System

Abstract

A system is provided.SOLUTION: The system includes a generation system artificial intelligence means for generating a question on the basis of a topic and a language selected by a user, an artificial intelligence means for receiving an answer input by the user and automatically generating a draft of a blog article on the basis of the answer, and an artificial intelligence means for evaluating the progress of language learning for the generated draft of the blog article and providing feedback.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] Conventional language learning and blogging methods face challenges, such as the difficulty of providing a truly immersive learning experience. Furthermore, the lack of practical experience in a real-world environment limits the effectiveness of learning. Furthermore, blogging requires skills and knowledge regarding text composition and editing, which can be challenging, especially in the early stages of language learning. To address these challenges, a system that utilizes generative artificial intelligence (AI) is needed to enable users to efficiently create blogs while learning a language. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that utilizes generative artificial intelligence. Specifically, the system includes a generative artificial intelligence means for generating questions based on a topic and language selected by a user, an artificial intelligence means for receiving answers entered by the user and automatically generating draft blog posts based on the answers, and an artificial intelligence means for evaluating the language learning progress of the generated draft blog posts and providing feedback. The system also includes an authentication means for users to create and log in to their accounts, and a means for providing a web interface accessed by the users, allowing users to use the system efficiently. The system further includes a means for transmitting the topic and language selected by the user to a server, which then uses the generative artificial intelligence means to create a question-and-answer session, and a means for transmitting the answers entered by the user to the server and storing draft blog posts generated based on the answers on the server, thereby providing a practical, integrated environment for language learning and blog creation for users.

[0006] "Generative AI means" is a technology for automatically generating questions and answers in natural language based on user input.

[0007] A "topic" is a theme or subject matter that a user selects as the content of a blog post.

[0008] "Language" refers to the language used by the user and the natural language that is the target of learning on the system.

[0009] A "user" is someone who uses this system to learn a language while creating blog posts.

[0010] A "blog post draft" is an initial version of a blog post that is automatically generated by the generative artificial intelligence means based on the user's responses.

[0011] The "progress evaluation means" is a technique for evaluating the progress of a user's language learning based on the generated draft blog article.

[0012] "Feedback" refers to guidance and suggestions for improvement provided to the user generated by the progress evaluation means.

[0013] "Authentication means" refers to technology used to verify a user's credentials when they create an account and log in.

[0014] "Web Interface" means an online page or application that allows a user to access and operate a system.

[0015] A "session" is a question and answer exchange based on a user-selected topic and language.

[0016] A "server" is a central computer that manages data and processing for the entire system and provides information to user terminals. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention relates to a system that supports users in creating blog articles while efficiently learning a language, and utilizes generative artificial intelligence (AI). The following describes in detail the embodiments of the invention.

[0039] System Overview

[0040] This system consists of a server, a terminal, and a user. The server manages the database and generative AI model, and processes and evaluates the content created by users. The terminal provides an interface for users to access and operate the system. Users use this system to learn languages ​​while creating blog articles.

[0041] Program processing flow

[0042] 1. Create an account and log in

[0043] The user accesses the web interface on their device and creates a new account, entering a username, password, and desired language of study.

[0044] The terminal sends the entered information to the server, which stores the new user information in a database.

[0045] When a user enters their username and password on the login page, the device sends it to the server, which authenticates them, and if successful, redirects them to the dashboard.

[0046] 2. Choose your blog topic and language

[0047] A user selects a blog topic and language on the dashboard.

[0048] The terminal transmits the selected information to the server, which accepts it.

[0049] 3. Question and Answer Generation

[0050] The server uses a generative AI model to generate questions based on the topic and language selected by the user.

[0051] The server sends the generated question to the terminal and presents it to the user.

[0052] 4. Drafting a blog post

[0053] The user enters an answer to the question.

[0054] The terminal sends the entered answer to the server, which receives it.

[0055] The server uses a generative AI model to automatically generate a draft blog post based on your answers.

[0056] The server transmits the generated draft to the terminal and presents it to the user.

[0057] 5. Writing Progress Assessment and Feedback

[0058] The server uses a generative AI model to evaluate users' drafts, including their language learning progress and writing quality.

[0059] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[0060] Users then edit the draft based on the feedback and finalize the blog post.

[0061] Specific examples

[0062] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" The user inputs answers to each question, answering, for example, "To relax and have new experiences," or "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[0063] The system allows users to improve their practical writing skills while learning a new language.

[0064] The processing flow will be explained below.

[0065] Program processing steps

[0066] 1. Create an account and log in

[0067] Step 1:

[0068] The user accesses the web interface on their device and opens the account creation page.

[0069] Step 2:

[0070] The user enters the required information (username, password, desired language to learn).

[0071] Step 3:

[0072] The terminal transmits the input information to the server.

[0073] Step 4:

[0074] The server stores the received user information in a database and returns a message indicating that the account has been created.

[0075] Step 5:

[0076] The user enters their username and password on the login page.

[0077] Step 6:

[0078] The terminal transmits the entered authentication information to the server.

[0079] Step 7:

[0080] The server checks the user against the database and performs authentication, and if authentication is successful, returns a login success message to the user and redirects them to the dashboard.

[0081] 2. Choose your blog topic and language

[0082] Step 1:

[0083] A user selects a blog topic and language on the dashboard.

[0084] Step 2:

[0085] The terminal transmits the selected information to the server.

[0086] Step 3:

[0087] The server accepts the selected topic and language and prepares for the next process.

[0088] 3. Question and Answer Generation

[0089] Step 1:

[0090] The server runs a generative AI model to generate questions based on the topic and language selected by the user.

[0091] Step 2:

[0092] The server sends the generated question to the terminal and presents it to the user.

[0093] Step 3:

[0094] The user uses the terminal to enter answers to the questions.

[0095] Step 4:

[0096] The terminal sends the user's answer to the server.

[0097] 4. Drafting a blog post

[0098] Step 1:

[0099] The server uses a generative AI model to automatically generate a draft blog post based on the user's responses.

[0100] Step 2:

[0101] The server transmits the generated draft to the terminal and presents it to the user.

[0102] Step 3:

[0103] The user reviews the draft and makes edits as necessary.

[0104] Step 4:

[0105] The user sends the edited content from the terminal to the server.

[0106] 5. Writing Progress Assessment and Feedback

[0107] Step 1:

[0108] The server uses a generative AI model to assess the progress of the user's draft.

[0109] Step 2:

[0110] The server generates feedback including the evaluation results and improvements.

[0111] Step 3:

[0112] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[0113] Step 4:

[0114] The user checks the feedback on the device and re-edits the draft if necessary.

[0115] Step 5:

[0116] The user completes the final blog post and sends it from the device to the server for storage.

[0117] Through these steps, users can efficiently learn a language while creating high-quality blog articles.

[0118] Example 1

[0119] 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."

[0120] In modern society, there is a demand for the use of generative artificial intelligence as a means of efficiently generating content while learning. Systems that allow learners to simultaneously study a language and write blog posts would be particularly useful, but few systems offer such functionality. Conventional systems often generate content based on user input inefficiently, limiting learning outcomes. Furthermore, they lack evaluation functions to provide appropriate feedback, making it difficult for users to grasp their own progress.

[0121] 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.

[0122] In this invention, the server includes a generative AI means for generating questions based on a theme and language selected by the user, an AI means for receiving responses entered by the user and automatically generating a draft document based on the responses, and an AI means for evaluating the learning progress of the generated draft document and providing feedback. This enables users to efficiently progress with language learning while creating practical blog articles.

[0123] "Generative AI means" refers to AI technology that generates appropriate questions based on themes and languages ​​selected by the user, and supports the automatic generation of content.

[0124] "Authentication means" refers to a function that verifies user login information and ensures that only users with legitimate access rights can access the system.

[0125] "Web interface means" refers to a user interface that allows a user to access and operate the system online.

[0126] "Means for constructing a question and answer session" refers to a function that uses generative artificial intelligence means to create appropriate questions based on user selections and sets up a process for obtaining user responses.

[0127] "Artificial intelligence means for automatically generating a draft" means an artificial intelligence function that automatically generates an initial version of a document based on responses entered by a user.

[0128] The "evaluation means" is a function that evaluates the grammar and content of the generated draft blog article and provides the results to the user.

[0129] "Means for providing feedback" refers to a function that supports learning by providing advice and corrections based on the quality of the draft generated by the user and the learning progress.

[0130] "Means for storage" refers to the ability to store user-created data or system-generated data in an internal database so that it can be reused later.

[0131] A "theme" is a particular topic or topic that a user chooses as the content of a blog post.

[0132] A "response" is text information that a user inputs in response to a question presented by the generative artificial intelligence means.

[0133] The present invention relates to a system that supports users in creating blog articles while learning a language effectively and efficiently. This system utilizes a generative artificial intelligence model. A specific embodiment of this system will be described below.

[0134] System Overview

[0135] This system consists of a server, a terminal, and a user. The server manages the database and generative AI model, and generates and evaluates sentences written by users. For example, the well-known GPT-3 can be used as a generative AI model. The terminal provides an interface for users to access and operate the system. Users use this system to progress through language learning while creating blog posts.

[0136] Account creation and login

[0137] A user uses a terminal to access the web interface and create a new account. They enter their username, password, and desired language to learn. The terminal sends this information to the server, which stores it in a database. When the user enters their username and password on the login page, the terminal sends these credentials to the server, which authenticates them. If authentication is successful, the user is redirected to the dashboard.

[0138] Choosing a blog topic and learning language

[0139] Users select a blog topic and learning language on the dashboard. The device sends the selected information to the server, which accepts it. The server uses a generative artificial intelligence model to generate questions based on the selected topic and language. For example, users can select the topics "travel" or "learning." Questions generated include "What is the purpose of your trip?" and "Where would you like to visit this summer?"

[0140] Question and answer generation

[0141] The server uses a generative artificial intelligence model to generate questions based on the topic and language selected by the user. The generated questions are presented to the user via the terminal. The user then enters a response, which is then sent to the server.

[0142] Drafting a blog post

[0143] Based on the received response, the server uses a generative AI model to generate a draft blog post. The draft is then presented to the user via their device. If the user responds with something like "To relax and have a new experience," the response is reflected in the document.

[0144] Writing progress assessment and feedback

[0145] The server uses a generative AI model to evaluate the learning progress and quality of the generated draft and provides feedback. The user then edits the draft based on the feedback and completes the final blog post. For example, the server provides advice such as "There are grammatical errors" or "The content is insufficient."

[0146] Specific examples

[0147] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative artificial intelligence model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" The user might respond with "To relax and have new experiences" or "Kyoto is a beautiful place rich in history and culture." The server generates a draft based on these answers and presents it to the user. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[0148] The system allows users to improve their practical writing skills while learning a new language.

[0149] Prompt Sentence Examples

[0150] Generate a question with the topic "Travel" and language "Japanese".

[0151] Generate a draft blog post that answers the question, "What is the purpose of your trip?"

[0152] By using this system, users can learn languages ​​effectively and efficiently.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1:

[0155] A user accesses a web interface on a terminal and creates a new account. The user enters a username, password, and desired language to learn. The terminal sends this information to the server, which stores it in a database as a new user. The input to this process is the username, password, and desired language entered by the user, and the output is the new user information stored in the database.

[0156] Step 2:

[0157] The user enters their username and password on the login page. The device sends this authentication information to the server. The server compares the received information with the database and performs authentication. If authentication is successful, the server redirects the user to the dashboard and sends a success notification to the device. The input is the username and password, and the output is the authentication success / failure result and the display of the dashboard.

[0158] Step 3:

[0159] The user selects a blog topic and learning language on the dashboard. The device sends the selected information to the server. The server uses the received information to create a prompt for the generative AI model and prepares to generate a question. The input is the topic and learning language selected by the user, and the output is a prompt for the generative AI model.

[0160] Step 4:

[0161] The server uses a generative AI model to generate questions based on the topic and language selected by the user. The generated questions are sent to the device and presented to the user. The input is the prompt from the generative AI model, and the output is the generated question. The server can generate questions like, "What is the purpose of your trip?"

[0162] Step 5:

[0163] The user inputs a response to a question generated by the terminal. The terminal sends the input response to the server. The input is the user's response, and the output is the data sent to the server. For example, the user inputs "To relax and have a new experience."

[0164] Step 6:

[0165] The server generates a draft of a blog post using a generative AI model based on the received response. The server then sends the generated draft to the device. The input is the user's response, and the output is the generated draft. For example, the server might generate a draft that reads, "Traveling is a great way to relax and experience new things. Kyoto, in particular, is worth visiting because it is rich in history and culture."

[0166] Step 7:

[0167] The server uses a generative AI model to evaluate the learning progress and quality of the generated draft and provides feedback. The server sends the evaluation results and feedback to the device and presents them to the user. The input is the generated draft, and the output is the evaluation results and feedback. Typically, feedback such as "The sentence structure is good, but there are some grammatical errors" is generated.

[0168] Step 8:

[0169] The user edits the draft based on the feedback and completes the final blog post. The device sends the edited post to the server, which then saves the final post in a database. The input is the edited draft, and the output is the saved final post. Specifically, the user corrects "grammatical errors" and saves the completed post.

[0170] Through the above processing steps, users can effectively advance their language learning while creating practical blog articles.

[0171] (Application example 1)

[0172] 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."

[0173] Conventional language learning systems have the drawback of making it difficult for users to effectively learn a language while engaging in practical writing. In particular, the process of users writing blog posts about topics of their interest and simultaneously evaluating and providing feedback on the content is complicated, making it difficult to improve language skills. Furthermore, many systems are not optimized for smartphone environments, often resulting in a loss of user convenience.

[0174] 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.

[0175] In this invention, the server includes generative artificial intelligence means for generating questions based on a topic and language selected by a user, artificial intelligence means for receiving answers entered by the user and automatically generating draft blog articles based on the answers, artificial intelligence means for evaluating the language learning progress of the generated draft blog articles and providing feedback, means for transmitting the topic and language selected by the user to the server and using the generative artificial intelligence means to generate questions and present them to the user, means for transmitting the answers entered by the user to the server and using the generative artificial intelligence means to generate and evaluate draft blog articles based on the answers, and means including an application to be installed on a smartphone, thereby enabling users to use their smartphones to easily and efficiently create blog articles while learning a language.

[0176] "User" refers to an individual who uses the system to create blog articles while studying a language.

[0177] A "topic" refers to a theme or topic that a user chooses as the content of a blog post.

[0178] "Language" refers to the language that a user learns and uses to create blog posts.

[0179] "Generative AI methods" refers to AI technology that generates questions based on the topic and language selected by the user, and automatically generates draft blog posts based on the answers.

[0180] "Question" refers to a question posed by the generative artificial intelligence means regarding content related to a topic selected by a user.

[0181] "Answer" refers to the content that a user inputs in response to a question generated by the generative artificial intelligence means.

[0182] "Draft blog post" refers to the initial version of a blog post that is automatically created by the generative artificial intelligence means based on responses entered by the user.

[0183] "Evaluation" refers to the process of measuring the language learning progress and writing quality of the generated draft blog posts and providing appropriate feedback.

[0184] "Feedback" refers to advice and corrections provided to the user as a result of evaluation of the generated draft blog post.

[0185] "Authentication means" refers to the technology that authenticates users when they create an account and log in.

[0186] "Web Interface" refers to a browser-based user interface that allows a user to access and operate the system.

[0187] "Server" refers to a computer system that manages a database and generative artificial intelligence means, and processes and stores data sent by users.

[0188] "Smartphone application" refers to a dedicated application that is installed on a smartphone and allows users to study languages ​​and write blog articles.

[0189] System Overview

[0190] This invention is a system that allows users to efficiently create blog articles while learning a language. The system consists of a server, a user's smartphone, and the user. The server manages the database and generative AI model, and generates and evaluates content according to the user's operations. The smartphone provides the user with an interface that allows them to access and operate the system. The user uses this system to learn a language while creating blog articles.

[0191] Hardware and Software Used

[0192] Hardware: Servers, smartphones

[0193] software:

[0194] Flask: A Python-based web framework for server-side use.

[0195] Generative AI model: AI model for question generation, article generation, and evaluation

[0196] Database system: stores user information and generated articles

[0197] Processing flow

[0198] 1. Create an account and log in:

[0199] A user creates an account through a smartphone application and logs in. During this process, authentication information is sent to the server and stored in a database.

[0200] 2. Topic and language selection:

[0201] The user uses the smartphone application dashboard to select the topics and languages ​​they are interested in, and the selected information is sent to the server.

[0202] 3. Question generation and answer input:

[0203] The server uses a generative AI model to generate questions based on the user's selections. The generated questions are presented to the user via a smartphone application, and the user inputs answers.

[0204] 4. Blog post draft generation:

[0205] The user's answers are sent to a server, and a generative AI model automatically generates a draft blog post based on the answers. The draft is then presented to the user via their smartphone.

[0206] 5. Writing evaluation and feedback:

[0207] The server uses a generative AI model to evaluate the generated drafts, including language learning progress and writing quality, and provides the resulting feedback to the user via a smartphone application.

[0208] Specific examples

[0209] For example, if a user wants to write a blog post in English about "latest technology trends," the following process would be performed:

[0210] Topic selection: The user selects the topic "Technology" and the language of study "English."

[0211] Question generation: A server-side generative AI model generates questions such as:

[0212] "What is the latest tech trend you are excited about?"

[0213] "Describe a technology you think will change the world."

[0214] Answer input: The user inputs the answer to each question and sends it to the server via the smartphone application.

[0215] Draft generation: Based on these answers, the server uses a generative AI model to automatically generate a draft blog post.

[0216] Evaluation and feedback: The generated draft is evaluated on the server, and the evaluation results and feedback are provided to the user.

[0217] This system allows users to use their smartphones to easily and efficiently create blog articles while learning a language.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] A user launches a smartphone application, creates an account, and logs in. The user enters their username, password, and language of study through the application interface, and this information is sent from the device to the server. The server stores the received information in a database and manages authentication information.

[0221] Input: Username, Password, Learning Language

[0222] Data processing: The server stores the received data in a database

[0223] Output: New account created, login authentication successful message

[0224] Step 2:

[0225] After logging in, users select the topic and language of their blog post from the application's dashboard. The selected information is sent from the device to the server, which then uses this information to provide prompts to the generative AI model.

[0226] Input: Selected topic, language to study

[0227] Data processing: The server sends the prompt to the generative AI model

[0228] Output: Generated questionnaire list

[0229] Step 3:

[0230] The server uses a generative AI model to generate questions related to the topic and language selected by the user, which are then presented to the user via their device.

[0231] Input: prompt, topic, language

[0232] Data processing: Generative AI models generate questions

[0233] Output: The question presented to the user

[0234] Step 4:

[0235] The user inputs answers to the questions presented to them, and the terminal sends the answers to the server, which then stores them in a database.

[0236] Input: User's answer

[0237] Data processing: The server stores the received data in a database

[0238] Output: Answer data saved on the server

[0239] Step 5:

[0240] The server uses a generative AI model to automatically generate a draft blog post based on the user's answers, and the draft is presented to the user via their device.

[0241] Input: User response data

[0242] Data processing: A generative AI model generates draft blog posts

[0243] Output: A draft blog post presented to the user

[0244] Step 6:

[0245] The server uses a generative AI model to evaluate the generated blog post drafts, including language learning progress and writing quality, and provides the evaluation results and feedback to the user via their device.

[0246] Input: Blog post draft

[0247] Data processing: Generative AI models evaluate drafts and generate feedback

[0248] Output: Evaluation results and feedback presented to the user

[0249] 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.

[0250] The present invention relates to a system that supports users in creating blog articles while efficiently learning a language, and utilizes generative artificial intelligence (AI) and an emotion engine. The following describes in detail the embodiments of the invention.

[0251] System Overview

[0252] This system consists of a server, a terminal, and a user. The server manages the database, generative AI model, and emotion engine, and processes, evaluates, and recognizes emotions in content created by users. The terminal provides an interface for users to access and operate the system. Users use this system to create blog posts and learn languages ​​while receiving feedback tailored to their emotional state.

[0253] Program processing flow

[0254] 1. Create an account and log in

[0255] A user accesses the web interface on a device and creates a new account. They enter a username, password, and desired language to learn. The device sends the information to the server, which stores the new user information in a database. The user enters their username and password on the login page, which the device sends to the server, which authenticates them. If authentication is successful, they are redirected to the dashboard.

[0256] 2. Choose your blog topic and language

[0257] The user selects the blog topic and language on the dashboard. The device sends the selected information to the server, which then activates the emotion engine and monitors the user's emotional state in real time.

