System

Abstract

A system is provided.SOLUTION: A system for improving financial literacy, comprising: a micro learning unit; a virtual trading unit; and a community platform unit.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] The lack of financial literacy among today's young people could have a significant impact on their future asset management and risk management abilities. Furthermore, there is a lack of appropriate educational tools for effectively learning financial knowledge. Many current educational programs require long study periods and require high-risk investments using actual funds, making effective learning difficult. Given these circumstances, there is a need for a system that allows young people to improve their financial literacy with peace of mind. [Means for solving the problem]

[0005] The present invention provides a system for effectively teaching young people financial literacy, the system including the following means:

[0006] 1. Providing micro-learning tools based on the user's progress and presenting the next quiz to study at the appropriate time, this tool allows users to acquire financial knowledge efficiently in a short amount of time.

[0007] 2. Providing a virtual trading tool that allows users to improve their trading skills while minimizing risk through AI-based investment suggestions and feedback, allowing users to gain trading experience without risking real capital.

[0008] 3. Provide a community platform where users can freely start discussions on financial topics, exchange comments, and share information with other users, thereby promoting mutual learning among users and helping to improve financial literacy.

[0009] This allows users to systematically learn financial knowledge and effectively improve their actual trading skills and risk management abilities.

[0010] A "microlearning tool" is a method of providing learning materials and quizzes to efficiently teach specific knowledge or skills in a short amount of time.

[0011] A "virtual trading vehicle" is a method that provides a simulation of trading stocks and other financial instruments in a virtual environment without using actual funds.

[0012] A "community platform means" is a method that provides an online forum or message board function for users to discuss and exchange opinions on a specific topic.

[0013] "Managing user progress" refers to recording what each user has achieved and their progress during the learning process, and then suggesting what they should learn next based on that data.

[0014] "AI (artificial intelligence) investment proposals" refers to the use of the latest market data and advanced algorithms to advise users on optimal investment strategies and trading methods.

[0015] "Providing feedback" refers to providing effective advice and information for improvement based on the user's learning and trading results.

[0016] "Starting a discussion" refers to creating a new thread or topic on an online platform to exchange opinions with other users about a particular topic.

[0017] "Posting a comment" means sending a text message to add opinion or information to an existing discussion or debate. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention provides an effective system for improving financial literacy among young people, which includes three main tools: a micro-learning tool, a virtual trading tool, and a community platform tool.

[0040] Microlearning Vehicles

[0041] This method provides quizzes that allow users to learn financial knowledge efficiently in a short amount of time. When a user sends a request from their terminal to take a quiz, the server checks the user's progress and selects and sends the next quiz that the user should study. When the user answers the quiz, the results are sent to the server, which determines whether the answer is correct or incorrect, and generates and returns feedback. The user receives the feedback and can gradually improve their financial knowledge.

[0042] For example, if a user takes a quiz on "Intro to the Stock Market," the server provides multiple-choice questions on that subject. The user answers the questions and their answers are sent to the server. The server evaluates the answers and provides feedback such as, "Correct answers: 8 / 10, very good. Let's learn about the bond market next."

[0043] Virtual Trading Instruments

[0044] This method provides a simulation for a user to trade stocks and other financial instruments in a virtual environment without using real funds. When a user initiates a new trading session through a terminal, the server initializes the virtual trading session with suggestions from an AI guide. The user places an order for virtual stocks, which is accepted by the server and provided to the user along with feedback from the AI ​​guide.

[0045] For example, if a user initiates "Invest in Technology Stocks," the server may suggest to the user, "Technology stocks are rising right now, so try investing in Apple." If the user places an order to buy 100 shares of Apple and the order is successful, the server may return feedback saying, "Your order is complete. Technology stocks are still on the rise."

[0046] Community Platform Means

[0047] This method provides an online forum function where users can freely start discussions and exchange opinions on financial topics. When a user starts a new discussion, the server notifies the entire community of the discussion content. Furthermore, when other users post comments to the discussion, the content is accepted by the server and added to the thread.

[0048] For example, if a user starts a new discussion about "Investment Risk Management," the server notifies the community, "A user has started a new topic called 'Investment Risk Management.'" If another user posts a comment saying, "Tell me how to diversify risk," the server adds that comment to the discussion thread, making it visible to all users.

[0049] This system allows users to acquire financial knowledge efficiently in a short period of time, safely trade in a virtual environment, and share their knowledge with other users within the community. This will improve financial literacy and enable future asset growth and risk management skills.

[0050] The processing flow will be explained below.

[0051] Microlearning Vehicles

[0052] Accessing the quiz

[0053] Step 1:

[0054] A user submits a request to access the finance quiz. The user launches the app's quiz learning module and presses the "Access Finance Quiz" button.

[0055] Step 2:

[0056] The device sends the user ID and request information to the server.

[0057] Step 3:

[0058] The server selects the next quiz to study based on the user's ID. The server reads the user's learning history from a database and runs an algorithm to determine the next quiz to proceed to.

[0059] Step 4:

[0060] The server sends the selected quiz questions and choices to the terminal.

[0061] Step 5:

[0062] The terminal displays a quiz screen and presents the quiz to the user.

[0063] Quiz Answering Process

[0064] Step 1:

[0065] The user answers the quiz and submits the answer.

[0066] Step 2:

[0067] The device sends the user ID, quiz ID, and answer to the server.

[0068] Step 3:

[0069] The server compares the received answer with the correct answer in its database.

[0070] Step 4:

[0071] The server determines whether each question is correct or incorrect and calculates the number of correct answers.

[0072] Step 5:

[0073] The server generates feedback messages based on the percentage of correct answers and updates the user's progress.

[0074] Step 6:

[0075] The server sends the generated feedback message to the terminal.

[0076] Step 7:

[0077] The device displays a feedback message.

[0078] Virtual Trading Instruments

[0079] Process of starting a new trading session

[0080] Step 1:

[0081] A user sends a request to start a new trading session. The user launches the app's trading module and presses the "Start a new trading session" button.

[0082] Step 2:

[0083] The device sends the user ID and request information to the server.

[0084] Step 3:

[0085] The server initializes a virtual trading session. The server generates a new trading session ID and allocates an initial virtual fund amount to the user.

[0086] Step 4:

[0087] The server references current market data and runs AI algorithms to generate investment recommendations.

[0088] Step 5:

[0089] The server sends the generated proposal message to the terminal.

[0090] Step 6:

[0091] The device displays a suggestion message.

[0092] The process of sending a trade order

[0093] Step 1:

[0094] A user submits a virtual trade order, selecting a specific stock and entering the quantity to buy or sell.

[0095] Step 2:

[0096] The terminal sends the user ID and order details (stock name, quantity, buy or sell) to the server.

[0097] Step 3:

[0098] The server updates the order details to the virtual market database.

[0099] Step 4:

[0100] The server determines the outcome of the order and generates a feedback message to report to the user.

[0101] Step 5:

[0102] The server sends the updated trade data and feedback messages to the terminal.

[0103] Step 6:

[0104] The device will display a confirmation and feedback message.

[0105] Community Platform Means

[0106] Action to start a new discussion

[0107] Step 1:

[0108] A user submits a request to start a new discussion: The user opens the app's Community module and presses the "Start a new discussion" button.

[0109] Step 2:

[0110] The device sends the user ID, discussion topic, and content to the server.

[0111] Step 3:

[0112] The server generates a new discussion ID and adds the topic and content to the discussion database.

[0113] Step 4:

[0114] The server notifies active users that a new discussion has been created.

[0115] Step 5:

[0116] The device receives the notification and displays it on the user's screen.

[0117] The process of posting a comment to a discussion

[0118] Step 1:

[0119] A user posts a comment to a discussion. A user opens a specific discussion and enters a comment.

[0120] Step 2:

[0121] The device sends the user ID, discussion ID, and comment content to the server.

[0122] Step 3:

[0123] The server generates a comment ID and adds the comment to the appropriate discussion thread.

[0124] Step 4:

[0125] The server sends the latest discussion thread to the device.

[0126] Step 5:

[0127] The device displays the updated discussion thread.

[0128] Example 1

[0129] 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."

[0130] Conventional financial literacy improvement systems have been considered ineffective for young people due to long learning times, trading requiring actual funds, limited communication methods, etc. The present invention aims to solve these problems and provide a system that allows people to acquire financial knowledge efficiently in a short period of time, safely experience trading, and share knowledge within the community.

[0131] 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.

[0132] In this invention, the server includes means for providing quizzes designed to enable users to learn efficiently in a short period of time, means for providing a simulation of trading financial products in a virtual environment, and means for providing an online forum where users can discuss financial topics, thereby enabling users to acquire financial knowledge efficiently in a short period of time, safely experience trading in a virtual environment, and share their knowledge with other users within the community.

[0133] "User" means any person who uses the System to improve their financial knowledge, participate in virtual trading simulations, or participate in the community forum.

[0134] "Terminal" refers to the device through which a User accesses the System and engages in activities such as answering quizzes, placing virtual trade orders, and discussing in the community forum.

[0135] A "server" is a central processing unit that processes requests from users and devices, provides quizzes, initializes trading sessions, manages community forums, and so on, while interacting with the database.

[0136] A "Quiz" is a multiple-choice or other type of learning assignment designed to help users efficiently learn financial knowledge.

[0137] "Virtual trading" refers to a user simulating the trading of stocks or other financial instruments in a virtual environment without using actual funds.

[0138] A "community forum" is an online platform for users to exchange opinions and discuss financial topics.

[0139] "Progress Management" is a feature that tracks a user's learning progress and selects the next quiz or topic to study.

[0140] An "AI model" is an algorithm that uses machine learning to analyze market trends and user behavior in order to provide investment suggestions and other guidance to users.

[0141] A "discussion thread" is a series of posts initiated by a user in a community forum to exchange opinions or information on a particular topic.

[0142] The present invention is a system aimed at improving financial literacy among young people. The system provides users with multiple means to efficiently acquire financial knowledge, experience trading in a virtual environment, and exchange opinions within a community. Specific embodiments of the present invention are described below.

[0143] Microlearning Vehicles

[0144] This method provides quizzes that allow users to learn financial knowledge effectively in a short amount of time. When a user launches a dedicated application and requests to take a quiz, the device sends this request as an HTTP request to the server. The server uses a MySQL database to check the user's learning progress, selects the next quiz based on the progress, and sends it in JSON format to the device. When the user answers the quiz and sends the answer from the device to the server, the server uses the Pandas library to determine whether the answer is correct or incorrect, creates feedback, and sends it to the device.

[0145] Examples:

[0146] For example, if a user requests from their device to take a quiz on "Introduction to the Stock Market," the server will check their progress and send them multiple choice questions on "Introduction to the Stock Market." After the user answers the quiz and clicks the "Submit Answers" button, the results are sent to the server, which then returns feedback to the device saying, "You got 8 / 10 right, which is very good. Let's learn about the bond market next."

[0147] Virtual Trading Instruments

[0148] This tool provides users with a simulation of trading stocks and other financial instruments in a virtual environment. When a user starts a new trading session from their terminal, the terminal sends this request as an HTTP request to the server. The server uses an AI model using TensorFlow to analyze market trends and sends appropriate trade suggestions in JSON format to the terminal. When a user places an order for virtual stocks, the order is sent from the terminal to the server, which accepts the order and records it in a virtual trading database. The results of the order and trading feedback are sent from the server to the terminal.

[0149] Examples:

[0150] When a user initiates an "invest in technology stocks" request, the device sends the request to the server, which analyzes market trends. The server then sends a suggestion to the device saying, "Technology stocks are rising right now, so try investing in Apple." When the user places an order to buy 100 shares of Apple stock and the order is sent to the server, the server returns feedback to the device saying, "Your order is complete. Technology stocks are still on the rise."

[0151] Community Platform Means

[0152] This method provides an online forum function that allows users to freely start discussions and exchange opinions on financial topics. When a user sends a request to start a new discussion from their device to the server, the server notifies the entire community of the discussion content. When another user posts a comment to the discussion, the comment is sent from the device to the server, and the server adds the comment to the discussion thread and notifies all users of the update.

[0153] Examples:

[0154] When a user starts a new discussion about "Investment Risk Management," the device sends the request to the server, which then sends a notification to the entire community saying, "A user has started a new topic called 'Investment Risk Management.'" When another user posts a comment saying, "Tell me how to diversify risk," the device sends the comment to the server, which adds the comment to the discussion thread and notifies all users.

[0155] Example prompts for generative AI models

[0156] You can ask the generative AI model for a detailed explanation of the system's processing by using the following prompt:

[0157] Example prompt sentence:

[0158] 1. "Please explain the detailed process flow of a microlearning feature that allows users to quickly and efficiently improve their financial literacy."

[0159] 2. Please explain, with specific examples, how users can use the virtual trading feature to learn investment strategies.

[0160] 3. "Please explain the process from starting a discussion on a community platform to other users adding comments."

[0161] As described above, the present invention enables users to acquire financial knowledge efficiently in a short period of time, experience trading in a safe virtual environment, and share their knowledge with other users within the community.

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] Microlearning Vehicles

[0164] Step 1:

[0165] Submit a quiz request

[0166] The user launches the dedicated application on their device and clicks the "Take Quiz" button. The device generates an HTTP request and sends it to the server. The input to this request includes information such as the user ID and the desired field of study.

[0167] Step 2:

[0168] Progress check and quiz selection

[0169] The server processes the received request and retrieves the user's learning progress data from the MySQL database based on the user ID. The server analyzes this data (data processing) and uses a Python script to select the next quiz to study. The output of the selected quiz is sent to the terminal in JSON format.

[0170] Step 3:

[0171] Quiz Answers and Submission

[0172] The user answers the quiz on the device and clicks the "Submit Answer" button. The device sends the answer data to the server as an HTTP POST request. This input includes the user's answer.

[0173] Step 4:

[0174] Correct / incorrect judgment and feedback generation

[0175] The server analyzes the received response data using the Pandas library (data calculation) and determines whether it is correct or incorrect. Based on the analysis results, it generates a feedback message and sends it to the terminal in JSON format. This output includes the evaluation results and what to learn next.

[0176] Step 5:

[0177] Feedback Check

[0178] Users can view feedback on their device and assess their financial knowledge, which will help them plan their future learning.

[0179] Virtual Trading Instruments

[0180] Step 1:

[0181] Trading Session Initialization

[0182] A user clicks a button to start a new trading session in the terminal application. The terminal sends this request to the server as an HTTP request. The input includes the user ID and the desired transaction details.

[0183] Step 2:

[0184] AI guide suggestions

[0185] The server processes the received requests and analyzes market trends using an AI model powered by TensorFlow. Based on the analysis results, the server generates appropriate trade proposals and sends them to the terminal in JSON format. This output contains investment recommendations from the AI.

[0186] Step 3:

[0187] Virtual Stock Orders

[0188] The user places an order for virtual stocks based on the proposed investment strategy. The terminal sends the order to the server as an HTTP POST request. This input contains the order information.

[0189] Step 4:

[0190] Order acceptance and processing

[0191] The server receives the order data and records it in a virtual transaction database. The server analyzes the transaction results and generates appropriate feedback messages. The output is feedback including order confirmation and transaction results.

[0192] Step 5:

[0193] Feedback Check

[0194] Users can view the feedback on their devices and use it to plan their next investment strategy, which will help improve the user's virtual trading experience.

[0195] Community Platform Means

[0196] Step 1:

[0197] Start a discussion

[0198] A user opens the community function on their device, enters a new discussion, and clicks the "Start a discussion" button. The device sends this request to the server as an HTTP POST request. The input includes the user ID and the discussion content.

[0199] Step 2:

[0200] Community Notifications

[0201] Based on the received request, the server saves the discussion content in the community database and generates a notification message to send to the entire community, which notifies the new discussion that has started.

[0202] Step 3:

[0203] Posting and accepting comments

[0204] Other users view the discussion and post comments. The device sends the comment content to the server as an HTTP POST request. This input includes the comment content.

[0205] Step 4:

[0206] Add comments and notifications

[0207] The server accepts the comment data and adds it to the original discussion thread. The server generates updated thread information and notifies all users. The output is a notification containing the latest discussion content.

[0208] Step 5:

[0209] Discussion and Feedback

[0210] Users can use their devices to check the latest discussion updates and rate how their comments were received, which helps them choose their next comment and topic.

[0211] (Application example 1)

[0212] 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."

[0213] To improve the financial literacy of young people, a platform that allows them to learn efficiently in a short amount of time is needed. It is also important to have opportunities to safely gain investment experience without using actual funds. Furthermore, there is a need for an environment where students can deepen their overall knowledge by sharing information and exchanging opinions with other students. However, conventional systems have had difficulty meeting all of these requirements. Therefore, a system that utilizes virtual space and enables interactive and effective learning is needed.

[0214] 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.

[0215] In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, a financial education means in a virtual space, a real-time feedback providing means, and an interactive learning means using prompt sentences, thereby enabling users to efficiently learn financial knowledge in a virtual space, safely gain investment experience, and exchange opinions with other users.

[0216] A "microlearning tool" is a tool that provides quizzes to help people learn financial knowledge efficiently in a short amount of time.

[0217] A "virtual trading vehicle" is a simulated vehicle for trading stocks and other financial instruments in a virtual environment without using actual funds.

[0218] A "community platform vehicle" is a vehicle that provides an online forum function where users can freely initiate discussions and exchange opinions on financial topics.

[0219] The "financial education tool in a virtual space" is an educational tool that allows users to take financial quizzes and conduct virtual trades in a virtual store.

[0220] The "real-time feedback providing means" is a means by which a user can receive immediate feedback on a quiz or trade simulation.

[0221] An "interactive learning method using prompt sentences" is a method that allows users to customize the learning content by inputting specific instructions and progress through learning interactively.

[0222] The present invention provides an effective system for improving financial literacy among young people, which includes a micro-learning means, a virtual trading means, a community platform means, a virtual financial education means, a real-time feedback providing means, and an interactive learning means using prompts.

[0223] Microlearning Vehicles

[0224] In this system, the server manages the user's progress and provides the next quiz to study. When the user takes the quiz, the results are sent to the server, which determines whether the quiz was correct or incorrect and generates feedback to return to the user. This allows the user to gradually improve their financial knowledge.

[0225] For example, if a user takes a quiz on "Intro to the Stock Market," the server provides multiple choice questions on that subject. After the user answers the questions and submits their answers to the server, the server returns feedback like, "Number of answers correct: 8 / 10, very good. Let's learn about the bond market next."

[0226] Virtual Trading Instruments

[0227] The server initiates a virtual trading session and provides the user with investment suggestions from the AI. When the user places an order for virtual stocks, the order is accepted by the server and provided to the user along with feedback from the AI ​​guide.

[0228] For example, when a user starts "investing in technology stocks," the server will suggest to the user, "Technology stocks are rising now, so try investing in high-tech company A." If the user places an order to purchase 100 shares of high-tech company A and the order is successful, the server will return feedback saying, "Your order has been completed. Technology stocks are still on the rise."

[0229] Community Platform Means

[0230] Users can freely start discussions and exchange opinions on financial topics. When a user starts a new discussion, the server notifies the entire community of the discussion content. Furthermore, when other users post comments to the discussion, the server accepts the content and adds it to the thread.

[0231] For example, if a user starts a new discussion about "Investment Risk Management," the server notifies the community, "A user has started a new topic called 'Investment Risk Management.'" If another user posts a comment saying, "Tell me how to diversify risk," the server adds that comment to the discussion thread, making it visible to all users.

[0232] Virtual financial education tools

[0233] The virtual financial education tool provides an environment where users can take financial quizzes and conduct virtual trades in a virtual store, allowing users to safely and effectively improve their financial literacy without using real money.

[0234] Real-time feedback

[0235] This is a means for users to receive immediate feedback on quizzes and trading simulations. The server generates feedback in real time according to the user's actions and provides it to the user.

[0236] Interactive learning tools using prompts

[0237] It is a means for users to customize their learning content and progress interactively by inputting specific instructions. For example, by using prompts such as "I want to take a quiz on introductory stock markets" or "I want to invest in technology stocks," the system can provide users with the optimal learning content and trading experience.

[0238] The system is implemented using hardware such as smartphones, smart glasses, and head-mounted displays, and the software used includes Python, Flask (a server framework), and SQLite (a database).

[0239] This will effectively improve the financial literacy of young people.

[0240] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0241] Step 1:

[0242] The server receives a quiz request from the user's terminal.

[0243] Input: A request from the user's device to "take a quiz."

[0244] Processing: The server checks the database to see the user's progress and selects the next quiz to study.

[0245] Output: Send the selected quiz to the device.

[0246] Step 2:

[0247] The user answers the selected quiz questions.

[0248] Input: Quiz questions sent by the server.

[0249] Process: The user answers the quiz and sends the answers to the server.

[0250] Output: The user's answer data is sent to the server.

[0251] Step 3:

[0252] The server receives the user's answer and determines whether it is correct or incorrect.

[0253] Input: User response data.

[0254] Processing: The server compares the correct answer information with the user's answer and determines whether it is correct or not. It then calculates the number of correct answers and generates feedback.

[0255] Output: Sends feedback information to the user terminal.

[0256] Step 4:

[0257] The server initializes a new trading session.

[0258] Input: "Start a new trading session" request from the user terminal.

[0259] Processing: Initialize the virtual trading environment and generate AI-guided investment recommendations.

[0260] Output: Sends the initialized trading session state and AI-guided suggestions to the user terminal.

