System

Abstract

An object of a system according to an embodiment is to effectively improve the financial literacy of young people.SOLUTION: A system in accordance with an embodiment includes a micro-learning financial app, a AI guided virtual stock trading game app, and a community platform for financial literacy improvement. The micro-learning financial application provides financial education content that can be learned in a short time. The virtual-guided virtual stock trading game app assists users in learning practical financial knowledge through AI stock trading. A community platform for improving financial literacy provides a place for users to share information and learn from each other.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] Previous technology lacked comprehensive services to effectively support improving financial literacy among young people.

[0005] The system according to the embodiment aims to effectively improve the financial literacy of young people. [Means for solving the problem]

[0006] The system according to the embodiment includes a microlearning financial app, an AI-guided virtual stock trading game app, and a community platform for improving financial literacy. The microlearning financial app provides financial education content that can be learned in a short amount of time. The AI-guided virtual stock trading game app helps users acquire practical financial knowledge through virtual stock trading. The community platform for improving financial literacy provides a place where users can share information and learn from each other. [Effects of the Invention]

[0007] The system according to the embodiment can effectively improve the financial literacy of young people. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) Fin-Edu Connect, an embodiment of the present invention, is a comprehensive service for efficiently improving financial literacy among young people. This service is provided by combining a micro-learning financial app, an AI-guided virtual stock trading game app, and a community platform for improving financial literacy. In this way, Fin-Edu Connect enables young people to efficiently improve their financial literacy.

[0029] In one embodiment, Fin-Edu Connect comprises a microlearning finance app, an AI-guided virtual stock trading game app, and a community platform for improving financial literacy. The microlearning finance app provides financial knowledge that can be learned in a short amount of time. For example, users can learn basic financial concepts and the fundamentals of investing through short, five-minute videos and quiz-style content. The microlearning finance app also allows users to learn at their own pace and track their progress. For example, it provides financial knowledge useful in everyday life, such as the importance of savings, how to use credit cards, and the basics of investing. The AI-guided virtual stock trading game app simulates a real investment experience through trading on a virtual stock market. For example, an AI guide supports users' trading and provides appropriate advice. When a user attempts to purchase a specific stock, the AI ​​guide analyzes the stock's past performance and market trends and advises on the timing and risks of the purchase. The generation AI receives inputs including prompts containing the stocks the user wants to trade and the conditions for the trade, and the generation AI then analyzes and provides advice based on the prompts. The community platform for improving financial literacy provides a forum for users to share and learn financial knowledge from each other. For example, through forums and chat functions, users can post questions and answer other users' questions. Experts also hold webinars and online seminars, where users can learn the latest financial information and investment tips. For example, by sharing success stories and failures in investments, users can acquire practical knowledge. In this way, Fin-Edu Connect according to embodiments can efficiently improve young people's financial literacy. For example, users can acquire comprehensive financial knowledge by learning basic knowledge through a microlearning finance app, gaining practical experience through a virtual stock trading game app, and sharing information with other users on the community platform.

[0030] A microlearning finance app can analyze a user's learning history and performance to generate an individually customized learning plan. For example, a microlearning finance app can analyze a user's past learning history to identify in which areas their understanding has deepened and in which areas their understanding is lacking. For example, the app can perform analysis based on the percentage of correct answers in quizzes and the amount of time spent watching videos. The microlearning finance app can also evaluate the user's performance and generate an individually customized learning plan. For example, the app can provide recommended learning content and a learning schedule based on the user's level of understanding. This makes it possible to provide the optimal learning plan for each user.

[0031] A microlearning finance app can assess a user's level of comprehension of the learning content in real time and provide additional supplementary explanations and practice questions according to the user's level of comprehension. For example, when a user takes a quiz or test, the microlearning finance app analyzes the answers in real time to assess the user's level of comprehension. For example, the microlearning finance app measures the level of comprehension based on the percentage of correct answers or the time it takes to answer. The microlearning finance app also provides additional supplementary explanations and practice questions according to the user's level of comprehension. For example, if the user's level of comprehension is low, the app provides detailed explanations and additional practice questions. This makes it possible to provide learning support according to the user's level of comprehension.

