System

Abstract

A system is provided.SOLUTION: This system is provided with a generation means for generating a new question, an analysis means for analyzing multimedia data transmitted from a terminal and generating contents for education, a disclosure means for receiving the game for education prepared by a user from the terminal and disclosing it to a store and a profit means for monetizing the use of the contents for education and the game.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Existing educational applications have limited means to capture children's attention, making it difficult to maintain their motivation to learn. They also lack tools and monetization mechanisms to bring out children's creativity. The present invention aims to solve these problems and provide an educational system that continuously increases children's motivation to learn and stimulates their creativity. [Means for solving the problem]

[0005] The present invention solves the problems by the following means. First, a generation means is provided that generates new questions and automatically generates new questions periodically. Next, an analysis means is provided that analyzes multimedia data sent from the terminal and generates educational content. Furthermore, by incorporating a publishing means that receives educational games created by users from the terminal and publishes them in a store, children's creativity can be brought out. Furthermore, by including a revenue means that monetizes the use of educational content and games, user motivation is increased. By combining these means, a system is provided that allows children to continue learning in a fun way.

[0006] "Generation means" refers to technologies and modules for automatically generating new questions and content.

[0007] The "analysis means" refers to a technology or module that analyzes multimedia data sent from a terminal and generates educational content based on that data.

[0008] "Publication means" refers to the technology or module that receives educational games created by users from their devices and publishes them in the store.

[0009] "Monetization methods" are mechanisms and technologies that generate revenue for the use of educational content and games.

[0010] "Questions" are in the form of quizzes or exercises provided for educational purposes to enhance learning.

[0011] "Educational content" means multimedia materials such as text, images, audio and video that are provided for learning or teaching purposes.

[0012] A "game" is an interactive application designed to help users learn while having fun.

[0013] The "Store" is a virtual marketplace for downloading user-published educational content and games.

[0014] "Terminal" refers to a portable device such as a smartphone or tablet. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0036] System Overview

[0037] This invention is an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones and tablets), and users (children).

[0038] Key Features

[0039] 1. Automatic generation of new poop drill questions

[0040] The server periodically generates new poop drill questions based on a specific schedule using a generation means, which uses an existing database and a machine learning algorithm. The generated drill questions are stored on the server.

[0041] The device periodically accesses the server and downloads new poop drill questions. These downloaded drill questions are displayed to the user and used for learning.

[0042] 2. Story Generation from Photos

[0043] The user takes a photo using the terminal. For example, the user can use a photo taken at a zoo or a landscape photo taken at a park.

[0044] The device uploads the captured photo to a server, which then analyzes the photo using analytical means to generate related poop stories and drill questions. This analysis uses image recognition and natural language generation technologies.

[0045] The generated stories and drill questions are stored on the server and delivered to the device the next time the app is launched, where the user can view and study them.

[0046] 3. Create and publish an original poop game

[0047] Users can use the in-app creator tools to create their own poop games, such as a "poop jumping game" or a "poop maze game."

[0048] The device collects the configuration information entered by the user and generates the game logic, which the user can then preview and modify as necessary.

[0049] The completed game data is uploaded to a server and published to the store using a publishing method. Games published in the store can be downloaded and played by other users.

[0050] 4. Monetize your content

[0051] The server records the number of downloads of games and exercises published in the store and generates revenue based on that. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[0052] Specific examples

[0053] 1. Creation and distribution of poop drill questions

[0054] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?"

[0055] When the device starts the app, it retrieves new poop drill questions from the server and notifies the user, "There are new poop questions today!"

[0056] 2. Story Generation from Photos

[0057] Users upload photos of elephants taken at the zoo to the app, and the server analyzes the photos and generates an "adventure story of elephants and poop."

[0058] The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of the elephant and the poop has begun."

[0059] 3. Create and publish an original poop game

[0060] The user creates a "Poo Soccer Game" using the creator tool. The device receives the settings and builds the game logic.

[0061] Once the game is complete, upload it to the server and publish it to the store. If your "Poo Soccer Game" becomes popular on the store, revenue will be reflected in your account every time other users download it.

[0062] Through these functions, the present invention is a system that enhances children's motivation to learn and stimulates their creativity.

[0063] The processing flow will be explained below.

[0064] Automatic generation of new poop drill questions

[0065] Processing Steps

[0066] Step 1:

[0067] The server runs a scheduled task every Monday at 3:00 AM, calling the Google Gemini API using the generated method.

[0068] Step 2:

[0069] The server analyzes the data received from Google Gemini and generates new poop drill questions.

[0070] Step 3:

[0071] The server stores the generated drill questions in the "unco_drills" database.

[0072] Step 4:

[0073] When the app is launched or at a regular interval, the device sends a request to the server to check whether there are any new poop drill questions.

[0074] Step 5:

[0075] The server receives the request and sends response data including the new poop drill question.

[0076] Step 6:

[0077] The device receives the response data, stores the new poop drill question in the app, and notifies the user.

[0078] Story generation from photos

[0079] Processing Steps

[0080] Step 1:

[0081] A user takes a photo using a smartphone.

[0082] Step 2:

[0083] The photos taken by the device are saved in the app and the user is prompted to review and select the photos.

[0084] Step 3:

[0085] The user confirms and uploads the selected photos to the server.

[0086] Step 4:

[0087] The device sends the photo data to the server.

[0088] Step 5:

[0089] The server passes the received photo data to the image analysis module, which analyzes the characteristics of the photo.

[0090] Step 6:

[0091] The server calls the Google Gemini API based on the characteristics of the photo and generates a related poop story.

[0092] Step 7:

[0093] The server stores the generated story data in a "stories" database and associates it with a user.

[0094] Step 8:

[0095] The terminal receives notification that the story has been created and notifies the user that a new story is available.

[0096] Step 9:

[0097] Users follow the notification to open the app and view and learn the generated story.

[0098] Create and publish an original poop game

[0099] Processing Steps

[0100] Step 1:

[0101] Users open the game creator tools within the app and set the game's theme, characters, and rules.

[0102] Step 2:

[0103] The terminal collects the user's input and stores it as initial configuration data.

[0104] Step 3:

[0105] The device calls templates and preset game modules based on the configuration data and generates the game logic and visuals.

[0106] Step 4:

[0107] The terminal provides the user with a preview of the game and receives correction instructions if necessary.

[0108] Step 5:

[0109] The user checks the final preview and gives the command to publish the game.

[0110] Step 6:

[0111] The device uploads the final game data to the server.

[0112] Step 7:

[0113] The server stores the uploaded game data in the "unco_games" database and publishes it to the store.

[0114] Step 8:

[0115] The server periodically compiles the number of downloads and user ratings of published games.

[0116] Step 9:

[0117] When the server generates revenue, it reflects the information in the corresponding user account.

[0118] Step 10:

[0119] Users can view revenue information and download numbers on the dashboard.

[0120] Example 1

[0121] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] Conventional educational systems have the problem that it is difficult to provide personalized educational content to individual users. In addition, it is difficult to monetize the content created by users, which means that users' creativity and motivation to learn cannot be fully utilized.

[0123] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0124] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data sent from the terminal and automatically generating stories and questions using a generative AI model, and a publishing means for receiving educational games created by users from the terminal and publishing them in the store, thereby making it possible to provide personalized educational content to users and monetize the content created by users.

[0125] A "generation means" is a technology or system for generating new questions and educational content periodically or as needed.

[0126] "Analysis means" refers to a technology or system that analyzes multimedia data sent from a terminal and automatically generates stories or questions using a generative AI model.

[0127] "Publication means" refers to a technology or system for receiving educational games and content created by users from terminals and publishing them in the store.

[0128] "Monetization Measures" are technologies or systems that monetize the use of educational content and games and reflect that revenue information in users' accounts.

[0129] A "generative AI model" is an artificial intelligence model that generates novel problems or stories based on a given prompt.

[0130] A "prompt" is a sentence or text that instructs a generative AI model to generate a novel problem or story.

[0131] "Server" means a computer system that functions as a central processing unit and executes the generating means, analyzing means, publishing means, and monetizing means.

[0132] "Terminal" refers to a device used by a user, such as a smartphone or tablet, that communicates with the server and displays and operates educational content and games.

[0133] System Overview

[0134] This invention is an educational application system designed to enhance users' (children's) motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones and tablets), and users.

[0135] Main features

[0136] 1. Automatic generation of new poop drill questions

[0137] The server generates new poop drill questions based on a specific schedule. This generation is done using a program written in Python and machine learning libraries such as TensorFlow. For example, to notify the user, "There's a new poop question today!", the new questions are saved on the server and distributed to the device.

[0138] As a concrete example, by inputting the prompt "Generate a unique question related to poop" into the generative AI model, a new poop drill question is generated.

[0139] 2. Story Generation from Photos

[0140] Users take photos using their devices and upload them to the server through the app. For example, you could use a photo of an elephant taken at the zoo.

[0141] The server analyzes the uploaded photos using image recognition models in OpenCV and TensorFlow and generates a related story. The story is generated by inputting a prompt such as "Write an essay about the adventures of an elephant and its poop" into the generative AI model.

[0142] The generated story is stored on the server and delivered to the device the next time the app is launched, providing users with stories such as "The Great Adventure of an Elephant and a Poop."

[0143] 3. Create and publish an original poop game

[0144] Users can use the creator tool to create their own original poop games. For example, they can create a "poop soccer game."

[0145] The device collects the user's input settings and generates game logic using game development frameworks such as JavaScript and Unity. The device then uses a preview function to allow users to check the game's operation and make any necessary adjustments.

[0146] The completed game is uploaded to the server and published in the store, where other users can download and play it.

[0147] 4. Monetize your content

[0148] The server records the number of downloads of games and exercises published in the store and generates revenue. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[0149] Specific example explanation

[0150] When generating a new poop drill question, the prompt text used is "Generate a unique question about poop."

[0151] In generating a story from a photo, the prompt used is "Write about the adventures of an elephant and its poop."

[0152] To create an original poop game, you will use the creator tool to set the rules and stage details of the "poop soccer game."

[0153] Through these functions, the system aims to increase users' motivation to learn and stimulate their creativity.

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] 1. Automatic generation of new poop drill questions

[0156] Step 1: Prepare for question generation

[0157] The server creates a prompt to generate new drill questions once a week on a specific schedule (such as midnight on Sundays). The prompt includes the instruction "Please generate a unique question about poop."

[0158] Step 2: Generate questions using a generative AI model

[0159] The server inputs the prompt sentence into the generative AI model and generates a new poop drill problem using an AI model (such as GPT). The input is the prompt sentence, and the output is a new problem sentence. Specifically, it calls the AI ​​model using Python and TensorFlow libraries.

[0160] Step 3: Save the question

[0161] The server saves the generated Poop Drill questions in a database (e.g., MySQL). This database stores the questions and their metadata (e.g., creation date) in JSON format.

[0162] Step 4: Distributing the problem

[0163] The device accesses the server to download new questions when the app starts or periodically. The new questions retrieved from the server are stored in a local SQLite database, and a notification of the new question is displayed to the user. The notification reads, "There's a new poop question for today!"

[0164] 2. Story Generation from Photos

[0165] Step 1: Take and upload a photo

[0166] A user takes a photo at a zoo or park, for example, a photo of an elephant, and uses the app's "upload photo" function to send the photo from their device to the server. The input is the captured JPEG image file.

[0167] Step 2: Photo Analysis

[0168] The server processes the received files using analytics. It uses image recognition models from OpenCV and TensorFlow to analyze the photos. The input is a JPEG image file, and the output is a label for the photo (e.g., "This is a photo of an elephant").

[0169] Step 3: Narrative generation

[0170] The server generates a prompt based on the analysis results. For example, it generates a sentence such as "Write an essay about the adventures of an elephant and a poop." This prompt is input into a generative AI model to generate a story. The input is the prompt, and the output is the text of the generated story.

[0171] Step 4: Save and share your story

[0172] The server stores the generated story in a database. Then, when the app is launched, the story is delivered to the device. The device displays a notification to the user about the new story, saying, "There is a new elephant and poop adventure story."

[0173] 3. Create and publish an original poop game

[0174] Step 1: Create a game

[0175] Users use the creator tool to create their own original poop game. Users input setting information (e.g., game rules, characters, and stage details) into their device.

[0176] Step 2: Generate the game logic

[0177] The device collects user input information and generates game logic using game development frameworks such as JavaScript and Unity. The input is the user's configuration information, and the output is the game's executable file. Specifically, it provides a preview of the game based on the configuration information, allowing the user to check and modify its behavior.

[0178] Step 3: Upload your game

[0179] The device uploads the completed game data to the server. The input is the game executable file, and the output is the game information that is posted on the store on the server.

[0180] Step 4: Publish your game

[0181] The server uses a publishing method to publish the uploaded game to the store, where other users can download and play the game.

[0182] 4. Monetize your content

[0183] Step 1: Record the number of downloads

[0184] The server records the number of downloads of games and exercises published in the store, specifically by recording them in a database using a download tracking system.

[0185] Step 2: Generate revenue information

[0186] The server calculates revenue based on the number of downloads and reflects it in the user's account. A Python script is used to calculate and update revenue information and display it on the dashboard. The input is the number of downloads data and the output is the updated revenue information.

[0187] (Application example 1)

[0188] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0189] Conventional educational application systems often contained monotonous content, which did not adequately stimulate children's motivation to learn or their creativity. They also lacked a means for users to easily share and monetize the content they created. Another issue was the difficulty of smoothly purchasing and using content in virtual shops.

[0190] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0191] In this invention, the server includes: a means for users to purchase and use educational content and games within the virtual store; a generation means for generating new questions; an analysis means for analyzing multimedia data sent from the terminal and generating educational content; a publishing means for receiving educational games created by users from the terminal and publishing them in the virtual store; a revenue generating means for monetizing the use of educational content and games; and a means for generating and providing stories based on multimedia data uploaded by users from the terminal. This increases children's motivation to learn, stimulates their creativity, and makes it easier to share and monetize content created by users. Furthermore, the purchase and use of content within the virtual store can be smoothly performed.

[0192] 1. "Virtual Store" means a virtual store that users can access via the Internet, where they can purchase and use educational content and games.

[0193] 2. "Educational content" refers to learning materials and questions, as well as stories and drills based on them, that are provided primarily to increase children's motivation to learn and stimulate their creativity.

[0194] 3. "Generation tools" are technologies and methods for generating new educational content and questions using existing databases and machine learning algorithms.

[0195] 4. "Multimedia data" refers to data in multiple media formats, including photos, videos, and audio data, that is transmitted by a user using a terminal.

[0196] 5. "Analysis means" means a method using image recognition technology and natural language generation technology to analyze transmitted multimedia data and generate educational content based on that data.

[0197] 6. "Publication means" means the technology and method for receiving educational games created by users from a terminal and publishing them in a virtual store so that other users can view and download them.

[0198] 7. "Monetization Method" means the technology and methods used to generate revenue from the use of educational content and games and credit it to users' accounts.

[0199] 8. "Story generation means" refers to the technology and method for generating original stories based on multimedia data uploaded by users from their devices and providing them as educational content.

[0200] System Overview

[0201] This invention is an educational application system for enhancing children's motivation to learn and stimulating their creativity. The system involves a server, terminals, and users working together to provide a purchasing and usage experience within a virtual shopping mall.

[0202] Specific implementation methods for major functions

[0203] 1. Purchases and use within the virtual store

[0204] The server runs a virtual store that users can access using their smartphones, smart glasses, or head-mounted displays.

[0205] Users can explore, purchase and use educational content and games within the virtual store.

[0206] 2. Generating new problems

[0207] The server periodically generates new poop drill questions using existing databases and machine learning algorithms.

[0208] The generated questions are stored on a server and distributed to the device.

[0209] 3. Story Generation from Photos

[0210] The user takes a photo using the terminal and uploads it to the server.

[0211] The server analyzes the uploaded photos using image recognition and natural language generation techniques to generate an associated story.

[0212] The generated story will be delivered to the device the next time the app is launched.

[0213] 4. Creating and publishing original games

[0214] Users use the in-app creator tools to create educational games.

[0215] The terminal collects the configuration information entered by the user and generates the game logic.

[0216] The completed game data is uploaded to the server and made available in the virtual store.

[0217] 5. Monetize your content

[0218] The server records the number of downloads of the games and drill questions published in the virtual store and generates revenue based on the number of downloads.

[0219] Revenue information is reflected in the user's account and can be viewed on the dashboard.

[0220] Hardware and software used

[0221] Flask: Used to build web servers.

[0222] Werkzeug: For file processing.

[0223] Generative AI models: Machine learning models used for story generation and game generation.

[0224] Smartphone: Used as the primary interface device.

[0225] Smart glasses and head-mounted displays: Used to enhance the virtual shopping experience.

[0226] Specific examples

[0227] 1. Purchases in virtual stores

[0228] Users access a virtual store using their smartphone and purchase a specific set of poop drill problems. After purchase, the problem set is downloaded to their device and they can begin learning.

[0229] 2. Narrative Generation

[0230] The user uploads a photo of an elephant taken at the zoo to the app. The server analyzes the photo and generates an "adventure story of an elephant and its poop." This story is then sent to the device the next time the app is launched, and is provided as learning content.

[0231] 3. Example prompts

[0232] "Generate a short story about the following photo: A photo of an elephant"

[0233] "Generate poop drill questions for primary school education"

[0234] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0235] Step 1:

[0236] A user accesses the virtual store using a smartphone or head-mounted display. The input requires the user's authentication information and device ID. The output is the virtual store's home screen, which displays a list of available educational content and games.

[0237] Step 2:

[0238] A user selects and purchases educational content or games in a virtual store. The inputs include the selected content ID and payment information. The server receives this information and confirms the payment. The output is a message indicating the purchase has been completed and a download link is provided to the user.

[0239] Step 3:

[0240] The user clicks on a download link on their device to download the educational content or game they purchased. The input is the download link, and the server sends the corresponding content data. The output is that the content is saved on the device and ready for use.

[0241] Step 4:

[0242] The user takes a photo using the device's camera and uploads it to the server through a dedicated upload screen in the virtual shop. The input required is the photo data taken by the user and a description text associated with the photo. The output is a message indicating that the upload is complete.

[0243] Step 5:

[0244] The server receives the uploaded photos and analyzes them using a generative AI model. The inputs are the received photo data and a description text. The output is a generated story based on image recognition technology. The server stores this story.

[0245] Step 6:

[0246] Users create their own educational games using the app's creator tools. Game configuration information and user-created materials are required as input. The device collects this information and generates the game logic. The output is a preview of the game the user has designed.

[0247] Step 7:

[0248] The user completes the creation of a game and sends a request to the server to publish it. The input requires the completed game data and publishing instructions. The server receives this and processes the game to publish it in the virtual store. The output is a message indicating that publishing is complete, and the game becomes available for other users to download and use.

[0249] Step 8:

[0250] The server records the number of downloads of games and exercises published in the virtual store and generates revenue. The inputs are the number of downloads and a revenue calculation algorithm. The output is the generated revenue information reflected in the user's account, which can be viewed on the dashboard.

[0251] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0252] System Overview

[0253] This invention is an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, devices (smartphones and tablets), and users (children). In particular, by combining it with an emotion engine that recognizes the user's emotions, the system provides an educational experience tailored to each individual user.

[0254] Key Features

[0255] 1. Automatic generation of new poop drill questions

[0256] The server periodically generates new poop drill questions based on a specific schedule using a generation means, which uses an existing database and a machine learning algorithm. The generated drill questions are stored on the server.

[0257] The device periodically accesses the server and downloads new poop drill questions. These downloaded drill questions are displayed to the user and used for learning.

[0258] 2. Story Generation from Photos

[0259] The user takes a photo using the terminal. For example, the user can use a photo taken at a zoo or a landscape photo taken at a park.

[0260] The device uploads the captured photo to a server, which then analyzes the photo using analytical means to generate related poop stories and drill questions. This analysis uses image recognition and natural language generation technologies.

[0261] The generated stories and drill questions are stored on the server and delivered to the device the next time the app is launched, where the user can view and study them.

[0262] 3. Create and publish an original poop game

[0263] Users can use the in-app creator tools to create their own poop games, such as a "poop jumping game" or a "poop maze game."

[0264] The device collects the configuration information entered by the user and generates the game logic, which the user can then preview and modify as necessary.

[0265] The completed game data is uploaded to a server and published to the store using a publishing method. Games published in the store can be downloaded and played by other users.

[0266] 4. Monetize your content

[0267] The server records the number of downloads of games and exercises published in the store and generates revenue based on that. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[0268] 5. Leveraging Emotional Engines

[0269] The device collects emotional data from the user's facial expressions and voice and sends it to the emotion engine.

[0270] The emotion engine analyzes the collected data and evaluates the user's emotional state.

[0271] Based on the analysis results of the emotion engine, the server dynamically adjusts the educational content and game difficulty to suit the user.

[0272] The device provides feedback based on the user's emotions and delivers appropriate content.

[0273] Specific examples

[0274] 1. Creation and distribution of poop drill questions

[0275] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?"

[0276] When the device starts the app, it retrieves new poop drill questions from the server and notifies the user, "There are new poop questions today!"

[0277] 2. Story Generation from Photos

[0278] Users upload photos of elephants taken at the zoo to the app, and the server analyzes the photos and generates an "adventure story of elephants and poop."

[0279] The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of the elephant and the poop has begun."

[0280] 3. Create and publish an original poop game

[0281] The user creates a "Poo Soccer Game" using the creator tool. The device receives the settings and builds the game logic.

