system

Abstract

The system according to this embodiment aims to efficiently improve the financial literacy of young people. [Solution] The system according to the embodiment comprises a microlearning finance app, a virtual stock trading game app, and a community platform for improving financial literacy. The microlearning finance app provides short daily learning sessions. The virtual stock trading game app provides trading advice from an AI guide. The community platform for improving financial literacy shares information through forums, Q&A sections, and online workshops.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that there was a lack of comprehensive support for young people to efficiently improve their financial literacy.

[0005] The system according to the embodiment aims to enable young people to efficiently improve their financial literacy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a microlearning finance app, a virtual stock trading game app, and a community platform for improving financial literacy. The microlearning finance app provides short daily learning sessions. The virtual stock trading game app provides trading advice from an AI guide. The community platform for improving financial literacy facilitates information sharing through forums, Q&A sections, and online workshops. [Effects of the Invention]

[0007] The system according to this embodiment can enable young people to efficiently improve their financial literacy. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The financial literacy improvement system according to an embodiment of the present invention is a comprehensive service that supports financial literacy improvement for young people. This system combines three elements: a microlearning finance app that provides short daily learning sessions, a virtual stock trading game app where an AI guide provides trading advice, and a community platform for financial literacy improvement where information is shared through forums, Q&A sections, and online workshops. For example, the microlearning finance app provides short daily learning sessions, allowing users to build up basic financial knowledge and skills by spending a small amount of time each day. The app incorporates concise lessons, quizzes, and game elements, allowing users to improve their financial literacy while having fun. Next, the AI-guided virtual stock trading game app allows users to virtually experience real stock trading, with the AI ​​guide providing trading advice. Users can improve their trading skills and learn about asset growth and risk management without taking risks. Finally, the community platform for financial literacy improvement allows users to share information with other members and receive advice from experts through forums, Q&A sections, online workshops, etc. A point system is introduced, linking contributions to the community with rewards to enhance user motivation. This allows the financial literacy improvement system to comprehensively support the improvement of financial literacy among young people.

[0029] The financial literacy improvement system according to this embodiment comprises a microlearning finance app, a virtual stock trading game app, and a community platform for improving financial literacy. The microlearning finance app provides short daily learning sessions. For example, the microlearning finance app allows users to build up basic financial knowledge and skills by spending a small amount of time each day. The microlearning finance app includes concise lessons, quizzes, and game elements. For example, the microlearning finance app provides 3-minute video lessons and summary texts. The microlearning finance app can also provide quizzes with multiple-choice and written answers. Furthermore, the microlearning finance app can also provide game elements including a point system and level-up features. The virtual stock trading game app provides trading advice using an AI guide. For example, the virtual stock trading game app includes an AI guide that generates advice using natural language processing and personalizes it using machine learning. The virtual stock trading game app allows users to virtually experience real stock trading and improve their trading skills without taking risks. A community platform for improving financial literacy shares information through forums, Q&A sections, and online workshops. For example, users can share information in forums, answer questions in Q&A sections, and receive advice from experts in online workshops. The community platform also implements a points system, linking contributions to the community with rewards. For instance, users can earn points for posting in forums and answering questions in Q&A sections, and then exchange those points for rewards. This allows the financial literacy system to comprehensively support financial literacy improvement for young people.

[0030] Microlearning finance apps offer short daily learning sessions. For example, they allow users to build up basic financial knowledge and skills by spending just a little time each day. Specifically, the app provides users with 3-minute video lessons and displays summarized text. This allows users to learn efficiently in a short amount of time. The app also provides quizzes with multiple-choice and written answers to check what users have learned. For example, multiple-choice questions may include definitions of financial terms and basic calculation problems, while written answers require users to explain things in their own words. Furthermore, the app offers gamified elements, including a point system and level-up features. Users earn points by answering quizzes correctly, and level up when they accumulate a certain number of points. Leveling up unlocks new lessons and quizzes, maintaining motivation to learn. In this way, microlearning finance apps allow users to acquire financial knowledge and improve their skills while having fun. The app also has a feature that records the user's learning history and visualizes their progress. This allows users to feel their own growth and further increase their motivation to learn. Furthermore, the app can provide a customized learning plan tailored to the user's learning style and pace. For example, if a user is interested in a particular area, it can prioritize providing lessons and quizzes related to that area. This allows microlearning finance apps to provide an optimal learning experience for each individual user and effectively support the improvement of financial literacy.