[0032] Microlearning finance apps can incorporate game elements into their learning content, making learning fun through quizzes and mini-games. For example, microlearning finance apps can add quiz-style mini-games to their learning content, allowing users to learn while having fun. For example, they can introduce a system where points are accumulated for each correct answer. Microlearning finance apps can also incorporate game elements into their learning content, allowing users to access new content by leveling up. For example, users can advance to the next level by clearing a specific quiz. This makes learning fun for users.

[0033] Microlearning finance apps can provide learning content that corresponds to different languages ​​and cultures, making them suitable for a global user base. For example, microlearning finance apps can translate learning content into multiple languages ​​to accommodate users who speak different languages. For example, they can provide content in languages ​​such as English, Spanish, and Chinese. Microlearning finance apps can also provide learning content that corresponds to different cultures. For example, they can provide information on the financial systems and investment cultures of each country. This allows them to cater to a global user base.

[0034] An AI-guided virtual stock trading game app can analyze a user's trading history, learn trading patterns and trends, and provide more accurate advice. For example, an AI-guided virtual stock trading game app can analyze a user's past trading history to identify trading patterns and trends. For example, it can analyze frequently traded stocks and trading timing. The AI-guided virtual stock trading game app can also learn trading patterns and trends to provide more accurate advice. For example, it can provide advice on risk management and investment strategies based on the user's trading history. This allows it to provide more accurate advice based on the user's trading patterns.

[0035] An AI-guided virtual stock trading game app incorporates real-time market data into a virtual stock market simulation, thereby providing a trading experience that is closer to reality. For example, an AI-guided virtual stock trading game app incorporates real-time market data into a virtual stock market to provide a simulation that reflects real market conditions. For example, stock price fluctuations and news events are reflected in real time. Furthermore, an AI-guided virtual stock trading game app provides a simulation that allows users to have a trading experience that is closer to reality. For example, a trading simulation is performed based on real-time market data. This allows for a trading experience that is closer to reality.

[0036] AI-guided virtual stock trading game apps add a simulation mode that handles different financial products, allowing them to provide a wide range of investment experiences. For example, in addition to the virtual stock market, AI-guided virtual stock trading game apps add a simulation mode that handles different financial products, such as virtual currencies and bonds. For example, they simulate trading of Bitcoin and government bonds. AI-guided virtual stock trading game apps also provide a simulation mode that allows users to gain a wide range of investment experiences. For example, by simulating trading of different financial products, users can gain a diverse range of investment experiences. This allows users to gain a wide range of investment experiences.

[0037] AI-guided virtual stock trading game apps have introduced a tournament mode in which users compete against each other, allowing users to hone their investment skills in a game-like manner. AI-guided virtual stock trading game apps have introduced a tournament mode in which users compete against each other in virtual trading, providing an opportunity to hone their investment skills. For example, the user who achieves the highest return within a certain period of time will be declared the winner. AI-guided virtual stock trading game apps also allow users to learn while competing with other users through the tournament mode. For example, a ranking system has been introduced, allowing users to earn rewards by ranking highly. This allows users to hone their investment skills in a game-like manner.

[0038] A community platform for improving financial literacy can provide a function that analyzes user posts and automatically matches users with common interests. A community platform for improving financial literacy, for example, analyzes user posts using natural language processing technology and automatically matches users with common interests. For example, it can connect users with an interest in the same investment theme. A community platform for improving financial literacy also provides a matching function that makes it easier for users to exchange information with each other. For example, it allows users with common interests to interact through forums and chats. This makes it possible to automatically match users with common interests.

[0039] A community platform for improving financial literacy can automatically summarize the content of webinars and online seminars by experts, extracting and providing key points. A community platform for improving financial literacy could, for example, analyze recordings of webinars and online seminars by experts and build a system that automatically summarizes the key points. For example, it could use natural language processing technology to generate summaries. The community platform for improving financial literacy could also extract and provide key points to enable users to learn efficiently. For example, it could display the main points of a webinar in bullet points. This allows users to efficiently learn the key points of expert webinars and online seminars.

