system

Abstract

The system according to this embodiment aims to improve investment literacy by providing personalized investment advice to young people who are new to investing. [Solution] The system according to the embodiment comprises an analysis unit, an advice unit, a monitoring unit, an education unit, and a risk management unit. The analysis unit analyzes the user's profile and investment behavior. The advice unit provides personalized advice based on the information obtained by the analysis unit. The monitoring unit monitors market trends based on the advice provided by the advice unit and sends alerts for important news and market trends. The education unit provides investment education content based on the information obtained by the monitoring unit. The risk management unit proposes a risk management strategy based on the education content provided by the education unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 is a problem that it is difficult to provide personalized investment advice to young novice investors, and the improvement of investment literacy has not been sufficiently achieved.

[0005] The system according to the embodiment aims to provide personalized investment advice to young novice investors and improve investment literacy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, an advice unit, a monitoring unit, an education unit, and a risk management unit. The analysis unit analyzes the user's profile and investment behavior. The advice unit provides personalized advice based on the information obtained by the analysis unit. The monitoring unit monitors market trends based on the advice provided by the advice unit and sends alerts for important news and market trends. The education unit provides investment education content based on the information obtained by the monitoring unit. The risk management unit proposes risk management strategies based on the education content provided by the education unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized investment advice to young, novice investors and improve their investment 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 signed communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 AI ​​investment partner system according to an embodiment of the present invention is an innovative AI agent that provides personalized investment advice to young, novice investors. This AI investment partner system aims to improve investment literacy by proposing optimal investment plans and stocks based on the user's individual needs and risk tolerance. For example, the AI ​​investment partner system proposes optimal investment plans and stocks based on information such as the user's age, income, investment experience, and risk tolerance. This allows the user to make investments that suit them. Next, the AI ​​investment partner system is equipped with a function to send alerts for important news and market trends based on real-time market monitoring. The AI ​​investment partner system analyzes the latest market trends and news and provides important information to the user in real time. For example, if there is a sudden market fluctuation or important news regarding a particular stock occurs, an alert is sent to the user. This allows the user to respond quickly. Furthermore, the AI ​​investment partner system is equipped with a function to provide investment education content. The AI ​​investment partner system provides educational content on investment to support the improvement of the user's investment literacy. For example, users can learn basic investment knowledge, risk management methods, and points to consider when selecting stocks. This allows the user to deepen their knowledge of investment. In addition, the AI ​​investment partner system is equipped with a natural conversational interface using generative AI. Users can receive investment advice through natural conversations via generated AI. For example, if a user asks, "What are the recommended investments for this month?", the generated AI will suggest the most suitable investments based on the user's profile and market trends. This allows users to receive investment advice intuitively. Furthermore, the AI ​​investment partner system utilizes risk management tools to suggest appropriate risk management strategies to users. The AI ​​investment partner system analyzes the user's risk tolerance and investment situation and provides specific advice for risk management. For example, it suggests portfolio diversification and risk hedging methods. This allows users to invest while appropriately managing risk.Thus, the AI ​​Investment Partner System is an innovative service that provides personalized investment advice to young, novice investors, supporting the improvement of their investment literacy. With the rapid growth of the fintech market, now is the optimal time to enter the market, especially given the high demand from young people. This agent aims to promote the financial independence and investment literacy of young people. As a result, the AI ​​Investment Partner System can provide personalized investment advice to young, novice investors, supporting the improvement of their investment literacy.

[0029] The AI ​​investment partner system according to this embodiment comprises an analysis unit, an advice unit, a monitoring unit, an education unit, and a risk management unit. The analysis unit analyzes the user's profile and investment behavior. The user's profile includes, but is not limited to, age, income, investment experience, and risk tolerance. For example, the analysis unit proposes an optimal investment plan based on the user's age and income. The analysis unit can also select appropriate stocks considering the user's investment experience and risk tolerance. For example, the analysis unit analyzes the user's past investment history and proposes a portfolio that matches their risk tolerance. The advice unit provides personalized advice based on the information obtained by the analysis unit. For example, the advice unit proposes an optimal investment plan and stocks based on the user's profile and investment behavior. The advice unit can also provide advice to the user through a natural conversational interface using generative AI. For example, when the user asks, "What are the recommended investments for this month?", the generative AI proposes the optimal investment based on the user's profile and market trends. The monitoring unit monitors market trends based on the advice provided by the advice unit and sends alerts for important news and market trends. The Monitoring Department analyzes the latest market trends and news, for example, and provides users with important information in real time. For example, the Monitoring Department sends alerts to users when there are sudden market fluctuations or important news regarding specific stocks. The Education Department provides investment education content based on the information obtained by the Monitoring Department. For example, the Education Department provides educational content such as basic investment knowledge, risk management methods, and points to consider when selecting stocks. For example, the Education Department provides online courses and materials to help users deepen their knowledge of investing. The Risk Management Department proposes risk management strategies based on the educational content provided by the Education Department. For example, the Risk Management Department analyzes users' risk tolerance and investment status and provides specific advice for risk management. For example, the Risk Management Department proposes methods for portfolio diversification and risk hedging.As a result, the AI ​​investment partner system according to this embodiment can analyze the user's profile and investment behavior, provide personalized advice, monitor market trends in real time, provide investment education content, and propose risk management strategies.

[0030] The analytics department conducts a detailed analysis of user profiles and investment behavior. User profiles include, but are not limited to, age, income, investment experience, and risk tolerance. For example, based on a user's age and income, it proposes an optimal investment plan tailored to their life stage. For younger users, it recommends investments in growth stocks with a higher risk profile, while for users nearing retirement, it recommends bonds and dividend stocks that offer stable returns. It also selects appropriate securities considering the user's investment experience and risk tolerance. For example, it suggests low-risk index funds for users with little investment experience, and individual stocks or options trading for experienced users. Furthermore, the analytics department conducts a detailed analysis of users' past investment history and constructs a portfolio tailored to their risk tolerance. For example, for users who have experienced losses due to taking too much risk in the past, it proposes a portfolio with reduced risk. In this way, the analytics department can provide an optimal investment strategy tailored to each user's individual circumstances.

[0031] The advisory department provides personalized advice to users based on information obtained by the analysis department. Based on the user's profile and investment history, the advisory department proposes optimal investment plans and stocks. For example, considering the user's age, income, investment experience, and risk tolerance, it provides investment plans tailored to whether the user aims for short-term profits or long-term wealth building. Furthermore, the advisory department provides advice through a natural, conversational interface using generative AI. For instance, if a user asks, "What are the recommended investments for this month?", the generative AI will suggest the most suitable investments based on the user's profile and market trends. The generative AI analyzes the user's past investment behavior and current market conditions in real time to select the most appropriate investments. In addition, the advisory department collects user feedback to continuously improve the accuracy of its advice. This allows the advisory department to always provide users with optimal investment advice based on the latest information.

[0032] The monitoring department monitors market trends based on advice provided by the advisory department and sends alerts to users regarding important news and market trends. The monitoring department analyzes the latest market trends and news and provides users with important information in real time. For example, if there is a sudden market fluctuation or important news regarding a particular stock, the monitoring department will immediately send an alert to the user. The monitoring department uses AI to analyze a large amount of news articles and market data and extract information that is important to the user. For example, it provides information in real time that may affect the user's investments, such as sudden rises or falls in the price of a particular stock, the release of economic indicators, and corporate earnings announcements. Furthermore, the monitoring department prioritizes providing information on specific stocks and market sectors based on the user's investment portfolio. In this way, the monitoring department helps users make investment decisions based on the latest information at all times.