[0258] 3. Question and Answer Generation

[0259] The server uses a generative AI model to generate a question based on the topic and language selected by the user. The server sends the generated question to the device and presents it to the user. The user uses the device to enter an answer to the question. The device sends the user's answer to the server.

[0260] 4. Drafting a blog post

[0261] The server uses a generative AI model to automatically generate a draft of a blog post based on the user's responses. The server then sends the generated draft to the device and presents it to the user. The user then reviews the draft and makes edits as necessary. The server uses an emotion engine to provide advice and additional feedback based on the user's emotional state.

[0262] 5. Writing Progress Assessment and Feedback

[0263] The server uses a generative AI model to evaluate the user's progress on the draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. The server generates feedback including the evaluation results and areas for improvement. The server sends the evaluation results and feedback to the device and presents them to the user. The user reviews the feedback on the device and re-edits the draft if necessary. The user completes the final blog post and sends it from the device to the server for storage.

[0264] Specific examples

[0265] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" At the same time, an emotion engine recognizes emotions from the user's input and behavior and evaluates their motivation and stress level. The user enters answers to each question, such as "To relax and have new experiences" and "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. Depending on the user's emotional state, the server provides additional advice and feedback. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[0266] The system allows users to learn a new language while gaining practical writing skills and a personalized learning experience that is tailored to their emotional state.

[0267] The processing flow will be explained below.

[0268] Program processing steps (including emotion engine)

[0269] 1. Create an account and log in

[0270] Step 1:

[0271] The user accesses the web interface on their device and opens the account creation page.

[0272] Step 2:

[0273] The user enters the required information (username, password, and desired language of study).

[0274] Step 3:

[0275] The terminal transmits the input information to the server.

[0276] Step 4:

[0277] The server stores the received user information in a database and returns a message indicating that the account has been created.

[0278] Step 5:

[0279] The user enters their username and password on the login page.

[0280] Step 6:

[0281] The terminal transmits the entered authentication information to the server.

[0282] Step 7:

[0283] The server checks the user against the database and performs authentication, and if authentication is successful, returns a login success message to the user and redirects them to the dashboard.

[0284] 2. Choose your blog topic and language

[0285] Step 1:

[0286] A user selects a blog topic and language on the dashboard.

[0287] Step 2:

[0288] The terminal transmits the selected information to the server.

[0289] Step 3:

[0290] The server accepts the selected topic and language and prepares for the next process.

[0291] Step 4:

[0292] The server activates an emotion engine and monitors the user's emotional state in real time.

[0293] 3. Question and Answer Generation

[0294] Step 1:

[0295] The server uses a generative AI model to generate questions based on the topic and language selected by the user.

[0296] Step 2:

[0297] The server sends the generated question to the terminal and presents it to the user.

[0298] Step 3:

[0299] The user uses the terminal to enter answers to the questions.

[0300] Step 4:

[0301] The terminal sends the user's answer to the server.

[0302] 4. Drafting a blog post

[0303] Step 1:

[0304] The server uses a generative AI model to automatically generate a draft blog post based on the user's responses.

[0305] Step 2:

[0306] The server transmits the generated draft to the terminal and presents it to the user.

[0307] Step 3:

[0308] An emotion engine analyzes the user's emotional state and generates advice or additional feedback as needed.

[0309] Step 4:

[0310] The server sends the advice and feedback generated by the emotion engine to the terminal and presents it to the user.

[0311] Step 5:

[0312] The user reviews the draft and makes edits as necessary.

[0313] Step 6:

[0314] The user sends the edited content from the terminal to the server.

[0315] 5. Writing Progress Assessment and Feedback

[0316] Step 1:

[0317] The server uses a generative AI model to evaluate the user's progress in their draft, including language learning progress, writing quality, and the user's emotional state.

[0318] Step 2:

[0319] The server generates feedback including the evaluation results and improvements.

[0320] Step 3:

[0321] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[0322] Step 4:

[0323] The user checks the feedback on the device and re-edits the draft if necessary.

[0324] Step 5:

[0325] After the final blog post is completed, the user sends it to the server via the device for storage.

[0326] Specific examples

[0327] For example, if a user wants to create a blog post about "travel" in Japanese, the process would be as follows:

[0328] Select your blog topic and language

[0329] Step 1:

[0330] A user selects the topic "Travel" and the language "Japanese" on the dashboard.

[0331] Step 2:

[0332] The terminal transmits the selected information to the server.

[0333] Step 3:

[0334] The server accepts the selected information and activates the emotion engine.

[0335] Question and answer generation

[0336] Step 1:

[0337] The server uses a generative AI model to generate questions such as, "What is the purpose of your trip?" and "What are the characteristics of the place you want to visit?"

[0338] Step 2:

[0339] The server sends the generated question to the terminal and presents it to the user.

[0340] Step 3:

[0341] In response to the questions, the user answers, "To relax and have new experiences," and "Kyoto is a beautiful place rich in history and culture."

[0342] Step 4:

[0343] The device sends the response to the server.

[0344] Drafting and editing blog posts

[0345] Step 1:

[0346] The server uses a generative AI model to generate a draft blog post based on the answers.

[0347] Step 2:

[0348] The server transmits the generated draft to the terminal and presents it to the user.

[0349] Step 3:

[0350] The emotion engine analyzes the user's emotional state and provides additional advice such as "relax and carry on" if the user is tired.

[0351] Step 4:

[0352] Users review and edit the draft and also take into account feedback from the emotion engine.

[0353] Writing progress assessment and feedback

[0354] Step 1:

[0355] The server evaluates the draft and generates feedback based on progress, quality, and emotional state.

[0356] Step 2:

[0357] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[0358] Step 3:

[0359] The user checks the feedback and makes edits as necessary.

[0360] Step 4:

[0361] After the final blog post is completed, the user submits the post to the server for storage.

[0362] The system allows users to learn a new language while also receiving practical writing skills and personalized instruction tailored to their emotional state.

[0363] Example 2

[0364] 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."

[0365] In conventional language learning systems, efficient language learning and content creation are often not integrated when users create blog posts. Furthermore, they lack a mechanism for providing personalized feedback based on the user's emotional state. As a result, users' motivation and stress management are insufficient, and learning outcomes cannot be expected to improve. Therefore, there is a need for a system that allows users to effectively and efficiently create blog posts while learning a language.

[0366] 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.

[0367] In this invention, the server includes a generative artificial intelligence means for generating questions based on a topic and language selected by a user, a generative artificial intelligence means for receiving answers entered by the user and automatically generating draft blog articles based on the answers, a generative artificial intelligence means for evaluating the language learning progress and writing quality of the generated draft blog articles and providing feedback, and an emotion recognition means for monitoring the user's emotional state in real time and providing feedback based on the same, thereby enabling users to receive effective feedback based on their individual emotional state while simultaneously learning a language and creating blog articles.

[0368] A "generative artificial intelligence means" is an artificial intelligence system that has the ability to generate questions based on specific topics and languages ​​selected by a user and automatically generate content based on the answers received.

[0369] "Emotion recognition means" is a technology that analyzes user input and behavior in real time to recognize and evaluate their emotional state.

[0370] An "authentication means" is a processing system that performs authentication using information such as a username and password when a user creates an account and logs in.

[0371] The "means for providing an interface" refers to a technique for providing a user interface that allows a user to access and operate the system.

[0372] The "means for constructing a question and answer session" is a technique for constructing a series of dialogues that generate questions and collect answers from users based on the topic and language selected by the user.

[0373] The "means for saving a draft of a blog article" is a technology that has the function of saving a draft of a blog article generated based on the answers entered by the user in a storage device such as a database.

[0374] The "generative AI means for providing feedback" is an AI system that evaluates the generated draft blog post and provides appropriate feedback based on the user's language learning progress and writing quality.

[0375] "Language learning progress" is an indicator that shows how much the user's ability to understand and express themselves in the language they have selected to learn has improved.

[0376] The present invention relates to a system for supporting a user in creating blog articles while efficiently learning a language. Hereinafter, an embodiment of the present invention will be described in detail.

[0377] System Configuration

[0378] This system consists of a server, a terminal, and a user. The server manages the database, generative artificial intelligence (AI) model, and emotion recognition engine, and processes, evaluates, and recognizes emotions in content created by users. The terminal provides an interface for users to access and operate the system.

[0379] Server Roles

[0380] The server has the following features:

[0381] Generative AI methods: Generate questions based on the topic and language selected by the user. This can be done using Natural Language Processing (NLP) libraries, etc.

[0382] Generative AI answer processing: Receive the answers entered by the user and automatically generate a draft blog post based on them. This process utilizes a generative AI model such as GPT-3.

[0383] Language learning progress assessment and feedback: The generated blog post drafts are assessed for language learning progress and writing quality, and feedback is provided, again using the generative AI model described above.

[0384] Emotion recognition: Monitor the user's emotional state in real time and provide feedback based on this. This can be achieved using emotion recognition libraries and algorithms (e.g., Facial Emotion Recognition API).

[0385] Device Role

[0386] The terminal has the following features:

[0387] Web Interface: Provides an interface for users to access the system using a web browser and web pages written in HTML, CSS, and JavaScript.

[0388] Authentication method: Provides a function for users to create an account and log in. Authentication information is sent from the terminal to the server and processed there.

[0389] User operations

[0390] A user uses the system in the following steps:

[0391] 1. Account Creation and Login: The user accesses the web interface on their device and creates a new account. They enter their username, password, and desired language to learn. The device sends the information they entered to the server, which stores the new user information in a database. The user enters their username and password on the login page, which the device sends to the server, which authenticates them. If authentication is successful, they are redirected to the dashboard.

[0392] 2. Blog topic and language selection: The user selects the blog topic and language on the dashboard. The device sends the selected information to the server, which then accepts it. The server then activates the emotion engine and monitors the user's emotional state in real time.

[0393] 3. Question and answer generation: The server uses a generative AI model to generate questions based on the topic and language selected by the user. The server sends the generated questions to the device and presents them to the user. The user uses the device to enter answers to the questions. The device sends the user's answers to the server.

[0394] 4. Blog post drafting: The server automatically generates a blog post draft using a generative AI model based on the user's responses. The server then sends the draft to the device and presents it to the user. The user reviews the draft and makes edits as necessary. The server uses an emotion engine to provide advice and additional feedback based on the user's emotional state.

[0395] 5. Writing Progress Evaluation and Feedback: The server uses a generative AI model to evaluate the user's progress on their draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. The server generates feedback including the evaluation results and areas for improvement. The server sends the evaluation results and feedback to the device and presents them to the user. The user reviews the feedback on the device and re-edits the draft if necessary. The user completes the final blog post and sends it from the device to the server for storage.

[0396] Specific examples

[0397] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" At the same time, an emotion engine recognizes emotions from the user's input and behavior and evaluates their motivation and stress level. The user answers each question with questions such as "To relax and have new experiences" and "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. Depending on the user's emotional state, the server provides additional advice and feedback. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[0398] Prompt Sentence Examples

[0399] "Think about your next travel destination. What is your purpose and what are the characteristics of the place you want to visit?"

[0400] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0401] Step 1: Create an account

[0402] A user accesses the web interface using a terminal and enters a username, password, and desired language to learn on the account creation page. Input: Username, password, language to learn. Output: Account creation request data.

[0403] The device sends the entered information to the server, and the server's endpoint accepts this data.

[0404] The server saves the received account creation request data in the database and creates a user account. Data processing: Saving account information.

[0405] Step 2: Log in

[0406] A user enters a username and password on the login page. Input: Username, Password. Output: Authentication request data.

[0407] The terminal sends this to the server.

[0408] The server performs the authentication process, verifying that the username and password match. Data operation: Authentication check. If successful, redirect the user to the dashboard, otherwise return an error message. Output: Dashboard URL or error message.

[0409] Step 3: Choose your blog topic and language

[0410] User selects blog topic and language in dashboard. Input: topic, language. Output: selected topic and language.

[0411] The device sends the selected topic and language to the server.

[0412] The server accepts and starts the emotion engine. The selection information is passed to the emotion engine, which starts real-time monitoring of the user's emotional state. Data calculation: Setting topic and language information.

[0413] Step 4: Generate questions and answers

[0414] The server uses a generative AI model to generate questions based on the topic and language selected by the user. Input: Topic, Language. Output: Generated question.

[0415] The server sends the generated question to the terminal and presents it to the user.

[0416] The user uses the terminal to enter answers to questions. Input: User's answers. Output: Answer data.

[0417] The terminal sends the user's answer to the server.

[0418] Step 5: Draft your blog post

[0419] Based on the user's answers received by the server, a generative AI model is used to automatically generate a draft blog post. Input: Answer data. Output: Draft data.

[0420] The server transmits the generated draft to the terminal and presents it to the user.

[0421] The user checks the draft and edits it as necessary. The user adds or corrects it on the terminal. Input: Edited content. Output: Corrected draft data.

[0422] The server uses an emotion engine to monitor the user's emotional state and provide appropriate feedback. Data processing: Emotion data analysis and feedback provision.

[0423] Step 6: Writing Progress Assessment and Feedback

[0424] The server uses a generative AI model to evaluate the user's progress in their draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. Input: Draft data. Output: Evaluation results.

[0425] The server generates feedback including the evaluation results and improvements. Data calculation: Generation of evaluation results.

[0426] The server sends the feedback to the terminal and presents it to the user.

[0427] The user reviews the feedback and re-edits the draft if necessary. Input: Feedback. Output: Improved draft data.

[0428] The user completes the final blog post, sends it from the device to the server, and saves it. Input: Final draft data. Output: Notification of save completion.

[0429] The above is a detailed description of the specific processing steps of this system.

[0430] (Application example 2)

[0431] 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."

[0432] It is difficult to provide language learners with the opportunity to not only acquire language skills but also to write in real contexts while receiving feedback based on their emotional state and generating high-quality text. Furthermore, there is a lack of systems that allow users to reflect their emotional state and receive appropriate advice for a personalized learning experience. This is necessary to maintain user motivation and reduce stress.

[0433] 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.

[0434] In this invention, the server includes an automated generation means for generating questions based on a topic and language selected by the user, an artificial intelligence means for receiving answers entered by the user and automatically generating draft articles based on the answers, an artificial intelligence means for evaluating the language acquisition progress of the generated draft articles and providing feedback, and an emotional evaluation means for evaluating the emotional state of the user while creating the articles and providing appropriate advice. This allows users to efficiently learn a language while creating product reviews and introductory articles, and to receive feedback based on their emotional state during the process. Furthermore, the personalized learning experience helps maintain motivation and reduce stress.

[0435] An "automated generation means" is a device or software that automatically generates appropriate questions based on the topic and language selected by the user.

[0436] "Artificial intelligence means" means a system or algorithm capable of receiving user-entered responses and automatically generating a draft of a written article based thereon.

[0437] The "artificial intelligence means for assessing language acquisition progress and providing feedback" is a system that has the function of analyzing the generated draft text, assessing the user's language learning progress, and providing appropriate advice and areas for improvement.

[0438] An "emotion evaluation means" is a device or software that monitors the user's emotional state in real time while they are creating an article, and provides appropriate advice and feedback based on that information.

[0439] An "authentication means" is a device or software with security functions for creating an account and logging in when a user accesses a system.

[0440] "Means for providing an online interface" refers to an interface provided in the form of a web browser or mobile app that allows users to access and operate the system via the Internet.

[0441] The "means for managing feedback sessions" is a system that has the function of managing the content and history of feedback provided on the generated draft of a written article, and supporting users to understand and use it appropriately.

[0442] The system for implementing this invention mainly comprises a server, a terminal (user device), and a user. The operation of this system will be described in detail below.

[0443] 1. System Overview

[0444] server

[0445] The server manages the database, generative AI model, and emotion evaluation engine, processes and evaluates content from users, and evaluates the user's emotional state and provides appropriate feedback.

[0446] Terminal

[0447] It provides an interface for users to access and operate the system. The terminal is provided as a web browser or mobile app and receives and sends user input.

[0448] 2. Program Description

[0449] Question generation based on user-selected topics and languages

[0450] The server uses automated generation methods to generate appropriate questions based on the topic and language selected by the user, and utilizes a generative AI model to present questions that prompt the user to create an article.

[0451] Software used: OpenAI API

[0452] Example: "Generate questions to help users write reviews about 'Travel Guidebook'."

[0453] Receiving user responses and generating sentences

[0454] The server receives the user's inputted answers and automatically generates a draft of the article using artificial intelligence means based on the answers, and presents the draft to the user for necessary editing.

[0455] Software used: Natural Language Processing (NLP) engine

[0456] Evaluating the progress of drafts and providing feedback

[0457] The server evaluates the language learning progress of the generated draft article and provides appropriate feedback, which evaluates the user's language learning progress and suggests specific areas for improvement.

[0458] Software used: Evaluation algorithm

[0459] Emotion assessment and advice provision

[0460] The server uses emotion assessment tools to evaluate the user's emotional state, understand the user's stress level and motivation during the article writing process, and provide appropriate advice based on this, thereby providing a more personalized learning experience.

[0461] Software used: Emotion Recognition API

[0462] 3. Specific Examples

[0463] For example, consider a user writing a review about a "travel guidebook" on a virtual store. The user first creates an account and logs in. Then, they select the topic "travel guidebook" and the language "Japanese." The server uses a generative AI model to generate questions such as "Which part of this guidebook was most helpful?" and "What information does it provide about the details of your travel destination?"

[0464] When the user enters answers to these questions, the server receives them and generates a draft article based on the user's answers. For example, a response such as "The most useful parts of this guidebook were the detailed maps and transportation information. It also provided detailed information about the history and culture of the destination, which was very helpful" is reflected in the draft.

[0465] The generated draft is presented to the user, who then reviews and edits it. The server then provides progress assessment and feedback. During this process, the server monitors the user's emotional state and provides appropriate advice (e.g., "You're making good progress. Keep going.").

[0466] In this way, users can efficiently learn languages ​​while creating high-quality content. Furthermore, by receiving appropriate support throughout the creation process through the emotion assessment tool, users can reduce stress and maintain motivation.

[0467] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0468] Step 1:

[0469] Account creation and login

[0470] Input: The user enters their username, password, and preferred language from the terminal.

[0471] Specific operation: The terminal sends this information to the server, and the server stores the received information in a database.

[0472] Data processing and calculation: The server hashes the entered user information and stores it.

[0473] Output: Sends a successful account creation message to the terminal to display to the user.

[0474] Step 2:

[0475] Topic Selection and Question Generation

[0476] Input: The user selects the topic and language on the device dashboard.

[0477] Specific operation: The terminal sends the selected information to the server, and the server uses automated generation means to generate a question based on the topic and language.

[0478] Data processing and computation: The server uses a generative AI model to generate questions based on the prompt.

[0479] Output: The generated question is sent to the terminal and displayed to the user.

[0480] Step 3:

[0481] Receiving user answers to questions

[0482] Input: The user types the answer to the question at the terminal.

[0483] Specific operation: The terminal sends the user's answer to the server.

[0484] Data processing and calculation: The server receives the user's answers, formats them, and stores them.

[0485] Output: A reply receipt confirmation message is sent to the terminal and displayed to the user.

[0486] Step 4:

[0487] Generate a draft

[0488] Input: User response data stored on the server.

[0489] Specific operation: The server uses artificial intelligence means to generate a draft of the article based on the user's answers.

[0490] Data processing and calculation: Using a generative AI model, natural-sounding sentences are generated based on the answers.

[0491] Output: The generated draft is sent to the terminal and displayed to the user.

[0492] Step 5:

[0493] Review and edit the draft

[0494] Input: The user reviews the draft on their device and makes edits as needed.

[0495] Specific operation: Send the edited draft to the server.

[0496] Data processing and calculation: The server receives the edited text and stores it again.

[0497] Output: Sends an edit confirmation message to the terminal for display to the user.

[0498] Step 6:

[0499] Emotion evaluation and feedback provision

[0500] Input: Final draft stored on the server and user behavior data.

[0501] Specific operation: The server evaluates the user's emotional state using the emotion evaluation means.

[0502] Data processing and calculation: Emotion recognition algorithms are used to analyze the user's emotional state from their input speed and content.

[0503] Output: Generate feedback based on the emotion evaluation, send it to the device and display it to the user.

[0504] Step 7:

[0505] Save and publish the final article

[0506] Input: User reviews the final draft on their device and chooses to save or publish.

[0507] Specific operation: The device sends a save or publish instruction to the server.

[0508] Data processing and calculation: The server stores the final article in a database and performs appropriate publishing processing according to the publishing settings.

[0509] Output: Sends a save or publish confirmation message to the terminal and displays it to the user.

[0510] 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.

[0511] 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.

