system

Abstract

The system according to this embodiment aims to propose an optimal strategy based on the financial situation of individuals and companies. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a risk assessment unit, a market analysis unit, and a provision unit. The data collection unit collects the user's financial data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal strategy based on the analysis results obtained by the analysis unit. The risk assessment unit performs a risk assessment based on the strategy proposed by the proposal unit. The market analysis unit analyzes market trends based on the risk assessment performed by the risk assessment unit. The provision unit provides information to the user based on the market trends analyzed by the market analysis 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully carried out to propose optimal investment, savings, and loan repayment strategies based on the financial situation of individuals or enterprises, and there is room for improvement.

[0005] [[ID=�9]]The system according to the embodiment aims to propose an optimal strategy based on the financial situation of individuals or enterprises.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a risk assessment unit, a market analysis unit, and a provision unit. The data collection unit collects the user's financial data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal strategy based on the analysis results obtained by the analysis unit. The risk assessment unit performs a risk assessment based on the strategy proposed by the proposal unit. The market analysis unit analyzes market trends based on the risk assessment performed by the risk assessment unit. The provision unit provides information to the user based on the market trends analyzed by the market analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal strategy based on the financial situation of individuals and companies. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls 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 reception 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 reception 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 generative AI agent system according to an embodiment of the present invention is a system that proposes optimal strategies for investment, saving, and loan repayment based on the financial situation of individuals and companies. This generative AI agent system collects the user's financial data, and the generative AI analyzes the collected data to automatically generate optimal investment, saving, and loan repayment strategies for the user. The generative AI analyzes the financial data using machine learning algorithms and also performs risk assessment and market trend analysis. This supports the user in efficient financial management. For example, the generative AI agent system collects the user's financial data. The data collected includes income, expenses, assets, and liabilities. For example, the transaction history of the user's bank account and credit card usage history are collected. This allows for an accurate understanding of the user's financial situation. Next, the generative AI agent system analyzes the collected data. The generative AI uses machine learning algorithms to analyze the user's financial data and automatically generates optimal strategies for investment, saving, and loan repayment. For example, it analyzes the user's income and expense patterns and proposes what kind of investment is optimal. It also analyzes the user's debt situation and proposes how to repay loans. Furthermore, the generating AI also performs risk assessment and market trend analysis. For example, it analyzes current market conditions and suggests avoiding high-risk investments. It also predicts future market trends and proposes long-term investment strategies. This allows users to manage their finances efficiently while minimizing risk. This system enables individual investors, those managing household finances, and small business owners to manage their finances efficiently without understanding complex financial products or investment strategies. It also saves time and costs associated with hiring professional consultants. For example, simply by inputting their financial data, the generating AI can propose an optimal investment strategy, and the user can invest according to that proposal. In this way, by utilizing generating AI, it is possible to realize a world where everyone can effectively manage their finances and achieve financial freedom and peace of mind. For example, simply by inputting their financial data, the generating AI can propose an optimal investment strategy, and the user can invest according to that proposal.This can improve the financial health of individuals and small businesses. The generating AI agent system will be able to efficiently collect, analyze, propose, assess risks, analyze market trends, and provide information based on users' financial data.

[0029] The generation AI agent system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a risk assessment unit, a market analysis unit, and a provision unit. The collection unit collects the user's financial data. For example, the collection unit collects data such as the user's income, expenses, assets, and liabilities. For example, the collection unit can collect the user's bank account transaction history and credit card usage history. The collection unit can also collect the user's pay stubs and household budget data. For example, the collection unit can collect the user's income data to understand monthly income fluctuations. For example, the collection unit can collect the user's expense data to understand the breakdown of expenses by category. For example, the collection unit can collect the user's asset data to understand the type and value of assets. For example, the collection unit can collect the user's liability data to understand the type and balance of liabilities. The analysis unit analyzes the data collected by the collection unit using machine learning algorithms. For example, the analysis unit analyzes the user's income and expense patterns and proposes what kind of investment is optimal. The analysis unit, for example, analyzes the user's debt situation and proposes how to repay loans. The analysis unit, for example, analyzes the user's asset situation and proposes how to manage those assets. The analysis unit, for example, analyzes the user's spending patterns and proposes how to save money. The analysis unit, for example, analyzes the user's income patterns and proposes how to increase income. The proposal unit proposes the optimal strategy based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal investment strategy based on the user's income and spending patterns. The proposal unit proposes the optimal loan repayment strategy based on the user's debt situation. The proposal unit proposes the optimal asset management strategy based on the user's asset situation. The proposal unit proposes the optimal saving strategy based on the user's spending patterns. The proposal unit proposes the optimal income increase strategy based on the user's income patterns. The risk assessment unit performs a risk assessment based on the strategies proposed by the proposal unit. The risk assessment unit, for example, analyzes the current market situation and proposes avoiding high-risk investments.The Risk Assessment Department, for example, advises users to avoid high-risk investments based on their financial situation. The Risk Assessment Department, for example, advises users to avoid high-risk loans based on their debt situation. The Risk Assessment Department, for example, advises users to avoid high-risk asset management based on their asset situation. The Risk Assessment Department, for example, advises users to avoid high-risk spending based on their spending patterns. The Market Analysis Department analyzes market trends based on the risk assessment conducted by the Risk Assessment Department. The Market Analysis Department, for example, predicts future market trends and proposes long-term investment strategies. The Market Analysis Department, for example, analyzes current market conditions and proposes short-term investment strategies. The Market Analysis Department, for example, analyzes historical market data and predicts current market trends. The Market Analysis Department, for example, predicts future economic conditions and proposes long-term asset management strategies. The Market Analysis Department, for example, analyzes current economic conditions and proposes short-term asset management strategies. The Provision Department provides information to users based on the market trends analyzed by the Market Analysis Department. The Provision Department, for example, provides users with the optimal investment strategy. The service provider can, for example, provide the user with an optimal loan repayment strategy. The service provider can, for example, provide the user with an optimal asset management strategy. The service provider can, for example, provide the user with an optimal savings strategy. The service provider can, for example, provide the user with an optimal income increase strategy. As a result, the generating AI agent system according to the embodiment can efficiently collect, analyze, propose, assess risks, analyze the market, and provide information based on the user's financial data.

[0030] The data collection unit collects users' financial data. For example, it collects data such as users' income, expenses, assets, and liabilities. Specifically, the data collection unit can collect transaction history from users' bank accounts and credit card usage history. This allows it to obtain detailed data on users' monthly income and expenses, and understand fluctuations in income and trends in spending. The data collection unit can also collect data from users' pay stubs and household budgets. This allows it to understand in detail the breakdown of users' income sources and expenses. Furthermore, the data collection unit can collect users' asset data, understanding the types and valuations of their assets. For example, it can collect asset data such as real estate, stocks, and deposits owned by users and calculate the valuations of those assets. The data collection unit can also collect users' liability data, understanding the types and balances of their liabilities. For example, it can collect liability data such as mortgages, car loans, and credit card balances, understanding the total amount of liabilities and repayment status. This allows the data collection unit to comprehensively understand the user's financial situation and collect basic data to provide to the analysis and proposal units. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit uses machine learning algorithms to analyze data collected by the data collection unit. For example, the analysis unit analyzes the user's income and expenditure patterns and proposes optimal investment strategies. Specifically, it analyzes income data over time to understand income fluctuation patterns. This allows it to determine whether income is stable or subject to seasonal fluctuations and propose appropriate investment strategies. It also analyzes expenditure data by category to identify spending trends and patterns of wasteful spending. This allows it to provide specific advice for saving money. The analysis unit analyzes the user's debt situation and proposes how to repay loans. For example, it considers interest rates and repayment periods to create an optimal repayment plan. Furthermore, it analyzes the user's asset situation and proposes how to manage assets. For example, it evaluates the risk and return of assets and constructs an optimal portfolio. The analysis unit analyzes the user's expenditure patterns and proposes how to save money. For example, it proposes specific methods for reducing wasteful spending. The analysis unit analyzes the user's income patterns and proposes how to increase income. For example, it proposes opportunities for side jobs or investments. This allows the analytics unit to analyze users' financial data in detail and provide the foundational data for proposing optimal strategies. Furthermore, the analytics unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict future income and expenses based on past income and expense data and formulate long-term financial plans. In addition, the analytics unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analytics unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The Proposal Department proposes optimal strategies based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes optimal investment strategies based on the user's income and expenditure patterns. Specifically, it proposes high-risk investments for users with stable incomes and low-risk investments for users with unstable incomes. It also proposes specific saving methods to reduce wasteful spending based on spending trends. The Proposal Department proposes optimal loan repayment strategies based on the user's debt situation. For example, it proposes methods to prioritize the repayment of high-interest loans or to shorten the repayment period. The Proposal Department proposes optimal asset management strategies based on the user's asset situation. For example, it proposes a portfolio that considers the balance between risk and return and recommends diversified investment in assets. The Proposal Department proposes optimal saving strategies based on the user's expenditure patterns. For example, it proposes reviewing fixed costs and methods to reduce wasteful spending. The Proposal Department proposes optimal income-increasing strategies based on the user's income patterns. For example, it proposes investments in side jobs or education for skill development. In this way, the Proposal Department can propose optimal strategies tailored to the user's financial situation and support the user in achieving their financial goals. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can revise and improve proposals based on feedback from users who have implemented them. The proposal department can also provide personalized proposals that take into account users' lifestyles and values. This allows the proposal department to offer personalized suggestions to users and improve user satisfaction.