[0261] Step 5:

[0262] A user places an order for virtual stock.

[0263] Input: User trade order (e.g., "Buy 100 shares of technology stock").

[0264] Processing: The server accepts user orders and applies them to the virtual trading environment. The AI ​​guide generates feedback based on the latest investment status.

[0265] Output: Sends notification of order completion and feedback on investment status to the user terminal.

[0266] Step 6:

[0267] A user starts a new discussion on the community platform.

[0268] Input: User discussion topic (e.g., "Investment Risk Management").

[0269] Action: The server notifies the community of the new topic and creates a discussion thread.

[0270] Output: Sends a new topic notification to the entire community.

[0271] Step 7:

[0272] Other users post comments to the discussion.

[0273] Input: Comments from other users (e.g., "Tell me how to spread risk.").

[0274] Processing: The server accepts the comment and adds it to the discussion thread.

[0275] Output: Notify all interested users of an updated discussion thread.

[0276] These steps enable users to efficiently learn financial knowledge in a virtual space, safely gain investment experience, and exchange opinions with other users.

[0277] 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.

[0278] The present invention aims to provide a system for improving financial literacy that combines an emotion engine to provide learning content, virtual trade suggestions, and community discussion feedback in response to a user's emotions.

[0279] Combining microlearning tools with emotion engines

[0280] This method provides quizzes that allow users to learn financial knowledge efficiently and quickly. When a user sends a request to take a quiz, the server checks the user's progress and selects and sends the next quiz to the user. At that time, an emotion engine recognizes the user's emotions and generates learning content and feedback according to those emotions.

[0281] For example, if a user is feeling stressed about the "Stock Market Introduction" quiz, the emotion engine will recognize that emotion and the server will provide a quiz with an adjusted difficulty level, and even provide humorous feedback to improve the user's learning experience.

[0282] Combining virtual trading instruments with an emotional engine

[0283] This tool allows users to simulate trading stocks and other financial instruments in a virtual environment without using real funds. When a user starts a new trading session, the server initializes the virtual trading session with AI-guided suggestions. At the same time, an emotion engine recognizes the user's emotions and adjusts appropriate investment suggestions.

[0284] For example, if a user shows a risk-averse emotion, the emotion engine recognizes that emotion and the server suggests low-risk investments. In this way, investment learning based on the user's emotional state becomes possible.

[0285] Combining community platform tools and emotion engines

[0286] This method provides an online forum where users can freely start discussions and exchange opinions on financial topics. When a new discussion is started, the server notifies the entire community of the discussion content. When other users post comments to the discussion, the server accepts the comments and adds them to the thread. At that time, an emotion engine recognizes the user's emotions and generates feedback according to the emotions.

[0287] For example, if a user feels anxious during a discussion about "investment risk management," the emotion engine will recognize that emotion and the server will provide gentle, thoughtful advice on risk management. Other users' comments will also be similarly given feedback that takes their emotions into account, helping to reassure the user.

[0288] As described above, by utilizing an emotion engine, this system can provide a learning experience tailored to the user's emotions. This allows users to efficiently acquire financial knowledge, gain safe trading experience in a virtual environment, and engage in constructive discussions with other users. This system for improving financial literacy incorporates user emotional care, and is expected to be more effective than conventional educational methods.

[0289] The processing flow will be explained below.

[0290] Combining microlearning tools with emotion engines

[0291] Accessing the quiz

[0292] Step 1:

[0293] A user submits a request to access the finance quiz. The user launches the app's quiz learning module and presses the "Access Finance Quiz" button.

[0294] Step 2:

[0295] The device sends the user ID and request information to the server.

[0296] Step 3:

[0297] The server retrieves user progress data to select the next quiz to study based on the user ID.

[0298] Step 4:

[0299] The server uses the camera images and audio data sent from the terminal, and the emotion engine recognizes the user's current emotional state.

[0300] Step 5:

[0301] The server and emotion engine work together to select appropriate quizzes based on the user's progress and emotional state. If the user is feeling stressed, a quiz with adjusted difficulty will be selected.

[0302] Step 6:

[0303] The server transmits the selected quiz and a feedback message corresponding to the emotion to the terminal.

[0304] Step 7:

[0305] The terminal displays a quiz screen and presents the quiz to the user.

[0306] Quiz Answering Process

[0307] Step 1:

[0308] The user answers the quiz and submits the answer.

[0309] Step 2:

[0310] The device sends the user ID, quiz ID, and answer to the server.

[0311] Step 3:

[0312] The server compares the received answer with the correct answer in its database.

[0313] Step 4:

[0314] The server determines whether each question is correct or incorrect and calculates the number of correct answers.

[0315] Step 5:

[0316] When the server creates a feedback message based on the accuracy rate, the emotion engine reconfirms the user's emotional state and adjusts the feedback message.

[0317] Step 6:

[0318] The server sends the generated feedback message to the terminal.

[0319] Step 7:

[0320] The device displays a feedback message.

[0321] Combining virtual trading instruments with an emotional engine

[0322] Process of starting a new trading session

[0323] Step 1:

[0324] A user sends a request to start a new trading session. The user launches the app's trading module and presses the "Start a new trading session" button.

[0325] Step 2:

[0326] The device sends the user ID and request information to the server.

[0327] Step 3:

[0328] The server initializes a virtual trading session, generates a trading session ID, and allocates initial virtual funds to the user.

[0329] Step 4:

[0330] The server uses camera footage and audio data from the device, and the emotion engine recognizes the user's current emotional state.

[0331] Step 5:

[0332] The server consults market data, and the emotion engine generates AI investment recommendations based on the user's emotional state: if the user is risk-averse, the server will suggest low-risk investments.

[0333] Step 6:

[0334] The server sends the generated proposal message to the terminal.

[0335] Step 7:

[0336] The device displays a suggestion message.

[0337] The process of sending a trade order

[0338] Step 1:

[0339] A user submits a virtual trade order, selecting a specific stock and entering the quantity to buy or sell.

[0340] Step 2:

[0341] The terminal sends the user ID and order details (stock name, quantity, buy or sell) to the server.

[0342] Step 3:

[0343] The server updates the order details to the virtual market database.

[0344] Step 4:

[0345] An emotion engine recognizes the user's emotional state as the server determines the outcome of the order and generates feedback messages to report to the user.

[0346] Step 5:

[0347] The server adjusts the feedback message based on the user's emotional state and sends it to the terminal.

[0348] Step 6:

[0349] The device will display a confirmation and feedback message.

[0350] Combining community platform tools and emotion engines

[0351] Action to start a new discussion

[0352] Step 1:

[0353] A user submits a request to start a new discussion: The user opens the app's Community module and presses the "Start a new discussion" button.

[0354] Step 2:

[0355] The device sends the user ID, discussion topic, and content to the server.

[0356] Step 3:

[0357] The server generates a new discussion ID and adds the topic and content to the discussion database.

[0358] Step 4:

[0359] The server notifies active users that a new discussion has been created.

[0360] Step 5:

[0361] The device receives the notification and displays it on the user's screen.

[0362] The process of posting a comment to a discussion

[0363] Step 1:

[0364] A user posts a comment to a discussion. A user opens a specific discussion and enters a comment.

[0365] Step 2:

[0366] The device sends the user ID, discussion ID, and comment content to the server.

[0367] Step 3:

[0368] The server generates a comment ID and adds the comment to the appropriate discussion thread.

[0369] Step 4:

[0370] The server uses the camera images and audio data sent from the terminal, and the emotion engine recognizes the user's current emotional state.

[0371] Step 5:

[0372] The server generates friendly feedback and additional advice based on the user's emotional state.

[0373] Step 6:

[0374] The server sends the latest discussion threads and feedback to the device.

[0375] Step 7:

[0376] The device displays updated discussion threads and feedback.

[0377] Example 2

[0378] 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."

[0379] Conventional financial literacy improvement systems have struggled to provide a learning experience that takes into account the user's emotional state. As a result, users tend to lose interest and the learning experience is inconsistent. Furthermore, in virtual trading and community discussions, it was not possible to provide appropriate support based on each user's emotions. This limited the effectiveness of the system, resulting in problems such as reduced user satisfaction and reduced learning effectiveness.

[0380] 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.

[0381] In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, an emotion recognition means, and a feedback generation means using a generative AI model. This makes it possible to provide learning content and investment suggestions that take the user's emotions into consideration, thereby improving the user's learning experience and satisfaction.

[0382] "Microlearning tools" are tools that provide quizzes and training to help users learn financial knowledge in a short amount of time.

[0383] A "virtual trading instrument" is an instrument that allows users to simulate trading financial instruments in a virtual environment without using real funds.

[0384] A "community platform vehicle" is a vehicle that provides an online forum for users to freely discuss and exchange ideas on financial topics.

[0385] An "emotion recognition means" is a means including technology for recognizing a user's emotional state in real time.

[0386] "Feedback generation means using a generative AI model" refers to a means that uses an AI model to generate feedback according to the user's emotions and learning progress.

[0387] "Means for managing the user's progress and providing the next quiz to study" refers to means for selecting and providing the appropriate next quiz based on the user's learning history and correct answer rate.

[0388] "Means for providing users with investment proposals based on a generative AI model" refers to means for providing users with investment proposals based on their emotional state and risk tolerance during virtual trading using a generative AI model.

[0389] MODE FOR CARRYING OUT THE INVENTION

[0390] This invention is a learning system aimed at improving financial literacy, which combines emotion recognition means and feedback generation means using a generative AI model to provide a learning experience that responds to the user's emotions. This system is mainly composed of the following means:

[0391] 1. Microlearning methods:

[0392] The system provides quiz-style learning content to enable users to learn financial knowledge efficiently and quickly. The server manages the user's progress, selects the next quiz to be studied, and sends it to the user's device. Specifically, the system incorporates an algorithm that selects the appropriate next quiz based on the user's learning history and correct answer rate.

[0393] For example, if a user is taking an "Introduction to the Stock Market" quiz and the emotion recognition means detects that the user is stressed, the server will adjust the difficulty of the quiz according to the user's emotion and generate humorous feedback. An example of a prompt sentence is, "Please explain how you would respond to a user who is stressed by a short quiz to improve financial literacy."

[0394] 2. Virtual trading instruments:

[0395] This feature allows users to simulate trading financial instruments in a virtual environment. The server initializes a virtual trading session at the user's request and provides investment suggestions based on generative AI models. This allows users to safely gain trading experience without using real funds.

[0396] For example, when a user starts a virtual trading session, if the emotion recognition means detects the user's emotional state (e.g., risk aversion), the server generates and provides low-risk investment suggestions based on the results. An example of a prompt sentence is, "Please explain how to make appropriate investment suggestions during a virtual trade to a user who exhibits risk aversion emotions."

[0397] 3. Community Platform Means:

[0398] It provides an online forum where users can freely exchange opinions and discuss financial topics. The server notifies the entire community when a new discussion starts and accepts comments from other users and adds them to the thread.

[0399] For example, if a user starts a discussion about "investment risk management," and the emotion recognition means detects anxiety among the participants, the server will provide gentle advice on risk management to alleviate the anxiety. An example of a prompt sentence is, "Please explain how to respond in an online forum to a user who is anxious about "investment risk management."

[0400] This invention uses facial recognition technology and biometric sensors as emotion recognition methods, and the latest natural language processing technology and machine learning algorithms as generative AI models, making it possible to adapt to the user's emotions and provide individually optimized learning experiences and investment recommendations in real time.

[0401] By using concrete examples, users can efficiently acquire financial knowledge and safely gain experience in virtual trading. In addition, by constructively exchanging opinions with other users through community discussions, users can deepen their practical knowledge. This is expected to improve users' financial literacy.

[0402] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0403] Step 1:

[0404] A user submits a request to take a quiz.

[0405] Input: User request (want to take a quiz)

[0406] Processing: The user clicks the take quiz button in the application, and a request is sent to the server.

[0407] Output: The request data (information about the quiz request) is sent to the server.

[0408] Step 2:

[0409] The server checks the user's progress.

[0410] Input: Request data and user learning history data

[0411] Processing: The server retrieves the user's past learning history and correct answer rate from the database and evaluates their progress.

[0412] Output: Data for selecting the next quiz to study

[0413] Step 3:

[0414] The server sends the selected quiz to the user.

[0415] Input: Selection data

[0416] Processing: The server sends the selected quiz to the terminal based on the user's progress.

[0417] Output: Quiz data

[0418] Step 4:

[0419] The terminal recognizes the user's emotions using an emotion engine.

[0420] Input: Real-time biometric and facial expression data of the user

[0421] Processing: The device uses facial recognition technology and biometric sensors to analyze and recognize the user's emotions in real time.

[0422] Output: Emotion recognition result (e.g., stress state)

[0423] Step 5:

[0424] The server adjusts the difficulty of the quiz and generates feedback.

[0425] Input: Emotion recognition results and quiz data

[0426] Processing: The server adjusts the difficulty of the quiz appropriately based on the emotion recognition results and generates humorous feedback using a generative AI model.

[0427] Output: Adjusted quiz and feedback data

[0428] Step 6:

[0429] A user submits a request for a new virtual trading session.

[0430] Input: User request (want to start virtual trading)

[0431] Processing: The user clicks the application's button to initiate a virtual trade, and a request is sent to the server.

[0432] Output: Request data (information on the virtual trade start request)

[0433] Step 7:

[0434] The server initializes a virtual trading session.

[0435] Input: Request data and market data

[0436] Processing: The server initializes the virtual trading session, retrieves the necessary market data, and generates investment recommendations using the generative AI model.

[0437] Output: Virtual trading session data and investment proposal data

[0438] Step 8:

[0439] The terminal recognizes the user's emotions using an emotion engine.

[0440] Input: Real-time biometric and facial expression data of the user

[0441] Processing: The device uses facial recognition technology and biometric sensors to analyze and recognize the user's emotions in real time.

[0442] Output: Emotion recognition result (e.g., risk aversion)

[0443] Step 9:

[0444] The server coordinates the investment proposals.

[0445] Input: Emotion recognition results and virtual trading session data

[0446] Processing: The server adjusts the risk level of the investment proposal based on the emotion recognition result and provides it to the user.

[0447] Output: Adjusted investment proposal data

[0448] Step 10:

[0449] A user submits a request to start a new discussion.

[0450] Input: User request (want to start a discussion)

[0451] Process: The user clicks the Start Discussion button in the application and a request is sent to the server.

[0452] Output: Request data (information you wish to start a discussion about)

[0453] Step 11:

[0454] The server notifies the entire community of the discussion content.

[0455] Input: Request data and discussion content

[0456] Process: The server notifies the entire community of new discussion content and creates a thread.

[0457] Output: Notification data

[0458] Step 12:

[0459] Other users post comments to the discussion.

[0460] Input: Comment data

[0461] Process: Other users post comments to the discussion thread, which are then sent to the server.

[0462] Output: Comment data

[0463] Step 13:

[0464] The server adds the comment to the thread.

[0465] Input: Comment data

[0466] Processing: The server adds the accepted comment to the discussion thread.

[0467] Output: Updated discussion thread data

[0468] Step 14:

[0469] The device uses an emotion engine to recognize the user's emotions and generate feedback.

[0470] Input: Real-time biometric information, facial expression data, and comment data of the user

[0471] Processing: The device uses facial recognition technology and biometric sensors to analyze emotions and generates feedback using generative AI models.

[0472] Output: Feedback data (e.g., kind words of advice)

[0473] These steps provide a learning experience that improves financial literacy according to the user's progress and emotions.

[0474] (Application example 2)

[0475] 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."

[0476] Conventional financial literacy improvement systems have been unable to provide a learning experience that takes into account the user's emotional state, resulting in reduced learning efficiency. Furthermore, in virtual trading, they have been unable to provide appropriate investment suggestions based on the user's emotions, limiting the effectiveness of the user's learning. Furthermore, in community discussions, it has been difficult to provide feedback that takes into account the user's emotions.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, a feedback means based on emotion recognition, and a means for providing interactive learning content. This makes it possible to provide learning content and feedback according to the user's emotional state, thereby effectively supporting the improvement of the user's financial literacy.

[0478] "Microlearning" is an educational method for acquiring knowledge efficiently in a short amount of time.

[0479] "Virtual trading" means the simulated activity of trading financial instruments in a virtual environment without using actual funds.

[0480] A "community platform" is an online space where users can exchange opinions and hold discussions on a specific topic.

[0481] "Emotion recognition" is a technology that identifies a user's emotions in real time from their facial expressions and voice.

[0482] "Feedback means" is a method for providing appropriate information based on the user's behavior or state, and for improving learning or behavior.

[0483] "Interactive learning content provision" is a method of providing learning materials and tasks that change dynamically depending on the user's reactions and situation.

[0484] "Progress management" is the process of tracking a user's learning or work progress and suggesting next tasks or activities to be done.

[0485] "Adjusting the difficulty of a quiz" is a method of changing the difficulty of the quiz provided based on the user's level of understanding and emotional state.

[0486] "Investment proposal adjustment" is a technology that provides optimal investment strategies and policies to users based on the results of user emotion recognition.

[0487] This invention is a system aimed at improving financial literacy, providing a learning experience that responds to the user's emotional state. Specifically, it includes the following processes performed between a server, a terminal, and a user.

[0488] Hardware and software used

[0489] Hardware: Smartphone (high-resolution camera, microphone), server

[0490] software:

[0491] Emotion Recognizer

[0492] Quiz Module (QuizModule)

[0493] Virtual trade simulation (TradeSimulation)

[0494] Community Platform

[0495] Interactive Feedback System

[0496] System processing overview

[0497] 1. Emotion recognition:

[0498] The smartphone's camera and microphone are used to capture the user's facial expressions and voice.

[0499] The captured data is sent to EmotionRecognizer, which identifies emotions in real time.

[0500] 2. Microlearning:

[0501] When a user requests to take a quiz, the server uses QuizModule to check the user's progress, select the next quiz to be studied, and send it.

[0502] At this time, the difficulty level of the quiz is adjusted based on the emotion recognition results.

[0503] 3. Virtual trading:

[0504] When a user starts a new trading session, the server initializes TradeSimulation to provide a virtual investment environment.

[0505] Based on the emotion recognition results, the system makes investment suggestions that are in line with the user's emotional state. For example, if the user indicates a desire to avoid risk, the system will suggest low-risk investments.

[0506] 4. Community Platform:

[0507] When a new discussion is started, the server notifies the entire community via CommunityPlatform.

[0508] It also accepts comments from other users and generates feedback based on emotion recognition results. For example, if a user is feeling anxious during a discussion, the emotion engine will recognize this and provide gentle feedback.

[0509] 5. Providing interactive learning content:

[0510] Based on the emotion recognition results and the user's progress information, the server generates and provides interactive learning content to the user.

[0511] The learning content dynamically changes based on the user's reactions, for example generating humorous feedback if the user is feeling stressed.

[0512] Examples of concrete examples and prompts

[0513] Examples:

[0514] If a user feels stressed while taking the "Stock Market 101" quiz, the emotion recognition engine will recognize that emotion and the server will provide a quiz with adjusted difficulty.

[0515] If a user feels anxious during a discussion about "investment risk management," the emotion engine will recognize that emotion and the server will provide gentle risk management advice.

[0516] Example prompt sentence:

[0517] "If your users are stressed, give them gentle feedback."

[0518] "If users are confident, offer them a high-difficulty quiz."

[0519] In this way, the financial literacy improvement system linked to the emotion engine improves the user's learning experience and enables them to acquire financial knowledge efficiently.

[0520] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0521] Step 1:

[0522] A user launches a smartphone application and requests learning content. The smartphone's camera and microphone are used to capture the user's facial expressions and voice, which are then sent to EmotionRecognizer. EmotionRecognizer processes this data and identifies the user's emotions. The input is the user's facial and voice data, and the output is their emotional state (e.g., stressed, relaxed).

[0523] Step 2:

[0524] The server receives the emotional state sent from EmotionRecognizer and checks the user's learning progress information (e.g., the results and progress of previously completed quizzes). It then sends the emotional state and progress information to QuizModule to select the next quiz content to study. The input is the user's emotional state and progress information, and the output is the content of the next quiz to study.

[0525] Step 3:

[0526] QuizModule adjusts the difficulty of the next quiz based on the emotional state and progress information. For example, if the emotional state is stressed, it lowers the difficulty, and if the emotional state is relaxed, it raises the difficulty. The adjusted quiz content is sent to the server. The input is the emotional state and progress information, and the output is the adjusted quiz content.

[0527] Step 4:

[0528] When a user answers a quiz, the results are sent to the server and progress information is updated. At the same time, the InteractiveFeedback system generates interactive feedback based on the user's emotional state and the quiz results. For example, a humorous compliment may be given if the answer is correct. The input is the quiz result and emotional state, and the output is the generated feedback.

[0529] Step 5:

[0530] When a user requests to start a virtual trading session, the server initializes TradeSimulation. Based on the user's emotional state, the AI ​​makes investment suggestions. For example, if the user indicates a risk-averse sentiment, it will suggest low-risk investments. The input is the emotional state, and the output is a proposed investment scenario.

[0531] Step 6:

[0532] A user accesses the community platform and starts a new discussion or posts a comment to an existing discussion. The server notifies other users based on the content and emotional state of the comment, and generates appropriate feedback and adds it to the thread. The input is the discussion content and emotional state, and the output is the generated feedback and notification.