[0033] The Education Department provides investment education content based on information obtained by the Monitoring Department. The Education Department provides educational content covering basic investment knowledge, risk management methods, and key points for selecting stocks. For example, the Education Department offers online courses and materials to deepen users' investment knowledge. This content is tailored to users of all levels, from beginners to advanced investors. For example, beginners receive courses explaining basic investment concepts and terminology, and the relationship between risk and return. Advanced investors receive detailed explanations of technical and fundamental analysis methods and portfolio optimization. Furthermore, the Education Department continuously updates its content based on users' investment behavior and feedback, providing the latest information. This allows the Education Department to support users in maintaining up-to-date knowledge and making appropriate investment decisions.

[0034] The Risk Management Department proposes risk management strategies based on educational content provided by the Education Department. The Risk Management Department analyzes the user's risk tolerance and investment status and provides specific advice for risk management. For example, the Risk Management Department proposes methods for portfolio diversification and risk hedging. Specifically, it analyzes the user's investment portfolio and checks for any bias in specific stocks or market sectors. If bias is found, it recommends investing in different stocks or market sectors to diversify the portfolio. It also proposes the use of options trading and futures trading as risk hedging methods. For example, it recommends purchasing put options to hedge against the price decline risk of a specific stock. Furthermore, the Risk Management Department regularly monitors the user's investment status and reviews the risk management strategy as needed. In this way, the Risk Management Department helps users manage risk appropriately and achieve stable investment results.

[0035] The analysis department can propose optimal investment plans and stocks based on information such as the user's age, income, investment experience, and risk tolerance. For example, the analysis department can propose an optimal investment plan based on the user's age and income. For example, the analysis department can propose an investment plan with long-term growth potential to younger users. The analysis department can also select appropriate stocks considering the user's investment experience and risk tolerance. For example, the analysis department can propose low-risk stocks to users with little investment experience. Furthermore, the analysis department can propose portfolio diversification according to the user's risk tolerance. For example, the analysis department can propose a portfolio including high-risk stocks to users with a high risk tolerance. In this way, the analysis department can propose optimal investment plans and stocks based on the individual needs of each user.

[0036] The monitoring unit can analyze the latest market trends and news and provide users with important information in real time. For example, the monitoring unit can analyze the latest market trends and news and provide users with important information in real time. For example, the monitoring unit can send alerts to users when there are sudden market fluctuations or important news concerning specific stocks. The monitoring unit can monitor market trends such as stock price fluctuations and changes in economic indicators and provide users with important information. In addition, the monitoring unit can monitor important news such as corporate earnings announcements and policy changes and send alerts to users. For example, the monitoring unit can notify users when corporate earnings announcements are made. This allows the monitoring unit to provide users with important information in real time.

[0037] The Ministry of Education can provide educational content on basic investment knowledge, risk management methods, and key points for selecting stocks. For example, the Ministry of Education can provide online courses and materials to deepen users' knowledge of investing. Furthermore, the Ministry of Education can also provide educational content on risk management methods. For example, the Ministry of Education can provide educational content on risk diversification methods and risk assessment criteria. In addition, the Ministry of Education can provide educational content on key points for selecting stocks. For example, the Ministry of Education can provide educational content on key points for selecting stocks, such as company performance and growth potential. This allows the Ministry to provide educational content on basic investment knowledge, risk management methods, and key points for selecting stocks.

[0038] The Risk Management Department can analyze users' risk tolerance and investment status and provide specific advice for risk management. For example, the Risk Management Department can propose portfolio diversification according to the user's risk tolerance. The Risk Management Department can also provide advice on risk hedging methods. For example, the Risk Management Department can propose specific risk hedging measures to the user. Furthermore, the Risk Management Department can analyze the user's investment status and propose risk management strategies. For example, the Risk Management Department can propose the optimal risk management strategy according to the user's investment status. This allows the department to analyze users' risk tolerance and investment status and provide specific advice for risk management.

[0039] The advisory unit can provide personalized advice to users through a natural conversational interface using generative AI. For example, if a user asks, "What are the recommended investments for this month?", the generative AI will suggest the optimal investment based on the user's profile and market trends. The advisory unit can also use generative AI to provide advice based on the user's investment behavior. For example, the advisory unit will analyze the user's past investment history and suggest the optimal investment plan. Furthermore, the advisory unit can use generative AI to provide advice tailored to the user's risk tolerance. For example, the advisory unit will suggest high-risk stocks to users with a high risk tolerance. In this way, personalized advice can be provided to users through a natural conversational interface using generative AI.

[0040] The analysis unit can analyze a user's past investment behavior and select the optimal analysis method. For example, the analysis unit can analyze a user's past investment behavior and select the optimal analysis method. For example, the analysis unit can select a similar analysis method based on investment patterns in which the user has succeeded in the past. Alternatively, the analysis unit can select a different analysis method to avoid investment patterns in which the user has failed in the past. Furthermore, the analysis unit can cluster a user's past investment behavior and select the optimal analysis method. For example, the analysis unit can cluster a user's past investment behavior and select the optimal analysis method for each cluster. In this way, the optimal analysis method can be selected by analyzing a user's past investment behavior.

[0041] The analysis unit can filter results based on the user's current financial situation and areas of interest during the analysis process. For example, the analysis unit can suggest an appropriate investment plan considering the user's current income and expenses. The analysis unit can also prioritize the analysis of relevant stocks based on the user's areas of interest (e.g., technology, healthcare, etc.). Furthermore, the analysis unit can suggest lower-risk investment plans according to the user's financial situation. For example, the analysis unit can suggest a lower-risk investment plan considering the user's financial situation. This allows for the suggestion of a more appropriate investment plan by filtering results based on the user's current financial situation and areas of interest.

[0042] The analytics department can prioritize analyzing highly relevant data by considering the user's geographical location during analysis. For example, the analytics department can prioritize analyzing relevant data by considering the economic conditions of the area where the user lives. Furthermore, if the user is traveling, the analytics department can analyze data by considering the economic conditions of their current location. In addition, the analytics department can prioritize analyzing region-specific investment opportunities based on the user's geographical location. For example, the analytics department can prioritize analyzing region-specific investment opportunities based on the user's geographical location. This allows for the proposal of more appropriate investment plans by prioritizing the analysis of highly relevant data while considering the user's geographical location.

[0043] The analytics department can analyze users' social media activity and obtain relevant data during analysis. For example, the analytics department can analyze relevant data based on topics that users have shown interest in on social media. The analytics department can also analyze data by referring to the investment behavior of users' followers and friends on social media. Furthermore, the analytics department can identify current interests from users' social media activity and prioritize the analysis of relevant data. For example, the analytics department can identify current interests from users' social media activity and prioritize the analysis of relevant data. This allows the analytics department to obtain relevant data by analyzing users' social media activity and propose more appropriate investment plans.

[0044] The advisory department can adjust the level of detail in its advice based on the importance of the investment. For example, it can provide detailed advice for important investments, while providing concise advice for general investments. Furthermore, it can provide prompt and detailed advice for urgent investments. By adjusting the level of detail based on the importance of the investment, it can provide more appropriate advice.