[0031] The virtual stock trading game app provides trading advice using an AI guide. For example, the app features an AI guide that generates advice using natural language processing and personalizes it using machine learning. Specifically, when a user engages in virtual stock trading, the AI ​​guide provides advice in real time. The AI ​​guide analyzes the user's trading history and current market conditions to propose the optimal trading strategy. For example, when a user is considering purchasing a particular stock, the AI ​​guide advises on the timing of purchase and sale based on the stock's past performance and current market trends. The AI ​​guide also provides personalized advice based on the user's trading style and risk tolerance. This allows users to learn trading strategies that suit them and improve their trading skills without taking risks. Furthermore, the virtual stock trading game app also includes a feedback function to allow users to review and learn from their trading results. For example, it analyzes the factors contributing to the success and failure of the user's trades and provides advice to help them improve in future trades. This allows users to gain experience in an environment similar to actual trading and effectively improve their trading skills. Furthermore, the virtual stock trading game app also features a ranking function that allows users to compete with each other, as well as a function to hold trading contests. This allows users to improve their trading skills while having fun and competing with other users. In addition, the app regularly provides the latest market information and trends, ensuring that users can always trade based on the most up-to-date information. As a result, the virtual stock trading game app is a powerful tool for users to hone their skills in a realistic trading environment and improve their financial literacy.

[0032] The financial literacy community platform shares information through forums, Q&A sections, and online workshops. For example, users can share information in forums, answer questions in the Q&A section, and receive advice from experts in online workshops. Specifically, the forums allow users to discuss and share knowledge on a variety of financial topics. For instance, a wide range of themes are discussed, such as investment fundamentals, risk management, and the latest market trends. In the Q&A section, users can post specific questions and receive answers from other users and experts. This allows users to resolve their doubts and gain a deeper understanding. In online workshops, financial experts lecture on specific topics, and users can ask questions and participate in discussions in real time. This allows users to directly learn expert knowledge and acquire practical skills. Furthermore, the financial literacy community platform implements a points system, linking community contributions with rewards. For example, users can earn points for posting in forums and answering questions in the Q&A section, and exchange these points for rewards. Rewards include access to premium content and opportunities for individual consultations with experts. This allows users to actively participate in the community and improve their financial literacy while collaborating with other users. The community platform also includes features to record user activity history and award badges and titles based on contributions. This allows users to participate with greater motivation as their contributions are recognized. Furthermore, the platform regularly hosts events and challenges, providing an environment where users can learn while having fun. In this way, the financial literacy community platform provides strong support for users to learn from and grow together.

[0033] Microlearning finance apps include concise lessons, quizzes, and game elements. For example, a microlearning finance app might offer 3-minute video lessons or summarized text. It can also provide quizzes with multiple-choice and written answers. Furthermore, it can offer game elements, including point systems and level-up features. This allows users to improve their financial literacy while having fun.

[0034] Virtual stock trading game apps provide trading advice using AI guides. For example, virtual stock trading game apps feature AI guides that generate advice using natural language processing and personalize it using machine learning. Virtual stock trading game apps allow users to virtually experience real stock trading and improve their trading skills without taking risks.

[0035] A community platform for improving financial literacy can implement a points system, linking community contributions with rewards. For example, users can earn points for posting in forums and answering questions in the Q&A section, and then exchange those points for rewards. This can increase user motivation.

[0036] Microlearning finance apps can analyze a user's past learning history and suggest the optimal learning path. For example, a microlearning finance app can suggest a learning path that focuses on reviewing topics the user has struggled with in the past. It can also suggest a learning path that further explores areas the user excels in. Furthermore, a microlearning finance app can automatically select the next topic to learn based on the user's learning progress. This can improve the user's learning efficiency.

[0037] Microlearning finance apps can automatically adjust the timing of learning sessions to match the user's daily routine. For example, a microlearning finance app can suggest a morning learning session based on the user's wake-up time. It can also provide short learning sessions that can be completed during the user's commute. Furthermore, a microlearning finance app can suggest a learning session with relaxing content before the user goes to bed. This allows for learning that is tailored to the user's lifestyle.

[0038] Microlearning finance apps can provide region-specific financial knowledge by taking into account the user's geographical location. For example, a microlearning finance app can provide information on the tax and financial systems of the area where the user lives. It can also provide information on the currency and financial customs of the region the user is traveling to. Furthermore, a microlearning finance app can provide information on financial institutions and investment opportunities in the area where the user plans to move. This allows for a deeper understanding of the user by providing region-specific financial knowledge.

[0039] Microlearning finance apps can analyze users' social media activity and recommend relevant learning content. For example, a microlearning finance app can provide learning content related to topics users have shown interest in on social media. It can also suggest learning content based on posts from financial experts users follow. Furthermore, a microlearning finance app can provide learning content related to topics in online communities users participate in. This allows for the delivery of learning content tailored to the user's interests.

[0040] Virtual stock trading game apps can analyze a user's past trading history and suggest optimal trading strategies. For example, a virtual stock trading game app can suggest similar strategies based on the user's past successful trading patterns. It can also suggest strategies to avoid past unsuccessful trading patterns. Furthermore, based on the user's trading history, a virtual stock trading game app can suggest strategies tailored to their risk tolerance. This allows for the optimization of the user's trading strategy.