[0282] Once the game is complete, upload it to the server and publish it to the store. If your "Poo Soccer Game" becomes popular on the store, revenue will be reflected in your account every time other users download it.

[0283] 4. Utilizing the Emotion Engine

[0284] The device uses a camera and microphone to collect the user's facial expressions and tone of voice, and sends them to the emotion engine. For example, it determines whether the user is smiling or whether their tone of voice sounds happy.

[0285] The emotion engine sends the analysis results to the server, and if it determines that the user is enjoying the content, it adjusts the content to provide more challenging educational content.

[0286] The server dynamically adjusts the difficulty of the content and distributes it to the terminal, where it is displayed to the user.

[0287] Through these functions, the present invention is a system that enhances children's motivation to learn and stimulates their creativity. By introducing an emotion engine, we can provide an educational experience that is tailored to each individual user, creating a more efficient and enjoyable learning environment.

[0288] The processing flow will be explained below.

[0289] Automatic generation of new poop drill questions

[0290] Processing Steps

[0291] Step 1:

[0292] The server runs a scheduled task every Monday at 3:00 AM and calls the API using the generated method.

[0293] Step 2:

[0294] The server analyzes the data received from the API and generates new poop drill questions.

[0295] Step 3:

[0296] The server stores the generated drill questions in the "unco_drills" database.

[0297] Step 4:

[0298] When the app is launched or at a regular interval, the device sends a request to the server to check whether there are any new poop drill questions.

[0299] Step 5:

[0300] The server receives the request and sends response data including the new poop drill question.

[0301] Step 6:

[0302] The device receives the response data, stores the new poop drill question in the app, and notifies the user.

[0303] Story generation from photos

[0304] Processing Steps

[0305] Step 1:

[0306] A user takes a photo using a smartphone.

[0307] Step 2:

[0308] The photos taken by the device are saved in the app and the user is prompted to review and select the photos.

[0309] Step 3:

[0310] The user confirms and uploads the selected photos to the server.

[0311] Step 4:

[0312] The device sends the photo data to the server.

[0313] Step 5:

[0314] The server passes the received photo data to the image analysis module, which analyzes the characteristics of the photo.

[0315] Step 6:

[0316] The server calls the API based on the characteristics of the photo and generates a related poop story.

[0317] Step 7:

[0318] The server stores the generated story data in a "stories" database and associates it with a user.

[0319] Step 8:

[0320] The terminal receives notification that the story has been created and notifies the user that a new story is available.

[0321] Step 9:

[0322] Users follow the notification to open the app and view and learn the generated story.

[0323] Create and publish an original poop game

[0324] Processing Steps

[0325] Step 1:

[0326] Users open the game creator tools within the app and set the game's theme, characters, and rules.

[0327] Step 2:

[0328] The terminal collects the user's input and stores it as initial configuration data.

[0329] Step 3:

[0330] The device calls templates and preset game modules based on the configuration data and generates the game logic and visuals.

[0331] Step 4:

[0332] The terminal provides the user with a preview of the game and receives correction instructions if necessary.

[0333] Step 5:

[0334] The user checks the final preview and gives the command to publish the game.

[0335] Step 6:

[0336] The device uploads the final game data to the server.

[0337] Step 7:

[0338] The server stores the uploaded game data in the "unco_games" database and publishes it to the store.

[0339] Step 8:

[0340] The server periodically compiles the number of downloads and user ratings of published games.

[0341] Step 9:

[0342] When the server generates revenue, it reflects the information in the corresponding user account.

[0343] Step 10:

[0344] Users can view revenue information and download numbers on the dashboard.

[0345] Utilizing the Emotion Engine

[0346] Processing Steps

[0347] Step 1:

[0348] While a user is using educational content or playing games, the device uses a camera and microphone to record the user's facial expressions and voice.

[0349] Step 2:

[0350] The facial expression and voice data collected by the device is sent to the emotion engine.

[0351] Step 3:

[0352] The emotion engine analyzes the received data and assesses the user's emotional state (e.g., enjoying, concentrating, tired, etc.).

[0353] Step 4:

[0354] The emotion engine sends the analysis results to the server and reports the user's emotional state.

[0355] Step 5:

[0356] Based on the analysis results of the emotion engine, the server dynamically adjusts the difficulty and content of educational content and games according to the user's learning progress and stress level.

[0357] Step 6:

[0358] The device follows instructions from the server and displays tailored content or games to the user.

[0359] Step 7:

[0360] As the user continues to learn based on new content and games, the emotion engine continues to monitor the user's emotions.

[0361] Specific examples

[0362] Step 1:

[0363] While the user is solving drill problems in the app, the device records the user's facial expressions (camera) and voice (microphone).

[0364] Step 2:

[0365] The device sends this data to the emotion engine.

[0366] Step 3:

[0367] The emotion engine performs analysis and assesses whether the user is struggling or enjoying solving the problem.

[0368] Step 4:

[0369] The server receives the emotion engine's evaluation results, and if it determines that the user is struggling, it instructs the server to deliver questions with a slightly lower level of difficulty.

[0370] Step 5:

[0371] The device displays new (adjusted) questions to the user, and the user continues learning.

[0372] This specific processing step allows the present invention to provide an optimal educational experience for each individual user, taking into account the user's emotional state.

[0373] Example 2

[0374] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0375] Previous educational applications lacked the ability to generate new questions, publish user-generated content, monetize it, and personalize it based on user emotions. This made it difficult to continuously stimulate users' learning and provide an optimal educational experience for each individual user. Furthermore, there were challenges in the efficiency of automatic content generation and in stimulating user creativity.

[0376] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0377] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data transmitted from the terminal and generating educational content, a publishing means for receiving educational games created by users from the terminal and publishing them, a revenue generating means for monetizing the use of the educational content and games, and an adjusting means for analyzing the user's emotions and dynamically adjusting the difficulty level of the content, thereby making it possible to provide an individually optimized educational experience while increasing the user's motivation to learn.

[0378] "Generation means" refers to the technology or method for automatically generating educational content and questions.

[0379] "Analysis means" refers to techniques or methods for analyzing multimedia data sent from a terminal and generating educational content based on that data.

[0380] "Publishing Means" refers to the technology or method for receiving educational games or content created by users and publishing them on a platform where other users can access them.

[0381] "Monetization methods" are technologies or methods for monitoring the usage of educational content or games, generating revenue based on that data, and reflecting it in users' accounts.

[0382] The "adjustment means" refers to a technique or method for analyzing the user's emotional data and dynamically adjusting the difficulty level of the content based on the analysis results.

[0383] A "server" is a computer system that stores and processes data and manages the entire system while communicating with terminals.

[0384] A "terminal" is a device used by a user, such as a smartphone or tablet, that communicates with a server to display content and send and receive data.

[0385] "Educational content" refers to teaching materials and activities such as questions, stories, and games that are provided to help users learn.

[0386] "Emotional data" is data collected from the user's facial expressions, voice, etc., and is information used to evaluate the user's emotional state.

[0387] A "user" is an individual who uses this system to enjoy educational content.

[0388] The present invention relates to an educational application system for enhancing children's motivation to learn and stimulating their creativity. The system involves a server, terminals (such as smartphones and tablets), and users working together. This system is particularly distinctive in that it includes a generation unit, an analysis unit, a publishing unit, a revenue generating unit, and an adjustment unit.

[0389] generation means

[0390] The server generates new poop drill questions every week based on a specific schedule. Python scripts and machine learning algorithms (e.g., TensorFlow) are used to generate the questions. For example, the generated questions are in the form of "Today's Poop Question: What did the poop find in the park?" The generated poop drill questions are stored in a MySQL database.

[0391] Analysis means

[0392] The system generates educational content using photos taken by users on their devices. Users upload photos of elephants taken at the zoo to the server via their devices. The server analyzes the photos using the Google Cloud Vision API and generates stories and poop drill questions related to the photos. The analysis results are generated using natural language generation models such as GPT-4, and one example is the "Adventure Story of an Elephant and Poop." This generated content is stored in a MySQL database.

[0393] Publication method

[0394] Users create educational games using the app's creator tools. The device collects the user's input settings and generates the game logic using a game development engine such as Unity or GameMaker. The completed game is uploaded to a server and published on platforms such as the Google Play Store. For example, a "Poo Soccer Game" could be published.

[0395] revenue stream

[0396] The server records the number of downloads of games and poop drills published in the store and generates revenue information based on that data. The revenue is reflected in the user's account, and the user can check the revenue information through the dashboard. For example, a certain amount of revenue is added to the user's account each time a game is downloaded.

[0397] Adjustment means

[0398] The device collects the user's emotional data using a camera and microphone and sends the collected data to an emotion engine. For example, Azure Cognitive Services can be used to analyze the user's emotional state. If the emotion engine determines that the user is "enjoying" the content, the server dynamically adjusts the difficulty level of the content based on this data. The adjusted content is then delivered to the device and displayed to the user.

[0399] Examples of concrete examples and prompts

[0400] 1. Creation and distribution of poop drill questions

[0401] The server generates new poop drill problems using Python scripts and TensorFlow.

[0402] The terminal obtains new drill questions from the server and notifies the user.

[0403] Example prompt: "Let's solve the new poop drill!"

[0404] 2. Story Generation from Photos

[0405] A user uploads a photo of an elephant taken at the zoo.

[0406] The server generates the "Adventure Story of an Elephant and Poop" using Google Cloud Vision API and GPT-4.

[0407] Example prompt: "A new story has been generated from your photos!"

[0408] By taking the above specific steps, this system can increase children's motivation to learn and provide an optimal educational experience for each individual user.

[0409] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0410] Program processing flow

[0411] 1. Automatic generation of new poop drill questions

[0412] Step 1:

[0413] The server runs a Python script according to a specific schedule to generate new poop drill questions. A machine learning model (e.g., TensorFlow) is used as the generation algorithm. The input is the existing training database and algorithm parameters, and the output is a new poop drill question.

[0414] Specific operation: The server periodically launches the script and executes the question generation logic.

[0415] Step 2:

[0416] The server stores the generated Poop Drill questions in a MySQL database. The input is the new question generated, and the output is a new entry in the database.

[0417] Specific behavior: Inserts the generated questions into the database using an SQL query.

[0418] Step 3:

[0419] When the app is launched, the device sends a request to the server's API endpoint to download new poop drill questions. The input is the API request, and the output is the retrieved new questions.

[0420] Specific operation: The terminal executes GET / api / v1 / drill_problems / latest to obtain new problems.

[0421] Step 4:

[0422] The device displays the poop drill questions it has acquired to the user. The input is the questions acquired from the server, and the output is the questions displayed on the app screen.

[0423] Specific behavior: The device displays the new problem on the UI and notifies the user, "There's a new poop problem today!"

[0424] 2. Story Generation from Photos

[0425] Step 1:

[0426] The user takes a photo using the device's camera. The input is the photo taken by the user, and the output is the captured image file.

[0427] Specific actions: The user launches the camera app and presses the photo button.

[0428] Step 2:

[0429] The device uploads the photograph to the server. The input is the photographed image file, and the output is the image data stored on the server.

[0430] Specific operation: The device sends image data using the POST / api / v1 / photos endpoint.

[0431] Step 3:

[0432] The server analyzes the photo using the Google Cloud Vision API and generates a story or drill questions based on the recognized objects. The input is the image data, and the output is the generated story text.

[0433] What it does: Send an API request to the Vision API to get the analysis results, then use GPT-4 to generate a story.

[0434] Step 4:

[0435] The server stores the generated stories and questions in a MySQL database: the input is the generated story text and the output is a new entry in the database.

[0436] What you'll do: Insert a story into a database using an SQL query.

[0437] Step 5:

[0438] The device downloads the generated content from the server and displays it to the user. The input is the story or problem retrieved from the server, and the output is the content displayed on the app screen.

[0439] Specific behavior: The device displays the new story in the UI and notifies the user that "The great adventure of the elephant and the poop has begun."

[0440] 3. Create and publish an original poop game

[0441] Step 1:

[0442] A user creates a game using the creator tools within the app. The input is the user's configuration information, and the output is game design data.

[0443] Specific operation: The user inputs the game name, number of stages, difficulty level, etc.

[0444] Step 2:

[0445] The device collects the input configuration information and generates the game logic using Unity or GameMaker. The input is the user's configuration information, and the output is the game executable file.

[0446] Specific operation: The device generates source code, compiles it, and creates the game.

[0447] Step 3:

[0448] The device uploads the completed game data to the server. The input is the game executable file, and the output is the game data stored on the server.

[0449] Specific behavior: The device sends game data using the POST / api / v1 / games endpoint.

[0450] Step 4:

[0451] The server publishes the game to the Google Play Store. The input is the game data and the output is the published game.

[0452] What happens: Your server uses the Google Play Developer API to submit your game and approve it for publication.

[0453] 4. Monetize your content

[0454] Step 1:

[0455] The server records the number of downloads in the store. The input is the download event, and the output is the number of downloads recorded in the database.

[0456] Specific behavior: Uses an API request to get the number of downloads and stores it in a database.

[0457] Step 2:

[0458] The server generates revenue information and reflects it in the user's account. The input is the number of downloads, and the output is the user revenue information.

[0459] What it does: Calculates revenue based on the number of downloads and adds it to the user's account.

[0460] Step 3:

[0461] The server displays the revenue information on the dashboard. The input is the revenue information, and the output is the content displayed on the dashboard screen.

[0462] What you'll do: Visually display revenue information using a web front end.

[0463] 5. Leveraging Emotional Engines

[0464] Step 1:

[0465] The device collects user emotion data using a camera and microphone. The input is the user's facial expression and voice, and the output is emotion data.

[0466] Specific operation: Capture data in real time using a camera or microphone.

[0467] Step 2:

[0468] The device sends the collected emotional data to the emotion engine. The input is the emotional data, and the output is the analysis result of the emotional state.

[0469] Specific operation: Encodes emotion data and sends it to the emotion engine via an API request.

[0470] Step 3:

[0471] The emotion engine analyzes the user's emotional state. The input is emotion data, and the output is the emotion analysis result.

[0472] Specific action: Run an analysis algorithm to identify the user's emotional state (e.g., "having fun").

[0473] Step 4:

[0474] The server receives the analysis results and dynamically adjusts the difficulty level of the content. The input is the sentiment analysis results, and the output is the adjusted content settings.

[0475] Specific operation: Analyze data from the emotion engine and set the appropriate level of difficulty for the content.

[0476] Step 5:

[0477] The device displays the adjusted content to the user. The input is the adjusted content settings, and the output is the content displayed on the app screen.

[0478] What happens: Applies the new content settings and tells the user "This content is perfect for you."

[0479] Through the above specific processing steps, the system can effectively increase the user's motivation to learn and provide an individually optimized educational experience.

[0480] (Application example 2)

[0481] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0482] Conventional educational application systems struggle to provide an appropriate learning experience that reflects each user's emotional state and lack the individualized support required to maintain learning motivation. Furthermore, they lack efficient management tools for monetizing and publishing real-time educational content and user-created games. Therefore, a unified and dynamic system is needed to enhance children's learning motivation.

[0483] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0484] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data transmitted from the terminal and generating educational content, a publishing means for receiving educational games created by users from the terminal and publishing them in a store, a revenue generating means for monetizing the use of the educational content and games, an emotion engine means for analyzing user emotion data and dynamically adjusting the difficulty level of the educational content, and a means for providing users with stories and questions generated based on the analysis of the multimedia data. This makes it possible to provide appropriate educational content according to the emotional state of each user, thereby realizing an advanced learning system that maintains children's motivation to learn and stimulates their creativity.

[0485] The "generation means" is a means for automatically generating new questions and educational content, and utilizes the algorithms and databases used by the server.

[0486] The "analysis means" is a means for analyzing multimedia data transmitted from a terminal and generating educational content based on the data.

[0487] The "publishing means" is a means for receiving an educational game created by a user from a terminal and publishing it in the store.

[0488] "Revenue instruments" are instruments for earning revenue based on the use of educational content and games, and for managing revenue information.

[0489] The "emotion engine means" is a means for analyzing the user's emotional data and dynamically adjusting the difficulty level and content of the educational content based on the analysis results.

[0490] "Multimedia data" is a general term for multiple types of digital data such as images, audio, and text sent from a terminal.

[0491] "Educational content" is digital content such as quizzes, stories, and games that are provided for educational purposes.

[0492] This invention provides an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones, tablets, service robots), and users (children).

[0493] Key Features

[0494] 1. Generating new problems

[0495] The server has a generating means for generating new educational content. This generating means includes an existing database and a generative AI model, and periodically generates new questions and quizzes using these. For example, it generates a quiz such as "Today's learning question: Which animal poops the biggest?" The generated quizzes are stored on the server and periodically distributed to the terminal.

[0496] 2. Multimedia Data Analysis

[0497] The user takes a photo using the device's camera function. This photo data is sent from the device to a server and analyzed by the server's analysis means. This analysis uses image recognition technology (e.g., Google Cloud Vision API) and natural language generation technology (e.g., OpenAI GPT-4). Based on the analysis results, relevant educational stories and questions are generated.

[0498] 3. Utilizing the Emotion Engine

[0499] The device collects emotional data from the user's facial expressions and voice and sends it to the server. The server then uses an emotion engine to analyze the user's emotional state and dynamically adjusts the difficulty of the learning content based on the analysis. For example, if the server determines that the user is enjoying the content, it will adjust the difficulty level to provide slightly more challenging questions.

[0500] 4. Publishing user-created games

[0501] Users can create their own educational games using the creator tools in their devices. For example, they can create a "poop jumping game" or a "poop maze game." This game data is sent from the device to a server and published to the store using the publishing means. Other users can download and play the published game, and revenue is managed using the revenue means.

[0502] Specific Examples

[0503] Example 1: Creating and distributing new questions

[0504] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?" When the device starts the app, it retrieves the new poop drill questions from the server and notifies the user, "There's a new poop question today!"

[0505] Example 2: Narrative generation from photos

[0506] The user uploads a photo of an elephant taken at the zoo to the app. The server analyzes the photo and generates an "adventure story of an elephant and its poop." The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of an elephant and its poop has begun."

[0507] Example 3: Using the Emotion Engine

[0508] The device collects the user's facial expressions and tone of voice through the camera and microphone, and sends them to the emotion engine. For example, it determines whether the user is smiling or whether their tone of voice sounds like they're having fun. The emotion engine sends the analysis results to the server, and if it determines that the user is having fun, it adjusts the educational content provided to them to be more challenging. The server then dynamically distributes the content with the adjusted difficulty to the device and displays it to the user.

[0509] Example 4: Utilizing generative AI models

[0510] Example prompt sentence:

[0511] "Generate a story about the adventures of an elephant and its poop."

[0512] This creates unique learning content that is of interest to the user.

[0513] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0514] Step 1:

[0515] The user takes a photo using the device's camera function.

[0516] Input: Photo data

[0517] Output: Photo data stored on the device

[0518] Specific behavior: The user launches the app, presses the "Take Photo" button, and takes a photo.

[0519] Step 2:

[0520] The terminal transmits the photographed photo data to the server.

[0521] Input: Photo data stored on the device

[0522] Output: Photo data uploaded to the server

[0523] Specific operation: The device selects the photo data and presses the "Upload" button, which sends the photo data to the server via the Internet.

[0524] Step 3:

[0525] The server receives the photograph data and analyzes the data using the analysis means.

[0526] Input: Photo data uploaded to the server

[0527] Output: Image recognition results (label information)

[0528] Specific operation: Uses an image analysis API (such as Google Cloud Vision API) to label the content of the photo.

[0529] Step 4:

[0530] The server generates a prompt sentence based on the analysis results and generates a story using a generative AI model (GPT-4).

[0531] Input: Image recognition results (label information)

[0532] Output: Generated narrative text

[0533] Specific operation: The label information is converted into a prompt sentence and input into the generative AI model. The generated story text is saved on the server.

[0534] Example prompt: "Generate a story about the great adventure of an elephant and a poop."

[0535] Step 5:

[0536] A server generates novel educational quizzes.

[0537] Input: Existing database, generation algorithm

[0538] Output: New quiz question

[0539] Specific operation: New quizzes are automatically generated periodically every Monday by the generation means and stored on the server.

[0540] Step 6:

[0541] The terminal accesses the server and downloads the generated story text and quiz questions.

[0542] Input: Story text stored on the server, quiz questions

[0543] Output: Story text downloaded to the device, quiz questions

[0544] Specific operation: When the app is launched, the device sends a query to the server to obtain new content.

[0545] Step 7:

[0546] The terminal displays the downloaded story text and quiz questions to the user.

[0547] Input: Story text downloaded to the device, quiz questions

[0548] Output: Educational content displayed to the user

[0549] Specific behavior: The app displays a story text and asks quiz questions. The user answers the questions.

[0550] Step 8:

[0551] The device collects the user's facial expressions and voice and transmits the emotional data to the server.

[0552] Input: User's facial expression and voice data

[0553] Output: Emotion data uploaded to the server

[0554] Specific operation: The device uses the camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data.

[0555] Step 9:

[0556] The server analyzes the collected emotional data to generate feedback and dynamically adjust the difficulty level.

[0557] Input: Emotion data uploaded to the server

[0558] Output: Tailored educational content

[0559] Specific operation: Using the emotion engine means, change the difficulty level of the educational content based on the analysis results and adjust the next distribution content.

[0560] Step 10:

[0561] A user creates a new game in the creator tool and uploads it to the server.

[0562] Input: User-created game data

[0563] Output: Game data uploaded to the server

[0564] Specific behavior: A user uses the creator tools within the app to create a game. The completed game is then sent to the server.