[0033] The Risk Assessment Department conducts risk assessments based on the strategies proposed by the Proposal Department. For example, the Risk Assessment Department analyzes current market conditions and advises against high-risk investments. Specifically, it analyzes stock market trends and economic indicators to identify high-risk investment opportunities. It also advises against high-risk investments based on the user's financial situation. For example, it recommends low-risk investments to users with unstable incomes. The Risk Assessment Department advises against high-risk loans based on the user's debt situation. For example, it provides specific advice on avoiding high-interest loans. The Risk Assessment Department advises against high-risk asset management based on the user's asset situation. For example, it recommends diversifying investments and proposes methods for diversifying risk. The Risk Assessment Department advises against high-risk spending based on the user's spending patterns. For example, it proposes specific methods for reducing wasteful spending. This allows the Risk Assessment Department to conduct risk assessments tailored to the user's financial situation and provide specific advice to minimize risk. Furthermore, the Risk Assessment Department can also conduct long-term risk assessments by utilizing historical data and statistical information. For example, it can predict future risks based on historical market data and propose long-term risk management strategies. Furthermore, the risk assessment unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the risk assessment unit to handle not only real-time risk assessment but also long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0034] The Market Analysis Department analyzes market trends based on risk assessments conducted by the Risk Assessment Department. For example, the Market Analysis Department predicts future market trends and proposes long-term investment strategies. Specifically, it analyzes economic indicators and market data to predict future market trends. This allows it to formulate and propose long-term investment strategies to users. It also analyzes current market conditions and proposes short-term investment strategies. For example, it analyzes current stock market trends and proposes short-term buying and selling timings. The Market Analysis Department analyzes historical market data to predict current market trends. For example, it predicts current stock price trends based on historical stock price data and proposes investment timings. The Market Analysis Department predicts future economic conditions and proposes long-term asset management strategies. For example, it predicts future economic growth rates and inflation rates and formulates long-term asset management policies. The Market Analysis Department analyzes current economic conditions and proposes short-term asset management strategies. For example, it analyzes current interest rate trends and proposes short-term asset management policies. This allows the Market Analysis Department to analyze market trends according to the user's financial situation and propose the optimal investment strategy. Furthermore, the market analysis department can utilize historical data and statistical information to predict long-term market trends. For example, it can predict future economic trends based on historical economic data and formulate long-term investment strategies. In addition, the market analysis department can use anomaly detection algorithms to detect unusual market trends and abnormal data, and issue warnings early. This allows the market analysis department to not only analyze market trends in real time but also to predict long-term market trends and detect anomalies, thereby improving the reliability and security of the entire system.

[0035] The service provider department provides information to users based on market trends analyzed by the market analysis department. For example, the service provider department provides users with optimal investment strategies. Specifically, it proposes optimal investment destinations and timing based on the user's financial situation and market trends. It also provides users with optimal loan repayment strategies. For example, it proposes an optimal repayment plan based on the user's debt situation and income situation. The service provider department provides users with optimal asset management strategies. For example, it proposes an optimal portfolio based on the user's asset situation and risk tolerance. The service provider department provides users with optimal savings strategies. For example, it proposes specific methods to reduce wasteful spending based on the user's spending patterns. The service provider department provides users with optimal income-increasing strategies. For example, it proposes investments in side jobs or education for skill development based on the user's skills and experience. In this way, the service provider department can provide optimal information tailored to the user's financial situation and support the user in achieving their financial goals. Furthermore, the service provider department can collect user feedback and continuously improve the accuracy and effectiveness of the services provided. For example, it can review and improve the services provided based on feedback from users who have implemented the provided information. Furthermore, the service provider can provide personalized information that takes into account the user's lifestyle and values. This allows the service provider to deliver personalized information to users and improve user satisfaction. In addition, the service provider can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the service provider to provide information to users quickly and reliably and support them in achieving their financial goals.

[0036] The data collection unit can collect data such as the user's income, expenses, assets, and liabilities. For example, the data collection unit can collect the user's income data to understand monthly fluctuations in income. For example, the data collection unit can collect the user's expense data to understand the breakdown of expenses by category. For example, the data collection unit can collect the user's asset data to understand the type and value of assets. For example, the data collection unit can collect the user's liability data to understand the type and balance of liabilities. In this way, the data collection unit can comprehensively collect the user's financial data. Income, expenses, assets, and liabilities include, but are not limited to, salaries, rent, savings, and loans. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's bank account transaction history into AI and have AI perform analysis of the transaction history.

[0037] The analysis unit can use machine learning algorithms to analyze a user's financial data and automatically generate optimal strategies for investment, saving, and loan repayment. For example, the analysis unit can analyze a user's income and expenditure patterns and suggest optimal investments. For example, the analysis unit can analyze a user's debt situation and suggest how to repay loans. For example, the analysis unit can analyze a user's asset situation and suggest how to manage assets. For example, the analysis unit can analyze a user's expenditure patterns and suggest how to save money. For example, the analysis unit can analyze a user's income patterns and suggest how to increase income. By using machine learning algorithms, the analysis unit can improve the accuracy of its financial data analysis. Machine learning algorithms include, but are not limited to, regression analysis, clustering, and neural networks. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's financial data into AI and have the AI ​​automatically generate optimal strategies.

[0038] The proposal unit can analyze a user's income and expenditure patterns and propose the optimal investment. For example, the proposal unit can analyze a user's income data and propose an investment strategy to increase income. For example, the proposal unit can analyze a user's expenditure data and propose an investment strategy to reduce expenditure. For example, the proposal unit can analyze a user's asset data and propose an investment strategy to optimize asset management. For example, the proposal unit can analyze a user's debt data and propose an investment strategy to make debt repayment more efficient. In this way, the proposal unit can propose the optimal investment strategy based on the user's income and expenditure patterns. Income and expenditure patterns include, but are not limited to, monthly income and expenditure categories. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the user's income and expenditure data into AI and have the AI ​​propose the optimal investment strategy.

[0039] The risk assessment unit can analyze current market conditions and propose strategies to avoid high-risk investments. For example, the risk assessment unit can analyze current market conditions and propose strategies to avoid high-risk investments. For example, the risk assessment unit can analyze the user's financial situation and propose strategies to avoid high-risk investments. For example, the risk assessment unit can analyze the user's debt situation and propose strategies to avoid high-risk loans. For example, the risk assessment unit can analyze the user's asset situation and propose strategies to avoid high-risk asset management. In this way, the risk assessment unit can minimize the user's risk by avoiding high-risk investments. Current market conditions include, but are not limited to, stock prices, interest rates, and economic indicators. Some or all of the above processing in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input data on current market conditions into AI and have the AI ​​perform the risk assessment.

[0040] The market analysis department can predict future market trends and propose long-term investment strategies. For example, the market analysis department can predict future market trends and propose long-term investment strategies. For example, the market analysis department can analyze current market conditions and propose short-term investment strategies. For example, the market analysis department can analyze historical market data and predict current market trends. For example, the market analysis department can predict future economic conditions and propose long-term asset management strategies. For example, the market analysis department can analyze current economic conditions and propose short-term asset management strategies. Thus, by predicting future market trends, the market analysis department can propose long-term investment strategies. Future market trends include, but are not limited to, economic forecasting models and scenario analysis. Some or all of the above processes in the market analysis department may be performed using, for example, AI, or not. For example, the market analysis department can input data on future market trends into AI and have the AI ​​perform market trend predictions.

[0041] The service provider can provide users with optimal investment, saving, and loan repayment strategies. For example, the service provider can provide users with the optimal investment strategy. For example, the service provider can provide users with the optimal loan repayment strategy. For example, the service provider can provide users with the optimal asset management strategy. For example, the service provider can provide users with the optimal saving strategy. For example, the service provider can provide users with the optimal income growth strategy. In this way, the service provider supports efficient financial management by providing users with the optimal strategy. Optimal investment, saving, and loan repayment strategies include, but are not limited to, types of investments, saving methods, and loan repayment plans. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's financial data into AI and have AI perform the task of providing the optimal strategy.

[0042] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the data collection unit may prioritize suggesting collection methods previously used by the user (manual input, API integration, etc.). For example, the data collection unit may select the most efficient collection method from the user's past collection history. For example, the data collection unit may analyze the user's past collection history and suggest improvements to the collection method. In this way, the data collection unit can select the optimal collection method by analyzing past collection history. The optimal collection method includes, but is not limited to, data collection frequency and collection methods. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past collection history data into AI and have the AI ​​select the optimal collection method.

[0043] The data collection unit can filter the collected financial data based on the user's current economic situation and areas of interest. For example, the data collection unit adjusts the scope of data to be collected based on the user's current income. For example, the data collection unit filters the data to be collected based on the user's areas of interest (investment, saving, etc.). For example, the data collection unit determines the priority of data to be collected according to the user's economic situation. This allows the data collection unit to collect highly relevant data by filtering the data based on the user's economic situation and areas of interest. The user's current economic situation and areas of interest include, but are not limited to, income, expenses, and investment history. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's economic situation data into AI and have the AI ​​perform the data filtering.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, the data collection unit prioritizes the collection of relevant data based on the economic conditions of the area where the user lives. For example, the data collection unit collects region-specific financial data based on the user's geographical location. For example, the data collection unit collects data from local financial institutions by considering the user's location. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. The user's geographical location includes, but is not limited to, local economic conditions and geographical characteristics. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location into AI and have AI perform the collection of highly relevant data.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data when collecting financial data. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant financial data. For example, the data collection unit can collect relevant data by referring to the activities of a user's social media followers and friends. For example, the data collection unit can adjust the scope of data to be collected based on the user's social media usage. In this way, the data collection unit can collect relevant data by analyzing a user's social media activity. A user's social media activity includes, but is not limited to, posts, number of followers, and number of likes. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's social media data into AI and have the AI ​​perform the collection of relevant data.