[0040] A community platform for improving financial literacy can set up sub-communities targeted at users of different age groups and occupations, facilitating more specific information exchange. A community platform for improving financial literacy can, for example, set up sub-communities targeted at users of different age groups and occupations, facilitating information exchange according to specific needs. For example, sub-communities can be set up for students, young working adults, and seniors. A community platform for improving financial literacy can also allow users to join sub-communities that suit them. For example, users can select sub-communities based on age or occupation. This allows users of different age groups and occupations to exchange specific information.

[0041] A community platform for improving financial literacy can add a function where AI automatically suggests related past posts and external resources for questions and answers posted by users. For example, a community platform for improving financial literacy could build a system where AI automatically suggests related past posts for questions and answers posted by users. For example, it could display past discussions on the same topic. In addition, a community platform for improving financial literacy could suggest related external resources for questions and answers posted by users. For example, it could display related websites or academic papers. This makes it possible to automatically suggest related information for questions and answers posted by users.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] Microlearning finance apps can analyze a user's learning history and performance to generate an individually customized learning plan. For example, they can analyze a user's past learning history to identify areas where their understanding has deepened and areas where their understanding is lacking. For example, they can perform analysis based on the percentage of correct answers to quizzes and the amount of time spent watching videos. Microlearning finance apps can also evaluate a user's performance and generate an individually customized learning plan. For example, they can provide recommended learning content and a learning schedule based on the user's level of understanding. This makes it possible to provide the optimal learning plan for each user.

[0044] A microlearning finance app can assess a user's level of comprehension of the learning content in real time and provide additional supplementary explanations or practice questions according to the user's level of comprehension. For example, when a user takes a quiz or test, the answers can be analyzed in real time to assess the user's level of comprehension. For example, the level of comprehension can be measured based on the percentage of correct answers or the time it takes to answer. Furthermore, a microlearning finance app can provide additional supplementary explanations or practice questions according to the user's level of comprehension. For example, if the user's level of comprehension is low, detailed explanations or additional practice questions can be provided. This makes it possible to provide learning support according to the user's level of comprehension.

[0045] Microlearning finance apps can incorporate game elements into their learning content, making learning fun through quizzes and mini-games. For example, they can add quiz-style mini-games to their learning content, allowing users to learn while having fun. For example, they can introduce a system where points are accumulated for each correct answer. Microlearning finance apps can also incorporate game elements into their learning content, allowing users to access new content by leveling up. For example, users can advance to the next level by clearing a specific quiz. This makes learning fun for users.

[0046] Microlearning finance apps can cater to a global user base by providing learning content tailored to different languages ​​and cultures. For example, learning content can be translated into multiple languages ​​to accommodate users who speak different languages. For example, content can be provided in languages ​​such as English, Spanish, and Chinese. Microlearning finance apps can also provide learning content tailored to different cultures. For example, they can provide information on the financial systems and investment cultures of each country. This allows them to cater to a global user base.

[0047] An AI-guided virtual stock trading game app can analyze a user's trading history, learn their trading patterns and trends, and provide more accurate advice. For example, it can analyze a user's past trading history to identify trading patterns and trends. For example, it can analyze frequently traded stocks and trading timing. The AI-guided virtual stock trading game app can also learn their trading patterns and trends to provide more accurate advice. For example, it can provide advice on risk management and investment strategies based on the user's trading history. This allows it to provide more accurate advice based on the user's trading patterns.

[0048] AI-guided virtual stock trading game apps incorporate real-time market data into their virtual stock market simulations to provide a trading experience that is closer to reality. For example, real-time market data is incorporated into the virtual stock market to provide a simulation that reflects real market conditions. For example, stock price fluctuations and news events are reflected in real time. AI-guided virtual stock trading game apps also provide simulations that allow users to have a trading experience that is closer to reality. For example, trading simulations are performed based on real-time market data. This allows for a trading experience that is closer to reality.