[0565] Step 11:

[0566] The server then publishes the received game on the store, where other users can download and use it.

[0567] Input: Game data stored on the server

[0568] Output: Games published in the store, record of downloads

[0569] Specific operation: The server uploads the game to the store using the publishing method and manages the number of downloads using the revenue method.

[0570] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0571] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0572] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0573] [Second embodiment]

[0574] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0575] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0576] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0577] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0578] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0579] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0580] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0581] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0582] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0583] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0584] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0585] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0586] System Overview

[0587] This invention is an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones and tablets), and users (children).

[0588] Key Features

[0589] 1. Automatic generation of new poop drill questions

[0590] The server periodically generates new poop drill questions based on a specific schedule using a generation means, which uses an existing database and a machine learning algorithm. The generated drill questions are stored on the server.

[0591] The device periodically accesses the server and downloads new poop drill questions. These downloaded drill questions are displayed to the user and used for learning.

[0592] 2. Story Generation from Photos

[0593] The user takes a photo using the terminal. For example, the user can use a photo taken at a zoo or a landscape photo taken at a park.

[0594] The device uploads the captured photo to a server, which then analyzes the photo using analytical means to generate related poop stories and drill questions. This analysis uses image recognition and natural language generation technologies.

[0595] The generated stories and drill questions are stored on the server and delivered to the device the next time the app is launched, where the user can view and study them.

[0596] 3. Create and publish an original poop game

[0597] Users can use the in-app creator tools to create their own poop games, such as a "poop jumping game" or a "poop maze game."

[0598] The device collects the configuration information entered by the user and generates the game logic, which the user can then preview and modify as necessary.

[0599] The completed game data is uploaded to a server and published to the store using a publishing method. Games published in the store can be downloaded and played by other users.

[0600] 4. Monetize your content

[0601] The server records the number of downloads of games and exercises published in the store and generates revenue based on that. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[0602] Specific examples

[0603] 1. Creation and distribution of poop drill questions

[0604] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?"

[0605] When the device starts the app, it retrieves new poop drill questions from the server and notifies the user, "There are new poop questions today!"

[0606] 2. Story Generation from Photos

[0607] Users upload photos of elephants taken at the zoo to the app, and the server analyzes the photos and generates an "adventure story of elephants and poop."

[0608] The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of the elephant and the poop has begun."

[0609] 3. Create and publish an original poop game

[0610] The user creates a "Poo Soccer Game" using the creator tool. The device receives the settings and builds the game logic.

[0611] Once the game is complete, upload it to the server and publish it to the store. If your "Poo Soccer Game" becomes popular on the store, revenue will be reflected in your account every time other users download it.

[0612] Through these functions, the present invention is a system that enhances children's motivation to learn and stimulates their creativity.

[0613] The processing flow will be explained below.

[0614] Automatic generation of new poop drill questions

[0615] Processing Steps

[0616] Step 1:

[0617] The server runs a scheduled task every Monday at 3:00 AM, calling the Google Gemini API using the generated method.

[0618] Step 2:

[0619] The server analyzes the data received from Google Gemini and generates new poop drill questions.

[0620] Step 3:

[0621] The server stores the generated drill questions in the "unco_drills" database.

[0622] Step 4:

[0623] When the app is launched or at a regular interval, the device sends a request to the server to check whether there are any new poop drill questions.

[0624] Step 5:

[0625] The server receives the request and sends response data including the new poop drill question.

[0626] Step 6:

[0627] The device receives the response data, stores the new poop drill question in the app, and notifies the user.

[0628] Story generation from photos

[0629] Processing Steps

[0630] Step 1:

[0631] A user takes a photo using a smartphone.

[0632] Step 2:

[0633] The photos taken by the device are saved in the app and the user is prompted to review and select the photos.

[0634] Step 3:

[0635] The user confirms and uploads the selected photos to the server.

[0636] Step 4:

[0637] The device sends the photo data to the server.

[0638] Step 5:

[0639] The server passes the received photo data to the image analysis module, which analyzes the characteristics of the photo.

[0640] Step 6:

[0641] The server calls the Google Gemini API based on the characteristics of the photo and generates a related poop story.

[0642] Step 7:

[0643] The server stores the generated story data in a "stories" database and associates it with a user.

[0644] Step 8:

[0645] The terminal receives notification that the story has been created and notifies the user that a new story is available.

[0646] Step 9:

[0647] Users follow the notification to open the app and view and learn the generated story.

[0648] Create and publish an original poop game

[0649] Processing Steps

[0650] Step 1:

[0651] Users open the game creator tools within the app and set the game's theme, characters, and rules.

[0652] Step 2:

[0653] The terminal collects the user's input and stores it as initial configuration data.

[0654] Step 3:

[0655] The device calls templates and preset game modules based on the configuration data and generates the game logic and visuals.

[0656] Step 4:

[0657] The terminal provides the user with a preview of the game and receives correction instructions if necessary.

[0658] Step 5:

[0659] The user checks the final preview and gives the command to publish the game.

[0660] Step 6:

[0661] The device uploads the final game data to the server.

[0662] Step 7:

[0663] The server stores the uploaded game data in the "unco_games" database and publishes it to the store.

[0664] Step 8:

[0665] The server periodically compiles the number of downloads and user ratings of published games.

[0666] Step 9:

[0667] When the server generates revenue, it reflects the information in the corresponding user account.

[0668] Step 10:

[0669] Users can view revenue information and download numbers on the dashboard.

[0670] Example 1

[0671] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0672] Conventional educational systems have the problem that it is difficult to provide personalized educational content to individual users. In addition, it is difficult to monetize the content created by users, which means that users' creativity and motivation to learn cannot be fully utilized.

[0673] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0674] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data sent from the terminal and automatically generating stories and questions using a generative AI model, and a publishing means for receiving educational games created by users from the terminal and publishing them in the store, thereby making it possible to provide personalized educational content to users and monetize the content created by users.

[0675] A "generation means" is a technology or system for generating new questions and educational content periodically or as needed.

[0676] "Analysis means" refers to a technology or system that analyzes multimedia data sent from a terminal and automatically generates stories or questions using a generative AI model.

[0677] "Publication means" refers to a technology or system for receiving educational games and content created by users from terminals and publishing them in the store.

[0678] "Monetization Measures" are technologies or systems that monetize the use of educational content and games and reflect that revenue information in users' accounts.

[0679] A "generative AI model" is an artificial intelligence model that generates novel problems or stories based on a given prompt.

[0680] A "prompt" is a sentence or text that instructs a generative AI model to generate a novel problem or story.

[0681] "Server" means a computer system that functions as a central processing unit and executes the generating means, analyzing means, publishing means, and monetizing means.

[0682] "Terminal" refers to a device used by a user, such as a smartphone or tablet, that communicates with the server and displays and operates educational content and games.

[0683] System Overview

[0684] This invention is an educational application system designed to enhance users' (children's) motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones and tablets), and users.

[0685] Main features

[0686] 1. Automatic generation of new poop drill questions

[0687] The server generates new poop drill questions based on a specific schedule. This generation is done using a program written in Python and machine learning libraries such as TensorFlow. For example, to notify the user, "There's a new poop question today!", the new questions are saved on the server and distributed to the device.

[0688] As a concrete example, by inputting the prompt "Generate a unique question related to poop" into the generative AI model, a new poop drill question is generated.

[0689] 2. Story Generation from Photos

[0690] Users take photos using their devices and upload them to the server through the app. For example, you could use a photo of an elephant taken at the zoo.

[0691] The server analyzes the uploaded photos using image recognition models in OpenCV and TensorFlow and generates a related story. The story is generated by inputting a prompt such as "Write an essay about the adventures of an elephant and its poop" into the generative AI model.

[0692] The generated story is stored on the server and delivered to the device the next time the app is launched, providing users with stories such as "The Great Adventure of an Elephant and a Poop."

[0693] 3. Create and publish an original poop game

[0694] Users can use the creator tool to create their own original poop games. For example, they can create a "poop soccer game."

[0695] The device collects the user's input settings and generates game logic using game development frameworks such as JavaScript and Unity. The device then uses a preview function to allow users to check the game's operation and make any necessary adjustments.

[0696] The completed game is uploaded to the server and published in the store, where other users can download and play it.

[0697] 4. Monetize your content

[0698] The server records the number of downloads of games and exercises published in the store and generates revenue. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[0699] Specific example explanation

[0700] When generating a new poop drill question, the prompt text used is "Generate a unique question about poop."

[0701] In generating a story from a photo, the prompt used is "Write about the adventures of an elephant and its poop."

[0702] To create an original poop game, you will use the creator tool to set the rules and stage details of the "poop soccer game."

[0703] Through these functions, the system aims to increase users' motivation to learn and stimulate their creativity.

[0704] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0705] 1. Automatic generation of new poop drill questions

[0706] Step 1: Prepare for question generation

[0707] The server creates a prompt to generate new drill questions once a week on a specific schedule (such as midnight on Sundays). The prompt includes the instruction "Please generate a unique question about poop."

[0708] Step 2: Generate questions using a generative AI model

[0709] The server inputs the prompt sentence into the generative AI model and generates a new poop drill problem using an AI model (such as GPT). The input is the prompt sentence, and the output is a new problem sentence. Specifically, it calls the AI ​​model using Python and TensorFlow libraries.

[0710] Step 3: Save the question

[0711] The server saves the generated Poop Drill questions in a database (e.g., MySQL). This database stores the questions and their metadata (e.g., creation date) in JSON format.

[0712] Step 4: Distributing the problem

[0713] The device accesses the server to download new questions when the app starts or periodically. The new questions retrieved from the server are stored in a local SQLite database, and a notification of the new question is displayed to the user. The notification reads, "There's a new poop question for today!"

[0714] 2. Story Generation from Photos

[0715] Step 1: Take and upload a photo

[0716] A user takes a photo at a zoo or park, for example, a photo of an elephant, and uses the app's "upload photo" function to send the photo from their device to the server. The input is the captured JPEG image file.

[0717] Step 2: Photo Analysis

[0718] The server processes the received files using analytics. It uses image recognition models from OpenCV and TensorFlow to analyze the photos. The input is a JPEG image file, and the output is a label for the photo (e.g., "This is a photo of an elephant").

[0719] Step 3: Narrative generation

[0720] The server generates a prompt based on the analysis results. For example, it generates a sentence such as "Write an essay about the adventures of an elephant and a poop." This prompt is input into a generative AI model to generate a story. The input is the prompt, and the output is the text of the generated story.

[0721] Step 4: Save and share your story

[0722] The server stores the generated story in a database. Then, when the app is launched, the story is delivered to the device. The device displays a notification to the user about the new story, saying, "There is a new elephant and poop adventure story."

[0723] 3. Create and publish an original poop game

[0724] Step 1: Create a game

[0725] Users use the creator tool to create their own original poop game. Users input setting information (e.g., game rules, characters, and stage details) into their device.

[0726] Step 2: Generate the game logic

[0727] The device collects user input information and generates game logic using game development frameworks such as JavaScript and Unity. The input is the user's configuration information, and the output is the game's executable file. Specifically, it provides a preview of the game based on the configuration information, allowing the user to check and modify its behavior.

[0728] Step 3: Upload your game

[0729] The device uploads the completed game data to the server. The input is the game executable file, and the output is the game information that is posted on the store on the server.

[0730] Step 4: Publish your game

[0731] The server uses a publishing method to publish the uploaded game to the store, where other users can download and play the game.

[0732] 4. Monetize your content

[0733] Step 1: Record the number of downloads

[0734] The server records the number of downloads of games and exercises published in the store, specifically by recording them in a database using a download tracking system.

[0735] Step 2: Generate revenue information

[0736] The server calculates revenue based on the number of downloads and reflects it in the user's account. A Python script is used to calculate and update revenue information and display it on the dashboard. The input is the number of downloads data and the output is the updated revenue information.

[0737] (Application example 1)

[0738] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0739] Conventional educational application systems often contained monotonous content, which did not adequately stimulate children's motivation to learn or their creativity. They also lacked a means for users to easily share and monetize the content they created. Another issue was the difficulty of smoothly purchasing and using content in virtual shops.

[0740] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0741] In this invention, the server includes: a means for users to purchase and use educational content and games within the virtual store; a generation means for generating new questions; an analysis means for analyzing multimedia data sent from the terminal and generating educational content; a publishing means for receiving educational games created by users from the terminal and publishing them in the virtual store; a revenue generating means for monetizing the use of educational content and games; and a means for generating and providing stories based on multimedia data uploaded by users from the terminal. This increases children's motivation to learn, stimulates their creativity, and makes it easier to share and monetize content created by users. Furthermore, the purchase and use of content within the virtual store can be smoothly performed.

[0742] 1. "Virtual Store" means a virtual store that users can access via the Internet, where they can purchase and use educational content and games.

[0743] 2. "Educational content" refers to learning materials and questions, as well as stories and drills based on them, that are provided primarily to increase children's motivation to learn and stimulate their creativity.

[0744] 3. "Generation tools" are technologies and methods for generating new educational content and questions using existing databases and machine learning algorithms.

[0745] 4. "Multimedia data" refers to data in multiple media formats, including photos, videos, and audio data, that is transmitted by a user using a terminal.

[0746] 5. "Analysis means" means a method using image recognition technology and natural language generation technology to analyze transmitted multimedia data and generate educational content based on that data.

[0747] 6. "Publication means" means the technology and method for receiving educational games created by users from a terminal and publishing them in a virtual store so that other users can view and download them.

[0748] 7. "Monetization Method" means the technology and methods used to generate revenue from the use of educational content and games and credit it to users' accounts.

[0749] 8. "Story generation means" refers to the technology and method for generating original stories based on multimedia data uploaded by users from their devices and providing them as educational content.

[0750] System Overview

[0751] This invention is an educational application system for enhancing children's motivation to learn and stimulating their creativity. The system involves a server, terminals, and users working together to provide a purchasing and usage experience within a virtual shopping mall.

[0752] Specific implementation methods for major functions

[0753] 1. Purchases and use within the virtual store

[0754] The server runs a virtual store that users can access using their smartphones, smart glasses, or head-mounted displays.

[0755] Users can explore, purchase and use educational content and games within the virtual store.

[0756] 2. Generating new problems

[0757] The server periodically generates new poop drill questions using existing databases and machine learning algorithms.

[0758] The generated questions are stored on a server and distributed to the device.

[0759] 3. Story Generation from Photos

[0760] The user takes a photo using the terminal and uploads it to the server.

[0761] The server analyzes the uploaded photos using image recognition and natural language generation techniques to generate an associated story.

[0762] The generated story will be delivered to the device the next time the app is launched.

[0763] 4. Creating and publishing original games

[0764] Users use the in-app creator tools to create educational games.

[0765] The terminal collects the configuration information entered by the user and generates the game logic.

[0766] The completed game data is uploaded to the server and made available in the virtual store.

[0767] 5. Monetize your content

[0768] The server records the number of downloads of the games and drill questions published in the virtual store and generates revenue based on the number of downloads.

[0769] Revenue information is reflected in the user's account and can be viewed on the dashboard.

[0770] Hardware and software used

[0771] Flask: Used to build web servers.

[0772] Werkzeug: For file processing.

[0773] Generative AI models: Machine learning models used for story generation and game generation.

[0774] Smartphone: Used as the primary interface device.

[0775] Smart glasses and head-mounted displays: Used to enhance the virtual shopping experience.

[0776] Specific examples

[0777] 1. Purchases in virtual stores

[0778] Users access a virtual store using their smartphone and purchase a specific set of poop drill problems. After purchase, the problem set is downloaded to their device and they can begin learning.

[0779] 2. Narrative Generation

[0780] The user uploads a photo of an elephant taken at the zoo to the app. The server analyzes the photo and generates an "adventure story of an elephant and its poop." This story is then sent to the device the next time the app is launched, and is provided as learning content.

[0781] 3. Example prompts

[0782] "Generate a short story about the following photo: A photo of an elephant"

[0783] "Generate poop drill questions for primary school education"

[0784] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0785] Step 1:

[0786] A user accesses the virtual store using a smartphone or head-mounted display. The input requires the user's authentication information and device ID. The output is the virtual store's home screen, which displays a list of available educational content and games.

[0787] Step 2:

[0788] A user selects and purchases educational content or games in a virtual store. The inputs include the selected content ID and payment information. The server receives this information and confirms the payment. The output is a message indicating the purchase has been completed and a download link is provided to the user.

[0789] Step 3:

[0790] The user clicks on a download link on their device to download the educational content or game they purchased. The input is the download link, and the server sends the corresponding content data. The output is that the content is saved on the device and ready for use.

[0791] Step 4:

[0792] The user takes a photo using the device's camera and uploads it to the server through a dedicated upload screen in the virtual shop. The input required is the photo data taken by the user and a description text associated with the photo. The output is a message indicating that the upload is complete.

[0793] Step 5:

[0794] The server receives the uploaded photos and analyzes them using a generative AI model. The inputs are the received photo data and a description text. The output is a generated story based on image recognition technology. The server stores this story.

[0795] Step 6:

[0796] Users create their own educational games using the app's creator tools. Game configuration information and user-created materials are required as input. The device collects this information and generates the game logic. The output is a preview of the game the user has designed.

[0797] Step 7:

[0798] The user completes the creation of a game and sends a request to the server to publish it. The input requires the completed game data and publishing instructions. The server receives this and processes the game to publish it in the virtual store. The output is a message indicating that publishing is complete, and the game becomes available for other users to download and use.

[0799] Step 8:

[0800] The server records the number of downloads of games and exercises published in the virtual store and generates revenue. The inputs are the number of downloads and a revenue calculation algorithm. The output is the generated revenue information reflected in the user's account, which can be viewed on the dashboard.

[0801] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0802] System Overview

[0803] This invention is an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, devices (smartphones and tablets), and users (children). In particular, by combining it with an emotion engine that recognizes the user's emotions, the system provides an educational experience tailored to each individual user.

[0804] Key Features

[0805] 1. Automatic generation of new poop drill questions

[0806] The server periodically generates new poop drill questions based on a specific schedule using a generation means, which uses an existing database and a machine learning algorithm. The generated drill questions are stored on the server.

[0807] The device periodically accesses the server and downloads new poop drill questions. These downloaded drill questions are displayed to the user and used for learning.

[0808] 2. Story Generation from Photos

[0809] The user takes a photo using the terminal. For example, the user can use a photo taken at a zoo or a landscape photo taken at a park.

[0810] The device uploads the captured photo to a server, which then analyzes the photo using analytical means to generate related poop stories and drill questions. This analysis uses image recognition and natural language generation technologies.

[0811] The generated stories and drill questions are stored on the server and delivered to the device the next time the app is launched, where the user can view and study them.

[0812] 3. Create and publish an original poop game

[0813] Users can use the in-app creator tools to create their own poop games, such as a "poop jumping game" or a "poop maze game."

[0814] The device collects the configuration information entered by the user and generates the game logic, which the user can then preview and modify as necessary.

[0815] The completed game data is uploaded to a server and published to the store using a publishing method. Games published in the store can be downloaded and played by other users.

[0816] 4. Monetize your content

[0817] The server records the number of downloads of games and exercises published in the store and generates revenue based on that. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[0818] 5. Leveraging Emotional Engines

[0819] The device collects emotional data from the user's facial expressions and voice and sends it to the emotion engine.

[0820] The emotion engine analyzes the collected data and evaluates the user's emotional state.

[0821] Based on the analysis results of the emotion engine, the server dynamically adjusts the educational content and game difficulty to suit the user.

[0822] The device provides feedback based on the user's emotions and delivers appropriate content.

[0823] Specific examples

[0824] 1. Creation and distribution of poop drill questions

[0825] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?"

[0826] When the device starts the app, it retrieves new poop drill questions from the server and notifies the user, "There are new poop questions today!"

[0827] 2. Story Generation from Photos

[0828] Users upload photos of elephants taken at the zoo to the app, and the server analyzes the photos and generates an "adventure story of elephants and poop."

[0829] The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of the elephant and the poop has begun."

[0830] 3. Create and publish an original poop game

[0831] The user creates a "Poo Soccer Game" using the creator tool. The device receives the settings and builds the game logic.

[0832] Once the game is complete, upload it to the server and publish it to the store. If your "Poo Soccer Game" becomes popular on the store, revenue will be reflected in your account every time other users download it.

[0833] 4. Utilizing the Emotion Engine

[0834] The device uses a camera and microphone to collect the user's facial expressions and tone of voice, and sends them to the emotion engine. For example, it determines whether the user is smiling or whether their tone of voice sounds happy.

[0835] The emotion engine sends the analysis results to the server, and if it determines that the user is enjoying the content, it adjusts the content to provide more challenging educational content.

[0836] The server dynamically adjusts the difficulty of the content and distributes it to the terminal, where it is displayed to the user.

[0837] Through these functions, the present invention is a system that enhances children's motivation to learn and stimulates their creativity. By introducing an emotion engine, we can provide an educational experience that is tailored to each individual user, creating a more efficient and enjoyable learning environment.

[0838] The processing flow will be explained below.

[0839] Automatic generation of new poop drill questions

[0840] Processing Steps

[0841] Step 1:

[0842] The server runs a scheduled task every Monday at 3:00 AM and calls the API using the generated method.

[0843] Step 2:

[0844] The server analyzes the data received from the API and generates new poop drill questions.

[0845] Step 3:

[0846] The server stores the generated drill questions in the "unco_drills" database.