[0512] 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.

[0513] [Second embodiment]

[0514] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0515] 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.

[0516] 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).

[0517] 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.

[0518] 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.

[0519] 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).

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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.

[0524] 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.

[0525] 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."

[0526] The present invention relates to a system that supports users in creating blog articles while efficiently learning a language, and utilizes generative artificial intelligence (AI). The following describes in detail the embodiments of the invention.

[0527] System Overview

[0528] This system consists of a server, a terminal, and a user. The server manages the database and generative AI model, and processes and evaluates the content created by users. The terminal provides an interface for users to access and operate the system. Users use this system to learn languages ​​while creating blog articles.

[0529] Program processing flow

[0530] 1. Create an account and log in

[0531] The user accesses the web interface on their device and creates a new account, entering a username, password, and desired language of study.

[0532] The terminal sends the entered information to the server, which stores the new user information in a database.

[0533] When a user enters their username and password on the login page, the device sends it to the server, which authenticates them, and if successful, redirects them to the dashboard.

[0534] 2. Choose your blog topic and language

[0535] A user selects a blog topic and language on the dashboard.

[0536] The terminal transmits the selected information to the server, which accepts it.

[0537] 3. Question and Answer Generation

[0538] The server uses a generative AI model to generate questions based on the topic and language selected by the user.

[0539] The server sends the generated question to the terminal and presents it to the user.

[0540] 4. Drafting a blog post

[0541] The user enters an answer to the question.

[0542] The terminal sends the entered answer to the server, which receives it.

[0543] The server uses a generative AI model to automatically generate a draft blog post based on your answers.

[0544] The server transmits the generated draft to the terminal and presents it to the user.

[0545] 5. Writing Progress Assessment and Feedback

[0546] The server uses a generative AI model to evaluate users' drafts, including their language learning progress and writing quality.

[0547] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[0548] Users then edit the draft based on the feedback and finalize the blog post.

[0549] Specific examples

[0550] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" The user inputs answers to each question, answering, for example, "To relax and have new experiences," or "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[0551] The system allows users to improve their practical writing skills while learning a new language.

[0552] The processing flow will be explained below.

[0553] Program processing steps

[0554] 1. Create an account and log in

[0555] Step 1:

[0556] The user accesses the web interface on their device and opens the account creation page.

[0557] Step 2:

[0558] The user enters the required information (username, password, desired language to learn).

[0559] Step 3:

[0560] The terminal transmits the input information to the server.

[0561] Step 4:

[0562] The server stores the received user information in a database and returns a message indicating that the account has been created.

[0563] Step 5:

[0564] The user enters their username and password on the login page.

[0565] Step 6:

[0566] The terminal transmits the entered authentication information to the server.

[0567] Step 7:

[0568] The server checks the user against the database and performs authentication, and if authentication is successful, returns a login success message to the user and redirects them to the dashboard.

[0569] 2. Choose your blog topic and language

[0570] Step 1:

[0571] A user selects a blog topic and language on the dashboard.

[0572] Step 2:

[0573] The terminal transmits the selected information to the server.

[0574] Step 3:

[0575] The server accepts the selected topic and language and prepares for the next process.

[0576] 3. Question and Answer Generation

[0577] Step 1:

[0578] The server runs a generative AI model to generate questions based on the topic and language selected by the user.

[0579] Step 2:

[0580] The server sends the generated question to the terminal and presents it to the user.

[0581] Step 3:

[0582] The user uses the terminal to enter answers to the questions.

[0583] Step 4:

[0584] The terminal sends the user's answer to the server.

[0585] 4. Drafting a blog post

[0586] Step 1:

[0587] The server uses a generative AI model to automatically generate a draft blog post based on the user's responses.

[0588] Step 2:

[0589] The server transmits the generated draft to the terminal and presents it to the user.

[0590] Step 3:

[0591] The user reviews the draft and makes edits as necessary.

[0592] Step 4:

[0593] The user sends the edited content from the terminal to the server.

[0594] 5. Writing Progress Assessment and Feedback

[0595] Step 1:

[0596] The server uses a generative AI model to assess the progress of the user's draft.

[0597] Step 2:

[0598] The server generates feedback including the evaluation results and improvements.

[0599] Step 3:

[0600] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[0601] Step 4:

[0602] The user checks the feedback on the device and re-edits the draft if necessary.

[0603] Step 5:

[0604] The user completes the final blog post and sends it from the device to the server for storage.

[0605] Through these steps, users can efficiently learn a language while creating high-quality blog articles.

[0606] Example 1

[0607] 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."

[0608] In modern society, there is a demand for the use of generative artificial intelligence as a means of efficiently generating content while learning. Systems that allow learners to simultaneously study a language and write blog posts would be particularly useful, but few systems offer such functionality. Conventional systems often generate content based on user input inefficiently, limiting learning outcomes. Furthermore, they lack evaluation functions to provide appropriate feedback, making it difficult for users to grasp their own progress.

[0609] 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.

[0610] In this invention, the server includes a generative AI means for generating questions based on a theme and language selected by the user, an AI means for receiving responses entered by the user and automatically generating a draft document based on the responses, and an AI means for evaluating the learning progress of the generated draft document and providing feedback. This enables users to efficiently progress with language learning while creating practical blog articles.

[0611] "Generative AI means" refers to AI technology that generates appropriate questions based on themes and languages ​​selected by the user, and supports the automatic generation of content.

[0612] "Authentication means" refers to a function that verifies user login information and ensures that only users with legitimate access rights can access the system.

[0613] "Web interface means" refers to a user interface that allows a user to access and operate the system online.

[0614] "Means for constructing a question and answer session" refers to a function that uses generative artificial intelligence means to create appropriate questions based on user selections and sets up a process for obtaining user responses.

[0615] "Artificial intelligence means for automatically generating a draft" means an artificial intelligence function that automatically generates an initial version of a document based on responses entered by a user.

[0616] The "evaluation means" is a function that evaluates the grammar and content of the generated draft blog article and provides the results to the user.

[0617] "Means for providing feedback" refers to a function that supports learning by providing advice and corrections based on the quality of the draft generated by the user and the learning progress.

[0618] "Means for storage" refers to the ability to store user-created data or system-generated data in an internal database so that it can be reused later.

[0619] A "theme" is a particular topic or topic that a user chooses as the content of a blog post.

[0620] A "response" is text information that a user inputs in response to a question presented by the generative artificial intelligence means.

[0621] The present invention relates to a system that supports users in creating blog articles while learning a language effectively and efficiently. This system utilizes a generative artificial intelligence model. A specific embodiment of this system will be described below.

[0622] System Overview

[0623] This system consists of a server, a terminal, and a user. The server manages the database and generative AI model, and generates and evaluates sentences written by users. For example, the well-known GPT-3 can be used as a generative AI model. The terminal provides an interface for users to access and operate the system. Users use this system to progress through language learning while creating blog posts.

[0624] Account creation and login

[0625] A user uses a terminal to access the web interface and create a new account. They enter their username, password, and desired language to learn. The terminal sends this information to the server, which stores it in a database. When the user enters their username and password on the login page, the terminal sends these credentials to the server, which authenticates them. If authentication is successful, the user is redirected to the dashboard.

[0626] Choosing a blog topic and learning language

[0627] Users select a blog topic and learning language on the dashboard. The device sends the selected information to the server, which accepts it. The server uses a generative artificial intelligence model to generate questions based on the selected topic and language. For example, users can select the topics "travel" or "learning." Questions generated include "What is the purpose of your trip?" and "Where would you like to visit this summer?"

[0628] Question and answer generation

[0629] The server uses a generative artificial intelligence model to generate questions based on the topic and language selected by the user. The generated questions are presented to the user via the terminal. The user then enters a response, which is then sent to the server.

[0630] Drafting a blog post

[0631] Based on the received response, the server uses a generative AI model to generate a draft blog post. The draft is then presented to the user via their device. If the user responds with something like "To relax and have a new experience," the response is reflected in the document.

[0632] Writing progress assessment and feedback

[0633] The server uses a generative AI model to evaluate the learning progress and quality of the generated draft and provides feedback. The user then edits the draft based on the feedback and completes the final blog post. For example, the server provides advice such as "There are grammatical errors" or "The content is insufficient."

[0634] Specific examples

[0635] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative artificial intelligence model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" The user might respond with "To relax and have new experiences" or "Kyoto is a beautiful place rich in history and culture." The server generates a draft based on these answers and presents it to the user. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[0636] The system allows users to improve their practical writing skills while learning a new language.

[0637] Prompt Sentence Examples

[0638] Generate a question with the topic "Travel" and language "Japanese".

[0639] Generate a draft blog post that answers the question, "What is the purpose of your trip?"

[0640] By using this system, users can learn languages ​​effectively and efficiently.

[0641] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0642] Step 1:

[0643] A user accesses a web interface on a terminal and creates a new account. The user enters a username, password, and desired language to learn. The terminal sends this information to the server, which stores it in a database as a new user. The input to this process is the username, password, and desired language entered by the user, and the output is the new user information stored in the database.

[0644] Step 2:

[0645] The user enters their username and password on the login page. The device sends this authentication information to the server. The server compares the received information with the database and performs authentication. If authentication is successful, the server redirects the user to the dashboard and sends a success notification to the device. The input is the username and password, and the output is the authentication success / failure result and the display of the dashboard.

[0646] Step 3:

[0647] The user selects a blog topic and learning language on the dashboard. The device sends the selected information to the server. The server uses the received information to create a prompt for the generative AI model and prepares to generate a question. The input is the topic and learning language selected by the user, and the output is a prompt for the generative AI model.

[0648] Step 4:

[0649] The server uses a generative AI model to generate questions based on the topic and language selected by the user. The generated questions are sent to the device and presented to the user. The input is the prompt from the generative AI model, and the output is the generated question. The server can generate questions like, "What is the purpose of your trip?"

[0650] Step 5:

[0651] The user inputs a response to a question generated by the terminal. The terminal sends the input response to the server. The input is the user's response, and the output is the data sent to the server. For example, the user inputs "To relax and have a new experience."

[0652] Step 6:

[0653] The server generates a draft of a blog post using a generative AI model based on the received response. The server then sends the generated draft to the device. The input is the user's response, and the output is the generated draft. For example, the server might generate a draft that reads, "Traveling is a great way to relax and experience new things. Kyoto, in particular, is worth visiting because it is rich in history and culture."

[0654] Step 7:

[0655] The server uses a generative AI model to evaluate the learning progress and quality of the generated draft and provides feedback. The server sends the evaluation results and feedback to the device and presents them to the user. The input is the generated draft, and the output is the evaluation results and feedback. Typically, feedback such as "The sentence structure is good, but there are some grammatical errors" is generated.

[0656] Step 8:

[0657] The user edits the draft based on the feedback and completes the final blog post. The device sends the edited post to the server, which then saves the final post in a database. The input is the edited draft, and the output is the saved final post. Specifically, the user corrects "grammatical errors" and saves the completed post.

[0658] Through the above processing steps, users can effectively advance their language learning while creating practical blog articles.

[0659] (Application example 1)

[0660] 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."

[0661] Conventional language learning systems have the drawback of making it difficult for users to effectively learn a language while engaging in practical writing. In particular, the process of users writing blog posts about topics of their interest and simultaneously evaluating and providing feedback on the content is complicated, making it difficult to improve language skills. Furthermore, many systems are not optimized for smartphone environments, often resulting in a loss of user convenience.

[0662] 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.

[0663] In this invention, the server includes generative artificial intelligence means for generating questions based on a topic and language selected by a user, artificial intelligence means for receiving answers entered by the user and automatically generating draft blog articles based on the answers, artificial intelligence means for evaluating the language learning progress of the generated draft blog articles and providing feedback, means for transmitting the topic and language selected by the user to the server and using the generative artificial intelligence means to generate questions and present them to the user, means for transmitting the answers entered by the user to the server and using the generative artificial intelligence means to generate and evaluate draft blog articles based on the answers, and means including an application to be installed on a smartphone, thereby enabling users to use their smartphones to easily and efficiently create blog articles while learning a language.

[0664] "User" refers to an individual who uses the system to create blog articles while studying a language.

[0665] A "topic" refers to a theme or topic that a user chooses as the content of a blog post.

[0666] "Language" refers to the language that a user learns and uses to create blog posts.

[0667] "Generative AI methods" refers to AI technology that generates questions based on the topic and language selected by the user, and automatically generates draft blog posts based on the answers.

[0668] "Question" refers to a question posed by the generative artificial intelligence means regarding content related to a topic selected by a user.

[0669] "Answer" refers to the content that a user inputs in response to a question generated by the generative artificial intelligence means.

[0670] "Draft blog post" refers to the initial version of a blog post that is automatically created by the generative artificial intelligence means based on responses entered by the user.

[0671] "Evaluation" refers to the process of measuring the language learning progress and writing quality of the generated draft blog posts and providing appropriate feedback.

[0672] "Feedback" refers to advice and corrections provided to the user as a result of evaluation of the generated draft blog post.

[0673] "Authentication means" refers to the technology that authenticates users when they create an account and log in.

[0674] "Web Interface" refers to a browser-based user interface that allows a user to access and operate the system.

[0675] "Server" refers to a computer system that manages a database and generative artificial intelligence means, and processes and stores data sent by users.

[0676] "Smartphone application" refers to a dedicated application that is installed on a smartphone and allows users to study languages ​​and write blog articles.

[0677] System Overview

[0678] This invention is a system that allows users to efficiently create blog articles while learning a language. The system consists of a server, a user's smartphone, and the user. The server manages the database and generative AI model, and generates and evaluates content according to the user's operations. The smartphone provides the user with an interface that allows them to access and operate the system. The user uses this system to learn a language while creating blog articles.

[0679] Hardware and Software Used

[0680] Hardware: Servers, smartphones

[0681] software:

[0682] Flask: A Python-based web framework for server-side use.

[0683] Generative AI model: AI model for question generation, article generation, and evaluation

[0684] Database system: stores user information and generated articles

[0685] Processing flow

[0686] 1. Create an account and log in:

[0687] A user creates an account through a smartphone application and logs in. During this process, authentication information is sent to the server and stored in a database.

[0688] 2. Topic and language selection:

[0689] The user uses the smartphone application dashboard to select the topics and languages ​​they are interested in, and the selected information is sent to the server.

[0690] 3. Question generation and answer input:

[0691] The server uses a generative AI model to generate questions based on the user's selections. The generated questions are presented to the user via a smartphone application, and the user inputs answers.

[0692] 4. Blog post draft generation:

[0693] The user's answers are sent to a server, and a generative AI model automatically generates a draft blog post based on the answers. The draft is then presented to the user via their smartphone.

[0694] 5. Writing evaluation and feedback:

[0695] The server uses a generative AI model to evaluate the generated drafts, including language learning progress and writing quality, and provides the resulting feedback to the user via a smartphone application.

[0696] Specific examples

[0697] For example, if a user wants to write a blog post in English about "latest technology trends," the following process would be performed:

[0698] Topic selection: The user selects the topic "Technology" and the language of study "English."

[0699] Question generation: A server-side generative AI model generates questions such as:

[0700] "What is the latest tech trend you are excited about?"

[0701] "Describe a technology you think will change the world."

[0702] Answer input: The user inputs the answer to each question and sends it to the server via the smartphone application.

[0703] Draft generation: Based on these answers, the server uses a generative AI model to automatically generate a draft blog post.

[0704] Evaluation and feedback: The generated draft is evaluated on the server, and the evaluation results and feedback are provided to the user.

[0705] This system allows users to use their smartphones to easily and efficiently create blog articles while learning a language.

[0706] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0707] Step 1:

[0708] A user launches a smartphone application, creates an account, and logs in. The user enters their username, password, and language of study through the application interface, and this information is sent from the device to the server. The server stores the received information in a database and manages authentication information.

[0709] Input: Username, Password, Learning Language

[0710] Data processing: The server stores the received data in a database

[0711] Output: New account created, login authentication successful message

[0712] Step 2:

[0713] After logging in, users select the topic and language of their blog post from the application's dashboard. The selected information is sent from the device to the server, which then uses this information to provide prompts to the generative AI model.

[0714] Input: Selected topic, language to study

[0715] Data processing: The server sends the prompt to the generative AI model

[0716] Output: Generated questionnaire list

[0717] Step 3:

[0718] The server uses a generative AI model to generate questions related to the topic and language selected by the user, which are then presented to the user via their device.

[0719] Input: prompt, topic, language

[0720] Data processing: Generative AI models generate questions

[0721] Output: The question presented to the user

[0722] Step 4:

[0723] The user inputs answers to the questions presented to them, and the terminal sends the answers to the server, which then stores them in a database.

[0724] Input: User's answer

[0725] Data processing: The server stores the received data in a database

[0726] Output: Answer data saved on the server

[0727] Step 5:

[0728] The server uses a generative AI model to automatically generate a draft blog post based on the user's answers, and the draft is presented to the user via their device.

[0729] Input: User response data

[0730] Data processing: A generative AI model generates draft blog posts

[0731] Output: A draft blog post presented to the user

[0732] Step 6:

[0733] The server uses a generative AI model to evaluate the generated blog post drafts, including language learning progress and writing quality, and provides the evaluation results and feedback to the user via their device.

[0734] Input: Blog post draft

[0735] Data processing: Generative AI models evaluate drafts and generate feedback

[0736] Output: Evaluation results and feedback presented to the user

[0737] 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.

[0738] The present invention relates to a system that supports users in creating blog articles while efficiently learning a language, and utilizes generative artificial intelligence (AI) and an emotion engine. The following describes in detail the embodiments of the invention.

[0739] System Overview

[0740] This system consists of a server, a terminal, and a user. The server manages the database, generative AI model, and emotion engine, and processes, evaluates, and recognizes emotions in content created by users. The terminal provides an interface for users to access and operate the system. Users use this system to create blog posts and learn languages ​​while receiving feedback tailored to their emotional state.

[0741] Program processing flow

[0742] 1. Create an account and log in

[0743] A user accesses the web interface on a device and creates a new account. They enter a username, password, and desired language to learn. The device sends the information to the server, which stores the new user information in a database. The user enters their username and password on the login page, which the device sends to the server, which authenticates them. If authentication is successful, they are redirected to the dashboard.

[0744] 2. Choose your blog topic and language

[0745] The user selects the blog topic and language on the dashboard. The device sends the selected information to the server, which then activates the emotion engine and monitors the user's emotional state in real time.

[0746] 3. Question and Answer Generation

[0747] The server uses a generative AI model to generate a question based on the topic and language selected by the user. The server sends the generated question to the device and presents it to the user. The user uses the device to enter an answer to the question. The device sends the user's answer to the server.

[0748] 4. Drafting a blog post

[0749] The server uses a generative AI model to automatically generate a draft of a blog post based on the user's responses. The server then sends the generated draft to the device and presents it to the user. The user then reviews the draft and makes edits as necessary. The server uses an emotion engine to provide advice and additional feedback based on the user's emotional state.

[0750] 5. Writing Progress Assessment and Feedback

[0751] The server uses a generative AI model to evaluate the user's progress on the draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. The server generates feedback including the evaluation results and areas for improvement. The server sends the evaluation results and feedback to the device and presents them to the user. The user reviews the feedback on the device and re-edits the draft if necessary. The user completes the final blog post and sends it from the device to the server for storage.

[0752] Specific examples

[0753] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" At the same time, an emotion engine recognizes emotions from the user's input and behavior and evaluates their motivation and stress level. The user enters answers to each question, such as "To relax and have new experiences" and "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. Depending on the user's emotional state, the server provides additional advice and feedback. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[0754] The system allows users to learn a new language while gaining practical writing skills and a personalized learning experience that is tailored to their emotional state.

[0755] The processing flow will be explained below.

[0756] Program processing steps (including emotion engine)

[0757] 1. Create an account and log in

[0758] Step 1:

[0759] The user accesses the web interface on their device and opens the account creation page.

[0760] Step 2:

[0761] The user enters the required information (username, password, and desired language of study).

[0762] Step 3:

[0763] The terminal transmits the input information to the server.

[0764] Step 4:

[0765] The server stores the received user information in a database and returns a message indicating that the account has been created.

[0766] Step 5:

[0767] The user enters their username and password on the login page.

[0768] Step 6:

[0769] The terminal transmits the entered authentication information to the server.

[0770] Step 7:

[0771] The server checks the user against the database and performs authentication, and if authentication is successful, returns a login success message to the user and redirects them to the dashboard.

[0772] 2. Choose your blog topic and language

[0773] Step 1:

[0774] A user selects a blog topic and language on the dashboard.

[0775] Step 2:

[0776] The terminal transmits the selected information to the server.