[0046] The analysis unit can adjust the level of detail of its analysis based on the importance of the financial data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the financial data. The importance of financial data includes, but is not limited to, income, expenses, assets, and liabilities. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of the financial data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of financial data during analysis. For example, the analysis unit applies a risk assessment algorithm to investment data. For example, the analysis unit applies an expenditure reduction algorithm to savings data. For example, the analysis unit applies a repayment planning algorithm to loan repayment data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of financial data. Categories of financial data include, but are not limited to, income, expenses, assets, and liabilities. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of financial data into the AI ​​and have the AI ​​perform the application of different analysis algorithms.

[0048] The analysis unit can determine the priority of analysis based on the submission date of financial data during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. For example, the analysis unit may postpone the analysis of older data. The analysis unit may dynamically adjust the analysis priority according to the submission date. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the submission date of financial data. The submission date of financial data includes, but is not limited to, monthly, quarterly, and annual data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the submission date of financial data into AI and have AI determine the analysis priority.

[0049] The analysis unit can adjust the order of analysis based on the relationships between financial data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, it may postpone the analysis of less relevant data. For example, the analysis unit may dynamically adjust the order of analysis according to the relationships between the data. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relationships between financial data. The relationships between financial data include, but are not limited to, correlation analysis and causal relationships. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relationships between financial data into AI and have AI perform the adjustment of the analysis order.

[0050] The proposal department can adjust the level of detail in its proposals based on the importance of the financial strategies. For example, it can provide detailed proposals for high-importance strategies and simplified proposals for low-importance strategies. The proposal department can also prioritize proposals based on the importance of the strategies. This allows the proposal department to make efficient proposals by adjusting the level of detail based on the importance of the financial strategies. The importance of financial strategies includes, but is not limited to, risk, return, and time. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of financial strategies into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0051] The proposal unit can apply different proposal algorithms depending on the category of financial strategy when making a proposal. For example, the proposal unit applies a risk assessment algorithm to an investment strategy. For example, the proposal unit applies an expenditure reduction algorithm to a savings strategy. For example, the proposal unit applies a repayment planning algorithm to a loan repayment strategy. This improves the accuracy of the proposal by applying the appropriate proposal algorithm according to the category of financial strategy. The categories of financial strategies include, but are not limited to, investment strategies, savings strategies, and loan repayment strategies. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the categories of financial strategies into the AI ​​and have the AI ​​apply different proposal algorithms.

[0052] The proposal department can prioritize proposals based on the timing of financial strategy submissions. For example, the proposal department might prioritize recently submitted strategies. For example, it might postpone the submission of older strategies. The proposal department might dynamically adjust the priority of proposals according to the submission timing. This allows the proposal department to make efficient proposals by prioritizing proposals based on the timing of financial strategy submissions. The timing of financial strategy submissions includes, but is not limited to, short-term, medium-term, and long-term. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not. For example, the proposal department could input the timing of financial strategy submissions into an AI and have the AI ​​determine the priority of proposals.

[0053] The proposal department can adjust the order of proposals based on the relevance of the financial strategies during the proposal process. For example, the proposal department may prioritize proposing strategies that are highly relevant. For example, it may postpone proposing strategies that are less relevant. For example, the proposal department may dynamically adjust the order of proposals according to the relevance of the strategies. This allows the proposal department to make efficient proposals by adjusting the order of proposals based on the relevance of the financial strategies. The relevance of financial strategies includes, but is not limited to, correlation analysis and causal relationships. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input the relevance of financial strategies into AI and have the AI ​​perform the adjustment of the order of proposals.

[0054] The risk assessment unit can improve the accuracy of its risk assessment by considering the interrelationships of financial data during the risk assessment process. For example, the risk assessment unit can perform a risk assessment by considering the balance between income and expenses. For example, the risk assessment unit can perform a risk assessment by considering the relationship between assets and liabilities. For example, the risk assessment unit can perform a risk assessment by considering the interrelationships between investments and loan repayments. In this way, the risk assessment unit improves the accuracy of its risk assessment by considering the interrelationships of financial data. The interrelationships of financial data include, but are not limited to, correlation analysis and causal relationships. Some or all of the above processing in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input the interrelationships of financial data into AI and have the AI ​​perform the risk assessment.

[0055] The risk assessment unit can perform risk assessments by considering the attribute information of the financial data submitter. For example, the risk assessment unit may consider the submitter's age and occupation when performing risk assessments. For example, the risk assessment unit may consider the submitter's past financial history when performing risk assessments. For example, the risk assessment unit may consider the submitter's credit score when performing risk assessments. This allows the risk assessment unit to perform more appropriate risk assessments by considering the submitter's attribute information. The submitter's attribute information includes, but is not limited to, age, occupation, and income. Some or all of the above processing in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input the submitter's attribute information into AI and have the AI ​​perform the risk assessment.

[0056] The risk assessment unit can perform risk assessments while considering the geographical distribution of financial data. For example, the risk assessment unit can perform risk assessments while considering the economic conditions of the user's region. For example, the risk assessment unit can perform risk assessments while considering region-specific risk factors. For example, the risk assessment unit can adjust the risk assessment criteria based on geographical factors. This allows the risk assessment unit to perform more appropriate risk assessments by considering the geographical distribution of financial data. The geographical distribution of financial data includes, but is not limited to, regional economic conditions and geographical characteristics. Some or all of the above processing in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input the geographical distribution of financial data into AI and have the AI ​​perform the risk assessment.

[0057] The risk assessment unit can improve the accuracy of its risk assessments by referring to relevant literature on financial data during the risk assessment process. For example, the risk assessment unit may refer to the latest economic reports when conducting risk assessments. For example, the risk assessment unit may refer to relevant academic papers when conducting risk assessments. For example, the risk assessment unit may refer to the opinions of industry experts when conducting risk assessments. In this way, the risk assessment unit can improve the accuracy of its risk assessments by referring to relevant literature on financial data. Relevant literature on financial data includes, but is not limited to, academic papers and industry reports. Some or all of the above processes in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit may input relevant literature on financial data into AI and have the AI ​​perform the risk assessment.

[0058] The market analysis department can predict current market trends by referring to historical market data during market analysis. For example, the market analysis department predicts current market trends based on historical market data. For example, the market analysis department predicts current market trends by analyzing patterns in historical market data. For example, the market analysis department analyzes changes in current market trends by referring to historical market data. This improves the accuracy of the market analysis department's predictions of current market trends by referring to historical market data. Historical market data includes, but is not limited to, past stock prices and economic indicators. Some or all of the above processes in the market analysis department may be performed using, for example, AI, or not using AI. For example, the market analysis department can input historical market data into AI and have the AI ​​perform market trend predictions.

[0059] The market analysis department can apply different market analysis methods to each category of financial data during market analysis. For example, the market analysis department can apply risk assessment methods to investment data. For example, the market analysis department can apply expenditure reduction methods to savings data. For example, the market analysis department can apply repayment planning methods to loan repayment data. This improves the accuracy of the analysis by applying the appropriate market analysis method to each category of financial data. Different market analysis methods for each category of financial data include, but are not limited to, income, expenses, assets, and liabilities. Some or all of the above processing in the market analysis department may be performed using AI, for example, or not using AI. For example, the market analysis department can input categories of financial data into AI and have the AI ​​perform the application of different market analysis methods.

[0060] The market analysis department can analyze changes in market trends based on the timing of financial data submissions during market analysis. For example, the market analysis department can analyze changes in market trends based on recently submitted data. For example, the market analysis department can postpone analyzing changes in market trends based on older data submissions. For example, the market analysis department can dynamically analyze changes in market trends according to submission timing. This enables efficient market analysis by allowing the market analysis department to analyze changes in market trends based on the timing of financial data submissions. Changes in market trends include, but are not limited to, historical data, current market conditions, and future forecasts. Some or all of the above processes in the market analysis department may be performed using, for example, AI, or not using AI. For example, the market analysis department can input the timing of financial data submissions into AI and have AI perform the analysis of changes in market trends.

[0061] The market analysis department can analyze market trends by referring to relevant market data from financial data during market analysis. For example, the market analysis department analyzes market trends based on relevant market data. For example, the market analysis department analyzes market trends by analyzing patterns in relevant market data. For example, the market analysis department analyzes changes in market trends by referring to relevant market data. This improves the accuracy of market trend analysis by the market analysis department by referring to relevant market data from financial data. Relevant market data from financial data includes, but is not limited to, relevant industry data and economic indicators. Some or all of the above processing in the market analysis department may be performed using, for example, AI, or not using AI. For example, the market analysis department can input relevant market data into AI and have the AI ​​perform market trend analysis.

[0062] The information provider can select the optimal information provision method by referring to the user's past operation history when providing information. For example, the information provider can select the optimal information provision method based on the user's past operation history. For example, the information provider can select the most efficient information provision method from the user's past operation history. For example, the information provider can analyze the user's past operation history and propose improvements to the information provision method. In this way, the information provider can select the optimal information provision method by referring to the user's past operation history. The user's past operation history includes, but is not limited to, past operation content and frequency. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past operation history into AI and have AI select the optimal information provision method.

