

[0526] Step 4:

[0527] The server creates animations based on the generated scenarios. This process uses animation tools such as Blender to model characters, set up backgrounds, and add audio data. Finally, the animation as a visual representation is completed, which is then encoded into a format for the user.

[0528] Step 5:

[0529] The server delivers the completed animation to the user's device. Using technologies such as HTTP streaming, users can instantly view the animation on their device. This allows users to share educational content with their children in real time and facilitate learning.

[0530] (Application Example 1)

[0531] 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."

[0532] Moral education within the home is a time-consuming task for parents and educators, and it presents challenges in maintaining children's interest. Furthermore, there is a need for effective educational methods that respond to children's specific behaviors. Therefore, there is a demand for a system that easily supports moral education within the home.

[0533] 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.

[0534] In this invention, the server includes a device for receiving educational information input by a user, a device for identifying a subject to be taught based on the educational information, a device for generating content based on the identified subject, and a device for automatically generating viewable motion images. This makes it possible for parents and educators to easily conduct moral education tailored to children's behavior within the home.

[0535] A "user" is the entity that uses the system to input educational information and receive motion images.

[0536] "Educational information" refers to the fundamental information that users provide to the system for conducting education.

[0537] A "server" is a computer device that receives input information from users and performs analysis processing and generates motion images.

[0538] "Device" refers to hardware or software used to perform various processes within a server.

[0539] A "subject" is a specific subject or theme of moral education, identified based on educational information.

[0540] "Content" refers to educational scenarios or stories generated based on a specific theme.

[0541] "Viewable motion images" refer to animations and video expressions that are automatically created based on the generated scenario.

[0542] To implement this invention, a system combining a smartphone and a server is used. The user inputs educational information via the smartphone, and this information is transferred to the server. The server receives the educational information and uses natural language processing technology to identify the topics to be covered based on its content.

[0543] The server can use Python and TensorFlow to extract important keywords from received information and select appropriate educational topics. Based on the selected topics, the server generates scenarios and then generates animated images based on those scenarios. In generating the animated images, elements such as character design, background, and sound are automatically integrated using AI technology to form viewable animations.

[0544] The generated animations are converted to the appropriate format and delivered from the server to the user's smartphone. This allows parents and educators to utilize video content to support children's moral education from the comfort of their homes.

[0545] For example, if a user enters the information "My child doesn't clean up," the server identifies the theme "tidying up" and generates a scenario. Then, a corresponding animation is created and sent to the user's smartphone. An example of a prompt is as follows: "My child says they want to do XX. Based on this, please select a relevant moral theme and generate a scenario and animation that will allow the child to learn in a fun way."

[0546] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0547] Step 1:

[0548] Users input educational information using a terminal. The information entered includes text data such as specific behaviors of children and educational content. This data is sent from the terminal to the server and used as basic data to initiate processing.

[0549] Step 2:

[0550] The server analyzes the received educational information. Here, natural language processing techniques are used to extract important keywords from the input text data. The input is text data, and the output is a set of keywords for theme selection. Specifically, a machine learning model using Python performs the text analysis.

[0551] Step 3:

[0552] The server identifies appropriate educational themes based on the extracted keywords. The selected themes are then used for scenario and animation generation. The input is the keywords extracted in step 2, and the output is the selected educational themes. In operation, a theme selection algorithm evaluates the set of keywords.

[0553] Step 4:

[0554] The server generates scenarios based on specified educational themes. These scenarios include a story, character actions, and dialogues, all based on the educational themes. The input is a selected educational theme, and the output is specific scenario data. Natural language generation technology is used for this operation.

[0555] Step 5:

[0556] The server generates action images based on the generated scenario. Here, AI technology is used for character design, background placement, and voice addition. The input is scenario data, and the output is viewable animation data. Specifically, a model using TensorFlow performs the animation generation.

[0557] Step 6:

[0558] The server converts the generated animation into the appropriate format and delivers it to the user's device. At this point, the user is ready to view the animation on their device. The input is the animation data, and the output is delivery to the user. The operation involves file format conversion and data transmission over the network.

[0559] 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.

[0560] This invention is a moral education animation generation system that takes user emotions into consideration. Designed to support children's moral education in the home environment, this system incorporates emotion recognition based on user input to provide a more personalized educational experience.

[0561] Main process

[0562] 1. User input and sentiment recognition

[0563] Users input educational content and behaviors related to their children through an application on their device. The text entered here also serves as data for emotion recognition.

[0564] Once input is complete on the device, the data is sent to the server, where an emotion engine runs to perform sentiment analysis on the input.

[0565] 2. Data Analysis and Theme Identification

[0566] The server analyzes the received data to extract emotional keywords and identify the emotions the user is feeling.

[0567] This emotional information assists in the selection process of educational themes for specific methods and serves as material for determining the most appropriate moral education themes.

[0568] 3. Scenario generation and animation settings

[0569] The server constructs a detailed story using scenario generation tools, based on educational themes and recognized emotions. In this process, recognized emotions are reflected in the characters' actions and dialogue, resulting in content that resonates more with the audience.

[0570] Through a generation process, elements such as characters, backgrounds, and sounds within the animation are set, and emotional elements are included.