[0777] Step 3:

[0778] The server accepts the selected topic and language and prepares for the next process.

[0779] Step 4:

[0780] The server activates an emotion engine and monitors the user's emotional state in real time.

[0781] 3. Question and Answer Generation

[0782] Step 1:

[0783] The server uses a generative AI model to generate questions based on the topic and language selected by the user.

[0784] Step 2:

[0785] The server sends the generated question to the terminal and presents it to the user.

[0786] Step 3:

[0787] The user uses the terminal to enter answers to the questions.

[0788] Step 4:

[0789] The terminal sends the user's answer to the server.

[0790] 4. Drafting a blog post

[0791] Step 1:

[0792] The server uses a generative AI model to automatically generate a draft blog post based on the user's responses.

[0793] Step 2:

[0794] The server transmits the generated draft to the terminal and presents it to the user.

[0795] Step 3:

[0796] An emotion engine analyzes the user's emotional state and generates advice or additional feedback as needed.

[0797] Step 4:

[0798] The server sends the advice and feedback generated by the emotion engine to the terminal and presents it to the user.

[0799] Step 5:

[0800] The user reviews the draft and makes edits as necessary.

[0801] Step 6:

[0802] The user sends the edited content from the terminal to the server.

[0803] 5. Writing Progress Assessment and Feedback

[0804] Step 1:

[0805] The server uses a generative AI model to evaluate the user's progress in their draft, including language learning progress, writing quality, and the user's emotional state.

[0806] Step 2:

[0807] The server generates feedback including the evaluation results and improvements.

[0808] Step 3:

[0809] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[0810] Step 4:

[0811] The user checks the feedback on the device and re-edits the draft if necessary.

[0812] Step 5:

[0813] After the final blog post is completed, the user sends it to the server via the device for storage.

[0814] Specific examples

[0815] For example, if a user wants to create a blog post about "travel" in Japanese, the process would be as follows:

[0816] Select your blog topic and language

[0817] Step 1:

[0818] A user selects the topic "Travel" and the language "Japanese" on the dashboard.

[0819] Step 2:

[0820] The terminal transmits the selected information to the server.

[0821] Step 3:

[0822] The server accepts the selected information and activates the emotion engine.

[0823] Question and answer generation

[0824] Step 1:

[0825] The server uses a generative AI model to generate questions such as, "What is the purpose of your trip?" and "What are the characteristics of the place you want to visit?"

[0826] Step 2:

[0827] The server sends the generated question to the terminal and presents it to the user.

[0828] Step 3:

[0829] In response to the questions, the user answers, "To relax and have new experiences," and "Kyoto is a beautiful place rich in history and culture."

[0830] Step 4:

[0831] The device sends the response to the server.

[0832] Drafting and editing blog posts

[0833] Step 1:

[0834] The server uses a generative AI model to generate a draft blog post based on the answers.

[0835] Step 2:

[0836] The server transmits the generated draft to the terminal and presents it to the user.

[0837] Step 3:

[0838] The emotion engine analyzes the user's emotional state and provides additional advice such as "relax and carry on" if the user is tired.

[0839] Step 4:

[0840] Users review and edit the draft and also take into account feedback from the emotion engine.

[0841] Writing progress assessment and feedback

[0842] Step 1:

[0843] The server evaluates the draft and generates feedback based on progress, quality, and emotional state.

[0844] Step 2:

[0845] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[0846] Step 3:

[0847] The user checks the feedback and makes edits as necessary.

[0848] Step 4:

[0849] After the final blog post is completed, the user submits the post to the server for storage.

[0850] The system allows users to learn a new language while also receiving practical writing skills and personalized instruction tailored to their emotional state.

[0851] Example 2

[0852] 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."

[0853] In conventional language learning systems, efficient language learning and content creation are often not integrated when users create blog posts. Furthermore, they lack a mechanism for providing personalized feedback based on the user's emotional state. As a result, users' motivation and stress management are insufficient, and learning outcomes cannot be expected to improve. Therefore, there is a need for a system that allows users to effectively and efficiently create blog posts while learning a language.

[0854] 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.

[0855] In this invention, the server includes a generative artificial intelligence means for generating questions based on a topic and language selected by a user, a generative artificial intelligence means for receiving answers entered by the user and automatically generating draft blog articles based on the answers, a generative artificial intelligence means for evaluating the language learning progress and writing quality of the generated draft blog articles and providing feedback, and an emotion recognition means for monitoring the user's emotional state in real time and providing feedback based on the same, thereby enabling users to receive effective feedback based on their individual emotional state while simultaneously learning a language and creating blog articles.

[0856] A "generative artificial intelligence means" is an artificial intelligence system that has the ability to generate questions based on specific topics and languages ​​selected by a user and automatically generate content based on the answers received.

[0857] "Emotion recognition means" is a technology that analyzes user input and behavior in real time to recognize and evaluate their emotional state.

[0858] An "authentication means" is a processing system that performs authentication using information such as a username and password when a user creates an account and logs in.

[0859] The "means for providing an interface" refers to a technique for providing a user interface that allows a user to access and operate the system.

[0860] The "means for constructing a question and answer session" is a technique for constructing a series of dialogues that generate questions and collect answers from users based on the topic and language selected by the user.

[0861] The "means for saving a draft of a blog article" is a technology that has the function of saving a draft of a blog article generated based on the answers entered by the user in a storage device such as a database.

[0862] The "generative AI means for providing feedback" is an AI system that evaluates the generated draft blog post and provides appropriate feedback based on the user's language learning progress and writing quality.

[0863] "Language learning progress" is an indicator that shows how much the user's ability to understand and express themselves in the language they have selected to learn has improved.

[0864] The present invention relates to a system for supporting a user in creating blog articles while efficiently learning a language. Hereinafter, an embodiment of the present invention will be described in detail.

[0865] System Configuration

[0866] This system consists of a server, a terminal, and a user. The server manages the database, generative artificial intelligence (AI) model, and emotion recognition engine, and processes, evaluates, and recognizes emotions in content created by users. The terminal provides an interface for users to access and operate the system.

[0867] Server Roles

[0868] The server has the following features:

[0869] Generative AI methods: Generate questions based on the topic and language selected by the user. This can be done using Natural Language Processing (NLP) libraries, etc.

[0870] Generative AI answer processing: Receive the answers entered by the user and automatically generate a draft blog post based on them. This process utilizes a generative AI model such as GPT-3.

[0871] Language learning progress assessment and feedback: The generated blog post drafts are assessed for language learning progress and writing quality, and feedback is provided, again using the generative AI model described above.

[0872] Emotion recognition: Monitor the user's emotional state in real time and provide feedback based on this. This can be achieved using emotion recognition libraries and algorithms (e.g., Facial Emotion Recognition API).

[0873] Device Role

[0874] The terminal has the following features:

[0875] Web Interface: Provides an interface for users to access the system using a web browser and web pages written in HTML, CSS, and JavaScript.

[0876] Authentication method: Provides a function for users to create an account and log in. Authentication information is sent from the terminal to the server and processed there.

[0877] User operations

[0878] A user uses the system in the following steps:

[0879] 1. Account Creation and Login: The user accesses the web interface on their device and creates a new account. They enter their username, password, and desired language to learn. The device sends the information they entered to the server, which stores the new user information in a database. The user enters their username and password on the login page, which the device sends to the server, which authenticates them. If authentication is successful, they are redirected to the dashboard.

[0880] 2. Blog topic and language selection: The user selects the blog topic and language on the dashboard. The device sends the selected information to the server, which then accepts it. The server then activates the emotion engine and monitors the user's emotional state in real time.

[0881] 3. Question and answer generation: The server uses a generative AI model to generate questions based on the topic and language selected by the user. The server sends the generated questions to the device and presents them to the user. The user uses the device to enter answers to the questions. The device sends the user's answers to the server.

[0882] 4. Blog post drafting: The server automatically generates a blog post draft using a generative AI model based on the user's responses. The server then sends the draft to the device and presents it to the user. The user reviews the draft and makes edits as necessary. The server uses an emotion engine to provide advice and additional feedback based on the user's emotional state.

[0883] 5. Writing Progress Evaluation and Feedback: The server uses a generative AI model to evaluate the user's progress on their draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. The server generates feedback including the evaluation results and areas for improvement. The server sends the evaluation results and feedback to the device and presents them to the user. The user reviews the feedback on the device and re-edits the draft if necessary. The user completes the final blog post and sends it from the device to the server for storage.

[0884] Specific examples

[0885] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" At the same time, an emotion engine recognizes emotions from the user's input and behavior and evaluates their motivation and stress level. The user answers each question with questions such as "To relax and have new experiences" and "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. Depending on the user's emotional state, the server provides additional advice and feedback. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[0886] Prompt Sentence Examples

[0887] "Think about your next travel destination. What is your purpose and what are the characteristics of the place you want to visit?"

[0888] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0889] Step 1: Create an account

[0890] A user accesses the web interface using a terminal and enters a username, password, and desired language to learn on the account creation page. Input: Username, password, language to learn. Output: Account creation request data.

[0891] The device sends the entered information to the server, and the server's endpoint accepts this data.

[0892] The server saves the received account creation request data in the database and creates a user account. Data processing: Saving account information.

[0893] Step 2: Log in

[0894] A user enters a username and password on the login page. Input: Username, Password. Output: Authentication request data.

[0895] The terminal sends this to the server.

[0896] The server performs the authentication process, verifying that the username and password match. Data operation: Authentication check. If successful, redirect the user to the dashboard, otherwise return an error message. Output: Dashboard URL or error message.

[0897] Step 3: Choose your blog topic and language

[0898] User selects blog topic and language in dashboard. Input: topic, language. Output: selected topic and language.

[0899] The device sends the selected topic and language to the server.

[0900] The server accepts and starts the emotion engine. The selection information is passed to the emotion engine, which starts real-time monitoring of the user's emotional state. Data calculation: Setting topic and language information.

[0901] Step 4: Generate questions and answers

[0902] The server uses a generative AI model to generate questions based on the topic and language selected by the user. Input: Topic, Language. Output: Generated question.

[0903] The server sends the generated question to the terminal and presents it to the user.

[0904] The user uses the terminal to enter answers to questions. Input: User's answers. Output: Answer data.

[0905] The terminal sends the user's answer to the server.

[0906] Step 5: Draft your blog post

[0907] Based on the user's answers received by the server, a generative AI model is used to automatically generate a draft blog post. Input: Answer data. Output: Draft data.

[0908] The server transmits the generated draft to the terminal and presents it to the user.

[0909] The user checks the draft and edits it as necessary. The user adds or corrects it on the terminal. Input: Edited content. Output: Corrected draft data.

[0910] The server uses an emotion engine to monitor the user's emotional state and provide appropriate feedback. Data processing: Emotion data analysis and feedback provision.

[0911] Step 6: Writing Progress Assessment and Feedback

[0912] The server uses a generative AI model to evaluate the user's progress in their draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. Input: Draft data. Output: Evaluation results.

[0913] The server generates feedback including the evaluation results and improvements. Data calculation: Generation of evaluation results.

[0914] The server sends the feedback to the terminal and presents it to the user.

[0915] The user reviews the feedback and re-edits the draft if necessary. Input: Feedback. Output: Improved draft data.

[0916] The user completes the final blog post, sends it from the device to the server, and saves it. Input: Final draft data. Output: Notification of save completion.

[0917] The above is a detailed description of the specific processing steps of this system.

[0918] (Application example 2)

[0919] 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."

[0920] It is difficult to provide language learners with the opportunity to not only acquire language skills but also to write in real contexts while receiving feedback based on their emotional state and generating high-quality text. Furthermore, there is a lack of systems that allow users to reflect their emotional state and receive appropriate advice for a personalized learning experience. This is necessary to maintain user motivation and reduce stress.

[0921] 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.

[0922] In this invention, the server includes an automated generation means for generating questions based on a topic and language selected by the user, an artificial intelligence means for receiving answers entered by the user and automatically generating draft articles based on the answers, an artificial intelligence means for evaluating the language acquisition progress of the generated draft articles and providing feedback, and an emotional evaluation means for evaluating the emotional state of the user while creating the articles and providing appropriate advice. This allows users to efficiently learn a language while creating product reviews and introductory articles, and to receive feedback based on their emotional state during the process. Furthermore, the personalized learning experience helps maintain motivation and reduce stress.

[0923] An "automated generation means" is a device or software that automatically generates appropriate questions based on the topic and language selected by the user.

[0924] "Artificial intelligence means" means a system or algorithm capable of receiving user-entered responses and automatically generating a draft of a written article based thereon.

[0925] The "artificial intelligence means for assessing language acquisition progress and providing feedback" is a system that has the function of analyzing the generated draft text, assessing the user's language learning progress, and providing appropriate advice and areas for improvement.

[0926] An "emotion evaluation means" is a device or software that monitors the user's emotional state in real time while they are creating an article, and provides appropriate advice and feedback based on that information.

[0927] An "authentication means" is a device or software with security functions for creating an account and logging in when a user accesses a system.

[0928] "Means for providing an online interface" refers to an interface provided in the form of a web browser or mobile app that allows users to access and operate the system via the Internet.

[0929] The "means for managing feedback sessions" is a system that has the function of managing the content and history of feedback provided on the generated draft of a written article, and supporting users to understand and use it appropriately.

[0930] The system for implementing this invention mainly comprises a server, a terminal (user device), and a user. The operation of this system will be described in detail below.

[0931] 1. System Overview

[0932] server

[0933] The server manages the database, generative AI model, and emotion evaluation engine, processes and evaluates content from users, and evaluates the user's emotional state and provides appropriate feedback.

[0934] Terminal

[0935] It provides an interface for users to access and operate the system. The terminal is provided as a web browser or mobile app and receives and sends user input.

[0936] 2. Program Description

[0937] Question generation based on user-selected topics and languages

[0938] The server uses automated generation methods to generate appropriate questions based on the topic and language selected by the user, and utilizes a generative AI model to present questions that prompt the user to create an article.

[0939] Software used: OpenAI API

[0940] Example: "Generate questions to help users write reviews about 'Travel Guidebook'."

[0941] Receiving user responses and generating sentences

[0942] The server receives the user's inputted answers and automatically generates a draft of the article using artificial intelligence means based on the answers, and presents the draft to the user for necessary editing.

[0943] Software used: Natural Language Processing (NLP) engine

[0944] Evaluating the progress of drafts and providing feedback

[0945] The server evaluates the language learning progress of the generated draft article and provides appropriate feedback, which evaluates the user's language learning progress and suggests specific areas for improvement.

[0946] Software used: Evaluation algorithm

[0947] Emotion assessment and advice provision

[0948] The server uses emotion assessment tools to evaluate the user's emotional state, understand the user's stress level and motivation during the article writing process, and provide appropriate advice based on this, thereby providing a more personalized learning experience.

[0949] Software used: Emotion Recognition API

[0950] 3. Specific Examples

[0951] For example, consider a user writing a review about a "travel guidebook" on a virtual store. The user first creates an account and logs in. Then, they select the topic "travel guidebook" and the language "Japanese." The server uses a generative AI model to generate questions such as "Which part of this guidebook was most helpful?" and "What information does it provide about the details of your travel destination?"

[0952] When the user enters answers to these questions, the server receives them and generates a draft article based on the user's answers. For example, a response such as "The most useful parts of this guidebook were the detailed maps and transportation information. It also provided detailed information about the history and culture of the destination, which was very helpful" is reflected in the draft.

[0953] The generated draft is presented to the user, who then reviews and edits it. The server then provides progress assessment and feedback. During this process, the server monitors the user's emotional state and provides appropriate advice (e.g., "You're making good progress. Keep going.").

[0954] In this way, users can efficiently learn languages ​​while creating high-quality content. Furthermore, by receiving appropriate support throughout the creation process through the emotion assessment tool, users can reduce stress and maintain motivation.

[0955] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0956] Step 1:

[0957] Account creation and login

[0958] Input: The user enters their username, password, and preferred language from the terminal.

[0959] Specific operation: The terminal sends this information to the server, and the server stores the received information in a database.

[0960] Data processing and calculation: The server hashes the entered user information and stores it.

[0961] Output: Sends a successful account creation message to the terminal to display to the user.

[0962] Step 2:

[0963] Topic Selection and Question Generation

[0964] Input: The user selects the topic and language on the device dashboard.

[0965] Specific operation: The terminal sends the selected information to the server, and the server uses automated generation means to generate a question based on the topic and language.

[0966] Data processing and computation: The server uses a generative AI model to generate questions based on the prompt.

[0967] Output: The generated question is sent to the terminal and displayed to the user.

[0968] Step 3:

[0969] Receiving user answers to questions

[0970] Input: The user types the answer to the question at the terminal.

[0971] Specific operation: The terminal sends the user's answer to the server.

[0972] Data processing and calculation: The server receives the user's answers, formats them, and stores them.

[0973] Output: A reply receipt confirmation message is sent to the terminal and displayed to the user.

[0974] Step 4:

[0975] Generate a draft

[0976] Input: User response data stored on the server.

[0977] Specific operation: The server uses artificial intelligence means to generate a draft of the article based on the user's answers.

[0978] Data processing and calculation: Using a generative AI model, natural-sounding sentences are generated based on the answers.

[0979] Output: The generated draft is sent to the terminal and displayed to the user.

[0980] Step 5:

[0981] Review and edit the draft

[0982] Input: The user reviews the draft on their device and makes edits as needed.

[0983] Specific operation: Send the edited draft to the server.

[0984] Data processing and calculation: The server receives the edited text and stores it again.

[0985] Output: Sends an edit confirmation message to the terminal for display to the user.

[0986] Step 6:

[0987] Emotion evaluation and feedback provision

[0988] Input: Final draft stored on the server and user behavior data.

[0989] Specific operation: The server evaluates the user's emotional state using the emotion evaluation means.

[0990] Data processing and calculation: Emotion recognition algorithms are used to analyze the user's emotional state from their input speed and content.

[0991] Output: Generate feedback based on the emotion evaluation, send it to the device and display it to the user.

[0992] Step 7:

[0993] Save and publish the final article

[0994] Input: User reviews the final draft on their device and chooses to save or publish.

[0995] Specific operation: The device sends a save or publish instruction to the server.

[0996] Data processing and calculation: The server stores the final article in a database and performs appropriate publishing processing according to the publishing settings.

[0997] Output: Sends a save or publish confirmation message to the terminal and displays it to the user.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] [Third embodiment]

[1002] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1003] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1004] 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).

[1005] 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.

[1006] 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.

[1007] 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).

[1008] 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.

[1009] 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.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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."

[1014] The present invention relates to a system that supports users in creating blog articles while efficiently learning a language, and utilizes generative artificial intelligence (AI). The following describes in detail the embodiments of the invention.

[1015] System Overview

[1016] This system consists of a server, a terminal, and a user. The server manages the database and generative AI model, and processes and evaluates the content created by users. The terminal provides an interface for users to access and operate the system. Users use this system to learn languages ​​while creating blog articles.

[1017] Program processing flow

[1018] 1. Create an account and log in

[1019] The user accesses the web interface on their device and creates a new account, entering a username, password, and desired language of study.

[1020] The terminal sends the entered information to the server, which stores the new user information in a database.

[1021] When a user enters their username and password on the login page, the device sends it to the server, which authenticates them, and if successful, redirects them to the dashboard.

[1022] 2. Choose your blog topic and language

[1023] A user selects a blog topic and language on the dashboard.

[1024] The terminal transmits the selected information to the server, which accepts it.

[1025] 3. Question and Answer Generation

[1026] The server uses a generative AI model to generate questions based on the topic and language selected by the user.

[1027] The server sends the generated question to the terminal and presents it to the user.

[1028] 4. Drafting a blog post

[1029] The user enters an answer to the question.

[1030] The terminal sends the entered answer to the server, which receives it.

[1031] The server uses a generative AI model to automatically generate a draft blog post based on your answers.

[1032] The server transmits the generated draft to the terminal and presents it to the user.

[1033] 5. Writing Progress Assessment and Feedback

[1034] The server uses a generative AI model to evaluate users' drafts, including their language learning progress and writing quality.

[1035] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[1036] Users then edit the draft based on the feedback and finalize the blog post.

[1037] Specific examples

[1038] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" The user inputs answers to each question, answering, for example, "To relax and have new experiences," or "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[1039] The system allows users to improve their practical writing skills while learning a new language.

[1040] The processing flow will be explained below.

[1041] Program processing steps

[1042] 1. Create an account and log in

[1043] Step 1:

[1044] The user accesses the web interface on their device and opens the account creation page.

[1045] Step 2:

[1046] The user enters the required information (username, password, desired language to learn).