[0533] Step 7:

[0534] After all the processing is completed, the server saves the updated user progress information in storage and prepares it for the next access. This allows the user to continue learning. The input is the updated progress information, and the output is the saved data.

[0535] The above processing steps provide a learning experience based on the user's emotional state, and a system is realized that effectively supports the improvement of financial literacy.

[0536] 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.

[0537] 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.

[0538] 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.

[0539] [Second embodiment]

[0540] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0541] 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.

[0542] 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).

[0543] 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.

[0544] 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.

[0545] 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).

[0546] 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.

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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."

[0552] The present invention provides an effective system for improving financial literacy among young people, which includes three main tools: a micro-learning tool, a virtual trading tool, and a community platform tool.

[0553] Microlearning Vehicles

[0554] This method provides quizzes that allow users to learn financial knowledge efficiently in a short amount of time. When a user sends a request from their terminal to take a quiz, the server checks the user's progress and selects and sends the next quiz that the user should study. When the user answers the quiz, the results are sent to the server, which determines whether the answer is correct or incorrect, and generates and returns feedback. The user receives the feedback and can gradually improve their financial knowledge.

[0555] For example, if a user takes a quiz on "Intro to the Stock Market," the server provides multiple-choice questions on that subject. The user answers the questions and their answers are sent to the server. The server evaluates the answers and provides feedback such as, "Correct answers: 8 / 10, very good. Let's learn about the bond market next."

[0556] Virtual Trading Instruments

[0557] This method provides a simulation for a user to trade stocks and other financial instruments in a virtual environment without using real funds. When a user initiates a new trading session through a terminal, the server initializes the virtual trading session with suggestions from an AI guide. The user places an order for virtual stocks, which is accepted by the server and provided to the user along with feedback from the AI ​​guide.

[0558] For example, if a user initiates "Invest in Technology Stocks," the server may suggest to the user, "Technology stocks are rising right now, so try investing in Apple." If the user places an order to buy 100 shares of Apple and the order is successful, the server may return feedback saying, "Your order is complete. Technology stocks are still on the rise."

[0559] Community Platform Means

[0560] This method provides an online forum function where users can freely start discussions and exchange opinions on financial topics. When a user starts a new discussion, the server notifies the entire community of the discussion content. Furthermore, when other users post comments to the discussion, the content is accepted by the server and added to the thread.

[0561] For example, if a user starts a new discussion about "Investment Risk Management," the server notifies the community, "A user has started a new topic called 'Investment Risk Management.'" If another user posts a comment saying, "Tell me how to diversify risk," the server adds that comment to the discussion thread, making it visible to all users.

[0562] This system allows users to acquire financial knowledge efficiently in a short period of time, safely trade in a virtual environment, and share their knowledge with other users within the community. This will improve financial literacy and enable future asset growth and risk management skills.

[0563] The processing flow will be explained below.

[0564] Microlearning Vehicles

[0565] Accessing the quiz

[0566] Step 1:

[0567] A user submits a request to access the finance quiz. The user launches the app's quiz learning module and presses the "Access Finance Quiz" button.

[0568] Step 2:

[0569] The device sends the user ID and request information to the server.

[0570] Step 3:

[0571] The server selects the next quiz to study based on the user's ID. The server reads the user's learning history from a database and runs an algorithm to determine the next quiz to proceed to.

[0572] Step 4:

[0573] The server sends the selected quiz questions and choices to the terminal.

[0574] Step 5:

[0575] The terminal displays a quiz screen and presents the quiz to the user.

[0576] Quiz Answering Process

[0577] Step 1:

[0578] The user answers the quiz and submits the answer.

[0579] Step 2:

[0580] The device sends the user ID, quiz ID, and answer to the server.

[0581] Step 3:

[0582] The server compares the received answer with the correct answer in its database.

[0583] Step 4:

[0584] The server determines whether each question is correct or incorrect and calculates the number of correct answers.

[0585] Step 5:

[0586] The server generates feedback messages based on the percentage of correct answers and updates the user's progress.

[0587] Step 6:

[0588] The server sends the generated feedback message to the terminal.

[0589] Step 7:

[0590] The device displays a feedback message.

[0591] Virtual Trading Instruments

[0592] Process of starting a new trading session

[0593] Step 1:

[0594] A user sends a request to start a new trading session. The user launches the app's trading module and presses the "Start a new trading session" button.

[0595] Step 2:

[0596] The device sends the user ID and request information to the server.

[0597] Step 3:

[0598] The server initializes a virtual trading session. The server generates a new trading session ID and allocates an initial virtual fund amount to the user.

[0599] Step 4:

[0600] The server references current market data and runs AI algorithms to generate investment recommendations.

[0601] Step 5:

[0602] The server sends the generated proposal message to the terminal.

[0603] Step 6:

[0604] The device displays a suggestion message.

[0605] The process of sending a trade order

[0606] Step 1:

[0607] A user submits a virtual trade order, selecting a specific stock and entering the quantity to buy or sell.

[0608] Step 2:

[0609] The terminal sends the user ID and order details (stock name, quantity, buy or sell) to the server.

[0610] Step 3:

[0611] The server updates the order details to the virtual market database.

[0612] Step 4:

[0613] The server determines the outcome of the order and generates a feedback message to report to the user.

[0614] Step 5:

[0615] The server sends the updated trade data and feedback messages to the terminal.

[0616] Step 6:

[0617] The device will display a confirmation and feedback message.

[0618] Community Platform Means

[0619] Action to start a new discussion

[0620] Step 1:

[0621] A user submits a request to start a new discussion: The user opens the app's Community module and presses the "Start a new discussion" button.

[0622] Step 2:

[0623] The device sends the user ID, discussion topic, and content to the server.

[0624] Step 3:

[0625] The server generates a new discussion ID and adds the topic and content to the discussion database.

[0626] Step 4:

[0627] The server notifies active users that a new discussion has been created.

[0628] Step 5:

[0629] The device receives the notification and displays it on the user's screen.

[0630] The process of posting a comment to a discussion

[0631] Step 1:

[0632] A user posts a comment to a discussion. A user opens a specific discussion and enters a comment.

[0633] Step 2:

[0634] The device sends the user ID, discussion ID, and comment content to the server.

[0635] Step 3:

[0636] The server generates a comment ID and adds the comment to the appropriate discussion thread.

[0637] Step 4:

[0638] The server sends the latest discussion thread to the device.

[0639] Step 5:

[0640] The device displays the updated discussion thread.

[0641] Example 1

[0642] 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."

[0643] Conventional financial literacy improvement systems have been considered ineffective for young people due to long learning times, trading requiring actual funds, limited communication methods, etc. The present invention aims to solve these problems and provide a system that allows people to acquire financial knowledge efficiently in a short period of time, safely experience trading, and share knowledge within the community.

[0644] 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.

[0645] In this invention, the server includes means for providing quizzes designed to enable users to learn efficiently in a short period of time, means for providing a simulation of trading financial products in a virtual environment, and means for providing an online forum where users can discuss financial topics, thereby enabling users to acquire financial knowledge efficiently in a short period of time, safely experience trading in a virtual environment, and share their knowledge with other users within the community.

[0646] "User" means any person who uses the System to improve their financial knowledge, participate in virtual trading simulations, or participate in the community forum.

[0647] "Terminal" refers to the device through which a User accesses the System and engages in activities such as answering quizzes, placing virtual trade orders, and discussing in the community forum.

[0648] A "server" is a central processing unit that processes requests from users and devices, provides quizzes, initializes trading sessions, manages community forums, and so on, while interacting with the database.

[0649] A "Quiz" is a multiple-choice or other type of learning assignment designed to help users efficiently learn financial knowledge.

[0650] "Virtual trading" refers to a user simulating the trading of stocks or other financial instruments in a virtual environment without using actual funds.

[0651] A "community forum" is an online platform for users to exchange opinions and discuss financial topics.

[0652] "Progress Management" is a feature that tracks a user's learning progress and selects the next quiz or topic to study.

[0653] An "AI model" is an algorithm that uses machine learning to analyze market trends and user behavior in order to provide investment suggestions and other guidance to users.

[0654] A "discussion thread" is a series of posts initiated by a user in a community forum to exchange opinions or information on a particular topic.

[0655] The present invention is a system aimed at improving financial literacy among young people. The system provides users with multiple means to efficiently acquire financial knowledge, experience trading in a virtual environment, and exchange opinions within a community. Specific embodiments of the present invention are described below.

[0656] Microlearning Vehicles

[0657] This method provides quizzes that allow users to learn financial knowledge effectively in a short amount of time. When a user launches a dedicated application and requests to take a quiz, the device sends this request as an HTTP request to the server. The server uses a MySQL database to check the user's learning progress, selects the next quiz based on the progress, and sends it in JSON format to the device. When the user answers the quiz and sends the answer from the device to the server, the server uses the Pandas library to determine whether the answer is correct or incorrect, creates feedback, and sends it to the device.

[0658] Examples:

[0659] For example, if a user requests from their device to take a quiz on "Introduction to the Stock Market," the server will check their progress and send them multiple choice questions on "Introduction to the Stock Market." After the user answers the quiz and clicks the "Submit Answers" button, the results are sent to the server, which then returns feedback to the device saying, "You got 8 / 10 right, which is very good. Let's learn about the bond market next."

[0660] Virtual Trading Instruments

[0661] This tool provides users with a simulation of trading stocks and other financial instruments in a virtual environment. When a user starts a new trading session from their terminal, the terminal sends this request as an HTTP request to the server. The server uses an AI model using TensorFlow to analyze market trends and sends appropriate trade suggestions in JSON format to the terminal. When a user places an order for virtual stocks, the order is sent from the terminal to the server, which accepts the order and records it in a virtual trading database. The results of the order and trading feedback are sent from the server to the terminal.

[0662] Examples:

[0663] When a user initiates an "invest in technology stocks" request, the device sends the request to the server, which analyzes market trends. The server then sends a suggestion to the device saying, "Technology stocks are rising right now, so try investing in Apple." When the user places an order to buy 100 shares of Apple stock and the order is sent to the server, the server returns feedback to the device saying, "Your order is complete. Technology stocks are still on the rise."

[0664] Community Platform Means

[0665] This method provides an online forum function that allows users to freely start discussions and exchange opinions on financial topics. When a user sends a request to start a new discussion from their device to the server, the server notifies the entire community of the discussion content. When another user posts a comment to the discussion, the comment is sent from the device to the server, and the server adds the comment to the discussion thread and notifies all users of the update.

[0666] Examples:

[0667] When a user starts a new discussion about "Investment Risk Management," the device sends the request to the server, which then sends a notification to the entire community saying, "A user has started a new topic called 'Investment Risk Management.'" When another user posts a comment saying, "Tell me how to diversify risk," the device sends the comment to the server, which adds the comment to the discussion thread and notifies all users.

[0668] Example prompts for generative AI models

[0669] You can ask the generative AI model for a detailed explanation of the system's processing by using the following prompt:

[0670] Example prompt sentence:

[0671] 1. "Please explain the detailed process flow of a microlearning feature that allows users to quickly and efficiently improve their financial literacy."

[0672] 2. Please explain, with specific examples, how users can use the virtual trading feature to learn investment strategies.

[0673] 3. "Please explain the process from starting a discussion on a community platform to other users adding comments."

[0674] As described above, the present invention enables users to acquire financial knowledge efficiently in a short period of time, experience trading in a safe virtual environment, and share their knowledge with other users within the community.

[0675] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0676] Microlearning Vehicles

[0677] Step 1:

[0678] Submit a quiz request

[0679] The user launches the dedicated application on their device and clicks the "Take Quiz" button. The device generates an HTTP request and sends it to the server. The input to this request includes information such as the user ID and the desired field of study.

[0680] Step 2:

[0681] Progress check and quiz selection

[0682] The server processes the received request and retrieves the user's learning progress data from the MySQL database based on the user ID. The server analyzes this data (data processing) and uses a Python script to select the next quiz to study. The output of the selected quiz is sent to the terminal in JSON format.

[0683] Step 3:

[0684] Quiz Answers and Submission

[0685] The user answers the quiz on the device and clicks the "Submit Answer" button. The device sends the answer data to the server as an HTTP POST request. This input includes the user's answer.

[0686] Step 4:

[0687] Correct / incorrect judgment and feedback generation

[0688] The server analyzes the received response data using the Pandas library (data calculation) and determines whether it is correct or incorrect. Based on the analysis results, it generates a feedback message and sends it to the terminal in JSON format. This output includes the evaluation results and what to learn next.

[0689] Step 5:

[0690] Feedback Check

[0691] Users can view feedback on their device and assess their financial knowledge, which will help them plan their future learning.

[0692] Virtual Trading Instruments

[0693] Step 1:

[0694] Trading Session Initialization

[0695] A user clicks a button to start a new trading session in the terminal application. The terminal sends this request to the server as an HTTP request. The input includes the user ID and the desired transaction details.

[0696] Step 2:

[0697] AI guide suggestions

[0698] The server processes the received requests and analyzes market trends using an AI model powered by TensorFlow. Based on the analysis results, the server generates appropriate trade proposals and sends them to the terminal in JSON format. This output contains investment recommendations from the AI.

[0699] Step 3:

[0700] Virtual Stock Orders

[0701] The user places an order for virtual stocks based on the proposed investment strategy. The terminal sends the order to the server as an HTTP POST request. This input contains the order information.

[0702] Step 4:

[0703] Order acceptance and processing

[0704] The server receives the order data and records it in a virtual transaction database. The server analyzes the transaction results and generates appropriate feedback messages. The output is feedback including order confirmation and transaction results.

[0705] Step 5:

[0706] Feedback Check

[0707] Users can view the feedback on their devices and use it to plan their next investment strategy, which will help improve the user's virtual trading experience.

[0708] Community Platform Means

[0709] Step 1:

[0710] Start a discussion

[0711] A user opens the community function on their device, enters a new discussion, and clicks the "Start a discussion" button. The device sends this request to the server as an HTTP POST request. The input includes the user ID and the discussion content.

[0712] Step 2:

[0713] Community Notifications

[0714] Based on the received request, the server saves the discussion content in the community database and generates a notification message to send to the entire community, which notifies the new discussion that has started.

[0715] Step 3:

[0716] Posting and accepting comments

[0717] Other users view the discussion and post comments. The device sends the comment content to the server as an HTTP POST request. This input includes the comment content.

[0718] Step 4:

[0719] Add comments and notifications

[0720] The server accepts the comment data and adds it to the original discussion thread. The server generates updated thread information and notifies all users. The output is a notification containing the latest discussion content.

[0721] Step 5:

[0722] Discussion and Feedback

[0723] Users can use their devices to check the latest discussion updates and rate how their comments were received, which helps them choose their next comment and topic.

[0724] (Application example 1)

[0725] 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."

[0726] To improve the financial literacy of young people, a platform that allows them to learn efficiently in a short amount of time is needed. It is also important to have opportunities to safely gain investment experience without using actual funds. Furthermore, there is a need for an environment where students can deepen their overall knowledge by sharing information and exchanging opinions with other students. However, conventional systems have had difficulty meeting all of these requirements. Therefore, a system that utilizes virtual space and enables interactive and effective learning is needed.

[0727] 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.

[0728] In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, a financial education means in a virtual space, a real-time feedback providing means, and an interactive learning means using prompt sentences, thereby enabling users to efficiently learn financial knowledge in a virtual space, safely gain investment experience, and exchange opinions with other users.

[0729] A "microlearning tool" is a tool that provides quizzes to help people learn financial knowledge efficiently in a short amount of time.

[0730] A "virtual trading vehicle" is a simulated vehicle for trading stocks and other financial instruments in a virtual environment without using actual funds.

[0731] A "community platform vehicle" is a vehicle that provides an online forum function where users can freely initiate discussions and exchange opinions on financial topics.

[0732] The "financial education tool in a virtual space" is an educational tool that allows users to take financial quizzes and conduct virtual trades in a virtual store.

[0733] The "real-time feedback providing means" is a means by which a user can receive immediate feedback on a quiz or trade simulation.

[0734] An "interactive learning method using prompt sentences" is a method that allows users to customize the learning content by inputting specific instructions and progress through learning interactively.

[0735] The present invention provides an effective system for improving financial literacy among young people, which includes a micro-learning means, a virtual trading means, a community platform means, a virtual financial education means, a real-time feedback providing means, and an interactive learning means using prompts.

[0736] Microlearning Vehicles

[0737] In this system, the server manages the user's progress and provides the next quiz to study. When the user takes the quiz, the results are sent to the server, which determines whether the quiz was correct or incorrect and generates feedback to return to the user. This allows the user to gradually improve their financial knowledge.

[0738] For example, if a user takes a quiz on "Intro to the Stock Market," the server provides multiple choice questions on that subject. After the user answers the questions and submits their answers to the server, the server returns feedback like, "Number of answers correct: 8 / 10, very good. Let's learn about the bond market next."

[0739] Virtual Trading Instruments

[0740] The server initiates a virtual trading session and provides the user with investment suggestions from the AI. When the user places an order for virtual stocks, the order is accepted by the server and provided to the user along with feedback from the AI ​​guide.

[0741] For example, when a user starts "investing in technology stocks," the server will suggest to the user, "Technology stocks are rising now, so try investing in high-tech company A." If the user places an order to purchase 100 shares of high-tech company A and the order is successful, the server will return feedback saying, "Your order has been completed. Technology stocks are still on the rise."

[0742] Community Platform Means

[0743] Users can freely start discussions and exchange opinions on financial topics. When a user starts a new discussion, the server notifies the entire community of the discussion content. Furthermore, when other users post comments to the discussion, the server accepts the content and adds it to the thread.

[0744] For example, if a user starts a new discussion about "Investment Risk Management," the server notifies the community, "A user has started a new topic called 'Investment Risk Management.'" If another user posts a comment saying, "Tell me how to diversify risk," the server adds that comment to the discussion thread, making it visible to all users.

[0745] Virtual financial education tools

[0746] The virtual financial education tool provides an environment where users can take financial quizzes and conduct virtual trades in a virtual store, allowing users to safely and effectively improve their financial literacy without using real money.

[0747] Real-time feedback

[0748] This is a means for users to receive immediate feedback on quizzes and trading simulations. The server generates feedback in real time according to the user's actions and provides it to the user.

[0749] Interactive learning tools using prompts

[0750] It is a means for users to customize their learning content and progress interactively by inputting specific instructions. For example, by using prompts such as "I want to take a quiz on introductory stock markets" or "I want to invest in technology stocks," the system can provide users with the optimal learning content and trading experience.

[0751] The system is implemented using hardware such as smartphones, smart glasses, and head-mounted displays, and the software used includes Python, Flask (a server framework), and SQLite (a database).

[0752] This will effectively improve the financial literacy of young people.

[0753] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0754] Step 1:

[0755] The server receives a quiz request from the user's terminal.

[0756] Input: A request from the user's device to "take a quiz."

[0757] Processing: The server checks the database to see the user's progress and selects the next quiz to study.

[0758] Output: Send the selected quiz to the device.

[0759] Step 2:

[0760] The user answers the selected quiz questions.

[0761] Input: Quiz questions sent by the server.

[0762] Process: The user answers the quiz and sends the answers to the server.

[0763] Output: The user's answer data is sent to the server.

[0764] Step 3:

[0765] The server receives the user's answer and determines whether it is correct or incorrect.

[0766] Input: User response data.

[0767] Processing: The server compares the correct answer information with the user's answer and determines whether it is correct or not. It then calculates the number of correct answers and generates feedback.

[0768] Output: Sends feedback information to the user terminal.

[0769] Step 4:

[0770] The server initializes a new trading session.

[0771] Input: "Start a new trading session" request from the user terminal.

[0772] Processing: Initialize the virtual trading environment and generate AI-guided investment recommendations.

[0773] Output: Sends the initialized trading session state and AI-guided suggestions to the user terminal.

[0774] Step 5:

[0775] A user places an order for virtual stock.

[0776] Input: User trade order (e.g., "Buy 100 shares of technology stock").

[0777] Processing: The server accepts user orders and applies them to the virtual trading environment. The AI ​​guide generates feedback based on the latest investment status.

[0778] Output: Sends notification of order completion and feedback on investment status to the user terminal.

[0779] Step 6:

[0780] A user starts a new discussion on the community platform.

[0781] Input: User discussion topic (e.g., "Investment Risk Management").

[0782] Action: The server notifies the community of the new topic and creates a discussion thread.

[0783] Output: Sends a new topic notification to the entire community.

[0784] Step 7:

[0785] Other users post comments to the discussion.

[0786] Input: Comments from other users (e.g., "Tell me how to spread risk.").

[0787] Processing: The server accepts the comment and adds it to the discussion thread.

[0788] Output: Notify all interested users of an updated discussion thread.

[0789] These steps enable users to efficiently learn financial knowledge in a virtual space, safely gain investment experience, and exchange opinions with other users.

[0790] 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.

[0791] The present invention aims to provide a system for improving financial literacy that combines an emotion engine to provide learning content, virtual trade suggestions, and community discussion feedback in response to a user's emotions.

[0792] Combining microlearning tools with emotion engines

[0793] This method provides quizzes that allow users to learn financial knowledge efficiently and quickly. When a user sends a request to take a quiz, the server checks the user's progress and selects and sends the next quiz to the user. At that time, an emotion engine recognizes the user's emotions and generates learning content and feedback according to those emotions.

[0794] For example, if a user is feeling stressed about the "Stock Market Introduction" quiz, the emotion engine will recognize that emotion and the server will provide a quiz with an adjusted difficulty level, and even provide humorous feedback to improve the user's learning experience.