[0571] 4. Animation generation and distribution

[0572] The final animation is generated on the server and rendered in a viewable format. This video is then delivered from the server to the user's device, making it accessible to children.

[0573] Specific example

[0574] For example, if a child expresses sadness in response to having a fight with a friend, the system will select the theme of "communication" and generate an animation that reflects the feeling of sadness. Specifically, the animation will depict a scene where the character reflects on the fight and tries to reconcile with their friend, providing viewers with empathy and moral lessons.

[0575] Thus, the present invention enables the generation of animations that reflect the user's emotions, providing more effective moral education for children.

[0576] The following describes the processing flow.

[0577] Step 1:

[0578] The user opens the application on their device and enters information about educational themes and recent activities related to their child. They describe specific actions or situations in the text box and press the submit button.

[0579] Step 2:

[0580] The terminal sends the input content to the server via a secure communication protocol. During this process, a transmission completion message is displayed to the user to confirm that the data has been sent correctly.

[0581] Step 3:

[0582] The server receives the input data through a receiving mechanism. The received data is temporarily stored in a database.

[0583] Step 4:

[0584] The emotion engine on the server analyzes the received data to recognize the user's emotions. Using natural language processing algorithms, it extracts emotion keywords from words and phrases and identifies emotion categories.

[0585] Step 5:

[0586] The server uses specific methods to determine educational themes based on recognized emotions. In this process, emotional information is used as a reference for theme selection, and more appropriate moral themes are identified.

[0587] Step 6:

[0588] Using a scenario generation method, the server generates scenarios that align with the theme and emotions. Dialogues and character actions that reflect these emotions are meticulously designed.

[0589] Step 7:

[0590] The server uses a generation mechanism to automatically generate animations from the scenario. Here, character designs, backgrounds, and voices are set, and emotional elements are reflected in the animation.

[0591] Step 8:

[0592] The server checks the quality of the completed animation and renders it into a viewable file format. This file is then optimized for smooth playback.

[0593] Step 9:

[0594] The server sends the animation file to the user's device via the distribution method. The device saves the received video in a format that allows it to be streamed or downloaded.

[0595] Step 10:

[0596] Users use their devices to play the received animations. They watch them with their children and use them as an opportunity to gain moral learning.

[0597] (Example 2)

[0598] 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."

[0599] In moral education for children within the home environment, it is difficult to provide educational content that is tailored to the individual feelings and circumstances of each child. Traditional educational methods primarily consist of standardized content and fail to provide educational experiences that fully reflect children's emotions. Furthermore, there were challenges in understanding children's emotions and generating animations that respond accordingly.

[0600] 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.

[0601] In this invention, the server includes means for receiving educational information or activities entered by a user, means for analysis using natural language processing technology to perform sentiment analysis, means for identifying themes for moral education based on the sentiment analysis results, means for generating a story, means for automatically generating video, and means for distributing it to the receiving side. This makes it possible to generate moral education animations that correspond to individual emotions and provide children with a personalized educational experience.

[0602] "Means for receiving educational information or activities entered by users" refers to a mechanism for a server to receive education-related data and activity content provided by users through their devices.

[0603] "An analysis method using natural language processing technology for sentiment analysis" refers to a function that has the ability to analyze the user's emotional state from received text data and extract necessary emotional information.

[0604] "A method for identifying moral education themes based on emotion analysis results" refers to the process of selecting appropriate moral education themes based on analyzed emotional information.

[0605] "A means of generating a story" is a function that combines selected themes and emotional information to construct a narrative that includes the actions and dialogues of the characters.

[0606] "Methods for automatically generating video" refers to the process of creating animations consisting of characters, backgrounds, sound, etc., based on a constructed storyline.

[0607] "Means of distribution to the receiving end" refers to a distribution mechanism that delivers the generated video to the user's device and makes it viewable.

[0608] This system aims to generate moral education animations that respond to the user's emotions. The system is primarily implemented using the following hardware and software.

[0609] Users input educational information and behavioral data about their children as text via their devices. A user interface implemented on the device is used for this input. The entered text data is sent to a server via the internet. Common digital communication technologies such as the HTTP protocol are typically used for communication.

[0610] The server analyzes the received user text data using an emotion recognition engine that implements natural language processing (NLP) technology. This engine is built using commonly used NLP libraries and frameworks. Through this analysis, emotion keywords are extracted from the text entered by the user.

[0611] The extracted emotional keywords are stored in the server's database. Based on this emotional information, the server selects appropriate moral education themes. This process utilizes a generative AI model related to the educational content.

[0612] The server generates a detailed story based on the selected educational theme and emotional information. The story generation utilizes a generation AI model with prompts. A specific example of a prompt is: "A child is feeling sad because they had a fight with a friend. Please generate an animation with a communication theme."

[0613] Once the story is generated, the server runs a program to create the animation. This uses 3DCG software and a speech synthesis engine, among other things. The generated animation is rendered on the server and finally delivered to the user's terminal via a digital streaming protocol.

[0614] In this way, users can provide children with personalized moral education animations tailored to their emotions. This system promotes the personalization of education and deepens children's understanding and empathy.

[0615] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0616] Step 1:

[0617] Users use their devices to input and submit educational information and data about their children's behavior. The information entered by the user is received as text data by the device's application. This data serves as input for sentiment analysis. The device uses the HTTP protocol to send this input data to the server over the internet.