[1047] Step 3:

[1048] The terminal transmits the input information to the server.

[1049] Step 4:

[1050] The server stores the received user information in a database and returns a message indicating that the account has been created.

[1051] Step 5:

[1052] The user enters their username and password on the login page.

[1053] Step 6:

[1054] The terminal transmits the entered authentication information to the server.

[1055] Step 7:

[1056] The server checks the user against the database and performs authentication, and if authentication is successful, returns a login success message to the user and redirects them to the dashboard.

[1057] 2. Choose your blog topic and language

[1058] Step 1:

[1059] A user selects a blog topic and language on the dashboard.

[1060] Step 2:

[1061] The terminal transmits the selected information to the server.

[1062] Step 3:

[1063] The server accepts the selected topic and language and prepares for the next process.

[1064] 3. Question and Answer Generation

[1065] Step 1:

[1066] The server runs a generative AI model to generate questions based on the topic and language selected by the user.

[1067] Step 2:

[1068] The server sends the generated question to the terminal and presents it to the user.

[1069] Step 3:

[1070] The user uses the terminal to enter answers to the questions.

[1071] Step 4:

[1072] The terminal sends the user's answer to the server.

[1073] 4. Drafting a blog post

[1074] Step 1:

[1075] The server uses a generative AI model to automatically generate a draft blog post based on the user's responses.

[1076] Step 2:

[1077] The server transmits the generated draft to the terminal and presents it to the user.

[1078] Step 3:

[1079] The user reviews the draft and makes edits as necessary.

[1080] Step 4:

[1081] The user sends the edited content from the terminal to the server.

[1082] 5. Writing Progress Assessment and Feedback

[1083] Step 1:

[1084] The server uses a generative AI model to assess the progress of the user's draft.

[1085] Step 2:

[1086] The server generates feedback including the evaluation results and improvements.

[1087] Step 3:

[1088] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[1089] Step 4:

[1090] The user checks the feedback on the device and re-edits the draft if necessary.

[1091] Step 5:

[1092] The user completes the final blog post and sends it from the device to the server for storage.

[1093] Through these steps, users can efficiently learn a language while creating high-quality blog articles.

[1094] Example 1

[1095] 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."

[1096] In modern society, there is a demand for the use of generative artificial intelligence as a means of efficiently generating content while learning. Systems that allow learners to simultaneously study a language and write blog posts would be particularly useful, but few systems offer such functionality. Conventional systems often generate content based on user input inefficiently, limiting learning outcomes. Furthermore, they lack evaluation functions to provide appropriate feedback, making it difficult for users to grasp their own progress.

[1097] 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.

[1098] In this invention, the server includes a generative AI means for generating questions based on a theme and language selected by the user, an AI means for receiving responses entered by the user and automatically generating a draft document based on the responses, and an AI means for evaluating the learning progress of the generated draft document and providing feedback. This enables users to efficiently progress with language learning while creating practical blog articles.

[1099] "Generative AI means" refers to AI technology that generates appropriate questions based on themes and languages ​​selected by the user, and supports the automatic generation of content.

[1100] "Authentication means" refers to a function that verifies user login information and ensures that only users with legitimate access rights can access the system.

[1101] "Web interface means" refers to a user interface that allows a user to access and operate the system online.

[1102] "Means for constructing a question and answer session" refers to a function that uses generative artificial intelligence means to create appropriate questions based on user selections and sets up a process for obtaining user responses.

[1103] "Artificial intelligence means for automatically generating a draft" means an artificial intelligence function that automatically generates an initial version of a document based on responses entered by a user.

[1104] The "evaluation means" is a function that evaluates the grammar and content of the generated draft blog article and provides the results to the user.

[1105] "Means for providing feedback" refers to a function that supports learning by providing advice and corrections based on the quality of the draft generated by the user and the learning progress.

[1106] "Means for storage" refers to the ability to store user-created data or system-generated data in an internal database so that it can be reused later.

[1107] A "theme" is a particular topic or topic that a user chooses as the content of a blog post.

[1108] A "response" is text information that a user inputs in response to a question presented by the generative artificial intelligence means.

[1109] The present invention relates to a system that supports users in creating blog articles while learning a language effectively and efficiently. This system utilizes a generative artificial intelligence model. A specific embodiment of this system will be described below.

[1110] System Overview

[1111] This system consists of a server, a terminal, and a user. The server manages the database and generative AI model, and generates and evaluates sentences written by users. For example, the well-known GPT-3 can be used as a generative AI model. The terminal provides an interface for users to access and operate the system. Users use this system to progress through language learning while creating blog posts.

[1112] Account creation and login

[1113] A user uses a terminal to access the web interface and create a new account. They enter their username, password, and desired language to learn. The terminal sends this information to the server, which stores it in a database. When the user enters their username and password on the login page, the terminal sends these credentials to the server, which authenticates them. If authentication is successful, the user is redirected to the dashboard.

[1114] Choosing a blog topic and learning language

[1115] Users select a blog topic and learning language on the dashboard. The device sends the selected information to the server, which accepts it. The server uses a generative artificial intelligence model to generate questions based on the selected topic and language. For example, users can select the topics "travel" or "learning." Questions generated include "What is the purpose of your trip?" and "Where would you like to visit this summer?"

[1116] Question and answer generation

[1117] The server uses a generative artificial intelligence model to generate questions based on the topic and language selected by the user. The generated questions are presented to the user via the terminal. The user then enters a response, which is then sent to the server.

[1118] Drafting a blog post

[1119] Based on the received response, the server uses a generative AI model to generate a draft blog post. The draft is then presented to the user via their device. If the user responds with something like "To relax and have a new experience," the response is reflected in the document.

[1120] Writing progress assessment and feedback

[1121] The server uses a generative AI model to evaluate the learning progress and quality of the generated draft and provides feedback. The user then edits the draft based on the feedback and completes the final blog post. For example, the server provides advice such as "There are grammatical errors" or "The content is insufficient."

[1122] Specific examples

[1123] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative artificial intelligence model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" The user might respond with "To relax and have new experiences" or "Kyoto is a beautiful place rich in history and culture." The server generates a draft based on these answers and presents it to the user. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[1124] The system allows users to improve their practical writing skills while learning a new language.

[1125] Prompt Sentence Examples

[1126] Generate a question with the topic "Travel" and language "Japanese".

[1127] Generate a draft blog post that answers the question, "What is the purpose of your trip?"

[1128] By using this system, users can learn languages ​​effectively and efficiently.

[1129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1130] Step 1:

[1131] A user accesses a web interface on a terminal and creates a new account. The user enters a username, password, and desired language to learn. The terminal sends this information to the server, which stores it in a database as a new user. The input to this process is the username, password, and desired language entered by the user, and the output is the new user information stored in the database.

[1132] Step 2:

[1133] The user enters their username and password on the login page. The device sends this authentication information to the server. The server compares the received information with the database and performs authentication. If authentication is successful, the server redirects the user to the dashboard and sends a success notification to the device. The input is the username and password, and the output is the authentication success / failure result and the display of the dashboard.

[1134] Step 3:

[1135] The user selects a blog topic and learning language on the dashboard. The device sends the selected information to the server. The server uses the received information to create a prompt for the generative AI model and prepares to generate a question. The input is the topic and learning language selected by the user, and the output is a prompt for the generative AI model.

[1136] Step 4:

[1137] The server uses a generative AI model to generate questions based on the topic and language selected by the user. The generated questions are sent to the device and presented to the user. The input is the prompt from the generative AI model, and the output is the generated question. The server can generate questions like, "What is the purpose of your trip?"

[1138] Step 5:

[1139] The user inputs a response to a question generated by the terminal. The terminal sends the input response to the server. The input is the user's response, and the output is the data sent to the server. For example, the user inputs "To relax and have a new experience."

[1140] Step 6:

[1141] The server generates a draft of a blog post using a generative AI model based on the received response. The server then sends the generated draft to the device. The input is the user's response, and the output is the generated draft. For example, the server might generate a draft that reads, "Traveling is a great way to relax and experience new things. Kyoto, in particular, is worth visiting because it is rich in history and culture."

[1142] Step 7:

[1143] The server uses a generative AI model to evaluate the learning progress and quality of the generated draft and provides feedback. The server sends the evaluation results and feedback to the device and presents them to the user. The input is the generated draft, and the output is the evaluation results and feedback. Typically, feedback such as "The sentence structure is good, but there are some grammatical errors" is generated.

[1144] Step 8:

[1145] The user edits the draft based on the feedback and completes the final blog post. The device sends the edited post to the server, which then saves the final post in a database. The input is the edited draft, and the output is the saved final post. Specifically, the user corrects "grammatical errors" and saves the completed post.

[1146] Through the above processing steps, users can effectively advance their language learning while creating practical blog articles.

[1147] (Application example 1)

[1148] 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."

[1149] Conventional language learning systems have the drawback of making it difficult for users to effectively learn a language while engaging in practical writing. In particular, the process of users writing blog posts about topics of their interest and simultaneously evaluating and providing feedback on the content is complicated, making it difficult to improve language skills. Furthermore, many systems are not optimized for smartphone environments, often resulting in a loss of user convenience.

[1150] 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.

[1151] In this invention, the server includes generative artificial intelligence means for generating questions based on a topic and language selected by a user, artificial intelligence means for receiving answers entered by the user and automatically generating draft blog articles based on the answers, artificial intelligence means for evaluating the language learning progress of the generated draft blog articles and providing feedback, means for transmitting the topic and language selected by the user to the server and using the generative artificial intelligence means to generate questions and present them to the user, means for transmitting the answers entered by the user to the server and using the generative artificial intelligence means to generate and evaluate draft blog articles based on the answers, and means including an application to be installed on a smartphone, thereby enabling users to use their smartphones to easily and efficiently create blog articles while learning a language.

[1152] "User" refers to an individual who uses the system to create blog articles while studying a language.

[1153] A "topic" refers to a theme or topic that a user chooses as the content of a blog post.

[1154] "Language" refers to the language that a user learns and uses to create blog posts.

[1155] "Generative AI methods" refers to AI technology that generates questions based on the topic and language selected by the user, and automatically generates draft blog posts based on the answers.

[1156] "Question" refers to a question posed by the generative artificial intelligence means regarding content related to a topic selected by a user.

[1157] "Answer" refers to the content that a user inputs in response to a question generated by the generative artificial intelligence means.

[1158] "Draft blog post" refers to the initial version of a blog post that is automatically created by the generative artificial intelligence means based on responses entered by the user.

[1159] "Evaluation" refers to the process of measuring the language learning progress and writing quality of the generated draft blog posts and providing appropriate feedback.

[1160] "Feedback" refers to advice and corrections provided to the user as a result of evaluation of the generated draft blog post.

[1161] "Authentication means" refers to the technology that authenticates users when they create an account and log in.

[1162] "Web Interface" refers to a browser-based user interface that allows a user to access and operate the system.

[1163] "Server" refers to a computer system that manages a database and generative artificial intelligence means, and processes and stores data sent by users.

[1164] "Smartphone application" refers to a dedicated application that is installed on a smartphone and allows users to study languages ​​and write blog articles.

[1165] System Overview

[1166] This invention is a system that allows users to efficiently create blog articles while learning a language. The system consists of a server, a user's smartphone, and the user. The server manages the database and generative AI model, and generates and evaluates content according to the user's operations. The smartphone provides the user with an interface that allows them to access and operate the system. The user uses this system to learn a language while creating blog articles.

[1167] Hardware and Software Used

[1168] Hardware: Servers, smartphones

[1169] software:

[1170] Flask: A Python-based web framework for server-side use.

[1171] Generative AI model: AI model for question generation, article generation, and evaluation

[1172] Database system: stores user information and generated articles

[1173] Processing flow

[1174] 1. Create an account and log in:

[1175] A user creates an account through a smartphone application and logs in. During this process, authentication information is sent to the server and stored in a database.

[1176] 2. Topic and language selection:

[1177] The user uses the smartphone application dashboard to select the topics and languages ​​they are interested in, and the selected information is sent to the server.

[1178] 3. Question generation and answer input:

[1179] The server uses a generative AI model to generate questions based on the user's selections. The generated questions are presented to the user via a smartphone application, and the user inputs answers.

[1180] 4. Blog post draft generation:

[1181] The user's answers are sent to a server, and a generative AI model automatically generates a draft blog post based on the answers. The draft is then presented to the user via their smartphone.

[1182] 5. Writing evaluation and feedback:

[1183] The server uses a generative AI model to evaluate the generated drafts, including language learning progress and writing quality, and provides the resulting feedback to the user via a smartphone application.

[1184] Specific examples

[1185] For example, if a user wants to write a blog post in English about "latest technology trends," the following process would be performed:

[1186] Topic selection: The user selects the topic "Technology" and the language of study "English."

[1187] Question generation: A server-side generative AI model generates questions such as:

[1188] "What is the latest tech trend you are excited about?"

[1189] "Describe a technology you think will change the world."

[1190] Answer input: The user inputs the answer to each question and sends it to the server via the smartphone application.

[1191] Draft generation: Based on these answers, the server uses a generative AI model to automatically generate a draft blog post.

[1192] Evaluation and feedback: The generated draft is evaluated on the server, and the evaluation results and feedback are provided to the user.

[1193] This system allows users to use their smartphones to easily and efficiently create blog articles while learning a language.

[1194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1195] Step 1:

[1196] A user launches a smartphone application, creates an account, and logs in. The user enters their username, password, and language of study through the application interface, and this information is sent from the device to the server. The server stores the received information in a database and manages authentication information.

[1197] Input: Username, Password, Learning Language

[1198] Data processing: The server stores the received data in a database

[1199] Output: New account created, login authentication successful message

[1200] Step 2:

[1201] After logging in, users select the topic and language of their blog post from the application's dashboard. The selected information is sent from the device to the server, which then uses this information to provide prompts to the generative AI model.

[1202] Input: Selected topic, language to study

[1203] Data processing: The server sends the prompt to the generative AI model

[1204] Output: Generated questionnaire list

[1205] Step 3:

[1206] The server uses a generative AI model to generate questions related to the topic and language selected by the user, which are then presented to the user via their device.

[1207] Input: prompt, topic, language

[1208] Data processing: Generative AI models generate questions

[1209] Output: The question presented to the user

[1210] Step 4:

[1211] The user inputs answers to the questions presented to them, and the terminal sends the answers to the server, which then stores them in a database.

[1212] Input: User's answer

[1213] Data processing: The server stores the received data in a database

[1214] Output: Answer data saved on the server

[1215] Step 5:

[1216] The server uses a generative AI model to automatically generate a draft blog post based on the user's answers, and the draft is presented to the user via their device.

[1217] Input: User response data

[1218] Data processing: A generative AI model generates draft blog posts

[1219] Output: A draft blog post presented to the user

[1220] Step 6:

[1221] The server uses a generative AI model to evaluate the generated blog post drafts, including language learning progress and writing quality, and provides the evaluation results and feedback to the user via their device.

[1222] Input: Blog post draft

[1223] Data processing: Generative AI models evaluate drafts and generate feedback

[1224] Output: Evaluation results and feedback presented to the user

[1225] 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.

[1226] The present invention relates to a system that supports users in creating blog articles while efficiently learning a language, and utilizes generative artificial intelligence (AI) and an emotion engine. The following describes in detail the embodiments of the invention.

[1227] System Overview

[1228] This system consists of a server, a terminal, and a user. The server manages the database, generative AI model, and emotion engine, and processes, evaluates, and recognizes emotions in content created by users. The terminal provides an interface for users to access and operate the system. Users use this system to create blog posts and learn languages ​​while receiving feedback tailored to their emotional state.

[1229] Program processing flow

[1230] 1. Create an account and log in

[1231] A user accesses the web interface on a device and creates a new account. They enter a username, password, and desired language to learn. The device sends the information to the server, which stores the new user information in a database. The user enters their username and password on the login page, which the device sends to the server, which authenticates them. If authentication is successful, they are redirected to the dashboard.

[1232] 2. Choose your blog topic and language

[1233] The user selects the blog topic and language on the dashboard. The device sends the selected information to the server, which then activates the emotion engine and monitors the user's emotional state in real time.

[1234] 3. Question and Answer Generation

[1235] The server uses a generative AI model to generate a question based on the topic and language selected by the user. The server sends the generated question to the device and presents it to the user. The user uses the device to enter an answer to the question. The device sends the user's answer to the server.

[1236] 4. Drafting a blog post

[1237] The server uses a generative AI model to automatically generate a draft of a blog post based on the user's responses. The server then sends the generated draft to the device and presents it to the user. The user then reviews the draft and makes edits as necessary. The server uses an emotion engine to provide advice and additional feedback based on the user's emotional state.

[1238] 5. Writing Progress Assessment and Feedback

[1239] The server uses a generative AI model to evaluate the user's progress on the draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. The server generates feedback including the evaluation results and areas for improvement. The server sends the evaluation results and feedback to the device and presents them to the user. The user reviews the feedback on the device and re-edits the draft if necessary. The user completes the final blog post and sends it from the device to the server for storage.

[1240] Specific examples

[1241] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" At the same time, an emotion engine recognizes emotions from the user's input and behavior and evaluates their motivation and stress level. The user enters answers to each question, such as "To relax and have new experiences" and "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. Depending on the user's emotional state, the server provides additional advice and feedback. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[1242] The system allows users to learn a new language while gaining practical writing skills and a personalized learning experience that is tailored to their emotional state.

[1243] The processing flow will be explained below.

[1244] Program processing steps (including emotion engine)

[1245] 1. Create an account and log in

[1246] Step 1:

[1247] The user accesses the web interface on their device and opens the account creation page.

[1248] Step 2:

[1249] The user enters the required information (username, password, and desired language of study).

[1250] Step 3:

[1251] The terminal transmits the input information to the server.

[1252] Step 4:

[1253] The server stores the received user information in a database and returns a message indicating that the account has been created.

[1254] Step 5:

[1255] The user enters their username and password on the login page.

[1256] Step 6:

[1257] The terminal transmits the entered authentication information to the server.

[1258] Step 7:

[1259] The server checks the user against the database and performs authentication, and if authentication is successful, returns a login success message to the user and redirects them to the dashboard.

[1260] 2. Choose your blog topic and language

[1261] Step 1:

[1262] A user selects a blog topic and language on the dashboard.

[1263] Step 2:

[1264] The terminal transmits the selected information to the server.

[1265] Step 3:

[1266] The server accepts the selected topic and language and prepares for the next process.

[1267] Step 4:

[1268] The server activates an emotion engine and monitors the user's emotional state in real time.

[1269] 3. Question and Answer Generation

[1270] Step 1:

[1271] The server uses a generative AI model to generate questions based on the topic and language selected by the user.

[1272] Step 2:

[1273] The server sends the generated question to the terminal and presents it to the user.

[1274] Step 3:

[1275] The user uses the terminal to enter answers to the questions.

[1276] Step 4:

[1277] The terminal sends the user's answer to the server.

[1278] 4. Drafting a blog post

[1279] Step 1:

[1280] The server uses a generative AI model to automatically generate a draft blog post based on the user's responses.

[1281] Step 2:

[1282] The server transmits the generated draft to the terminal and presents it to the user.

[1283] Step 3:

[1284] An emotion engine analyzes the user's emotional state and generates advice or additional feedback as needed.

[1285] Step 4:

[1286] The server sends the advice and feedback generated by the emotion engine to the terminal and presents it to the user.

[1287] Step 5:

[1288] The user reviews the draft and makes edits as necessary.

[1289] Step 6:

[1290] The user sends the edited content from the terminal to the server.

[1291] 5. Writing Progress Assessment and Feedback

[1292] Step 1:

[1293] The server uses a generative AI model to evaluate the user's progress in their draft, including language learning progress, writing quality, and the user's emotional state.

[1294] Step 2:

[1295] The server generates feedback including the evaluation results and improvements.

[1296] Step 3:

[1297] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[1298] Step 4:

[1299] The user checks the feedback on the device and re-edits the draft if necessary.

[1300] Step 5:

[1301] After the final blog post is completed, the user sends it to the server via the device for storage.

[1302] Specific examples

[1303] For example, if a user wants to create a blog post about "travel" in Japanese, the process would be as follows:

[1304] Select your blog topic and language

[1305] Step 1:

[1306] A user selects the topic "Travel" and the language "Japanese" on the dashboard.

[1307] Step 2:

[1308] The terminal transmits the selected information to the server.

[1309] Step 3:

[1310] The server accepts the selected information and activates the emotion engine.

[1311] Question and answer generation

[1312] Step 1:

[1313] The server uses a generative AI model to generate questions such as, "What is the purpose of your trip?" and "What are the characteristics of the place you want to visit?"