[0045] The advisory unit can apply different advisory algorithms depending on the investment category when providing advice. For example, the advisory unit can apply a stock-specific advisory algorithm for stock investments. It can also apply a real estate-specific advisory algorithm for real estate investments. Furthermore, it can apply a cryptocurrency-specific advisory algorithm for cryptocurrency investments. By applying different advisory algorithms depending on the investment category, it can provide more appropriate advice.

[0046] The advisory department can prioritize advice based on the timing of investment submissions when providing advice. For example, the advisory department will prioritize advice for urgent investment projects. It can also provide prompt advice for investment projects with approaching submission deadlines. Furthermore, it can provide detailed advice for investment projects with distant submission deadlines. By prioritizing advice based on the timing of investment submissions, the advisory department can provide more appropriate advice.

[0047] The advisory department can prioritize advice based on the timing of investment submissions when providing advice. For example, the advisory department will prioritize advice for urgent investment projects. It can also provide prompt advice for investment projects with approaching submission deadlines. Furthermore, it can provide detailed advice for investment projects with distant submission deadlines. By prioritizing advice based on the timing of investment submissions, the advisory department can provide more appropriate advice.

[0048] The advisory department can adjust the order of advice based on the relevance of the investments when providing advice. For example, the advisory department can prioritize providing advice on highly relevant investment projects. Conversely, the advisory department can postpone providing advice on less relevant investment projects. Furthermore, the advisory department can analyze the relevance of investment projects and provide advice in the optimal order. For example, the advisory department can analyze the relevance of investment projects and provide advice in the optimal order. By adjusting the order of advice based on the relevance of the investments, it is possible to provide more appropriate advice.

[0049] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of investments during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by considering the interrelationships of investments during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by analyzing the correlations between investment projects. The monitoring unit can also improve the accuracy of monitoring by considering the interdependence of investment projects. Furthermore, the monitoring unit can improve the accuracy of monitoring by clustering the interrelationships of investment projects. For example, the monitoring unit can cluster the interrelationships of investment projects and apply the optimal monitoring method to each cluster. This allows for more accurate monitoring by improving the accuracy of monitoring by considering the interrelationships of investments.

[0050] The monitoring unit can perform monitoring while considering the attribute information of the investment submitter. For example, the monitoring unit can perform monitoring while considering the attribute information of the investment submitter. For example, the monitoring unit can perform monitoring while considering the past performance of the investment submitter. The monitoring unit can also evaluate the reliability of the investment submitter and improve the accuracy of monitoring. Furthermore, the monitoring unit can cluster the attribute information of the investment submitter to improve the accuracy of monitoring. For example, the monitoring unit can cluster the attribute information of the investment submitter and apply the optimal monitoring method to each cluster. This allows for more appropriate monitoring by considering the attribute information of the investment submitter.

[0051] The monitoring unit can perform monitoring while considering the geographical distribution of investments. For example, the monitoring unit can analyze the geographical distribution of investment projects to improve monitoring accuracy. The monitoring unit can also improve monitoring accuracy by considering the regional characteristics of investment projects. Furthermore, the monitoring unit can cluster the geographical distribution of investment projects to improve monitoring accuracy. For example, the monitoring unit can cluster the geographical distribution of investment projects and apply the optimal monitoring method to each cluster. This allows for more appropriate monitoring by considering the geographical distribution of investments.

[0052] The monitoring unit can improve the accuracy of its monitoring by referring to relevant investment literature during monitoring. For example, the monitoring unit can improve the accuracy of its monitoring by referring to relevant investment literature during monitoring. For example, the monitoring unit can improve the accuracy of its monitoring by referring to literature related to investment projects. The monitoring unit can also improve the accuracy of its monitoring by analyzing relevant literature related to investment projects. Furthermore, the monitoring unit can improve the accuracy of its monitoring by clustering relevant literature related to investment projects. For example, the monitoring unit can cluster relevant literature related to investment projects and apply the optimal monitoring method to each cluster. This allows for more accurate monitoring by improving the accuracy of monitoring by referring to relevant investment literature.

[0053] The Ministry of Education can optimize current educational content by referring to past educational data when providing educational content. For example, the Ministry of Education can provide optimal educational content based on a user's past learning history. The Ministry of Education can also analyze a user's past educational data to optimize current educational content. Furthermore, the Ministry of Education can cluster a user's past educational data and provide optimal educational content. For example, the Ministry of Education can cluster a user's past educational data and provide optimal educational content for each cluster. By optimizing current educational content by referring to past educational data, the Ministry can provide more appropriate education.

[0054] The Ministry of Education can apply different educational methods to each investment category when providing educational content. For example, the Ministry of Education can apply a stock-specific educational method to stock investments. It can also apply a real estate-specific educational method to real estate investments. Furthermore, the Ministry of Education can apply a cryptocurrency-specific educational method to cryptocurrency investments. By applying different educational methods to each investment category, it can provide more appropriate education.

[0055] The Ministry of Education can analyze changes in educational content based on the timing of investment proposals when providing educational content. For example, the Ministry of Education can provide educational content quickly for investment projects with approaching proposal deadlines. It can also provide detailed educational content for investment projects with later proposal deadlines. Furthermore, the Ministry of Education can prioritize educational content based on the proposal deadlines. By analyzing changes in educational content based on the timing of investment proposals, it can provide more appropriate education.

[0056] The Ministry of Education can analyze educational content by referring to investment-related market data when providing educational content. For example, the Ministry of Education can provide educational content based on market data related to investment projects. The Ministry of Education can also analyze market data of investment projects and optimize educational content. Furthermore, the Ministry of Education can cluster the market data of investment projects and provide optimal educational content. For example, the Ministry of Education can cluster the market data of investment projects and provide optimal educational content for each cluster. This allows for the provision of more appropriate education by analyzing educational content by referring to investment-related market data.

[0057] The risk management department can analyze the user's past investment behavior and select the optimal risk management method during risk management. For example, the risk management department can select the optimal risk management method based on the user's past investment behavior. The risk management department can also analyze the user's past investment behavior and select a low-risk method. Furthermore, the risk management department can cluster the user's past investment behavior and select the optimal risk management method. For example, the risk management department can cluster the user's past investment behavior and select the optimal risk management method for each cluster. This allows for more appropriate risk management by analyzing the user's past investment behavior and selecting the optimal risk management method.

[0058] The risk management department can customize risk management methods based on the user's current financial situation during risk management. For example, the risk management department can customize risk management methods by considering the user's current income and expenses. The risk management department can also suggest lower-risk methods depending on the user's financial situation. Furthermore, the risk management department can analyze the user's financial situation and suggest the optimal risk management method. For example, the risk management department can analyze the user's financial situation and suggest the optimal risk management method. By customizing risk management methods based on the user's current financial situation, more appropriate risk management can be achieved.

[0059] The risk management department can select the optimal risk management method when managing risks, taking into account the user's geographical location. For example, the risk management department can select the optimal risk management method when managing risks, taking into account the economic conditions of the area where the user lives. Furthermore, if the user is traveling, the risk management department can also perform risk management considering the economic conditions of their current location. In addition, the risk management department can select region-specific risk management methods based on the user's geographical location. For example, the risk management department can select region-specific risk management methods based on the user's geographical location. By selecting the optimal risk management method while considering the user's geographical location, more appropriate risk management can be achieved.