[0063] The information provider can select the optimal information delivery method by considering the user's device information when providing information. For example, if the user is using a smartphone, the information provider will provide an information delivery method that is adapted to the screen size. For example, if the user is using a tablet, the information provider will provide an information delivery method optimized for a large screen. For example, if the user is using a smartwatch, the information provider will provide a concise and highly visible information delivery method. In this way, the information provider can select the optimal information delivery method by considering the user's device information. User device information includes, but is not limited to, the type of device and usage status. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input the user's device information into AI and have the AI ​​select the optimal information delivery method.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The analysis unit can predict future behavior by considering the user's past financial behavior patterns when analyzing the user's financial data. For example, if a user has repeatedly made a specific investment pattern in the past, the analysis unit can predict future investment behavior based on that pattern. For example, if a user has had a specific spending pattern in the past, the analysis unit can predict future spending behavior based on that pattern. For example, if a user has had a specific loan repayment pattern in the past, the analysis unit can predict future loan repayment behavior based on that pattern. In this way, the analysis unit can make more accurate future predictions by considering the user's past behavior patterns.

[0066] The proposal department can propose financial strategies tailored to the user's life events based on the results of analyzing the user's financial data. For example, if a user is planning to get married, the proposal department can propose a savings strategy that takes into account the expenses associated with marriage. If a user is planning to buy a house, the proposal department can propose an optimal mortgage repayment plan. If a user is considering the cost of their children's education, the proposal department can propose an investment strategy for education expenses. In this way, the proposal department can provide support closely tailored to the user's life by proposing financial strategies that match the user's life events.

[0067] The risk assessment department can perform risk assessments that take into account the user's health status when analyzing the user's financial data. For example, if a user provides the results of a health checkup, the risk assessment department can perform a risk assessment based on those results. For example, if a user has a specific health risk, the risk assessment department can propose an investment strategy that takes that risk into account. For example, if a user's income is expected to fluctuate depending on their health status, the risk assessment department can perform a risk assessment that takes that fluctuation into account. In this way, the risk assessment department can perform more appropriate risk assessments by taking the user's health status into account.

[0068] The market analysis department can perform market analysis by considering the characteristics of the user's occupation and industry when analyzing the user's financial data. For example, if the user is engaged in a specific industry, the market analysis department can propose investment strategies considering the market trends of that industry. For example, if the user is engaged in a specific occupation, the market analysis department can perform risk assessments considering the income stability of that occupation. For example, the market analysis department can propose long-term investment strategies considering the economic conditions of the user's specific industry. In this way, the market analysis department can perform more appropriate market analysis by considering the characteristics of the user's occupation and industry.

[0069] The service provider can provide information tailored to the user's financial goals based on the results of analyzing the user's financial data. For example, if a user has a specific savings goal, the service provider can provide a saving strategy to reach that goal. If a user has a specific investment goal, the service provider can provide an investment strategy to reach that goal. If a user has a specific loan repayment goal, the service provider can provide a repayment plan to reach that goal. In this way, the service provider can support the user in achieving their goals by providing information tailored to the user's financial goals.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection unit collects the user's financial data. For example, the data collection unit collects data such as the user's income, expenses, assets, and liabilities. The data collection unit can collect the user's bank account transaction history, credit card usage history, pay stubs, and household budget data. Step 2: The analysis unit uses machine learning algorithms to analyze the data collected by the data collection unit. The analysis unit analyzes the user's income and expenditure patterns, debt situation, and asset situation, and proposes the optimal investments, loan repayments, asset management, savings, and income increases. Step 3: The proposal department proposes the optimal strategy based on the analysis results obtained by the analysis department. Based on the user's income and expenditure patterns, debt situation, and asset situation, the proposal department proposes the optimal investment strategy, loan repayment strategy, asset management strategy, savings strategy, and income increase strategy. Step 4: The Risk Assessment Department conducts a risk assessment based on the strategy proposed by the Proposal Department. Based on current market conditions and the user's financial situation, debt situation, asset situation, and spending patterns, the Risk Assessment Department recommends avoiding high-risk investments, loans, asset management, and spending. Step 5: The Market Analysis Department analyzes market trends based on the risk assessment conducted by the Risk Assessment Department. The Market Analysis Department forecasts future market trends and economic conditions and proposes long-term and short-term investment strategies and asset management strategies. Step 6: The provision department provides information to users based on market trends analyzed by the market analysis department. The provision department provides users with optimal investment strategies, loan repayment strategies, asset management strategies, savings strategies, and income increase strategies.

[0072] (Example of form 2) The generative AI agent system according to an embodiment of the present invention is a system that proposes optimal strategies for investment, saving, and loan repayment based on the financial situation of individuals and companies. This generative AI agent system collects the user's financial data, and the generative AI analyzes the collected data to automatically generate optimal investment, saving, and loan repayment strategies for the user. The generative AI analyzes the financial data using machine learning algorithms and also performs risk assessment and market trend analysis. This supports the user in efficient financial management. For example, the generative AI agent system collects the user's financial data. The data collected includes income, expenses, assets, and liabilities. For example, the transaction history of the user's bank account and credit card usage history are collected. This allows for an accurate understanding of the user's financial situation. Next, the generative AI agent system analyzes the collected data. The generative AI uses machine learning algorithms to analyze the user's financial data and automatically generates optimal strategies for investment, saving, and loan repayment. For example, it analyzes the user's income and expense patterns and proposes what kind of investment is optimal. It also analyzes the user's debt situation and proposes how to repay loans. Furthermore, the generating AI also performs risk assessment and market trend analysis. For example, it analyzes current market conditions and suggests avoiding high-risk investments. It also predicts future market trends and proposes long-term investment strategies. This allows users to manage their finances efficiently while minimizing risk. This system enables individual investors, those managing household finances, and small business owners to manage their finances efficiently without understanding complex financial products or investment strategies. It also saves time and costs associated with hiring professional consultants. For example, simply by inputting their financial data, the generating AI can propose an optimal investment strategy, and the user can invest according to that proposal. In this way, by utilizing generating AI, it is possible to realize a world where everyone can effectively manage their finances and achieve financial freedom and peace of mind. For example, simply by inputting their financial data, the generating AI can propose an optimal investment strategy, and the user can invest according to that proposal.This can improve the financial health of individuals and small businesses. The generating AI agent system will be able to efficiently collect, analyze, propose, assess risks, analyze market trends, and provide information based on users' financial data.

[0073] The generation AI agent system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a risk assessment unit, a market analysis unit, and a provision unit. The collection unit collects the user's financial data. For example, the collection unit collects data such as the user's income, expenses, assets, and liabilities. For example, the collection unit can collect the user's bank account transaction history and credit card usage history. The collection unit can also collect the user's pay stubs and household budget data. For example, the collection unit can collect the user's income data to understand monthly income fluctuations. For example, the collection unit can collect the user's expense data to understand the breakdown of expenses by category. For example, the collection unit can collect the user's asset data to understand the type and value of assets. For example, the collection unit can collect the user's liability data to understand the type and balance of liabilities. The analysis unit analyzes the data collected by the collection unit using machine learning algorithms. For example, the analysis unit analyzes the user's income and expense patterns and proposes what kind of investment is optimal. The analysis unit, for example, analyzes the user's debt situation and proposes how to repay loans. The analysis unit, for example, analyzes the user's asset situation and proposes how to manage those assets. The analysis unit, for example, analyzes the user's spending patterns and proposes how to save money. The analysis unit, for example, analyzes the user's income patterns and proposes how to increase income. The proposal unit proposes the optimal strategy based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal investment strategy based on the user's income and spending patterns. The proposal unit proposes the optimal loan repayment strategy based on the user's debt situation. The proposal unit proposes the optimal asset management strategy based on the user's asset situation. The proposal unit proposes the optimal saving strategy based on the user's spending patterns. The proposal unit proposes the optimal income increase strategy based on the user's income patterns. The risk assessment unit performs a risk assessment based on the strategies proposed by the proposal unit. The risk assessment unit, for example, analyzes the current market situation and proposes avoiding high-risk investments.The Risk Assessment Department, for example, advises users to avoid high-risk investments based on their financial situation. The Risk Assessment Department, for example, advises users to avoid high-risk loans based on their debt situation. The Risk Assessment Department, for example, advises users to avoid high-risk asset management based on their asset situation. The Risk Assessment Department, for example, advises users to avoid high-risk spending based on their spending patterns. The Market Analysis Department analyzes market trends based on the risk assessment conducted by the Risk Assessment Department. The Market Analysis Department, for example, predicts future market trends and proposes long-term investment strategies. The Market Analysis Department, for example, analyzes current market conditions and proposes short-term investment strategies. The Market Analysis Department, for example, analyzes historical market data and predicts current market trends. The Market Analysis Department, for example, predicts future economic conditions and proposes long-term asset management strategies. The Market Analysis Department, for example, analyzes current economic conditions and proposes short-term asset management strategies. The Provision Department provides information to users based on the market trends analyzed by the Market Analysis Department. The Provision Department, for example, provides users with the optimal investment strategy. The service provider can, for example, provide the user with an optimal loan repayment strategy. The service provider can, for example, provide the user with an optimal asset management strategy. The service provider can, for example, provide the user with an optimal savings strategy. The service provider can, for example, provide the user with an optimal income increase strategy. As a result, the generating AI agent system according to the embodiment can efficiently collect, analyze, propose, assess risks, analyze the market, and provide information based on the user's financial data.

