System

Abstract

To provide a system according to an embodiment that analyzes data on a user's finance and performs appropriate investment proposals and risk assessment.SOLUTION: A system comprises a collection unit, an analysis unit, an investment proposal unit, and a risk assessment unit. The collection unit collects data on a user's finance. The analysis unit analyzes the finance data collected by the collection unit. The investment proposal unit carries out investment proposals on the basis of an analysis result obtained by the analysis unit. The risk assessment unit assesses the risk of an investment strategy proposed by the investment proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately provide investment suggestions or risk assessments based on users' financial data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's financial data and provide appropriate investment suggestions and risk assessments. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an investment proposal unit, and a risk assessment unit. The collection unit collects financial data of a user. The analysis unit analyzes the financial data collected by the collection unit. The investment proposal unit makes an investment proposal based on the analysis result obtained by the analysis unit. The risk assessment unit assesses the risk of the investment strategy proposed by the investment proposal unit. Effect of the Invention

[0007] The system according to the embodiment can analyze a user's financial data and provide appropriate investment suggestions and risk assessments. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The AI ​​assistant system for financial planners according to an embodiment of the present invention is a system that collects and analyzes the financial data of a user, and performs investment proposals, risk assessments, tax-saving measures, retirement fund planning, and insurance needs analysis. This system collects the user's financial data and collects information such as income, expenditures, assets, and liabilities. Next, the collected data is analyzed to grasp the user's financial situation. Based on the analysis result, an investment strategy appropriate for the user is proposed, and the risk of the proposed investment strategy is evaluated. In addition, a tax-saving method appropriate for the user is proposed, and retirement funds are planned by predicting future income. Furthermore, insurance products are proposed based on the user's financial situation and lifestyle. For example, a collection unit that collects the user's financial data collects data on the user's income, expenditures, assets, and liabilities. Next, an analysis unit that analyzes the collected data analyzes the data using a machine learning model to grasp the user's financial situation. Based on the analysis result, an investment proposal unit proposes an investment strategy appropriate for the user. The investment proposal unit proposes an investment strategy based on the user's financial situation and market data, and ensures transparency using blockchain technology. In addition, a risk assessment unit evaluates the risk of the proposed investment strategy. The risk assessment unit performs risk assessment using a genetic algorithm. Furthermore, based on the analysis results, the tax saving proposal unit proposes tax saving methods suited to the user. The prediction unit predicts the user's future income using a machine learning model, and the retirement fund proposal unit calculates the user's living expenses after retirement and proposes retirement funds. The insurance product proposal unit proposes insurance products based on the user's financial situation and lifestyle. This allows the financial planner's AI assistant system to comprehensively analyze the user's financial situation and make investment proposals, risk assessments, tax saving measures, retirement fund planning, and insurance needs analysis. This allows the financial planner's AI assistant system to comprehensively analyze the user's financial situation and make appropriate proposals.

[0029] The AI ​​assistant system for financial planners according to the embodiment includes a collection unit, an analysis unit, an investment proposal unit, and a risk assessment unit. The collection unit collects financial data of a user. The financial data of a user includes, for example, income, expenditure, assets, and liabilities, but is not limited to such examples. The collection unit collects, for example, the income data of a user from a pay slip or a bank transaction statement. The collection unit can also collect the expenditure data of a user from a credit card statement or a household accounting application. Furthermore, the collection unit can collect the asset data of a user from the assessed value of real estate and the holding status of stocks. For example, the collection unit automatically acquires the income data of a user through an API and stores it in a database. The expenditure data can be manually entered by a user or can be automatically acquired from a credit card company. The asset data is periodically updated with the assessed value of real estate to keep the latest information. The analysis unit analyzes the financial data collected by the collection unit using a machine learning model. The analysis unit analyzes the trend of the user's income using, for example, a regression model. The analysis unit can also analyze the spending pattern of a user using a clustering model. Furthermore, the analysis unit can also evaluate the risk of the user's assets using a classification model. For example, the analysis unit uses a regression model to predict how the user's income will fluctuate in the future. The clustering model groups the user's expenditure data and analyzes which category has the most expenditure. The classification model classifies the user's asset data into high-risk assets and low-risk assets, and performs risk evaluation. The investment proposal unit proposes an investment strategy according to the user based on the analysis results obtained by the analysis unit. The investment proposal unit proposes an investment strategy such as stock investment, bond investment, real estate investment, etc., according to the user's risk tolerance. The investment proposal unit can also ensure the transparency of the investment proposal using blockchain technology. For example, the investment proposal unit evaluates the user's risk tolerance from a questionnaire survey or past investment behavior, and proposes an appropriate investment strategy. The blockchain technology records the history of the investment proposal, making it impossible to tamper with it. The risk evaluation unit evaluates the risk of the investment strategy proposed by the investment proposal unit.The risk assessment unit evaluates the risk of the investment strategy using, for example, a genetic algorithm. The risk assessment unit can also evaluate the risk of the investment strategy using cosine similarity or Jaccard coefficient. For example, the risk assessment unit optimizes the risk of the investment strategy using a genetic algorithm. Cosine similarity calculates the angle between the vectors of the investment strategy to evaluate the risk. The Jaccard coefficient is a value obtained by dividing the size of the common part of the investment strategy by the total size, and evaluates the risk. In this way, the AI ​​assistant system for financial planners according to the embodiment can collect and analyze the financial data of the user, and make investment proposals and risk assessments.