[0049] AI-guided virtual stock trading game apps can add simulation modes that handle different financial products, providing a wide range of investment experiences. For example, in addition to the virtual stock market, a simulation mode can be added that handles different financial products such as virtual currencies and bonds. For example, trading of Bitcoin and government bonds can be simulated. AI-guided virtual stock trading game apps also provide simulation modes that allow users to gain a wide range of investment experiences. For example, by simulating trading of different financial products, users can gain a diverse range of investment experiences. This allows users to gain a wide range of investment experiences.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: Microlearning finance apps provide financial knowledge that can be learned in a short amount of time. For example, they can teach basic financial concepts and the fundamentals of investing through short, five-minute videos and quiz-style content. Users can also learn at their own pace and check their progress. For example, they provide financial knowledge that is useful in everyday life, such as the importance of savings, how to use credit cards, and the basics of investing. Step 2: The AI-guided virtual stock trading game app simulates a real investment experience through trading in a virtual stock market. For example, an AI guide supports the user's trading and provides appropriate advice. When a user attempts to purchase a specific stock, the AI ​​guide analyzes the stock's past performance and market trends, and provides advice on the timing and risks of the purchase. The input to the generation AI is a prompt containing the stock the user wants to trade and its conditions, and the generation AI analyzes and provides advice based on the prompt. Step 3: The financial literacy improvement community platform provides a forum for users to share and learn from each other about financial knowledge. For example, through the forum and chat functions, users can post questions and answer other users' questions. Experts also host webinars and online seminars where users can learn the latest financial information and investment tips. For example, users can gain practical knowledge by sharing success stories and failures in investing.

[0052] (Example 2) Fin-Edu Connect, an embodiment of the present invention, is a comprehensive service for efficiently improving financial literacy among young people. This service is provided by combining a micro-learning financial app, an AI-guided virtual stock trading game app, and a community platform for improving financial literacy. In this way, Fin-Edu Connect enables young people to efficiently improve their financial literacy.

[0053] In one embodiment, Fin-Edu Connect comprises a microlearning finance app, an AI-guided virtual stock trading game app, and a community platform for improving financial literacy. The microlearning finance app provides financial knowledge that can be learned in a short amount of time. For example, users can learn basic financial concepts and the fundamentals of investing through short, five-minute videos and quiz-style content. The microlearning finance app also allows users to learn at their own pace and track their progress. For example, it provides financial knowledge useful in everyday life, such as the importance of savings, how to use credit cards, and the basics of investing. The AI-guided virtual stock trading game app simulates a real investment experience through trading on a virtual stock market. For example, an AI guide supports users' trading and provides appropriate advice. When a user attempts to purchase a specific stock, the AI ​​guide analyzes the stock's past performance and market trends and advises on the timing and risks of the purchase. The generation AI receives inputs including prompts containing the stocks the user wants to trade and the conditions for the trade, and the generation AI then analyzes and provides advice based on the prompts. The community platform for improving financial literacy provides a forum for users to share and learn financial knowledge from each other. For example, through forums and chat functions, users can post questions and answer other users' questions. Experts also hold webinars and online seminars, where users can learn the latest financial information and investment tips. For example, by sharing success stories and failures in investments, users can acquire practical knowledge. In this way, Fin-Edu Connect according to embodiments can efficiently improve young people's financial literacy. For example, users can acquire comprehensive financial knowledge by learning basic knowledge through a microlearning finance app, gaining practical experience through a virtual stock trading game app, and sharing information with other users on the community platform.

[0054] A microlearning finance app can analyze a user's learning history and performance to generate an individually customized learning plan. For example, a microlearning finance app can analyze a user's past learning history to identify in which areas their understanding has deepened and in which areas their understanding is lacking. For example, the app can perform analysis based on the percentage of correct answers in quizzes and the amount of time spent watching videos. The microlearning finance app can also evaluate the user's performance and generate an individually customized learning plan. For example, the app can provide recommended learning content and a learning schedule based on the user's level of understanding. This makes it possible to provide the optimal learning plan for each user.