[0074] The data collection unit collects users' financial data. For example, it collects data such as users' income, expenses, assets, and liabilities. Specifically, the data collection unit can collect transaction history from users' bank accounts and credit card usage history. This allows it to obtain detailed data on users' monthly income and expenses, and understand fluctuations in income and trends in spending. The data collection unit can also collect data from users' pay stubs and household budgets. This allows it to understand in detail the breakdown of users' income sources and expenses. Furthermore, the data collection unit can collect users' asset data, understanding the types and valuations of their assets. For example, it can collect asset data such as real estate, stocks, and deposits owned by users and calculate the valuations of those assets. The data collection unit can also collect users' liability data, understanding the types and balances of their liabilities. For example, it can collect liability data such as mortgages, car loans, and credit card balances, understanding the total amount of liabilities and repayment status. This allows the data collection unit to comprehensively understand the user's financial situation and collect basic data to provide to the analysis and proposal units. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0075] The analysis unit uses machine learning algorithms to analyze data collected by the data collection unit. For example, the analysis unit analyzes the user's income and expenditure patterns and proposes optimal investment strategies. Specifically, it analyzes income data over time to understand income fluctuation patterns. This allows it to determine whether income is stable or subject to seasonal fluctuations and propose appropriate investment strategies. It also analyzes expenditure data by category to identify spending trends and patterns of wasteful spending. This allows it to provide specific advice for saving money. The analysis unit analyzes the user's debt situation and proposes how to repay loans. For example, it considers interest rates and repayment periods to create an optimal repayment plan. Furthermore, it analyzes the user's asset situation and proposes how to manage assets. For example, it evaluates the risk and return of assets and constructs an optimal portfolio. The analysis unit analyzes the user's expenditure patterns and proposes how to save money. For example, it proposes specific methods for reducing wasteful spending. The analysis unit analyzes the user's income patterns and proposes how to increase income. For example, it proposes opportunities for side jobs or investments. This allows the analytics unit to analyze users' financial data in detail and provide the foundational data for proposing optimal strategies. Furthermore, the analytics unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict future income and expenses based on past income and expense data and formulate long-term financial plans. In addition, the analytics unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analytics unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0076] The Proposal Department proposes optimal strategies based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes optimal investment strategies based on the user's income and expenditure patterns. Specifically, it proposes high-risk investments for users with stable incomes and low-risk investments for users with unstable incomes. It also proposes specific saving methods to reduce wasteful spending based on spending trends. The Proposal Department proposes optimal loan repayment strategies based on the user's debt situation. For example, it proposes methods to prioritize the repayment of high-interest loans or to shorten the repayment period. The Proposal Department proposes optimal asset management strategies based on the user's asset situation. For example, it proposes a portfolio that considers the balance between risk and return and recommends diversified investment in assets. The Proposal Department proposes optimal saving strategies based on the user's expenditure patterns. For example, it proposes reviewing fixed costs and methods to reduce wasteful spending. The Proposal Department proposes optimal income-increasing strategies based on the user's income patterns. For example, it proposes investments in side jobs or education for skill development. In this way, the Proposal Department can propose optimal strategies tailored to the user's financial situation and support the user in achieving their financial goals. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can revise and improve proposals based on feedback from users who have implemented them. The proposal department can also provide personalized proposals that take into account users' lifestyles and values. This allows the proposal department to offer personalized suggestions to users and improve user satisfaction.

[0077] The Risk Assessment Department conducts risk assessments based on the strategies proposed by the Proposal Department. For example, the Risk Assessment Department analyzes current market conditions and advises against high-risk investments. Specifically, it analyzes stock market trends and economic indicators to identify high-risk investment opportunities. It also advises against high-risk investments based on the user's financial situation. For example, it recommends low-risk investments to users with unstable incomes. The Risk Assessment Department advises against high-risk loans based on the user's debt situation. For example, it provides specific advice on avoiding high-interest loans. The Risk Assessment Department advises against high-risk asset management based on the user's asset situation. For example, it recommends diversifying investments and proposes methods for diversifying risk. The Risk Assessment Department advises against high-risk spending based on the user's spending patterns. For example, it proposes specific methods for reducing wasteful spending. This allows the Risk Assessment Department to conduct risk assessments tailored to the user's financial situation and provide specific advice to minimize risk. Furthermore, the Risk Assessment Department can also conduct long-term risk assessments by utilizing historical data and statistical information. For example, it can predict future risks based on historical market data and propose long-term risk management strategies. Furthermore, the risk assessment unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the risk assessment unit to handle not only real-time risk assessment but also long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0078] The Market Analysis Department analyzes market trends based on risk assessments conducted by the Risk Assessment Department. For example, the Market Analysis Department predicts future market trends and proposes long-term investment strategies. Specifically, it analyzes economic indicators and market data to predict future market trends. This allows it to formulate and propose long-term investment strategies to users. It also analyzes current market conditions and proposes short-term investment strategies. For example, it analyzes current stock market trends and proposes short-term buying and selling timings. The Market Analysis Department analyzes historical market data to predict current market trends. For example, it predicts current stock price trends based on historical stock price data and proposes investment timings. The Market Analysis Department predicts future economic conditions and proposes long-term asset management strategies. For example, it predicts future economic growth rates and inflation rates and formulates long-term asset management policies. The Market Analysis Department analyzes current economic conditions and proposes short-term asset management strategies. For example, it analyzes current interest rate trends and proposes short-term asset management policies. This allows the Market Analysis Department to analyze market trends according to the user's financial situation and propose the optimal investment strategy. Furthermore, the market analysis department can utilize historical data and statistical information to predict long-term market trends. For example, it can predict future economic trends based on historical economic data and formulate long-term investment strategies. In addition, the market analysis department can use anomaly detection algorithms to detect unusual market trends and abnormal data, and issue warnings early. This allows the market analysis department to not only analyze market trends in real time but also to predict long-term market trends and detect anomalies, thereby improving the reliability and security of the entire system.

[0079] The service provider department provides information to users based on market trends analyzed by the market analysis department. For example, the service provider department provides users with optimal investment strategies. Specifically, it proposes optimal investment destinations and timing based on the user's financial situation and market trends. It also provides users with optimal loan repayment strategies. For example, it proposes an optimal repayment plan based on the user's debt situation and income situation. The service provider department provides users with optimal asset management strategies. For example, it proposes an optimal portfolio based on the user's asset situation and risk tolerance. The service provider department provides users with optimal savings strategies. For example, it proposes specific methods to reduce wasteful spending based on the user's spending patterns. The service provider department provides users with optimal income-increasing strategies. For example, it proposes investments in side jobs or education for skill development based on the user's skills and experience. In this way, the service provider department can provide optimal information tailored to the user's financial situation and support the user in achieving their financial goals. Furthermore, the service provider department can collect user feedback and continuously improve the accuracy and effectiveness of the services provided. For example, it can review and improve the services provided based on feedback from users who have implemented the provided information. Furthermore, the service provider can provide personalized information that takes into account the user's lifestyle and values. This allows the service provider to deliver personalized information to users and improve user satisfaction. In addition, the service provider can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the service provider to provide information to users quickly and reliably and support them in achieving their financial goals.

[0080] The data collection unit can collect data such as the user's income, expenses, assets, and liabilities. For example, the data collection unit can collect the user's income data to understand monthly fluctuations in income. For example, the data collection unit can collect the user's expense data to understand the breakdown of expenses by category. For example, the data collection unit can collect the user's asset data to understand the type and value of assets. For example, the data collection unit can collect the user's liability data to understand the type and balance of liabilities. In this way, the data collection unit can comprehensively collect the user's financial data. Income, expenses, assets, and liabilities include, but are not limited to, salaries, rent, savings, and loans. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's bank account transaction history into AI and have AI perform analysis of the transaction history.

[0081] The analysis unit can use machine learning algorithms to analyze a user's financial data and automatically generate optimal strategies for investment, saving, and loan repayment. For example, the analysis unit can analyze a user's income and expenditure patterns and suggest optimal investments. For example, the analysis unit can analyze a user's debt situation and suggest how to repay loans. For example, the analysis unit can analyze a user's asset situation and suggest how to manage assets. For example, the analysis unit can analyze a user's expenditure patterns and suggest how to save money. For example, the analysis unit can analyze a user's income patterns and suggest how to increase income. By using machine learning algorithms, the analysis unit can improve the accuracy of its financial data analysis. Machine learning algorithms include, but are not limited to, regression analysis, clustering, and neural networks. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's financial data into AI and have the AI ​​automatically generate optimal strategies.

[0082] The proposal unit can analyze a user's income and expenditure patterns and propose the optimal investment. For example, the proposal unit can analyze a user's income data and propose an investment strategy to increase income. For example, the proposal unit can analyze a user's expenditure data and propose an investment strategy to reduce expenditure. For example, the proposal unit can analyze a user's asset data and propose an investment strategy to optimize asset management. For example, the proposal unit can analyze a user's debt data and propose an investment strategy to make debt repayment more efficient. In this way, the proposal unit can propose the optimal investment strategy based on the user's income and expenditure patterns. Income and expenditure patterns include, but are not limited to, monthly income and expenditure categories. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the user's income and expenditure data into AI and have the AI ​​propose the optimal investment strategy.

[0083] The risk assessment unit can analyze current market conditions and propose strategies to avoid high-risk investments. For example, the risk assessment unit can analyze current market conditions and propose strategies to avoid high-risk investments. For example, the risk assessment unit can analyze the user's financial situation and propose strategies to avoid high-risk investments. For example, the risk assessment unit can analyze the user's debt situation and propose strategies to avoid high-risk loans. For example, the risk assessment unit can analyze the user's asset situation and propose strategies to avoid high-risk asset management. In this way, the risk assessment unit can minimize the user's risk by avoiding high-risk investments. Current market conditions include, but are not limited to, stock prices, interest rates, and economic indicators. Some or all of the above processing in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input data on current market conditions into AI and have the AI ​​perform the risk assessment.