[0030] The collection unit collects the user's financial data. The user's financial data includes, but is not limited to, income, expenses, assets, and liabilities. The collection unit collects the user's income data from, for example, a pay slip or a bank statement. Specifically, the collection unit analyzes a digital copy of the pay slip provided by the user and extracts income information. From the bank statement, regular payroll transfers and other sources of income are identified and added to the income database. Furthermore, the collection unit can collect the user's expenditure data from a credit card statement or a household accounting app. The credit card statement is automatically obtained through an API and classified by expenditure category. The data from the household accounting app includes information manually entered by the user, which is also automatically collected. Furthermore, the collection unit can collect the user's asset data from the valuation of real estate and stock holdings. The valuation of real estate is obtained from a public valuation database that is updated periodically and reflected in the user's asset database. The stock holdings are updated in real time through a securities company's API and added to the user's portfolio. For example, the collection department automatically obtains a user's income data through an API and stores it in a database. Expense data can be entered manually by the user or automatically obtained from credit card companies. Asset data regularly updates the assessed value of real estate and keeps the information up to date. This allows the collection department to get a comprehensive view of the user's financial situation and organize and store the data so that other departments can use it.

[0031] The analysis unit uses a machine learning model to analyze the financial data collected by the collection unit. The analysis unit, for example, uses a regression model to analyze the user's income trends. Specifically, a regression model is constructed to predict future income fluctuations based on past income data. This model predicts increases and decreases in income by taking into account variables such as the user's occupation, age, and years of experience. The analysis unit can also analyze the user's spending patterns using a clustering model. The clustering model groups the user's spending data and analyzes which category has the most spending. For example, it classifies the data into categories such as food expenses, transportation expenses, and entertainment expenses, and calculates the spending ratio of each category. Furthermore, the analysis unit can evaluate the risk of the user's assets using a classification model. The classification model classifies the user's asset data into high-risk assets and low-risk assets, and performs a risk evaluation. For example, a risk evaluation is performed for each asset class, such as stocks, real estate, and bonds, and the risk of the user's entire portfolio is calculated. This allows the analysis unit to analyze the collected data from multiple angles and grasp the user's financial situation in detail. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term financial planning and risk management. For example, based on past expenditure data, future expenditure trends can be predicted and budget plans can be created. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term financial management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The investment proposal unit proposes an investment strategy according to the user based on the analysis results obtained by the analysis unit. The investment proposal unit proposes an investment strategy such as stock investment, bond investment, real estate investment, etc., according to the user's risk tolerance. Specifically, the investment proposal unit evaluates the user's risk tolerance from a questionnaire survey and past investment behavior, and proposes an appropriate investment strategy. In the questionnaire survey, questions are asked about the user's investment experience, attitude toward risk, investment goals, etc., and the risk tolerance is quantified. For past investment behavior, the history of investments made by the user in the past is analyzed, and the risk tolerance is evaluated. In addition, the investment proposal unit can also ensure the transparency of the investment proposal using blockchain technology. Blockchain technology records the history of investment proposals and makes them impossible to tamper with. This allows the user to confirm the reliability of the proposed investment strategy. In addition, the investment proposal unit can also predict market trends using AI and propose the optimal investment timing. For example, a machine learning model is used to predict stock market trends and notify the user of the timing of buying and selling. In addition, the investment proposal unit can also periodically review the user's portfolio and propose rebalancing. This enables the investment proposal unit to provide optimal investment strategies in accordance with the user's financial goals and support long-term asset formation.

[0033] The risk assessment unit evaluates the risk of the investment strategy proposed by the investment proposal unit. The risk assessment unit evaluates the risk of the investment strategy by using, for example, a genetic algorithm. Specifically, the genetic algorithm optimizes the parameters of the investment strategy and evaluates the balance between risk and return. The risk assessment unit can also evaluate the risk of the investment strategy by using cosine similarity or Jaccard coefficient. Cosine similarity calculates the angle between the vectors of the investment strategy and evaluates the risk. The Jaccard coefficient is a value obtained by dividing the size of the common part of the investment strategies by the total size, and evaluates the risk. This allows the risk assessment unit to evaluate the risk of the investment strategy from multiple angles using multiple methods. Furthermore, the risk assessment unit can also use scenario analysis to evaluate the performance of the investment strategy under different market conditions. For example, scenarios such as economic growth periods, recession periods, and financial crises are set, and the risk and return of the investment strategy in each scenario are simulated. This allows the risk assessment unit to provide the user with a more realistic risk assessment. The risk assessment unit can also perform portfolio optimization for risk management. For example, the modern portfolio theory is used to optimize the balance between risk and return, and propose an optimal portfolio according to the user's risk tolerance. This enables the risk assessment unit to comprehensively assess the risks of the user's investment strategy and support appropriate risk management.