[0041] Virtual stock trading game apps can customize trading game scenarios according to the user's skill level. For example, a virtual stock trading game app can provide basic trading scenarios for beginner users. It can also provide trading scenarios of moderate difficulty for intermediate users. Furthermore, it can provide advanced trading scenarios for expert users. This allows for the provision of trading scenarios tailored to the user's skill level.

[0042] Virtual stock trading game apps can provide region-specific market data by taking into account the user's geographical location. For example, a virtual stock trading game app can provide stock market data for the region where the user lives. It can also provide market data for the region the user is traveling to. Furthermore, a virtual stock trading game app can provide market data for regions the user is interested in. This allows for a deeper understanding of the user by providing region-specific market data.

[0043] Virtual stock trading game apps can analyze users' social media activity and recommend relevant trading scenarios. For example, a virtual stock trading game app can provide stock scenarios for companies that users have shown interest in on social media. It can also suggest trading scenarios based on posts from investors that users follow. Furthermore, a virtual stock trading game app can provide trading scenarios related to topics in online communities that users participate in. This allows for the provision of trading scenarios tailored to the user's interests.

[0044] A community platform for improving financial literacy can analyze users' past community activities and suggest the most suitable methods for information sharing. For example, it can suggest relevant topics based on discussions users have previously participated in. It can also provide relevant information based on content users have previously posted. Furthermore, it can suggest the most suitable methods for information sharing based on users' past activity history. This allows the platform to provide the most appropriate information sharing methods based on users' past activities.

[0045] A community platform for improving financial literacy can offer special rewards and benefits based on users' contributions within the community. For example, a financial literacy community platform could offer special badges or titles to users who make many posts. It could also award points to users who receive high ratings from other members. Furthermore, a financial literacy community platform could offer benefits to users who participate in community events. By offering rewards and benefits based on user contributions, it can increase motivation to participate in the community.

[0046] A community platform for improving financial literacy can provide region-specific financial event information, taking into account the user's geographical location. For example, it could provide information on financial seminars and workshops in the user's area of ​​residence. It could also provide information on financial events in the area the user is traveling to. Furthermore, it could provide information on financial events in areas of interest to the user. This allows for a deeper understanding of regional financial events by providing relevant information.

[0047] A community platform for improving financial literacy can analyze users' social media activity and recommend relevant online workshops. For example, it can offer online workshops related to topics users have shown interest in on social media. It can also suggest online workshops based on posts from financial experts users follow. Furthermore, it can offer online workshops related to topics within online communities users participate in. This allows for the provision of online workshops tailored to user interests.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The financial literacy improvement system can analyze a user's past learning history and suggest the optimal learning path. For example, it can suggest a learning path that focuses on reviewing topics the user has struggled with in the past. It can also suggest a learning path that further explores areas the user excels in. Furthermore, it can automatically select the next topic to learn based on the user's learning progress. This improves the user's learning efficiency.

[0050] The financial literacy improvement system can automatically adjust the timing of learning sessions to match the user's daily routine. For example, it can suggest a morning learning session to coincide with the user's wake-up time. It can also provide short learning sessions that can be completed during the user's commute. Furthermore, it can suggest a learning session with relaxing content before the user goes to bed. This allows for learning that is tailored to the user's lifestyle.

[0051] A financial literacy enhancement system can provide region-specific financial knowledge by taking into account the user's geographical location. For example, it can provide information on the tax and financial systems of the area where the user lives. It can also provide information on the currency and financial customs of the region the user is traveling to. Furthermore, it can provide information on financial institutions and investment opportunities in the area where the user plans to move. In this way, by providing region-specific financial knowledge, it can deepen the user's understanding.

[0052] The financial literacy improvement system can analyze users' social media activity and recommend relevant learning content. For example, it can provide learning content related to topics users have shown interest in on social media. It can also suggest learning content based on posts from financial experts users follow. Furthermore, it can provide learning content related to topics in online communities users participate in. This allows for the provision of learning content tailored to the user's interests.

[0053] The financial literacy improvement system can analyze a user's past trading history and suggest optimal trading strategies. For example, it can suggest similar strategies based on the user's past successful trading patterns. It can also suggest strategies to avoid past unsuccessful trading patterns. Furthermore, it can suggest strategies tailored to the user's risk tolerance based on their trading history. This allows for the optimization of the user's trading strategy.