[0055] A microlearning finance app can assess a user's level of comprehension of the learning content in real time and provide additional supplementary explanations and practice questions according to the user's level of comprehension. For example, when a user takes a quiz or test, the microlearning finance app analyzes the answers in real time to assess the user's level of comprehension. For example, the microlearning finance app measures the level of comprehension based on the percentage of correct answers or the time it takes to answer. The microlearning finance app also provides additional supplementary explanations and practice questions according to the user's level of comprehension. For example, if the user's level of comprehension is low, the app provides detailed explanations and additional practice questions. This makes it possible to provide learning support according to the user's level of comprehension.

[0056] A microlearning finance app can use an emotion estimation function to monitor a user's emotions while studying and provide relaxing content when the user feels stressed or fatigued. For example, the microlearning finance app analyzes the user's facial expressions and voice while studying to estimate emotions in real time. For example, it can use a camera or microphone to detect signs of stress or fatigue. The microlearning finance app can also provide relaxing content when the user feels stressed or fatigued. For example, it can provide relaxation music or a meditation guide. This reduces the user's stress and fatigue and improves learning efficiency.

[0057] Microlearning finance apps can incorporate game elements into their learning content, making learning fun through quizzes and mini-games. For example, microlearning finance apps can add quiz-style mini-games to their learning content, allowing users to learn while having fun. For example, they can introduce a system where points are accumulated for each correct answer. Microlearning finance apps can also incorporate game elements into their learning content, allowing users to access new content by leveling up. For example, users can advance to the next level by clearing a specific quiz. This makes learning fun for users.

[0058] Microlearning finance apps can provide learning content that corresponds to different languages ​​and cultures, making them suitable for a global user base. For example, microlearning finance apps can translate learning content into multiple languages ​​to accommodate users who speak different languages. For example, they can provide content in languages ​​such as English, Spanish, and Chinese. Microlearning finance apps can also provide learning content that corresponds to different cultures. For example, they can provide information on the financial systems and investment cultures of each country. This allows them to cater to a global user base.

[0059] A microlearning finance app can use the emotion estimation function to identify topics in which a user is most interested and prioritize providing learning content related to those topics. For example, the microlearning finance app analyzes the user's emotions while studying and identifies topics of interest. For example, it prioritizes displaying topics with a high number of positive emotional responses. The microlearning finance app also prioritizes providing learning content related to topics in which a user is most interested. For example, it prioritizes displaying content related to investment areas in which a user is interested. This makes it possible to provide learning content that matches the user's interests.

[0060] An AI-guided virtual stock trading game app can analyze a user's trading history, learn trading patterns and trends, and provide more accurate advice. For example, an AI-guided virtual stock trading game app can analyze a user's past trading history to identify trading patterns and trends. For example, it can analyze frequently traded stocks and trading timing. The AI-guided virtual stock trading game app can also learn trading patterns and trends to provide more accurate advice. For example, it can provide advice on risk management and investment strategies based on the user's trading history. This allows it to provide more accurate advice based on the user's trading patterns.

[0061] An AI-guided virtual stock trading game app incorporates real-time market data into a virtual stock market simulation, thereby providing a trading experience that is closer to reality. For example, an AI-guided virtual stock trading game app incorporates real-time market data into a virtual stock market to provide a simulation that reflects real market conditions. For example, stock price fluctuations and news events are reflected in real time. Furthermore, an AI-guided virtual stock trading game app provides a simulation that allows users to have a trading experience that is closer to reality. For example, a trading simulation is performed based on real-time market data. This allows for a trading experience that is closer to reality.

[0062] An AI-guided virtual stock trading game app uses an emotion estimation function to monitor a user's emotions while trading and can issue a warning if the user is taking too much risk. For example, the AI-guided virtual stock trading game app analyzes the user's facial expressions and voice while trading to estimate emotions in real time. For example, it uses a camera or microphone to detect signs of excitement or impatience. The AI-guided virtual stock trading game app also issues a warning if the user is taking too much risk. For example, it issues a warning with a pop-up notification or audio alert when a high-risk trade is about to be made. This can warn the user not to take too much risk.