[0034] The collection unit can collect data on the user's income, expenditure, assets, and liabilities. The collection unit, for example, collects the user's income data from a pay slip or a bank transaction statement. The income data includes, for example, salary income, business income, investment income, and the like, but is not limited to such examples. The collection unit can also collect the user's expenditure data from a credit card statement or a household accounting application. The expenditure data includes, for example, living expenses, education expenses, medical expenses, and the like, but is not limited to such examples. The collection unit can also collect the user's asset data from the appraisal value of real estate and the holding status of stocks. The asset data includes, for example, real estate, stocks, deposits, and the like, but is not limited to such examples. The collection unit can also collect the user's liability data from mortgages and credit card debts. The liability data includes, for example, mortgages, credit card debts, and the like, but is not limited to such examples. As a result, the collection unit can grasp the financial situation by collecting the user's income, expenditure, assets, and liability data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's income data into the AI ​​and cause the AI ​​to collect the income data.

[0035] The investment proposal unit can propose an investment strategy based on the user's financial situation and market data. The investment proposal unit proposes an investment strategy such as stock investment, bond investment, real estate investment, etc., depending on the user's risk tolerance, for example. The user's financial situation includes, but is not limited to, income, expenditure, assets, and liabilities. The market data includes, but is not limited to, stock price data, economic indicators, and industry trends. The investment proposal unit evaluates the user's risk tolerance from a questionnaire survey and past investment behavior, and proposes an appropriate investment strategy. For example, the investment proposal unit evaluates the user's risk tolerance through a questionnaire survey, and proposes stock investment to users with high risk tolerance, and proposes bond investment to users with low risk tolerance. The investment proposal unit can also analyze the user's past investment behavior and evaluate the risk tolerance. For example, the investment proposal unit analyzes the user's past investment behavior, and proposes a high-risk investment strategy to users with high risk tolerance, and a low-risk investment strategy to users with low risk tolerance. This allows the investment proposal unit to propose an appropriate investment strategy based on the user's financial situation and market data. Some or all of the above-mentioned processing in the investment proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the investment proposal unit may input the user's financial data and market data to the AI ​​and have the AI ​​execute a proposal for an investment strategy.

[0036] The risk assessment unit can assess the risk of the proposed investment strategy. The risk assessment unit assesses the risk of the investment strategy, for example, using a genetic algorithm. The genetic algorithm is an algorithm that searches for an optimal solution by performing operations such as selection, crossover, and mutation. The risk assessment unit optimizes the risk of the investment strategy using a genetic algorithm. For example, the risk assessment unit minimizes the risk of the investment strategy using a genetic algorithm. The risk assessment unit can also assess the risk of the investment strategy using cosine similarity or Jaccard coefficient. Cosine similarity calculates the angle between two vectors in a vector space model, and determines that the smaller the angle, the higher the similarity. The Jaccard coefficient is a value obtained by dividing the size of the common part of two sets by the total size, and indicates the degree of overlap of the sets. The risk assessment unit assesses the risk of the investment strategy using cosine similarity. For example, the risk assessment unit calculates the angle between the vectors of the investment strategies and evaluates the risk. The Jaccard coefficient is a value obtained by dividing the size of the common part of the investment strategies by the total size, and evaluates the risk. This allows the risk assessment unit to evaluate the risk of the proposed investment strategy, thereby enabling risk management. A part or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit may input data on the investment strategy to AI and have the AI ​​execute risk assessment.

[0037] The analysis unit can analyze the user's financial data using a machine learning model. The analysis unit, for example, uses a regression model to analyze the user's income trend. The regression model is a model for expressing data trends with a straight line or a curve and predicting future values. The analysis unit uses the regression model to predict how the user's income will fluctuate in the future. For example, the analysis unit uses the user's past income data to predict future income. The analysis unit can also analyze the user's spending pattern using a clustering model. The clustering model is a model for dividing data into groups, and classifies similar data into the same group. The analysis unit uses the clustering model to group the user's spending data and analyze which category has the most spending. For example, the analysis unit uses the user's spending data to classify into categories such as food expenses, transportation expenses, and entertainment expenses, and grasps the spending trend. Furthermore, the analysis unit can also evaluate the risk of the user's assets using a classification model. The classification model is a model for classifying data into categories, and predicts which category the data belongs to. The analysis unit uses a classification model to classify the user's asset data into high-risk assets and low-risk assets, and performs risk assessment. For example, the analysis unit uses the user's asset data to classify into high-risk stocks and low-risk bonds, and performs risk assessment. As a result, the analysis unit improves the accuracy of analyzing financial data by using a machine learning model. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's financial data into AI, and have the AI ​​analyze the data.

[0038] The investment proposal unit can ensure the transparency of the investment proposal by using the blockchain technology. The investment proposal unit, for example, uses the blockchain technology to record the history of the investment proposal and prevent tampering. Blockchain technology is a distributed ledger technology that makes it difficult to tamper with data, and therefore transparency can be ensured. The investment proposal unit uses the blockchain technology to record the history of the investment proposal and enable the user to check the contents of the proposal. For example, the investment proposal unit records the history of the investment strategy proposed to the user in the blockchain and enables the user to check the contents of the proposal at any time. The investment proposal unit can also use the blockchain technology to build a system for ensuring the transparency of the investment proposal. For example, the investment proposal unit uses the blockchain technology to build a system for recording the history of the investment proposal and enabling the user to check the contents of the proposal. As a result, the investment proposal unit uses the blockchain technology to improve the transparency of the investment proposal. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input the history of the investment proposal into AI and have the AI ​​execute the recording using the blockchain technology.