[0618] Step 2:

[0619] The server receives the text data and activates an emotion recognition engine using natural language processing technology. The engine analyzes the text content and extracts emotion keywords. Based on this analysis, emotions such as "sadness" or "joy" are output from the user's input text. The analysis results are recorded in the server's database.

[0620] Step 3:

[0621] The server initiates a process to identify moral education themes based on emotional keywords recorded in the database. In this process, the aforementioned emotional keywords serve as input, and an appropriate theme is selected as output. The selected educational theme is then passed on to the story generation process.

[0622] Step 4:

[0623] The server uses a generative AI model to construct a story based on the selected theme and emotional information. Here, the AI ​​model outputs a specific story in response to a prompt. For example, a prompt such as, "A child is feeling sad because they had a fight with a friend. Please generate an animation with a communication theme," will generate a detailed story.

[0624] Step 5:

[0625] The server performs a process to automatically generate animation based on the generated story. Elements such as character movements and backgrounds are set. In this process, the story is the input and visual animation data is output. 3DCG software and speech synthesis tools are used to capture character designs and voices.

[0626] Step 6:

[0627] The server renders the final animation and converts it into a viewable video format. The rendered video is delivered from the server to the device, allowing the user to receive moral education through the video. The video is transmitted and played on the device using digital streaming technology.

[0628] (Application Example 2)

[0629] 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."

[0630] A challenge in providing moral education within the home is ensuring a personalized educational experience that appropriately considers children's emotions. Traditional educational methods often fail to adequately incorporate emotionally-based personalization, limiting the educational effectiveness for children.

[0631] 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.

[0632] In this invention, the server includes receiving means for receiving educational content or actions input by the user, identifying means for recognizing emotions and identifying themes for moral education, and scenario generating means for generating scenarios based on the themes and emotional information. This makes it possible to generate personalized moral education animations that respond to the child's emotions.

[0633] A "receiving means" is a device or system that receives input data from a user and incorporates educational content and actions into the server as an initial step in processing.

[0634] "Specific means" refers to a method or process for selecting appropriate themes for moral education by using natural language processing technology to recognize emotions and keywords based on received educational content and actions.

[0635] A "scenario generation method" is a system or program for creating detailed educational stories based on a specified theme and recognized emotional information.

[0636] "Generation means" refers to the process of setting animation elements, including character designs, backgrounds, and voice acting, according to the story created by the scenario generation means, and automatically generating animation.

[0637] "Distribution means" refers to communication technologies and software that transmit the generated animation to the user's device and make it viewable or playable.

[0638] This invention relates to a system that generates personalized educational animations based on a child's emotions in order to support moral education within the home. The system consists of input performed on the user's terminal, processing performed on a server, and distribution means for the child to view.

[0639] The user first inputs information about their child's educational content and behavior through their device. This input information is sent to the server as text data. The server receives this information, uses natural language processing techniques to recognize emotions, and extracts keywords. Specifically, the Python libraries NLTK and Transformers can be used for this purpose. Based on the recognized emotions and keywords, educational themes are identified.

[0640] Next, the server generates a detailed story based on the recognized emotional information and themes. This process uses a generative AI model, specifically AI-powered scenario generation. Subsequently, the server generates story-based animations using animation software such as Unity or Blender. The generated animations are converted to the appropriate format and delivered to the terminal.

[0641] For example, if a child expresses frustration because they are struggling with their homework, the server recognizes this as a theme of "overcoming difficulties" and generates an animation that reflects this emotion. The animation depicts a scene where the character faces a challenge and ultimately succeeds. This allows children to learn emotionally through a relatable story.

[0642] An example of a prompt given to a generative AI model might be, "If a child is feeling frustrated, how should the animation depict how to overcome difficulties?" In this way, the system can consider the user's emotions and provide more effective moral education animations.

[0643] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0644] Step 1:

[0645] The terminal receives user input. The user enters information about educational content and the child's behavior and emotions into the terminal in text format. The entered data becomes the initial data to be sent to the server via the terminal's input device.

[0646] Step 2:

[0647] The server receives data transmitted from the terminal using a receiving device. The received data is used as raw material for analyzing user emotions and educational content.

[0648] Step 3:

[0649] The server uses specific methods to perform natural language processing on the received data. Specifically, it uses the "Transformers" library in "Python" to analyze the structure of sentences and extract sentiment keywords. The input is the user's text data, and the output is the extracted sentiments and keywords.

[0650] Step 4:

[0651] The server selects a theme using specific means based on the emotions and keywords obtained. The theme selected here forms the basis for the next scenario generation. The input is the extracted results, and the output is a theme suitable for moral education.

[0652] Step 5:

[0653] The server generates scenarios using a generative AI model. Taking a theme selected by specific means and recognized emotional information as input, the generative AI model forms the framework of the story. The output is a specific story plot.

[0654] Step 6:

[0655] The server uses a generation mechanism to create animations based on the generated story plot. Unity and Blender are used to design characters and backgrounds and render the animations. The input is the story plot idea, and the output is a digital file of the animation.

[0656] Step 7:

[0657] The server sends the final generated animation to the terminal via the distribution method. At this point, the animation is ready to be displayed on the terminal. The input is the generated animation data, and the output is the display output for the child to view.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] [Fourth Embodiment]

[0662] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0663] 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.