[1314] Step 2:

[1315] The server sends the generated question to the terminal and presents it to the user.

[1316] Step 3:

[1317] In response to the questions, the user answers, "To relax and have new experiences," and "Kyoto is a beautiful place rich in history and culture."

[1318] Step 4:

[1319] The device sends the response to the server.

[1320] Drafting and editing blog posts

[1321] Step 1:

[1322] The server uses a generative AI model to generate a draft blog post based on the answers.

[1323] Step 2:

[1324] The server transmits the generated draft to the terminal and presents it to the user.

[1325] Step 3:

[1326] The emotion engine analyzes the user's emotional state and provides additional advice such as "relax and carry on" if the user is tired.

[1327] Step 4:

[1328] Users review and edit the draft and also take into account feedback from the emotion engine.

[1329] Writing progress assessment and feedback

[1330] Step 1:

[1331] The server evaluates the draft and generates feedback based on progress, quality, and emotional state.

[1332] Step 2:

[1333] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[1334] Step 3:

[1335] The user checks the feedback and makes edits as necessary.

[1336] Step 4:

[1337] After the final blog post is completed, the user submits the post to the server for storage.

[1338] The system allows users to learn a new language while also receiving practical writing skills and personalized instruction tailored to their emotional state.

[1339] Example 2

[1340] 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."

[1341] In conventional language learning systems, efficient language learning and content creation are often not integrated when users create blog posts. Furthermore, they lack a mechanism for providing personalized feedback based on the user's emotional state. As a result, users' motivation and stress management are insufficient, and learning outcomes cannot be expected to improve. Therefore, there is a need for a system that allows users to effectively and efficiently create blog posts while learning a language.

[1342] 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.

[1343] In this invention, the server includes a generative artificial intelligence means for generating questions based on a topic and language selected by a user, a generative artificial intelligence means for receiving answers entered by the user and automatically generating draft blog articles based on the answers, a generative artificial intelligence means for evaluating the language learning progress and writing quality of the generated draft blog articles and providing feedback, and an emotion recognition means for monitoring the user's emotional state in real time and providing feedback based on the same, thereby enabling users to receive effective feedback based on their individual emotional state while simultaneously learning a language and creating blog articles.

[1344] A "generative artificial intelligence means" is an artificial intelligence system that has the ability to generate questions based on specific topics and languages ​​selected by a user and automatically generate content based on the answers received.

[1345] "Emotion recognition means" is a technology that analyzes user input and behavior in real time to recognize and evaluate their emotional state.

[1346] An "authentication means" is a processing system that performs authentication using information such as a username and password when a user creates an account and logs in.

[1347] The "means for providing an interface" refers to a technique for providing a user interface that allows a user to access and operate the system.

[1348] The "means for constructing a question and answer session" is a technique for constructing a series of dialogues that generate questions and collect answers from users based on the topic and language selected by the user.

[1349] The "means for saving a draft of a blog article" is a technology that has the function of saving a draft of a blog article generated based on the answers entered by the user in a storage device such as a database.

[1350] The "generative AI means for providing feedback" is an AI system that evaluates the generated draft blog post and provides appropriate feedback based on the user's language learning progress and writing quality.

[1351] "Language learning progress" is an indicator that shows how much the user's ability to understand and express themselves in the language they have selected to learn has improved.

[1352] The present invention relates to a system for supporting a user in creating blog articles while efficiently learning a language. Hereinafter, an embodiment of the present invention will be described in detail.

[1353] System Configuration

[1354] This system consists of a server, a terminal, and a user. The server manages the database, generative artificial intelligence (AI) model, and emotion recognition engine, and processes, evaluates, and recognizes emotions in content created by users. The terminal provides an interface for users to access and operate the system.

[1355] Server Roles

[1356] The server has the following features:

[1357] Generative AI methods: Generate questions based on the topic and language selected by the user. This can be done using Natural Language Processing (NLP) libraries, etc.

[1358] Generative AI answer processing: Receive the answers entered by the user and automatically generate a draft blog post based on them. This process utilizes a generative AI model such as GPT-3.

[1359] Language learning progress assessment and feedback: The generated blog post drafts are assessed for language learning progress and writing quality, and feedback is provided, again using the generative AI model described above.

[1360] Emotion recognition: Monitor the user's emotional state in real time and provide feedback based on this. This can be achieved using emotion recognition libraries and algorithms (e.g., Facial Emotion Recognition API).

[1361] Device Role

[1362] The terminal has the following features:

[1363] Web Interface: Provides an interface for users to access the system using a web browser and web pages written in HTML, CSS, and JavaScript.

[1364] Authentication method: Provides a function for users to create an account and log in. Authentication information is sent from the terminal to the server and processed there.

[1365] User operations

[1366] A user uses the system in the following steps:

[1367] 1. Account Creation and Login: The user accesses the web interface on their device and creates a new account. They enter their username, password, and desired language to learn. The device sends the information they entered to the server, which stores the new user information in a database. The user enters their username and password on the login page, which the device sends to the server, which authenticates them. If authentication is successful, they are redirected to the dashboard.

[1368] 2. Blog topic and language selection: The user selects the blog topic and language on the dashboard. The device sends the selected information to the server, which then accepts it. The server then activates the emotion engine and monitors the user's emotional state in real time.

[1369] 3. Question and answer generation: The server uses a generative AI model to generate questions based on the topic and language selected by the user. The server sends the generated questions to the device and presents them to the user. The user uses the device to enter answers to the questions. The device sends the user's answers to the server.

[1370] 4. Blog post drafting: The server automatically generates a blog post draft using a generative AI model based on the user's responses. The server then sends the draft to the device and presents it to the user. The user reviews the draft and makes edits as necessary. The server uses an emotion engine to provide advice and additional feedback based on the user's emotional state.

[1371] 5. Writing Progress Evaluation and Feedback: The server uses a generative AI model to evaluate the user's progress on their draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. The server generates feedback including the evaluation results and areas for improvement. The server sends the evaluation results and feedback to the device and presents them to the user. The user reviews the feedback on the device and re-edits the draft if necessary. The user completes the final blog post and sends it from the device to the server for storage.

[1372] Specific examples

[1373] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" At the same time, an emotion engine recognizes emotions from the user's input and behavior and evaluates their motivation and stress level. The user answers each question with questions such as "To relax and have new experiences" and "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. Depending on the user's emotional state, the server provides additional advice and feedback. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[1374] Prompt Sentence Examples

[1375] "Think about your next travel destination. What is your purpose and what are the characteristics of the place you want to visit?"

[1376] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1377] Step 1: Create an account

[1378] A user accesses the web interface using a terminal and enters a username, password, and desired language to learn on the account creation page. Input: Username, password, language to learn. Output: Account creation request data.

[1379] The device sends the entered information to the server, and the server's endpoint accepts this data.

[1380] The server saves the received account creation request data in the database and creates a user account. Data processing: Saving account information.

[1381] Step 2: Log in

[1382] A user enters a username and password on the login page. Input: Username, Password. Output: Authentication request data.

[1383] The terminal sends this to the server.

[1384] The server performs the authentication process, verifying that the username and password match. Data operation: Authentication check. If successful, redirect the user to the dashboard, otherwise return an error message. Output: Dashboard URL or error message.

[1385] Step 3: Choose your blog topic and language

[1386] User selects blog topic and language in dashboard. Input: topic, language. Output: selected topic and language.

[1387] The device sends the selected topic and language to the server.

[1388] The server accepts and starts the emotion engine. The selection information is passed to the emotion engine, which starts real-time monitoring of the user's emotional state. Data calculation: Setting topic and language information.

[1389] Step 4: Generate questions and answers

[1390] The server uses a generative AI model to generate questions based on the topic and language selected by the user. Input: Topic, Language. Output: Generated question.

[1391] The server sends the generated question to the terminal and presents it to the user.

[1392] The user uses the terminal to enter answers to questions. Input: User's answers. Output: Answer data.

[1393] The terminal sends the user's answer to the server.

[1394] Step 5: Draft your blog post

[1395] Based on the user's answers received by the server, a generative AI model is used to automatically generate a draft blog post. Input: Answer data. Output: Draft data.

[1396] The server transmits the generated draft to the terminal and presents it to the user.

[1397] The user checks the draft and edits it as necessary. The user adds or corrects it on the terminal. Input: Edited content. Output: Corrected draft data.

[1398] The server uses an emotion engine to monitor the user's emotional state and provide appropriate feedback. Data processing: Emotion data analysis and feedback provision.

[1399] Step 6: Writing Progress Assessment and Feedback

[1400] The server uses a generative AI model to evaluate the user's progress in their draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. Input: Draft data. Output: Evaluation results.

[1401] The server generates feedback including the evaluation results and improvements. Data calculation: Generation of evaluation results.

[1402] The server sends the feedback to the terminal and presents it to the user.

[1403] The user reviews the feedback and re-edits the draft if necessary. Input: Feedback. Output: Improved draft data.

[1404] The user completes the final blog post, sends it from the device to the server, and saves it. Input: Final draft data. Output: Notification of save completion.

[1405] The above is a detailed description of the specific processing steps of this system.

[1406] (Application example 2)

[1407] 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."

[1408] It is difficult to provide language learners with the opportunity to not only acquire language skills but also to write in real contexts while receiving feedback based on their emotional state and generating high-quality text. Furthermore, there is a lack of systems that allow users to reflect their emotional state and receive appropriate advice for a personalized learning experience. This is necessary to maintain user motivation and reduce stress.

[1409] 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.

[1410] In this invention, the server includes an automated generation means for generating questions based on a topic and language selected by the user, an artificial intelligence means for receiving answers entered by the user and automatically generating draft articles based on the answers, an artificial intelligence means for evaluating the language acquisition progress of the generated draft articles and providing feedback, and an emotional evaluation means for evaluating the emotional state of the user while creating the articles and providing appropriate advice. This allows users to efficiently learn a language while creating product reviews and introductory articles, and to receive feedback based on their emotional state during the process. Furthermore, the personalized learning experience helps maintain motivation and reduce stress.

[1411] An "automated generation means" is a device or software that automatically generates appropriate questions based on the topic and language selected by the user.

[1412] "Artificial intelligence means" means a system or algorithm capable of receiving user-entered responses and automatically generating a draft of a written article based thereon.

[1413] The "artificial intelligence means for assessing language acquisition progress and providing feedback" is a system that has the function of analyzing the generated draft text, assessing the user's language learning progress, and providing appropriate advice and areas for improvement.

[1414] An "emotion evaluation means" is a device or software that monitors the user's emotional state in real time while they are creating an article, and provides appropriate advice and feedback based on that information.

[1415] An "authentication means" is a device or software with security functions for creating an account and logging in when a user accesses a system.

[1416] "Means for providing an online interface" refers to an interface provided in the form of a web browser or mobile app that allows users to access and operate the system via the Internet.

[1417] The "means for managing feedback sessions" is a system that has the function of managing the content and history of feedback provided on the generated draft of a written article, and supporting users to understand and use it appropriately.

[1418] The system for implementing this invention mainly comprises a server, a terminal (user device), and a user. The operation of this system will be described in detail below.

[1419] 1. System Overview

[1420] server

[1421] The server manages the database, generative AI model, and emotion evaluation engine, processes and evaluates content from users, and evaluates the user's emotional state and provides appropriate feedback.

[1422] Terminal

[1423] It provides an interface for users to access and operate the system. The terminal is provided as a web browser or mobile app and receives and sends user input.

[1424] 2. Program Description

[1425] Question generation based on user-selected topics and languages

[1426] The server uses automated generation methods to generate appropriate questions based on the topic and language selected by the user, and utilizes a generative AI model to present questions that prompt the user to create an article.

[1427] Software used: OpenAI API

[1428] Example: "Generate questions to help users write reviews about 'Travel Guidebook'."

[1429] Receiving user responses and generating sentences

[1430] The server receives the user's inputted answers and automatically generates a draft of the article using artificial intelligence means based on the answers, and presents the draft to the user for necessary editing.

[1431] Software used: Natural Language Processing (NLP) engine

[1432] Evaluating the progress of drafts and providing feedback

[1433] The server evaluates the language learning progress of the generated draft article and provides appropriate feedback, which evaluates the user's language learning progress and suggests specific areas for improvement.

[1434] Software used: Evaluation algorithm

[1435] Emotion assessment and advice provision

[1436] The server uses emotion assessment tools to evaluate the user's emotional state, understand the user's stress level and motivation during the article writing process, and provide appropriate advice based on this, thereby providing a more personalized learning experience.

[1437] Software used: Emotion Recognition API

[1438] 3. Specific Examples

[1439] For example, consider a user writing a review about a "travel guidebook" on a virtual store. The user first creates an account and logs in. Then, they select the topic "travel guidebook" and the language "Japanese." The server uses a generative AI model to generate questions such as "Which part of this guidebook was most helpful?" and "What information does it provide about the details of your travel destination?"

[1440] When the user enters answers to these questions, the server receives them and generates a draft article based on the user's answers. For example, a response such as "The most useful parts of this guidebook were the detailed maps and transportation information. It also provided detailed information about the history and culture of the destination, which was very helpful" is reflected in the draft.

[1441] The generated draft is presented to the user, who then reviews and edits it. The server then provides progress assessment and feedback. During this process, the server monitors the user's emotional state and provides appropriate advice (e.g., "You're making good progress. Keep going.").

[1442] In this way, users can efficiently learn languages ​​while creating high-quality content. Furthermore, by receiving appropriate support throughout the creation process through the emotion assessment tool, users can reduce stress and maintain motivation.

[1443] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1444] Step 1:

[1445] Account creation and login

[1446] Input: The user enters their username, password, and preferred language from the terminal.

[1447] Specific operation: The terminal sends this information to the server, and the server stores the received information in a database.

[1448] Data processing and calculation: The server hashes the entered user information and stores it.

[1449] Output: Sends a successful account creation message to the terminal to display to the user.

[1450] Step 2:

[1451] Topic Selection and Question Generation

[1452] Input: The user selects the topic and language on the device dashboard.

[1453] Specific operation: The terminal sends the selected information to the server, and the server uses automated generation means to generate a question based on the topic and language.

[1454] Data processing and computation: The server uses a generative AI model to generate questions based on the prompt.

[1455] Output: The generated question is sent to the terminal and displayed to the user.

[1456] Step 3:

[1457] Receiving user answers to questions

[1458] Input: The user types the answer to the question at the terminal.

[1459] Specific operation: The terminal sends the user's answer to the server.

[1460] Data processing and calculation: The server receives the user's answers, formats them, and stores them.

[1461] Output: A reply receipt confirmation message is sent to the terminal and displayed to the user.

[1462] Step 4:

[1463] Generate a draft

[1464] Input: User response data stored on the server.

[1465] Specific operation: The server uses artificial intelligence means to generate a draft of the article based on the user's answers.

[1466] Data processing and calculation: Using a generative AI model, natural-sounding sentences are generated based on the answers.

[1467] Output: The generated draft is sent to the terminal and displayed to the user.

[1468] Step 5:

[1469] Review and edit the draft

[1470] Input: The user reviews the draft on their device and makes edits as needed.

[1471] Specific operation: Send the edited draft to the server.

[1472] Data processing and calculation: The server receives the edited text and stores it again.

[1473] Output: Sends an edit confirmation message to the terminal for display to the user.

[1474] Step 6:

[1475] Emotion evaluation and feedback provision

[1476] Input: Final draft stored on the server and user behavior data.

[1477] Specific operation: The server evaluates the user's emotional state using the emotion evaluation means.

[1478] Data processing and calculation: Emotion recognition algorithms are used to analyze the user's emotional state from their input speed and content.

[1479] Output: Generate feedback based on the emotion evaluation, send it to the device and display it to the user.

[1480] Step 7:

[1481] Save and publish the final article

[1482] Input: User reviews the final draft on their device and chooses to save or publish.

[1483] Specific operation: The device sends a save or publish instruction to the server.

[1484] Data processing and calculation: The server stores the final article in a database and performs appropriate publishing processing according to the publishing settings.

[1485] Output: Sends a save or publish confirmation message to the terminal and displays it to the user.

[1486] 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.

[1487] 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.

[1488] 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.

[1489] [Fourth embodiment]

[1490] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1491] 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.

[1492] 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).

[1493] 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.

[1494] 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.

[1495] 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).

[1496] 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.

[1497] 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.

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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."

[1503] The present invention relates to a system that supports users in creating blog articles while efficiently learning a language, and utilizes generative artificial intelligence (AI). The following describes in detail the embodiments of the invention.

[1504] System Overview

[1505] This system consists of a server, a terminal, and a user. The server manages the database and generative AI model, and processes and evaluates the content created by users. The terminal provides an interface for users to access and operate the system. Users use this system to learn languages ​​while creating blog articles.

[1506] Program processing flow

[1507] 1. Create an account and log in

[1508] The user accesses the web interface on their device and creates a new account, entering a username, password, and desired language of study.

[1509] The terminal sends the entered information to the server, which stores the new user information in a database.

[1510] When a user enters their username and password on the login page, the device sends it to the server, which authenticates them, and if successful, redirects them to the dashboard.

[1511] 2. Choose your blog topic and language

[1512] A user selects a blog topic and language on the dashboard.

[1513] The terminal transmits the selected information to the server, which accepts it.

[1514] 3. Question and Answer Generation

[1515] The server uses a generative AI model to generate questions based on the topic and language selected by the user.

[1516] The server sends the generated question to the terminal and presents it to the user.

[1517] 4. Drafting a blog post

[1518] The user enters an answer to the question.

[1519] The terminal sends the entered answer to the server, which receives it.

[1520] The server uses a generative AI model to automatically generate a draft blog post based on your answers.

[1521] The server transmits the generated draft to the terminal and presents it to the user.

[1522] 5. Writing Progress Assessment and Feedback

[1523] The server uses a generative AI model to evaluate users' drafts, including their language learning progress and writing quality.

[1524] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[1525] Users then edit the draft based on the feedback and finalize the blog post.

[1526] Specific examples

[1527] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" The user inputs answers to each question, answering, for example, "To relax and have new experiences," or "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[1528] The system allows users to improve their practical writing skills while learning a new language.

[1529] The processing flow will be explained below.

[1530] Program processing steps

[1531] 1. Create an account and log in

[1532] Step 1:

[1533] The user accesses the web interface on their device and opens the account creation page.

[1534] Step 2:

[1535] The user enters the required information (username, password, desired language to learn).

[1536] Step 3:

[1537] The terminal transmits the input information to the server.

[1538] Step 4:

[1539] The server stores the received user information in a database and returns a message indicating that the account has been created.

[1540] Step 5:

[1541] The user enters their username and password on the login page.

[1542] Step 6:

[1543] The terminal transmits the entered authentication information to the server.

[1544] Step 7:

[1545] The server checks the user against the database and performs authentication, and if authentication is successful, returns a login success message to the user and redirects them to the dashboard.

[1546] 2. Choose your blog topic and language

[1547] Step 1:

[1548] A user selects a blog topic and language on the dashboard.

[1549] Step 2:

[1550] The terminal transmits the selected information to the server.

[1551] Step 3:

[1552] The server accepts the selected topic and language and prepares for the next process.

[1553] 3. Question and Answer Generation

[1554] Step 1:

[1555] The server runs a generative AI model to generate questions based on the topic and language selected by the user.

[1556] Step 2:

[1557] The server sends the generated question to the terminal and presents it to the user.

[1558] Step 3:

[1559] The user uses the terminal to enter answers to the questions.

[1560] Step 4:

[1561] The terminal sends the user's answer to the server.

[1562] 4. Drafting a blog post

[1563] Step 1:

[1564] The server uses a generative AI model to automatically generate a draft blog post based on the user's responses.

[1565] Step 2:

[1566] The server transmits the generated draft to the terminal and presents it to the user.

[1567] Step 3:

[1568] The user reviews the draft and makes edits as necessary.

[1569] Step 4:

[1570] The user sends the edited content from the terminal to the server.

[1571] 5. Writing Progress Assessment and Feedback

[1572] Step 1:

[1573] The server uses a generative AI model to assess the progress of the user's draft.

[1574] Step 2:

[1575] The server generates feedback including the evaluation results and improvements.

[1576] Step 3:

[1577] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[1578] Step 4:

[1579] The user checks the feedback on the device and re-edits the draft if necessary.

[1580] Step 5:

[1581] The user completes the final blog post and sends it from the device to the server for storage.

[1582] Through these steps, users can efficiently learn a language while creating high-quality blog articles.

[1583] Example 1

[1584] 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."