[0039] The risk assessment unit can perform risk assessment using a genetic algorithm. The risk assessment unit, for example, uses a genetic algorithm to assess the risk of the investment strategy. The genetic algorithm is an algorithm that searches for an optimal solution by performing operations such as selection, crossover, and mutation. The risk assessment unit optimizes the risk of the investment strategy using a genetic algorithm. For example, the risk assessment unit minimizes the risk of the investment strategy using a genetic algorithm. The risk assessment unit can also assess the risk of the investment strategy using cosine similarity or Jaccard coefficient. Cosine similarity calculates the angle between two vectors in a vector space model, and determines that the smaller the angle, the higher the similarity. The Jaccard coefficient is a value obtained by dividing the size of the common part of two sets by the total size, and indicates the degree of overlap of the sets. The risk assessment unit evaluates the risk of the investment strategy using cosine similarity. For example, the risk assessment unit calculates the angle between the vectors of the investment strategies and evaluates the risk. The Jaccard coefficient is a value obtained by dividing the size of the common part of the investment strategies by the total size, and evaluates the risk. As a result, the risk assessment unit improves the accuracy of risk assessment by using a genetic algorithm. A part or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit may input data on the investment strategy to AI and have the AI ​​execute risk assessment.

[0040] The system may include a tax-saving proposal unit that proposes a tax-saving method based on the analysis result. The tax-saving proposal unit, for example, analyzes data on the user's income, expenditure, assets, and liabilities, and proposes an optimal tax-saving method. The tax-saving methods include, for example, tax deductions, tax credits, and tax preferential treatment, but are not limited to such examples. The tax-saving proposal unit analyzes the user's income data and proposes applicable tax deductions. For example, the tax-saving proposal unit analyzes the user's income data and proposes tax deductions such as medical expenses deductions and housing loan deductions. The tax-saving proposal unit can also analyze the user's expenditure data and propose applicable tax deductions. For example, the tax-saving proposal unit analyzes the user's expenditure data and proposes tax deductions such as education expenses deductions and donation deductions. Furthermore, the tax-saving proposal unit can analyze the user's asset data and propose applicable tax preferential treatment. For example, the tax-saving proposal unit analyzes the user's asset data and proposes tax preferential treatment such as investment trusts and personal pension insurance. As a result, the tax-saving proposal unit can reduce the user's tax burden by proposing a tax-saving method based on the analysis result. Some or all of the above-mentioned processing in the tax saving suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the tax saving suggestion unit can input the user's financial data into the AI ​​and have the AI ​​execute the proposal of tax saving methods.

[0041] The system may include a prediction unit that predicts the user's future income and a retirement fund proposal unit that proposes retirement funds. The prediction unit, for example, predicts the user's income using a machine learning model. The machine learning model is a model for learning past data and predicting future values. The prediction unit predicts how the user's income will fluctuate in the future using the machine learning model. For example, the prediction unit predicts the user's future income using the user's past income data. The retirement fund proposal unit calculates the user's living expenses after retirement based on the user's current financial situation and the prediction result by the prediction unit, and proposes retirement funds. Retirement funds include, for example, pensions, retirement benefits, investment income, etc., but are not limited to such examples. The retirement fund proposal unit analyzes the user's current financial situation and calculates the living expenses after retirement. For example, the retirement fund proposal unit analyzes the user's income, expenditure, assets, and liabilities data, and calculates the living expenses after retirement. In addition, the retirement fund proposal unit predicts the user's income after retirement based on the prediction result by the prediction unit and proposes retirement funds. For example, the retirement fund proposal unit predicts the user's income after retirement using the income prediction result by the prediction unit, and proposes necessary retirement funds. As a result, the prediction unit and the retirement fund proposal unit can support the user's life after retirement by predicting future income and planning retirement funds. Some or all of the above-mentioned processing in the prediction unit and the retirement fund proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's income data into the AI ​​and have the AI ​​execute an income prediction. The retirement fund proposal unit can input the income prediction results by the prediction unit into the AI ​​and have the AI ​​execute a retirement fund proposal.

[0042] The insurance system may include an insurance product proposal unit that proposes insurance products based on the financial situation and lifestyle of the user. The insurance product proposal unit, for example, analyzes data on the user's income, expenditure, assets, and liabilities, and proposes an optimal insurance product. Insurance products include, for example, life insurance, medical insurance, and non-life insurance, but are not limited to such examples. The insurance product proposal unit analyzes the user's financial situation and proposes applicable insurance products. For example, the insurance product proposal unit analyzes the user's income data and proposes income protection insurance. The insurance product proposal unit can also analyze the user's expenditure data and propose medical insurance. Furthermore, the insurance product proposal unit can analyze the user's asset data and propose non-life insurance. For example, the insurance product proposal unit analyzes the user's asset data and proposes fire insurance for real estate. This allows the insurance product proposal unit to propose an appropriate insurance product based on the user's financial situation and lifestyle. A part or all of the above-mentioned processing in the insurance product proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the insurance product proposal unit can input the user's financial data into AI and have the AI ​​execute the proposal of insurance products.