[0847] Step 4:

[0848] When the app is launched or at a regular interval, the device sends a request to the server to check whether there are any new poop drill questions.

[0849] Step 5:

[0850] The server receives the request and sends response data including the new poop drill question.

[0851] Step 6:

[0852] The device receives the response data, stores the new poop drill question in the app, and notifies the user.

[0853] Story generation from photos

[0854] Processing Steps

[0855] Step 1:

[0856] A user takes a photo using a smartphone.

[0857] Step 2:

[0858] The photos taken by the device are saved in the app and the user is prompted to review and select the photos.

[0859] Step 3:

[0860] The user confirms and uploads the selected photos to the server.

[0861] Step 4:

[0862] The device sends the photo data to the server.

[0863] Step 5:

[0864] The server passes the received photo data to the image analysis module, which analyzes the characteristics of the photo.

[0865] Step 6:

[0866] The server calls the API based on the characteristics of the photo and generates a related poop story.

[0867] Step 7:

[0868] The server stores the generated story data in a "stories" database and associates it with a user.

[0869] Step 8:

[0870] The terminal receives notification that the story has been created and notifies the user that a new story is available.

[0871] Step 9:

[0872] Users follow the notification to open the app and view and learn the generated story.

[0873] Create and publish an original poop game

[0874] Processing Steps

[0875] Step 1:

[0876] Users open the game creator tools within the app and set the game's theme, characters, and rules.

[0877] Step 2:

[0878] The terminal collects the user's input and stores it as initial configuration data.

[0879] Step 3:

[0880] The device calls templates and preset game modules based on the configuration data and generates the game logic and visuals.

[0881] Step 4:

[0882] The terminal provides the user with a preview of the game and receives correction instructions if necessary.

[0883] Step 5:

[0884] The user checks the final preview and gives the command to publish the game.

[0885] Step 6:

[0886] The device uploads the final game data to the server.

[0887] Step 7:

[0888] The server stores the uploaded game data in the "unco_games" database and publishes it to the store.

[0889] Step 8:

[0890] The server periodically compiles the number of downloads and user ratings of published games.

[0891] Step 9:

[0892] When the server generates revenue, it reflects the information in the corresponding user account.

[0893] Step 10:

[0894] Users can view revenue information and download numbers on the dashboard.

[0895] Utilizing the Emotion Engine

[0896] Processing Steps

[0897] Step 1:

[0898] While a user is using educational content or playing games, the device uses a camera and microphone to record the user's facial expressions and voice.

[0899] Step 2:

[0900] The facial expression and voice data collected by the device is sent to the emotion engine.

[0901] Step 3:

[0902] The emotion engine analyzes the received data and assesses the user's emotional state (e.g., enjoying, concentrating, tired, etc.).

[0903] Step 4:

[0904] The emotion engine sends the analysis results to the server and reports the user's emotional state.

[0905] Step 5:

[0906] Based on the analysis results of the emotion engine, the server dynamically adjusts the difficulty and content of educational content and games according to the user's learning progress and stress level.

[0907] Step 6:

[0908] The device follows instructions from the server and displays tailored content or games to the user.

[0909] Step 7:

[0910] As the user continues to learn based on new content and games, the emotion engine continues to monitor the user's emotions.

[0911] Specific examples

[0912] Step 1:

[0913] While the user is solving drill problems in the app, the device records the user's facial expressions (camera) and voice (microphone).

[0914] Step 2:

[0915] The device sends this data to the emotion engine.

[0916] Step 3:

[0917] The emotion engine performs analysis and assesses whether the user is struggling or enjoying solving the problem.

[0918] Step 4:

[0919] The server receives the emotion engine's evaluation results, and if it determines that the user is struggling, it instructs the server to deliver questions with a slightly lower level of difficulty.

[0920] Step 5:

[0921] The device displays new (adjusted) questions to the user, and the user continues learning.

[0922] This specific processing step allows the present invention to provide an optimal educational experience for each individual user, taking into account the user's emotional state.

[0923] Example 2

[0924] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0925] Previous educational applications lacked the ability to generate new questions, publish user-generated content, monetize it, and personalize it based on user emotions. This made it difficult to continuously stimulate users' learning and provide an optimal educational experience for each individual user. Furthermore, there were challenges in the efficiency of automatic content generation and in stimulating user creativity.

[0926] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0927] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data transmitted from the terminal and generating educational content, a publishing means for receiving educational games created by users from the terminal and publishing them, a revenue generating means for monetizing the use of the educational content and games, and an adjusting means for analyzing the user's emotions and dynamically adjusting the difficulty level of the content, thereby making it possible to provide an individually optimized educational experience while increasing the user's motivation to learn.

[0928] "Generation means" refers to the technology or method for automatically generating educational content and questions.

[0929] "Analysis means" refers to techniques or methods for analyzing multimedia data sent from a terminal and generating educational content based on that data.

[0930] "Publishing Means" refers to the technology or method for receiving educational games or content created by users and publishing them on a platform where other users can access them.

[0931] "Monetization methods" are technologies or methods for monitoring the usage of educational content or games, generating revenue based on that data, and reflecting it in users' accounts.

[0932] The "adjustment means" refers to a technique or method for analyzing the user's emotional data and dynamically adjusting the difficulty level of the content based on the analysis results.

[0933] A "server" is a computer system that stores and processes data and manages the entire system while communicating with terminals.

[0934] A "terminal" is a device used by a user, such as a smartphone or tablet, that communicates with a server to display content and send and receive data.

[0935] "Educational content" refers to teaching materials and activities such as questions, stories, and games that are provided to help users learn.

[0936] "Emotional data" is data collected from the user's facial expressions, voice, etc., and is information used to evaluate the user's emotional state.

[0937] A "user" is an individual who uses this system to enjoy educational content.

[0938] The present invention relates to an educational application system for enhancing children's motivation to learn and stimulating their creativity. The system involves a server, terminals (such as smartphones and tablets), and users working together. This system is particularly distinctive in that it includes a generation unit, an analysis unit, a publishing unit, a revenue generating unit, and an adjustment unit.

[0939] generation means

[0940] The server generates new poop drill questions every week based on a specific schedule. Python scripts and machine learning algorithms (e.g., TensorFlow) are used to generate the questions. For example, the generated questions are in the form of "Today's Poop Question: What did the poop find in the park?" The generated poop drill questions are stored in a MySQL database.

[0941] Analysis means

[0942] The system generates educational content using photos taken by users on their devices. Users upload photos of elephants taken at the zoo to the server via their devices. The server analyzes the photos using the Google Cloud Vision API and generates stories and poop drill questions related to the photos. The analysis results are generated using natural language generation models such as GPT-4, and one example is the "Adventure Story of an Elephant and Poop." This generated content is stored in a MySQL database.

[0943] Publication method

[0944] Users create educational games using the app's creator tools. The device collects the user's input settings and generates the game logic using a game development engine such as Unity or GameMaker. The completed game is uploaded to a server and published on platforms such as the Google Play Store. For example, a "Poo Soccer Game" could be published.

[0945] revenue stream

[0946] The server records the number of downloads of games and poop drills published in the store and generates revenue information based on that data. The revenue is reflected in the user's account, and the user can check the revenue information through the dashboard. For example, a certain amount of revenue is added to the user's account each time a game is downloaded.

[0947] Adjustment means

[0948] The device collects the user's emotional data using a camera and microphone and sends the collected data to an emotion engine. For example, Azure Cognitive Services can be used to analyze the user's emotional state. If the emotion engine determines that the user is "enjoying" the content, the server dynamically adjusts the difficulty level of the content based on this data. The adjusted content is then delivered to the device and displayed to the user.

[0949] Examples of concrete examples and prompts

[0950] 1. Creation and distribution of poop drill questions

[0951] The server generates new poop drill problems using Python scripts and TensorFlow.

[0952] The terminal obtains new drill questions from the server and notifies the user.

[0953] Example prompt: "Let's solve the new poop drill!"

[0954] 2. Story Generation from Photos

[0955] A user uploads a photo of an elephant taken at the zoo.

[0956] The server generates the "Adventure Story of an Elephant and Poop" using Google Cloud Vision API and GPT-4.

[0957] Example prompt: "A new story has been generated from your photos!"

[0958] By taking the above specific steps, this system can increase children's motivation to learn and provide an optimal educational experience for each individual user.

[0959] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0960] Program processing flow

[0961] 1. Automatic generation of new poop drill questions

[0962] Step 1:

[0963] The server runs a Python script according to a specific schedule to generate new poop drill questions. A machine learning model (e.g., TensorFlow) is used as the generation algorithm. The input is the existing training database and algorithm parameters, and the output is a new poop drill question.

[0964] Specific operation: The server periodically launches the script and executes the question generation logic.

[0965] Step 2:

[0966] The server stores the generated Poop Drill questions in a MySQL database. The input is the new question generated, and the output is a new entry in the database.

[0967] Specific behavior: Inserts the generated questions into the database using an SQL query.

[0968] Step 3:

[0969] When the app is launched, the device sends a request to the server's API endpoint to download new poop drill questions. The input is the API request, and the output is the retrieved new questions.

[0970] Specific operation: The terminal executes GET / api / v1 / drill_problems / latest to obtain new problems.

[0971] Step 4:

[0972] The device displays the poop drill questions it has acquired to the user. The input is the questions acquired from the server, and the output is the questions displayed on the app screen.

[0973] Specific behavior: The device displays the new problem on the UI and notifies the user, "There's a new poop problem today!"

[0974] 2. Story Generation from Photos

[0975] Step 1:

[0976] The user takes a photo using the device's camera. The input is the photo taken by the user, and the output is the captured image file.

[0977] Specific actions: The user launches the camera app and presses the photo button.

[0978] Step 2:

[0979] The device uploads the photograph to the server. The input is the photographed image file, and the output is the image data stored on the server.

[0980] Specific operation: The device sends image data using the POST / api / v1 / photos endpoint.

[0981] Step 3:

[0982] The server analyzes the photo using the Google Cloud Vision API and generates a story or drill questions based on the recognized objects. The input is the image data, and the output is the generated story text.

[0983] What it does: Send an API request to the Vision API to get the analysis results, then use GPT-4 to generate a story.

[0984] Step 4:

[0985] The server stores the generated stories and questions in a MySQL database: the input is the generated story text and the output is a new entry in the database.

[0986] What you'll do: Insert a story into a database using an SQL query.

[0987] Step 5:

[0988] The device downloads the generated content from the server and displays it to the user. The input is the story or problem retrieved from the server, and the output is the content displayed on the app screen.

[0989] Specific behavior: The device displays the new story in the UI and notifies the user that "The great adventure of the elephant and the poop has begun."

[0990] 3. Create and publish an original poop game

[0991] Step 1:

[0992] A user creates a game using the creator tools within the app. The input is the user's configuration information, and the output is game design data.

[0993] Specific operation: The user inputs the game name, number of stages, difficulty level, etc.

[0994] Step 2:

[0995] The device collects the input configuration information and generates the game logic using Unity or GameMaker. The input is the user's configuration information, and the output is the game executable file.

[0996] Specific operation: The device generates source code, compiles it, and creates the game.

[0997] Step 3:

[0998] The device uploads the completed game data to the server. The input is the game executable file, and the output is the game data stored on the server.

[0999] Specific behavior: The device sends game data using the POST / api / v1 / games endpoint.

[1000] Step 4:

[1001] The server publishes the game to the Google Play Store. The input is the game data and the output is the published game.

[1002] What happens: Your server uses the Google Play Developer API to submit your game and approve it for publication.

[1003] 4. Monetize your content

[1004] Step 1:

[1005] The server records the number of downloads in the store. The input is the download event, and the output is the number of downloads recorded in the database.

[1006] Specific behavior: Uses an API request to get the number of downloads and stores it in a database.

[1007] Step 2:

[1008] The server generates revenue information and reflects it in the user's account. The input is the number of downloads, and the output is the user revenue information.

[1009] What it does: Calculates revenue based on the number of downloads and adds it to the user's account.

[1010] Step 3:

[1011] The server displays the revenue information on the dashboard. The input is the revenue information, and the output is the content displayed on the dashboard screen.

[1012] What you'll do: Visually display revenue information using a web front end.

[1013] 5. Leveraging Emotional Engines

[1014] Step 1:

[1015] The device collects user emotion data using a camera and microphone. The input is the user's facial expression and voice, and the output is emotion data.

[1016] Specific operation: Capture data in real time using a camera or microphone.

[1017] Step 2:

[1018] The device sends the collected emotional data to the emotion engine. The input is the emotional data, and the output is the analysis result of the emotional state.

[1019] Specific operation: Encodes emotion data and sends it to the emotion engine via an API request.

[1020] Step 3:

[1021] The emotion engine analyzes the user's emotional state. The input is emotion data, and the output is the emotion analysis result.

[1022] Specific action: Run an analysis algorithm to identify the user's emotional state (e.g., "having fun").

[1023] Step 4:

[1024] The server receives the analysis results and dynamically adjusts the difficulty level of the content. The input is the sentiment analysis results, and the output is the adjusted content settings.

[1025] Specific operation: Analyze data from the emotion engine and set the appropriate level of difficulty for the content.

[1026] Step 5:

[1027] The device displays the adjusted content to the user. The input is the adjusted content settings, and the output is the content displayed on the app screen.

[1028] What happens: Applies the new content settings and tells the user "This content is perfect for you."

[1029] Through the above specific processing steps, the system can effectively increase the user's motivation to learn and provide an individually optimized educational experience.

[1030] (Application example 2)

[1031] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1032] Conventional educational application systems struggle to provide an appropriate learning experience that reflects each user's emotional state and lack the individualized support required to maintain learning motivation. Furthermore, they lack efficient management tools for monetizing and publishing real-time educational content and user-created games. Therefore, a unified and dynamic system is needed to enhance children's learning motivation.

[1033] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1034] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data transmitted from the terminal and generating educational content, a publishing means for receiving educational games created by users from the terminal and publishing them in a store, a revenue generating means for monetizing the use of the educational content and games, an emotion engine means for analyzing user emotion data and dynamically adjusting the difficulty level of the educational content, and a means for providing users with stories and questions generated based on the analysis of the multimedia data. This makes it possible to provide appropriate educational content according to the emotional state of each user, thereby realizing an advanced learning system that maintains children's motivation to learn and stimulates their creativity.

[1035] The "generation means" is a means for automatically generating new questions and educational content, and utilizes the algorithms and databases used by the server.

[1036] The "analysis means" is a means for analyzing multimedia data transmitted from a terminal and generating educational content based on the data.

[1037] The "publishing means" is a means for receiving an educational game created by a user from a terminal and publishing it in the store.

[1038] "Revenue instruments" are instruments for earning revenue based on the use of educational content and games, and for managing revenue information.

[1039] The "emotion engine means" is a means for analyzing the user's emotional data and dynamically adjusting the difficulty level and content of the educational content based on the analysis results.

[1040] "Multimedia data" is a general term for multiple types of digital data such as images, audio, and text sent from a terminal.

[1041] "Educational content" is digital content such as quizzes, stories, and games that are provided for educational purposes.

[1042] This invention provides an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones, tablets, service robots), and users (children).

[1043] Key Features

[1044] 1. Generating new problems

[1045] The server has a generating means for generating new educational content. This generating means includes an existing database and a generative AI model, and periodically generates new questions and quizzes using these. For example, it generates a quiz such as "Today's learning question: Which animal poops the biggest?" The generated quizzes are stored on the server and periodically distributed to the terminal.

[1046] 2. Multimedia Data Analysis

[1047] The user takes a photo using the device's camera function. This photo data is sent from the device to a server and analyzed by the server's analysis means. This analysis uses image recognition technology (e.g., Google Cloud Vision API) and natural language generation technology (e.g., OpenAI GPT-4). Based on the analysis results, relevant educational stories and questions are generated.

[1048] 3. Utilizing the Emotion Engine

[1049] The device collects emotional data from the user's facial expressions and voice and sends it to the server. The server then uses an emotion engine to analyze the user's emotional state and dynamically adjusts the difficulty of the learning content based on the analysis. For example, if the server determines that the user is enjoying the content, it will adjust the difficulty level to provide slightly more challenging questions.

[1050] 4. Publishing user-created games

[1051] Users can create their own educational games using the creator tools in their devices. For example, they can create a "poop jumping game" or a "poop maze game." This game data is sent from the device to a server and published to the store using the publishing means. Other users can download and play the published game, and revenue is managed using the revenue means.

[1052] Specific Examples

[1053] Example 1: Creating and distributing new questions

[1054] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?" When the device starts the app, it retrieves the new poop drill questions from the server and notifies the user, "There's a new poop question today!"

[1055] Example 2: Narrative generation from photos

[1056] The user uploads a photo of an elephant taken at the zoo to the app. The server analyzes the photo and generates an "adventure story of an elephant and its poop." The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of an elephant and its poop has begun."

[1057] Example 3: Using the Emotion Engine

[1058] The device collects the user's facial expressions and tone of voice through the camera and microphone, and sends them to the emotion engine. For example, it determines whether the user is smiling or whether their tone of voice sounds like they're having fun. The emotion engine sends the analysis results to the server, and if it determines that the user is having fun, it adjusts the educational content provided to them to be more challenging. The server then dynamically distributes the content with the adjusted difficulty to the device and displays it to the user.

[1059] Example 4: Utilizing generative AI models

[1060] Example prompt sentence:

[1061] "Generate a story about the adventures of an elephant and its poop."

[1062] This creates unique learning content that is of interest to the user.

[1063] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1064] Step 1:

[1065] The user takes a photo using the device's camera function.

[1066] Input: Photo data

[1067] Output: Photo data stored on the device

[1068] Specific behavior: The user launches the app, presses the "Take Photo" button, and takes a photo.

[1069] Step 2:

[1070] The terminal transmits the photographed photo data to the server.

[1071] Input: Photo data stored on the device

[1072] Output: Photo data uploaded to the server

[1073] Specific operation: The device selects the photo data and presses the "Upload" button, which sends the photo data to the server via the Internet.

[1074] Step 3:

[1075] The server receives the photograph data and analyzes the data using the analysis means.

[1076] Input: Photo data uploaded to the server

[1077] Output: Image recognition results (label information)

[1078] Specific operation: Uses an image analysis API (such as Google Cloud Vision API) to label the content of the photo.

[1079] Step 4:

[1080] The server generates a prompt sentence based on the analysis results and generates a story using a generative AI model (GPT-4).

[1081] Input: Image recognition results (label information)

[1082] Output: Generated narrative text

[1083] Specific operation: The label information is converted into a prompt sentence and input into the generative AI model. The generated story text is saved on the server.

[1084] Example prompt: "Generate a story about the great adventure of an elephant and a poop."

[1085] Step 5:

[1086] A server generates novel educational quizzes.

[1087] Input: Existing database, generation algorithm

[1088] Output: New quiz question

[1089] Specific operation: New quizzes are automatically generated periodically every Monday by the generation means and stored on the server.

[1090] Step 6:

[1091] The terminal accesses the server and downloads the generated story text and quiz questions.

[1092] Input: Story text stored on the server, quiz questions

[1093] Output: Story text downloaded to the device, quiz questions

[1094] Specific operation: When the app is launched, the device sends a query to the server to obtain new content.

[1095] Step 7:

[1096] The terminal displays the downloaded story text and quiz questions to the user.

[1097] Input: Story text downloaded to the device, quiz questions

[1098] Output: Educational content displayed to the user

[1099] Specific behavior: The app displays a story text and asks quiz questions. The user answers the questions.

[1100] Step 8:

[1101] The device collects the user's facial expressions and voice and transmits the emotional data to the server.

[1102] Input: User's facial expression and voice data

[1103] Output: Emotion data uploaded to the server

[1104] Specific operation: The device uses the camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data.

[1105] Step 9:

[1106] The server analyzes the collected emotional data to generate feedback and dynamically adjust the difficulty level.

[1107] Input: Emotion data uploaded to the server

[1108] Output: Tailored educational content

[1109] Specific operation: Using the emotion engine means, change the difficulty level of the educational content based on the analysis results and adjust the next distribution content.

[1110] Step 10:

[1111] A user creates a new game in the creator tool and uploads it to the server.

[1112] Input: User-created game data

[1113] Output: Game data uploaded to the server

[1114] Specific behavior: A user uses the creator tools within the app to create a game. The completed game is then sent to the server.

[1115] Step 11:

[1116] The server then publishes the received game on the store, where other users can download and use it.

[1117] Input: Game data stored on the server

[1118] Output: Games published in the store, record of downloads

[1119] Specific operation: The server uploads the game to the store using the publishing method and manages the number of downloads using the revenue method.

[1120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1122] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1123] [Third embodiment]

[1124] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1128] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1132] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1134] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1135] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1136] System Overview

[1137] This invention is an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones and tablets), and users (children).

[1138] Key Features

[1139] 1. Automatic generation of new poop drill questions

[1140] The server periodically generates new poop drill questions based on a specific schedule using a generation means, which uses an existing database and a machine learning algorithm. The generated drill questions are stored on the server.

[1141] The device periodically accesses the server and downloads new poop drill questions. These downloaded drill questions are displayed to the user and used for learning.

[1142] 2. Story Generation from Photos

[1143] The user takes a photo using the terminal. For example, the user can use a photo taken at a zoo or a landscape photo taken at a park.

[1144] The device uploads the captured photo to a server, which then analyzes the photo using analytical means to generate related poop stories and drill questions. This analysis uses image recognition and natural language generation technologies.

[1145] The generated stories and drill questions are stored on the server and delivered to the device the next time the app is launched, where the user can view and study them.