[0054] The financial literacy improvement system can customize trading game scenarios according to the user's skill level. For example, it can provide basic trading scenarios for beginner users, moderately difficult scenarios for intermediate users, and advanced scenarios for expert users. This allows the system to provide trading scenarios tailored to each user's skill level.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: Microlearning finance apps provide short daily learning sessions. For example, users can build up basic financial knowledge and skills by spending a little time each day. Specifically, they offer 3-minute video lessons, summary texts, quizzes with multiple-choice and written answers, and game elements including a points system and level-up features. Step 2: The virtual stock trading game app provides trading advice using an AI guide. For example, it features an AI guide that generates advice using natural language processing and personalizes it using machine learning. This allows users to virtually experience real stock trading and improve their trading skills without taking risks. Step 3: The community platform for improving financial literacy will share information through forums, Q&A sections, and online workshops. For example, users can share information in forums, answer questions in the Q&A section, and receive advice from experts in online workshops. A points system will also be implemented, linking community contributions with rewards. This will allow users to earn points for posting in forums and answering questions in the Q&A section, which can then be exchanged for rewards.

[0057] (Example of form 2) The financial literacy improvement system according to an embodiment of the present invention is a comprehensive service that supports financial literacy improvement for young people. This system combines three elements: a microlearning finance app that provides short daily learning sessions, a virtual stock trading game app where an AI guide provides trading advice, and a community platform for financial literacy improvement where information is shared through forums, Q&A sections, and online workshops. For example, the microlearning finance app provides short daily learning sessions, allowing users to build up basic financial knowledge and skills by spending a small amount of time each day. The app incorporates concise lessons, quizzes, and game elements, allowing users to improve their financial literacy while having fun. Next, the AI-guided virtual stock trading game app allows users to virtually experience real stock trading, with the AI ​​guide providing trading advice. Users can improve their trading skills and learn about asset growth and risk management without taking risks. Finally, the community platform for financial literacy improvement allows users to share information with other members and receive advice from experts through forums, Q&A sections, online workshops, etc. A point system is introduced, linking contributions to the community with rewards to enhance user motivation. This allows the financial literacy improvement system to comprehensively support the improvement of financial literacy among young people.

[0058] The financial literacy improvement system according to this embodiment comprises a microlearning finance app, a virtual stock trading game app, and a community platform for improving financial literacy. The microlearning finance app provides short daily learning sessions. For example, the microlearning finance app allows users to build up basic financial knowledge and skills by spending a small amount of time each day. The microlearning finance app includes concise lessons, quizzes, and game elements. For example, the microlearning finance app provides 3-minute video lessons and summary texts. The microlearning finance app can also provide quizzes with multiple-choice and written answers. Furthermore, the microlearning finance app can also provide game elements including a point system and level-up features. The virtual stock trading game app provides trading advice using an AI guide. For example, the virtual stock trading game app includes an AI guide that generates advice using natural language processing and personalizes it using machine learning. The virtual stock trading game app allows users to virtually experience real stock trading and improve their trading skills without taking risks. A community platform for improving financial literacy shares information through forums, Q&A sections, and online workshops. For example, users can share information in forums, answer questions in Q&A sections, and receive advice from experts in online workshops. The community platform also implements a points system, linking contributions to the community with rewards. For instance, users can earn points for posting in forums and answering questions in Q&A sections, and then exchange those points for rewards. This allows the financial literacy system to comprehensively support financial literacy improvement for young people.

[0059] Microlearning finance apps offer short daily learning sessions. For example, they allow users to build up basic financial knowledge and skills by spending just a little time each day. Specifically, the app provides users with 3-minute video lessons and displays summarized text. This allows users to learn efficiently in a short amount of time. The app also provides quizzes with multiple-choice and written answers to check what users have learned. For example, multiple-choice questions may include definitions of financial terms and basic calculation problems, while written answers require users to explain things in their own words. Furthermore, the app offers gamified elements, including a point system and level-up features. Users earn points by answering quizzes correctly, and level up when they accumulate a certain number of points. Leveling up unlocks new lessons and quizzes, maintaining motivation to learn. In this way, microlearning finance apps allow users to acquire financial knowledge and improve their skills while having fun. The app also has a feature that records the user's learning history and visualizes their progress. This allows users to feel their own growth and further increase their motivation to learn. Furthermore, the app can provide a customized learning plan tailored to the user's learning style and pace. For example, if a user is interested in a particular area, it can prioritize providing lessons and quizzes related to that area. This allows microlearning finance apps to provide an optimal learning experience for each individual user and effectively support the improvement of financial literacy.

[0060] The virtual stock trading game app provides trading advice using an AI guide. For example, the app features an AI guide that generates advice using natural language processing and personalizes it using machine learning. Specifically, when a user engages in virtual stock trading, the AI ​​guide provides advice in real time. The AI ​​guide analyzes the user's trading history and current market conditions to propose the optimal trading strategy. For example, when a user is considering purchasing a particular stock, the AI ​​guide advises on the timing of purchase and sale based on the stock's past performance and current market trends. The AI ​​guide also provides personalized advice based on the user's trading style and risk tolerance. This allows users to learn trading strategies that suit them and improve their trading skills without taking risks. Furthermore, the virtual stock trading game app also includes a feedback function to allow users to review and learn from their trading results. For example, it analyzes the factors contributing to the success and failure of the user's trades and provides advice to help them improve in future trades. This allows users to gain experience in an environment similar to actual trading and effectively improve their trading skills. Furthermore, the virtual stock trading game app also features a ranking function that allows users to compete with each other, as well as a function to hold trading contests. This allows users to improve their trading skills while having fun and competing with other users. In addition, the app regularly provides the latest market information and trends, ensuring that users can always trade based on the most up-to-date information. As a result, the virtual stock trading game app is a powerful tool for users to hone their skills in a realistic trading environment and improve their financial literacy.