[0060] The Risk Management Department can analyze users' social media activity and propose risk management measures during risk management. For example, the Risk Management Department can propose risk management measures based on topics that users have shown interest in on social media. The Risk Management Department can also propose risk management measures by referring to the investment behavior of users' followers and friends on social media. Furthermore, the Risk Management Department can identify current interests from users' social media activity and propose risk management measures. For example, the Risk Management Department can identify current interests from users' social media activity and propose risk management measures. By analyzing users' social media activity and proposing risk management measures, more appropriate risk management can be achieved.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The analysis unit can analyze a user's past investment behavior and select the optimal analysis method. For example, it can select a similar analysis method based on the user's past successful investment patterns. It can also select a different analysis method to avoid investment patterns that the user has previously failed at. Furthermore, it can cluster the user's past investment behavior and select the optimal analysis method. In this way, the optimal analysis method can be selected by analyzing the user's past investment behavior.

[0063] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships between investments during the monitoring process. For example, it can improve monitoring accuracy by analyzing the correlations between investment projects. It can also improve monitoring accuracy by considering the interdependencies between investment projects. Furthermore, it can cluster the interrelationships of investment projects and apply the optimal monitoring method to each cluster. By improving monitoring accuracy by considering the interrelationships of investments, more accurate monitoring can be performed.

[0064] The Ministry of Education can optimize current educational content by referencing past educational data when providing educational content. For example, it can provide optimal educational content based on a user's past learning history. It can also analyze a user's past educational data to optimize current educational content. Furthermore, it can cluster a user's past educational data and provide optimal educational content to each cluster. By optimizing current educational content by referencing past educational data, it is possible to provide more appropriate education.

[0065] The risk management department can customize risk management methods based on the user's current financial situation. For example, it can customize risk management methods by considering the user's current income and expenses. It can also suggest lower-risk methods depending on the user's financial situation. Furthermore, it can analyze the user's financial situation and suggest the optimal risk management method. By customizing risk management methods based on the user's current financial situation, more appropriate risk management can be achieved.

[0066] The Ministry of Education can apply different educational methods to each investment category when providing educational content. For example, a stock-specific educational method can be applied to stock investment. Similarly, a real estate-specific educational method can be applied to real estate investment. Furthermore, a cryptocurrency-specific educational method can be applied to cryptocurrency investment. By applying different educational methods to each investment category, more appropriate education can be provided.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The analysis department analyzes the user's profile and investment behavior. The user's profile includes age, income, investment experience, and risk tolerance. Based on this information, the analysis department proposes an optimal investment plan and selects appropriate stocks considering the user's investment experience and risk tolerance. Step 2: The advice department provides personalized advice based on the information obtained by the analysis department. The advice department proposes optimal investment plans and stocks based on the user's profile and investment behavior, and provides advice through a natural conversational interface using generative AI. Step 3: The monitoring unit monitors market trends based on the advice provided by the advisory unit and sends alerts for important news and market trends. The monitoring unit analyzes the latest market trends and news and provides users with important information in real time. Step 4: The Ministry of Education provides investment education content based on information obtained by the Monitoring Department. The Ministry of Education provides educational content such as basic investment knowledge, risk management methods, and points for selecting stocks, and provides online courses and materials to deepen users' knowledge of investing. Step 5: The Risk Management Department proposes a risk management strategy based on the educational content provided by the Education Department. The Risk Management Department analyzes the user's risk tolerance and investment situation and provides specific advice for risk management.

[0069] (Example of form 2) The AI ​​investment partner system according to an embodiment of the present invention is an innovative AI agent that provides personalized investment advice to young, novice investors. This AI investment partner system aims to improve investment literacy by proposing optimal investment plans and stocks based on the user's individual needs and risk tolerance. For example, the AI ​​investment partner system proposes optimal investment plans and stocks based on information such as the user's age, income, investment experience, and risk tolerance. This allows the user to make investments that suit them. Next, the AI ​​investment partner system is equipped with a function to send alerts for important news and market trends based on real-time market monitoring. The AI ​​investment partner system analyzes the latest market trends and news and provides important information to the user in real time. For example, if there is a sudden market fluctuation or important news regarding a particular stock occurs, an alert is sent to the user. This allows the user to respond quickly. Furthermore, the AI ​​investment partner system is equipped with a function to provide investment education content. The AI ​​investment partner system provides educational content on investment to support the improvement of the user's investment literacy. For example, users can learn basic investment knowledge, risk management methods, and points to consider when selecting stocks. This allows the user to deepen their knowledge of investment. In addition, the AI ​​investment partner system is equipped with a natural conversational interface using generative AI. Users can receive investment advice through natural conversations via generated AI. For example, if a user asks, "What are the recommended investments for this month?", the generated AI will suggest the most suitable investments based on the user's profile and market trends. This allows users to receive investment advice intuitively. Furthermore, the AI ​​investment partner system utilizes risk management tools to suggest appropriate risk management strategies to users. The AI ​​investment partner system analyzes the user's risk tolerance and investment situation and provides specific advice for risk management. For example, it suggests portfolio diversification and risk hedging methods. This allows users to invest while appropriately managing risk.Thus, the AI ​​Investment Partner System is an innovative service that provides personalized investment advice to young, novice investors, supporting the improvement of their investment literacy. With the rapid growth of the fintech market, now is the optimal time to enter the market, especially given the high demand from young people. This agent aims to promote the financial independence and investment literacy of young people. As a result, the AI ​​Investment Partner System can provide personalized investment advice to young, novice investors, supporting the improvement of their investment literacy.

[0070] The AI ​​investment partner system according to this embodiment comprises an analysis unit, an advice unit, a monitoring unit, an education unit, and a risk management unit. The analysis unit analyzes the user's profile and investment behavior. The user's profile includes, but is not limited to, age, income, investment experience, and risk tolerance. For example, the analysis unit proposes an optimal investment plan based on the user's age and income. The analysis unit can also select appropriate stocks considering the user's investment experience and risk tolerance. For example, the analysis unit analyzes the user's past investment history and proposes a portfolio that matches their risk tolerance. The advice unit provides personalized advice based on the information obtained by the analysis unit. For example, the advice unit proposes an optimal investment plan and stocks based on the user's profile and investment behavior. The advice unit can also provide advice to the user through a natural conversational interface using generative AI. For example, when the user asks, "What are the recommended investments for this month?", the generative AI proposes the optimal investment based on the user's profile and market trends. The monitoring unit monitors market trends based on the advice provided by the advice unit and sends alerts for important news and market trends. The Monitoring Department analyzes the latest market trends and news, for example, and provides users with important information in real time. For example, the Monitoring Department sends alerts to users when there are sudden market fluctuations or important news regarding specific stocks. The Education Department provides investment education content based on the information obtained by the Monitoring Department. For example, the Education Department provides educational content such as basic investment knowledge, risk management methods, and points to consider when selecting stocks. For example, the Education Department provides online courses and materials to help users deepen their knowledge of investing. The Risk Management Department proposes risk management strategies based on the educational content provided by the Education Department. For example, the Risk Management Department analyzes users' risk tolerance and investment status and provides specific advice for risk management. For example, the Risk Management Department proposes methods for portfolio diversification and risk hedging.As a result, the AI ​​investment partner system according to this embodiment can analyze the user's profile and investment behavior, provide personalized advice, monitor market trends in real time, provide investment education content, and propose risk management strategies.