[0084] The market analysis department can predict future market trends and propose long-term investment strategies. For example, the market analysis department can predict future market trends and propose long-term investment strategies. For example, the market analysis department can analyze current market conditions and propose short-term investment strategies. For example, the market analysis department can analyze historical market data and predict current market trends. For example, the market analysis department can predict future economic conditions and propose long-term asset management strategies. For example, the market analysis department can analyze current economic conditions and propose short-term asset management strategies. Thus, by predicting future market trends, the market analysis department can propose long-term investment strategies. Future market trends include, but are not limited to, economic forecasting models and scenario analysis. Some or all of the above processes in the market analysis department may be performed using, for example, AI, or not. For example, the market analysis department can input data on future market trends into AI and have the AI ​​perform market trend predictions.

[0085] The service provider can provide users with optimal investment, saving, and loan repayment strategies. For example, the service provider can provide users with the optimal investment strategy. For example, the service provider can provide users with the optimal loan repayment strategy. For example, the service provider can provide users with the optimal asset management strategy. For example, the service provider can provide users with the optimal saving strategy. For example, the service provider can provide users with the optimal income growth strategy. In this way, the service provider supports efficient financial management by providing users with the optimal strategy. Optimal investment, saving, and loan repayment strategies include, but are not limited to, types of investments, saving methods, and loan repayment plans. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's financial data into AI and have AI perform the task of providing the optimal strategy.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of financial data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection and collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can immediately collect financial data and start analysis quickly. For example, if the user is in a hurry, the data collection unit can advance the collection timing and collect data quickly. In this way, the data collection unit can collect data at a more appropriate time by adjusting the collection timing according to the user's emotions. User emotions include, but are not limited to, emotion analysis algorithms and survey results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​perform the adjustment of the collection timing.

[0087] The data collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the data collection unit may prioritize suggesting collection methods previously used by the user (manual input, API integration, etc.). For example, the data collection unit may select the most efficient collection method from the user's past collection history. For example, the data collection unit may analyze the user's past collection history and suggest improvements to the collection method. In this way, the data collection unit can select the optimal collection method by analyzing past collection history. The optimal collection method includes, but is not limited to, data collection frequency and collection methods. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past collection history data into AI and have the AI ​​select the optimal collection method.

[0088] The data collection unit can filter the collected financial data based on the user's current economic situation and areas of interest. For example, the data collection unit adjusts the scope of data to be collected based on the user's current income. For example, the data collection unit filters the data to be collected based on the user's areas of interest (investment, saving, etc.). For example, the data collection unit determines the priority of data to be collected according to the user's economic situation. This allows the data collection unit to collect highly relevant data by filtering the data based on the user's economic situation and areas of interest. The user's current economic situation and areas of interest include, but are not limited to, income, expenses, and investment history. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's economic situation data into AI and have the AI ​​perform the data filtering.

[0089] The data collection unit can estimate the user's emotions and prioritize the financial data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit will collect all data at once. For example, if the user is in a hurry, the data collection unit will prioritize the collection of highly important data. In this way, the data collection unit can prioritize the collection of important data by prioritizing data according to the user's emotions. Prioritization of financial data to be collected includes, but is not limited to, importance and urgency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform the data prioritization.

[0090] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial data. For example, the data collection unit prioritizes the collection of relevant data based on the economic conditions of the area where the user lives. For example, the data collection unit collects region-specific financial data based on the user's geographical location. For example, the data collection unit collects data from local financial institutions by considering the user's location. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. The user's geographical location includes, but is not limited to, local economic conditions and geographical characteristics. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location into AI and have AI perform the collection of highly relevant data.

[0091] The data collection unit can analyze a user's social media activity and collect relevant data when collecting financial data. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant financial data. For example, the data collection unit can collect relevant data by referring to the activities of a user's social media followers and friends. For example, the data collection unit can adjust the scope of data to be collected based on the user's social media usage. In this way, the data collection unit can collect relevant data by analyzing a user's social media activity. A user's social media activity includes, but is not limited to, posts, number of followers, and number of likes. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's social media data into AI and have the AI ​​perform the collection of relevant data.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Presentation methods of the analysis include, but are not limited to, graphs, charts, and report formats. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0093] The analysis unit can adjust the level of detail of its analysis based on the importance of the financial data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the financial data. The importance of financial data includes, but is not limited to, income, expenses, assets, and liabilities. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of the financial data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the category of financial data during analysis. For example, the analysis unit applies a risk assessment algorithm to investment data. For example, the analysis unit applies an expenditure reduction algorithm to savings data. For example, the analysis unit applies a repayment planning algorithm to loan repayment data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of financial data. Categories of financial data include, but are not limited to, income, expenses, assets, and liabilities. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of financial data into the AI ​​and have the AI ​​perform the application of different analysis algorithms.

[0095] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit will perform a detailed analysis. If the user is excited, the analysis unit will perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide more appropriate analysis results. The length of the analysis includes, but is not limited to, detailed analysis and summary analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the length of the analysis.

[0096] The analysis unit can determine the priority of analysis based on the submission date of financial data during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. For example, the analysis unit may postpone the analysis of older data. The analysis unit may dynamically adjust the analysis priority according to the submission date. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the submission date of financial data. The submission date of financial data includes, but is not limited to, monthly, quarterly, and annual data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the submission date of financial data into AI and have AI determine the analysis priority.

[0097] The analysis unit can adjust the order of analysis based on the relationships between financial data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, it may postpone the analysis of less relevant data. For example, the analysis unit may dynamically adjust the order of analysis according to the relationships between the data. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relationships between financial data. The relationships between financial data include, but are not limited to, correlation analysis and causal relationships. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relationships between financial data into AI and have AI perform the adjustment of the analysis order.

[0098] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit will provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way it presents suggestions according to the user's emotions. The presentation of suggestions includes, but is not limited to, text, graphs, and charts. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the way it presents suggestions.

[0099] The proposal department can adjust the level of detail in its proposals based on the importance of the financial strategies. For example, it can provide detailed proposals for high-importance strategies and simplified proposals for low-importance strategies. The proposal department can also prioritize proposals based on the importance of the strategies. This allows the proposal department to make efficient proposals by adjusting the level of detail based on the importance of the financial strategies. The importance of financial strategies includes, but is not limited to, risk, return, and time. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of financial strategies into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0100] The proposal unit can apply different proposal algorithms depending on the category of financial strategy when making a proposal. For example, the proposal unit applies a risk assessment algorithm to an investment strategy. For example, the proposal unit applies an expenditure reduction algorithm to a savings strategy. For example, the proposal unit applies a repayment planning algorithm to a loan repayment strategy. This improves the accuracy of the proposal by applying the appropriate proposal algorithm according to the category of financial strategy. The categories of financial strategies include, but are not limited to, investment strategies, savings strategies, and loan repayment strategies. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the categories of financial strategies into the AI ​​and have the AI ​​apply different proposal algorithms.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is excited, the suggestion unit will provide visually stimulating suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. Suggestion lengths include, but are not limited to, detailed suggestions and summary suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the length of the suggestions.

[0102] The proposal department can prioritize proposals based on the timing of financial strategy submissions. For example, the proposal department might prioritize recently submitted strategies. For example, it might postpone the submission of older strategies. The proposal department might dynamically adjust the priority of proposals according to the submission timing. This allows the proposal department to make efficient proposals by prioritizing proposals based on the timing of financial strategy submissions. The timing of financial strategy submissions includes, but is not limited to, short-term, medium-term, and long-term. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not. For example, the proposal department could input the timing of financial strategy submissions into an AI and have the AI ​​determine the priority of proposals.

[0103] The proposal department can adjust the order of proposals based on the relevance of the financial strategies during the proposal process. For example, the proposal department may prioritize proposing strategies that are highly relevant. For example, it may postpone proposing strategies that are less relevant. For example, the proposal department may dynamically adjust the order of proposals according to the relevance of the strategies. This allows the proposal department to make efficient proposals by adjusting the order of proposals based on the relevance of the financial strategies. The relevance of financial strategies includes, but is not limited to, correlation analysis and causal relationships. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input the relevance of financial strategies into AI and have the AI ​​perform the adjustment of the order of proposals.

[0104] The risk assessment unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated user emotions. For example, if the user is tense, the risk assessment unit will tighten the risk assessment criteria. For example, if the user is relaxed, the risk assessment unit will loosen the risk assessment criteria. For example, if the user is in a hurry, the risk assessment unit will perform a rapid risk assessment. In this way, the risk assessment unit can perform a more appropriate risk assessment by adjusting the risk assessment criteria according to the user's emotions. The risk assessment criteria include, but are not limited to, the type of risk, the assessment method, and the assessment standards. 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 risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the risk assessment criteria.

[0105] The risk assessment unit can improve the accuracy of its risk assessment by considering the interrelationships of financial data during the risk assessment process. For example, the risk assessment unit can perform a risk assessment by considering the balance between income and expenses. For example, the risk assessment unit can perform a risk assessment by considering the relationship between assets and liabilities. For example, the risk assessment unit can perform a risk assessment by considering the interrelationships between investments and loan repayments. In this way, the risk assessment unit improves the accuracy of its risk assessment by considering the interrelationships of financial data. The interrelationships of financial data include, but are not limited to, correlation analysis and causal relationships. Some or all of the above processing in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input the interrelationships of financial data into AI and have the AI ​​perform the risk assessment.