[0061] The financial literacy community platform shares information through forums, Q&A sections, and online workshops. For example, users can share information in forums, answer questions in the Q&A section, and receive advice from experts in online workshops. Specifically, the forums allow users to discuss and share knowledge on a variety of financial topics. For instance, a wide range of themes are discussed, such as investment fundamentals, risk management, and the latest market trends. In the Q&A section, users can post specific questions and receive answers from other users and experts. This allows users to resolve their doubts and gain a deeper understanding. In online workshops, financial experts lecture on specific topics, and users can ask questions and participate in discussions in real time. This allows users to directly learn expert knowledge and acquire practical skills. Furthermore, the financial literacy community platform implements a points system, linking community contributions with rewards. For example, users can earn points for posting in forums and answering questions in the Q&A section, and exchange these points for rewards. Rewards include access to premium content and opportunities for individual consultations with experts. This allows users to actively participate in the community and improve their financial literacy while collaborating with other users. The community platform also includes features to record user activity history and award badges and titles based on contributions. This allows users to participate with greater motivation as their contributions are recognized. Furthermore, the platform regularly hosts events and challenges, providing an environment where users can learn while having fun. In this way, the financial literacy community platform provides strong support for users to learn from and grow together.

[0062] Microlearning finance apps include concise lessons, quizzes, and game elements. For example, a microlearning finance app might offer 3-minute video lessons or summarized text. It can also provide quizzes with multiple-choice and written answers. Furthermore, it can offer game elements, including point systems and level-up features. This allows users to improve their financial literacy while having fun.

[0063] Virtual stock trading game apps provide trading advice using AI guides. For example, virtual stock trading game apps feature AI guides that generate advice using natural language processing and personalize it using machine learning. Virtual stock trading game apps allow users to virtually experience real stock trading and improve their trading skills without taking risks.

[0064] A community platform for improving financial literacy can implement a points system, linking community contributions with rewards. For example, users can earn points for posting in forums and answering questions in the Q&A section, and then exchange those points for rewards. This can increase user motivation.

[0065] Microlearning finance apps can estimate a user's emotions and adjust the content and difficulty of learning sessions based on those emotions. For example, if a user is feeling stressed, the microlearning finance app can offer easy quizzes or relaxing learning sessions. Conversely, if a user is excited, it can offer challenging problems or advanced learning sessions. Furthermore, if a user is tired, it can offer short, easily completed learning sessions. This allows for the provision of an optimal learning experience tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0066] Microlearning finance apps can analyze a user's past learning history and suggest the optimal learning path. For example, a microlearning finance app can suggest a learning path that focuses on reviewing topics the user has struggled with in the past. It can also suggest a learning path that further explores areas the user excels in. Furthermore, a microlearning finance app can automatically select the next topic to learn based on the user's learning progress. This can improve the user's learning efficiency.

[0067] Microlearning finance apps can automatically adjust the timing of learning sessions to match the user's daily routine. For example, a microlearning finance app can suggest a morning learning session based on the user's wake-up time. It can also provide short learning sessions that can be completed during the user's commute. Furthermore, a microlearning finance app can suggest a learning session with relaxing content before the user goes to bed. This allows for learning that is tailored to the user's lifestyle.

[0068] A microlearning finance app can estimate the user's emotions and change the order of learning modules based on those emotions. For example, if the user is stressed, the microlearning finance app can provide relaxing learning modules first. It can also provide challenging learning modules first if the user is excited. Furthermore, if the user is tired, it can provide easy learning modules first. This allows for the delivery of learning modules tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] Microlearning finance apps can provide region-specific financial knowledge by taking into account the user's geographical location. For example, a microlearning finance app can provide information on the tax and financial systems of the area where the user lives. It can also provide information on the currency and financial customs of the region the user is traveling to. Furthermore, a microlearning finance app can provide information on financial institutions and investment opportunities in the area where the user plans to move. This allows for a deeper understanding of the user by providing region-specific financial knowledge.

[0070] Microlearning finance apps can analyze users' social media activity and recommend relevant learning content. For example, a microlearning finance app can provide learning content related to topics users have shown interest in on social media. It can also suggest learning content based on posts from financial experts users follow. Furthermore, a microlearning finance app can provide learning content related to topics in online communities users participate in. This allows for the delivery of learning content tailored to the user's interests.