[0664] 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).

[0665] 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.

[0666] 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.

[0667] 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).

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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".

[0675] This invention is an animation generation system for supporting moral education in the home. This system is implemented as a web or mobile application and serves as a tool for parents and educators to share with children.

[0676] Main process

[0677] 1. User input

[0678] Users access the application using their device and enter specific details about what they want to teach their child and their recent behavior.

[0679] 2. Data reception and analysis

[0680] The server receives information sent by the user. The received information is stored on the server as text data.

[0681] The server utilizes natural language processing technology to analyze the input text and extract keywords and educational themes.

[0682] 3. Identifying educational themes

[0683] Using specific methods, the server identifies appropriate moral education themes from the analysis results.

[0684] For example, if the input is "I don't clean up," the theme will be selected to be about tidying up and rules.

[0685] 4. Scenario Generation

[0686] The server generates scenarios based on the selected educational theme. These scenarios include character actions, dialogues, and learning points.

[0687] Scenarios are dynamically generated using AI-generated models.

[0688] 5. Animation generation

[0689] The server generates animations based on the scenario. This includes character design, scene setting, and the addition of audio data.

[0690] We will use AI technology to construct a continuous story as an animation.

[0691] 6. Streaming and Viewing

[0692] The generated animation is converted to the appropriate format and delivered from the server to the user's terminal.

[0693] Users can download or stream animations on their devices, making it possible to watch them with children.

[0694] Specific example

[0695] For example, if a child inputs the action description "I had a fight with a friend," the system will generate a scenario themed around "communication" and "empathy." The animation will include scenes of understanding and reconciliation between friends, structured so that the child can learn from it.

[0696] This invention is configured in this way and is a system that assists parents and educators in easily conducting moral education at home.

[0697] The following describes the processing flow.

[0698] Step 1:

[0699] Users access the application using their device's interface and input educational content and behaviors related to their children. The input is in text format and includes specific situations and episodes.

[0700] Step 2:

[0701] The terminal sends the entered information to the server. A communication protocol is used to securely transfer the information and ensure that the data reaches the server accurately.

[0702] Step 3:

[0703] The server receives input data via a receiving device and stores it in the database.

[0704] Step 4:

[0705] The server uses specific methods and natural language processing techniques to extract specific keywords and themes from the input text. This allows for the development of concrete directions for moral education.

[0706] Step 5:

[0707] The server consults a database of educational themes and determines an appropriate moral theme based on extracted keywords. This process refers to a predefined set of themes.

[0708] Step 6:

[0709] Using a scenario generation mechanism, the server generates scenarios based on a specified theme. These scenarios include story development, character dialogue, and learning focus points.

[0710] Step 7:

[0711] Using the generation method, the server generates animations based on the scenario. This process includes character design, animation sequence setup, and the addition of voice and sound effects.

[0712] Step 8:

[0713] The server renders the generated animation into the appropriate video format and prepares it as a viewable file.

[0714] Step 9:

[0715] Using a distribution method, the server sends the completed animation file to the user's terminal.

[0716] Step 10:

[0717] The user plays the animation received on their device and watches it together with their child. The viewing experience supports the child in gaining moral lessons.

[0718] (Example 1)

[0719] 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".

[0720] In traditional home education, it was often burdensome for parents and educators to directly provide moral education to children, and selecting appropriate educational content was difficult. Furthermore, there were limited means of conveying educational content in a format that children could easily engage with, thus creating a need for a system that would promote efficient learning.

[0721] 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.

[0722] In this invention, the server includes means for receiving input information, means for analyzing the input information to identify educational themes, means for generating scenarios, means for automatically generating visual representations, and means for transferring the generated visual representations to the user. This makes it possible for users to provide appropriate moral education to children and promote effective learning through engaging visual materials simply by providing input information.

[0723] "Means for receiving input information" refers to a system in which a server acquires information about educational content and actions provided by users using their devices via a network.

[0724] "Methods for analyzing input information to identify educational themes" refers to the process of analyzing received information using language analysis technology and selecting themes suitable for moral education from that analysis.

[0725] "Means for generating scenarios" refers to a function that uses AI technology to create the structure of an educational story based on a specific educational theme.

[0726] "Means for automatically generating visual representations" refer to tools and processes that enable the construction of visual elements such as character movements and backgrounds based on generated scenarios.

[0727] "Means for transferring generated visual representations to users" refers to technologies for encoding completed visual teaching materials in an appropriate format and transmitting them in a way that users can access.

[0728] This invention is a system for supporting moral education at home, and in particular, provides educational content to children by generating animations. Specific embodiments are shown below.

[0729] Users access this system using a device. First, users input specific text about the educational content they want to teach their child and their child's recent behavior through a particular application. This input information is sent to the server via the device.

[0730] The server stores the received text data and performs analysis using natural language processing techniques. This analysis utilizes libraries such as Python's NLTK and spaCy. Keywords are extracted from the analysis results and compared with predefined libraries within the system to identify the most suitable educational themes.