[1585] In modern society, there is a demand for the use of generative artificial intelligence as a means of efficiently generating content while learning. Systems that allow learners to simultaneously study a language and write blog posts would be particularly useful, but few systems offer such functionality. Conventional systems often generate content based on user input inefficiently, limiting learning outcomes. Furthermore, they lack evaluation functions to provide appropriate feedback, making it difficult for users to grasp their own progress.

[1586] 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.

[1587] In this invention, the server includes a generative AI means for generating questions based on a theme and language selected by the user, an AI means for receiving responses entered by the user and automatically generating a draft document based on the responses, and an AI means for evaluating the learning progress of the generated draft document and providing feedback. This enables users to efficiently progress with language learning while creating practical blog articles.

[1588] "Generative AI means" refers to AI technology that generates appropriate questions based on themes and languages ​​selected by the user, and supports the automatic generation of content.

[1589] "Authentication means" refers to a function that verifies user login information and ensures that only users with legitimate access rights can access the system.

[1590] "Web interface means" refers to a user interface that allows a user to access and operate the system online.

[1591] "Means for constructing a question and answer session" refers to a function that uses generative artificial intelligence means to create appropriate questions based on user selections and sets up a process for obtaining user responses.

[1592] "Artificial intelligence means for automatically generating a draft" means an artificial intelligence function that automatically generates an initial version of a document based on responses entered by a user.

[1593] The "evaluation means" is a function that evaluates the grammar and content of the generated draft blog article and provides the results to the user.

[1594] "Means for providing feedback" refers to a function that supports learning by providing advice and corrections based on the quality of the draft generated by the user and the learning progress.

[1595] "Means for storage" refers to the ability to store user-created data or system-generated data in an internal database so that it can be reused later.

[1596] A "theme" is a particular topic or topic that a user chooses as the content of a blog post.

[1597] A "response" is text information that a user inputs in response to a question presented by the generative artificial intelligence means.

[1598] The present invention relates to a system that supports users in creating blog articles while learning a language effectively and efficiently. This system utilizes a generative artificial intelligence model. A specific embodiment of this system will be described below.

[1599] System Overview

[1600] This system consists of a server, a terminal, and a user. The server manages the database and generative AI model, and generates and evaluates sentences written by users. For example, the well-known GPT-3 can be used as a generative AI model. The terminal provides an interface for users to access and operate the system. Users use this system to progress through language learning while creating blog posts.

[1601] Account creation and login

[1602] A user uses a terminal to access the web interface and create a new account. They enter their username, password, and desired language to learn. The terminal sends this information to the server, which stores it in a database. When the user enters their username and password on the login page, the terminal sends these credentials to the server, which authenticates them. If authentication is successful, the user is redirected to the dashboard.

[1603] Choosing a blog topic and learning language

[1604] Users select a blog topic and learning language on the dashboard. The device sends the selected information to the server, which accepts it. The server uses a generative artificial intelligence model to generate questions based on the selected topic and language. For example, users can select the topics "travel" or "learning." Questions generated include "What is the purpose of your trip?" and "Where would you like to visit this summer?"

[1605] Question and answer generation

[1606] The server uses a generative artificial intelligence model to generate questions based on the topic and language selected by the user. The generated questions are presented to the user via the terminal. The user then enters a response, which is then sent to the server.

[1607] Drafting a blog post

[1608] Based on the received response, the server uses a generative AI model to generate a draft blog post. The draft is then presented to the user via their device. If the user responds with something like "To relax and have a new experience," the response is reflected in the document.

[1609] Writing progress assessment and feedback

[1610] The server uses a generative AI model to evaluate the learning progress and quality of the generated draft and provides feedback. The user then edits the draft based on the feedback and completes the final blog post. For example, the server provides advice such as "There are grammatical errors" or "The content is insufficient."

[1611] Specific examples

[1612] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative artificial intelligence model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" The user might respond with "To relax and have new experiences" or "Kyoto is a beautiful place rich in history and culture." The server generates a draft based on these answers and presents it to the user. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[1613] The system allows users to improve their practical writing skills while learning a new language.

[1614] Prompt Sentence Examples

[1615] Generate a question with the topic "Travel" and language "Japanese".

[1616] Generate a draft blog post that answers the question, "What is the purpose of your trip?"

[1617] By using this system, users can learn languages ​​effectively and efficiently.

[1618] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1619] Step 1:

[1620] A user accesses a web interface on a terminal and creates a new account. The user enters a username, password, and desired language to learn. The terminal sends this information to the server, which stores it in a database as a new user. The input to this process is the username, password, and desired language entered by the user, and the output is the new user information stored in the database.

[1621] Step 2:

[1622] The user enters their username and password on the login page. The device sends this authentication information to the server. The server compares the received information with the database and performs authentication. If authentication is successful, the server redirects the user to the dashboard and sends a success notification to the device. The input is the username and password, and the output is the authentication success / failure result and the display of the dashboard.

[1623] Step 3:

[1624] The user selects a blog topic and learning language on the dashboard. The device sends the selected information to the server. The server uses the received information to create a prompt for the generative AI model and prepares to generate a question. The input is the topic and learning language selected by the user, and the output is a prompt for the generative AI model.

[1625] Step 4:

[1626] The server uses a generative AI model to generate questions based on the topic and language selected by the user. The generated questions are sent to the device and presented to the user. The input is the prompt from the generative AI model, and the output is the generated question. The server can generate questions like, "What is the purpose of your trip?"

[1627] Step 5:

[1628] The user inputs a response to a question generated by the terminal. The terminal sends the input response to the server. The input is the user's response, and the output is the data sent to the server. For example, the user inputs "To relax and have a new experience."

[1629] Step 6:

[1630] The server generates a draft of a blog post using a generative AI model based on the received response. The server then sends the generated draft to the device. The input is the user's response, and the output is the generated draft. For example, the server might generate a draft that reads, "Traveling is a great way to relax and experience new things. Kyoto, in particular, is worth visiting because it is rich in history and culture."

[1631] Step 7:

[1632] The server uses a generative AI model to evaluate the learning progress and quality of the generated draft and provides feedback. The server sends the evaluation results and feedback to the device and presents them to the user. The input is the generated draft, and the output is the evaluation results and feedback. Typically, feedback such as "The sentence structure is good, but there are some grammatical errors" is generated.

[1633] Step 8:

[1634] The user edits the draft based on the feedback and completes the final blog post. The device sends the edited post to the server, which then saves the final post in a database. The input is the edited draft, and the output is the saved final post. Specifically, the user corrects "grammatical errors" and saves the completed post.

[1635] Through the above processing steps, users can effectively advance their language learning while creating practical blog articles.

[1636] (Application example 1)

[1637] 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."

[1638] Conventional language learning systems have the drawback of making it difficult for users to effectively learn a language while engaging in practical writing. In particular, the process of users writing blog posts about topics of their interest and simultaneously evaluating and providing feedback on the content is complicated, making it difficult to improve language skills. Furthermore, many systems are not optimized for smartphone environments, often resulting in a loss of user convenience.

[1639] 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.

[1640] In this invention, the server includes generative artificial intelligence means for generating questions based on a topic and language selected by a user, artificial intelligence means for receiving answers entered by the user and automatically generating draft blog articles based on the answers, artificial intelligence means for evaluating the language learning progress of the generated draft blog articles and providing feedback, means for transmitting the topic and language selected by the user to the server and using the generative artificial intelligence means to generate questions and present them to the user, means for transmitting the answers entered by the user to the server and using the generative artificial intelligence means to generate and evaluate draft blog articles based on the answers, and means including an application to be installed on a smartphone, thereby enabling users to use their smartphones to easily and efficiently create blog articles while learning a language.

[1641] "User" refers to an individual who uses the system to create blog articles while studying a language.

[1642] A "topic" refers to a theme or topic that a user chooses as the content of a blog post.

[1643] "Language" refers to the language that a user learns and uses to create blog posts.

[1644] "Generative AI methods" refers to AI technology that generates questions based on the topic and language selected by the user, and automatically generates draft blog posts based on the answers.

[1645] "Question" refers to a question posed by the generative artificial intelligence means regarding content related to a topic selected by a user.

[1646] "Answer" refers to the content that a user inputs in response to a question generated by the generative artificial intelligence means.

[1647] "Draft blog post" refers to the initial version of a blog post that is automatically created by the generative artificial intelligence means based on responses entered by the user.

[1648] "Evaluation" refers to the process of measuring the language learning progress and writing quality of the generated draft blog posts and providing appropriate feedback.

[1649] "Feedback" refers to advice and corrections provided to the user as a result of evaluation of the generated draft blog post.

[1650] "Authentication means" refers to the technology that authenticates users when they create an account and log in.

[1651] "Web Interface" refers to a browser-based user interface that allows a user to access and operate the system.

[1652] "Server" refers to a computer system that manages a database and generative artificial intelligence means, and processes and stores data sent by users.

[1653] "Smartphone application" refers to a dedicated application that is installed on a smartphone and allows users to study languages ​​and write blog articles.

[1654] System Overview

[1655] This invention is a system that allows users to efficiently create blog articles while learning a language. The system consists of a server, a user's smartphone, and the user. The server manages the database and generative AI model, and generates and evaluates content according to the user's operations. The smartphone provides the user with an interface that allows them to access and operate the system. The user uses this system to learn a language while creating blog articles.

[1656] Hardware and Software Used

[1657] Hardware: Servers, smartphones

[1658] software:

[1659] Flask: A Python-based web framework for server-side use.

[1660] Generative AI model: AI model for question generation, article generation, and evaluation

[1661] Database system: stores user information and generated articles

[1662] Processing flow

[1663] 1. Create an account and log in:

[1664] A user creates an account through a smartphone application and logs in. During this process, authentication information is sent to the server and stored in a database.

[1665] 2. Topic and language selection:

[1666] The user uses the smartphone application dashboard to select the topics and languages ​​they are interested in, and the selected information is sent to the server.

[1667] 3. Question generation and answer input:

[1668] The server uses a generative AI model to generate questions based on the user's selections. The generated questions are presented to the user via a smartphone application, and the user inputs answers.

[1669] 4. Blog post draft generation:

[1670] The user's answers are sent to a server, and a generative AI model automatically generates a draft blog post based on the answers. The draft is then presented to the user via their smartphone.

[1671] 5. Writing evaluation and feedback:

[1672] The server uses a generative AI model to evaluate the generated drafts, including language learning progress and writing quality, and provides the resulting feedback to the user via a smartphone application.

[1673] Specific examples

[1674] For example, if a user wants to write a blog post in English about "latest technology trends," the following process would be performed:

[1675] Topic selection: The user selects the topic "Technology" and the language of study "English."

[1676] Question generation: A server-side generative AI model generates questions such as:

[1677] "What is the latest tech trend you are excited about?"

[1678] "Describe a technology you think will change the world."

[1679] Answer input: The user inputs the answer to each question and sends it to the server via the smartphone application.

[1680] Draft generation: Based on these answers, the server uses a generative AI model to automatically generate a draft blog post.

[1681] Evaluation and feedback: The generated draft is evaluated on the server, and the evaluation results and feedback are provided to the user.

[1682] This system allows users to use their smartphones to easily and efficiently create blog articles while learning a language.

[1683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1684] Step 1:

[1685] A user launches a smartphone application, creates an account, and logs in. The user enters their username, password, and language of study through the application interface, and this information is sent from the device to the server. The server stores the received information in a database and manages authentication information.

[1686] Input: Username, Password, Learning Language

[1687] Data processing: The server stores the received data in a database

[1688] Output: New account created, login authentication successful message

[1689] Step 2:

[1690] After logging in, users select the topic and language of their blog post from the application's dashboard. The selected information is sent from the device to the server, which then uses this information to provide prompts to the generative AI model.

[1691] Input: Selected topic, language to study

[1692] Data processing: The server sends the prompt to the generative AI model

[1693] Output: Generated questionnaire list

[1694] Step 3:

[1695] The server uses a generative AI model to generate questions related to the topic and language selected by the user, which are then presented to the user via their device.

[1696] Input: prompt, topic, language

[1697] Data processing: Generative AI models generate questions

[1698] Output: The question presented to the user

[1699] Step 4:

[1700] The user inputs answers to the questions presented to them, and the terminal sends the answers to the server, which then stores them in a database.

[1701] Input: User's answer

[1702] Data processing: The server stores the received data in a database

[1703] Output: Answer data saved on the server

[1704] Step 5:

[1705] The server uses a generative AI model to automatically generate a draft blog post based on the user's answers, and the draft is presented to the user via their device.

[1706] Input: User response data

[1707] Data processing: A generative AI model generates draft blog posts

[1708] Output: A draft blog post presented to the user

[1709] Step 6:

[1710] The server uses a generative AI model to evaluate the generated blog post drafts, including language learning progress and writing quality, and provides the evaluation results and feedback to the user via their device.

[1711] Input: Blog post draft

[1712] Data processing: Generative AI models evaluate drafts and generate feedback

[1713] Output: Evaluation results and feedback presented to the user

[1714] 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.

[1715] The present invention relates to a system that supports users in creating blog articles while efficiently learning a language, and utilizes generative artificial intelligence (AI) and an emotion engine. The following describes in detail the embodiments of the invention.

[1716] System Overview

[1717] This system consists of a server, a terminal, and a user. The server manages the database, generative AI model, and emotion engine, and processes, evaluates, and recognizes emotions in content created by users. The terminal provides an interface for users to access and operate the system. Users use this system to create blog posts and learn languages ​​while receiving feedback tailored to their emotional state.

[1718] Program processing flow

[1719] 1. Create an account and log in

[1720] A user accesses the web interface on a device and creates a new account. They enter a username, password, and desired language to learn. The device sends the information to the server, which stores the new user information in a database. The user enters their username and password on the login page, which the device sends to the server, which authenticates them. If authentication is successful, they are redirected to the dashboard.

[1721] 2. Choose your blog topic and language

[1722] The user selects the blog topic and language on the dashboard. The device sends the selected information to the server, which then activates the emotion engine and monitors the user's emotional state in real time.

[1723] 3. Question and Answer Generation

[1724] The server uses a generative AI model to generate a question based on the topic and language selected by the user. The server sends the generated question to the device and presents it to the user. The user uses the device to enter an answer to the question. The device sends the user's answer to the server.

[1725] 4. Drafting a blog post

[1726] The server uses a generative AI model to automatically generate a draft of a blog post based on the user's responses. The server then sends the generated draft to the device and presents it to the user. The user then reviews the draft and makes edits as necessary. The server uses an emotion engine to provide advice and additional feedback based on the user's emotional state.

[1727] 5. Writing Progress Assessment and Feedback

[1728] The server uses a generative AI model to evaluate the user's progress on the draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. The server generates feedback including the evaluation results and areas for improvement. The server sends the evaluation results and feedback to the device and presents them to the user. The user reviews the feedback on the device and re-edits the draft if necessary. The user completes the final blog post and sends it from the device to the server for storage.

[1729] Specific examples

[1730] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" At the same time, an emotion engine recognizes emotions from the user's input and behavior and evaluates their motivation and stress level. The user enters answers to each question, such as "To relax and have new experiences" and "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. Depending on the user's emotional state, the server provides additional advice and feedback. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[1731] The system allows users to learn a new language while gaining practical writing skills and a personalized learning experience that is tailored to their emotional state.

[1732] The processing flow will be explained below.

[1733] Program processing steps (including emotion engine)

[1734] 1. Create an account and log in

[1735] Step 1:

[1736] The user accesses the web interface on their device and opens the account creation page.

[1737] Step 2:

[1738] The user enters the required information (username, password, and desired language of study).

[1739] Step 3:

[1740] The terminal transmits the input information to the server.

[1741] Step 4:

[1742] The server stores the received user information in a database and returns a message indicating that the account has been created.

[1743] Step 5:

[1744] The user enters their username and password on the login page.

[1745] Step 6:

[1746] The terminal transmits the entered authentication information to the server.

[1747] Step 7:

[1748] The server checks the user against the database and performs authentication, and if authentication is successful, returns a login success message to the user and redirects them to the dashboard.

[1749] 2. Choose your blog topic and language

[1750] Step 1:

[1751] A user selects a blog topic and language on the dashboard.

[1752] Step 2:

[1753] The terminal transmits the selected information to the server.

[1754] Step 3:

[1755] The server accepts the selected topic and language and prepares for the next process.

[1756] Step 4:

[1757] The server activates an emotion engine and monitors the user's emotional state in real time.

[1758] 3. Question and Answer Generation

[1759] Step 1:

[1760] The server uses a generative AI model to generate questions based on the topic and language selected by the user.

[1761] Step 2:

[1762] The server sends the generated question to the terminal and presents it to the user.

[1763] Step 3:

[1764] The user uses the terminal to enter answers to the questions.

[1765] Step 4:

[1766] The terminal sends the user's answer to the server.

[1767] 4. Drafting a blog post

[1768] Step 1:

[1769] The server uses a generative AI model to automatically generate a draft blog post based on the user's responses.

[1770] Step 2:

[1771] The server transmits the generated draft to the terminal and presents it to the user.

[1772] Step 3:

[1773] An emotion engine analyzes the user's emotional state and generates advice or additional feedback as needed.

[1774] Step 4:

[1775] The server sends the advice and feedback generated by the emotion engine to the terminal and presents it to the user.

[1776] Step 5:

[1777] The user reviews the draft and makes edits as necessary.

[1778] Step 6:

[1779] The user sends the edited content from the terminal to the server.

[1780] 5. Writing Progress Assessment and Feedback

[1781] Step 1:

[1782] The server uses a generative AI model to evaluate the user's progress in their draft, including language learning progress, writing quality, and the user's emotional state.

[1783] Step 2:

[1784] The server generates feedback including the evaluation results and improvements.

[1785] Step 3:

[1786] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[1787] Step 4:

[1788] The user checks the feedback on the device and re-edits the draft if necessary.

[1789] Step 5:

[1790] After the final blog post is completed, the user sends it to the server via the device for storage.

[1791] Specific examples

[1792] For example, if a user wants to create a blog post about "travel" in Japanese, the process would be as follows:

[1793] Select your blog topic and language

[1794] Step 1:

[1795] A user selects the topic "Travel" and the language "Japanese" on the dashboard.

[1796] Step 2:

[1797] The terminal transmits the selected information to the server.

[1798] Step 3:

[1799] The server accepts the selected information and activates the emotion engine.

[1800] Question and answer generation

[1801] Step 1:

[1802] The server uses a generative AI model to generate questions such as, "What is the purpose of your trip?" and "What are the characteristics of the place you want to visit?"

[1803] Step 2:

[1804] The server sends the generated question to the terminal and presents it to the user.

[1805] Step 3:

[1806] In response to the questions, the user answers, "To relax and have new experiences," and "Kyoto is a beautiful place rich in history and culture."

[1807] Step 4:

[1808] The device sends the response to the server.

[1809] Drafting and editing blog posts

[1810] Step 1:

[1811] The server uses a generative AI model to generate a draft blog post based on the answers.

[1812] Step 2:

[1813] The server transmits the generated draft to the terminal and presents it to the user.

[1814] Step 3:

[1815] The emotion engine analyzes the user's emotional state and provides additional advice such as "relax and carry on" if the user is tired.

[1816] Step 4:

[1817] Users review and edit the draft and also take into account feedback from the emotion engine.

[1818] Writing progress assessment and feedback

[1819] Step 1:

[1820] The server evaluates the draft and generates feedback based on progress, quality, and emotional state.

[1821] Step 2:

[1822] The server sends the evaluation results and feedback to the terminal and presents them to the user.

[1823] Step 3:

[1824] The user checks the feedback and makes edits as necessary.

[1825] Step 4:

[1826] After the final blog post is completed, the user submits the post to the server for storage.

[1827] The system allows users to learn a new language while also receiving practical writing skills and personalized instruction tailored to their emotional state.

[1828] Example 2

[1829] 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."

[1830] In conventional language learning systems, efficient language learning and content creation are often not integrated when users create blog posts. Furthermore, they lack a mechanism for providing personalized feedback based on the user's emotional state. As a result, users' motivation and stress management are insufficient, and learning outcomes cannot be expected to improve. Therefore, there is a need for a system that allows users to effectively and efficiently create blog posts while learning a language.

[1831] 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.

[1832] In this invention, the server includes a generative artificial intelligence means for generating questions based on a topic and language selected by a user, a generative artificial intelligence means for receiving answers entered by the user and automatically generating draft blog articles based on the answers, a generative artificial intelligence means for evaluating the language learning progress and writing quality of the generated draft blog articles and providing feedback, and an emotion recognition means for monitoring the user's emotional state in real time and providing feedback based on the same, thereby enabling users to receive effective feedback based on their individual emotional state while simultaneously learning a language and creating blog articles.