[0071] The analytics department conducts a detailed analysis of user profiles and investment behavior. User profiles include, but are not limited to, age, income, investment experience, and risk tolerance. For example, based on a user's age and income, it proposes an optimal investment plan tailored to their life stage. For younger users, it recommends investments in growth stocks with a higher risk profile, while for users nearing retirement, it recommends bonds and dividend stocks that offer stable returns. It also selects appropriate securities considering the user's investment experience and risk tolerance. For example, it suggests low-risk index funds for users with little investment experience, and individual stocks or options trading for experienced users. Furthermore, the analytics department conducts a detailed analysis of users' past investment history and constructs a portfolio tailored to their risk tolerance. For example, for users who have experienced losses due to taking too much risk in the past, it proposes a portfolio with reduced risk. In this way, the analytics department can provide an optimal investment strategy tailored to each user's individual circumstances.

[0072] The advisory department provides personalized advice to users based on information obtained by the analysis department. Based on the user's profile and investment history, the advisory department proposes optimal investment plans and stocks. For example, considering the user's age, income, investment experience, and risk tolerance, it provides investment plans tailored to whether the user aims for short-term profits or long-term wealth building. Furthermore, the advisory department provides advice through a natural, conversational interface using generative AI. For instance, if a user asks, "What are the recommended investments for this month?", the generative AI will suggest the most suitable investments based on the user's profile and market trends. The generative AI analyzes the user's past investment behavior and current market conditions in real time to select the most appropriate investments. In addition, the advisory department collects user feedback to continuously improve the accuracy of its advice. This allows the advisory department to always provide users with optimal investment advice based on the latest information.

[0073] The monitoring department monitors market trends based on advice provided by the advisory department and sends alerts to users regarding important news and market trends. The monitoring department analyzes the latest market trends and news and provides users with important information in real time. For example, if there is a sudden market fluctuation or important news regarding a particular stock, the monitoring department will immediately send an alert to the user. The monitoring department uses AI to analyze a large amount of news articles and market data and extract information that is important to the user. For example, it provides information in real time that may affect the user's investments, such as sudden rises or falls in the price of a particular stock, the release of economic indicators, and corporate earnings announcements. Furthermore, the monitoring department prioritizes providing information on specific stocks and market sectors based on the user's investment portfolio. In this way, the monitoring department helps users make investment decisions based on the latest information at all times.

[0074] The Education Department provides investment education content based on information obtained by the Monitoring Department. The Education Department provides educational content covering basic investment knowledge, risk management methods, and key points for selecting stocks. For example, the Education Department offers online courses and materials to deepen users' investment knowledge. This content is tailored to users of all levels, from beginners to advanced investors. For example, beginners receive courses explaining basic investment concepts and terminology, and the relationship between risk and return. Advanced investors receive detailed explanations of technical and fundamental analysis methods and portfolio optimization. Furthermore, the Education Department continuously updates its content based on users' investment behavior and feedback, providing the latest information. This allows the Education Department to support users in maintaining up-to-date knowledge and making appropriate investment decisions.

[0075] The Risk Management Department proposes risk management strategies based on educational content provided by the Education Department. The Risk Management Department analyzes the user's risk tolerance and investment status and provides specific advice for risk management. For example, the Risk Management Department proposes methods for portfolio diversification and risk hedging. Specifically, it analyzes the user's investment portfolio and checks for any bias in specific stocks or market sectors. If bias is found, it recommends investing in different stocks or market sectors to diversify the portfolio. It also proposes the use of options trading and futures trading as risk hedging methods. For example, it recommends purchasing put options to hedge against the price decline risk of a specific stock. Furthermore, the Risk Management Department regularly monitors the user's investment status and reviews the risk management strategy as needed. In this way, the Risk Management Department helps users manage risk appropriately and achieve stable investment results.

[0076] The analysis department can propose optimal investment plans and stocks based on information such as the user's age, income, investment experience, and risk tolerance. For example, the analysis department can propose an optimal investment plan based on the user's age and income. For example, the analysis department can propose an investment plan with long-term growth potential to younger users. The analysis department can also select appropriate stocks considering the user's investment experience and risk tolerance. For example, the analysis department can propose low-risk stocks to users with little investment experience. Furthermore, the analysis department can propose portfolio diversification according to the user's risk tolerance. For example, the analysis department can propose a portfolio including high-risk stocks to users with a high risk tolerance. In this way, the analysis department can propose optimal investment plans and stocks based on the individual needs of each user.

[0077] The monitoring unit can analyze the latest market trends and news and provide users with important information in real time. For example, the monitoring unit can analyze the latest market trends and news and provide users with important information in real time. For example, the monitoring unit can send alerts to users when there are sudden market fluctuations or important news concerning specific stocks. The monitoring unit can monitor market trends such as stock price fluctuations and changes in economic indicators and provide users with important information. In addition, the monitoring unit can monitor important news such as corporate earnings announcements and policy changes and send alerts to users. For example, the monitoring unit can notify users when corporate earnings announcements are made. This allows the monitoring unit to provide users with important information in real time.

[0078] The Ministry of Education can provide educational content on basic investment knowledge, risk management methods, and key points for selecting stocks. For example, the Ministry of Education can provide online courses and materials to deepen users' knowledge of investing. Furthermore, the Ministry of Education can also provide educational content on risk management methods. For example, the Ministry of Education can provide educational content on risk diversification methods and risk assessment criteria. In addition, the Ministry of Education can provide educational content on key points for selecting stocks. For example, the Ministry of Education can provide educational content on key points for selecting stocks, such as company performance and growth potential. This allows the Ministry to provide educational content on basic investment knowledge, risk management methods, and key points for selecting stocks.

[0079] The Risk Management Department can analyze users' risk tolerance and investment status and provide specific advice for risk management. For example, the Risk Management Department can propose portfolio diversification according to the user's risk tolerance. The Risk Management Department can also provide advice on risk hedging methods. For example, the Risk Management Department can propose specific risk hedging measures to the user. Furthermore, the Risk Management Department can analyze the user's investment status and propose risk management strategies. For example, the Risk Management Department can propose the optimal risk management strategy according to the user's investment status. This allows the department to analyze users' risk tolerance and investment status and provide specific advice for risk management.

[0080] The advisory unit can provide personalized advice to users through a natural conversational interface using generative AI. For example, if a user asks, "What are the recommended investments for this month?", the generative AI will suggest the optimal investment based on the user's profile and market trends. The advisory unit can also use generative AI to provide advice based on the user's investment behavior. For example, the advisory unit will analyze the user's past investment history and suggest the optimal investment plan. Furthermore, the advisory unit can use generative AI to provide advice tailored to the user's risk tolerance. For example, the advisory unit will suggest high-risk stocks to users with a high risk tolerance. In this way, personalized advice can be provided to users through a natural conversational interface using generative AI.

[0081] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit may delay the analysis until the user is relaxed. Alternatively, if the user is agitated, the analysis unit may start the analysis immediately and provide rapid feedback. Furthermore, if the user is tired, the analysis unit may postpone the analysis until the next day. This allows for analysis to be performed at a more appropriate time by adjusting the timing of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can analyze a user's past investment behavior and select the optimal analysis method. For example, the analysis unit can analyze a user's past investment behavior and select the optimal analysis method. For example, the analysis unit can select a similar analysis method based on investment patterns in which the user has succeeded in the past. Alternatively, the analysis unit can select a different analysis method to avoid investment patterns in which the user has failed in the past. Furthermore, the analysis unit can cluster a user's past investment behavior and select the optimal analysis method. For example, the analysis unit can cluster a user's past investment behavior and select the optimal analysis method for each cluster. In this way, the optimal analysis method can be selected by analyzing a user's past investment behavior.