[0731] The server then dynamically generates scenarios based on the identified theme using a generative AI model (for example, a general natural language generation model). By inputting prompt sentences into the AI ​​model, an educational story is constructed. Examples of prompt sentences used at this time include "My child has been fighting with friends a lot lately. I want to create an animation that teaches about this," or "My child doesn't clean up, so I want to teach them about tidiness."

[0732] Subsequently, based on the scenario, the server generates a visual representation. This process involves combining character designs, backgrounds, and audio data using animation production tools such as Blender. Once the visual representation is complete, it is encoded into the appropriate video format and delivered from the server to the user's terminal.

[0733] Users can watch this animation with their children through an application on their device. This system allows parents and educators to provide effective and engaging moral education to children.

[0734] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0735] Step 1:

[0736] Users log in to the application via their device and enter information about the educational content they wish to teach and their child's behavior. This input includes text data describing specific situations and educational objectives. The entered data is sent to the server when the submit button is pressed.

[0737] Step 2:

[0738] The server receives text data sent from the terminal and stores it in a database. Next, the server analyzes the text using natural language processing techniques. In this analysis process, libraries such as NLTK and spaCy are used to extract keywords and theme-related features from the text. As a result, educational themes are identified.

[0739] Step 3:

[0740] The server generates scenarios using AI technology based on identified educational themes. The generative AI model used here automatically constructs a story based on prompts for the aforementioned keywords. The scenarios include character actions, dialogues, and educational points, which form the basis for subsequent visual representation generation.

[0741] Step 4:

[0742] The server creates animations based on the generated scenarios. This process uses animation tools such as Blender to model characters, set up backgrounds, and add audio data. Finally, the animation as a visual representation is completed, which is then encoded into a format for the user.

[0743] Step 5:

[0744] The server delivers the completed animation to the user's device. Using technologies such as HTTP streaming, users can instantly view the animation on their device. This allows users to share educational content with their children in real time and facilitate learning.

[0745] (Application Example 1)

[0746] 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".

[0747] Moral education within the home is a time-consuming task for parents and educators, and it presents challenges in maintaining children's interest. Furthermore, there is a need for effective educational methods that respond to children's specific behaviors. Therefore, there is a demand for a system that easily supports moral education within the home.

[0748] 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.

[0749] In this invention, the server includes a device for receiving educational information input by a user, a device for identifying a subject to be taught based on the educational information, a device for generating content based on the identified subject, and a device for automatically generating viewable motion images. This makes it possible for parents and educators to easily conduct moral education tailored to children's behavior within the home.

[0750] A "user" is the entity that uses the system to input educational information and receive motion images.

[0751] "Educational information" refers to the fundamental information that users provide to the system for conducting education.

[0752] A "server" is a computer device that receives input information from users and performs analysis processing and generates motion images.

[0753] "Device" refers to hardware or software used to perform various processes within a server.

[0754] A "subject" is a specific subject or theme of moral education, identified based on educational information.

[0755] "Content" refers to educational scenarios or stories generated based on a specific theme.

[0756] "Viewable motion images" refer to animations and video expressions that are automatically created based on the generated scenario.

[0757] To implement this invention, a system combining a smartphone and a server is used. The user inputs educational information via the smartphone, and this information is transferred to the server. The server receives the educational information and uses natural language processing technology to identify the topics to be covered based on its content.

[0758] The server can use Python and TensorFlow to extract important keywords from received information and select appropriate educational topics. Based on the selected topics, the server generates scenarios and then generates animated images based on those scenarios. In generating the animated images, elements such as character design, background, and sound are automatically integrated using AI technology to form viewable animations.

[0759] The generated animations are converted to the appropriate format and delivered from the server to the user's smartphone. This allows parents and educators to utilize video content to support children's moral education from the comfort of their homes.

[0760] For example, if a user enters the information "My child doesn't clean up," the server identifies the theme "tidying up" and generates a scenario. Then, a corresponding animation is created and sent to the user's smartphone. An example of a prompt is as follows: "My child says they want to do XX. Based on this, please select a relevant moral theme and generate a scenario and animation that will allow the child to learn in a fun way."

[0761] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0762] Step 1:

[0763] Users input educational information using a terminal. The information entered includes text data such as specific behaviors of children and educational content. This data is sent from the terminal to the server and used as basic data to initiate processing.

[0764] Step 2:

[0765] The server analyzes the received educational information. Here, natural language processing techniques are used to extract important keywords from the input text data. The input is text data, and the output is a set of keywords for theme selection. Specifically, a machine learning model using Python performs the text analysis.

[0766] Step 3:

[0767] The server identifies appropriate educational themes based on the extracted keywords. The selected themes are then used for scenario and animation generation. The input is the keywords extracted in step 2, and the output is the selected educational themes. In operation, a theme selection algorithm evaluates the set of keywords.

[0768] Step 4:

[0769] The server generates scenarios based on specified educational themes. These scenarios include a story, character actions, and dialogues, all based on the educational themes. The input is a selected educational theme, and the output is specific scenario data. Natural language generation technology is used for this operation.

[0770] Step 5:

[0771] The server generates action images based on the generated scenario. Here, AI technology is used for character design, background placement, and voice addition. The input is scenario data, and the output is viewable animation data. Specifically, a model using TensorFlow performs the animation generation.