[0106] The risk assessment unit can perform risk assessments by considering the attribute information of the financial data submitter. For example, the risk assessment unit may consider the submitter's age and occupation when performing risk assessments. For example, the risk assessment unit may consider the submitter's past financial history when performing risk assessments. For example, the risk assessment unit may consider the submitter's credit score when performing risk assessments. This allows the risk assessment unit to perform more appropriate risk assessments by considering the submitter's attribute information. The submitter's attribute information includes, but is not limited to, age, occupation, and income. Some or all of the above processing in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input the submitter's attribute information into AI and have the AI ​​perform the risk assessment.

[0107] The risk assessment unit can estimate the user's emotions and adjust the order in which the risk assessment results are displayed based on the estimated emotions. For example, if the user is nervous, the risk assessment unit will display the low-risk results first. If the user is relaxed, the risk assessment unit will display all results at once. If the user is in a hurry, the risk assessment unit will display the important results first. In this way, the risk assessment unit can provide more appropriate risk assessment results by adjusting the order in which the risk assessment results are displayed according to the user's emotions. The order in which the risk assessment results are displayed may include, but is not limited to, importance, urgency, etc. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may include, 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 risk assessment unit may be performed using AI, for example, or not using AI. For example, the risk assessment unit can input user emotion data into an AI and have the AI ​​adjust the display order of the risk assessment results.

[0108] The risk assessment unit can perform risk assessments while considering the geographical distribution of financial data. For example, the risk assessment unit can perform risk assessments while considering the economic conditions of the user's region. For example, the risk assessment unit can perform risk assessments while considering region-specific risk factors. For example, the risk assessment unit can adjust the risk assessment criteria based on geographical factors. This allows the risk assessment unit to perform more appropriate risk assessments by considering the geographical distribution of financial data. The geographical distribution of financial data includes, but is not limited to, regional economic conditions and geographical characteristics. Some or all of the above processing in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input the geographical distribution of financial data into AI and have the AI ​​perform the risk assessment.

[0109] The risk assessment unit can improve the accuracy of its risk assessments by referring to relevant literature on financial data during the risk assessment process. For example, the risk assessment unit may refer to the latest economic reports when conducting risk assessments. For example, the risk assessment unit may refer to relevant academic papers when conducting risk assessments. For example, the risk assessment unit may refer to the opinions of industry experts when conducting risk assessments. In this way, the risk assessment unit can improve the accuracy of its risk assessments by referring to relevant literature on financial data. Relevant literature on financial data includes, but is not limited to, academic papers and industry reports. Some or all of the above processes in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit may input relevant literature on financial data into AI and have the AI ​​perform the risk assessment.

[0110] The market analysis department can estimate the user's emotions and adjust the display method of the market analysis based on the estimated user emotions. For example, if the user is stressed, the market analysis department provides a simple and highly visible display method. For example, if the user is relaxed, the market analysis department provides a display method that includes detailed information. For example, if the user is in a hurry, the market analysis department provides a display method that gets straight to the point. In this way, the market analysis department can provide more appropriate market analysis results by adjusting the display method of the market analysis according to the user's emotions. Display methods of the market analysis include, but are not limited to, graphs, charts, and report formats. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the market analysis department may be performed using AI, for example, or not using AI. For example, the market analysis department can input user emotion data into AI and have the AI ​​perform the adjustment of the display method of the market analysis.

[0111] The market analysis department can predict current market trends by referring to historical market data during market analysis. For example, the market analysis department predicts current market trends based on historical market data. For example, the market analysis department predicts current market trends by analyzing patterns in historical market data. For example, the market analysis department analyzes changes in current market trends by referring to historical market data. This improves the accuracy of the market analysis department's predictions of current market trends by referring to historical market data. Historical market data includes, but is not limited to, past stock prices and economic indicators. Some or all of the above processes in the market analysis department may be performed using, for example, AI, or not using AI. For example, the market analysis department can input historical market data into AI and have the AI ​​perform market trend predictions.

[0112] The market analysis department can apply different market analysis methods to each category of financial data during market analysis. For example, the market analysis department can apply risk assessment methods to investment data. For example, the market analysis department can apply expenditure reduction methods to savings data. For example, the market analysis department can apply repayment planning methods to loan repayment data. This improves the accuracy of the analysis by applying the appropriate market analysis method to each category of financial data. Different market analysis methods for each category of financial data include, but are not limited to, income, expenses, assets, and liabilities. Some or all of the above processing in the market analysis department may be performed using AI, for example, or not using AI. For example, the market analysis department can input categories of financial data into AI and have the AI ​​perform the application of different market analysis methods.

[0113] The market analysis department can estimate the user's emotions and adjust the importance of market analysis based on those emotions. For example, if the user is stressed, the market analysis department will postpone less important information. If the user is relaxed, the market analysis department will provide all information at once. If the user is in a hurry, the market analysis department will prioritize providing highly important information. In this way, the market analysis department can provide more appropriate market analysis results by adjusting the importance of market analysis according to the user's emotions. The importance of market analysis includes, but is not limited to, risk, return, and time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the market analysis department may be performed using AI or not. For example, the market analysis department can input user emotion data into AI and have the AI ​​perform the adjustment of market analysis importance.

[0114] The market analysis department can analyze changes in market trends based on the timing of financial data submissions during market analysis. For example, the market analysis department can analyze changes in market trends based on recently submitted data. For example, the market analysis department can postpone analyzing changes in market trends based on older data submissions. For example, the market analysis department can dynamically analyze changes in market trends according to submission timing. This enables efficient market analysis by allowing the market analysis department to analyze changes in market trends based on the timing of financial data submissions. Changes in market trends include, but are not limited to, historical data, current market conditions, and future forecasts. Some or all of the above processes in the market analysis department may be performed using, for example, AI, or not using AI. For example, the market analysis department can input the timing of financial data submissions into AI and have AI perform the analysis of changes in market trends.

[0115] The market analysis department can analyze market trends by referring to relevant market data from financial data during market analysis. For example, the market analysis department analyzes market trends based on relevant market data. For example, the market analysis department analyzes market trends by analyzing patterns in relevant market data. For example, the market analysis department analyzes changes in market trends by referring to relevant market data. This improves the accuracy of market trend analysis by the market analysis department by referring to relevant market data from financial data. Relevant market data from financial data includes, but is not limited to, relevant industry data and economic indicators. Some or all of the above processing in the market analysis department may be performed using, for example, AI, or not using AI. For example, the market analysis department can input relevant market data into AI and have the AI ​​perform market trend analysis.

[0116] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible method of information delivery. For example, if the user is relaxed, the information provider can provide a detailed method of information delivery. For example, if the user is in a hurry, the information provider can provide a concise method of information delivery. This allows the information provider to provide more appropriate information by adjusting the method of information delivery according to the user's emotions. Methods of information delivery include, but are not limited to, email, notifications, and reports. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input user emotion data into AI and have the AI ​​adjust the method of information delivery.

[0117] The information provider can select the optimal information provision method by referring to the user's past operation history when providing information. For example, the information provider can select the optimal information provision method based on the user's past operation history. For example, the information provider can select the most efficient information provision method from the user's past operation history. For example, the information provider can analyze the user's past operation history and propose improvements to the information provision method. In this way, the information provider can select the optimal information provision method by referring to the user's past operation history. The user's past operation history includes, but is not limited to, past operation content and frequency. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past operation history into AI and have AI select the optimal information provision method.

[0118] The information provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is nervous, the provider will postpone the delivery of less important information. For example, if the user is relaxed, the provider will deliver all information at once. For example, if the user is in a hurry, the provider will prioritize the delivery of highly important information. In this way, the provider can deliver important information preferentially by determining the priority of information delivery according to the user's emotions. The priority of information delivery includes, but is not limited to, importance and urgency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input user emotion data into AI and have the AI ​​perform the determination of information delivery priorities.

[0119] The information provider can select the optimal information delivery method by considering the user's device information when providing information. For example, if the user is using a smartphone, the information provider will provide an information delivery method that is adapted to the screen size. For example, if the user is using a tablet, the information provider will provide an information delivery method optimized for a large screen. For example, if the user is using a smartwatch, the information provider will provide a concise and highly visible information delivery method. In this way, the information provider can select the optimal information delivery method by considering the user's device information. User device information includes, but is not limited to, the type of device and usage status. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input the user's device information into AI and have the AI ​​select the optimal information delivery method.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The analysis unit can predict future behavior by considering the user's past financial behavior patterns when analyzing the user's financial data. For example, if a user has repeatedly made a specific investment pattern in the past, the analysis unit can predict future investment behavior based on that pattern. For example, if a user has had a specific spending pattern in the past, the analysis unit can predict future spending behavior based on that pattern. For example, if a user has had a specific loan repayment pattern in the past, the analysis unit can predict future loan repayment behavior based on that pattern. In this way, the analysis unit can make more accurate future predictions by considering the user's past behavior patterns.

[0122] The proposal department can propose financial strategies tailored to the user's life events based on the results of analyzing the user's financial data. For example, if a user is planning to get married, the proposal department can propose a savings strategy that takes into account the expenses associated with marriage. If a user is planning to buy a house, the proposal department can propose an optimal mortgage repayment plan. If a user is considering the cost of their children's education, the proposal department can propose an investment strategy for education expenses. In this way, the proposal department can provide support closely tailored to the user's life by proposing financial strategies that match the user's life events.