[0083] The analysis unit can filter results based on the user's current financial situation and areas of interest during the analysis process. For example, the analysis unit can suggest an appropriate investment plan considering the user's current income and expenses. The analysis unit can also prioritize the analysis of relevant stocks based on the user's areas of interest (e.g., technology, healthcare, etc.). Furthermore, the analysis unit can suggest lower-risk investment plans according to the user's financial situation. For example, the analysis unit can suggest a lower-risk investment plan considering the user's financial situation. This allows for the suggestion of a more appropriate investment plan by filtering results based on the user's current financial situation and areas of interest.

[0084] The analysis unit can estimate the user's emotions and determine the priority of data to analyze based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit may prioritize analyzing low-risk data. Similarly, if the user is excited, the analysis unit may prioritize analyzing high-risk data. Furthermore, if the user is relaxed, the analysis unit may prioritize analyzing balanced data. This allows for the prioritization of more appropriate data by determining the priority of data to analyze based on 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. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The analytics department can prioritize analyzing highly relevant data by considering the user's geographical location during analysis. For example, the analytics department can prioritize analyzing relevant data by considering the economic conditions of the area where the user lives. Furthermore, if the user is traveling, the analytics department can analyze data by considering the economic conditions of their current location. In addition, the analytics department can prioritize analyzing region-specific investment opportunities based on the user's geographical location. For example, the analytics department can prioritize analyzing region-specific investment opportunities based on the user's geographical location. This allows for the proposal of more appropriate investment plans by prioritizing the analysis of highly relevant data while considering the user's geographical location.

[0086] The analytics department can analyze users' social media activity and obtain relevant data during analysis. For example, the analytics department can analyze relevant data based on topics that users have shown interest in on social media. The analytics department can also analyze data by referring to the investment behavior of users' followers and friends on social media. Furthermore, the analytics department can identify current interests from users' social media activity and prioritize the analysis of relevant data. For example, the analytics department can identify current interests from users' social media activity and prioritize the analysis of relevant data. This allows the analytics department to obtain relevant data by analyzing users' social media activity and propose more appropriate investment plans.

[0087] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is feeling anxious, the advice unit can provide advice in a reassuring way. If the user is excited, the advice unit can also provide advice in a calm way. Furthermore, if the user is relaxed, the advice unit can provide advice in a friendly way. By adjusting the way advice is expressed based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, or not using AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The advisory department can adjust the level of detail in its advice based on the importance of the investment. For example, it can provide detailed advice for important investments, while providing concise advice for general investments. Furthermore, it can provide prompt and detailed advice for urgent investments. By adjusting the level of detail based on the importance of the investment, it can provide more appropriate advice.

[0089] The advisory unit can apply different advisory algorithms depending on the investment category when providing advice. For example, the advisory unit can apply a stock-specific advisory algorithm for stock investments. It can also apply a real estate-specific advisory algorithm for real estate investments. Furthermore, it can apply a cryptocurrency-specific advisory algorithm for cryptocurrency investments. By applying different advisory algorithms depending on the investment category, it can provide more appropriate advice.

[0090] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the advice unit can provide short, concise advice. If the user is relaxed, the advice unit can provide longer advice with more detailed explanations. Furthermore, if the user is agitated, the advice unit can provide concise and calm advice. By adjusting the length of the advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The advisory department can prioritize advice based on the timing of investment submissions when providing advice. For example, the advisory department will prioritize advice for urgent investment projects. It can also provide prompt advice for investment projects with approaching submission deadlines. Furthermore, it can provide detailed advice for investment projects with distant submission deadlines. By prioritizing advice based on the timing of investment submissions, the advisory department can provide more appropriate advice.

[0092] The advisory department can prioritize advice based on the timing of investment submissions when providing advice. For example, the advisory department will prioritize advice for urgent investment projects. It can also provide prompt advice for investment projects with approaching submission deadlines. Furthermore, it can provide detailed advice for investment projects with distant submission deadlines. By prioritizing advice based on the timing of investment submissions, the advisory department can provide more appropriate advice.

[0093] The advisory department can adjust the order of advice based on the relevance of the investments when providing advice. For example, the advisory department can prioritize providing advice on highly relevant investment projects. Conversely, the advisory department can postpone providing advice on less relevant investment projects. Furthermore, the advisory department can analyze the relevance of investment projects and provide advice in the optimal order. For example, the advisory department can analyze the relevance of investment projects and provide advice in the optimal order. By adjusting the order of advice based on the relevance of the investments, it is possible to provide more appropriate advice.

[0094] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit may monitor at a low-risk level. It may also monitor at a high-risk level if the user is excited. Furthermore, if the user is relaxed, it may monitor at a balanced level. This allows for more appropriate monitoring by adjusting the monitoring criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of investments during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by considering the interrelationships of investments during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by analyzing the correlations between investment projects. The monitoring unit can also improve the accuracy of monitoring by considering the interdependence of investment projects. Furthermore, the monitoring unit can improve the accuracy of monitoring by clustering the interrelationships of investment projects. For example, the monitoring unit can cluster the interrelationships of investment projects and apply the optimal monitoring method to each cluster. This allows for more accurate monitoring by improving the accuracy of monitoring by considering the interrelationships of investments.

[0096] The monitoring unit can perform monitoring while considering the attribute information of the investment submitter. For example, the monitoring unit can perform monitoring while considering the attribute information of the investment submitter. For example, the monitoring unit can perform monitoring while considering the past performance of the investment submitter. The monitoring unit can also evaluate the reliability of the investment submitter and improve the accuracy of monitoring. Furthermore, the monitoring unit can cluster the attribute information of the investment submitter to improve the accuracy of monitoring. For example, the monitoring unit can cluster the attribute information of the investment submitter and apply the optimal monitoring method to each cluster. This allows for more appropriate monitoring by considering the attribute information of the investment submitter.

[0097] The monitoring unit can estimate the user's emotions and adjust the order in which monitoring results are displayed based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit may prioritize displaying low-risk results. It may also prioritize displaying high-risk results if the user is excited. Furthermore, if the user is relaxed, the monitoring unit may display balanced results. This allows for the provision of more appropriate information by adjusting the order in which monitoring results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The monitoring unit can perform monitoring while considering the geographical distribution of investments. For example, the monitoring unit can analyze the geographical distribution of investment projects to improve monitoring accuracy. The monitoring unit can also improve monitoring accuracy by considering the regional characteristics of investment projects. Furthermore, the monitoring unit can cluster the geographical distribution of investment projects to improve monitoring accuracy. For example, the monitoring unit can cluster the geographical distribution of investment projects and apply the optimal monitoring method to each cluster. This allows for more appropriate monitoring by considering the geographical distribution of investments.

[0099] The monitoring unit can improve the accuracy of its monitoring by referring to relevant investment literature during monitoring. For example, the monitoring unit can improve the accuracy of its monitoring by referring to relevant investment literature during monitoring. For example, the monitoring unit can improve the accuracy of its monitoring by referring to literature related to investment projects. The monitoring unit can also improve the accuracy of its monitoring by analyzing relevant literature related to investment projects. Furthermore, the monitoring unit can improve the accuracy of its monitoring by clustering relevant literature related to investment projects. For example, the monitoring unit can cluster relevant literature related to investment projects and apply the optimal monitoring method to each cluster. This allows for more accurate monitoring by improving the accuracy of monitoring by referring to relevant investment literature.