[0043] The prediction unit can predict the user's income using a machine learning model. The machine learning model is a model for learning past data and predicting future values. The prediction unit uses the machine learning model to predict how the user's income will fluctuate in the future. For example, the prediction unit predicts the future income using the user's past income data. The machine learning model includes, but is not limited to, for example, a regression model, a classification model, a clustering model, and the like. The prediction unit predicts the user's income tendency using the regression model. For example, the prediction unit predicts the future income using the user's past income data. The classification model is a model for classifying data into categories, and predicts which category the data belongs to. The prediction unit uses the classification model to classify the user's income data and predict the fluctuation of income. The clustering model is a model for dividing data into groups, and classifies similar data into the same group. The prediction unit uses the clustering model to group the user's income data and predict the income tendency. As a result, the prediction unit improves the accuracy of income prediction by using the machine learning model. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input the user's income data into the AI ​​and have the AI ​​perform income prediction.

[0044] The investment proposal unit can dynamically evaluate the risk tolerance of the user and propose an investment strategy. The investment proposal unit, for example, evaluates the risk tolerance of the user from a questionnaire survey or past investment behavior, and proposes an appropriate investment strategy. The risk tolerance is an index that indicates how much risk the user can accept. The investment proposal unit dynamically evaluates the risk tolerance of the user and proposes an investment strategy. For example, the investment proposal unit evaluates the risk tolerance of the user through a questionnaire survey, proposes stock investment to users with high risk tolerance, and proposes bond investment to users with low risk tolerance. The investment proposal unit can also analyze the past investment behavior of the user and evaluate the risk tolerance. For example, the investment proposal unit analyzes the past investment behavior of the user, proposes a high-risk investment strategy to users with high risk tolerance, and proposes a low-risk investment strategy to users with low risk tolerance. This allows the investment proposal unit to propose an investment strategy according to the risk tolerance of the user. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input a user's risk tolerance data into the AI ​​and have the AI ​​execute investment strategy proposals.

[0045] The collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the collection unit preferentially proposes a collection method (manual input, API integration, etc.) that the user has used in the past. The collection unit analyzes the user's past financial data collection history and selects the optimal collection method. For example, the collection unit selects the most efficient collection method from the user's past collection history. The collection unit can also analyze the user's past collection history and select a collection method with fewer errors. For example, the collection unit analyzes the user's past collection history and selects a collection method with fewer errors. In this way, the collection unit can select the optimal collection method by analyzing the past collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history data to AI and cause AI to select the optimal collection method.

[0046] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting financial data. The collection unit collects only necessary data based on the user's living situation and field of interest, for example. The collection unit collects only necessary data based on the user's living situation. For example, the collection unit collects only necessary data based on the user's living situation. The collection unit can also preferentially collect related financial data based on the user's field of interest. For example, the collection unit preferentially collects related financial data based on the user's field of interest. Furthermore, the collection unit can also filter unnecessary data based on the user's living situation and field of interest. For example, the collection unit filters unnecessary data based on the user's living situation and field of interest. This allows the collection unit to collect only necessary data based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and field of interest data to AI and cause the AI ​​to perform filtering.

[0047] When collecting financial data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user. For example, if the user lives in a specific area, the collection unit preferentially collects financial data related to the area. The collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user. For example, if the user is traveling, the collection unit collects financial data related to the current location. The collection unit can also collect region-specific financial data based on the geographical location information of the user. For example, the collection unit collects region-specific financial data based on the geographical location information of the user. This allows the collection unit to preferentially collect highly relevant data based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to AI and cause the AI ​​to execute collection of highly relevant data.

[0048] The collection unit can analyze the user's social media activity and collect related data when collecting financial data. The collection unit, for example, collects financial data related to the field of interest from the user's social media activity. The collection unit analyzes the user's social media activity and collects related data. For example, the collection unit analyzes the content of the user's social media posts and collects related financial data. The collection unit can also collect related financial data with reference to the activities of the user's social media followers and friends. For example, the collection unit collects related financial data with reference to the activities of the user's social media followers and friends. This allows the collection unit to collect related data based on the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and cause AI to execute collection of related data.

[0049] The analysis unit can adjust the level of detail of the analysis based on the importance of the financial data. For example, the analysis unit performs a detailed analysis on important financial data. The analysis unit adjusts the level of detail of the analysis based on the importance of the financial data. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the financial data. For example, the analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the financial data. As a result, the analysis unit adjusts the level of detail of the analysis according to the importance of the financial data, thereby enabling efficient analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the financial data to AI and cause AI to adjust the level of detail of the analysis.

[0050] The analysis unit can apply different analysis algorithms depending on the category of financial data. For example, the analysis unit applies an income prediction algorithm to income data. The analysis unit applies different analysis algorithms depending on the category of financial data. For example, the analysis unit applies an expenditure pattern analysis algorithm to expenditure data. The analysis unit can also apply an asset evaluation algorithm to asset data. For example, the analysis unit applies an income prediction algorithm to income data. Thereby, the analysis unit applies an appropriate analysis algorithm depending on the category of financial data, thereby improving the analysis accuracy. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of financial data to AI and cause AI to apply the analysis algorithm.