[1146] 3. Create and publish an original poop game

[1147] Users can use the in-app creator tools to create their own poop games, such as a "poop jumping game" or a "poop maze game."

[1148] The device collects the configuration information entered by the user and generates the game logic, which the user can then preview and modify as necessary.

[1149] The completed game data is uploaded to a server and published to the store using a publishing method. Games published in the store can be downloaded and played by other users.

[1150] 4. Monetize your content

[1151] The server records the number of downloads of games and exercises published in the store and generates revenue based on that. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[1152] Specific examples

[1153] 1. Creation and distribution of poop drill questions

[1154] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?"

[1155] When the device starts the app, it retrieves new poop drill questions from the server and notifies the user, "There are new poop questions today!"

[1156] 2. Story Generation from Photos

[1157] Users upload photos of elephants taken at the zoo to the app, and the server analyzes the photos and generates an "adventure story of elephants and poop."

[1158] The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of the elephant and the poop has begun."

[1159] 3. Create and publish an original poop game

[1160] The user creates a "Poo Soccer Game" using the creator tool. The device receives the settings and builds the game logic.

[1161] Once the game is complete, upload it to the server and publish it to the store. If your "Poo Soccer Game" becomes popular on the store, revenue will be credited to your account every time another user downloads it.

[1162] Through these functions, the present invention is a system that enhances children's motivation to learn and stimulates their creativity.

[1163] The processing flow will be explained below.

[1164] Automatic generation of new poop drill questions

[1165] Processing Steps

[1166] Step 1:

[1167] The server runs a scheduled task every Monday at 3:00 AM, calling the Google Gemini API using the generated method.

[1168] Step 2:

[1169] The server analyzes the data received from Google Gemini and generates new poop drill questions.

[1170] Step 3:

[1171] The server stores the generated drill questions in the "unco_drills" database.

[1172] Step 4:

[1173] When the app is launched or at a regular interval, the device sends a request to the server to check whether there are any new poop drill questions.

[1174] Step 5:

[1175] The server receives the request and sends response data including the new poop drill question.

[1176] Step 6:

[1177] The device receives the response data, stores the new poop drill question in the app, and notifies the user.

[1178] Story generation from photos

[1179] Processing Steps

[1180] Step 1:

[1181] A user takes a photo using a smartphone.

[1182] Step 2:

[1183] The photos taken by the device are saved in the app and the user is prompted to review and select the photos.

[1184] Step 3:

[1185] The user confirms and uploads the selected photos to the server.

[1186] Step 4:

[1187] The device sends the photo data to the server.

[1188] Step 5:

[1189] The server passes the received photo data to the image analysis module, which analyzes the characteristics of the photo.

[1190] Step 6:

[1191] The server calls the Google Gemini API based on the characteristics of the photo and generates a related poop story.

[1192] Step 7:

[1193] The server stores the generated story data in a "stories" database and associates it with a user.

[1194] Step 8:

[1195] The terminal receives notification that the story has been created and notifies the user that a new story is available.

[1196] Step 9:

[1197] Users follow the notification to open the app and view and learn the generated story.

[1198] Create and publish an original poop game

[1199] Processing Steps

[1200] Step 1:

[1201] Users open the game creator tools within the app and set the game's theme, characters, and rules.

[1202] Step 2:

[1203] The terminal collects the user's input and stores it as initial configuration data.

[1204] Step 3:

[1205] The device calls templates and preset game modules based on the configuration data and generates the game logic and visuals.

[1206] Step 4:

[1207] The terminal provides the user with a preview of the game and receives correction instructions if necessary.

[1208] Step 5:

[1209] The user checks the final preview and gives the command to publish the game.

[1210] Step 6:

[1211] The device uploads the final game data to the server.

[1212] Step 7:

[1213] The server stores the uploaded game data in the "unco_games" database and publishes it to the store.

[1214] Step 8:

[1215] The server periodically compiles the number of downloads and user ratings of published games.

[1216] Step 9:

[1217] When the server generates revenue, it reflects the information in the corresponding user account.

[1218] Step 10:

[1219] Users can view revenue information and download numbers on the dashboard.

[1220] Example 1

[1221] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1222] Conventional educational systems have the problem that it is difficult to provide personalized educational content to individual users. In addition, it is difficult to monetize the content created by users, which means that users' creativity and motivation to learn cannot be fully utilized.

[1223] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1224] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data sent from the terminal and automatically generating stories and questions using a generative AI model, and a publishing means for receiving educational games created by users from the terminal and publishing them in the store, thereby making it possible to provide personalized educational content to users and monetize the content created by users.

[1225] A "generation means" is a technology or system for generating new questions and educational content periodically or as needed.

[1226] "Analysis means" refers to a technology or system that analyzes multimedia data sent from a terminal and automatically generates stories or questions using a generative AI model.

[1227] "Publication means" refers to a technology or system for receiving educational games and content created by users from terminals and publishing them in the store.

[1228] "Monetization Measures" are technologies or systems that monetize the use of educational content and games and reflect that revenue information in users' accounts.

[1229] A "generative AI model" is an artificial intelligence model that generates novel problems or stories based on a given prompt.

[1230] A "prompt" is a sentence or text that instructs a generative AI model to generate a novel problem or story.

[1231] "Server" means a computer system that functions as a central processing unit and executes the generating means, analyzing means, publishing means, and monetizing means.

[1232] "Terminal" refers to a device used by a user, such as a smartphone or tablet, that communicates with the server and displays and operates educational content and games.

[1233] System Overview

[1234] This invention is an educational application system designed to enhance users' (children's) motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones and tablets), and users.

[1235] Main features

[1236] 1. Automatic generation of new poop drill questions

[1237] The server generates new poop drill questions based on a specific schedule. This generation is done using a program written in Python and machine learning libraries such as TensorFlow. For example, to notify the user, "There's a new poop question today!", the new questions are saved on the server and distributed to the device.

[1238] As a concrete example, by inputting the prompt "Generate a unique question related to poop" into the generative AI model, a new poop drill question is generated.

[1239] 2. Story Generation from Photos

[1240] Users take photos using their devices and upload them to the server through the app. For example, you could use a photo of an elephant taken at the zoo.

[1241] The server analyzes the uploaded photos using image recognition models in OpenCV and TensorFlow and generates a related story. The story is generated by inputting a prompt such as "Write an essay about the adventures of an elephant and its poop" into the generative AI model.

[1242] The generated story is stored on the server and delivered to the device the next time the app is launched, providing users with stories such as "The Great Adventure of an Elephant and a Poop."

[1243] 3. Create and publish an original poop game

[1244] Users can use the creator tool to create their own original poop games. For example, they can create a "poop soccer game."

[1245] The device collects the user's input settings and generates game logic using game development frameworks such as JavaScript and Unity. The device then uses a preview function to allow users to check the game's operation and make any necessary adjustments.

[1246] The completed game is uploaded to the server and published in the store, where other users can download and play it.

[1247] 4. Monetize your content

[1248] The server records the number of downloads of games and exercises published in the store and generates revenue. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[1249] Specific example explanation

[1250] When generating a new poop drill question, the prompt text used is "Generate a unique question about poop."

[1251] In generating a story from a photo, the prompt used is "Write about the adventures of an elephant and its poop."

[1252] To create an original poop game, you will use the creator tool to set the rules and stage details of the "poop soccer game."

[1253] Through these functions, the system aims to increase users' motivation to learn and stimulate their creativity.

[1254] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1255] 1. Automatic generation of new poop drill questions

[1256] Step 1: Prepare for question generation

[1257] The server creates a prompt to generate new drill questions once a week on a specific schedule (such as midnight on Sundays). The prompt includes the instruction "Please generate a unique question about poop."

[1258] Step 2: Generate questions using a generative AI model

[1259] The server inputs the prompt sentence into the generative AI model and generates a new poop drill problem using an AI model (such as GPT). The input is the prompt sentence, and the output is a new problem sentence. Specifically, it calls the AI ​​model using Python and TensorFlow libraries.

[1260] Step 3: Save the question

[1261] The server saves the generated Poop Drill questions in a database (e.g., MySQL). This database stores the questions and their metadata (e.g., creation date) in JSON format.

[1262] Step 4: Distributing the problem

[1263] The device accesses the server to download new questions when the app starts or periodically. The new questions retrieved from the server are stored in a local SQLite database, and a notification of the new question is displayed to the user. The notification reads, "There's a new poop question for today!"

[1264] 2. Story Generation from Photos

[1265] Step 1: Take and upload a photo

[1266] A user takes a photo at a zoo or park, for example, a photo of an elephant, and uses the app's "upload photo" function to send the photo from their device to the server. The input is the captured JPEG image file.

[1267] Step 2: Photo Analysis

[1268] The server processes the received files using analytics. It uses image recognition models from OpenCV and TensorFlow to analyze the photos. The input is a JPEG image file, and the output is a label for the photo (e.g., "This is a photo of an elephant").

[1269] Step 3: Narrative generation

[1270] The server generates a prompt based on the analysis results. For example, it generates a sentence such as "Write an essay about the adventures of an elephant and a poop." This prompt is input into a generative AI model to generate a story. The input is the prompt, and the output is the text of the generated story.

[1271] Step 4: Save and share your story

[1272] The server stores the generated story in a database. Then, when the app is launched, the story is delivered to the device. The device displays a notification to the user about the new story, saying, "There is a new elephant and poop adventure story."

[1273] 3. Create and publish an original poop game

[1274] Step 1: Create a game

[1275] Users use the creator tool to create their own original poop game. Users input setting information (e.g., game rules, characters, and stage details) into their device.

[1276] Step 2: Generate the game logic

[1277] The device collects user input information and generates game logic using game development frameworks such as JavaScript and Unity. The input is the user's configuration information, and the output is the game's executable file. Specifically, it provides a preview of the game based on the configuration information, allowing the user to check and modify its behavior.

[1278] Step 3: Upload your game

[1279] The device uploads the completed game data to the server. The input is the game executable file, and the output is the game information that is posted on the store on the server.

[1280] Step 4: Publish your game

[1281] The server uses a publishing method to publish the uploaded game to the store, where other users can download and play the game.

[1282] 4. Monetize your content

[1283] Step 1: Record the number of downloads

[1284] The server records the number of downloads of games and exercises published in the store, specifically by recording them in a database using a download tracking system.

[1285] Step 2: Generate revenue information

[1286] The server calculates revenue based on the number of downloads and reflects it in the user's account. A Python script is used to calculate and update revenue information and display it on the dashboard. The input is the number of downloads data and the output is the updated revenue information.

[1287] (Application example 1)

[1288] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1289] Conventional educational application systems often contained monotonous content, which did not adequately stimulate children's motivation to learn or their creativity. They also lacked a means for users to easily share and monetize the content they created. Another issue was the difficulty of smoothly purchasing and using content in virtual shops.

[1290] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1291] In this invention, the server includes: a means for users to purchase and use educational content and games within the virtual store; a generation means for generating new questions; an analysis means for analyzing multimedia data sent from the terminal and generating educational content; a publishing means for receiving educational games created by users from the terminal and publishing them in the virtual store; a revenue generating means for monetizing the use of educational content and games; and a means for generating and providing stories based on multimedia data uploaded by users from the terminal. This increases children's motivation to learn, stimulates their creativity, and makes it easier to share and monetize content created by users. Furthermore, the purchase and use of content within the virtual store can be smoothly performed.

[1292] 1. "Virtual Store" means a virtual store that users can access via the Internet, where they can purchase and use educational content and games.

[1293] 2. "Educational content" refers to learning materials and questions, as well as stories and drills based on them, that are provided primarily to increase children's motivation to learn and stimulate their creativity.

[1294] 3. "Generation tools" are technologies and methods for generating new educational content and questions using existing databases and machine learning algorithms.

[1295] 4. "Multimedia data" refers to data in multiple media formats, including photos, videos, and audio data, that is transmitted by a user using a terminal.

[1296] 5. "Analysis means" means a method using image recognition technology and natural language generation technology to analyze transmitted multimedia data and generate educational content based on that data.

[1297] 6. "Publication means" means the technology and method for receiving educational games created by users from a terminal and publishing them in a virtual store so that other users can view and download them.

[1298] 7. "Monetization Method" means the technology and methods used to generate revenue from the use of educational content and games and credit it to users' accounts.

[1299] 8. "Story generation means" refers to the technology and method for generating original stories based on multimedia data uploaded by users from their devices and providing them as educational content.

[1300] System Overview

[1301] This invention is an educational application system for enhancing children's motivation to learn and stimulating their creativity. The system involves a server, terminals, and users working together to provide a purchasing and usage experience within a virtual shopping mall.

[1302] Specific implementation methods for major functions

[1303] 1. Purchases and use within the virtual store

[1304] The server runs a virtual store that users can access using their smartphones, smart glasses, or head-mounted displays.

[1305] Users can explore, purchase and use educational content and games within the virtual store.

[1306] 2. Generating new problems

[1307] The server periodically generates new poop drill questions using existing databases and machine learning algorithms.

[1308] The generated questions are stored on a server and distributed to the device.

[1309] 3. Story Generation from Photos

[1310] The user takes a photo using the terminal and uploads it to the server.

[1311] The server analyzes the uploaded photos using image recognition and natural language generation techniques to generate an associated story.

[1312] The generated story will be delivered to the device the next time the app is launched.

[1313] 4. Creating and publishing original games

[1314] Users use the in-app creator tools to create educational games.

[1315] The terminal collects the configuration information entered by the user and generates the game logic.

[1316] The completed game data is uploaded to the server and made available in the virtual store.

[1317] 5. Monetize your content

[1318] The server records the number of downloads of the games and drill questions published in the virtual store and generates revenue based on the number of downloads.

[1319] Revenue information is reflected in the user's account and can be viewed on the dashboard.

[1320] Hardware and software used

[1321] Flask: Used to build web servers.

[1322] Werkzeug: For file processing.

[1323] Generative AI models: Machine learning models used for story generation and game generation.

[1324] Smartphone: Used as the primary interface device.

[1325] Smart glasses and head-mounted displays: Used to enhance the virtual shopping experience.

[1326] Specific examples

[1327] 1. Purchases in virtual stores

[1328] Users access a virtual store using their smartphone and purchase a specific set of poop drill problems. After purchase, the problem set is downloaded to their device and they can begin learning.

[1329] 2. Narrative Generation

[1330] The user uploads a photo of an elephant taken at the zoo to the app. The server analyzes the photo and generates an "adventure story of an elephant and its poop." This story is then sent to the device the next time the app is launched, and is provided as learning content.

[1331] 3. Example prompts

[1332] "Generate a short story about the following photo: A photo of an elephant"

[1333] "Generate poop drill questions for primary school education"

[1334] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1335] Step 1:

[1336] A user accesses the virtual store using a smartphone or head-mounted display. The input requires the user's authentication information and device ID. The output is the virtual store's home screen, which displays a list of available educational content and games.

[1337] Step 2:

[1338] A user selects and purchases educational content or games in a virtual store. The inputs include the selected content ID and payment information. The server receives this information and confirms the payment. The output is a message indicating the purchase has been completed and a download link is provided to the user.

[1339] Step 3:

[1340] The user clicks on a download link on their device to download the educational content or game they purchased. The input is the download link, and the server sends the corresponding content data. The output is that the content is saved on the device and ready for use.

[1341] Step 4:

[1342] The user takes a photo using the device's camera and uploads it to the server through a dedicated upload screen in the virtual shop. The input required is the photo data taken by the user and a description text associated with the photo. The output is a message indicating that the upload is complete.

[1343] Step 5:

[1344] The server receives the uploaded photos and analyzes them using a generative AI model. The inputs are the received photo data and a description text. The output is a generated story based on image recognition technology. The server stores this story.

[1345] Step 6:

[1346] Users create their own educational games using the app's creator tools. Game configuration information and user-created materials are required as input. The device collects this information and generates the game logic. The output is a preview of the game the user has designed.

[1347] Step 7:

[1348] The user completes the creation of a game and sends a request to the server to publish it. The input requires the completed game data and publishing instructions. The server receives this and processes the game to publish it in the virtual store. The output is a message indicating that publishing is complete, and the game becomes available for other users to download and use.

[1349] Step 8:

[1350] The server records the number of downloads of games and exercises published in the virtual store and generates revenue. The inputs are the number of downloads and a revenue calculation algorithm. The output is the generated revenue information reflected in the user's account and can be viewed on the dashboard.

[1351] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1352] System Overview

[1353] This invention is an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, devices (smartphones and tablets), and users (children). In particular, by combining it with an emotion engine that recognizes the user's emotions, the system provides an educational experience tailored to each individual user.

[1354] Key Features

[1355] 1. Automatic generation of new poop drill questions

[1356] The server periodically generates new poop drill questions based on a specific schedule using a generation means, which uses an existing database and a machine learning algorithm. The generated drill questions are stored on the server.

[1357] The device periodically accesses the server and downloads new poop drill questions. These downloaded drill questions are displayed to the user and used for learning.

[1358] 2. Story Generation from Photos

[1359] The user takes a photo using the terminal. For example, the user can use a photo taken at a zoo or a landscape photo taken at a park.

[1360] The device uploads the captured photo to a server, which then analyzes the photo using analytical means to generate related poop stories and drill questions. This analysis uses image recognition and natural language generation technologies.

[1361] The generated stories and drill questions are stored on the server and delivered to the device the next time the app is launched, where the user can view and study them.

[1362] 3. Create and publish an original poop game

[1363] Users can use the in-app creator tools to create their own poop games, such as a "poop jumping game" or a "poop maze game."

[1364] The device collects the configuration information entered by the user and generates the game logic, which the user can then preview and modify as necessary.

[1365] The completed game data is uploaded to a server and published to the store using a publishing method. Games published in the store can be downloaded and played by other users.

[1366] 4. Monetize your content

[1367] The server records the number of downloads of games and exercises published in the store and generates revenue based on that. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[1368] 5. Leveraging Emotional Engines

[1369] The device collects emotional data from the user's facial expressions and voice and sends it to the emotion engine.

[1370] The emotion engine analyzes the collected data and evaluates the user's emotional state.

[1371] Based on the analysis results of the emotion engine, the server dynamically adjusts the educational content and game difficulty to suit the user.

[1372] The device provides feedback based on the user's emotions and delivers appropriate content.

[1373] Specific examples

[1374] 1. Creation and distribution of poop drill questions

[1375] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?"

[1376] When the device starts the app, it retrieves new poop drill questions from the server and notifies the user, "There are new poop questions today!"

[1377] 2. Story Generation from Photos

[1378] Users upload photos of elephants taken at the zoo to the app, and the server analyzes the photos and generates an "adventure story of elephants and poop."

[1379] The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of the elephant and the poop has begun."

[1380] 3. Create and publish an original poop game

[1381] The user creates a "Poo Soccer Game" using the creator tool. The device receives the settings and builds the game logic.

[1382] Once the game is complete, upload it to the server and publish it to the store. If your "Poo Soccer Game" becomes popular on the store, revenue will be reflected in your account every time other users download it.

[1383] 4. Utilizing the Emotion Engine

[1384] The device uses a camera and microphone to collect the user's facial expressions and tone of voice, and sends them to the emotion engine. For example, it determines whether the user is smiling or whether their tone of voice sounds happy.

[1385] The emotion engine sends the analysis results to the server, and if it determines that the user is enjoying the content, it adjusts the content to provide more challenging educational content.

[1386] The server dynamically adjusts the difficulty of the content and distributes it to the terminal, where it is displayed to the user.

[1387] Through these functions, the present invention is a system that enhances children's motivation to learn and stimulates their creativity. By introducing an emotion engine, we can provide an educational experience that is tailored to each individual user, creating a more efficient and enjoyable learning environment.

[1388] The processing flow will be explained below.

[1389] Automatic generation of new poop drill questions

[1390] Processing Steps

[1391] Step 1:

[1392] The server runs a scheduled task every Monday at 3:00 AM and calls the API using the generated method.

[1393] Step 2:

[1394] The server analyzes the data received from the API and generates new poop drill questions.

[1395] Step 3:

[1396] The server stores the generated drill questions in the "unco_drills" database.

[1397] Step 4:

[1398] When the app is launched or at a regular interval, the device sends a request to the server to check whether there are any new poop drill questions.

[1399] Step 5:

[1400] The server receives the request and sends response data including the new poop drill question.

[1401] Step 6:

[1402] The device receives the response data, stores the new poop drill question in the app, and notifies the user.

[1403] Story generation from photos

[1404] Processing Steps

[1405] Step 1:

[1406] A user takes a photo using a smartphone.

[1407] Step 2:

[1408] The photos taken by the device are saved in the app and the user is prompted to review and select the photos.

[1409] Step 3:

[1410] The user confirms and uploads the selected photos to the server.

[1411] Step 4:

[1412] The device sends the photo data to the server.

[1413] Step 5:

[1414] The server passes the received photo data to the image analysis module, which analyzes the characteristics of the photo.

[1415] Step 6:

[1416] The server calls the API based on the characteristics of the photo and generates a related poop story.

[1417] Step 7:

[1418] The server stores the generated story data in a "stories" database and associates it with a user.

[1419] Step 8:

[1420] The terminal receives notification that the story has been created and notifies the user that a new story is available.

[1421] Step 9:

[1422] Users follow the notification to open the app and view and learn the generated story.

[1423] Create and publish an original poop game

[1424] Processing Steps

[1425] Step 1:

[1426] Users open the game creator tools within the app and set the game's theme, characters, and rules.

[1427] Step 2:

[1428] The terminal collects the user's input and stores it as initial configuration data.