[0795] Combining virtual trading instruments with an emotional engine

[0796] This tool allows users to simulate trading stocks and other financial instruments in a virtual environment without using real funds. When a user starts a new trading session, the server initializes the virtual trading session with AI-guided suggestions. At the same time, an emotion engine recognizes the user's emotions and adjusts appropriate investment suggestions.

[0797] For example, if a user shows a risk-averse emotion, the emotion engine recognizes that emotion and the server suggests low-risk investments. In this way, investment learning based on the user's emotional state becomes possible.

[0798] Combining community platform tools and emotion engines

[0799] This method provides an online forum where users can freely start discussions and exchange opinions on financial topics. When a new discussion is started, the server notifies the entire community of the discussion content. When other users post comments to the discussion, the server accepts the comments and adds them to the thread. At that time, an emotion engine recognizes the user's emotions and generates feedback according to the emotions.

[0800] For example, if a user feels anxious during a discussion about "investment risk management," the emotion engine will recognize that emotion and the server will provide gentle, thoughtful advice on risk management. Other users' comments will also be similarly given feedback that takes their emotions into account, helping to reassure the user.

[0801] As described above, by utilizing an emotion engine, this system can provide a learning experience tailored to the user's emotions. This allows users to efficiently acquire financial knowledge, gain safe trading experience in a virtual environment, and engage in constructive discussions with other users. This system for improving financial literacy incorporates user emotional care, and is expected to be more effective than conventional educational methods.

[0802] The processing flow will be explained below.

[0803] Combining microlearning tools with emotion engines

[0804] Accessing the quiz

[0805] Step 1:

[0806] A user submits a request to access the finance quiz. The user launches the app's quiz learning module and presses the "Access Finance Quiz" button.

[0807] Step 2:

[0808] The device sends the user ID and request information to the server.

[0809] Step 3:

[0810] The server retrieves user progress data to select the next quiz to study based on the user ID.

[0811] Step 4:

[0812] The server uses the camera images and audio data sent from the terminal, and the emotion engine recognizes the user's current emotional state.

[0813] Step 5:

[0814] The server and emotion engine work together to select appropriate quizzes based on the user's progress and emotional state. If the user is feeling stressed, a quiz with adjusted difficulty will be selected.

[0815] Step 6:

[0816] The server transmits the selected quiz and a feedback message corresponding to the emotion to the terminal.

[0817] Step 7:

[0818] The terminal displays a quiz screen and presents the quiz to the user.

[0819] Quiz Answering Process

[0820] Step 1:

[0821] The user answers the quiz and submits the answer.

[0822] Step 2:

[0823] The device sends the user ID, quiz ID, and answer to the server.

[0824] Step 3:

[0825] The server compares the received answer with the correct answer in its database.

[0826] Step 4:

[0827] The server determines whether each question is correct or incorrect and calculates the number of correct answers.

[0828] Step 5:

[0829] When the server creates a feedback message based on the accuracy rate, the emotion engine reconfirms the user's emotional state and adjusts the feedback message.

[0830] Step 6:

[0831] The server sends the generated feedback message to the terminal.

[0832] Step 7:

[0833] The device displays a feedback message.

[0834] Combining virtual trading instruments with an emotional engine

[0835] Process of starting a new trading session

[0836] Step 1:

[0837] A user sends a request to start a new trading session. The user launches the app's trading module and presses the "Start a new trading session" button.

[0838] Step 2:

[0839] The device sends the user ID and request information to the server.

[0840] Step 3:

[0841] The server initializes a virtual trading session, generates a trading session ID, and allocates initial virtual funds to the user.

[0842] Step 4:

[0843] The server uses camera footage and audio data from the device, and the emotion engine recognizes the user's current emotional state.

[0844] Step 5:

[0845] The server consults market data, and the emotion engine generates AI investment recommendations based on the user's emotional state: if the user is risk-averse, the server will suggest low-risk investments.

[0846] Step 6:

[0847] The server sends the generated proposal message to the terminal.

[0848] Step 7:

[0849] The device displays a suggestion message.

[0850] The process of sending a trade order

[0851] Step 1:

[0852] A user submits a virtual trade order, selecting a specific stock and entering the quantity to buy or sell.

[0853] Step 2:

[0854] The terminal sends the user ID and order details (stock name, quantity, buy or sell) to the server.

[0855] Step 3:

[0856] The server updates the order details to the virtual market database.

[0857] Step 4:

[0858] An emotion engine recognizes the user's emotional state as the server determines the outcome of the order and generates feedback messages to report to the user.

[0859] Step 5:

[0860] The server adjusts the feedback message based on the user's emotional state and sends it to the terminal.

[0861] Step 6:

[0862] The device will display a confirmation and feedback message.

[0863] Combining community platform tools and emotion engines

[0864] Action to start a new discussion

[0865] Step 1:

[0866] A user submits a request to start a new discussion: The user opens the app's Community module and presses the "Start a new discussion" button.

[0867] Step 2:

[0868] The device sends the user ID, discussion topic, and content to the server.

[0869] Step 3:

[0870] The server generates a new discussion ID and adds the topic and content to the discussion database.

[0871] Step 4:

[0872] The server notifies active users that a new discussion has been created.

[0873] Step 5:

[0874] The device receives the notification and displays it on the user's screen.

[0875] The process of posting a comment to a discussion

[0876] Step 1:

[0877] A user posts a comment to a discussion. A user opens a specific discussion and enters a comment.

[0878] Step 2:

[0879] The device sends the user ID, discussion ID, and comment content to the server.

[0880] Step 3:

[0881] The server generates a comment ID and adds the comment to the appropriate discussion thread.

[0882] Step 4:

[0883] The server uses the camera images and audio data sent from the terminal, and the emotion engine recognizes the user's current emotional state.

[0884] Step 5:

[0885] The server generates friendly feedback and additional advice based on the user's emotional state.

[0886] Step 6:

[0887] The server sends the latest discussion threads and feedback to the device.

[0888] Step 7:

[0889] The device displays updated discussion threads and feedback.

[0890] Example 2

[0891] 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."

[0892] Conventional financial literacy improvement systems have struggled to provide a learning experience that takes into account the user's emotional state. As a result, users tend to lose interest and the learning experience is inconsistent. Furthermore, in virtual trading and community discussions, it was not possible to provide appropriate support based on each user's emotions. This limited the effectiveness of the system, resulting in problems such as reduced user satisfaction and reduced learning effectiveness.

[0893] 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.

[0894] In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, an emotion recognition means, and a feedback generation means using a generative AI model. This makes it possible to provide learning content and investment suggestions that take the user's emotions into consideration, thereby improving the user's learning experience and satisfaction.

[0895] "Microlearning tools" are tools that provide quizzes and training to help users learn financial knowledge in a short amount of time.

[0896] A "virtual trading instrument" is an instrument that allows users to simulate trading financial instruments in a virtual environment without using real funds.

[0897] A "community platform vehicle" is a vehicle that provides an online forum for users to freely discuss and exchange ideas on financial topics.

[0898] An "emotion recognition means" is a means including technology for recognizing a user's emotional state in real time.

[0899] "Feedback generation means using a generative AI model" refers to a means that uses an AI model to generate feedback according to the user's emotions and learning progress.

[0900] "Means for managing the user's progress and providing the next quiz to study" refers to means for selecting and providing the appropriate next quiz based on the user's learning history and correct answer rate.

[0901] "Means for providing users with investment proposals based on a generative AI model" refers to means for providing users with investment proposals based on their emotional state and risk tolerance during virtual trading using a generative AI model.

[0902] MODE FOR CARRYING OUT THE INVENTION

[0903] This invention is a learning system aimed at improving financial literacy, which combines emotion recognition means and feedback generation means using a generative AI model to provide a learning experience that responds to the user's emotions. This system is mainly composed of the following means:

[0904] 1. Microlearning methods:

[0905] The system provides quiz-style learning content to enable users to learn financial knowledge efficiently and quickly. The server manages the user's progress, selects the next quiz to be studied, and sends it to the user's device. Specifically, the system incorporates an algorithm that selects the appropriate next quiz based on the user's learning history and correct answer rate.

[0906] For example, if a user is taking an "Introduction to the Stock Market" quiz and the emotion recognition means detects that the user is stressed, the server will adjust the difficulty of the quiz according to the user's emotion and generate humorous feedback. An example of a prompt sentence is, "Please explain how you would respond to a user who is stressed by a short quiz to improve financial literacy."

[0907] 2. Virtual trading instruments:

[0908] This feature allows users to simulate trading financial instruments in a virtual environment. The server initializes a virtual trading session at the user's request and provides investment suggestions based on generative AI models. This allows users to safely gain trading experience without using real funds.

[0909] For example, when a user starts a virtual trading session, if the emotion recognition means detects the user's emotional state (e.g., risk aversion), the server generates and provides low-risk investment suggestions based on the results. An example of a prompt sentence is, "Please explain how to make appropriate investment suggestions during a virtual trade to a user who exhibits risk aversion emotions."

[0910] 3. Community Platform Means:

[0911] It provides an online forum where users can freely exchange opinions and discuss financial topics. The server notifies the entire community when a new discussion starts and accepts comments from other users and adds them to the thread.

[0912] For example, if a user starts a discussion about "investment risk management," and the emotion recognition means detects anxiety among the participants, the server will provide gentle advice on risk management to alleviate the anxiety. An example of a prompt sentence is, "Please explain how to respond in an online forum to a user who is anxious about "investment risk management."

[0913] This invention uses facial recognition technology and biometric sensors as emotion recognition methods, and the latest natural language processing technology and machine learning algorithms as generative AI models, making it possible to adapt to the user's emotions and provide individually optimized learning experiences and investment recommendations in real time.

[0914] By using concrete examples, users can efficiently acquire financial knowledge and safely gain experience in virtual trading. In addition, by constructively exchanging opinions with other users through community discussions, users can deepen their practical knowledge. This is expected to improve users' financial literacy.

[0915] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0916] Step 1:

[0917] A user submits a request to take a quiz.

[0918] Input: User request (want to take a quiz)

[0919] Processing: The user clicks the take quiz button in the application, and a request is sent to the server.

[0920] Output: The request data (information about the quiz request) is sent to the server.

[0921] Step 2:

[0922] The server checks the user's progress.

[0923] Input: Request data and user learning history data

[0924] Processing: The server retrieves the user's past learning history and correct answer rate from the database and evaluates their progress.

[0925] Output: Data for selecting the next quiz to study

[0926] Step 3:

[0927] The server sends the selected quiz to the user.

[0928] Input: Selection data

[0929] Processing: The server sends the selected quiz to the terminal based on the user's progress.

[0930] Output: Quiz data

[0931] Step 4:

[0932] The terminal recognizes the user's emotions using an emotion engine.

[0933] Input: Real-time biometric and facial expression data of the user

[0934] Processing: The device uses facial recognition technology and biometric sensors to analyze and recognize the user's emotions in real time.

[0935] Output: Emotion recognition result (e.g., stress state)

[0936] Step 5:

[0937] The server adjusts the difficulty of the quiz and generates feedback.

[0938] Input: Emotion recognition results and quiz data

[0939] Processing: The server adjusts the difficulty of the quiz appropriately based on the emotion recognition results and generates humorous feedback using a generative AI model.

[0940] Output: Adjusted quiz and feedback data

[0941] Step 6:

[0942] A user submits a request for a new virtual trading session.

[0943] Input: User request (want to start virtual trading)

[0944] Processing: The user clicks the application's button to initiate a virtual trade, and a request is sent to the server.

[0945] Output: Request data (information on the virtual trade start request)

[0946] Step 7:

[0947] The server initializes a virtual trading session.

[0948] Input: Request data and market data

[0949] Processing: The server initializes the virtual trading session, retrieves the necessary market data, and generates investment recommendations using the generative AI model.

[0950] Output: Virtual trading session data and investment proposal data

[0951] Step 8:

[0952] The terminal recognizes the user's emotions using an emotion engine.

[0953] Input: Real-time biometric and facial expression data of the user

[0954] Processing: The device uses facial recognition technology and biometric sensors to analyze and recognize the user's emotions in real time.

[0955] Output: Emotion recognition result (e.g., risk aversion)

[0956] Step 9:

[0957] The server coordinates the investment proposals.

[0958] Input: Emotion recognition results and virtual trading session data

[0959] Processing: The server adjusts the risk level of the investment proposal based on the emotion recognition result and provides it to the user.

[0960] Output: Adjusted investment proposal data

[0961] Step 10:

[0962] A user submits a request to start a new discussion.

[0963] Input: User request (want to start a discussion)

[0964] Process: The user clicks the Start Discussion button in the application and a request is sent to the server.

[0965] Output: Request data (information you wish to start a discussion about)

[0966] Step 11:

[0967] The server notifies the entire community of the discussion content.

[0968] Input: Request data and discussion content

[0969] Process: The server notifies the entire community of new discussion content and creates a thread.

[0970] Output: Notification data

[0971] Step 12:

[0972] Other users post comments to the discussion.

[0973] Input: Comment data

[0974] Process: Other users post comments to the discussion thread, which are then sent to the server.

[0975] Output: Comment data

[0976] Step 13:

[0977] The server adds the comment to the thread.

[0978] Input: Comment data

[0979] Processing: The server adds the accepted comment to the discussion thread.

[0980] Output: Updated discussion thread data

[0981] Step 14:

[0982] The device uses an emotion engine to recognize the user's emotions and generate feedback.

[0983] Input: Real-time biometric information, facial expression data, and comment data of the user

[0984] Processing: The device uses facial recognition technology and biometric sensors to analyze emotions and generates feedback using generative AI models.

[0985] Output: Feedback data (e.g., kind words of advice)

[0986] These steps provide a learning experience that improves financial literacy according to the user's progress and emotions.

[0987] (Application example 2)

[0988] 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."

[0989] Conventional financial literacy improvement systems have been unable to provide a learning experience that takes into account the user's emotional state, resulting in reduced learning efficiency. Furthermore, in virtual trading, they have been unable to provide appropriate investment suggestions based on the user's emotions, limiting the effectiveness of the user's learning. Furthermore, in community discussions, it has been difficult to provide feedback that takes into account the user's emotions.

[0990] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, a feedback means based on emotion recognition, and a means for providing interactive learning content. This makes it possible to provide learning content and feedback according to the user's emotional state, thereby effectively supporting the improvement of the user's financial literacy.

[0991] "Microlearning" is an educational method for acquiring knowledge efficiently in a short amount of time.

[0992] "Virtual trading" means the simulated activity of trading financial instruments in a virtual environment without using actual funds.

[0993] A "community platform" is an online space where users can exchange opinions and hold discussions on a specific topic.

[0994] "Emotion recognition" is a technology that identifies a user's emotions in real time from their facial expressions and voice.

[0995] "Feedback means" is a method for providing appropriate information based on the user's behavior or state, and for improving learning or behavior.

[0996] "Interactive learning content provision" is a method of providing learning materials and tasks that change dynamically depending on the user's reactions and situation.

[0997] "Progress management" is the process of tracking a user's learning or work progress and suggesting next tasks or activities to be done.

[0998] "Adjusting the difficulty of a quiz" is a method of changing the difficulty of the quiz provided based on the user's level of understanding and emotional state.

[0999] "Investment proposal adjustment" is a technology that provides optimal investment strategies and policies to users based on the results of user emotion recognition.

[1000] This invention is a system aimed at improving financial literacy, providing a learning experience that responds to the user's emotional state. Specifically, it includes the following processes performed between a server, a terminal, and a user.

[1001] Hardware and software used

[1002] Hardware: Smartphone (high-resolution camera, microphone), server

[1003] software:

[1004] Emotion Recognizer

[1005] Quiz Module (QuizModule)

[1006] Virtual trade simulation (TradeSimulation)

[1007] Community Platform

[1008] Interactive Feedback System

[1009] System processing overview

[1010] 1. Emotion recognition:

[1011] The smartphone's camera and microphone are used to capture the user's facial expressions and voice.

[1012] The captured data is sent to EmotionRecognizer, which identifies emotions in real time.

[1013] 2. Microlearning:

[1014] When a user requests to take a quiz, the server uses QuizModule to check the user's progress, select the next quiz to be studied, and send it.

[1015] At this time, the difficulty level of the quiz is adjusted based on the emotion recognition results.

[1016] 3. Virtual trading:

[1017] When a user starts a new trading session, the server initializes TradeSimulation to provide a virtual investment environment.

[1018] Based on the emotion recognition results, the system makes investment suggestions that are in line with the user's emotional state. For example, if the user indicates a desire to avoid risk, the system will suggest low-risk investments.

[1019] 4. Community Platform:

[1020] When a new discussion is started, the server notifies the entire community via CommunityPlatform.

[1021] It also accepts comments from other users and generates feedback based on emotion recognition results. For example, if a user is feeling anxious during a discussion, the emotion engine will recognize this and provide gentle feedback.

[1022] 5. Providing interactive learning content:

[1023] Based on the emotion recognition results and the user's progress information, the server generates and provides interactive learning content to the user.

[1024] The learning content dynamically changes based on the user's reactions, for example generating humorous feedback if the user is feeling stressed.

[1025] Examples of concrete examples and prompts

[1026] Examples:

[1027] If a user feels stressed while taking the "Stock Market 101" quiz, the emotion recognition engine will recognize that emotion and the server will provide a quiz with adjusted difficulty.

[1028] If a user feels anxious during a discussion about "investment risk management," the emotion engine will recognize that emotion and the server will provide gentle risk management advice.

[1029] Example prompt sentence:

[1030] "If your users are stressed, give them gentle feedback."

[1031] "If users are confident, offer them a high-difficulty quiz."

[1032] In this way, the financial literacy improvement system linked to the emotion engine improves the user's learning experience and enables them to acquire financial knowledge efficiently.

[1033] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1034] Step 1:

[1035] A user launches a smartphone application and requests learning content. The smartphone's camera and microphone are used to capture the user's facial expressions and voice, which are then sent to EmotionRecognizer. EmotionRecognizer processes this data and identifies the user's emotions. The input is the user's facial and voice data, and the output is their emotional state (e.g., stressed, relaxed).

[1036] Step 2:

[1037] The server receives the emotional state sent from EmotionRecognizer and checks the user's learning progress information (e.g., the results and progress of previously completed quizzes). It then sends the emotional state and progress information to QuizModule to select the next quiz content to study. The input is the user's emotional state and progress information, and the output is the content of the next quiz to study.

[1038] Step 3:

[1039] QuizModule adjusts the difficulty of the next quiz based on the emotional state and progress information. For example, if the emotional state is stressed, it lowers the difficulty, and if the emotional state is relaxed, it raises the difficulty. The adjusted quiz content is sent to the server. The input is the emotional state and progress information, and the output is the adjusted quiz content.

[1040] Step 4:

[1041] When a user answers a quiz, the results are sent to the server and progress information is updated. At the same time, the InteractiveFeedback system generates interactive feedback based on the user's emotional state and the quiz results. For example, a humorous compliment may be given if the answer is correct. The input is the quiz result and emotional state, and the output is the generated feedback.

[1042] Step 5:

[1043] When a user requests to start a virtual trading session, the server initializes TradeSimulation. Based on the user's emotional state, the AI ​​makes investment suggestions. For example, if the user indicates a risk-averse sentiment, it will suggest low-risk investments. The input is the emotional state, and the output is a proposed investment scenario.

[1044] Step 6:

[1045] A user accesses the community platform and starts a new discussion or posts a comment to an existing discussion. The server notifies other users based on the content and emotional state of the comment, and generates appropriate feedback and adds it to the thread. The input is the discussion content and emotional state, and the output is the generated feedback and notification.

[1046] Step 7:

[1047] After all the processing is completed, the server saves the updated user progress information in storage and prepares it for the next access. This allows the user to continue learning. The input is the updated progress information, and the output is the saved data.

[1048] The above processing steps provide a learning experience based on the user's emotional state, and a system is realized that effectively supports the improvement of financial literacy.

[1049] 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.

[1050] 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.

[1051] 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.

[1052] [Third embodiment]

[1053] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1054] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1055] 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).

[1056] 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.

[1057] 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.

[1058] 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).

[1059] 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.

[1060] 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.

[1061] 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.

[1062] 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.

[1063] 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.

[1064] 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."

[1065] The present invention provides an effective system for improving financial literacy among young people, which includes three main tools: a micro-learning tool, a virtual trading tool, and a community platform tool.

[1066] Microlearning Vehicles

[1067] This method provides quizzes that allow users to learn financial knowledge efficiently in a short amount of time. When a user sends a request from their terminal to take a quiz, the server checks the user's progress and selects and sends the next quiz that the user should study. When the user answers the quiz, the results are sent to the server, which determines whether the answer is correct or incorrect, and generates and returns feedback. The user receives the feedback and can gradually improve their financial knowledge.

[1068] For example, if a user takes a quiz on "Intro to the Stock Market," the server provides multiple-choice questions on that subject. The user answers the questions and their answers are sent to the server. The server evaluates the answers and provides feedback such as, "Correct answers: 8 / 10, very good. Let's learn about the bond market next."

[1069] Virtual Trading Instruments

[1070] This method provides a simulation for a user to trade stocks and other financial instruments in a virtual environment without using real funds. When a user initiates a new trading session through a terminal, the server initializes the virtual trading session with suggestions from an AI guide. The user places an order for virtual stocks, which is accepted by the server and provided to the user along with feedback from the AI ​​guide.