[0100] The Ministry of Education can estimate a user's emotions and adjust how educational content is displayed based on those emotions. For example, if a user is feeling anxious, the Ministry of Education can provide educational content in a reassuring way. If a user is excited, the Ministry of Education can also provide educational content in a calm way. Furthermore, if a user is relaxed, the Ministry of Education can provide educational content in a friendly way. By adjusting how educational content is displayed based on the user's emotions, more appropriate education can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The Ministry of Education can optimize current educational content by referring to past educational data when providing educational content. For example, the Ministry of Education can provide optimal educational content based on a user's past learning history. The Ministry of Education can also analyze a user's past educational data to optimize current educational content. Furthermore, the Ministry of Education can cluster a user's past educational data and provide optimal educational content. For example, the Ministry of Education can cluster a user's past educational data and provide optimal educational content for each cluster. By optimizing current educational content by referring to past educational data, the Ministry can provide more appropriate education.

[0102] The Ministry of Education can apply different educational methods to each investment category when providing educational content. For example, the Ministry of Education can apply a stock-specific educational method to stock investments. It can also apply a real estate-specific educational method to real estate investments. Furthermore, the Ministry of Education can apply a cryptocurrency-specific educational method to cryptocurrency investments. By applying different educational methods to each investment category, it can provide more appropriate education.

[0103] The Ministry of Education can estimate a user's emotions and adjust the importance of educational content based on those emotions. For example, if a user is feeling anxious, the Ministry of Education may prioritize providing educational content on risk management. Similarly, if a user is excited, the Ministry of Education may prioritize providing educational content on investment opportunities. Furthermore, if a user is relaxed, the Ministry of Education may provide balanced educational content. This allows for more appropriate education by adjusting the importance of educational content based on 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. Some or all of the above processing in the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The Ministry of Education can analyze changes in educational content based on the timing of investment proposals when providing educational content. For example, the Ministry of Education can provide educational content quickly for investment projects with approaching proposal deadlines. It can also provide detailed educational content for investment projects with later proposal deadlines. Furthermore, the Ministry of Education can prioritize educational content based on the proposal deadlines. By analyzing changes in educational content based on the timing of investment proposals, it can provide more appropriate education.

[0105] The Ministry of Education can analyze educational content by referring to investment-related market data when providing educational content. For example, the Ministry of Education can provide educational content based on market data related to investment projects. The Ministry of Education can also analyze market data of investment projects and optimize educational content. Furthermore, the Ministry of Education can cluster the market data of investment projects and provide optimal educational content. For example, the Ministry of Education can cluster the market data of investment projects and provide optimal educational content for each cluster. This allows for the provision of more appropriate education by analyzing educational content by referring to investment-related market data.

[0106] The risk management unit can estimate the user's emotions and adjust its risk management methods based on those estimated emotions. For example, if the user is feeling anxious, the risk management unit can implement risk management in a low-risk manner. If the user is excited, the risk management unit can implement risk management in a high-risk manner. Furthermore, if the user is relaxed, the risk management unit can implement risk management in a balanced manner. This allows for more appropriate risk management by adjusting risk management methods based on 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. Some or all of the above-described processes in the risk management unit may be performed using AI, or not. For example, the risk management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The risk management department can analyze the user's past investment behavior and select the optimal risk management method during risk management. For example, the risk management department can select the optimal risk management method based on the user's past investment behavior. The risk management department can also analyze the user's past investment behavior and select a low-risk method. Furthermore, the risk management department can cluster the user's past investment behavior and select the optimal risk management method. For example, the risk management department can cluster the user's past investment behavior and select the optimal risk management method for each cluster. This allows for more appropriate risk management by analyzing the user's past investment behavior and selecting the optimal risk management method.

[0108] The risk management department can customize risk management methods based on the user's current financial situation during risk management. For example, the risk management department can customize risk management methods by considering the user's current income and expenses. The risk management department can also suggest lower-risk methods depending on the user's financial situation. Furthermore, the risk management department can analyze the user's financial situation and suggest the optimal risk management method. For example, the risk management department can analyze the user's financial situation and suggest the optimal risk management method. By customizing risk management methods based on the user's current financial situation, more appropriate risk management can be achieved.

[0109] The risk management unit can estimate the user's emotions and determine the priority of risk management based on the estimated emotions. For example, if the user is feeling anxious, the risk management unit will prioritize low-risk management. The risk management unit may also prioritize high-risk management if the user is excited. Furthermore, if the user is relaxed, the risk management unit may prioritize balanced management. This allows for more appropriate risk management by determining the priority of risk management based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the risk management unit may be performed using AI, or not. For example, the risk management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The risk management department can select the optimal risk management method when managing risks, taking into account the user's geographical location. For example, the risk management department can select the optimal risk management method when managing risks, taking into account the economic conditions of the area where the user lives. Furthermore, if the user is traveling, the risk management department can also perform risk management considering the economic conditions of their current location. In addition, the risk management department can select region-specific risk management methods based on the user's geographical location. For example, the risk management department can select region-specific risk management methods based on the user's geographical location. By selecting the optimal risk management method while considering the user's geographical location, more appropriate risk management can be achieved.

[0111] The Risk Management Department can analyze users' social media activity and propose risk management measures during risk management. For example, the Risk Management Department can propose risk management measures based on topics that users have shown interest in on social media. The Risk Management Department can also propose risk management measures by referring to the investment behavior of users' followers and friends on social media. Furthermore, the Risk Management Department can identify current interests from users' social media activity and propose risk management measures. For example, the Risk Management Department can identify current interests from users' social media activity and propose risk management measures. By analyzing users' social media activity and proposing risk management measures, more appropriate risk management can be achieved.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on those emotions. For example, if the user is stressed, the analysis can be delayed until they are relaxed. Conversely, if the user is agitated, the analysis can be started immediately to provide rapid feedback. Furthermore, if the user is tired, the analysis can be postponed until the next day. This allows for more appropriate timing of the analysis by adjusting the timing based on the user's emotions.

[0114] The advice function can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is feeling anxious, it can provide advice in a reassuring way. If the user is excited, it can provide advice in a calm way. Furthermore, if the user is relaxed, it can provide advice in a friendly way. By adjusting the way advice is presented based on the user's emotions, it can provide more appropriate advice.

[0115] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on those estimates. For example, if the user is feeling anxious, monitoring can be performed at a lower risk level. Conversely, if the user is excited, monitoring can be performed at a higher risk level. Furthermore, if the user is relaxed, monitoring can be performed at a balanced level. By adjusting the monitoring criteria based on the user's emotions, more appropriate monitoring can be achieved.

[0116] The Ministry of Education can estimate users' emotions and adjust how educational content is displayed based on those emotions. For example, if a user is feeling anxious, the educational content can be presented in a way that provides reassurance. If a user is excited, the content can be presented in a calming way. Furthermore, if a user is relaxed, the content can be presented in a friendly way. By adjusting how educational content is presented based on users' emotions, more appropriate education can be provided.

[0117] The risk management department can estimate the user's emotions and adjust risk management methods based on those estimates. For example, if the user is feeling anxious, risk management can be carried out using a low-risk method. Conversely, if the user is excited, risk management can be carried out using a high-risk method. Furthermore, if the user is relaxed, risk management can be carried out using a balanced method. By adjusting risk management methods based on the user's emotions, more appropriate risk management can be achieved.