[0071] A virtual stock trading game app can estimate the user's emotions and adjust the content and timing of trading advice based on those emotions. For example, if the user is feeling nervous, the app can provide low-risk trading advice. Conversely, if the user is excited, it can provide high-risk but potentially high-return trading advice. Furthermore, if the user is tired, it can provide simple and easy-to-understand trading advice. This allows the app to provide trading advice tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] Virtual stock trading game apps can analyze a user's past trading history and suggest optimal trading strategies. For example, a virtual stock trading game app can suggest similar strategies based on the user's past successful trading patterns. It can also suggest strategies to avoid past unsuccessful trading patterns. Furthermore, based on the user's trading history, a virtual stock trading game app can suggest strategies tailored to their risk tolerance. This allows for the optimization of the user's trading strategy.

[0073] Virtual stock trading game apps can customize trading game scenarios according to the user's skill level. For example, a virtual stock trading game app can provide basic trading scenarios for beginner users. It can also provide trading scenarios of moderate difficulty for intermediate users. Furthermore, it can provide advanced trading scenarios for expert users. This allows for the provision of trading scenarios tailored to the user's skill level.

[0074] A virtual stock trading game app can estimate the user's emotions and adjust the difficulty of the trading simulation based on those emotions. For example, if the user is stressed, the app can provide a low-difficulty simulation. Conversely, if the user is excited, it can provide a high-difficulty simulation. Furthermore, if the user is relaxed, it can provide a simulation of moderate difficulty. This allows the app to provide trading simulations tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] Virtual stock trading game apps can provide region-specific market data by taking into account the user's geographical location. For example, a virtual stock trading game app can provide stock market data for the region where the user lives. It can also provide market data for the region the user is traveling to. Furthermore, a virtual stock trading game app can provide market data for regions the user is interested in. This allows for a deeper understanding of the user by providing region-specific market data.

[0076] Virtual stock trading game apps can analyze users' social media activity and recommend relevant trading scenarios. For example, a virtual stock trading game app can provide stock scenarios for companies that users have shown interest in on social media. It can also suggest trading scenarios based on posts from investors that users follow. Furthermore, a virtual stock trading game app can provide trading scenarios related to topics in online communities that users participate in. This allows for the provision of trading scenarios tailored to the user's interests.

[0077] A community platform for improving financial literacy can estimate a user's emotions and adjust the content displayed on the forum based on that estimation. For example, if a user is feeling stressed, the platform can prioritize displaying relaxing topics and positive posts. It can also prioritize displaying challenging topics and discussions if the user is feeling agitated. Furthermore, if the user is feeling tired, it can prioritize displaying simple and easy-to-understand posts. This allows the platform to provide forum content tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] A community platform for improving financial literacy can analyze users' past community activities and suggest the most suitable methods for information sharing. For example, it can suggest relevant topics based on discussions users have previously participated in. It can also provide relevant information based on content users have previously posted. Furthermore, it can suggest the most suitable methods for information sharing based on users' past activity history. This allows the platform to provide the most appropriate information sharing methods based on users' past activities.

[0079] A community platform for improving financial literacy can offer special rewards and benefits based on users' contributions within the community. For example, a financial literacy community platform could offer special badges or titles to users who make many posts. It could also award points to users who receive high ratings from other members. Furthermore, a financial literacy community platform could offer benefits to users who participate in community events. By offering rewards and benefits based on user contributions, it can increase motivation to participate in the community.

[0080] A community platform for improving financial literacy can estimate a user's emotions and change the display order of the Q&A section based on the estimated emotions. For example, if a user is feeling stressed, the platform can prioritize displaying simple and easy-to-understand questions and answers. It can also prioritize displaying challenging questions and answers if the user is feeling agitated. Furthermore, if the user is tired, it can prioritize displaying short and concise questions and answers. This provides a display order for the Q&A section that is tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] A community platform for improving financial literacy can provide region-specific financial event information, taking into account the user's geographical location. For example, it could provide information on financial seminars and workshops in the user's area of ​​residence. It could also provide information on financial events in the area the user is traveling to. Furthermore, it could provide information on financial events in areas of interest to the user. This allows for a deeper understanding of regional financial events by providing relevant information.

[0082] A community platform for improving financial literacy can analyze users' social media activity and recommend relevant online workshops. For example, it can offer online workshops related to topics users have shown interest in on social media. It can also suggest online workshops based on posts from financial experts users follow. Furthermore, it can offer online workshops related to topics within online communities users participate in. This allows for the provision of online workshops tailored to user interests.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The financial literacy improvement system can estimate the user's emotions and adjust the content and difficulty of learning sessions based on those emotions. For example, if the user is stressed, it can provide easy quizzes or relaxing learning sessions. If the user is excited, it can provide challenging problems or advanced learning sessions. Furthermore, if the user is tired, it can provide learning sessions that can be completed in a short time. This allows for the provision of an optimal learning experience tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The financial literacy improvement system can analyze a user's past learning history and suggest the optimal learning path. For example, it can suggest a learning path that focuses on reviewing topics the user has struggled with in the past. It can also suggest a learning path that further explores areas the user excels in. Furthermore, it can automatically select the next topic to learn based on the user's learning progress. This improves the user's learning efficiency.