[1429] Step 3:

[1430] The device calls templates and preset game modules based on the configuration data and generates the game logic and visuals.

[1431] Step 4:

[1432] The terminal provides the user with a preview of the game and receives correction instructions if necessary.

[1433] Step 5:

[1434] The user checks the final preview and gives the command to publish the game.

[1435] Step 6:

[1436] The device uploads the final game data to the server.

[1437] Step 7:

[1438] The server stores the uploaded game data in the "unco_games" database and publishes it to the store.

[1439] Step 8:

[1440] The server periodically compiles the number of downloads and user ratings of published games.

[1441] Step 9:

[1442] When the server generates revenue, it reflects the information in the corresponding user account.

[1443] Step 10:

[1444] Users can view revenue information and download numbers on the dashboard.

[1445] Utilizing the Emotion Engine

[1446] Processing Steps

[1447] Step 1:

[1448] While a user is using educational content or playing games, the device uses a camera and microphone to record the user's facial expressions and voice.

[1449] Step 2:

[1450] The facial expression and voice data collected by the device is sent to the emotion engine.

[1451] Step 3:

[1452] The emotion engine analyzes the received data and assesses the user's emotional state (e.g., enjoying, concentrating, tired, etc.).

[1453] Step 4:

[1454] The emotion engine sends the analysis results to the server and reports the user's emotional state.

[1455] Step 5:

[1456] Based on the analysis results of the emotion engine, the server dynamically adjusts the difficulty and content of educational content and games according to the user's learning progress and stress level.

[1457] Step 6:

[1458] The device follows instructions from the server and displays tailored content or games to the user.

[1459] Step 7:

[1460] As the user continues to learn based on new content and games, the emotion engine continues to monitor the user's emotions.

[1461] Specific examples

[1462] Step 1:

[1463] While the user is solving drill problems in the app, the device records the user's facial expressions (camera) and voice (microphone).

[1464] Step 2:

[1465] The device sends this data to the emotion engine.

[1466] Step 3:

[1467] The emotion engine performs analysis and assesses whether the user is struggling or enjoying solving the problem.

[1468] Step 4:

[1469] The server receives the emotion engine's evaluation results, and if it determines that the user is struggling, it instructs the server to deliver questions with a slightly lower level of difficulty.

[1470] Step 5:

[1471] The device displays new (adjusted) questions to the user, and the user continues learning.

[1472] This specific processing step allows the present invention to provide an optimal educational experience for each individual user, taking into account the user's emotional state.

[1473] Example 2

[1474] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1475] Previous educational applications lacked the ability to generate new questions, publish user-generated content, monetize it, and personalize it based on user emotions. This made it difficult to continuously stimulate users' learning and provide an optimal educational experience for each individual user. Furthermore, there were challenges in the efficiency of automatic content generation and in stimulating user creativity.

[1476] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1477] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data transmitted from the terminal and generating educational content, a publishing means for receiving educational games created by users from the terminal and publishing them, a revenue generating means for monetizing the use of the educational content and games, and an adjusting means for analyzing the user's emotions and dynamically adjusting the difficulty level of the content, thereby making it possible to provide an individually optimized educational experience while increasing the user's motivation to learn.

[1478] "Generation means" refers to the technology or method for automatically generating educational content and questions.

[1479] "Analysis means" refers to techniques or methods for analyzing multimedia data sent from a terminal and generating educational content based on that data.

[1480] "Publishing Means" refers to the technology or method for receiving educational games or content created by users and publishing them on a platform where other users can access them.

[1481] "Monetization methods" are technologies or methods for monitoring the usage of educational content or games, generating revenue based on that data, and reflecting it in users' accounts.

[1482] The "adjustment means" refers to a technique or method for analyzing the user's emotional data and dynamically adjusting the difficulty level of the content based on the analysis results.

[1483] A "server" is a computer system that stores and processes data and manages the entire system while communicating with terminals.

[1484] A "terminal" is a device used by a user, such as a smartphone or tablet, that communicates with a server to display content and send and receive data.

[1485] "Educational content" refers to teaching materials and activities such as questions, stories, and games that are provided to help users learn.

[1486] "Emotional data" is data collected from the user's facial expressions, voice, etc., and is information used to evaluate the user's emotional state.

[1487] A "user" is an individual who uses this system to enjoy educational content.

[1488] The present invention relates to an educational application system for enhancing children's motivation to learn and stimulating their creativity. The system involves a server, terminals (such as smartphones and tablets), and users working together. This system is particularly distinctive in that it includes a generation unit, an analysis unit, a publishing unit, a revenue generating unit, and an adjustment unit.

[1489] generation means

[1490] The server generates new poop drill questions every week based on a specific schedule. Python scripts and machine learning algorithms (e.g., TensorFlow) are used to generate the questions. For example, the generated questions are in the form of "Today's Poop Question: What did the poop find in the park?" The generated poop drill questions are stored in a MySQL database.

[1491] Analysis means

[1492] The system generates educational content using photos taken by users on their devices. Users upload photos of elephants taken at the zoo to the server via their devices. The server analyzes the photos using the Google Cloud Vision API and generates stories and poop drill questions related to the photos. The analysis results are generated using natural language generation models such as GPT-4, and one example is the "Adventure Story of an Elephant and Poop." This generated content is stored in a MySQL database.

[1493] Publication method

[1494] Users create educational games using the app's creator tools. The device collects the user's input settings and generates the game logic using a game development engine such as Unity or GameMaker. The completed game is uploaded to a server and published on platforms such as the Google Play Store. For example, a "Poo Soccer Game" could be published.

[1495] revenue stream

[1496] The server records the number of downloads of games and poop drills published in the store and generates revenue information based on that data. The revenue is reflected in the user's account, and the user can check the revenue information through the dashboard. For example, a certain amount of revenue is added to the user's account each time a game is downloaded.

[1497] Adjustment means

[1498] The device collects the user's emotional data using a camera and microphone and sends the collected data to an emotion engine. For example, Azure Cognitive Services can be used to analyze the user's emotional state. If the emotion engine determines that the user is "enjoying" the content, the server dynamically adjusts the difficulty level of the content based on this data. The adjusted content is then delivered to the device and displayed to the user.

[1499] Examples of concrete examples and prompts

[1500] 1. Creation and distribution of poop drill questions

[1501] The server generates new poop drill problems using Python scripts and TensorFlow.

[1502] The terminal obtains new drill questions from the server and notifies the user.

[1503] Example prompt: "Let's solve the new poop drill!"

[1504] 2. Story Generation from Photos

[1505] A user uploads a photo of an elephant taken at the zoo.

[1506] The server generates the "Adventure Story of an Elephant and Poop" using Google Cloud Vision API and GPT-4.

[1507] Example prompt: "A new story has been generated from your photos!"

[1508] By taking the above specific steps, this system can increase children's motivation to learn and provide an optimal educational experience for each individual user.

[1509] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1510] Program processing flow

[1511] 1. Automatic generation of new poop drill questions

[1512] Step 1:

[1513] The server runs a Python script according to a specific schedule to generate new poop drill questions. A machine learning model (e.g., TensorFlow) is used as the generation algorithm. The input is the existing training database and algorithm parameters, and the output is a new poop drill question.

[1514] Specific operation: The server periodically launches the script and executes the question generation logic.

[1515] Step 2:

[1516] The server stores the generated Poop Drill questions in a MySQL database. The input is the new question generated, and the output is a new entry in the database.

[1517] Specific behavior: Inserts the generated questions into the database using an SQL query.

[1518] Step 3:

[1519] When the app is launched, the device sends a request to the server's API endpoint to download new poop drill questions. The input is the API request, and the output is the retrieved new questions.

[1520] Specific operation: The terminal executes GET / api / v1 / drill_problems / latest to obtain new problems.

[1521] Step 4:

[1522] The device displays the poop drill questions it has acquired to the user. The input is the questions acquired from the server, and the output is the questions displayed on the app screen.

[1523] Specific behavior: The device displays the new problem on the UI and notifies the user, "There's a new poop problem today!"

[1524] 2. Story Generation from Photos

[1525] Step 1:

[1526] The user takes a photo using the device's camera. The input is the photo taken by the user, and the output is the captured image file.

[1527] Specific actions: The user launches the camera app and presses the photo button.

[1528] Step 2:

[1529] The device uploads the photograph to the server. The input is the photographed image file, and the output is the image data stored on the server.

[1530] Specific operation: The device sends image data using the POST / api / v1 / photos endpoint.

[1531] Step 3:

[1532] The server analyzes the photo using the Google Cloud Vision API and generates a story or drill questions based on the recognized objects. The input is the image data, and the output is the generated story text.

[1533] What it does: Send an API request to the Vision API to get the analysis results, then use GPT-4 to generate a story.

[1534] Step 4:

[1535] The server stores the generated stories and questions in a MySQL database: the input is the generated story text and the output is a new entry in the database.

[1536] What you'll do: Insert a story into a database using an SQL query.

[1537] Step 5:

[1538] The device downloads the generated content from the server and displays it to the user. The input is the story or problem retrieved from the server, and the output is the content displayed on the app screen.

[1539] Specific behavior: The device displays the new story in the UI and notifies the user that "The great adventure of the elephant and the poop has begun."

[1540] 3. Create and publish an original poop game

[1541] Step 1:

[1542] A user creates a game using the creator tools within the app. The input is the user's configuration information, and the output is game design data.

[1543] Specific operation: The user inputs the game name, number of stages, difficulty level, etc.

[1544] Step 2:

[1545] The device collects the input configuration information and generates the game logic using Unity or GameMaker. The input is the user's configuration information, and the output is the game executable file.

[1546] Specific operation: The device generates source code, compiles it, and creates the game.

[1547] Step 3:

[1548] The device uploads the completed game data to the server. The input is the game executable file, and the output is the game data stored on the server.

[1549] Specific behavior: The device sends game data using the POST / api / v1 / games endpoint.

[1550] Step 4:

[1551] The server publishes the game to the Google Play Store. The input is the game data and the output is the published game.

[1552] What happens: Your server uses the Google Play Developer API to submit your game and approve it for publication.

[1553] 4. Monetize your content

[1554] Step 1:

[1555] The server records the number of downloads in the store. The input is the download event, and the output is the number of downloads recorded in the database.

[1556] Specific behavior: Uses an API request to get the number of downloads and stores it in a database.

[1557] Step 2:

[1558] The server generates revenue information and reflects it in the user's account. The input is the number of downloads, and the output is the user revenue information.

[1559] What it does: Calculates revenue based on the number of downloads and adds it to the user's account.

[1560] Step 3:

[1561] The server displays the revenue information on the dashboard. The input is the revenue information, and the output is the content displayed on the dashboard screen.

[1562] What you'll do: Visually display revenue information using a web front end.

[1563] 5. Leveraging Emotional Engines

[1564] Step 1:

[1565] The device collects user emotion data using a camera and microphone. The input is the user's facial expression and voice, and the output is emotion data.

[1566] Specific operation: Capture data in real time using a camera or microphone.

[1567] Step 2:

[1568] The device sends the collected emotional data to the emotion engine. The input is the emotional data, and the output is the analysis result of the emotional state.

[1569] Specific operation: Encodes emotion data and sends it to the emotion engine via an API request.

[1570] Step 3:

[1571] The emotion engine analyzes the user's emotional state. The input is emotion data, and the output is the emotion analysis result.

[1572] Specific action: Run an analysis algorithm to identify the user's emotional state (e.g., "having fun").

[1573] Step 4:

[1574] The server receives the analysis results and dynamically adjusts the difficulty level of the content. The input is the sentiment analysis results, and the output is the adjusted content settings.

[1575] Specific operation: Analyze data from the emotion engine and set the appropriate level of difficulty for the content.

[1576] Step 5:

[1577] The device displays the adjusted content to the user. The input is the adjusted content settings, and the output is the content displayed on the app screen.

[1578] What happens: Applies the new content settings and tells the user "This content is perfect for you."

[1579] Through the above specific processing steps, the system can effectively increase the user's motivation to learn and provide an individually optimized educational experience.

[1580] (Application example 2)

[1581] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1582] Conventional educational application systems struggle to provide an appropriate learning experience that reflects each user's emotional state and lack the individualized support required to maintain learning motivation. Furthermore, they lack efficient management tools for monetizing and publishing real-time educational content and user-created games. Therefore, a unified and dynamic system is needed to enhance children's learning motivation.

[1583] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1584] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data transmitted from the terminal and generating educational content, a publishing means for receiving educational games created by users from the terminal and publishing them in a store, a revenue generating means for monetizing the use of the educational content and games, an emotion engine means for analyzing user emotion data and dynamically adjusting the difficulty level of the educational content, and a means for providing users with stories and questions generated based on the analysis of the multimedia data. This makes it possible to provide appropriate educational content according to the emotional state of each user, thereby realizing an advanced learning system that maintains children's motivation to learn and stimulates their creativity.

[1585] The "generation means" is a means for automatically generating new questions and educational content, and utilizes the algorithms and databases used by the server.

[1586] The "analysis means" is a means for analyzing multimedia data transmitted from a terminal and generating educational content based on the data.

[1587] The "publishing means" is a means for receiving an educational game created by a user from a terminal and publishing it in the store.

[1588] "Revenue instruments" are instruments for earning revenue based on the use of educational content and games, and for managing revenue information.

[1589] The "emotion engine means" is a means for analyzing the user's emotional data and dynamically adjusting the difficulty level and content of the educational content based on the analysis results.

[1590] "Multimedia data" is a general term for multiple types of digital data such as images, audio, and text sent from a terminal.

[1591] "Educational content" is digital content such as quizzes, stories, and games that are provided for educational purposes.

[1592] This invention provides an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones, tablets, service robots), and users (children).

[1593] Key Features

[1594] 1. Generating new problems

[1595] The server has a generating means for generating new educational content. This generating means includes an existing database and a generative AI model, and periodically generates new questions and quizzes using these. For example, it generates a quiz such as "Today's learning question: Which animal poops the biggest?" The generated quizzes are stored on the server and periodically distributed to the terminal.

[1596] 2. Multimedia Data Analysis

[1597] The user takes a photo using the device's camera function. This photo data is sent from the device to a server and analyzed by the server's analysis means. This analysis uses image recognition technology (e.g., Google Cloud Vision API) and natural language generation technology (e.g., OpenAI GPT-4). Based on the analysis results, relevant educational stories and questions are generated.

[1598] 3. Utilizing the Emotion Engine

[1599] The device collects emotional data from the user's facial expressions and voice and sends it to the server. The server then uses an emotion engine to analyze the user's emotional state and dynamically adjusts the difficulty of the learning content based on the analysis. For example, if the server determines that the user is enjoying the content, it will adjust the difficulty level to provide slightly more challenging questions.

[1600] 4. Publishing user-created games

[1601] Users can create their own educational games using the creator tools in their devices. For example, they can create a "poop jumping game" or a "poop maze game." This game data is sent from the device to a server and published to the store using the publishing means. Other users can download and play the published game, and revenue is managed using the revenue means.

[1602] Specific Examples

[1603] Example 1: Creating and distributing new questions

[1604] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?" When the device starts the app, it retrieves the new poop drill questions from the server and notifies the user, "There's a new poop question today!"

[1605] Example 2: Narrative generation from photos

[1606] The user uploads a photo of an elephant taken at the zoo to the app. The server analyzes the photo and generates an "adventure story of an elephant and its poop." The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of an elephant and its poop has begun."

[1607] Example 3: Using the Emotion Engine

[1608] The device collects the user's facial expressions and tone of voice through the camera and microphone, and sends them to the emotion engine. For example, it determines whether the user is smiling or whether their tone of voice sounds like they're having fun. The emotion engine sends the analysis results to the server, and if it determines that the user is having fun, it adjusts the educational content provided to them to be more challenging. The server then dynamically distributes the content with the adjusted difficulty to the device and displays it to the user.

[1609] Example 4: Utilizing generative AI models

[1610] Example prompt sentence:

[1611] "Generate a story about the adventures of an elephant and its poop."

[1612] This creates unique learning content that is of interest to the user.

[1613] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1614] Step 1:

[1615] The user takes a photo using the device's camera function.

[1616] Input: Photo data

[1617] Output: Photo data stored on the device

[1618] Specific behavior: The user launches the app, presses the "Take Photo" button, and takes a photo.

[1619] Step 2:

[1620] The terminal transmits the photographed photo data to the server.

[1621] Input: Photo data stored on the device

[1622] Output: Photo data uploaded to the server

[1623] Specific operation: The device selects the photo data and presses the "Upload" button, which sends the photo data to the server via the Internet.

[1624] Step 3:

[1625] The server receives the photograph data and analyzes the data using the analysis means.

[1626] Input: Photo data uploaded to the server

[1627] Output: Image recognition results (label information)

[1628] Specific operation: Uses an image analysis API (such as Google Cloud Vision API) to label the content of the photo.

[1629] Step 4:

[1630] The server generates a prompt sentence based on the analysis results and generates a story using a generative AI model (GPT-4).

[1631] Input: Image recognition results (label information)

[1632] Output: Generated narrative text

[1633] Specific operation: The label information is converted into a prompt sentence and input into the generative AI model. The generated story text is saved on the server.

[1634] Example prompt: "Generate a story about the great adventure of an elephant and a poop."

[1635] Step 5:

[1636] A server generates novel educational quizzes.

[1637] Input: Existing database, generation algorithm

[1638] Output: New quiz question

[1639] Specific operation: New quizzes are automatically generated periodically every Monday by the generation means and stored on the server.

[1640] Step 6:

[1641] The terminal accesses the server and downloads the generated story text and quiz questions.

[1642] Input: Story text stored on the server, quiz questions

[1643] Output: Story text downloaded to the device, quiz questions

[1644] Specific operation: When the app is launched, the device sends a query to the server to obtain new content.

[1645] Step 7:

[1646] The terminal displays the downloaded story text and quiz questions to the user.

[1647] Input: Story text downloaded to the device, quiz questions

[1648] Output: Educational content displayed to the user

[1649] Specific behavior: The app displays a story text and asks quiz questions. The user answers the questions.

[1650] Step 8:

[1651] The device collects the user's facial expressions and voice and transmits the emotional data to the server.

[1652] Input: User's facial expression and voice data

[1653] Output: Emotion data uploaded to the server

[1654] Specific operation: The device uses the camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data.

[1655] Step 9:

[1656] The server analyzes the collected emotional data to generate feedback and dynamically adjust the difficulty level.

[1657] Input: Emotion data uploaded to the server

[1658] Output: Tailored educational content

[1659] Specific operation: Using the emotion engine means, change the difficulty level of the educational content based on the analysis results and adjust the next distribution content.

[1660] Step 10:

[1661] A user creates a new game in the creator tool and uploads it to the server.

[1662] Input: User-created game data

[1663] Output: Game data uploaded to the server

[1664] Specific behavior: A user uses the creator tools within the app to create a game. The completed game is then sent to the server.

[1665] Step 11:

[1666] The server then publishes the received game on the store, where other users can download and use it.

[1667] Input: Game data stored on the server

[1668] Output: Games published in the store, record of downloads

[1669] Specific operation: The server uploads the game to the store using the publishing method and manages the number of downloads using the revenue method.

[1670] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1671] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1672] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1673] [Fourth embodiment]

[1674] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1675] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1676] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1677] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1678] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1679] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1680] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1681] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1682] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1683] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1684] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1685] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1686] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1687] System Overview

[1688] This invention is an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones and tablets), and users (children).

[1689] Key Features

[1690] 1. Automatic generation of new poop drill questions

[1691] The server periodically generates new poop drill questions based on a specific schedule using a generation means, which uses an existing database and a machine learning algorithm. The generated drill questions are stored on the server.

[1692] The device periodically accesses the server and downloads new poop drill questions. These downloaded drill questions are displayed to the user and used for learning.

[1693] 2. Story Generation from Photos

[1694] The user takes a photo using the terminal. For example, the user can use a photo taken at a zoo or a landscape photo taken at a park.

[1695] The device uploads the captured photo to a server, which then analyzes the photo using analytical means to generate related poop stories and drill questions. This analysis uses image recognition and natural language generation technologies.

[1696] The generated stories and drill questions are stored on the server and delivered to the device the next time the app is launched, where the user can view and study them.

[1697] 3. Create and publish an original poop game

[1698] Users can use the in-app creator tools to create their own poop games, such as a "poop jumping game" or a "poop maze game."

[1699] The device collects the configuration information entered by the user and generates the game logic, which the user can then preview and modify as necessary.

[1700] The completed game data is uploaded to a server and published to the store using a publishing method. Games published in the store can be downloaded and played by other users.

[1701] 4. Monetize your content

[1702] The server records the number of downloads of games and exercises published in the store and generates revenue based on that. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[1703] Specific examples

[1704] 1. Creation and distribution of poop drill questions

[1705] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?"

[1706] When the device starts the app, it retrieves new poop drill questions from the server and notifies the user, "There are new poop questions today!"

[1707] 2. Story Generation from Photos

[1708] Users upload photos of elephants taken at the zoo to the app, and the server analyzes the photos and generates an "adventure story of elephants and poop."

[1709] The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of the elephant and the poop has begun."

[1710] 3. Create and publish an original poop game

[1711] The user creates a "Poo Soccer Game" using the creator tool. The device receives the settings and builds the game logic.

[1712] Once the game is complete, upload it to the server and publish it to the store. If your "Poo Soccer Game" becomes popular on the store, revenue will be reflected in your account every time other users download it.

[1713] Through these functions, the present invention is a system that enhances children's motivation to learn and stimulates their creativity.