[1833] A "generative artificial intelligence means" is an artificial intelligence system that has the ability to generate questions based on specific topics and languages ​​selected by a user and automatically generate content based on the answers received.

[1834] "Emotion recognition means" is a technology that analyzes user input and behavior in real time to recognize and evaluate their emotional state.

[1835] An "authentication means" is a processing system that performs authentication using information such as a username and password when a user creates an account and logs in.

[1836] The "means for providing an interface" refers to a technique for providing a user interface that allows a user to access and operate the system.

[1837] The "means for constructing a question and answer session" is a technique for constructing a series of dialogues that generate questions and collect answers from users based on the topic and language selected by the user.

[1838] The "means for saving a draft of a blog article" is a technology that has the function of saving a draft of a blog article generated based on the answers entered by the user in a storage device such as a database.

[1839] The "generative AI means for providing feedback" is an AI system that evaluates the generated draft blog post and provides appropriate feedback based on the user's language learning progress and writing quality.

[1840] "Language learning progress" is an indicator that shows how much the user's ability to understand and express themselves in the language they have selected to learn has improved.

[1841] The present invention relates to a system for supporting a user in creating blog articles while efficiently learning a language. Hereinafter, an embodiment of the present invention will be described in detail.

[1842] System Configuration

[1843] This system consists of a server, a terminal, and a user. The server manages the database, generative artificial intelligence (AI) model, and emotion recognition engine, and processes, evaluates, and recognizes emotions in content created by users. The terminal provides an interface for users to access and operate the system.

[1844] Server Roles

[1845] The server has the following features:

[1846] Generative AI methods: Generate questions based on the topic and language selected by the user. This can be done using Natural Language Processing (NLP) libraries, etc.

[1847] Generative AI answer processing: Receive the answers entered by the user and automatically generate a draft blog post based on them. This process utilizes a generative AI model such as GPT-3.

[1848] Language learning progress assessment and feedback: The generated blog post drafts are assessed for language learning progress and writing quality, and feedback is provided, again using the generative AI model described above.

[1849] Emotion recognition: Monitor the user's emotional state in real time and provide feedback based on this. This can be achieved using emotion recognition libraries and algorithms (e.g., Facial Emotion Recognition API).

[1850] Device Role

[1851] The terminal has the following features:

[1852] Web Interface: Provides an interface for users to access the system using a web browser and web pages written in HTML, CSS, and JavaScript.

[1853] Authentication method: Provides a function for users to create an account and log in. Authentication information is sent from the terminal to the server and processed there.

[1854] User operations

[1855] A user uses the system in the following steps:

[1856] 1. Account Creation and Login: The user accesses the web interface on their device and creates a new account. They enter their username, password, and desired language to learn. The device sends the information they entered to the server, which stores the new user information in a database. The user enters their username and password on the login page, which the device sends to the server, which authenticates them. If authentication is successful, they are redirected to the dashboard.

[1857] 2. Blog topic and language selection: The user selects the blog topic and language on the dashboard. The device sends the selected information to the server, which then accepts it. The server then activates the emotion engine and monitors the user's emotional state in real time.

[1858] 3. Question and answer generation: The server uses a generative AI model to generate questions based on the topic and language selected by the user. The server sends the generated questions to the device and presents them to the user. The user uses the device to enter answers to the questions. The device sends the user's answers to the server.

[1859] 4. Blog post drafting: The server automatically generates a blog post draft using a generative AI model based on the user's responses. The server then sends the draft to the device and presents it to the user. The user reviews the draft and makes edits as necessary. The server uses an emotion engine to provide advice and additional feedback based on the user's emotional state.

[1860] 5. Writing Progress Evaluation and Feedback: The server uses a generative AI model to evaluate the user's progress on their draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. The server generates feedback including the evaluation results and areas for improvement. The server sends the evaluation results and feedback to the device and presents them to the user. The user reviews the feedback on the device and re-edits the draft if necessary. The user completes the final blog post and sends it from the device to the server for storage.

[1861] Specific examples

[1862] For example, if a user wants to write a blog post about "travel" in Japanese, they first create an account and log in. Next, they select the topic "travel" and the language "Japanese." The server uses a generative AI model to generate questions such as "What is the purpose of your trip?" and "What are the characteristics of the places you want to visit?" At the same time, an emotion engine recognizes emotions from the user's input and behavior and evaluates their motivation and stress level. The user answers each question with questions such as "To relax and have new experiences" and "Kyoto is a beautiful place rich in history and culture." Based on these answers, the server generates a draft and presents it to the user. Depending on the user's emotional state, the server provides additional advice and feedback. The user then reviews and edits the draft, receives feedback from the server, and completes the final blog post.

[1863] Prompt Sentence Examples

[1864] "Think about your next travel destination. What is your purpose and what are the characteristics of the place you want to visit?"

[1865] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1866] Step 1: Create an account

[1867] A user accesses the web interface using a terminal and enters a username, password, and desired language to learn on the account creation page. Input: Username, password, language to learn. Output: Account creation request data.

[1868] The device sends the entered information to the server, and the server's endpoint accepts this data.

[1869] The server saves the received account creation request data in the database and creates a user account. Data processing: Saving account information.

[1870] Step 2: Log in

[1871] A user enters a username and password on the login page. Input: Username, Password. Output: Authentication request data.

[1872] The terminal sends this to the server.

[1873] The server performs the authentication process, verifying that the username and password match. Data operation: Authentication check. If successful, redirect the user to the dashboard, otherwise return an error message. Output: Dashboard URL or error message.

[1874] Step 3: Choose your blog topic and language

[1875] User selects blog topic and language in dashboard. Input: topic, language. Output: selected topic and language.

[1876] The device sends the selected topic and language to the server.

[1877] The server accepts and starts the emotion engine. The selection information is passed to the emotion engine, which starts real-time monitoring of the user's emotional state. Data calculation: Setting topic and language information.

[1878] Step 4: Generate questions and answers

[1879] The server uses a generative AI model to generate questions based on the topic and language selected by the user. Input: Topic, Language. Output: Generated question.

[1880] The server sends the generated question to the terminal and presents it to the user.

[1881] The user uses the terminal to enter answers to questions. Input: User's answers. Output: Answer data.

[1882] The terminal sends the user's answer to the server.

[1883] Step 5: Draft your blog post

[1884] Based on the user's answers received by the server, a generative AI model is used to automatically generate a draft blog post. Input: Answer data. Output: Draft data.

[1885] The server transmits the generated draft to the terminal and presents it to the user.

[1886] The user checks the draft and edits it as necessary. The user adds or corrects it on the terminal. Input: Edited content. Output: Corrected draft data.

[1887] The server uses an emotion engine to monitor the user's emotional state and provide appropriate feedback. Data processing: Emotion data analysis and feedback provision.

[1888] Step 6: Writing Progress Assessment and Feedback

[1889] The server uses a generative AI model to evaluate the user's progress in their draft. The evaluation includes language learning progress, writing quality, and the user's emotional state. Input: Draft data. Output: Evaluation results.

[1890] The server generates feedback including the evaluation results and improvements. Data calculation: Generation of evaluation results.

[1891] The server sends the feedback to the terminal and presents it to the user.

[1892] The user reviews the feedback and re-edits the draft if necessary. Input: Feedback. Output: Improved draft data.

[1893] The user completes the final blog post, sends it from the device to the server, and saves it. Input: Final draft data. Output: Notification of save completion.

[1894] The above is a detailed description of the specific processing steps of this system.

[1895] (Application example 2)

[1896] 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."

[1897] It is difficult to provide language learners with the opportunity to not only acquire language skills but also to write in real contexts while receiving feedback based on their emotional state and generating high-quality text. Furthermore, there is a lack of systems that allow users to reflect their emotional state and receive appropriate advice for a personalized learning experience. This is necessary to maintain user motivation and reduce stress.

[1898] 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.

[1899] In this invention, the server includes an automated generation means for generating questions based on a topic and language selected by the user, an artificial intelligence means for receiving answers entered by the user and automatically generating draft articles based on the answers, an artificial intelligence means for evaluating the language acquisition progress of the generated draft articles and providing feedback, and an emotional evaluation means for evaluating the emotional state of the user while creating the articles and providing appropriate advice. This allows users to efficiently learn a language while creating product reviews and introductory articles, and to receive feedback based on their emotional state during the process. Furthermore, the personalized learning experience helps maintain motivation and reduce stress.

[1900] An "automated generation means" is a device or software that automatically generates appropriate questions based on the topic and language selected by the user.

[1901] "Artificial intelligence means" means a system or algorithm capable of receiving user-entered responses and automatically generating a draft of a written article based thereon.

[1902] The "artificial intelligence means for assessing language acquisition progress and providing feedback" is a system that has the function of analyzing the generated draft text, assessing the user's language learning progress, and providing appropriate advice and areas for improvement.

[1903] An "emotion evaluation means" is a device or software that monitors the user's emotional state in real time while they are creating an article, and provides appropriate advice and feedback based on that information.

[1904] An "authentication means" is a device or software with security functions for creating an account and logging in when a user accesses a system.

[1905] "Means for providing an online interface" refers to an interface provided in the form of a web browser or mobile app that allows users to access and operate the system via the Internet.

[1906] The "means for managing feedback sessions" is a system that has the function of managing the content and history of feedback provided on the generated draft of a written article, and supporting users to understand and use it appropriately.

[1907] The system for implementing this invention mainly comprises a server, a terminal (user device), and a user. The operation of this system will be described in detail below.

[1908] 1. System Overview

[1909] server

[1910] The server manages the database, generative AI model, and emotion evaluation engine, processes and evaluates content from users, and evaluates the user's emotional state and provides appropriate feedback.

[1911] Terminal

[1912] It provides an interface for users to access and operate the system. The terminal is provided as a web browser or mobile app and receives and sends user input.

[1913] 2. Program Description

[1914] Question generation based on user-selected topics and languages

[1915] The server uses automated generation methods to generate appropriate questions based on the topic and language selected by the user, and utilizes a generative AI model to present questions that prompt the user to create an article.

[1916] Software used: OpenAI API

[1917] Example: "Generate questions to help users write reviews about 'Travel Guidebook'."

[1918] Receiving user responses and generating sentences

[1919] The server receives the user's inputted answers and automatically generates a draft of the article using artificial intelligence means based on the answers, and presents the draft to the user for necessary editing.

[1920] Software used: Natural Language Processing (NLP) engine

[1921] Evaluating the progress of drafts and providing feedback

[1922] The server evaluates the language learning progress of the generated draft article and provides appropriate feedback, which evaluates the user's language learning progress and suggests specific areas for improvement.

[1923] Software used: Evaluation algorithm

[1924] Emotion assessment and advice provision

[1925] The server uses emotion assessment tools to evaluate the user's emotional state, understand the user's stress level and motivation during the article writing process, and provide appropriate advice based on this, thereby providing a more personalized learning experience.

[1926] Software used: Emotion Recognition API

[1927] 3. Specific Examples

[1928] For example, consider a user writing a review about a "travel guidebook" on a virtual store. The user first creates an account and logs in. Then, they select the topic "travel guidebook" and the language "Japanese." The server uses a generative AI model to generate questions such as "Which part of this guidebook was most helpful?" and "What information does it provide about the details of your travel destination?"

[1929] When the user enters answers to these questions, the server receives them and generates a draft article based on the user's answers. For example, a response such as "The most useful parts of this guidebook were the detailed maps and transportation information. It also provided detailed information about the history and culture of the destination, which was very helpful" is reflected in the draft.

[1930] The generated draft is presented to the user, who then reviews and edits it. The server then provides progress assessment and feedback. During this process, the server monitors the user's emotional state and provides appropriate advice (e.g., "You're making good progress. Keep going.").

[1931] In this way, users can efficiently learn languages ​​while creating high-quality content. Furthermore, by receiving appropriate support throughout the creation process through the emotion assessment tool, users can reduce stress and maintain motivation.

[1932] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1933] Step 1:

[1934] Account creation and login

[1935] Input: The user enters their username, password, and preferred language from the terminal.

[1936] Specific operation: The terminal sends this information to the server, and the server stores the received information in a database.

[1937] Data processing and calculation: The server hashes the entered user information and stores it.

[1938] Output: Sends a successful account creation message to the terminal to display to the user.

[1939] Step 2:

[1940] Topic Selection and Question Generation

[1941] Input: The user selects the topic and language on the device dashboard.

[1942] Specific operation: The terminal sends the selected information to the server, and the server uses automated generation means to generate a question based on the topic and language.

[1943] Data processing and computation: The server uses a generative AI model to generate questions based on the prompt.

[1944] Output: The generated question is sent to the terminal and displayed to the user.

[1945] Step 3:

[1946] Receiving user answers to questions

[1947] Input: The user types the answer to the question at the terminal.

[1948] Specific operation: The terminal sends the user's answer to the server.

[1949] Data processing and calculation: The server receives the user's answers, formats them, and stores them.

[1950] Output: A reply receipt confirmation message is sent to the terminal and displayed to the user.

[1951] Step 4:

[1952] Generate a draft

[1953] Input: User response data stored on the server.

[1954] Specific operation: The server uses artificial intelligence means to generate a draft of the article based on the user's answers.

[1955] Data processing and calculation: Using a generative AI model, natural-sounding sentences are generated based on the answers.

[1956] Output: The generated draft is sent to the terminal and displayed to the user.

[1957] Step 5:

[1958] Review and edit the draft

[1959] Input: The user reviews the draft on their device and makes edits as needed.

[1960] Specific operation: Send the edited draft to the server.

[1961] Data processing and calculation: The server receives the edited text and stores it again.

[1962] Output: Sends an edit confirmation message to the terminal for display to the user.

[1963] Step 6:

[1964] Emotion evaluation and feedback provision

[1965] Input: Final draft stored on the server and user behavior data.

[1966] Specific operation: The server evaluates the user's emotional state using the emotion evaluation means.

[1967] Data processing and calculation: Emotion recognition algorithms are used to analyze the user's emotional state from their input speed and content.

[1968] Output: Generate feedback based on the emotion evaluation, send it to the device and display it to the user.

[1969] Step 7:

[1970] Save and publish the final article

[1971] Input: User reviews the final draft on their device and chooses to save or publish.

[1972] Specific operation: The device sends a save or publish instruction to the server.

[1973] Data processing and calculation: The server stores the final article in a database and performs appropriate publishing processing according to the publishing settings.

[1974] Output: Sends a save or publish confirmation message to the terminal and displays it to the user.

[1975] 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.

[1976] 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.

[1977] 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.

[1978] 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.

[1979] 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.

[1980] 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.

[1981] 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).

[1982] 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.

[1983] 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."

[1984] 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.

[1985] 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).

[1986] 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.

[1987] 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.

[1988] 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.

[1989] 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.

[1990] 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.

[1991] 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.

[1992] 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.

[1993] 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.

[1994] 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.

[1995] 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.

[1996] The following is further disclosed regarding the above embodiment.

[1997] (Claim 1)

[1998] a generative artificial intelligence means for generating questions based on a user-selected topic and language;

[1999] an artificial intelligence means for receiving the user-entered answers and automatically generating a draft blog post based thereon;

[2000] an artificial intelligence means for evaluating language learning progress and providing feedback on the generated draft blog post;

[2001] A system including:

[2002] (Claim 2)

[2003] authentication means for users to create accounts and log in;

[2004] means for providing a web interface for user access;

[2005] The system of claim 1 further comprising:

[2006] (Claim 3)

[2007] means for transmitting the user's selected topic and language to a server, which uses generative artificial intelligence means to construct a question and answer session;

[2008] a means for transmitting the answers entered by the user to a server and storing a draft of a blog post generated based on the answers on the server;

[2009] The system of claim 1 further comprising:

[2010] "Example 1"

[2011] (Claim 1)

[2012] a generative artificial intelligence means for generating questions based on a theme and language selected by a user;

[2013] an artificial intelligence means for receiving the user-entered responses and automatically generating a draft document based thereon;

[2014] an artificial intelligence means for evaluating the learning progress of the generated document draft and providing feedback;

[2015] an authentication method for users to create accounts and log in;

[2016] means for providing a web interface for user access;

[2017] A system including:

[2018] (Claim 2)

[2019] means for transmitting the user's selected topic and language to a server, which uses generative artificial intelligence means to construct a question and answer session;

[2020] means for transmitting responses entered by the user to a server and storing a draft document generated based on the responses on the server;

[2021] The system of claim 1 further comprising:

[2022] (Claim 3)

[2023] A means for the server to use a generative artificial intelligence model to generate questions based on the theme and language selected by the user and send the questions to the terminal;

[2024] a means for the server to generate a draft document based on the user's response using a generative artificial intelligence model and transmit the draft document to the terminal;

[2025] a means for the server to evaluate the document draft using a generative artificial intelligence model and send the evaluation result as feedback to the terminal;

[2026] 10. The system of claim 1, comprising:

[2027] "Application Example 1"

[2028] (Claim 1)

[2029] a generative artificial intelligence means for generating questions based on a user-selected topic and language;

[2030] an artificial intelligence means for receiving the user-entered answers and automatically generating a draft blog post based thereon;

[2031] an artificial intelligence means for evaluating language learning progress and providing feedback on the generated draft blog post;

[2032] means for transmitting the user's selected topic and language to a server, and generating questions using generative artificial intelligence means to present the questions to the user;

[2033] means for transmitting the user-entered answers to a server and using generative artificial intelligence means to generate and evaluate a draft blog post based on the answers;

[2034] A system including:

[2035] (Claim 2)

[2036] authentication means for users to create accounts and log in;

[2037] means for providing a web interface for user access;

[2038] Including applications installed on smartphones,

[2039] 10. The system of claim 1.

[2040] (Claim 3)

[2041] means for transmitting the user's selected topic and language to a server, which uses generative artificial intelligence means to construct a question and answer session;

[2042] a means for transmitting the answers entered by the user to a server and storing a draft of a blog post generated based on the answers on the server;

[2043] a means for making it accessible via a smartphone application;

[2044] 10. The system of claim 1, comprising:

[2045] "Example 2: Combining Emotion Engines"

[2046] (Claim 1)

[2047] a generative artificial intelligence means for generating questions based on a user-selected topic and language;

[2048] a generative artificial intelligence means for receiving the answers entered by the user and automatically generating a draft of a blog post based on the answers;

[2049] A generative artificial intelligence means for evaluating the language learning progress and writing quality of the generated blog post draft and providing feedback;

[2050] an emotion recognition means for monitoring the user's emotional state in real time and providing feedback based thereon;

[2051] A system including:

[2052] (Claim 2)

[2053] authentication means for users to create accounts and log in;

[2054] means for providing an interface for user access;

[2055] The system of claim 1 further comprising:

[2056] (Claim 3)

[2057] means for transmitting user-selected topics and languages ​​and constructing a question and answer session using generative artificial intelligence means;

[2058] means for submitting the user-entered answers and saving a draft blog post generated based on the answers;

[2059] The system of claim 1 further comprising:

[2060] "Application example 2 when combining emotion engines"

[2061] (Claim 1)

[2062] an automated generation means for generating questions based on a user-selected topic and language;

[2063] an artificial intelligence means for receiving the answers entered by the user and automatically generating a draft of the written article based thereon;

[2064] an artificial intelligence means for evaluating the language acquisition progress of the generated draft text article and providing feedback;

[2065] An emotion evaluation means for evaluating an emotion state of a user while the user is creating an article and providing appropriate advice;

[2066] A system including:

[2067] (Claim 2)

[2068] authentication means for users to create accounts and log in;

[2069] means for providing an online interface for user access;

[2070] The system of claim 1 further comprising:

[2071] (Claim 3)

[2072] means for transmitting user-selected topics and languages ​​to a server, which uses automated generation means to construct a question and answer session;

[2073] a means for transmitting the answers entered by the user to a server and storing a draft of a written article generated based on the answers in the server;

[2074] a means for managing feedback sessions on the saved draft;

[2075] The system of claim 1 further comprising: [Explanation of symbols]

[2076] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a generative artificial intelligence means for generating questions based on a user-selected topic and language; an artificial intelligence means for receiving the user-entered answers and automatically generating a draft blog post based thereon; an artificial intelligence means for evaluating language learning progress and providing feedback on the generated draft blog post; A system including:

2. authentication means for users to create accounts and log in; means for providing a web interface for user access; The system of claim 1 further comprising:

3. means for transmitting the user's selected topic and language to a server, which uses generative artificial intelligence means to construct a question and answer session; a means for transmitting the answers entered by the user to a server and storing a draft of a blog post generated based on the answers on the server; The system of claim 1 further comprising:

Images