[0772] Step 6:

[0773] The server converts the generated animation into the appropriate format and delivers it to the user's device. At this point, the user is ready to view the animation on their device. The input is the animation data, and the output is delivery to the user. The operation involves file format conversion and data transmission over the network.

[0774] 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.

[0775] This invention is a moral education animation generation system that takes user emotions into consideration. Designed to support children's moral education in the home environment, this system incorporates emotion recognition based on user input to provide a more personalized educational experience.

[0776] Main process

[0777] 1. User input and sentiment recognition

[0778] Users input educational content and behaviors related to their children through an application on their device. The text entered here also serves as data for emotion recognition.

[0779] Once input is complete on the device, the data is sent to the server, where an emotion engine runs to perform sentiment analysis on the input.

[0780] 2. Data Analysis and Theme Identification

[0781] The server analyzes the received data to extract emotional keywords and identify the emotions the user is feeling.

[0782] This emotional information assists in the selection process of educational themes for specific methods and serves as material for determining the most appropriate moral education themes.

[0783] 3. Scenario generation and animation settings

[0784] The server constructs a detailed story using scenario generation tools, based on educational themes and recognized emotions. In this process, recognized emotions are reflected in the characters' actions and dialogue, resulting in content that resonates more with the audience.

[0785] Through a generation process, elements such as characters, backgrounds, and sounds within the animation are set, and emotional elements are included.

[0786] 4. Animation generation and distribution

[0787] The final animation is generated on the server and rendered in a viewable format. This video is then delivered from the server to the user's device, making it accessible to children.

[0788] Specific example

[0789] For example, if a child expresses sadness in response to having a fight with a friend, the system will select the theme of "communication" and generate an animation that reflects the feeling of sadness. Specifically, the animation will depict a scene where the character reflects on the fight and tries to reconcile with their friend, providing viewers with empathy and moral lessons.

[0790] Thus, the present invention enables the generation of animations that reflect the user's emotions, providing more effective moral education for children.

[0791] The following describes the processing flow.

[0792] Step 1:

[0793] The user opens the application on their device and enters information about educational themes and recent activities related to their child. They describe specific actions or situations in the text box and press the submit button.

[0794] Step 2:

[0795] The terminal sends the input content to the server via a secure communication protocol. During this process, a transmission completion message is displayed to the user to confirm that the data has been sent correctly.

[0796] Step 3:

[0797] The server receives the input data through a receiving mechanism. The received data is temporarily stored in a database.

[0798] Step 4:

[0799] The emotion engine on the server analyzes the received data to recognize the user's emotions. Using natural language processing algorithms, it extracts emotion keywords from words and phrases and identifies emotion categories.

[0800] Step 5:

[0801] The server uses specific methods to determine educational themes based on recognized emotions. In this process, emotional information is used as a reference for theme selection, and more appropriate moral themes are identified.

[0802] Step 6:

[0803] Using a scenario generation method, the server generates scenarios that align with the theme and emotions. Dialogues and character actions that reflect these emotions are meticulously designed.

[0804] Step 7:

[0805] The server uses a generation mechanism to automatically generate animations from the scenario. Here, character designs, backgrounds, and voices are set, and emotional elements are reflected in the animation.

[0806] Step 8:

[0807] The server checks the quality of the completed animation and renders it into a viewable file format. This file is then optimized for smooth playback.

[0808] Step 9:

[0809] The server sends the animation file to the user's device via the distribution method. The device saves the received video in a format that allows it to be streamed or downloaded.

[0810] Step 10:

[0811] Users use their devices to play the received animations. They watch them with their children and use them as an opportunity to gain moral learning.

[0812] (Example 2)

[0813] 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".

[0814] In moral education for children within the home environment, it is difficult to provide educational content that is tailored to the individual feelings and circumstances of each child. Traditional educational methods primarily consist of standardized content and fail to provide educational experiences that fully reflect children's emotions. Furthermore, there were challenges in understanding children's emotions and generating animations that respond accordingly.

[0815] 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.

[0816] In this invention, the server includes means for receiving educational information or activities entered by a user, means for analysis using natural language processing technology to perform sentiment analysis, means for identifying themes for moral education based on the sentiment analysis results, means for generating a story, means for automatically generating video, and means for distributing it to the receiving side. This makes it possible to generate moral education animations that correspond to individual emotions and provide children with a personalized educational experience.

[0817] "Means for receiving educational information or activities entered by users" refers to a mechanism for a server to receive education-related data and activity content provided by users through their devices.

[0818] "An analysis method using natural language processing technology for sentiment analysis" refers to a function that has the ability to analyze the user's emotional state from received text data and extract necessary emotional information.

[0819] "A method for identifying moral education themes based on emotion analysis results" refers to the process of selecting appropriate moral education themes based on analyzed emotional information.

[0820] "A means of generating a story" is a function that combines selected themes and emotional information to construct a narrative that includes the actions and dialogues of the characters.

[0821] "Methods for automatically generating video" refers to the process of creating animations consisting of characters, backgrounds, sound, etc., based on a constructed storyline.

[0822] "Means of distribution to the receiving end" refers to a distribution mechanism that delivers the generated video to the user's device and makes it viewable.

[0823] This system aims to generate moral education animations that respond to the user's emotions. The system is primarily implemented using the following hardware and software.