[0063] AI-guided virtual stock trading game apps add a simulation mode that handles different financial products, allowing them to provide a wide range of investment experiences. For example, in addition to the virtual stock market, AI-guided virtual stock trading game apps add a simulation mode that handles different financial products, such as virtual currencies and bonds. For example, they simulate trading of Bitcoin and government bonds. AI-guided virtual stock trading game apps also provide a simulation mode that allows users to gain a wide range of investment experiences. For example, by simulating trading of different financial products, users can gain a diverse range of investment experiences. This allows users to gain a wide range of investment experiences.

[0064] AI-guided virtual stock trading game apps have introduced a tournament mode in which users compete against each other, allowing users to hone their investment skills in a game-like manner. AI-guided virtual stock trading game apps have introduced a tournament mode in which users compete against each other in virtual trading, providing an opportunity to hone their investment skills. For example, the user who achieves the highest return within a certain period of time will be declared the winner. AI-guided virtual stock trading game apps also allow users to learn while competing with other users through the tournament mode. For example, a ranking system has been introduced, allowing users to earn rewards by ranking highly. This allows users to hone their investment skills in a game-like manner.

[0065] The AI-guided virtual stock trading game app can use an emotion estimation function to identify the investment areas in which a user is most interested and prioritize providing simulations related to those areas. For example, the AI-guided virtual stock trading game app can analyze the user's emotions during trading and identify the investment areas in which the user is interested. For example, areas with a high number of positive emotional responses can be prioritized. The AI-guided virtual stock trading game app can also prioritize providing simulations related to the investment areas in which the user is most interested. For example, simulations related to stocks or virtual currencies in which the user is interested can be prioritized. This makes it possible to provide investment simulations that match the user's interests.

[0066] A community platform for improving financial literacy can provide a function that analyzes user posts and automatically matches users with common interests. A community platform for improving financial literacy, for example, analyzes user posts using natural language processing technology and automatically matches users with common interests. For example, it can connect users with an interest in the same investment theme. A community platform for improving financial literacy also provides a matching function that makes it easier for users to exchange information with each other. For example, it allows users with common interests to interact through forums and chats. This makes it possible to automatically match users with common interests.

[0067] A community platform for improving financial literacy can automatically summarize the content of webinars and online seminars by experts, extracting and providing key points. A community platform for improving financial literacy could, for example, analyze recordings of webinars and online seminars by experts and build a system that automatically summarizes the key points. For example, it could use natural language processing technology to generate summaries. The community platform for improving financial literacy could also extract and provide key points to enable users to learn efficiently. For example, it could display the main points of a webinar in bullet points. This allows users to efficiently learn the key points of expert webinars and online seminars.

[0068] A community platform for improving financial literacy can use an emotion estimation function to analyze emotional responses to users' posts and prioritize displaying posts that receive a lot of positive responses. A community platform for improving financial literacy can, for example, build a system that analyzes emotional responses to users' posts in real time and prioritizes displaying posts that receive a lot of positive responses. For example, it can adjust the display order of posts based on the emotion score. Furthermore, a community platform for improving financial literacy can analyze the content of posts to make it easier for users to receive positive responses. For example, it can determine the display order based on the number of positive comments and likes. This allows posts that receive a lot of positive responses to be displayed preferentially.

[0069] A community platform for improving financial literacy can set up sub-communities targeted at users of different age groups and occupations, facilitating more specific information exchange. A community platform for improving financial literacy can, for example, set up sub-communities targeted at users of different age groups and occupations, facilitating information exchange according to specific needs. For example, sub-communities can be set up for students, young working adults, and seniors. A community platform for improving financial literacy can also allow users to join sub-communities that suit them. For example, users can select sub-communities based on age or occupation. This allows users of different age groups and occupations to exchange specific information.

[0070] A community platform for improving financial literacy can add a function where AI automatically suggests related past posts and external resources for questions and answers posted by users. For example, a community platform for improving financial literacy could build a system where AI automatically suggests related past posts for questions and answers posted by users. For example, it could display past discussions on the same topic. In addition, a community platform for improving financial literacy could suggest related external resources for questions and answers posted by users. For example, it could display related websites or academic papers. This makes it possible to automatically suggest related information for questions and answers posted by users.