[0118] The analysis unit can analyze a user's past investment behavior and select the optimal analysis method. For example, it can select a similar analysis method based on the user's past successful investment patterns. It can also select a different analysis method to avoid investment patterns that the user has previously failed at. Furthermore, it can cluster the user's past investment behavior and select the optimal analysis method. In this way, the optimal analysis method can be selected by analyzing the user's past investment behavior.

[0119] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships between investments during the monitoring process. For example, it can improve monitoring accuracy by analyzing the correlations between investment projects. It can also improve monitoring accuracy by considering the interdependencies between investment projects. Furthermore, it can cluster the interrelationships of investment projects and apply the optimal monitoring method to each cluster. By improving monitoring accuracy by considering the interrelationships of investments, more accurate monitoring can be performed.

[0120] The Ministry of Education can optimize current educational content by referencing past educational data when providing educational content. For example, it can provide optimal educational content based on a user's past learning history. It can also analyze a user's past educational data to optimize current educational content. Furthermore, it can cluster a user's past educational data and provide optimal educational content to each cluster. By optimizing current educational content by referencing past educational data, it is possible to provide more appropriate education.

[0121] The risk management department can customize risk management methods based on the user's current financial situation. For example, it can customize risk management methods by considering the user's current income and expenses. It can also suggest lower-risk methods depending on the user's financial situation. Furthermore, it can analyze the user's financial situation and suggest the optimal risk management method. By customizing risk management methods based on the user's current financial situation, more appropriate risk management can be achieved.

[0122] The Ministry of Education can apply different educational methods to each investment category when providing educational content. For example, a stock-specific educational method can be applied to stock investment. Similarly, a real estate-specific educational method can be applied to real estate investment. Furthermore, a cryptocurrency-specific educational method can be applied to cryptocurrency investment. By applying different educational methods to each investment category, more appropriate education can be provided.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The analysis department analyzes the user's profile and investment behavior. The user's profile includes age, income, investment experience, and risk tolerance. Based on this information, the analysis department proposes an optimal investment plan and selects appropriate stocks considering the user's investment experience and risk tolerance. Step 2: The advice department provides personalized advice based on the information obtained by the analysis department. The advice department proposes optimal investment plans and stocks based on the user's profile and investment behavior, and provides advice through a natural conversational interface using generative AI. Step 3: The monitoring unit monitors market trends based on the advice provided by the advisory unit and sends alerts for important news and market trends. The monitoring unit analyzes the latest market trends and news and provides users with important information in real time. Step 4: The Ministry of Education provides investment education content based on information obtained by the Monitoring Department. The Ministry of Education provides educational content such as basic investment knowledge, risk management methods, and points for selecting stocks, and provides online courses and materials to deepen users' knowledge of investing. Step 5: The Risk Management Department proposes a risk management strategy based on the educational content provided by the Education Department. The Risk Management Department analyzes the user's risk tolerance and investment situation and provides specific advice for risk management.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the analysis unit, advice unit, monitoring unit, education unit, and risk management unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The monitoring unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The education unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The risk management unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the analysis unit, advice unit, monitoring unit, education unit, and risk management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The education unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The risk management unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the analysis unit, advice unit, monitoring unit, education unit, and risk management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The monitoring unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The education unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The risk management unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the analysis unit, advice unit, monitoring unit, education unit, and risk management unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The education unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The risk management unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The analytics department analyzes user profiles and investment behavior, An advice unit provides personalized advice based on the information obtained by the analysis unit, A monitoring unit monitors market trends based on the advice provided by the aforementioned advisory unit and sends alerts for important news and market trends. The Education Department provides investment education content based on the information obtained by the Monitoring Department, The system comprises a Risk Management Department that proposes a risk management strategy based on educational content provided by the aforementioned Education Department. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Based on information such as the user's age, income, investment experience, and risk tolerance, we propose the optimal investment plan and stocks. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned monitoring unit, We analyze the latest market trends and news to provide users with important information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned Ministry of Education, We provide educational content covering basic investment knowledge, risk management methods, and key points for selecting stocks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned risk management department, We analyze users' risk tolerance and investment status and provide specific advice for risk management. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, It provides personalized advice to users through a natural conversational interface using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is It estimates the user's emotions and adjusts the timing of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Analyze the user's past investment behavior and select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, filtering is performed based on the user's current economic situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates user sentiment and prioritizes data to analyze based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, the system prioritizes analyzing highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, When providing advice, we adjust the level of detail based on the importance of the investment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, When providing advice, we apply different advisory algorithms depending on the investment category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, When providing advice, we prioritize the advice based on when the investment was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, we prioritize the advice based on when the investment was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, we adjust the order of advice based on the relevance of the investments. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, We estimate user sentiment and adjust monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, During monitoring, consider the interrelationships between investments to improve the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, During monitoring, the attribute information of the investor is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned monitoring unit, It estimates the user's sentiment and adjusts the order in which monitoring results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, During monitoring, the geographical distribution of investments should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, During monitoring, refer to relevant investment literature to improve the accuracy of the monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Ministry of Education, It estimates user sentiment and adjusts how educational content is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Ministry of Education, When providing educational content, we optimize current educational content by referring to past educational data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Ministry of Education, When providing educational content, different educational methods are applied to each investment category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Ministry of Education, It estimates user sentiment and adjusts the importance of educational content based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Ministry of Education, When providing educational content, analyze changes in educational content based on the timing of investment submissions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Ministry of Education, When providing educational content, we analyze the educational material by referring to market data related to investment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned risk management department, We estimate user sentiment and adjust risk management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned risk management department, During risk management, the system analyzes the user's past investment behavior to select the optimal risk management method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned risk management department, During risk management, customize risk management methods based on the user's current economic situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned risk management department, The system estimates user sentiment and prioritizes risk management based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned risk management department, When managing risks, the optimal risk management method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned risk management department, During risk management, we analyze users' social media activity and propose risk management strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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. The analytics department analyzes user profiles and investment behavior, An advice unit provides personalized advice based on the information obtained by the analysis unit, A monitoring unit monitors market trends based on the advice provided by the aforementioned advisory unit and sends alerts for important news and market trends. The Education Department provides investment education content based on the information obtained by the Monitoring Department, The system comprises a Risk Management Department that proposes a risk management strategy based on educational content provided by the aforementioned Education Department. A system characterized by the following features.

2. The aforementioned analysis unit is Based on information such as the user's age, income, investment experience, and risk tolerance, we propose the optimal investment plan and stocks. The system according to feature 1.

3. The aforementioned monitoring unit, We analyze the latest market trends and news to provide users with important information in real time. The system according to feature 1.

4. The aforementioned Ministry of Education, We provide educational content covering basic investment knowledge, risk management methods, and key points for selecting stocks. The system according to feature 1.

5. The aforementioned risk management department, We analyze users' risk tolerance and investment status and provide specific advice for risk management. The system according to feature 1.

6. The aforementioned advice section, It provides personalized advice to users through a natural, conversational interface using generative AI. The system according to feature 1.

7. The aforementioned analysis unit is It estimates the user's emotions and adjusts the timing of the analysis based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit is Analyze the user's past investment behavior and select the optimal analysis method. The system according to feature 1.

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