[0051] The analysis unit can determine the priority of the analysis based on the submission time of the financial data. The analysis unit, for example, prioritizes the analysis of the latest financial data. The analysis unit determines the priority of the analysis based on the submission time of the financial data. For example, the analysis unit analyzes data that has been submitted earlier later. The analysis unit can also dynamically adjust the priority of the analysis based on the submission time. For example, the analysis unit dynamically adjusts the priority of the analysis based on the submission time. Thereby, the analysis unit can prioritize the analysis of the latest data by determining the priority of the analysis based on the submission time of the financial data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the financial data to AI and cause AI to execute the determination of the priority of the analysis.

[0052] The analysis unit can adjust the order of analysis based on the relevance of the financial data. For example, the analysis unit preferentially analyzes data with high relevance. The analysis unit adjusts the order of analysis based on the relevance of the financial data. For example, the analysis unit analyzes data with low relevance later. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the financial data. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the financial data. As a result, the analysis unit adjusts the order of analysis based on the relevance of the financial data, thereby enabling efficient analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the financial data to AI and cause AI to adjust the order of analysis.

[0053] The investment proposal unit can adjust the level of detail of the proposal based on the importance of the investment strategy. For example, the investment proposal unit makes a detailed proposal for an important investment strategy. The investment proposal unit adjusts the level of detail of the proposal based on the importance of the investment strategy. For example, the investment proposal unit makes a simplified proposal for an investment strategy with a low importance. The investment proposal unit can also dynamically adjust the level of detail of the proposal according to the importance of the investment strategy. For example, the investment proposal unit dynamically adjusts the level of detail of the proposal according to the importance of the investment strategy. As a result, the investment proposal unit can adjust the level of detail of the proposal according to the importance of the investment strategy, thereby enabling efficient proposals. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input importance data of the investment strategy to AI and cause AI to adjust the level of detail of the proposal.

[0054] The investment proposal unit can apply different proposal algorithms depending on the category of the investment strategy. For example, the investment proposal unit applies a stock investment algorithm to a stock investment. The investment proposal unit applies different proposal algorithms depending on the category of the investment strategy. For example, the investment proposal unit applies a real estate investment algorithm to a real estate investment. The investment proposal unit can also apply a bond investment algorithm to a bond investment. For example, the investment proposal unit applies a stock investment algorithm to a stock investment. Thereby, the investment proposal unit applies an appropriate proposal algorithm depending on the category of the investment strategy, thereby improving the proposal accuracy. A part or all of the above-mentioned processing in the investment proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the investment proposal unit can input category data of the investment strategy to the AI ​​and cause the AI ​​to apply the proposal algorithm.

[0055] The investment proposal unit can determine the priority of proposals based on the submission time of the investment strategy. For example, the investment proposal unit preferentially proposes the latest investment strategy. The investment proposal unit determines the priority of proposals based on the submission time of the investment strategy. For example, the investment proposal unit proposes an investment strategy with an older submission time later. The investment proposal unit can also dynamically adjust the priority of proposals based on the submission time. For example, the investment proposal unit dynamically adjusts the priority of proposals based on the submission time. Thereby, the investment proposal unit can preferentially propose the latest strategy by determining the priority of proposals based on the submission time of the investment strategy. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input the submission time data of the investment strategy to AI and cause AI to execute the determination of the priority of proposals.

[0056] The investment proposal unit can adjust the order of proposals based on the relevance of the investment strategies. For example, the investment proposal unit preferentially proposes highly relevant investment strategies. The investment proposal unit adjusts the order of proposals based on the relevance of the investment strategies. For example, the investment proposal unit proposes less relevant investment strategies later. The investment proposal unit can also dynamically adjust the order of proposals based on the relevance of the investment strategies. For example, the investment proposal unit dynamically adjusts the order of proposals based on the relevance of the investment strategies. As a result, the investment proposal unit adjusts the order of proposals based on the relevance of the investment strategies, thereby enabling efficient proposals. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input relevance data of the investment strategies to AI and cause AI to adjust the order of proposals.

[0057] The risk assessment unit can improve the accuracy of the assessment by taking into account the interrelationships between the investment strategies. The risk assessment unit, for example, analyzes the interrelationships between a plurality of investment strategies and improves the accuracy of the risk assessment. The risk assessment unit improves the accuracy of the assessment by taking into account the interrelationships between the investment strategies. For example, the risk assessment unit performs risk assessment by taking into account the interdependence between the investment strategies. The risk assessment unit can also adjust the result of the risk assessment by taking into account the interrelationships between the investment strategies. For example, the risk assessment unit adjusts the result of the risk assessment by taking into account the interrelationships between the investment strategies. As a result, the risk assessment unit improves the accuracy of the risk assessment by taking into account the interrelationships between the investment strategies. A part or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input interrelationship data between the investment strategies into AI and cause the AI ​​to execute the improvement of the accuracy of the assessment.