[0824] Users input educational information and behavioral data about their children as text via their devices. A user interface implemented on the device is used for this input. The entered text data is sent to a server via the internet. Common digital communication technologies such as the HTTP protocol are typically used for communication.

[0825] The server analyzes the received user text data using an emotion recognition engine that implements natural language processing (NLP) technology. This engine is built using commonly used NLP libraries and frameworks. Through this analysis, emotion keywords are extracted from the text entered by the user.

[0826] The extracted emotional keywords are stored in the server's database. Based on this emotional information, the server selects appropriate moral education themes. This process utilizes a generative AI model related to the educational content.

[0827] The server generates a detailed story based on the selected educational theme and emotional information. The story generation utilizes a generation AI model with prompts. A specific example of a prompt is: "A child is feeling sad because they had a fight with a friend. Please generate an animation with a communication theme."

[0828] Once the story is generated, the server runs a program to create the animation. This uses 3DCG software and a speech synthesis engine, among other things. The generated animation is rendered on the server and finally delivered to the user's terminal via a digital streaming protocol.

[0829] In this way, users can provide children with personalized moral education animations tailored to their emotions. This system promotes the personalization of education and deepens children's understanding and empathy.

[0830] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0831] Step 1:

[0832] Users use their devices to input and submit educational information and data about their children's behavior. The information entered by the user is received as text data by the device's application. This data serves as input for sentiment analysis. The device uses the HTTP protocol to send this input data to the server over the internet.

[0833] Step 2:

[0834] The server receives the text data and activates an emotion recognition engine using natural language processing technology. The engine analyzes the text content and extracts emotion keywords. Based on this analysis, emotions such as "sadness" or "joy" are output from the user's input text. The analysis results are recorded in the server's database.

[0835] Step 3:

[0836] The server initiates a process to identify moral education themes based on emotional keywords recorded in the database. In this process, the aforementioned emotional keywords serve as input, and an appropriate theme is selected as output. The selected educational theme is then passed on to the story generation process.

[0837] Step 4:

[0838] The server uses a generative AI model to construct a story based on the selected theme and emotional information. Here, the AI ​​model outputs a specific story in response to a prompt. For example, a prompt such as, "A child is feeling sad because they had a fight with a friend. Please generate an animation with a communication theme," will generate a detailed story.

[0839] Step 5:

[0840] The server performs a process to automatically generate animation based on the generated story. Elements such as character movements and backgrounds are set. In this process, the story is the input and visual animation data is output. 3DCG software and speech synthesis tools are used to capture character designs and voices.

[0841] Step 6:

[0842] The server renders the final animation and converts it into a viewable video format. The rendered video is delivered from the server to the device, allowing the user to receive moral education through the video. The video is transmitted and played on the device using digital streaming technology.

[0843] (Application Example 2)

[0844] 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".

[0845] A challenge in providing moral education within the home is ensuring a personalized educational experience that appropriately considers children's emotions. Traditional educational methods often fail to adequately incorporate emotionally-based personalization, limiting the educational effectiveness for children.

[0846] 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.

[0847] In this invention, the server includes receiving means for receiving educational content or actions input by the user, identifying means for recognizing emotions and identifying themes for moral education, and scenario generating means for generating scenarios based on the themes and emotional information. This makes it possible to generate personalized moral education animations that respond to the child's emotions.

[0848] A "receiving means" is a device or system that receives input data from a user and incorporates educational content and actions into the server as an initial step in processing.

[0849] "Specific means" refers to a method or process for selecting appropriate themes for moral education by using natural language processing technology to recognize emotions and keywords based on received educational content and actions.

[0850] A "scenario generation method" is a system or program for creating detailed educational stories based on a specified theme and recognized emotional information.

[0851] "Generation means" refers to the process of setting animation elements, including character designs, backgrounds, and voice acting, according to the story created by the scenario generation means, and automatically generating animation.

[0852] "Distribution means" refers to communication technologies and software that transmit the generated animation to the user's device and make it viewable or playable.

[0853] This invention relates to a system that generates personalized educational animations based on a child's emotions in order to support moral education within the home. The system consists of input performed on the user's terminal, processing performed on a server, and distribution means for the child to view.

[0854] The user first inputs information about their child's educational content and behavior through their device. This input information is sent to the server as text data. The server receives this information, uses natural language processing techniques to recognize emotions, and extracts keywords. Specifically, the Python libraries NLTK and Transformers can be used for this purpose. Based on the recognized emotions and keywords, educational themes are identified.

[0855] Next, the server generates a detailed story based on the recognized emotional information and themes. This process uses a generative AI model, specifically AI-powered scenario generation. Subsequently, the server generates story-based animations using animation software such as Unity or Blender. The generated animations are converted to the appropriate format and delivered to the terminal.

[0856] For example, if a child expresses frustration because they are struggling with their homework, the server recognizes this as a theme of "overcoming difficulties" and generates an animation that reflects this emotion. The animation depicts a scene where the character faces a challenge and ultimately succeeds. This allows children to learn emotionally through a relatable story.

[0857] An example of a prompt given to a generative AI model might be, "If a child is feeling frustrated, how should the animation depict how to overcome difficulties?" In this way, the system can consider the user's emotions and provide more effective moral education animations.