[1714] The processing flow will be explained below.

[1715] Automatic generation of new poop drill questions

[1716] Processing Steps

[1717] Step 1:

[1718] The server runs a scheduled task every Monday at 3:00 AM, calling the Google Gemini API using the generated method.

[1719] Step 2:

[1720] The server analyzes the data received from Google Gemini and generates new poop drill questions.

[1721] Step 3:

[1722] The server stores the generated drill questions in the "unco_drills" database.

[1723] Step 4:

[1724] When the app is launched or at a regular interval, the device sends a request to the server to check whether there are any new poop drill questions.

[1725] Step 5:

[1726] The server receives the request and sends response data including the new poop drill question.

[1727] Step 6:

[1728] The device receives the response data, stores the new poop drill question in the app, and notifies the user.

[1729] Story generation from photos

[1730] Processing Steps

[1731] Step 1:

[1732] A user takes a photo using a smartphone.

[1733] Step 2:

[1734] The photos taken by the device are saved in the app and the user is prompted to review and select the photos.

[1735] Step 3:

[1736] The user confirms and uploads the selected photos to the server.

[1737] Step 4:

[1738] The device sends the photo data to the server.

[1739] Step 5:

[1740] The server passes the received photo data to the image analysis module, which analyzes the characteristics of the photo.

[1741] Step 6:

[1742] The server calls the Google Gemini API based on the characteristics of the photo and generates a related poop story.

[1743] Step 7:

[1744] The server stores the generated story data in a "stories" database and associates it with a user.

[1745] Step 8:

[1746] The terminal receives notification that the story has been created and notifies the user that a new story is available.

[1747] Step 9:

[1748] Users follow the notification to open the app and view and learn the generated story.

[1749] Create and publish an original poop game

[1750] Processing Steps

[1751] Step 1:

[1752] Users open the game creator tools within the app and set the game's theme, characters, and rules.

[1753] Step 2:

[1754] The terminal collects the user's input and stores it as initial configuration data.

[1755] Step 3:

[1756] The device calls templates and preset game modules based on the configuration data and generates the game logic and visuals.

[1757] Step 4:

[1758] The terminal provides the user with a preview of the game and receives correction instructions if necessary.

[1759] Step 5:

[1760] The user checks the final preview and gives the command to publish the game.

[1761] Step 6:

[1762] The device uploads the final game data to the server.

[1763] Step 7:

[1764] The server stores the uploaded game data in the "unco_games" database and publishes it to the store.

[1765] Step 8:

[1766] The server periodically compiles the number of downloads and user ratings of published games.

[1767] Step 9:

[1768] When the server generates revenue, it reflects the information in the corresponding user account.

[1769] Step 10:

[1770] Users can view revenue information and download numbers on the dashboard.

[1771] Example 1

[1772] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1773] Conventional educational systems have the problem that it is difficult to provide personalized educational content to individual users. In addition, it is difficult to monetize the content created by users, which means that users' creativity and motivation to learn cannot be fully utilized.

[1774] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1775] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data sent from the terminal and automatically generating stories and questions using a generative AI model, and a publishing means for receiving educational games created by users from the terminal and publishing them in the store, thereby making it possible to provide personalized educational content to users and monetize the content created by users.

[1776] A "generation means" is a technology or system for generating new questions and educational content periodically or as needed.

[1777] "Analysis means" refers to a technology or system that analyzes multimedia data sent from a terminal and automatically generates stories or questions using a generative AI model.

[1778] "Publication means" refers to a technology or system for receiving educational games and content created by users from terminals and publishing them in the store.

[1779] "Monetization Measures" are technologies or systems that monetize the use of educational content and games and reflect that revenue information in users' accounts.

[1780] A "generative AI model" is an artificial intelligence model that generates novel problems or stories based on a given prompt.

[1781] A "prompt" is a sentence or text that instructs a generative AI model to generate a novel problem or story.

[1782] "Server" means a computer system that functions as a central processing unit and executes the generating means, analyzing means, publishing means, and monetizing means.

[1783] "Terminal" refers to a device used by a user, such as a smartphone or tablet, that communicates with the server and displays and operates educational content and games.

[1784] System Overview

[1785] This invention is an educational application system designed to enhance users' (children's) motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones and tablets), and users.

[1786] Main features

[1787] 1. Automatic generation of new poop drill questions

[1788] The server generates new poop drill questions based on a specific schedule. This generation is done using a program written in Python and machine learning libraries such as TensorFlow. For example, to notify the user, "There's a new poop question today!", the new questions are saved on the server and distributed to the device.

[1789] As a concrete example, by inputting the prompt "Generate a unique question related to poop" into the generative AI model, a new poop drill question is generated.

[1790] 2. Story Generation from Photos

[1791] Users take photos using their devices and upload them to the server through the app. For example, you could use a photo of an elephant taken at the zoo.

[1792] The server analyzes the uploaded photos using image recognition models in OpenCV and TensorFlow and generates a related story. The story is generated by inputting a prompt such as "Write an essay about the adventures of an elephant and its poop" into the generative AI model.

[1793] The generated story is stored on the server and delivered to the device the next time the app is launched, providing users with stories such as "The Great Adventure of an Elephant and a Poop."

[1794] 3. Create and publish an original poop game

[1795] Users can use the creator tool to create their own original poop games. For example, they can create a "poop soccer game."

[1796] The device collects the user's input settings and generates game logic using game development frameworks such as JavaScript and Unity. The device then uses a preview function to allow users to check the game's operation and make any necessary adjustments.

[1797] The completed game is uploaded to the server and published in the store, where other users can download and play it.

[1798] 4. Monetize your content

[1799] The server records the number of downloads of games and exercises published in the store and generates revenue. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[1800] Specific example explanation

[1801] When generating a new poop drill question, the prompt text used is "Generate a unique question about poop."

[1802] In generating a story from a photo, the prompt used is "Write about the adventures of an elephant and its poop."

[1803] To create an original poop game, you will use the creator tool to set the rules and stage details of the "poop soccer game."

[1804] Through these functions, the system aims to increase users' motivation to learn and stimulate their creativity.

[1805] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1806] 1. Automatic generation of new poop drill questions

[1807] Step 1: Prepare for question generation

[1808] The server creates a prompt to generate new drill questions once a week on a specific schedule (such as midnight on Sundays). The prompt includes the instruction "Please generate a unique question about poop."

[1809] Step 2: Generate questions using a generative AI model

[1810] The server inputs the prompt sentence into the generative AI model and generates a new poop drill problem using an AI model (such as GPT). The input is the prompt sentence, and the output is a new problem sentence. Specifically, it calls the AI ​​model using Python and TensorFlow libraries.

[1811] Step 3: Save the question

[1812] The server saves the generated Poop Drill questions in a database (e.g., MySQL). This database stores the questions and their metadata (e.g., creation date) in JSON format.

[1813] Step 4: Distributing the problem

[1814] The device accesses the server to download new questions when the app starts or periodically. The new questions retrieved from the server are stored in a local SQLite database, and a notification of the new question is displayed to the user. The notification reads, "There's a new poop question for today!"

[1815] 2. Story Generation from Photos

[1816] Step 1: Take and upload a photo

[1817] A user takes a photo at a zoo or park, for example, a photo of an elephant, and uses the app's "upload photo" function to send the photo from their device to the server. The input is the captured JPEG image file.

[1818] Step 2: Photo Analysis

[1819] The server processes the received files using analytics. It uses image recognition models from OpenCV and TensorFlow to analyze the photos. The input is a JPEG image file, and the output is a label for the photo (e.g., "This is a photo of an elephant").

[1820] Step 3: Narrative generation

[1821] The server generates a prompt based on the analysis results. For example, it generates a sentence such as "Write an essay about the adventures of an elephant and a poop." This prompt is input into a generative AI model to generate a story. The input is the prompt, and the output is the text of the generated story.

[1822] Step 4: Save and share your story

[1823] The server stores the generated story in a database. Then, when the app is launched, the story is delivered to the device. The device displays a notification to the user about the new story, saying, "There is a new elephant and poop adventure story."

[1824] 3. Create and publish an original poop game

[1825] Step 1: Create a game

[1826] Users use the creator tool to create their own original poop game. Users input setting information (e.g., game rules, characters, and stage details) into their device.

[1827] Step 2: Generate the game logic

[1828] The device collects user input information and generates game logic using game development frameworks such as JavaScript and Unity. The input is the user's configuration information, and the output is the game's executable file. Specifically, it provides a preview of the game based on the configuration information, allowing the user to check and modify its behavior.

[1829] Step 3: Upload your game

[1830] The device uploads the completed game data to the server. The input is the game executable file, and the output is the game information that is posted on the store on the server.

[1831] Step 4: Publish your game

[1832] The server uses a publishing method to publish the uploaded game to the store, where other users can download and play the game.

[1833] 4. Monetize your content

[1834] Step 1: Record the number of downloads

[1835] The server records the number of downloads of games and exercises published in the store, specifically by recording them in a database using a download tracking system.

[1836] Step 2: Generate revenue information

[1837] The server calculates revenue based on the number of downloads and reflects it in the user's account. A Python script is used to calculate and update revenue information and display it on the dashboard. The input is the number of downloads data and the output is the updated revenue information.

[1838] (Application example 1)

[1839] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1840] Conventional educational application systems often contained monotonous content, which did not adequately stimulate children's motivation to learn or their creativity. They also lacked a means for users to easily share and monetize the content they created. Another issue was the difficulty of smoothly purchasing and using content in virtual shops.

[1841] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1842] In this invention, the server includes: a means for users to purchase and use educational content and games within the virtual store; a generation means for generating new questions; an analysis means for analyzing multimedia data sent from the terminal and generating educational content; a publishing means for receiving educational games created by users from the terminal and publishing them in the virtual store; a revenue generating means for monetizing the use of educational content and games; and a means for generating and providing stories based on multimedia data uploaded by users from the terminal. This increases children's motivation to learn, stimulates their creativity, and makes it easier to share and monetize content created by users. Furthermore, the purchase and use of content within the virtual store can be smoothly performed.

[1843] 1. "Virtual Store" means a virtual store that users can access via the Internet, where they can purchase and use educational content and games.

[1844] 2. "Educational content" refers to learning materials and questions, as well as stories and drills based on them, that are provided primarily to increase children's motivation to learn and stimulate their creativity.

[1845] 3. "Generation tools" are technologies and methods for generating new educational content and questions using existing databases and machine learning algorithms.

[1846] 4. "Multimedia data" refers to data in multiple media formats, including photos, videos, and audio data, that is transmitted by a user using a terminal.

[1847] 5. "Analysis means" means a method using image recognition technology and natural language generation technology to analyze transmitted multimedia data and generate educational content based on that data.

[1848] 6. "Publication means" means the technology and method for receiving educational games created by users from a terminal and publishing them in a virtual store so that other users can view and download them.

[1849] 7. "Monetization Method" means the technology and methods used to generate revenue from the use of educational content and games and credit it to users' accounts.

[1850] 8. "Story generation means" refers to the technology and method for generating original stories based on multimedia data uploaded by users from their devices and providing them as educational content.

[1851] System Overview

[1852] This invention is an educational application system for enhancing children's motivation to learn and stimulating their creativity. The system involves a server, terminals, and users working together to provide a purchasing and usage experience within a virtual shopping mall.

[1853] Specific implementation methods for major functions

[1854] 1. Purchases and use within the virtual store

[1855] The server runs a virtual store that users can access using their smartphones, smart glasses, or head-mounted displays.

[1856] Users can explore, purchase and use educational content and games within the virtual store.

[1857] 2. Generating new problems

[1858] The server periodically generates new poop drill questions using existing databases and machine learning algorithms.

[1859] The generated questions are stored on a server and distributed to the device.

[1860] 3. Story Generation from Photos

[1861] The user takes a photo using the terminal and uploads it to the server.

[1862] The server analyzes the uploaded photos using image recognition and natural language generation techniques to generate an associated story.

[1863] The generated story will be delivered to the device the next time the app is launched.

[1864] 4. Creating and publishing original games

[1865] Users use the in-app creator tools to create educational games.

[1866] The terminal collects the configuration information entered by the user and generates the game logic.

[1867] The completed game data is uploaded to the server and made available in the virtual store.

[1868] 5. Monetize your content

[1869] The server records the number of downloads of the games and drill questions published in the virtual store and generates revenue based on the number of downloads.

[1870] Revenue information is reflected in the user's account and can be viewed on the dashboard.

[1871] Hardware and software used

[1872] Flask: Used to build web servers.

[1873] Werkzeug: For file processing.

[1874] Generative AI models: Machine learning models used for story generation and game generation.

[1875] Smartphone: Used as the primary interface device.

[1876] Smart glasses and head-mounted displays: Used to enhance the virtual shopping experience.

[1877] Specific examples

[1878] 1. Purchases in virtual stores

[1879] Users access a virtual store using their smartphone and purchase a specific set of poop drill problems. After purchase, the problem set is downloaded to their device and they can begin learning.

[1880] 2. Narrative Generation

[1881] The user uploads a photo of an elephant taken at the zoo to the app. The server analyzes the photo and generates an "adventure story of an elephant and its poop." This story is then sent to the device the next time the app is launched, and is provided as learning content.

[1882] 3. Example prompts

[1883] "Generate a short story about the following photo: A photo of an elephant"

[1884] "Generate poop drill questions for primary school education"

[1885] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1886] Step 1:

[1887] A user accesses the virtual store using a smartphone or head-mounted display. The input requires the user's authentication information and device ID. The output is the virtual store's home screen, which displays a list of available educational content and games.

[1888] Step 2:

[1889] A user selects and purchases educational content or games in a virtual store. The inputs include the selected content ID and payment information. The server receives this information and confirms the payment. The output is a message indicating the purchase has been completed and a download link is provided to the user.

[1890] Step 3:

[1891] The user clicks on a download link on their device to download the educational content or game they purchased. The input is the download link, and the server sends the corresponding content data. The output is that the content is saved on the device and ready for use.

[1892] Step 4:

[1893] The user takes a photo using the device's camera and uploads it to the server through a dedicated upload screen in the virtual shop. The input required is the photo data taken by the user and a description text associated with the photo. The output is a message indicating that the upload is complete.

[1894] Step 5:

[1895] The server receives the uploaded photos and analyzes them using a generative AI model. The inputs are the received photo data and a description text. The output is a generated story based on image recognition technology. The server stores this story.

[1896] Step 6:

[1897] Users create their own educational games using the app's creator tools. Game configuration information and user-created materials are required as input. The device collects this information and generates the game logic. The output is a preview of the game the user has designed.

[1898] Step 7:

[1899] The user completes the creation of a game and sends a request to the server to publish it. The input requires the completed game data and publishing instructions. The server receives this and processes the game to publish it in the virtual store. The output is a message indicating that publishing is complete, and the game becomes available for other users to download and use.

[1900] Step 8:

[1901] The server records the number of downloads of games and exercises published in the virtual store and generates revenue. The inputs are the number of downloads and a revenue calculation algorithm. The output is the generated revenue information reflected in the user's account, which can be viewed on the dashboard.

[1902] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1903] System Overview

[1904] This invention is an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, devices (smartphones and tablets), and users (children). In particular, by combining it with an emotion engine that recognizes the user's emotions, the system provides an educational experience tailored to each individual user.

[1905] Key Features

[1906] 1. Automatic generation of new poop drill questions

[1907] The server periodically generates new poop drill questions based on a specific schedule using a generation means, which uses an existing database and a machine learning algorithm. The generated drill questions are stored on the server.

[1908] The device periodically accesses the server and downloads new poop drill questions. These downloaded drill questions are displayed to the user and used for learning.

[1909] 2. Story Generation from Photos

[1910] The user takes a photo using the terminal. For example, the user can use a photo taken at a zoo or a landscape photo taken at a park.

[1911] The device uploads the captured photo to a server, which then analyzes the photo using analytical means to generate related poop stories and drill questions. This analysis uses image recognition and natural language generation technologies.

[1912] The generated stories and drill questions are stored on the server and delivered to the device the next time the app is launched, where the user can view and study them.

[1913] 3. Create and publish an original poop game

[1914] Users can use the in-app creator tools to create their own poop games, such as a "poop jumping game" or a "poop maze game."

[1915] The device collects the configuration information entered by the user and generates the game logic, which the user can then preview and modify as necessary.

[1916] The completed game data is uploaded to a server and published to the store using a publishing method. Games published in the store can be downloaded and played by other users.

[1917] 4. Monetize your content

[1918] The server records the number of downloads of games and exercises published in the store and generates revenue based on that. The revenue information is reflected in the user's account and can be viewed on the dashboard.

[1919] 5. Leveraging Emotional Engines

[1920] The device collects emotional data from the user's facial expressions and voice and sends it to the emotion engine.

[1921] The emotion engine analyzes the collected data and evaluates the user's emotional state.

[1922] Based on the analysis results of the emotion engine, the server dynamically adjusts the educational content and game difficulty to suit the user.

[1923] The device provides feedback based on the user's emotions and delivers appropriate content.

[1924] Specific examples

[1925] 1. Creation and distribution of poop drill questions

[1926] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?"

[1927] When the device starts the app, it retrieves new poop drill questions from the server and notifies the user, "There are new poop questions today!"

[1928] 2. Story Generation from Photos

[1929] Users upload photos of elephants taken at the zoo to the app, and the server analyzes the photos and generates an "adventure story of elephants and poop."

[1930] The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of the elephant and the poop has begun."

[1931] 3. Create and publish an original poop game

[1932] The user creates a "Poo Soccer Game" using the creator tool. The device receives the settings and builds the game logic.

[1933] Once the game is complete, upload it to the server and publish it to the store. If your "Poo Soccer Game" becomes popular on the store, revenue will be reflected in your account every time other users download it.

[1934] 4. Utilizing the Emotion Engine

[1935] The device uses a camera and microphone to collect the user's facial expressions and tone of voice, and sends them to the emotion engine. For example, it determines whether the user is smiling or whether their tone of voice sounds happy.

[1936] The emotion engine sends the analysis results to the server, and if it determines that the user is enjoying the content, it adjusts the content to provide more challenging educational content.

[1937] The server dynamically adjusts the difficulty of the content and distributes it to the terminal, where it is displayed to the user.

[1938] Through these functions, the present invention is a system that enhances children's motivation to learn and stimulates their creativity. By introducing an emotion engine, we can provide an educational experience that is tailored to each individual user, creating a more efficient and enjoyable learning environment.

[1939] The processing flow will be explained below.

[1940] Automatic generation of new poop drill questions

[1941] Processing Steps

[1942] Step 1:

[1943] The server runs a scheduled task every Monday at 3:00 AM and calls the API using the generated method.

[1944] Step 2:

[1945] The server analyzes the data received from the API and generates new poop drill questions.

[1946] Step 3:

[1947] The server stores the generated drill questions in the "unco_drills" database.

[1948] Step 4:

[1949] When the app is launched or at a regular interval, the device sends a request to the server to check whether there are any new poop drill questions.

[1950] Step 5:

[1951] The server receives the request and sends response data including the new poop drill question.

[1952] Step 6:

[1953] The device receives the response data, stores the new poop drill question in the app, and notifies the user.

[1954] Story generation from photos

[1955] Processing Steps

[1956] Step 1:

[1957] A user takes a photo using a smartphone.

[1958] Step 2:

[1959] The photos taken by the device are saved in the app and the user is prompted to review and select the photos.

[1960] Step 3:

[1961] The user confirms and uploads the selected photos to the server.

[1962] Step 4:

[1963] The device sends the photo data to the server.

[1964] Step 5:

[1965] The server passes the received photo data to the image analysis module, which analyzes the characteristics of the photo.

[1966] Step 6:

[1967] The server calls the API based on the characteristics of the photo and generates a related poop story.

[1968] Step 7:

[1969] The server stores the generated story data in a "stories" database and associates it with a user.

[1970] Step 8:

[1971] The terminal receives notification that the story has been created and notifies the user that a new story is available.

[1972] Step 9:

[1973] Users follow the notification to open the app and view and learn the generated story.

[1974] Create and publish an original poop game

[1975] Processing Steps

[1976] Step 1:

[1977] Users open the game creator tools within the app and set the game's theme, characters, and rules.

[1978] Step 2:

[1979] The terminal collects the user's input and stores it as initial configuration data.

[1980] Step 3:

[1981] The device calls templates and preset game modules based on the configuration data and generates the game logic and visuals.

[1982] Step 4:

[1983] The terminal provides the user with a preview of the game and receives correction instructions if necessary.

[1984] Step 5:

[1985] The user checks the final preview and gives the command to publish the game.

[1986] Step 6:

[1987] The device uploads the final game data to the server.

[1988] Step 7:

[1989] The server stores the uploaded game data in the "unco_games" database and publishes it to the store.

[1990] Step 8:

[1991] The server periodically compiles the number of downloads and user ratings of published games.

[1992] Step 9:

[1993] When the server generates revenue, it reflects the information in the corresponding user account.

[1994] Step 10:

[1995] Users can view revenue information and download numbers on the dashboard.

[1996] Utilizing the Emotion Engine

[1997] Processing Steps

[1998] Step 1:

[1999] While a user is using educational content or playing games, the device uses a camera and microphone to record the user's facial expressions and voice.

[2000] Step 2:

[2001] The facial expression and voice data collected by the device is sent to the emotion engine.

[2002] Step 3:

[2003] The emotion engine analyzes the received data and assesses the user's emotional state (e.g., enjoying, concentrating, tired, etc.).