[0123] The risk assessment department can perform risk assessments that take into account the user's health status when analyzing the user's financial data. For example, if a user provides the results of a health checkup, the risk assessment department can perform a risk assessment based on those results. For example, if a user has a specific health risk, the risk assessment department can propose an investment strategy that takes that risk into account. For example, if a user's income is expected to fluctuate depending on their health status, the risk assessment department can perform a risk assessment that takes that fluctuation into account. In this way, the risk assessment department can perform more appropriate risk assessments by taking the user's health status into account.

[0124] The market analysis department can perform market analysis by considering the characteristics of the user's occupation and industry when analyzing the user's financial data. For example, if the user is engaged in a specific industry, the market analysis department can propose investment strategies considering the market trends of that industry. For example, if the user is engaged in a specific occupation, the market analysis department can perform risk assessments considering the income stability of that occupation. For example, the market analysis department can propose long-term investment strategies considering the economic conditions of the user's specific industry. In this way, the market analysis department can perform more appropriate market analysis by considering the characteristics of the user's occupation and industry.

[0125] The service provider can provide information tailored to the user's financial goals based on the results of analyzing the user's financial data. For example, if a user has a specific savings goal, the service provider can provide a saving strategy to reach that goal. If a user has a specific investment goal, the service provider can provide an investment strategy to reach that goal. If a user has a specific loan repayment goal, the service provider can provide a repayment plan to reach that goal. In this way, the service provider can support the user in achieving their goals by providing information tailored to the user's financial goals.

[0126] The data collection unit can estimate the user's emotions and adjust the types of data collected based on those emotions. For example, if the user is stressed, the types of data collected are reduced to lessen the user's burden. If the user is relaxed, for example, the data collection unit collects detailed data for more accurate analysis. If the user is in a hurry, for example, the data collection unit prioritizes collecting only the most important data. In this way, the data collection unit can reduce the user's burden while still collecting appropriate data by adjusting the types of data collected according to the user's emotions.

[0127] 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 timing can be delayed to perform the analysis when the user is relaxed. If the user is relaxed, the analysis unit can perform the analysis immediately and provide results quickly. If the user is in a hurry, the analysis unit can speed up the analysis timing and provide results quickly. In this way, the analysis unit can perform the analysis at a more appropriate time by adjusting the timing according to the user's emotions.

[0128] The suggestion department can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, it will prioritize low-risk suggestions. If the user is relaxed, for example, the suggestion department will provide detailed suggestions, including high-risk ones. If the user is in a hurry, for example, the suggestion department will provide concise and to-the-point suggestions. In this way, the suggestion department can provide more appropriate suggestions by adjusting the content of its suggestions according to the user's emotions.

[0129] The risk assessment unit can estimate the user's emotions and adjust how it displays the risk assessment results based on those emotions. For example, if the user is stressed, it can display lower-risk results first. If the user is relaxed, it can display all results at once. If the user is in a hurry, it can display important results first. In this way, the risk assessment unit can provide more appropriate risk assessment results by adjusting how it displays the results according to the user's emotions.

[0130] The market analysis department can estimate user emotions and adjust how market analysis results are displayed based on those estimated emotions. For example, if a user is stressed, it can provide a simple and highly visible display method. If a user is relaxed, it can provide a display method that includes detailed information. If a user is in a hurry, it can provide a display method that gets straight to the point. In this way, the market analysis department can provide more appropriate market analysis results by adjusting how market analysis results are displayed according to user emotions.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The data collection unit collects the user's financial data. For example, the data collection unit collects data such as the user's income, expenses, assets, and liabilities. The data collection unit can collect the user's bank account transaction history, credit card usage history, pay stubs, and household budget data. Step 2: The analysis unit uses machine learning algorithms to analyze the data collected by the data collection unit. The analysis unit analyzes the user's income and expenditure patterns, debt situation, and asset situation, and proposes the optimal investments, loan repayments, asset management, savings, and income increases. Step 3: The proposal department proposes the optimal strategy based on the analysis results obtained by the analysis department. Based on the user's income and expenditure patterns, debt situation, and asset situation, the proposal department proposes the optimal investment strategy, loan repayment strategy, asset management strategy, savings strategy, and income increase strategy. Step 4: The Risk Assessment Department conducts a risk assessment based on the strategy proposed by the Proposal Department. Based on current market conditions and the user's financial situation, debt situation, asset situation, and spending patterns, the Risk Assessment Department recommends avoiding high-risk investments, loans, asset management, and spending. Step 5: The Market Analysis Department analyzes market trends based on the risk assessment conducted by the Risk Assessment Department. The Market Analysis Department forecasts future market trends and economic conditions and proposes long-term and short-term investment strategies and asset management strategies. Step 6: The provision department provides information to users based on market trends analyzed by the market analysis department. The provision department provides users with optimal investment strategies, loan repayment strategies, asset management strategies, savings strategies, and income increase strategies.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, risk assessment unit, market analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's bank account transaction history and credit card usage history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a machine learning algorithm. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal strategy based on the analysis results. The risk assessment unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a risk assessment based on the proposed strategy. The market analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes market trends based on the risk assessment. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the user with the optimal strategy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, risk assessment unit, market analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's bank account transaction history and credit card usage history. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a machine learning algorithm. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal strategy based on the analysis results. The risk assessment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and performs a risk assessment based on the proposed strategy. The market analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes market trends based on the risk assessment. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the user with the optimal strategy. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, risk assessment unit, market analysis unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's bank account transaction history and credit card usage history. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a machine learning algorithm. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal strategy based on the analysis results. The risk assessment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and performs a risk assessment based on the proposed strategy. The market analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes market trends based on the risk assessment. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the user with the optimal strategy. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, risk assessment unit, market analysis unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects the user's bank account transaction history and credit card usage history. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a machine learning algorithm. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal strategy based on the analysis results. The risk assessment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs a risk assessment based on the proposed strategy. The market analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes market trends based on the risk assessment. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the user with the optimal strategy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A data collection unit that collects users' financial data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes an optimal strategy based on the analysis results obtained by the aforementioned analysis unit, A risk assessment unit conducts a risk assessment based on the strategy proposed by the aforementioned proposal unit, A market analysis department analyzes market trends based on the risk assessment conducted by the aforementioned risk assessment department, The system includes a provisioning unit that provides information to users based on market trends analyzed by the aforementioned market analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects user data such as income, expenses, assets, and liabilities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Using machine learning algorithms, the system analyzes users' financial data and automatically generates optimal strategies for investment, saving, and loan repayment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We analyze the user's income and spending patterns and suggest the optimal investment strategies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned risk assessment unit, We analyze the current market situation and advise against high-risk investments. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned market analysis department, We predict future market trends and propose long-term investment strategies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, We provide users with optimal investment, saving, and loan repayment strategies. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past financial data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting financial data, 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 11) The aforementioned collection unit is It estimates user sentiment and prioritizes the financial data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting financial data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting financial data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the financial data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analytical algorithms are applied depending on the category of financial data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the financial data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the financial data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the financial strategy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of financial strategy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the financial strategy will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the financial strategy. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned risk assessment unit, We estimate user sentiment and adjust risk assessment criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned risk assessment unit, When assessing risk, consider the interrelationships of financial data to improve the accuracy of the risk assessment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned risk assessment unit, When conducting a risk assessment, the attribute information of the financial data submitter should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned risk assessment unit, It estimates the user's emotions and adjusts the order in which the risk assessment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned risk assessment unit, When conducting a risk assessment, the geographical distribution of financial data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned risk assessment unit, When conducting risk assessments, referencing relevant literature on financial data can improve the accuracy of the risk assessment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned market analysis department, It estimates user sentiment and adjusts how market analysis is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned market analysis department, When conducting market analysis, historical market data is used to predict current market trends. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned market analysis department, When conducting market analysis, different market analysis methods are applied to each category of financial data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned market analysis department, We estimate user sentiment and adjust the importance of market analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned market analysis department, When conducting market analysis, we analyze changes in market trends based on the timing of financial data submission. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned market analysis department, When conducting market analysis, we analyze market trends by referring to relevant market data from financial data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned supply unit is, When providing information, the system selects the most suitable method of information delivery by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned supply unit is, When providing information, the optimal method of information delivery is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects users' financial data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes an optimal strategy based on the analysis results obtained by the aforementioned analysis unit, A risk assessment unit conducts a risk assessment based on the strategy proposed by the aforementioned proposal unit, A market analysis department analyzes market trends based on the risk assessment conducted by the aforementioned risk assessment department, The system includes a provisioning unit that provides information to users based on market trends analyzed by the aforementioned market analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects user data such as income, expenses, assets, and liabilities. The system according to feature 1.

3. The aforementioned analysis unit, Using machine learning algorithms, the system analyzes users' financial data and automatically generates optimal strategies for investment, saving, and loan repayment. The system according to feature 1.

4. The aforementioned proposal section is, We analyze the user's income and spending patterns and suggest the optimal investment strategies. The system according to feature 1.

5. The aforementioned risk assessment unit, We analyze the current market situation and advise against high-risk investments. The system according to feature 1.

6. The aforementioned market analysis department, We predict future market trends and propose long-term investment strategies. The system according to feature 1.

7. The aforementioned supply unit is, We provide users with optimal investment, saving, and loan repayment strategies. The system according to feature 1.

8. The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial data collection based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past financial data collection history and select the optimal collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting financial data, filtering is performed based on the user's current economic situation and areas of interest. The system according to feature 1.

Images