[0086] The financial literacy improvement system can automatically adjust the timing of learning sessions to match the user's daily routine. For example, it can suggest a morning learning session to coincide with the user's wake-up time. It can also provide short learning sessions that can be completed during the user's commute. Furthermore, it can suggest a learning session with relaxing content before the user goes to bed. This allows for learning that is tailored to the user's lifestyle.

[0087] The financial literacy improvement system can estimate the user's emotions and change the order of learning modules based on those emotions. For example, if the user is stressed, relaxing learning modules can be provided first. If the user is excited, challenging learning modules can be provided first. Furthermore, if the user is tired, easy learning modules can be provided first. This makes it possible to provide learning modules that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] A financial literacy enhancement system can provide region-specific financial knowledge by taking into account the user's geographical location. For example, it can provide information on the tax and financial systems of the area where the user lives. It can also provide information on the currency and financial customs of the region the user is traveling to. Furthermore, it can provide information on financial institutions and investment opportunities in the area where the user plans to move. In this way, by providing region-specific financial knowledge, it can deepen the user's understanding.

[0089] The financial literacy improvement system can analyze users' social media activity and recommend relevant learning content. For example, it can provide learning content related to topics users have shown interest in on social media. It can also suggest learning content based on posts from financial experts users follow. Furthermore, it can provide learning content related to topics in online communities users participate in. This allows for the provision of learning content tailored to the user's interests.

[0090] The financial literacy improvement system can estimate the user's emotions and adjust the content and timing of trading advice based on those emotions. For example, if the user is stressed, it can provide low-risk trading advice. If the user is excited, it can provide high-risk but high-return trading advice. Furthermore, if the user is tired, it can provide simple and easy-to-understand trading advice. This allows the system to provide trading advice tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The financial literacy improvement system can analyze a user's past trading history and suggest optimal trading strategies. For example, it can suggest similar strategies based on the user's past successful trading patterns. It can also suggest strategies to avoid past unsuccessful trading patterns. Furthermore, it can suggest strategies tailored to the user's risk tolerance based on their trading history. This allows for the optimization of the user's trading strategy.

[0092] The financial literacy improvement system can customize trading game scenarios according to the user's skill level. For example, it can provide basic trading scenarios for beginner users, moderately difficult scenarios for intermediate users, and advanced scenarios for expert users. This allows the system to provide trading scenarios tailored to each user's skill level.

[0093] The financial literacy improvement system can estimate the user's emotions and adjust the difficulty of the trading simulation based on those emotions. For example, if the user is stressed, it can provide a simulation with a lower difficulty level. If the user is excited, it can provide a simulation with a higher difficulty level. Furthermore, if the user is relaxed, it can provide a simulation with a moderate difficulty level. This allows the system to provide trading simulations that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: Microlearning finance apps provide short daily learning sessions. For example, users can build up basic financial knowledge and skills by spending a little time each day. Specifically, they offer 3-minute video lessons, summary texts, quizzes with multiple-choice and written answers, and game elements including a points system and level-up features. Step 2: The virtual stock trading game app provides trading advice using an AI guide. For example, it features an AI guide that generates advice using natural language processing and personalizes it using machine learning. This allows users to virtually experience real stock trading and improve their trading skills without taking risks. Step 3: The community platform for improving financial literacy will share information through forums, Q&A sections, and online workshops. For example, users can share information in forums, answer questions in the Q&A section, and receive advice from experts in online workshops. A points system will also be implemented, linking community contributions with rewards. This will allow users to earn points for posting in forums and answering questions in the Q&A section, which can then be exchanged for rewards.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0098] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0099] Each of the elements described above, including the microlearning finance app, the virtual stock trading game app, and the community platform for improving financial literacy, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the microlearning finance app is implemented by the control unit 46A of the smart device 14, allowing users to receive short daily learning sessions. The virtual stock trading game app is implemented by the specific processing unit 290 of the data processing device 12, where an AI guide provides trading advice. The community platform for improving financial literacy is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12, where information is shared through forums, Q&A sections, and online workshops. The correspondence between each part and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0106] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0107] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0108] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0109] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0110] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0114] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the elements described above, including the microlearning finance app, the virtual stock trading game app, and the community platform for improving financial literacy, is implemented by at least one of the smart glasses 214 and the data processing device 12. For example, the microlearning finance app is implemented by the control unit 46A of the smart glasses 214, allowing the user to receive short daily learning sessions. The virtual stock trading game app is implemented by the specific processing unit 290 of the data processing device 12, where an AI guide provides trading advice. The community platform for improving financial literacy is implemented by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, where information is shared through forums, Q&A sections, and online workshops. The correspondence between each part and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the microlearning finance app, the virtual stock trading game app, and the community platform for improving financial literacy, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the microlearning finance app is implemented by the control unit 46A of the headset terminal 314, allowing users to receive short daily learning sessions. The virtual stock trading game app is implemented by the specific processing unit 290 of the data processing device 12, where an AI guide provides trading advice. The community platform for improving financial literacy is implemented by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12, where information is shared through forums, Q&A sections, and online workshops. The correspondence between each part and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0140] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the elements described above, including the microlearning finance app, the virtual stock trading game app, and the community platform for improving financial literacy, is implemented by, for example, at least one of the robot 414 and the data processing device 12. For example, the microlearning finance app is implemented by the control unit 46A of the robot 414, allowing users to receive short daily learning sessions. The virtual stock trading game app is implemented by, for example, the specific processing unit 290 of the data processing device 12, where an AI guide provides trading advice. The community platform for improving financial literacy is implemented by, for example, the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12, where information is shared through forums, Q&A sections, and online workshops. The correspondence between each part and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0149] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0151] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0152] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0153] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0157] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0158] 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.