[0058] The risk assessment unit can perform the assessment by taking into consideration the attribute information of the person who submitted the investment strategy. The risk assessment unit performs the risk assessment by taking into consideration, for example, the past performance of the person who submitted the investment strategy. The risk assessment unit performs the assessment by taking into consideration the attribute information of the person who submitted the investment strategy. For example, the risk assessment unit performs the risk assessment by taking into consideration the expertise and experience of the person who submitted the investment strategy. The risk assessment unit can also adjust the result of the risk assessment based on the attribute information of the person who submitted the investment strategy. For example, the risk assessment unit adjusts the result of the risk assessment based on the attribute information of the person who submitted the investment strategy. As a result, the risk assessment unit improves the accuracy of the risk assessment by taking into consideration the attribute information of the person who submitted the investment strategy. A part or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input attribute information data of the person who submitted the investment strategy into AI and have the AI ​​execute the assessment.

[0059] The risk assessment unit can perform the assessment taking into account the geographical distribution of the investment strategy. For example, the risk assessment unit analyzes the geographical distribution of the investment strategy and performs the risk assessment. The risk assessment unit performs the assessment taking into account the geographical distribution of the investment strategy. For example, the risk assessment unit adjusts the result of the risk assessment taking into account the geographical risk. The risk assessment unit can also improve the accuracy of the risk assessment based on the geographical distribution of the investment strategy. For example, the risk assessment unit improves the accuracy of the risk assessment based on the geographical distribution of the investment strategy. As a result, the risk assessment unit improves the accuracy of the risk assessment by taking into account the geographical distribution of the investment strategy. A part or all of the above-mentioned processing in the risk assessment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the risk assessment unit can input the geographical distribution data of the investment strategy to the AI ​​and cause the AI ​​to execute the assessment.

[0060] The risk assessment unit can improve the accuracy of the assessment by referring to literature related to the investment strategy. The risk assessment unit, for example, improves the accuracy of the risk assessment by referring to literature related to the investment strategy. The risk assessment unit improves the accuracy of the assessment by referring to literature related to the investment strategy. For example, the risk assessment unit adjusts the result of the risk assessment based on information on the related literature. The risk assessment unit can also analyze literature related to the investment strategy and improve the accuracy of the risk assessment. For example, the risk assessment unit analyzes literature related to the investment strategy and improves the accuracy of the risk assessment. As a result, the risk assessment unit improves the accuracy of the risk assessment by referring to literature related to the investment strategy. A part or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input literature data related to the investment strategy into AI and cause AI to execute the improvement of the accuracy of the assessment.

[0061] The tax saving suggestion unit can select the optimal suggested method by analyzing the user's past tax history. The tax saving suggestion unit, for example, analyzes the user's past tax history and makes an optimal tax saving suggestion. The tax saving suggestion unit analyzes the user's past tax history and selects the optimal suggested method. For example, the tax saving suggestion unit adjusts the detail level of the tax saving suggestion based on the user's past tax history. The tax saving suggestion unit can also suggest the optimal tax saving method by referring to the user's past tax history. For example, the tax saving suggestion unit suggests the optimal tax saving method by referring to the user's past tax history. As a result, the tax saving suggestion unit can analyze the user's past tax history and make the optimal tax saving suggestion. A part or all of the above-mentioned processing in the tax saving suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the tax saving suggestion unit can input the user's past tax history data into AI and cause AI to select the optimal suggested method.

[0062] The tax saving proposal unit can select the optimal proposed method in consideration of the geographical location information of the user. For example, the tax saving proposal unit adjusts the tax saving proposal in consideration of differences in geographical tax systems. The tax saving proposal unit can also propose the optimal tax saving method with reference to the geographical location information of the user. For example, the tax saving proposal unit proposes the optimal tax saving method with reference to the geographical location information of the user. This enables the tax saving proposal unit to propose the optimal tax saving method based on the geographical location information of the user. A part or all of the above-mentioned processing in the tax saving proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the tax saving proposal unit can input the geographical location information data of the user to AI and cause the AI ​​to select the optimal proposed method.

[0063] The prediction unit can select the optimal prediction method by analyzing the user's past income history when predicting income. For example, the prediction unit adjusts the level of detail of the income prediction based on the user's past income history. The prediction unit can also select the optimal income prediction method by referring to the user's past income history. For example, the prediction unit selects the optimal income prediction method by referring to the user's past income history. As a result, the prediction unit can analyze the user's past income history to perform optimal income prediction. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the user's past income history data into AI and cause AI to select the optimal prediction method.

[0064] The prediction unit can select the optimal prediction method in consideration of the geographical location information of the user when predicting income. For example, the prediction unit adjusts the income prediction in consideration of the geographical economic situation. The prediction unit can also select the optimal income prediction method with reference to the geographical location information of the user. For example, the prediction unit selects the optimal income prediction method with reference to the geographical location information of the user. This enables the prediction unit to perform optimal income prediction based on the geographical location information of the user. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the geographical location information data of the user to the AI ​​and cause the AI ​​to select the optimal prediction method.