[1071] For example, if a user initiates "Invest in Technology Stocks," the server may suggest to the user, "Technology stocks are rising right now, so try investing in Apple." If the user places an order to buy 100 shares of Apple and the order is successful, the server may return feedback saying, "Your order is complete. Technology stocks are still on the rise."

[1072] Community Platform Means

[1073] This method provides an online forum function where users can freely start discussions and exchange opinions on financial topics. When a user starts a new discussion, the server notifies the entire community of the discussion content. Furthermore, when other users post comments to the discussion, the content is accepted by the server and added to the thread.

[1074] For example, if a user starts a new discussion about "Investment Risk Management," the server notifies the community, "A user has started a new topic called 'Investment Risk Management.'" If another user posts a comment saying, "Tell me how to diversify risk," the server adds that comment to the discussion thread, making it visible to all users.

[1075] This system allows users to acquire financial knowledge efficiently in a short period of time, safely trade in a virtual environment, and share their knowledge with other users within the community. This will improve financial literacy and enable future asset growth and risk management skills.

[1076] The processing flow will be explained below.

[1077] Microlearning Vehicles

[1078] Accessing the quiz

[1079] Step 1:

[1080] A user submits a request to access the finance quiz. The user launches the app's quiz learning module and presses the "Access Finance Quiz" button.

[1081] Step 2:

[1082] The device sends the user ID and request information to the server.

[1083] Step 3:

[1084] The server selects the next quiz to study based on the user's ID. The server reads the user's learning history from a database and runs an algorithm to determine the next quiz to proceed to.

[1085] Step 4:

[1086] The server sends the selected quiz questions and choices to the terminal.

[1087] Step 5:

[1088] The terminal displays a quiz screen and presents the quiz to the user.

[1089] Quiz Answering Process

[1090] Step 1:

[1091] The user answers the quiz and submits the answer.

[1092] Step 2:

[1093] The device sends the user ID, quiz ID, and answer to the server.

[1094] Step 3:

[1095] The server compares the received answer with the correct answer in its database.

[1096] Step 4:

[1097] The server determines whether each question is correct or incorrect and calculates the number of correct answers.

[1098] Step 5:

[1099] The server generates feedback messages based on the percentage of correct answers and updates the user's progress.

[1100] Step 6:

[1101] The server sends the generated feedback message to the terminal.

[1102] Step 7:

[1103] The device displays a feedback message.

[1104] Virtual Trading Instruments

[1105] Process of starting a new trading session

[1106] Step 1:

[1107] A user sends a request to start a new trading session. The user launches the app's trading module and presses the "Start a new trading session" button.

[1108] Step 2:

[1109] The device sends the user ID and request information to the server.

[1110] Step 3:

[1111] The server initializes a virtual trading session. The server generates a new trading session ID and allocates an initial virtual fund amount to the user.

[1112] Step 4:

[1113] The server references current market data and runs AI algorithms to generate investment recommendations.

[1114] Step 5:

[1115] The server sends the generated proposal message to the terminal.

[1116] Step 6:

[1117] The device displays a suggestion message.

[1118] The process of sending a trade order

[1119] Step 1:

[1120] A user submits a virtual trade order, selecting a specific stock and entering the quantity to buy or sell.

[1121] Step 2:

[1122] The terminal sends the user ID and order details (stock name, quantity, buy or sell) to the server.

[1123] Step 3:

[1124] The server updates the order details to the virtual market database.

[1125] Step 4:

[1126] The server determines the outcome of the order and generates a feedback message to report to the user.

[1127] Step 5:

[1128] The server sends the updated trade data and feedback messages to the terminal.

[1129] Step 6:

[1130] The device will display a confirmation and feedback message.

[1131] Community Platform Means

[1132] Action to start a new discussion

[1133] Step 1:

[1134] A user submits a request to start a new discussion: The user opens the app's Community module and presses the "Start a new discussion" button.

[1135] Step 2:

[1136] The device sends the user ID, discussion topic, and content to the server.

[1137] Step 3:

[1138] The server generates a new discussion ID and adds the topic and content to the discussion database.

[1139] Step 4:

[1140] The server notifies active users that a new discussion has been created.

[1141] Step 5:

[1142] The device receives the notification and displays it on the user's screen.

[1143] The process of posting a comment to a discussion

[1144] Step 1:

[1145] A user posts a comment to a discussion. A user opens a specific discussion and enters a comment.

[1146] Step 2:

[1147] The device sends the user ID, discussion ID, and comment content to the server.

[1148] Step 3:

[1149] The server generates a comment ID and adds the comment to the appropriate discussion thread.

[1150] Step 4:

[1151] The server sends the latest discussion thread to the device.

[1152] Step 5:

[1153] The device displays the updated discussion thread.

[1154] Example 1

[1155] 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."

[1156] Conventional financial literacy improvement systems have been considered ineffective for young people due to long learning times, trading requiring actual funds, limited communication methods, etc. The present invention aims to solve these problems and provide a system that allows people to acquire financial knowledge efficiently in a short period of time, safely experience trading, and share knowledge within the community.

[1157] 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.

[1158] In this invention, the server includes means for providing quizzes designed to enable users to learn efficiently in a short period of time, means for providing a simulation of trading financial products in a virtual environment, and means for providing an online forum where users can discuss financial topics, thereby enabling users to acquire financial knowledge efficiently in a short period of time, safely experience trading in a virtual environment, and share their knowledge with other users within the community.

[1159] "User" means any person who uses the System to improve their financial knowledge, participate in virtual trading simulations, or participate in the community forum.

[1160] "Terminal" refers to the device through which a User accesses the System and engages in activities such as answering quizzes, placing virtual trade orders, and discussing in the community forum.

[1161] A "server" is a central processing unit that processes requests from users and devices, provides quizzes, initializes trading sessions, manages community forums, and so on, while interacting with the database.

[1162] A "Quiz" is a multiple-choice or other type of learning assignment designed to help users efficiently learn financial knowledge.

[1163] "Virtual trading" refers to a user simulating the trading of stocks or other financial instruments in a virtual environment without using actual funds.

[1164] A "community forum" is an online platform for users to exchange opinions and discuss financial topics.

[1165] "Progress Management" is a feature that tracks a user's learning progress and selects the next quiz or topic to study.

[1166] An "AI model" is an algorithm that uses machine learning to analyze market trends and user behavior in order to provide investment suggestions and other guidance to users.

[1167] A "discussion thread" is a series of posts initiated by a user in a community forum to exchange opinions or information on a particular topic.

[1168] The present invention is a system aimed at improving financial literacy among young people. The system provides users with multiple means to efficiently acquire financial knowledge, experience trading in a virtual environment, and exchange opinions within a community. Specific embodiments of the present invention are described below.

[1169] Microlearning Vehicles

[1170] This method provides quizzes that allow users to learn financial knowledge effectively in a short amount of time. When a user launches a dedicated application and requests to take a quiz, the device sends this request as an HTTP request to the server. The server uses a MySQL database to check the user's learning progress, selects the next quiz based on the progress, and sends it in JSON format to the device. When the user answers the quiz and sends the answer from the device to the server, the server uses the Pandas library to determine whether the answer is correct or incorrect, creates feedback, and sends it to the device.

[1171] Examples:

[1172] For example, if a user requests from their device to take a quiz on "Introduction to the Stock Market," the server will check their progress and send them multiple choice questions on "Introduction to the Stock Market." After the user answers the quiz and clicks the "Submit Answers" button, the results are sent to the server, which then returns feedback to the device saying, "You got 8 / 10 right, which is very good. Let's learn about the bond market next."

[1173] Virtual Trading Instruments

[1174] This tool provides users with a simulation of trading stocks and other financial instruments in a virtual environment. When a user starts a new trading session from their terminal, the terminal sends this request as an HTTP request to the server. The server uses an AI model using TensorFlow to analyze market trends and sends appropriate trade suggestions in JSON format to the terminal. When a user places an order for virtual stocks, the order is sent from the terminal to the server, which accepts the order and records it in a virtual trading database. The results of the order and trading feedback are sent from the server to the terminal.

[1175] Examples:

[1176] When a user initiates an "invest in technology stocks" request, the device sends the request to the server, which analyzes market trends. The server then sends a suggestion to the device saying, "Technology stocks are rising right now, so try investing in Apple." When the user places an order to buy 100 shares of Apple stock and the order is sent to the server, the server returns feedback to the device saying, "Your order is complete. Technology stocks are still on the rise."

[1177] Community Platform Means

[1178] This method provides an online forum function that allows users to freely start discussions and exchange opinions on financial topics. When a user sends a request to start a new discussion from their device to the server, the server notifies the entire community of the discussion content. When another user posts a comment to the discussion, the comment is sent from the device to the server, and the server adds the comment to the discussion thread and notifies all users of the update.

[1179] Examples:

[1180] When a user starts a new discussion about "Investment Risk Management," the device sends the request to the server, which then sends a notification to the entire community saying, "A user has started a new topic called 'Investment Risk Management.'" When another user posts a comment saying, "Tell me how to diversify risk," the device sends the comment to the server, which adds the comment to the discussion thread and notifies all users.

[1181] Example prompts for generative AI models

[1182] You can ask the generative AI model for a detailed explanation of the system's processing by using the following prompt:

[1183] Example prompt sentence:

[1184] 1. "Please explain the detailed process flow of a microlearning feature that allows users to quickly and efficiently improve their financial literacy."

[1185] 2. Please explain, with specific examples, how users can use the virtual trading feature to learn investment strategies.

[1186] 3. "Please explain the process from starting a discussion on a community platform to other users adding comments."

[1187] As described above, the present invention enables users to acquire financial knowledge efficiently in a short period of time, experience trading in a safe virtual environment, and share their knowledge with other users within the community.

[1188] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1189] Microlearning Vehicles

[1190] Step 1:

[1191] Submit a quiz request

[1192] The user launches the dedicated application on their device and clicks the "Take Quiz" button. The device generates an HTTP request and sends it to the server. The input to this request includes information such as the user ID and the desired field of study.

[1193] Step 2:

[1194] Progress check and quiz selection

[1195] The server processes the received request and retrieves the user's learning progress data from the MySQL database based on the user ID. The server analyzes this data (data processing) and uses a Python script to select the next quiz to study. The output of the selected quiz is sent to the terminal in JSON format.

[1196] Step 3:

[1197] Quiz Answers and Submission

[1198] The user answers the quiz on the device and clicks the "Submit Answer" button. The device sends the answer data to the server as an HTTP POST request. This input includes the user's answer.

[1199] Step 4:

[1200] Correct / incorrect judgment and feedback generation

[1201] The server analyzes the received response data using the Pandas library (data calculation) and determines whether it is correct or incorrect. Based on the analysis results, it generates a feedback message and sends it to the terminal in JSON format. This output includes the evaluation results and what to learn next.

[1202] Step 5:

[1203] Feedback Check

[1204] Users can view feedback on their device and assess their financial knowledge, which will help them plan their future learning.

[1205] Virtual Trading Instruments

[1206] Step 1:

[1207] Trading Session Initialization

[1208] A user clicks a button to start a new trading session in the terminal application. The terminal sends this request to the server as an HTTP request. The input includes the user ID and the desired transaction details.

[1209] Step 2:

[1210] AI guide suggestions

[1211] The server processes the received requests and analyzes market trends using an AI model powered by TensorFlow. Based on the analysis results, the server generates appropriate trade proposals and sends them to the terminal in JSON format. This output contains investment recommendations from the AI.

[1212] Step 3:

[1213] Virtual Stock Orders

[1214] The user places an order for virtual stocks based on the proposed investment strategy. The terminal sends the order to the server as an HTTP POST request. This input contains the order information.

[1215] Step 4:

[1216] Order acceptance and processing

[1217] The server receives the order data and records it in a virtual transaction database. The server analyzes the transaction results and generates appropriate feedback messages. The output is feedback including order confirmation and transaction results.

[1218] Step 5:

[1219] Feedback Check

[1220] Users can view the feedback on their devices and use it to plan their next investment strategy, which will help improve the user's virtual trading experience.

[1221] Community Platform Means

[1222] Step 1:

[1223] Start a discussion

[1224] A user opens the community function on their device, enters a new discussion, and clicks the "Start a discussion" button. The device sends this request to the server as an HTTP POST request. The input includes the user ID and the discussion content.

[1225] Step 2:

[1226] Community Notifications

[1227] Based on the received request, the server saves the discussion content in the community database and generates a notification message to send to the entire community, which notifies the new discussion that has started.

[1228] Step 3:

[1229] Posting and accepting comments

[1230] Other users view the discussion and post comments. The device sends the comment content to the server as an HTTP POST request. This input includes the comment content.

[1231] Step 4:

[1232] Add comments and notifications

[1233] The server accepts the comment data and adds it to the original discussion thread. The server generates updated thread information and notifies all users. The output is a notification containing the latest discussion content.

[1234] Step 5:

[1235] Discussion and Feedback

[1236] Users can use their devices to check the latest discussion updates and rate how their comments were received, which helps them choose their next comment and topic.

[1237] (Application example 1)

[1238] 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."

[1239] To improve the financial literacy of young people, a platform that allows them to learn efficiently in a short amount of time is needed. It is also important to have opportunities to safely gain investment experience without using actual funds. Furthermore, there is a need for an environment where students can deepen their overall knowledge by sharing information and exchanging opinions with other students. However, conventional systems have had difficulty meeting all of these requirements. Therefore, a system that utilizes virtual space and enables interactive and effective learning is needed.

[1240] 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.

[1241] In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, a financial education means in a virtual space, a real-time feedback providing means, and an interactive learning means using prompt sentences, thereby enabling users to efficiently learn financial knowledge in a virtual space, safely gain investment experience, and exchange opinions with other users.

[1242] A "microlearning tool" is a tool that provides quizzes to help people learn financial knowledge efficiently in a short amount of time.

[1243] A "virtual trading vehicle" is a simulated vehicle for trading stocks and other financial instruments in a virtual environment without using actual funds.

[1244] A "community platform vehicle" is a vehicle that provides an online forum function where users can freely initiate discussions and exchange opinions on financial topics.

[1245] The "financial education tool in a virtual space" is an educational tool that allows users to take financial quizzes and conduct virtual trades in a virtual store.

[1246] The "real-time feedback providing means" is a means by which a user can receive immediate feedback on a quiz or trade simulation.

[1247] An "interactive learning method using prompt sentences" is a method that allows users to customize the learning content by inputting specific instructions and progress through learning interactively.

[1248] The present invention provides an effective system for improving financial literacy among young people, which includes a micro-learning means, a virtual trading means, a community platform means, a virtual financial education means, a real-time feedback providing means, and an interactive learning means using prompts.

[1249] Microlearning Vehicles

[1250] In this system, the server manages the user's progress and provides the next quiz to study. When the user takes the quiz, the results are sent to the server, which determines whether the quiz was correct or incorrect and generates feedback to return to the user. This allows the user to gradually improve their financial knowledge.

[1251] For example, if a user takes a quiz on "Intro to the Stock Market," the server provides multiple choice questions on that subject. After the user answers the questions and submits their answers to the server, the server returns feedback like, "Number of answers correct: 8 / 10, very good. Let's learn about the bond market next."

[1252] Virtual Trading Instruments

[1253] The server initiates a virtual trading session and provides the user with investment suggestions from the AI. When the user places an order for virtual stocks, the order is accepted by the server and provided to the user along with feedback from the AI ​​guide.

[1254] For example, when a user starts "investing in technology stocks," the server will suggest to the user, "Technology stocks are rising now, so try investing in high-tech company A." If the user places an order to purchase 100 shares of high-tech company A and the order is successful, the server will return feedback saying, "Your order has been completed. Technology stocks are still on the rise."

[1255] Community Platform Means

[1256] Users can freely start discussions and exchange opinions on financial topics. When a user starts a new discussion, the server notifies the entire community of the discussion content. Furthermore, when other users post comments to the discussion, the server accepts the content and adds it to the thread.

[1257] For example, if a user starts a new discussion about "Investment Risk Management," the server notifies the community, "A user has started a new topic called 'Investment Risk Management.'" If another user posts a comment saying, "Tell me how to diversify risk," the server adds that comment to the discussion thread, making it visible to all users.

[1258] Virtual financial education tools

[1259] The virtual financial education tool provides an environment where users can take financial quizzes and conduct virtual trades in a virtual store, allowing users to safely and effectively improve their financial literacy without using real money.

[1260] Real-time feedback

[1261] This is a means for users to receive immediate feedback on quizzes and trading simulations. The server generates feedback in real time according to the user's actions and provides it to the user.

[1262] Interactive learning tools using prompts

[1263] It is a means for users to customize their learning content and progress interactively by inputting specific instructions. For example, by using prompts such as "I want to take a quiz on introductory stock markets" or "I want to invest in technology stocks," the system can provide users with the optimal learning content and trading experience.

[1264] The system is implemented using hardware such as smartphones, smart glasses, and head-mounted displays, and the software used includes Python, Flask (a server framework), and SQLite (a database).

[1265] This will effectively improve the financial literacy of young people.

[1266] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1267] Step 1:

[1268] The server receives a quiz request from the user's terminal.

[1269] Input: A request from the user's device to "take a quiz."

[1270] Processing: The server checks the database to see the user's progress and selects the next quiz to study.

[1271] Output: Send the selected quiz to the device.

[1272] Step 2:

[1273] The user answers the selected quiz questions.

[1274] Input: Quiz questions sent by the server.

[1275] Process: The user answers the quiz and sends the answers to the server.

[1276] Output: The user's answer data is sent to the server.

[1277] Step 3:

[1278] The server receives the user's answer and determines whether it is correct or incorrect.

[1279] Input: User response data.

[1280] Processing: The server compares the correct answer information with the user's answer and determines whether it is correct or not. It then calculates the number of correct answers and generates feedback.

[1281] Output: Sends feedback information to the user terminal.

[1282] Step 4:

[1283] The server initializes a new trading session.

[1284] Input: "Start a new trading session" request from the user terminal.

[1285] Processing: Initialize the virtual trading environment and generate AI-guided investment recommendations.

[1286] Output: Sends the initialized trading session state and AI-guided suggestions to the user terminal.

[1287] Step 5:

[1288] A user places an order for virtual stock.

[1289] Input: User trade order (e.g., "Buy 100 shares of technology stock").

[1290] Processing: The server accepts user orders and applies them to the virtual trading environment. The AI ​​guide generates feedback based on the latest investment status.

[1291] Output: Sends notification of order completion and feedback on investment status to the user terminal.

[1292] Step 6:

[1293] A user starts a new discussion on the community platform.

[1294] Input: User discussion topic (e.g., "Investment Risk Management").

[1295] Action: The server notifies the community of the new topic and creates a discussion thread.

[1296] Output: Sends a new topic notification to the entire community.

[1297] Step 7:

[1298] Other users post comments to the discussion.

[1299] Input: Comments from other users (e.g., "Tell me how to spread risk.").

[1300] Processing: The server accepts the comment and adds it to the discussion thread.

[1301] Output: Notify all interested users of an updated discussion thread.

[1302] These steps enable users to efficiently learn financial knowledge in a virtual space, safely gain investment experience, and exchange opinions with other users.

[1303] 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.

[1304] The present invention aims to provide a system for improving financial literacy that combines an emotion engine to provide learning content, virtual trade suggestions, and community discussion feedback in response to a user's emotions.

[1305] Combining microlearning tools with emotion engines

[1306] This method provides quizzes that allow users to learn financial knowledge efficiently and quickly. When a user sends a request to take a quiz, the server checks the user's progress and selects and sends the next quiz to the user. At that time, an emotion engine recognizes the user's emotions and generates learning content and feedback according to those emotions.

[1307] For example, if a user is feeling stressed about the "Stock Market Introduction" quiz, the emotion engine will recognize that emotion and the server will provide a quiz with an adjusted difficulty level, and even provide humorous feedback to improve the user's learning experience.

[1308] Combining virtual trading instruments with an emotional engine

[1309] This tool allows users to simulate trading stocks and other financial instruments in a virtual environment without using real funds. When a user starts a new trading session, the server initializes the virtual trading session with AI-guided suggestions. At the same time, an emotion engine recognizes the user's emotions and adjusts appropriate investment suggestions.

[1310] For example, if a user shows a risk-averse emotion, the emotion engine recognizes that emotion and the server suggests low-risk investments. In this way, investment learning based on the user's emotional state becomes possible.

[1311] Combining community platform tools and emotion engines

[1312] This method provides an online forum where users can freely start discussions and exchange opinions on financial topics. When a new discussion is started, the server notifies the entire community of the discussion content. When other users post comments to the discussion, the server accepts the comments and adds them to the thread. At that time, an emotion engine recognizes the user's emotions and generates feedback according to the emotions.

[1313] For example, if a user feels anxious during a discussion about "investment risk management," the emotion engine will recognize that emotion and the server will provide gentle, thoughtful advice on risk management. Other users' comments will also be similarly given feedback that takes their emotions into account, helping to reassure the user.

[1314] As described above, by utilizing an emotion engine, this system can provide a learning experience tailored to the user's emotions. This allows users to efficiently acquire financial knowledge, gain safe trading experience in a virtual environment, and engage in constructive discussions with other users. This system for improving financial literacy incorporates user emotional care, and is expected to be more effective than conventional educational methods.

[1315] The processing flow will be explained below.