[0071] The community platform for improving financial literacy can use the emotion estimation function to identify topics in which users are most interested and prioritize displaying discussions related to those topics. The community platform for improving financial literacy, for example, analyzes emotional responses to users' posts and comments to identify topics in which users are most interested. For example, topics with many positive emotional responses are prioritized for display. The community platform for improving financial literacy also prioritizes displaying discussions related to topics in which users are most interested. For example, discussions on investment themes in which users are interested are prioritized for display. This makes it possible to prioritize displaying discussions related to topics in which users are most interested.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] Microlearning finance apps can analyze a user's learning history and performance to generate an individually customized learning plan. For example, they can analyze a user's past learning history to identify areas where their understanding has deepened and areas where their understanding is lacking. For example, they can perform analysis based on the percentage of correct answers to quizzes and the amount of time spent watching videos. Microlearning finance apps can also evaluate a user's performance and generate an individually customized learning plan. For example, they can provide recommended learning content and a learning schedule based on the user's level of understanding. This makes it possible to provide the optimal learning plan for each user.

[0074] A microlearning finance app can assess a user's level of comprehension of the learning content in real time and provide additional supplementary explanations or practice questions according to the user's level of comprehension. For example, when a user takes a quiz or test, the answers can be analyzed in real time to assess the user's level of comprehension. For example, the level of comprehension can be measured based on the percentage of correct answers or the time it takes to answer. Furthermore, a microlearning finance app can provide additional supplementary explanations or practice questions according to the user's level of comprehension. For example, if the user's level of comprehension is low, detailed explanations or additional practice questions can be provided. This makes it possible to provide learning support according to the user's level of comprehension.

[0075] A microlearning finance app can use an emotion estimation function to monitor a user's emotions while studying and provide content that helps them relax when they feel stressed or fatigued. For example, it can analyze the user's facial expressions and voice while studying and estimate their emotions in real time. For example, it can use a camera or microphone to detect signs of stress or fatigue. The microlearning finance app can also provide content that helps users relax when they feel stressed or fatigued. For example, it can provide relaxation music or a meditation guide. This reduces the user's stress and fatigue and improves learning efficiency.

[0076] Microlearning finance apps can incorporate game elements into their learning content, making learning fun through quizzes and mini-games. For example, they can add quiz-style mini-games to their learning content, allowing users to learn while having fun. For example, they can introduce a system where points are accumulated for each correct answer. Microlearning finance apps can also incorporate game elements into their learning content, allowing users to access new content by leveling up. For example, users can advance to the next level by clearing a specific quiz. This makes learning fun for users.

[0077] Microlearning finance apps can cater to a global user base by providing learning content tailored to different languages ​​and cultures. For example, learning content can be translated into multiple languages ​​to accommodate users who speak different languages. For example, content can be provided in languages ​​such as English, Spanish, and Chinese. Microlearning finance apps can also provide learning content tailored to different cultures. For example, they can provide information on the financial systems and investment cultures of each country. This allows them to cater to a global user base.

[0078] The microlearning finance app can use the emotion estimation function to identify the topics in which a user is most interested and prioritize providing learning content related to those topics. For example, the app can analyze the user's emotions while studying and identify topics of interest. For example, it can prioritize displaying topics with a high number of positive emotional responses. The microlearning finance app also prioritizes providing learning content related to the topics in which a user is most interested. For example, it can prioritize displaying content related to investment areas in which a user is interested. This makes it possible to provide learning content that matches the user's interests.

[0079] An AI-guided virtual stock trading game app can analyze a user's trading history, learn their trading patterns and trends, and provide more accurate advice. For example, it can analyze a user's past trading history to identify trading patterns and trends. For example, it can analyze frequently traded stocks and trading timing. The AI-guided virtual stock trading game app can also learn their trading patterns and trends to provide more accurate advice. For example, it can provide advice on risk management and investment strategies based on the user's trading history. This allows it to provide more accurate advice based on the user's trading patterns.