[0159] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0160] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0161] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0162] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0164] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0165] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0166] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0167] (Note 1) A microlearning finance app that provides short daily learning sessions, A virtual stock trading game app where an AI guide provides trading advice, It features a community platform for improving financial literacy, sharing information through forums, Q&A sections, and online workshops. A system characterized by the following features. (Note 2) The aforementioned microlearning finance app is Includes concise lessons, quizzes, and game elements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned virtual stock trading game app is We provide trading advice using AI guides. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned community platform for improving financial literacy is We will introduce a points system and link community contributions to rewards. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned microlearning finance app is It estimates the user's emotions and adjusts the content and difficulty level of the learning session based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned microlearning finance app is It analyzes the user's past learning history and suggests the optimal learning path. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned microlearning finance app is The timing of learning sessions is automatically adjusted to match the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned microlearning finance app is It estimates the user's emotions and changes the order of the learning modules based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned microlearning finance app is Providing region-specific financial knowledge while taking the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned microlearning finance app is Analyze users' social media activity and recommend relevant learning content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned virtual stock trading game app is It estimates the user's emotions and adjusts the content and timing of trading advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned virtual stock trading game app is We analyze the user's past trading history and propose the optimal trading strategy. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned virtual stock trading game app is Customize trading game scenarios according to the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned virtual stock trading game app is The system estimates the user's emotions and adjusts the difficulty of the trading simulation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned virtual stock trading game app is Providing region-specific market data while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned virtual stock trading game app is Analyze users' social media activity and recommend relevant trading scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned community platform for improving financial literacy is The system estimates user sentiment and adjusts the forum's display content based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned community platform for improving financial literacy is We analyze users' past community activities and suggest the most suitable information sharing methods. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned community platform for improving financial literacy is We offer special rewards and benefits based on the user's contribution within the community. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned community platform for improving financial literacy is It estimates the user's sentiment and changes the display order of the Q&A section based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned community platform for improving financial literacy is Providing region-specific financial event information while taking the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned community platform for improving financial literacy is Analyze users' social media activity and recommend relevant online workshops. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A microlearning finance app that provides short daily learning sessions, A virtual stock trading game app where an AI guide provides trading advice, It features a community platform for improving financial literacy, sharing information through forums, Q&A sections, and online workshops. A system characterized by the following features.

2. The aforementioned microlearning finance app is Includes concise lessons, quizzes, and game elements. The system according to feature 1.

3. The aforementioned virtual stock trading game app is We provide trading advice using AI guides. The system according to feature 1.

4. The aforementioned community platform for improving financial literacy is We will introduce a points system and link community contributions to rewards. The system according to feature 1.

5. The aforementioned microlearning finance app is It estimates the user's emotions and adjusts the content and difficulty level of the learning session based on the estimated user emotions. The system according to feature 1.

6. The aforementioned microlearning finance app is It analyzes the user's past learning history and suggests the optimal learning path. The system according to feature 1.

7. The aforementioned microlearning finance app is The timing of learning sessions is automatically adjusted to match the user's daily routine. The system according to feature 1.

8. The aforementioned microlearning finance app is It estimates the user's emotions and changes the order of the learning modules based on the estimated user emotions. The system according to feature 1.

9. The aforementioned microlearning finance app is Providing region-specific financial knowledge while taking the user's geographical location into consideration. The system according to feature 1.

10. The aforementioned microlearning finance app is Analyze users' social media activity and recommend relevant learning content. The system according to feature 1.

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