[1316] Combining microlearning tools with emotion engines

[1317] Accessing the quiz

[1318] Step 1:

[1319] A user submits a request to access the finance quiz. The user launches the app's quiz learning module and presses the "Access Finance Quiz" button.

[1320] Step 2:

[1321] The device sends the user ID and request information to the server.

[1322] Step 3:

[1323] The server retrieves user progress data to select the next quiz to study based on the user ID.

[1324] Step 4:

[1325] The server uses the camera images and audio data sent from the terminal, and the emotion engine recognizes the user's current emotional state.

[1326] Step 5:

[1327] The server and emotion engine work together to select appropriate quizzes based on the user's progress and emotional state. If the user is feeling stressed, a quiz with adjusted difficulty will be selected.

[1328] Step 6:

[1329] The server transmits the selected quiz and a feedback message corresponding to the emotion to the terminal.

[1330] Step 7:

[1331] The terminal displays a quiz screen and presents the quiz to the user.

[1332] Quiz Answering Process

[1333] Step 1:

[1334] The user answers the quiz and submits the answer.

[1335] Step 2:

[1336] The device sends the user ID, quiz ID, and answer to the server.

[1337] Step 3:

[1338] The server compares the received answer with the correct answer in its database.

[1339] Step 4:

[1340] The server determines whether each question is correct or incorrect and calculates the number of correct answers.

[1341] Step 5:

[1342] When the server creates a feedback message based on the accuracy rate, the emotion engine reconfirms the user's emotional state and adjusts the feedback message.

[1343] Step 6:

[1344] The server sends the generated feedback message to the terminal.

[1345] Step 7:

[1346] The device displays a feedback message.

[1347] Combining virtual trading instruments with an emotional engine

[1348] Process of starting a new trading session

[1349] Step 1:

[1350] A user sends a request to start a new trading session. The user launches the app's trading module and presses the "Start a new trading session" button.

[1351] Step 2:

[1352] The device sends the user ID and request information to the server.

[1353] Step 3:

[1354] The server initializes a virtual trading session, generates a trading session ID, and allocates initial virtual funds to the user.

[1355] Step 4:

[1356] The server uses camera footage and audio data from the device, and the emotion engine recognizes the user's current emotional state.

[1357] Step 5:

[1358] The server consults market data, and the emotion engine generates AI investment recommendations based on the user's emotional state: if the user is risk-averse, the server will suggest low-risk investments.

[1359] Step 6:

[1360] The server sends the generated proposal message to the terminal.

[1361] Step 7:

[1362] The device displays a suggestion message.

[1363] The process of sending a trade order

[1364] Step 1:

[1365] A user submits a virtual trade order, selecting a specific stock and entering the quantity to buy or sell.

[1366] Step 2:

[1367] The terminal sends the user ID and order details (stock name, quantity, buy or sell) to the server.

[1368] Step 3:

[1369] The server updates the order details to the virtual market database.

[1370] Step 4:

[1371] An emotion engine recognizes the user's emotional state as the server determines the outcome of the order and generates feedback messages to report to the user.

[1372] Step 5:

[1373] The server adjusts the feedback message based on the user's emotional state and sends it to the terminal.

[1374] Step 6:

[1375] The device will display a confirmation and feedback message.

[1376] Combining community platform tools and emotion engines

[1377] Action to start a new discussion

[1378] Step 1:

[1379] A user submits a request to start a new discussion: The user opens the app's Community module and presses the "Start a new discussion" button.

[1380] Step 2:

[1381] The device sends the user ID, discussion topic, and content to the server.

[1382] Step 3:

[1383] The server generates a new discussion ID and adds the topic and content to the discussion database.

[1384] Step 4:

[1385] The server notifies active users that a new discussion has been created.

[1386] Step 5:

[1387] The device receives the notification and displays it on the user's screen.

[1388] The process of posting a comment to a discussion

[1389] Step 1:

[1390] A user posts a comment to a discussion. A user opens a specific discussion and enters a comment.

[1391] Step 2:

[1392] The device sends the user ID, discussion ID, and comment content to the server.

[1393] Step 3:

[1394] The server generates a comment ID and adds the comment to the appropriate discussion thread.

[1395] Step 4:

[1396] The server uses the camera images and audio data sent from the terminal, and the emotion engine recognizes the user's current emotional state.

[1397] Step 5:

[1398] The server generates friendly feedback and additional advice based on the user's emotional state.

[1399] Step 6:

[1400] The server sends the latest discussion threads and feedback to the device.

[1401] Step 7:

[1402] The device displays updated discussion threads and feedback.

[1403] Example 2

[1404] 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."

[1405] Conventional financial literacy improvement systems have struggled to provide a learning experience that takes into account the user's emotional state. As a result, users tend to lose interest and the learning experience is inconsistent. Furthermore, in virtual trading and community discussions, it was not possible to provide appropriate support based on each user's emotions. This limited the effectiveness of the system, resulting in problems such as reduced user satisfaction and reduced learning effectiveness.

[1406] 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.

[1407] In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, an emotion recognition means, and a feedback generation means using a generative AI model. This makes it possible to provide learning content and investment suggestions that take the user's emotions into consideration, thereby improving the user's learning experience and satisfaction.

[1408] "Microlearning tools" are tools that provide quizzes and training to help users learn financial knowledge in a short amount of time.

[1409] A "virtual trading instrument" is an instrument that allows users to simulate trading financial instruments in a virtual environment without using real funds.

[1410] A "community platform vehicle" is a vehicle that provides an online forum for users to freely discuss and exchange ideas on financial topics.

[1411] An "emotion recognition means" is a means including technology for recognizing a user's emotional state in real time.

[1412] "Feedback generation means using a generative AI model" refers to a means that uses an AI model to generate feedback according to the user's emotions and learning progress.

[1413] "Means for managing the user's progress and providing the next quiz to study" refers to means for selecting and providing the appropriate next quiz based on the user's learning history and correct answer rate.

[1414] "Means for providing users with investment proposals based on a generative AI model" refers to means for providing users with investment proposals based on their emotional state and risk tolerance during virtual trading using a generative AI model.

[1415] MODE FOR CARRYING OUT THE INVENTION

[1416] This invention is a learning system aimed at improving financial literacy, which combines emotion recognition means and feedback generation means using a generative AI model to provide a learning experience that responds to the user's emotions. This system is mainly composed of the following means:

[1417] 1. Microlearning methods:

[1418] The system provides quiz-style learning content to enable users to learn financial knowledge efficiently and quickly. The server manages the user's progress, selects the next quiz to be studied, and sends it to the user's device. Specifically, the system incorporates an algorithm that selects the appropriate next quiz based on the user's learning history and correct answer rate.

[1419] For example, if a user is taking an "Introduction to the Stock Market" quiz and the emotion recognition means detects that the user is stressed, the server will adjust the difficulty of the quiz according to the user's emotion and generate humorous feedback. An example of a prompt sentence is, "Please explain how you would respond to a user who is stressed by a short quiz to improve financial literacy."

[1420] 2. Virtual trading instruments:

[1421] This feature allows users to simulate trading financial instruments in a virtual environment. The server initializes a virtual trading session at the user's request and provides investment suggestions based on generative AI models. This allows users to safely gain trading experience without using real funds.

[1422] For example, when a user starts a virtual trading session, if the emotion recognition means detects the user's emotional state (e.g., risk aversion), the server generates and provides low-risk investment suggestions based on the results. An example of a prompt sentence is, "Please explain how to make appropriate investment suggestions during a virtual trade to a user who exhibits risk aversion emotions."

[1423] 3. Community Platform Means:

[1424] It provides an online forum where users can freely exchange opinions and discuss financial topics. The server notifies the entire community when a new discussion starts and accepts comments from other users and adds them to the thread.

[1425] For example, if a user starts a discussion about "investment risk management," and the emotion recognition means detects anxiety among the participants, the server will provide gentle advice on risk management to alleviate the anxiety. An example of a prompt sentence is, "Please explain how to respond in an online forum to a user who is anxious about "investment risk management."

[1426] This invention uses facial recognition technology and biometric sensors as emotion recognition methods, and the latest natural language processing technology and machine learning algorithms as generative AI models, making it possible to adapt to the user's emotions and provide individually optimized learning experiences and investment recommendations in real time.

[1427] By using concrete examples, users can efficiently acquire financial knowledge and safely gain experience in virtual trading. In addition, by constructively exchanging opinions with other users through community discussions, users can deepen their practical knowledge. This is expected to improve users' financial literacy.

[1428] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1429] Step 1:

[1430] A user submits a request to take a quiz.

[1431] Input: User request (want to take a quiz)

[1432] Processing: The user clicks the take quiz button in the application, and a request is sent to the server.

[1433] Output: The request data (information about the quiz request) is sent to the server.

[1434] Step 2:

[1435] The server checks the user's progress.

[1436] Input: Request data and user learning history data

[1437] Processing: The server retrieves the user's past learning history and correct answer rate from the database and evaluates their progress.

[1438] Output: Data for selecting the next quiz to study

[1439] Step 3:

[1440] The server sends the selected quiz to the user.

[1441] Input: Selection data

[1442] Processing: The server sends the selected quiz to the terminal based on the user's progress.

[1443] Output: Quiz data

[1444] Step 4:

[1445] The terminal recognizes the user's emotions using an emotion engine.

[1446] Input: Real-time biometric and facial expression data of the user

[1447] Processing: The device uses facial recognition technology and biometric sensors to analyze and recognize the user's emotions in real time.

[1448] Output: Emotion recognition result (e.g., stress state)

[1449] Step 5:

[1450] The server adjusts the difficulty of the quiz and generates feedback.

[1451] Input: Emotion recognition results and quiz data

[1452] Processing: The server adjusts the difficulty of the quiz appropriately based on the emotion recognition results and generates humorous feedback using a generative AI model.

[1453] Output: Adjusted quiz and feedback data

[1454] Step 6:

[1455] A user submits a request for a new virtual trading session.

[1456] Input: User request (want to start virtual trading)

[1457] Processing: The user clicks the application's button to initiate a virtual trade, and a request is sent to the server.

[1458] Output: Request data (information on the virtual trade start request)

[1459] Step 7:

[1460] The server initializes a virtual trading session.

[1461] Input: Request data and market data

[1462] Processing: The server initializes the virtual trading session, retrieves the necessary market data, and generates investment recommendations using the generative AI model.

[1463] Output: Virtual trading session data and investment proposal data

[1464] Step 8:

[1465] The terminal recognizes the user's emotions using an emotion engine.

[1466] Input: Real-time biometric and facial expression data of the user

[1467] Processing: The device uses facial recognition technology and biometric sensors to analyze and recognize the user's emotions in real time.

[1468] Output: Emotion recognition result (e.g., risk aversion)

[1469] Step 9:

[1470] The server coordinates the investment proposals.

[1471] Input: Emotion recognition results and virtual trading session data

[1472] Processing: The server adjusts the risk level of the investment proposal based on the emotion recognition result and provides it to the user.

[1473] Output: Adjusted investment proposal data

[1474] Step 10:

[1475] A user submits a request to start a new discussion.

[1476] Input: User request (want to start a discussion)

[1477] Process: The user clicks the Start Discussion button in the application and a request is sent to the server.

[1478] Output: Request data (information you wish to start a discussion about)

[1479] Step 11:

[1480] The server notifies the entire community of the discussion content.

[1481] Input: Request data and discussion content

[1482] Process: The server notifies the entire community of new discussion content and creates a thread.

[1483] Output: Notification data

[1484] Step 12:

[1485] Other users post comments to the discussion.

[1486] Input: Comment data

[1487] Process: Other users post comments to the discussion thread, which are then sent to the server.

[1488] Output: Comment data

[1489] Step 13:

[1490] The server adds the comment to the thread.

[1491] Input: Comment data

[1492] Processing: The server adds the accepted comment to the discussion thread.

[1493] Output: Updated discussion thread data

[1494] Step 14:

[1495] The device uses an emotion engine to recognize the user's emotions and generate feedback.

[1496] Input: Real-time biometric information, facial expression data, and comment data of the user

[1497] Processing: The device uses facial recognition technology and biometric sensors to analyze emotions and generates feedback using generative AI models.

[1498] Output: Feedback data (e.g., kind words of advice)

[1499] These steps provide a learning experience that improves financial literacy according to the user's progress and emotions.

[1500] (Application example 2)

[1501] 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."

[1502] Conventional financial literacy improvement systems have been unable to provide a learning experience that takes into account the user's emotional state, resulting in reduced learning efficiency. Furthermore, in virtual trading, they have been unable to provide appropriate investment suggestions based on the user's emotions, limiting the effectiveness of the user's learning. Furthermore, in community discussions, it has been difficult to provide feedback that takes into account the user's emotions.

[1503] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, a feedback means based on emotion recognition, and a means for providing interactive learning content. This makes it possible to provide learning content and feedback according to the user's emotional state, thereby effectively supporting the improvement of the user's financial literacy.

[1504] "Microlearning" is an educational method for acquiring knowledge efficiently in a short amount of time.

[1505] "Virtual trading" means the simulated activity of trading financial instruments in a virtual environment without using actual funds.

[1506] A "community platform" is an online space where users can exchange opinions and hold discussions on a specific topic.

[1507] "Emotion recognition" is a technology that identifies a user's emotions in real time from their facial expressions and voice.

[1508] "Feedback means" is a method for providing appropriate information based on the user's behavior or state, and for improving learning or behavior.

[1509] "Interactive learning content provision" is a method of providing learning materials and tasks that change dynamically depending on the user's reactions and situation.

[1510] "Progress management" is the process of tracking a user's learning or work progress and suggesting next tasks or activities to be done.

[1511] "Adjusting the difficulty of a quiz" is a method of changing the difficulty of the quiz provided based on the user's level of understanding and emotional state.

[1512] "Investment proposal adjustment" is a technology that provides optimal investment strategies and policies to users based on the results of user emotion recognition.

[1513] This invention is a system aimed at improving financial literacy, providing a learning experience that responds to the user's emotional state. Specifically, it includes the following processes performed between a server, a terminal, and a user.

[1514] Hardware and software used

[1515] Hardware: Smartphone (high-resolution camera, microphone), server

[1516] software:

[1517] Emotion Recognizer

[1518] Quiz Module (QuizModule)

[1519] Virtual trade simulation (TradeSimulation)

[1520] Community Platform

[1521] Interactive Feedback System

[1522] System processing overview

[1523] 1. Emotion recognition:

[1524] The smartphone's camera and microphone are used to capture the user's facial expressions and voice.

[1525] The captured data is sent to EmotionRecognizer, which identifies emotions in real time.

[1526] 2. Microlearning:

[1527] When a user requests to take a quiz, the server uses QuizModule to check the user's progress, select the next quiz to be studied, and send it.

[1528] At this time, the difficulty level of the quiz is adjusted based on the emotion recognition results.

[1529] 3. Virtual trading:

[1530] When a user starts a new trading session, the server initializes TradeSimulation to provide a virtual investment environment.

[1531] Based on the emotion recognition results, the system makes investment suggestions that are in line with the user's emotional state. For example, if the user indicates a desire to avoid risk, the system will suggest low-risk investments.

[1532] 4. Community Platform:

[1533] When a new discussion is started, the server notifies the entire community via CommunityPlatform.

[1534] It also accepts comments from other users and generates feedback based on emotion recognition results. For example, if a user is feeling anxious during a discussion, the emotion engine will recognize this and provide gentle feedback.

[1535] 5. Providing interactive learning content:

[1536] Based on the emotion recognition results and the user's progress information, the server generates and provides interactive learning content to the user.

[1537] The learning content dynamically changes based on the user's reactions, for example generating humorous feedback if the user is feeling stressed.

[1538] Examples of concrete examples and prompts

[1539] Examples:

[1540] If a user feels stressed while taking the "Stock Market 101" quiz, the emotion recognition engine will recognize that emotion and the server will provide a quiz with adjusted difficulty.

[1541] If a user feels anxious during a discussion about "investment risk management," the emotion engine will recognize that emotion and the server will provide gentle risk management advice.

[1542] Example prompt sentence:

[1543] "If your users are stressed, give them gentle feedback."

[1544] "If users are confident, offer them a high-difficulty quiz."

[1545] In this way, the financial literacy improvement system linked to the emotion engine improves the user's learning experience and enables them to acquire financial knowledge efficiently.

[1546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1547] Step 1:

[1548] A user launches a smartphone application and requests learning content. The smartphone's camera and microphone are used to capture the user's facial expressions and voice, which are then sent to EmotionRecognizer. EmotionRecognizer processes this data and identifies the user's emotions. The input is the user's facial and voice data, and the output is their emotional state (e.g., stressed, relaxed).

[1549] Step 2:

[1550] The server receives the emotional state sent from EmotionRecognizer and checks the user's learning progress information (e.g., the results and progress of previously completed quizzes). It then sends the emotional state and progress information to QuizModule to select the next quiz content to study. The input is the user's emotional state and progress information, and the output is the content of the next quiz to study.

[1551] Step 3:

[1552] QuizModule adjusts the difficulty of the next quiz based on the emotional state and progress information. For example, if the emotional state is stressed, it lowers the difficulty, and if the emotional state is relaxed, it raises the difficulty. The adjusted quiz content is sent to the server. The input is the emotional state and progress information, and the output is the adjusted quiz content.

[1553] Step 4:

[1554] When a user answers a quiz, the results are sent to the server and progress information is updated. At the same time, the InteractiveFeedback system generates interactive feedback based on the user's emotional state and the quiz results. For example, a humorous compliment may be given if the answer is correct. The input is the quiz result and emotional state, and the output is the generated feedback.

[1555] Step 5:

[1556] When a user requests to start a virtual trading session, the server initializes TradeSimulation. Based on the user's emotional state, the AI ​​makes investment suggestions. For example, if the user indicates a risk-averse sentiment, it will suggest low-risk investments. The input is the emotional state, and the output is a proposed investment scenario.

[1557] Step 6:

[1558] A user accesses the community platform and starts a new discussion or posts a comment to an existing discussion. The server notifies other users based on the content and emotional state of the comment, and generates appropriate feedback and adds it to the thread. The input is the discussion content and emotional state, and the output is the generated feedback and notification.

[1559] Step 7:

[1560] After all the processing is completed, the server saves the updated user progress information in storage and prepares it for the next access. This allows the user to continue learning. The input is the updated progress information, and the output is the saved data.

[1561] The above processing steps provide a learning experience based on the user's emotional state, and a system is realized that effectively supports the improvement of financial literacy.

[1562] 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.

[1563] 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.

[1564] 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.

[1565] [Fourth embodiment]

[1566] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1567] 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.

[1568] 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).

[1569] 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.

[1570] 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.

[1571] 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).

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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."

[1579] The present invention provides an effective system for improving financial literacy among young people, which includes three main tools: a micro-learning tool, a virtual trading tool, and a community platform tool.

[1580] Microlearning Vehicles

[1581] This method provides quizzes that allow users to learn financial knowledge efficiently in a short amount of time. When a user sends a request from their terminal to take a quiz, the server checks the user's progress and selects and sends the next quiz that the user should study. When the user answers the quiz, the results are sent to the server, which determines whether the answer is correct or incorrect, and generates and returns feedback. The user receives the feedback and can gradually improve their financial knowledge.

[1582] For example, if a user takes a quiz on "Intro to the Stock Market," the server provides multiple-choice questions on that subject. The user answers the questions and their answers are sent to the server. The server evaluates the answers and provides feedback such as, "Correct answers: 8 / 10, very good. Let's learn about the bond market next."

[1583] Virtual Trading Instruments

[1584] This method provides a simulation for a user to trade stocks and other financial instruments in a virtual environment without using real funds. When a user initiates a new trading session through a terminal, the server initializes the virtual trading session with suggestions from an AI guide. The user places an order for virtual stocks, which is accepted by the server and provided to the user along with feedback from the AI ​​guide.

[1585] For example, if a user initiates "Invest in Technology Stocks," the server may suggest to the user, "Technology stocks are rising right now, so try investing in Apple." If the user places an order to buy 100 shares of Apple and the order is successful, the server may return feedback saying, "Your order is complete. Technology stocks are still on the rise."

[1586] Community Platform Means

[1587] This method provides an online forum function where users can freely start discussions and exchange opinions on financial topics. When a user starts a new discussion, the server notifies the entire community of the discussion content. Furthermore, when other users post comments to the discussion, the content is accepted by the server and added to the thread.

[1588] For example, if a user starts a new discussion about "Investment Risk Management," the server notifies the community, "A user has started a new topic called 'Investment Risk Management.'" If another user posts a comment saying, "Tell me how to diversify risk," the server adds that comment to the discussion thread, making it visible to all users.

[1589] This system allows users to acquire financial knowledge efficiently in a short period of time, safely trade in a virtual environment, and share their knowledge with other users within the community. This will improve financial literacy and enable future asset growth and risk management skills.

[1590] The processing flow will be explained below.

[1591] Microlearning Vehicles

[1592] Accessing the quiz

[1593] Step 1:

[1594] A user submits a request to access the finance quiz. The user launches the app's quiz learning module and presses the "Access Finance Quiz" button.

[1595] Step 2:

[1596] The device sends the user ID and request information to the server.

[1597] Step 3:

[1598] The server selects the next quiz to study based on the user's ID. The server reads the user's learning history from a database and runs an algorithm to determine the next quiz to proceed to.

[1599] Step 4:

[1600] The server sends the selected quiz questions and choices to the terminal.

[1601] Step 5:

[1602] The terminal displays a quiz screen and presents the quiz to the user.