[0080] AI-guided virtual stock trading game apps incorporate real-time market data into their virtual stock market simulations to provide a trading experience that is closer to reality. For example, real-time market data is incorporated into the virtual stock market to provide a simulation that reflects real market conditions. For example, stock price fluctuations and news events are reflected in real time. AI-guided virtual stock trading game apps also provide simulations that allow users to have a trading experience that is closer to reality. For example, trading simulations are performed based on real-time market data. This allows for a trading experience that is closer to reality.

[0081] The AI-guided virtual stock trading game app uses an emotion estimation function to monitor a user's emotions while trading and warn them if they are taking too much risk. For example, it analyzes the user's facial expressions and voice while trading to estimate their emotions in real time. For example, it uses a camera and microphone to detect signs of excitement or impatience. The AI-guided virtual stock trading game app also warns users if they are taking too much risk. For example, it warns users by issuing a pop-up notification or audio alert when they are about to make a high-risk trade. This can warn users not to take too much risk.

[0082] AI-guided virtual stock trading game apps can add simulation modes that handle different financial products, providing a wide range of investment experiences. For example, in addition to the virtual stock market, a simulation mode can be added that handles different financial products such as virtual currencies and bonds. For example, trading of Bitcoin and government bonds can be simulated. AI-guided virtual stock trading game apps also provide simulation modes that allow users to gain a wide range of investment experiences. For example, by simulating trading of different financial products, users can gain a diverse range of investment experiences. This allows users to gain a wide range of investment experiences.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: Microlearning finance apps provide financial knowledge that can be learned in a short amount of time. For example, they can teach basic financial concepts and the fundamentals of investing through short, five-minute videos and quiz-style content. Users can also learn at their own pace and check their progress. For example, they provide financial knowledge that is useful in everyday life, such as the importance of savings, how to use credit cards, and the basics of investing. Step 2: The AI-guided virtual stock trading game app simulates a real investment experience through trading in a virtual stock market. For example, an AI guide supports the user's trading and provides appropriate advice. When a user attempts to purchase a specific stock, the AI ​​guide analyzes the stock's past performance and market trends, and provides advice on the timing and risks of the purchase. The input to the generation AI is a prompt containing the stock the user wants to trade and its conditions, and the generation AI analyzes and provides advice based on the prompt. Step 3: The financial literacy improvement community platform provides a forum for users to share and learn from each other about financial knowledge. For example, through the forum and chat functions, users can post questions and answer other users' questions. Experts also host webinars and online seminars where users can learn the latest financial information and investment tips. For example, users can gain practical knowledge by sharing success stories and failures in investing.

[0085] 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.

[0086] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0091] 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.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0106] 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] 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.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0126] 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.

[0127] 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.

[0128] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] 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.

[0135] 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 encompasses both emotions 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.

[0136] 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.

[0137] 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).

[0138] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0146] The hardware resource that executes the specific process 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 process may be a single processor.

[0147] 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.

[0148] 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.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Microlearning financial apps and AI-guided virtual stock trading game app, A community platform for improving financial literacy. A system characterized by:

2. The microlearning finance app: Analyzes user learning history and performance to generate personalized learning plans 2. The system of claim 1.

3. The AI-guided virtual stock trading game app Analyzes user trading history, learns trading patterns and trends, and provides more accurate advice 2. The system of claim 1.

4. The financial literacy improvement community platform is: Provide a function that analyzes user posts and automatically matches users who share common interests.

2. The system of claim 1.

5. The microlearning finance app: Using emotion estimation functionality, the system monitors the user's emotions while studying and provides relaxing content when the user feels stressed or fatigued.

2. The system of claim 1.

6. The AI-guided virtual stock trading game app Using emotion estimation, it monitors users' emotions while trading and warns them if they are taking too much risk.

2. The system of claim 1.

7. The financial literacy improvement community platform is: Using emotion estimation, the app analyzes users' emotional reactions to posts and prioritizes displaying posts with a high number of positive reactions.

2. The system of claim 1.

8. The financial literacy improvement community platform is: Using sentiment estimation, the app identifies topics that users are most interested in and prioritizes discussions related to those topics.

2. The system of claim 1.

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