[0858] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0859] Step 1:

[0860] The terminal receives user input. The user enters information about educational content and the child's behavior and emotions into the terminal in text format. The entered data becomes the initial data to be sent to the server via the terminal's input device.

[0861] Step 2:

[0862] The server receives data transmitted from the terminal using a receiving device. The received data is used as raw material for analyzing user emotions and educational content.

[0863] Step 3:

[0864] The server uses specific methods to perform natural language processing on the received data. Specifically, it uses the "Transformers" library in "Python" to analyze the structure of sentences and extract sentiment keywords. The input is the user's text data, and the output is the extracted sentiments and keywords.

[0865] Step 4:

[0866] The server selects a theme using specific means based on the emotions and keywords obtained. The theme selected here forms the basis for the next scenario generation. The input is the extracted results, and the output is a theme suitable for moral education.

[0867] Step 5:

[0868] The server generates scenarios using a generative AI model. Taking a theme selected by specific means and recognized emotional information as input, the generative AI model forms the framework of the story. The output is a specific story plot.

[0869] Step 6:

[0870] The server uses a generation mechanism to create animations based on the generated story plot. Unity and Blender are used to design characters and backgrounds and render the animations. The input is the story plot idea, and the output is a digital file of the animation.

[0871] Step 7:

[0872] The server sends the final generated animation to the terminal via the distribution method. At this point, the animation is ready to be displayed on the terminal. The input is the generated animation data, and the output is the display output for the child to view.

[0873] 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.

[0874] 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.

[0875] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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."

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] The following is further disclosed regarding the embodiments described above.

[0895] (Claim 1)

[0896] A receiving means for receiving educational content or actions entered by the user,

[0897] A means of identifying themes for moral education based on the aforementioned educational content or behavior,

[0898] A scenario generation means for generating a scenario based on the aforementioned theme,

[0899] Based on the aforementioned scenario, a generation means for automatically generating animations,

[0900] A distribution means for delivering the generated animation to the user,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, wherein the identifying means extracts keywords using natural language processing technology.

[0904] (Claim 3)

[0905] The system according to claim 1, wherein the generation means sets animation elements including character designs, backgrounds, and sound.

[0906] "Example 1"

[0907] (Claim 1)

[0908] A means for receiving input information,

[0909] A means for analyzing the aforementioned input information to identify an educational theme,

[0910] A means for generating a scenario based on the aforementioned educational theme,

[0911] Based on the above scenario, means for automatically generating a visual representation,

[0912] Means for transferring the generated visual representation to the user,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, wherein the analysis means extracts keywords using language analysis technology.

[0916] (Claim 3)

[0917] The system according to claim 1, wherein the visual representation generation means sets animation components including character placement, background settings, and audio data.

[0918] "Application Example 1"

[0919] (Claim 1)

[0920] A device that receives educational information entered by the user,

[0921] A device for identifying the subject of education based on the aforementioned educational information,

[0922] A device for generating content based on the aforementioned subject,

[0923] Based on the above, a device that automatically generates viewable motion images,

[0924] A device for transmitting the generated motion image to the user,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, wherein the device used to identify important words extracts important phrases using natural language processing.

[0928] (Claim 3)

[0929] The system according to claim 1, wherein the automatically generating device sets elements including character designs, backgrounds, and sound for the motion image.

[0930] "Example 2 of combining an emotion engine"

[0931] (Claim 1)

[0932] Means for receiving educational information or activities entered by the user,

[0933] An analysis means using natural language processing technology to perform sentiment analysis based on the aforementioned educational information or activities,

[0934] A means of identifying themes for moral education based on the results of the aforementioned sentiment analysis,

[0935] A means for generating a story based on the aforementioned theme and sentiment analysis results,

[0936] A means for automatically generating video based on the story generated above,

[0937] A means for distributing the generated video to the receiving side,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, wherein the analysis means extracts emotional keywords using emotion recognition technology.

[0941] (Claim 3)

[0942] The system according to claim 1, wherein the automatic generation means sets the character designs and backgrounds, and video elements including sound.

[0943] "Application example 2 when combining with an emotional engine"

[0944] (Claim 1)

[0945] A receiving means for receiving educational content or actions entered by the user,

[0946] A means for recognizing emotions and identifying themes for moral education based on the aforementioned educational content or actions,

[0947] A scenario generation means that generates a scenario based on the aforementioned theme and emotional information,

[0948] Based on the aforementioned scenario, a generation means for automatically generating animations,

[0949] A distribution means for delivering and displaying the generated animation to a terminal,

[0950] A system that includes this.

[0951] (Claim 2)

[0952] The system according to claim 1, wherein the identifying means extracts emotions and keywords using natural language processing technology.

[0953] (Claim 3)

[0954] The system according to claim 1, wherein the generation means sets animation elements including character design, background, and voice according to emotion. [Explanation of Symbols]

[0955] 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 device that receives educational information entered by the user, A device for identifying the subject of education based on the aforementioned educational information, A device for generating content based on the aforementioned subject, Based on the above, a device that automatically generates viewable motion images, A device for transmitting the generated motion image to the user, A system that includes this.

2. The system according to claim 1, wherein the device used to identify important words extracts important phrases using natural language processing.

3. The system according to claim 1, wherein the automatically generating device sets elements including character designs, backgrounds, and sound for the motion image.

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