[1603] Quiz Answering Process

[1604] Step 1:

[1605] The user answers the quiz and submits the answer.

[1606] Step 2:

[1607] The device sends the user ID, quiz ID, and answer to the server.

[1608] Step 3:

[1609] The server compares the received answer with the correct answer in its database.

[1610] Step 4:

[1611] The server determines whether each question is correct or incorrect and calculates the number of correct answers.

[1612] Step 5:

[1613] The server generates feedback messages based on the percentage of correct answers and updates the user's progress.

[1614] Step 6:

[1615] The server sends the generated feedback message to the terminal.

[1616] Step 7:

[1617] The device displays a feedback message.

[1618] Virtual Trading Instruments

[1619] Process of starting a new trading session

[1620] Step 1:

[1621] A user sends a request to start a new trading session. The user launches the app's trading module and presses the "Start a new trading session" button.

[1622] Step 2:

[1623] The device sends the user ID and request information to the server.

[1624] Step 3:

[1625] The server initializes a virtual trading session. The server generates a new trading session ID and allocates an initial virtual fund amount to the user.

[1626] Step 4:

[1627] The server references current market data and runs AI algorithms to generate investment recommendations.

[1628] Step 5:

[1629] The server sends the generated proposal message to the terminal.

[1630] Step 6:

[1631] The device displays a suggestion message.

[1632] The process of sending a trade order

[1633] Step 1:

[1634] A user submits a virtual trade order, selecting a specific stock and entering the quantity to buy or sell.

[1635] Step 2:

[1636] The terminal sends the user ID and order details (stock name, quantity, buy or sell) to the server.

[1637] Step 3:

[1638] The server updates the order details to the virtual market database.

[1639] Step 4:

[1640] The server determines the outcome of the order and generates a feedback message to report to the user.

[1641] Step 5:

[1642] The server sends the updated trade data and feedback messages to the terminal.

[1643] Step 6:

[1644] The device will display a confirmation and feedback message.

[1645] Community Platform Means

[1646] Action to start a new discussion

[1647] Step 1:

[1648] A user submits a request to start a new discussion: The user opens the app's Community module and presses the "Start a new discussion" button.

[1649] Step 2:

[1650] The device sends the user ID, discussion topic, and content to the server.

[1651] Step 3:

[1652] The server generates a new discussion ID and adds the topic and content to the discussion database.

[1653] Step 4:

[1654] The server notifies active users that a new discussion has been created.

[1655] Step 5:

[1656] The device receives the notification and displays it on the user's screen.

[1657] The process of posting a comment to a discussion

[1658] Step 1:

[1659] A user posts a comment to a discussion. A user opens a specific discussion and enters a comment.

[1660] Step 2:

[1661] The device sends the user ID, discussion ID, and comment content to the server.

[1662] Step 3:

[1663] The server generates a comment ID and adds the comment to the appropriate discussion thread.

[1664] Step 4:

[1665] The server sends the latest discussion thread to the device.

[1666] Step 5:

[1667] The device displays the updated discussion thread.

[1668] Example 1

[1669] 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."

[1670] Conventional financial literacy improvement systems have been considered ineffective for young people due to long learning times, trading requiring actual funds, limited communication methods, etc. The present invention aims to solve these problems and provide a system that allows people to acquire financial knowledge efficiently in a short period of time, safely experience trading, and share knowledge within the community.

[1671] 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.

[1672] In this invention, the server includes means for providing quizzes designed to enable users to learn efficiently in a short period of time, means for providing a simulation of trading financial products in a virtual environment, and means for providing an online forum where users can discuss financial topics, thereby enabling users to acquire financial knowledge efficiently in a short period of time, safely experience trading in a virtual environment, and share their knowledge with other users within the community.

[1673] "User" means any person who uses the System to improve their financial knowledge, participate in virtual trading simulations, or participate in the community forum.

[1674] "Terminal" refers to the device through which a User accesses the System and engages in activities such as answering quizzes, placing virtual trade orders, and discussing in the community forum.

[1675] A "server" is a central processing unit that processes requests from users and devices, provides quizzes, initializes trading sessions, manages community forums, and so on, while interacting with the database.

[1676] A "Quiz" is a multiple-choice or other type of learning assignment designed to help users efficiently learn financial knowledge.

[1677] "Virtual trading" refers to a user simulating the trading of stocks or other financial instruments in a virtual environment without using actual funds.

[1678] A "community forum" is an online platform for users to exchange opinions and discuss financial topics.

[1679] "Progress Management" is a feature that tracks a user's learning progress and selects the next quiz or topic to study.

[1680] An "AI model" is an algorithm that uses machine learning to analyze market trends and user behavior in order to provide investment suggestions and other guidance to users.

[1681] A "discussion thread" is a series of posts initiated by a user in a community forum to exchange opinions or information on a particular topic.

[1682] The present invention is a system aimed at improving financial literacy among young people. The system provides users with multiple means to efficiently acquire financial knowledge, experience trading in a virtual environment, and exchange opinions within a community. Specific embodiments of the present invention are described below.

[1683] Microlearning Vehicles

[1684] This method provides quizzes that allow users to learn financial knowledge effectively in a short amount of time. When a user launches a dedicated application and requests to take a quiz, the device sends this request as an HTTP request to the server. The server uses a MySQL database to check the user's learning progress, selects the next quiz based on the progress, and sends it in JSON format to the device. When the user answers the quiz and sends the answer from the device to the server, the server uses the Pandas library to determine whether the answer is correct or incorrect, creates feedback, and sends it to the device.

[1685] Examples:

[1686] For example, if a user requests from their device to take a quiz on "Introduction to the Stock Market," the server will check their progress and send them multiple choice questions on "Introduction to the Stock Market." After the user answers the quiz and clicks the "Submit Answers" button, the results are sent to the server, which then returns feedback to the device saying, "You got 8 / 10 right, which is very good. Let's learn about the bond market next."

[1687] Virtual Trading Instruments

[1688] This tool provides users with a simulation of trading stocks and other financial instruments in a virtual environment. When a user starts a new trading session from their terminal, the terminal sends this request as an HTTP request to the server. The server uses an AI model using TensorFlow to analyze market trends and sends appropriate trade suggestions in JSON format to the terminal. When a user places an order for virtual stocks, the order is sent from the terminal to the server, which accepts the order and records it in a virtual trading database. The results of the order and trading feedback are sent from the server to the terminal.

[1689] Examples:

[1690] When a user initiates an "invest in technology stocks" request, the device sends the request to the server, which analyzes market trends. The server then sends a suggestion to the device saying, "Technology stocks are rising right now, so try investing in Apple." When the user places an order to buy 100 shares of Apple stock and the order is sent to the server, the server returns feedback to the device saying, "Your order is complete. Technology stocks are still on the rise."

[1691] Community Platform Means

[1692] This method provides an online forum function that allows users to freely start discussions and exchange opinions on financial topics. When a user sends a request to start a new discussion from their device to the server, the server notifies the entire community of the discussion content. When another user posts a comment to the discussion, the comment is sent from the device to the server, and the server adds the comment to the discussion thread and notifies all users of the update.

[1693] Examples:

[1694] When a user starts a new discussion about "Investment Risk Management," the device sends the request to the server, which then sends a notification to the entire community saying, "A user has started a new topic called 'Investment Risk Management.'" When another user posts a comment saying, "Tell me how to diversify risk," the device sends the comment to the server, which adds the comment to the discussion thread and notifies all users.

[1695] Example prompts for generative AI models

[1696] You can ask the generative AI model for a detailed explanation of the system's processing by using the following prompt:

[1697] Example prompt sentence:

[1698] 1. "Please explain the detailed process flow of a microlearning feature that allows users to quickly and efficiently improve their financial literacy."

[1699] 2. Please explain, with specific examples, how users can use the virtual trading feature to learn investment strategies.

[1700] 3. "Please explain the process from starting a discussion on a community platform to other users adding comments."

[1701] As described above, the present invention enables users to acquire financial knowledge efficiently in a short period of time, experience trading in a safe virtual environment, and share their knowledge with other users within the community.

[1702] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1703] Microlearning Vehicles

[1704] Step 1:

[1705] Submit a quiz request

[1706] The user launches the dedicated application on their device and clicks the "Take Quiz" button. The device generates an HTTP request and sends it to the server. The input to this request includes information such as the user ID and the desired field of study.

[1707] Step 2:

[1708] Progress check and quiz selection

[1709] The server processes the received request and retrieves the user's learning progress data from the MySQL database based on the user ID. The server analyzes this data (data processing) and uses a Python script to select the next quiz to study. The output of the selected quiz is sent to the terminal in JSON format.

[1710] Step 3:

[1711] Quiz Answers and Submission

[1712] The user answers the quiz on the device and clicks the "Submit Answer" button. The device sends the answer data to the server as an HTTP POST request. This input includes the user's answer.

[1713] Step 4:

[1714] Correct / incorrect judgment and feedback generation

[1715] The server analyzes the received response data using the Pandas library (data calculation) and determines whether it is correct or incorrect. Based on the analysis results, it generates a feedback message and sends it to the terminal in JSON format. This output includes the evaluation results and what to learn next.

[1716] Step 5:

[1717] Feedback Check

[1718] Users can view feedback on their device and assess their financial knowledge, which will help them plan their future learning.

[1719] Virtual Trading Instruments

[1720] Step 1:

[1721] Trading Session Initialization

[1722] A user clicks a button to start a new trading session in the terminal application. The terminal sends this request to the server as an HTTP request. The input includes the user ID and the desired transaction details.

[1723] Step 2:

[1724] AI guide suggestions

[1725] The server processes the received requests and analyzes market trends using an AI model powered by TensorFlow. Based on the analysis results, the server generates appropriate trade proposals and sends them to the terminal in JSON format. This output contains investment recommendations from the AI.

[1726] Step 3:

[1727] Virtual Stock Orders

[1728] The user places an order for virtual stocks based on the proposed investment strategy. The terminal sends the order to the server as an HTTP POST request. This input contains the order information.

[1729] Step 4:

[1730] Order acceptance and processing

[1731] The server receives the order data and records it in a virtual transaction database. The server analyzes the transaction results and generates appropriate feedback messages. The output is feedback including order confirmation and transaction results.

[1732] Step 5:

[1733] Feedback Check

[1734] Users can view the feedback on their devices and use it to plan their next investment strategy, which will help improve the user's virtual trading experience.

[1735] Community Platform Means

[1736] Step 1:

[1737] Start a discussion

[1738] A user opens the community function on their device, enters a new discussion, and clicks the "Start a discussion" button. The device sends this request to the server as an HTTP POST request. The input includes the user ID and the discussion content.

[1739] Step 2:

[1740] Community Notifications

[1741] Based on the received request, the server saves the discussion content in the community database and generates a notification message to send to the entire community, which notifies the new discussion that has started.

[1742] Step 3:

[1743] Posting and accepting comments

[1744] Other users view the discussion and post comments. The device sends the comment content to the server as an HTTP POST request. This input includes the comment content.

[1745] Step 4:

[1746] Add comments and notifications

[1747] The server accepts the comment data and adds it to the original discussion thread. The server generates updated thread information and notifies all users. The output is a notification containing the latest discussion content.

[1748] Step 5:

[1749] Discussion and Feedback

[1750] Users can use their devices to check the latest discussion updates and rate how their comments were received, which helps them choose their next comment and topic.

[1751] (Application example 1)

[1752] 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."

[1753] To improve the financial literacy of young people, a platform that allows them to learn efficiently in a short amount of time is needed. It is also important to have opportunities to safely gain investment experience without using actual funds. Furthermore, there is a need for an environment where students can deepen their overall knowledge by sharing information and exchanging opinions with other students. However, conventional systems have had difficulty meeting all of these requirements. Therefore, a system that utilizes virtual space and enables interactive and effective learning is needed.

[1754] 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.

[1755] In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, a financial education means in a virtual space, a real-time feedback providing means, and an interactive learning means using prompt sentences, thereby enabling users to efficiently learn financial knowledge in a virtual space, safely gain investment experience, and exchange opinions with other users.

[1756] A "microlearning tool" is a tool that provides quizzes to help people learn financial knowledge efficiently in a short amount of time.

[1757] A "virtual trading vehicle" is a simulated vehicle for trading stocks and other financial instruments in a virtual environment without using actual funds.

[1758] A "community platform vehicle" is a vehicle that provides an online forum function where users can freely initiate discussions and exchange opinions on financial topics.

[1759] The "financial education tool in a virtual space" is an educational tool that allows users to take financial quizzes and conduct virtual trades in a virtual store.

[1760] The "real-time feedback providing means" is a means by which a user can receive immediate feedback on a quiz or trade simulation.

[1761] An "interactive learning method using prompt sentences" is a method that allows users to customize the learning content by inputting specific instructions and progress through learning interactively.

[1762] The present invention provides an effective system for improving financial literacy among young people, which includes a micro-learning means, a virtual trading means, a community platform means, a virtual financial education means, a real-time feedback providing means, and an interactive learning means using prompts.

[1763] Microlearning Vehicles

[1764] In this system, the server manages the user's progress and provides the next quiz to study. When the user takes the quiz, the results are sent to the server, which determines whether the quiz was correct or incorrect and generates feedback to return to the user. This allows the user to gradually improve their financial knowledge.

[1765] For example, if a user takes a quiz on "Intro to the Stock Market," the server provides multiple choice questions on that subject. After the user answers the questions and submits their answers to the server, the server returns feedback like, "Number of answers correct: 8 / 10, very good. Let's learn about the bond market next."

[1766] Virtual Trading Instruments

[1767] The server initiates a virtual trading session and provides the user with investment suggestions from the AI. When the user places an order for virtual stocks, the order is accepted by the server and provided to the user along with feedback from the AI ​​guide.

[1768] For example, when a user starts "investing in technology stocks," the server will suggest to the user, "Technology stocks are rising now, so try investing in high-tech company A." If the user places an order to purchase 100 shares of high-tech company A and the order is successful, the server will return feedback saying, "Your order has been completed. Technology stocks are still on the rise."

[1769] Community Platform Means

[1770] Users can freely start discussions and exchange opinions on financial topics. When a user starts a new discussion, the server notifies the entire community of the discussion content. Furthermore, when other users post comments to the discussion, the server accepts the content and adds it to the thread.

[1771] For example, if a user starts a new discussion about "Investment Risk Management," the server notifies the community, "A user has started a new topic called 'Investment Risk Management.'" If another user posts a comment saying, "Tell me how to diversify risk," the server adds that comment to the discussion thread, making it visible to all users.

[1772] Virtual financial education tools

[1773] The virtual financial education tool provides an environment where users can take financial quizzes and conduct virtual trades in a virtual store, allowing users to safely and effectively improve their financial literacy without using real money.

[1774] Real-time feedback

[1775] This is a means for users to receive immediate feedback on quizzes and trading simulations. The server generates feedback in real time according to the user's actions and provides it to the user.

[1776] Interactive learning tools using prompts

[1777] It is a means for users to customize their learning content and progress interactively by inputting specific instructions. For example, by using prompts such as "I want to take a quiz on introductory stock markets" or "I want to invest in technology stocks," the system can provide users with the optimal learning content and trading experience.

[1778] The system is implemented using hardware such as smartphones, smart glasses, and head-mounted displays, and the software used includes Python, Flask (a server framework), and SQLite (a database).

[1779] This will effectively improve the financial literacy of young people.

[1780] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1781] Step 1:

[1782] The server receives a quiz request from the user's terminal.

[1783] Input: A request from the user's device to "take a quiz."

[1784] Processing: The server checks the database to see the user's progress and selects the next quiz to study.

[1785] Output: Send the selected quiz to the device.

[1786] Step 2:

[1787] The user answers the selected quiz questions.

[1788] Input: Quiz questions sent by the server.

[1789] Process: The user answers the quiz and sends the answers to the server.

[1790] Output: The user's answer data is sent to the server.

[1791] Step 3:

[1792] The server receives the user's answer and determines whether it is correct or incorrect.

[1793] Input: User response data.

[1794] Processing: The server compares the correct answer information with the user's answer and determines whether it is correct or not. It then calculates the number of correct answers and generates feedback.

[1795] Output: Sends feedback information to the user terminal.

[1796] Step 4:

[1797] The server initializes a new trading session.

[1798] Input: "Start a new trading session" request from the user terminal.

[1799] Processing: Initialize the virtual trading environment and generate AI-guided investment recommendations.

[1800] Output: Sends the initialized trading session state and AI-guided suggestions to the user terminal.

[1801] Step 5:

[1802] A user places an order for virtual stock.

[1803] Input: User trade order (e.g., "Buy 100 shares of technology stock").

[1804] Processing: The server accepts user orders and applies them to the virtual trading environment. The AI ​​guide generates feedback based on the latest investment status.

[1805] Output: Sends notification of order completion and feedback on investment status to the user terminal.

[1806] Step 6:

[1807] A user starts a new discussion on the community platform.

[1808] Input: User discussion topic (e.g., "Investment Risk Management").

[1809] Action: The server notifies the community of the new topic and creates a discussion thread.

[1810] Output: Sends a new topic notification to the entire community.

[1811] Step 7:

[1812] Other users post comments to the discussion.

[1813] Input: Comments from other users (e.g., "Tell me how to spread risk.").

[1814] Processing: The server accepts the comment and adds it to the discussion thread.

[1815] Output: Notify all interested users of an updated discussion thread.

[1816] These steps enable users to efficiently learn financial knowledge in a virtual space, safely gain investment experience, and exchange opinions with other users.

[1817] 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.

[1818] The present invention aims to provide a system for improving financial literacy that combines an emotion engine to provide learning content, virtual trade suggestions, and community discussion feedback in response to a user's emotions.

[1819] Combining microlearning tools with emotion engines

[1820] This method provides quizzes that allow users to learn financial knowledge efficiently and quickly. When a user sends a request to take a quiz, the server checks the user's progress and selects and sends the next quiz to the user. At that time, an emotion engine recognizes the user's emotions and generates learning content and feedback according to those emotions.

[1821] For example, if a user is feeling stressed about the "Stock Market Introduction" quiz, the emotion engine will recognize that emotion and the server will provide a quiz with an adjusted difficulty level, and even provide humorous feedback to improve the user's learning experience.

[1822] Combining virtual trading instruments with an emotional engine

[1823] This tool allows users to simulate trading stocks and other financial instruments in a virtual environment without using real funds. When a user starts a new trading session, the server initializes the virtual trading session with AI-guided suggestions. At the same time, an emotion engine recognizes the user's emotions and adjusts appropriate investment suggestions.

[1824] For example, if a user shows a risk-averse emotion, the emotion engine recognizes that emotion and the server suggests low-risk investments. In this way, investment learning based on the user's emotional state becomes possible.

[1825] Combining community platform tools and emotion engines

[1826] This method provides an online forum where users can freely start discussions and exchange opinions on financial topics. When a new discussion is started, the server notifies the entire community of the discussion content. When other users post comments to the discussion, the server accepts the comments and adds them to the thread. At that time, an emotion engine recognizes the user's emotions and generates feedback according to the emotions.

[1827] For example, if a user feels anxious during a discussion about "investment risk management," the emotion engine will recognize that emotion and the server will provide gentle, thoughtful advice on risk management. Other users' comments will also be similarly given feedback that takes their emotions into account, helping to reassure the user.

[1828] As described above, by utilizing an emotion engine, this system can provide a learning experience tailored to the user's emotions. This allows users to efficiently acquire financial knowledge, gain safe trading experience in a virtual environment, and engage in constructive discussions with other users. This system for improving financial literacy incorporates user emotional care, and is expected to be more effective than conventional educational methods.

[1829] The processing flow will be explained below.

[1830] Combining microlearning tools with emotion engines

[1831] Accessing the quiz

[1832] Step 1:

[1833] A user submits a request to access the finance quiz. The user launches the app's quiz learning module and presses the "Access Finance Quiz" button.

[1834] Step 2:

[1835] The device sends the user ID and request information to the server.

[1836] Step 3:

[1837] The server retrieves user progress data to select the next quiz to study based on the user ID.

[1838] Step 4:

[1839] The server uses the camera images and audio data sent from the terminal, and the emotion engine recognizes the user's current emotional state.

[1840] Step 5:

[1841] The server and emotion engine work together to select appropriate quizzes based on the user's progress and emotional state. If the user is feeling stressed, a quiz with adjusted difficulty will be selected.

[1842] Step 6:

[1843] The server transmits the selected quiz and a feedback message corresponding to the emotion to the terminal.

[1844] Step 7:

[1845] The terminal displays a quiz screen and presents the quiz to the user.

[1846] Quiz Answering Process

[1847] Step 1:

[1848] The user answers the quiz and submits the answer.

[1849] Step 2:

[1850] The device sends the user ID, quiz ID, and answer to the server.

[1851] Step 3:

[1852] The server compares the received answer with the correct answer in its database.

[1853] Step 4:

[1854] The server determines whether each question is correct or incorrect and calculates the number of correct answers.

[1855] Step 5:

[1856] When the server creates a feedback message based on the accuracy rate, the emotion engine reconfirms the user's emotional state and adjusts the feedback message.

[1857] Step 6:

[1858] The server sends the generated feedback message to the terminal.

[1859] Step 7:

[1860] The device displays a feedback message.

[1861] Combining virtual trading instruments with an emotional engine

[1862] Process of starting a new trading session

[1863] Step 1:

[1864] A user sends a request to start a new trading session. The user launches the app's trading module and presses the "Start a new trading session" button.