[2004] Step 4:

[2005] The emotion engine sends the analysis results to the server and reports the user's emotional state.

[2006] Step 5:

[2007] Based on the analysis results of the emotion engine, the server dynamically adjusts the difficulty and content of educational content and games according to the user's learning progress and stress level.

[2008] Step 6:

[2009] The device follows instructions from the server and displays tailored content or games to the user.

[2010] Step 7:

[2011] As the user continues to learn based on new content and games, the emotion engine continues to monitor the user's emotions.

[2012] Specific examples

[2013] Step 1:

[2014] While the user is solving drill problems in the app, the device records the user's facial expressions (camera) and voice (microphone).

[2015] Step 2:

[2016] The device sends this data to the emotion engine.

[2017] Step 3:

[2018] The emotion engine performs analysis and assesses whether the user is struggling or enjoying solving the problem.

[2019] Step 4:

[2020] The server receives the emotion engine's evaluation results, and if it determines that the user is struggling, it instructs the server to deliver questions with a slightly lower level of difficulty.

[2021] Step 5:

[2022] The device displays new (adjusted) questions to the user, and the user continues learning.

[2023] This specific processing step allows the present invention to provide an optimal educational experience for each individual user, taking into account the user's emotional state.

[2024] Example 2

[2025] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2026] Previous educational applications lacked the ability to generate new questions, publish user-generated content, monetize it, and personalize it based on user emotions. This made it difficult to continuously stimulate users' learning and provide an optimal educational experience for each individual user. Furthermore, there were challenges in the efficiency of automatic content generation and in stimulating user creativity.

[2027] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2028] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data transmitted from the terminal and generating educational content, a publishing means for receiving educational games created by users from the terminal and publishing them, a revenue generating means for monetizing the use of the educational content and games, and an adjusting means for analyzing the user's emotions and dynamically adjusting the difficulty level of the content, thereby making it possible to provide an individually optimized educational experience while increasing the user's motivation to learn.

[2029] "Generation means" refers to the technology or method for automatically generating educational content and questions.

[2030] "Analysis means" refers to techniques or methods for analyzing multimedia data sent from a terminal and generating educational content based on that data.

[2031] "Publishing Means" refers to the technology or method for receiving educational games or content created by users and publishing them on a platform where other users can access them.

[2032] "Monetization methods" are technologies or methods for monitoring the usage of educational content or games, generating revenue based on that data, and reflecting it in users' accounts.

[2033] The "adjustment means" refers to a technique or method for analyzing the user's emotional data and dynamically adjusting the difficulty level of the content based on the analysis results.

[2034] A "server" is a computer system that stores and processes data and manages the entire system while communicating with terminals.

[2035] A "terminal" is a device used by a user, such as a smartphone or tablet, that communicates with a server to display content and send and receive data.

[2036] "Educational content" refers to teaching materials and activities such as questions, stories, and games that are provided to help users learn.

[2037] "Emotional data" is data collected from the user's facial expressions, voice, etc., and is information used to evaluate the user's emotional state.

[2038] A "user" is an individual who uses this system to enjoy educational content.

[2039] The present invention relates to an educational application system for enhancing children's motivation to learn and stimulating their creativity. The system involves a server, terminals (such as smartphones and tablets), and users working together. This system is particularly distinctive in that it includes a generation unit, an analysis unit, a publishing unit, a revenue generating unit, and an adjustment unit.

[2040] generation means

[2041] The server generates new poop drill questions every week based on a specific schedule. Python scripts and machine learning algorithms (e.g., TensorFlow) are used to generate the questions. For example, the generated questions are in the form of "Today's Poop Question: What did the poop find in the park?" The generated poop drill questions are stored in a MySQL database.

[2042] Analysis means

[2043] The system generates educational content using photos taken by users on their devices. Users upload photos of elephants taken at the zoo to the server via their devices. The server analyzes the photos using the Google Cloud Vision API and generates stories and poop drill questions related to the photos. The analysis results are generated using natural language generation models such as GPT-4, and one example is the "Adventure Story of an Elephant and Poop." This generated content is stored in a MySQL database.

[2044] Publication method

[2045] Users create educational games using the app's creator tools. The device collects the user's input settings and generates the game logic using a game development engine such as Unity or GameMaker. The completed game is uploaded to a server and published on platforms such as the Google Play Store. For example, a "Poo Soccer Game" could be published.

[2046] revenue stream

[2047] The server records the number of downloads of games and poop drills published in the store and generates revenue information based on that data. The revenue is reflected in the user's account, and the user can check the revenue information through the dashboard. For example, a certain amount of revenue is added to the user's account each time a game is downloaded.

[2048] Adjustment means

[2049] The device collects the user's emotional data using a camera and microphone and sends the collected data to an emotion engine. For example, Azure Cognitive Services can be used to analyze the user's emotional state. If the emotion engine determines that the user is "enjoying" the content, the server dynamically adjusts the difficulty level of the content based on this data. The adjusted content is then delivered to the device and displayed to the user.

[2050] Examples of concrete examples and prompts

[2051] 1. Creation and distribution of poop drill questions

[2052] The server generates new poop drill problems using Python scripts and TensorFlow.

[2053] The terminal obtains new drill questions from the server and notifies the user.

[2054] Example prompt: "Let's solve the new poop drill!"

[2055] 2. Story Generation from Photos

[2056] A user uploads a photo of an elephant taken at the zoo.

[2057] The server generates the "Adventure Story of an Elephant and Poop" using Google Cloud Vision API and GPT-4.

[2058] Example prompt: "A new story has been generated from your photos!"

[2059] By taking the above specific steps, this system can increase children's motivation to learn and provide an optimal educational experience for each individual user.

[2060] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2061] Program processing flow

[2062] 1. Automatic generation of new poop drill questions

[2063] Step 1:

[2064] The server runs a Python script according to a specific schedule to generate new poop drill questions. A machine learning model (e.g., TensorFlow) is used as the generation algorithm. The input is the existing training database and algorithm parameters, and the output is a new poop drill question.

[2065] Specific operation: The server periodically launches the script and executes the question generation logic.

[2066] Step 2:

[2067] The server stores the generated Poop Drill questions in a MySQL database. The input is the new question generated, and the output is a new entry in the database.

[2068] Specific behavior: Inserts the generated questions into the database using an SQL query.

[2069] Step 3:

[2070] When the app is launched, the device sends a request to the server's API endpoint to download new poop drill questions. The input is the API request, and the output is the retrieved new questions.

[2071] Specific operation: The terminal executes GET / api / v1 / drill_problems / latest to obtain new problems.

[2072] Step 4:

[2073] The device displays the poop drill questions it has acquired to the user. The input is the questions acquired from the server, and the output is the questions displayed on the app screen.

[2074] Specific behavior: The device displays the new problem on the UI and notifies the user, "There's a new poop problem today!"

[2075] 2. Story Generation from Photos

[2076] Step 1:

[2077] The user takes a photo using the device's camera. The input is the photo taken by the user, and the output is the captured image file.

[2078] Specific actions: The user launches the camera app and presses the photo button.

[2079] Step 2:

[2080] The device uploads the photograph to the server. The input is the photographed image file, and the output is the image data stored on the server.

[2081] Specific operation: The device sends image data using the POST / api / v1 / photos endpoint.

[2082] Step 3:

[2083] The server analyzes the photo using the Google Cloud Vision API and generates a story or drill questions based on the recognized objects. The input is the image data, and the output is the generated story text.

[2084] What it does: Send an API request to the Vision API to get the analysis results, then use GPT-4 to generate a story.

[2085] Step 4:

[2086] The server stores the generated stories and questions in a MySQL database: the input is the generated story text and the output is a new entry in the database.

[2087] What you'll do: Insert a story into a database using an SQL query.

[2088] Step 5:

[2089] The device downloads the generated content from the server and displays it to the user. The input is the story or problem retrieved from the server, and the output is the content displayed on the app screen.

[2090] Specific behavior: The device displays the new story in the UI and notifies the user that "The great adventure of the elephant and the poop has begun."

[2091] 3. Create and publish an original poop game

[2092] Step 1:

[2093] A user creates a game using the creator tools within the app. The input is the user's configuration information, and the output is game design data.

[2094] Specific operation: The user inputs the game name, number of stages, difficulty level, etc.

[2095] Step 2:

[2096] The device collects the input configuration information and generates the game logic using Unity or GameMaker. The input is the user's configuration information, and the output is the game executable file.

[2097] Specific operation: The device generates source code, compiles it, and creates the game.

[2098] Step 3:

[2099] The device uploads the completed game data to the server. The input is the game executable file, and the output is the game data stored on the server.

[2100] Specific behavior: The device sends game data using the POST / api / v1 / games endpoint.

[2101] Step 4:

[2102] The server publishes the game to the Google Play Store. The input is the game data and the output is the published game.

[2103] What happens: Your server uses the Google Play Developer API to submit your game and approve it for publication.

[2104] 4. Monetize your content

[2105] Step 1:

[2106] The server records the number of downloads in the store. The input is the download event, and the output is the number of downloads recorded in the database.

[2107] Specific behavior: Uses an API request to get the number of downloads and stores it in a database.

[2108] Step 2:

[2109] The server generates revenue information and reflects it in the user's account. The input is the number of downloads, and the output is the user revenue information.

[2110] What it does: Calculates revenue based on the number of downloads and adds it to the user's account.

[2111] Step 3:

[2112] The server displays the revenue information on the dashboard. The input is the revenue information, and the output is the content displayed on the dashboard screen.

[2113] What you'll do: Visually display revenue information using a web front end.

[2114] 5. Leveraging Emotional Engines

[2115] Step 1:

[2116] The device collects user emotion data using a camera and microphone. The input is the user's facial expression and voice, and the output is emotion data.

[2117] Specific operation: Capture data in real time using a camera or microphone.

[2118] Step 2:

[2119] The device sends the collected emotional data to the emotion engine. The input is the emotional data, and the output is the analysis result of the emotional state.

[2120] Specific operation: Encodes emotion data and sends it to the emotion engine via an API request.

[2121] Step 3:

[2122] The emotion engine analyzes the user's emotional state. The input is emotion data, and the output is the emotion analysis result.

[2123] Specific action: Run an analysis algorithm to identify the user's emotional state (e.g., "having fun").

[2124] Step 4:

[2125] The server receives the analysis results and dynamically adjusts the difficulty level of the content. The input is the sentiment analysis results, and the output is the adjusted content settings.

[2126] Specific operation: Analyze data from the emotion engine and set the appropriate level of difficulty for the content.

[2127] Step 5:

[2128] The device displays the adjusted content to the user. The input is the adjusted content settings, and the output is the content displayed on the app screen.

[2129] What happens: Applies the new content settings and tells the user "This content is perfect for you."

[2130] Through the above specific processing steps, the system can effectively increase the user's motivation to learn and provide an individually optimized educational experience.

[2131] (Application example 2)

[2132] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2133] Conventional educational application systems struggle to provide an appropriate learning experience that reflects each user's emotional state and lack the individualized support required to maintain learning motivation. Furthermore, they lack efficient management tools for monetizing and publishing real-time educational content and user-created games. Therefore, a unified and dynamic system is needed to enhance children's learning motivation.

[2134] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2135] In this invention, the server includes a generating means for generating new questions, an analyzing means for analyzing multimedia data transmitted from the terminal and generating educational content, a publishing means for receiving educational games created by users from the terminal and publishing them in a store, a revenue generating means for monetizing the use of the educational content and games, an emotion engine means for analyzing user emotion data and dynamically adjusting the difficulty level of the educational content, and a means for providing users with stories and questions generated based on the analysis of the multimedia data. This makes it possible to provide appropriate educational content according to the emotional state of each user, thereby realizing an advanced learning system that maintains children's motivation to learn and stimulates their creativity.

[2136] The "generation means" is a means for automatically generating new questions and educational content, and utilizes the algorithms and databases used by the server.

[2137] The "analysis means" is a means for analyzing multimedia data transmitted from a terminal and generating educational content based on the data.

[2138] The "publishing means" is a means for receiving an educational game created by a user from a terminal and publishing it in the store.

[2139] "Revenue instruments" are instruments for earning revenue based on the use of educational content and games, and for managing revenue information.

[2140] The "emotion engine means" is a means for analyzing the user's emotional data and dynamically adjusting the difficulty level and content of the educational content based on the analysis results.

[2141] "Multimedia data" is a general term for multiple types of digital data such as images, audio, and text sent from a terminal.

[2142] "Educational content" is digital content such as quizzes, stories, and games that are provided for educational purposes.

[2143] This invention provides an educational application system that aims to increase children's motivation to learn and stimulate their creativity. This system operates in cooperation with a server, terminals (smartphones, tablets, service robots), and users (children).

[2144] Key Features

[2145] 1. Generating new problems

[2146] The server has a generating means for generating new educational content. This generating means includes an existing database and a generative AI model, and periodically generates new questions and quizzes using these. For example, it generates a quiz such as "Today's learning question: Which animal poops the biggest?" The generated quizzes are stored on the server and periodically distributed to the terminal.

[2147] 2. Multimedia Data Analysis

[2148] The user takes a photo using the device's camera function. This photo data is sent from the device to a server and analyzed by the server's analysis means. This analysis uses image recognition technology (e.g., Google Cloud Vision API) and natural language generation technology (e.g., OpenAI GPT-4). Based on the analysis results, relevant educational stories and questions are generated.

[2149] 3. Utilizing the Emotion Engine

[2150] The device collects emotional data from the user's facial expressions and voice and sends it to the server. The server then uses an emotion engine to analyze the user's emotional state and dynamically adjusts the difficulty of the learning content based on the analysis. For example, if the server determines that the user is enjoying the content, it will adjust the difficulty level to provide slightly more challenging questions.

[2151] 4. Publishing user-created games

[2152] Users can create their own educational games using the creator tools in their devices. For example, they can create a "poop jumping game" or a "poop maze game." This game data is sent from the device to a server and published to the store using the publishing means. Other users can download and play the published game, and revenue is managed using the revenue means.

[2153] Specific Examples

[2154] Example 1: Creating and distributing new questions

[2155] The server generates new poop drill questions every Monday and stores them in the database. For example, it automatically generates questions such as "Today's poop question: What did the poop find in the park?" When the device starts the app, it retrieves the new poop drill questions from the server and notifies the user, "There's a new poop question today!"

[2156] Example 2: Narrative generation from photos

[2157] The user uploads a photo of an elephant taken at the zoo to the app. The server analyzes the photo and generates an "adventure story of an elephant and its poop." The device displays the generated story to the user, providing it as learning content, saying, "The great adventure of an elephant and its poop has begun."

[2158] Example 3: Using the Emotion Engine

[2159] The device collects the user's facial expressions and tone of voice through the camera and microphone, and sends them to the emotion engine. For example, it determines whether the user is smiling or whether their tone of voice sounds like they're having fun. The emotion engine sends the analysis results to the server, and if it determines that the user is having fun, it adjusts the educational content provided to them to be more challenging. The server then dynamically distributes the content with the adjusted difficulty to the device and displays it to the user.

[2160] Example 4: Utilizing generative AI models

[2161] Example prompt sentence:

[2162] "Generate a story about the adventures of an elephant and its poop."

[2163] This creates unique learning content that is of interest to the user.

[2164] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2165] Step 1:

[2166] The user takes a photo using the device's camera function.

[2167] Input: Photo data

[2168] Output: Photo data stored on the device

[2169] Specific behavior: The user launches the app, presses the "Take Photo" button, and takes a photo.

[2170] Step 2:

[2171] The terminal transmits the photographed photo data to the server.

[2172] Input: Photo data stored on the device

[2173] Output: Photo data uploaded to the server

[2174] Specific operation: The device selects the photo data and presses the "Upload" button, which sends the photo data to the server via the Internet.

[2175] Step 3:

[2176] The server receives the photograph data and analyzes the data using the analysis means.

[2177] Input: Photo data uploaded to the server

[2178] Output: Image recognition results (label information)

[2179] Specific operation: Uses an image analysis API (such as Google Cloud Vision API) to label the content of the photo.

[2180] Step 4:

[2181] The server generates a prompt sentence based on the analysis results and generates a story using a generative AI model (GPT-4).

[2182] Input: Image recognition results (label information)

[2183] Output: Generated narrative text

[2184] Specific operation: The label information is converted into a prompt sentence and input into the generative AI model. The generated story text is saved on the server.

[2185] Example prompt: "Generate a story about the great adventure of an elephant and a poop."

[2186] Step 5:

[2187] A server generates novel educational quizzes.

[2188] Input: Existing database, generation algorithm

[2189] Output: New quiz question

[2190] Specific operation: New quizzes are automatically generated periodically every Monday by the generation means and stored on the server.

[2191] Step 6:

[2192] The terminal accesses the server and downloads the generated story text and quiz questions.

[2193] Input: Story text stored on the server, quiz questions

[2194] Output: Story text downloaded to the device, quiz questions

[2195] Specific operation: When the app is launched, the device sends a query to the server to obtain new content.

[2196] Step 7:

[2197] The terminal displays the downloaded story text and quiz questions to the user.

[2198] Input: Story text downloaded to the device, quiz questions

[2199] Output: Educational content displayed to the user

[2200] Specific behavior: The app displays a story text and asks quiz questions. The user answers the questions.

[2201] Step 8:

[2202] The device collects the user's facial expressions and voice and transmits the emotional data to the server.

[2203] Input: User's facial expression and voice data

[2204] Output: Emotion data uploaded to the server

[2205] Specific operation: The device uses the camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data.

[2206] Step 9:

[2207] The server analyzes the collected emotional data to generate feedback and dynamically adjust the difficulty level.

[2208] Input: Emotion data uploaded to the server

[2209] Output: Tailored educational content

[2210] Specific operation: Using the emotion engine means, change the difficulty level of the educational content based on the analysis results and adjust the next distribution content.

[2211] Step 10:

[2212] A user creates a new game in the creator tool and uploads it to the server.

[2213] Input: User-created game data

[2214] Output: Game data uploaded to the server

[2215] Specific behavior: A user uses the creator tools within the app to create a game. The completed game is then sent to the server.

[2216] Step 11:

[2217] The server then publishes the received game on the store, where other users can download and use it.

[2218] Input: Game data stored on the server

[2219] Output: Games published in the store, record of downloads

[2220] Specific operation: The server uploads the game to the store using the publishing method and manages the number of downloads using the revenue method.

[2221] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2222] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2223] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2224] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2225] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2226] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2227] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2228] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2229] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2230] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2231] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2232] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2233] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2234] 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.

[2235] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2236] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2237] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2238] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2239] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2240] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2241] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2242] The following is further disclosed regarding the above embodiment.

[2243] (Claim 1)

[2244] a generating means for generating new problems;

[2245] analysis means for analyzing multimedia data transmitted from the terminal and generating educational content;

[2246] a publishing means for receiving the educational game created by the user from the terminal and publishing it in the store;

[2247] and a revenue means for monetizing the use of educational content and games.

[2248] (Claim 2)

[2249] 2. The system according to claim 1, wherein the generating means further comprises means for storing the generated educational content on a server and distributing it to the terminal.

[2250] (Claim 3)

[2251] 10. The system of claim 1, wherein the analyzing means further comprises means for analyzing the multimedia data to automatically generate a story or question.

[2252] "Example 1"

[2253] (Claim 1)

[2254] a generating means for generating new problems;

[2255] analysis means for analyzing multimedia data transmitted from the terminal and generating educational content;

[2256] a publishing means for receiving the educational game created by the user from the terminal and publishing it in the store;

[2257] and a revenue means for monetizing the use of educational content and games.

[2258] (Claim 2)

[2259] 10. The system of claim 1, wherein the generating means further comprises means for storing the generated educational content in the central processing unit and distributing it to the terminal.

[2260] (Claim 3)

[2261] 10. The system of claim 1, wherein the analysis means further comprises means for analyzing image data transmitted from the terminal and automatically generating stories and questions using a generative AI model.

[2262] "Application Example 1"

[2263] (Claim 1)

[2264] A means for users to purchase and use educational content and games within the virtual store;

[2265] a generating means for generating new problems;

[2266] analysis means for analyzing multimedia data transmitted from the terminal and generating educational content;

[2267] publishing means for receiving the educational game created by the user from the terminal and publishing it in the virtual store;

[2268] revenue vehicles to monetize the use of educational content and games;

[2269] A means for generating and providing a story based on multimedia data uploaded by a user from a terminal;

[2270] A system including:

[2271] (Claim 2)

[2272] 2. The system according to claim 1, wherein the generating means further comprises means for storing the generated educational content on a server and distributing it to the terminal.

[2273] (Claim 3)

[2274] 10. The system of claim 1, wherein the analyzing means further comprises means for analyzing the multimedia data to automatically generate a story or question.

[2275] "Example 2: Combining Emotion Engines"

[2276] (Claim 1)

[2277] a generating means for generating new problems;

[2278] analysis means for analyzing multimedia data transmitted from the terminal and generating educational content;

[2279] a publishing means for receiving an educational game created by a user from a terminal and publishing the game;

[2280] revenue vehicles to monetize the...

Claims

1. a generating means for generating new problems; analysis means for analyzing multimedia data transmitted from the terminal and generating educational content; a publishing means for receiving the educational game created by the user from the terminal and publishing it in the store; and a revenue means for monetizing the use of educational content and games.

2. 2. The system according to claim 1, wherein the generating means further comprises means for storing the generated educational content in a server and distributing it to the terminal.

3. 10. The system of claim 1, wherein the analyzing means further comprises means for analyzing the multimedia data to automatically generate a story or question.

Images