[1865] Step 2:

[1866] The device sends the user ID and request information to the server.

[1867] Step 3:

[1868] The server initializes a virtual trading session, generates a trading session ID, and allocates initial virtual funds to the user.

[1869] Step 4:

[1870] The server uses camera footage and audio data from the device, and the emotion engine recognizes the user's current emotional state.

[1871] Step 5:

[1872] The server consults market data, and the emotion engine generates AI investment recommendations based on the user's emotional state: if the user is risk-averse, the server will suggest low-risk investments.

[1873] Step 6:

[1874] The server sends the generated proposal message to the terminal.

[1875] Step 7:

[1876] The device displays a suggestion message.

[1877] The process of sending a trade order

[1878] Step 1:

[1879] A user submits a virtual trade order, selecting a specific stock and entering the quantity to buy or sell.

[1880] Step 2:

[1881] The terminal sends the user ID and order details (stock name, quantity, buy or sell) to the server.

[1882] Step 3:

[1883] The server updates the order details to the virtual market database.

[1884] Step 4:

[1885] An emotion engine recognizes the user's emotional state as the server determines the outcome of the order and generates feedback messages to report to the user.

[1886] Step 5:

[1887] The server adjusts the feedback message based on the user's emotional state and sends it to the terminal.

[1888] Step 6:

[1889] The device will display a confirmation and feedback message.

[1890] Combining community platform tools and emotion engines

[1891] Action to start a new discussion

[1892] Step 1:

[1893] A user submits a request to start a new discussion: The user opens the app's Community module and presses the "Start a new discussion" button.

[1894] Step 2:

[1895] The device sends the user ID, discussion topic, and content to the server.

[1896] Step 3:

[1897] The server generates a new discussion ID and adds the topic and content to the discussion database.

[1898] Step 4:

[1899] The server notifies active users that a new discussion has been created.

[1900] Step 5:

[1901] The device receives the notification and displays it on the user's screen.

[1902] The process of posting a comment to a discussion

[1903] Step 1:

[1904] A user posts a comment to a discussion. A user opens a specific discussion and enters a comment.

[1905] Step 2:

[1906] The device sends the user ID, discussion ID, and comment content to the server.

[1907] Step 3:

[1908] The server generates a comment ID and adds the comment to the appropriate discussion thread.

[1909] Step 4:

[1910] The server uses the camera images and audio data sent from the terminal, and the emotion engine recognizes the user's current emotional state.

[1911] Step 5:

[1912] The server generates friendly feedback and additional advice based on the user's emotional state.

[1913] Step 6:

[1914] The server sends the latest discussion threads and feedback to the device.

[1915] Step 7:

[1916] The device displays updated discussion threads and feedback.

[1917] Example 2

[1918] 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."

[1919] Conventional financial literacy improvement systems have struggled to provide a learning experience that takes into account the user's emotional state. As a result, users tend to lose interest and the learning experience is inconsistent. Furthermore, in virtual trading and community discussions, it was not possible to provide appropriate support based on each user's emotions. This limited the effectiveness of the system, resulting in problems such as reduced user satisfaction and reduced learning effectiveness.

[1920] 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.

[1921] In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, an emotion recognition means, and a feedback generation means using a generative AI model. This makes it possible to provide learning content and investment suggestions that take the user's emotions into consideration, thereby improving the user's learning experience and satisfaction.

[1922] "Microlearning tools" are tools that provide quizzes and training to help users learn financial knowledge in a short amount of time.

[1923] A "virtual trading instrument" is an instrument that allows users to simulate trading financial instruments in a virtual environment without using real funds.

[1924] A "community platform vehicle" is a vehicle that provides an online forum for users to freely discuss and exchange ideas on financial topics.

[1925] An "emotion recognition means" is a means including technology for recognizing a user's emotional state in real time.

[1926] "Feedback generation means using a generative AI model" refers to a means that uses an AI model to generate feedback according to the user's emotions and learning progress.

[1927] "Means for managing the user's progress and providing the next quiz to study" refers to means for selecting and providing the appropriate next quiz based on the user's learning history and correct answer rate.

[1928] "Means for providing users with investment proposals based on a generative AI model" refers to means for providing users with investment proposals based on their emotional state and risk tolerance during virtual trading using a generative AI model.

[1929] MODE FOR CARRYING OUT THE INVENTION

[1930] This invention is a learning system aimed at improving financial literacy, which combines emotion recognition means and feedback generation means using a generative AI model to provide a learning experience that responds to the user's emotions. This system is mainly composed of the following means:

[1931] 1. Microlearning methods:

[1932] The system provides quiz-style learning content to enable users to learn financial knowledge efficiently and quickly. The server manages the user's progress, selects the next quiz to be studied, and sends it to the user's device. Specifically, the system incorporates an algorithm that selects the appropriate next quiz based on the user's learning history and correct answer rate.

[1933] For example, if a user is taking an "Introduction to the Stock Market" quiz and the emotion recognition means detects that the user is stressed, the server will adjust the difficulty of the quiz according to the user's emotion and generate humorous feedback. An example of a prompt sentence is, "Please explain how you would respond to a user who is stressed by a short quiz to improve financial literacy."

[1934] 2. Virtual trading instruments:

[1935] This feature allows users to simulate trading financial instruments in a virtual environment. The server initializes a virtual trading session at the user's request and provides investment suggestions based on generative AI models. This allows users to safely gain trading experience without using real funds.

[1936] For example, when a user starts a virtual trading session, if the emotion recognition means detects the user's emotional state (e.g., risk aversion), the server generates and provides low-risk investment suggestions based on the results. An example of a prompt sentence is, "Please explain how to make appropriate investment suggestions during a virtual trade to a user who exhibits risk aversion emotions."

[1937] 3. Community Platform Means:

[1938] It provides an online forum where users can freely exchange opinions and discuss financial topics. The server notifies the entire community when a new discussion starts and accepts comments from other users and adds them to the thread.

[1939] For example, if a user starts a discussion about "investment risk management," and the emotion recognition means detects anxiety among the participants, the server will provide gentle advice on risk management to alleviate the anxiety. An example of a prompt sentence is, "Please explain how to respond in an online forum to a user who is anxious about "investment risk management."

[1940] This invention uses facial recognition technology and biometric sensors as emotion recognition methods, and the latest natural language processing technology and machine learning algorithms as generative AI models, making it possible to adapt to the user's emotions and provide individually optimized learning experiences and investment recommendations in real time.

[1941] By using concrete examples, users can efficiently acquire financial knowledge and safely gain experience in virtual trading. In addition, by constructively exchanging opinions with other users through community discussions, users can deepen their practical knowledge. This is expected to improve users' financial literacy.

[1942] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1943] Step 1:

[1944] A user submits a request to take a quiz.

[1945] Input: User request (want to take a quiz)

[1946] Processing: The user clicks the take quiz button in the application, and a request is sent to the server.

[1947] Output: The request data (information about the quiz request) is sent to the server.

[1948] Step 2:

[1949] The server checks the user's progress.

[1950] Input: Request data and user learning history data

[1951] Processing: The server retrieves the user's past learning history and correct answer rate from the database and evaluates their progress.

[1952] Output: Data for selecting the next quiz to study

[1953] Step 3:

[1954] The server sends the selected quiz to the user.

[1955] Input: Selection data

[1956] Processing: The server sends the selected quiz to the terminal based on the user's progress.

[1957] Output: Quiz data

[1958] Step 4:

[1959] The terminal recognizes the user's emotions using an emotion engine.

[1960] Input: Real-time biometric and facial expression data of the user

[1961] Processing: The device uses facial recognition technology and biometric sensors to analyze and recognize the user's emotions in real time.

[1962] Output: Emotion recognition result (e.g., stress state)

[1963] Step 5:

[1964] The server adjusts the difficulty of the quiz and generates feedback.

[1965] Input: Emotion recognition results and quiz data

[1966] Processing: The server adjusts the difficulty of the quiz appropriately based on the emotion recognition results and generates humorous feedback using a generative AI model.

[1967] Output: Adjusted quiz and feedback data

[1968] Step 6:

[1969] A user submits a request for a new virtual trading session.

[1970] Input: User request (want to start virtual trading)

[1971] Processing: The user clicks the application's button to initiate a virtual trade, and a request is sent to the server.

[1972] Output: Request data (information on the virtual trade start request)

[1973] Step 7:

[1974] The server initializes a virtual trading session.

[1975] Input: Request data and market data

[1976] Processing: The server initializes the virtual trading session, retrieves the necessary market data, and generates investment recommendations using the generative AI model.

[1977] Output: Virtual trading session data and investment proposal data

[1978] Step 8:

[1979] The terminal recognizes the user's emotions using an emotion engine.

[1980] Input: Real-time biometric and facial expression data of the user

[1981] Processing: The device uses facial recognition technology and biometric sensors to analyze and recognize the user's emotions in real time.

[1982] Output: Emotion recognition result (e.g., risk aversion)

[1983] Step 9:

[1984] The server coordinates the investment proposals.

[1985] Input: Emotion recognition results and virtual trading session data

[1986] Processing: The server adjusts the risk level of the investment proposal based on the emotion recognition result and provides it to the user.

[1987] Output: Adjusted investment proposal data

[1988] Step 10:

[1989] A user submits a request to start a new discussion.

[1990] Input: User request (want to start a discussion)

[1991] Process: The user clicks the Start Discussion button in the application and a request is sent to the server.

[1992] Output: Request data (information you wish to start a discussion about)

[1993] Step 11:

[1994] The server notifies the entire community of the discussion content.

[1995] Input: Request data and discussion content

[1996] Process: The server notifies the entire community of new discussion content and creates a thread.

[1997] Output: Notification data

[1998] Step 12:

[1999] Other users post comments to the discussion.

[2000] Input: Comment data

[2001] Process: Other users post comments to the discussion thread, which are then sent to the server.

[2002] Output: Comment data

[2003] Step 13:

[2004] The server adds the comment to the thread.

[2005] Input: Comment data

[2006] Processing: The server adds the accepted comment to the discussion thread.

[2007] Output: Updated discussion thread data

[2008] Step 14:

[2009] The device uses an emotion engine to recognize the user's emotions and generate feedback.

[2010] Input: Real-time biometric information, facial expression data, and comment data of the user

[2011] Processing: The device uses facial recognition technology and biometric sensors to analyze emotions and generates feedback using generative AI models.

[2012] Output: Feedback data (e.g., kind words of advice)

[2013] These steps provide a learning experience that improves financial literacy according to the user's progress and emotions.

[2014] (Application example 2)

[2015] 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."

[2016] Conventional financial literacy improvement systems have been unable to provide a learning experience that takes into account the user's emotional state, resulting in reduced learning efficiency. Furthermore, in virtual trading, they have been unable to provide appropriate investment suggestions based on the user's emotions, limiting the effectiveness of the user's learning. Furthermore, in community discussions, it has been difficult to provide feedback that takes into account the user's emotions.

[2017] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a microlearning means, a virtual trading means, a community platform means, a feedback means based on emotion recognition, and a means for providing interactive learning content. This makes it possible to provide learning content and feedback according to the user's emotional state, thereby effectively supporting the improvement of the user's financial literacy.

[2018] "Microlearning" is an educational method for acquiring knowledge efficiently in a short amount of time.

[2019] "Virtual trading" means the simulated activity of trading financial instruments in a virtual environment without using actual funds.

[2020] A "community platform" is an online space where users can exchange opinions and hold discussions on a specific topic.

[2021] "Emotion recognition" is a technology that identifies a user's emotions in real time from their facial expressions and voice.

[2022] "Feedback means" is a method for providing appropriate information based on the user's behavior or state, and for improving learning or behavior.

[2023] "Interactive learning content provision" is a method of providing learning materials and tasks that change dynamically depending on the user's reactions and situation.

[2024] "Progress management" is the process of tracking a user's learning or work progress and suggesting next tasks or activities to be done.

[2025] "Adjusting the difficulty of a quiz" is a method of changing the difficulty of the quiz provided based on the user's level of understanding and emotional state.

[2026] "Investment proposal adjustment" is a technology that provides optimal investment strategies and policies to users based on the results of user emotion recognition.

[2027] This invention is a system aimed at improving financial literacy, providing a learning experience that responds to the user's emotional state. Specifically, it includes the following processes performed between a server, a terminal, and a user.

[2028] Hardware and software used

[2029] Hardware: Smartphone (high-resolution camera, microphone), server

[2030] software:

[2031] Emotion Recognizer

[2032] Quiz Module (QuizModule)

[2033] Virtual trade simulation (TradeSimulation)

[2034] Community Platform

[2035] Interactive Feedback System

[2036] System processing overview

[2037] 1. Emotion recognition:

[2038] The smartphone's camera and microphone are used to capture the user's facial expressions and voice.

[2039] The captured data is sent to EmotionRecognizer, which identifies emotions in real time.

[2040] 2. Microlearning:

[2041] When a user requests to take a quiz, the server uses QuizModule to check the user's progress, select the next quiz to be studied, and send it.

[2042] At this time, the difficulty level of the quiz is adjusted based on the emotion recognition results.

[2043] 3. Virtual trading:

[2044] When a user starts a new trading session, the server initializes TradeSimulation to provide a virtual investment environment.

[2045] Based on the emotion recognition results, the system makes investment suggestions that are in line with the user's emotional state. For example, if the user indicates a desire to avoid risk, the system will suggest low-risk investments.

[2046] 4. Community Platform:

[2047] When a new discussion is started, the server notifies the entire community via CommunityPlatform.

[2048] It also accepts comments from other users and generates feedback based on emotion recognition results. For example, if a user is feeling anxious during a discussion, the emotion engine will recognize this and provide gentle feedback.

[2049] 5. Providing interactive learning content:

[2050] Based on the emotion recognition results and the user's progress information, the server generates and provides interactive learning content to the user.

[2051] The learning content dynamically changes based on the user's reactions, for example generating humorous feedback if the user is feeling stressed.

[2052] Examples of concrete examples and prompts

[2053] Examples:

[2054] If a user feels stressed while taking the "Stock Market 101" quiz, the emotion recognition engine will recognize that emotion and the server will provide a quiz with adjusted difficulty.

[2055] If a user feels anxious during a discussion about "investment risk management," the emotion engine will recognize that emotion and the server will provide gentle risk management advice.

[2056] Example prompt sentence:

[2057] "If your users are stressed, give them gentle feedback."

[2058] "If users are confident, offer them a high-difficulty quiz."

[2059] In this way, the financial literacy improvement system linked to the emotion engine improves the user's learning experience and enables them to acquire financial knowledge efficiently.

[2060] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2061] Step 1:

[2062] A user launches a smartphone application and requests learning content. The smartphone's camera and microphone are used to capture the user's facial expressions and voice, which are then sent to EmotionRecognizer. EmotionRecognizer processes this data and identifies the user's emotions. The input is the user's facial and voice data, and the output is their emotional state (e.g., stressed, relaxed).

[2063] Step 2:

[2064] The server receives the emotional state sent from EmotionRecognizer and checks the user's learning progress information (e.g., the results and progress of previously completed quizzes). It then sends the emotional state and progress information to QuizModule to select the next quiz content to study. The input is the user's emotional state and progress information, and the output is the content of the next quiz to study.

[2065] Step 3:

[2066] QuizModule adjusts the difficulty of the next quiz based on the emotional state and progress information. For example, if the emotional state is stressed, it lowers the difficulty, and if the emotional state is relaxed, it raises the difficulty. The adjusted quiz content is sent to the server. The input is the emotional state and progress information, and the output is the adjusted quiz content.

[2067] Step 4:

[2068] When a user answers a quiz, the results are sent to the server and progress information is updated. At the same time, the InteractiveFeedback system generates interactive feedback based on the user's emotional state and the quiz results. For example, a humorous compliment may be given if the answer is correct. The input is the quiz result and emotional state, and the output is the generated feedback.

[2069] Step 5:

[2070] When a user requests to start a virtual trading session, the server initializes TradeSimulation. Based on the user's emotional state, the AI ​​makes investment suggestions. For example, if the user indicates a risk-averse sentiment, it will suggest low-risk investments. The input is the emotional state, and the output is a proposed investment scenario.

[2071] Step 6:

[2072] A user accesses the community platform and starts a new discussion or posts a comment to an existing discussion. The server notifies other users based on the content and emotional state of the comment, and generates appropriate feedback and adds it to the thread. The input is the discussion content and emotional state, and the output is the generated feedback and notification.

[2073] Step 7:

[2074] After all the processing is completed, the server saves the updated user progress information in storage and prepares it for the next access. This allows the user to continue learning. The input is the updated progress information, and the output is the saved data.

[2075] The above processing steps provide a learning experience based on the user's emotional state, and a system is realized that effectively supports the improvement of financial literacy.

[2076] 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.

[2077] 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.

[2078] 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.

[2079] 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.

[2080] FIG. 9 illustrates 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 behaviors 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.

[2081] 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.

[2082] 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).

[2083] 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.

[2084] 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."

[2085] 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.

[2086] 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).

[2087] 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.

[2088] 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.

[2089] 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.

[2090] 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.

[2091] 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.

[2092] 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.

[2093] 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.

[2094] 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.

[2095] 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.

[2096] 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.

[2097] The following is further disclosed regarding the above embodiment.

[2098] (Claim 1)

[2099] Microlearning vehicles;

[2100] Virtual trading instruments;

[2101] a community platform means;

[2102] A financial literacy improvement system including:

[2103] (Claim 2)

[2104] 10. The system of claim 1, further comprising means for managing the user's progress and providing the next quiz to be studied.

[2105] (Claim 3)

[2106] 10. The system of claim 1, further comprising means for initiating a virtual trading session and providing AI-generated investment suggestions to the user.

[2107] (Claim 4)

[2108] 10. The system of claim 1, further comprising means for a user to initiate a discussion and notify the community thereof.

[2109] (Claim 5)

[2110] 3. The system of claim 2, further comprising means for evaluating the user's answers and providing feedback.

[2111] (Claim 6)

[2112] 4. The system of claim 3, further comprising means for processing virtual trade orders and providing AI feedback.

[2113] (Claim 7)

[2114] 5. The system of claim 4, further comprising means for accepting comments on the discussion and adding them to the associated thread.

[2115] "Example 1"

[2116] (Claim 1)

[2117] A means of providing quizzes designed to help users learn quickly and efficiently;

[2118] means for providing a simulation of trading financial instruments in a virtual environment;

[2119] a means for providing an online forum for users to discuss financial topics;

[2120] A system including:

[2121] (Claim 2)

[2122] 2. The system according to claim 1, further comprising means for checking the user's learning progress and selecting and providing the next quiz to be studied.

[2123] (Claim 3)

[2124] 10. The system of claim 1, further comprising means for initiating a virtual trading session and providing investment suggestions to a user using the generative AI model.

[2125] "Application Example 1"

[2126] (Claim 1)

[2127] Microlearning vehicles;

[2128] Virtual trading instruments;

[2129] a community platform means;

[2130] Virtual financial education tools and

[2131] a means for providing real-time feedback;

[2132] An interactive learning method using prompt sentences;

[2133] A system including:

[2134] (Claim 2)

[2135] 10. The system of claim 1, further comprising means for managing the user's progress and providing the next quiz to be studied.

[2136] (Claim 3)

[2137] 10. The system of claim 1, further comprising means for initiating a virtual trading session and providing AI-generated investment suggestions to the user.

[2138] "Example 2: Combining Emotion Engines"

[2139] (Claim 1)

[2140] Microlearning vehicles;

[2141] Virtual trading instruments;

[2142] a community platform means;

[2143] An emotion recognition means;

[2144] A means of generating feedback using a generative AI model;

[2145] A system including:

[2146] (Claim 2)

[2147] 10. The system of claim 1, further comprising means for managing the user's progress and providing the next quiz to be studied.

[2148] (Claim 3)

[2149] 10. The system of claim 1, further comprising means for initiating a virtual trading session and providing a user with investment suggestions from the generative AI model.

[2150] "Application example 2 when combining emotion engines"

[2151] (Claim 1)

[2152] Microlearning vehicles;

[2153] Virtual trading instruments;

[2154] a community platform means;

[2155] Emotion recognition feedback means;

[2156] Interactive learning content delivery methods,

[2157] A system including:

[2158] (Claim 2)

[2159] A way to track the user's progress and provide the next quiz to study;

[2160] a means for adjusting the difficulty of the quiz based on the emotional state;

[2161] 10. The system of claim 1, comprising:

[2162] (Claim 3)

[2163] A means to initialize virtual trading sessions and provide users with AI-driven investment suggestions;

[2164] A means for adjusting investment proposals based on emotion recognition;

[2165] 10. The system of claim 1, comprising: [Explanation of symbols]

[2166] 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. Microlearning instruments; Virtual trading instruments; a community platform means; A financial literacy improvement system including:

2. 2. The system according to claim 1, further comprising means for managing the user's progress and providing the next quiz to be studied.

3. The system of claim 1, further comprising means for initiating a virtual trading session and providing AI-generated investment suggestions to the user.

4. 2. The system of claim 1, further comprising means for a user to initiate a discussion and notify the community of the same.

5. 3. The system of claim 2, further comprising means for evaluating the user's answers and providing feedback.

6. 4. The system of claim 3, further comprising means for processing virtual trade orders and providing AI feedback.

7. 5. The system of claim 4, further comprising means for accepting comments on the discussion and adding them to the associated thread.

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