[0065] The retirement fund proposal unit can select the optimal proposal method by analyzing the user's past financial history when proposing retirement funds. For example, the retirement fund proposal unit adjusts the level of detail of the retirement fund proposal based on the user's past financial history. The retirement fund proposal unit can also select the optimal retirement fund proposal method by referring to the user's past financial history. For example, the retirement fund proposal unit selects the optimal retirement fund proposal method by referring to the user's past financial history. As a result, the retirement fund proposal unit can analyze the user's past financial history to propose optimal retirement funds. Some or all of the above-mentioned processing in the retirement fund proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the retirement fund proposal unit can input the user's past financial history data into AI and have the AI ​​select the optimal proposal method.

[0066] The retirement fund proposal unit can select the optimal proposal method by taking into consideration the geographic location information of the user when proposing retirement funds. For example, the retirement fund proposal unit adjusts the retirement fund proposal by taking into consideration the geographical difference in living expenses. The retirement fund proposal unit can also select the optimal retirement fund proposal method by referring to the geographic location information of the user. For example, the retirement fund proposal unit selects the optimal retirement fund proposal method by referring to the geographic location information of the user. This enables the retirement fund proposal unit to propose the optimal retirement fund based on the geographic location information of the user. Some or all of the above-mentioned processing in the retirement fund proposal unit may be performed, for example, using AI or may be performed without using AI. For example, the retirement fund proposal unit can input the geographic location information data of the user to the AI ​​and have the AI ​​select the optimal proposal method.

[0067] The insurance product proposal unit can select the optimal proposal method by analyzing the user's past insurance history when proposing an insurance product. For example, the insurance product proposal unit adjusts the level of detail of the insurance product proposal based on the user's past insurance history. The insurance product proposal unit can also select the optimal insurance product proposal method by referring to the user's past insurance history. For example, the insurance product proposal unit selects the optimal insurance product proposal method by referring to the user's past insurance history. As a result, the insurance product proposal unit can propose the optimal insurance product by analyzing the user's past insurance history. A part or all of the above-mentioned processing in the insurance product proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the insurance product proposal unit can input the user's past insurance history data into AI and cause AI to select the optimal proposal method.

[0068] The insurance product proposal unit can select the optimal proposal method in consideration of the geographical location information of the user when proposing an insurance product. For example, the insurance product proposal unit adjusts the insurance product proposal in consideration of differences in geographical insurance systems. The insurance product proposal unit can also select the optimal insurance product proposal method with reference to the geographical location information of the user. For example, the insurance product proposal unit selects the optimal insurance product proposal method with reference to the geographical location information of the user. This enables the insurance product proposal unit to propose the optimal insurance product based on the geographical location information of the user. A part or all of the above-mentioned processing in the insurance product proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the insurance product proposal unit can input the geographical location information data of the user to AI and cause the AI ​​to select the optimal proposal method.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] When collecting the user's financial data, the financial planner AI assistant system can analyze the user's social media activities and collect related data. For example, the collection unit analyzes the content of the user's social media posts and collects financial data related to the user's field of interest. The collection unit can also collect related financial data by referring to the activities of the user's social media followers and friends. This allows the collection unit to collect related data based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the AI ​​and cause the AI ​​to collect related data.

[0071] When collecting financial data of a user, the AI ​​assistant system for financial planners can preferentially collect highly relevant data taking into account the geographical location information of the user. For example, if the user lives in a particular area, the collection unit preferentially collects financial data related to that area. In addition, if the user is traveling, the collection unit can also collect financial data related to the current location. This allows the collection unit to preferentially collect highly relevant data based on the geographical location information of the user. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to the AI ​​and cause the AI ​​to collect highly relevant data.

[0072] The financial planner's AI assistant system can analyze the user's past financial data collection history and select the optimal collection method. For example, the collection unit preferentially suggests collection methods (manual input, API integration, etc.) that the user has used in the past. The collection unit can also select the most efficient collection method from the user's past collection history. This allows the collection unit to select the optimal collection method by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history data into AI and have the AI ​​select the optimal collection method.

[0073] The process flow of the first embodiment will be briefly described below.

[0074] Step 1: The collection unit collects the user's financial data. The user's financial data includes income, expenses, assets, and liabilities. For example, the collection unit collects income data from pay slips and bank transaction statements, and collects expense data from credit card statements and a household accounting app. It can also collect asset data from real estate appraisals and stock holdings. The collection unit can automatically obtain data through the API and store it in a database. Step 2: The analysis unit uses a machine learning model to analyze the financial data collected by the collection unit. The analysis unit uses a regression model to analyze the user's income trends and a clustering model to analyze the user's spending patterns. Furthermore, the analysis unit uses a classification model to evaluate the risk of the user's assets. For example, the regression model is used to predict future fluctuations in income, the clustering model is used to group spending data, and the classification model is used to classify asset data into high-risk and low-risk assets. Step 3: The investment proposal unit proposes an investment strategy appropriate to the user based on the analysis results obtained by the analysis unit. The investment proposal unit proposes investment strategies such as stock investment, bond investment, and real estate investment according to the user's risk tolerance. In addition, blockchain technology is used to ensure the transparency of investment proposals. For example, the user's risk tolerance is evaluated based on questionnaire surveys and past investment behavior, and an appropriate investment strategy is proposed. Blockchain technology records the history of investment proposals, preventing tampering. Step 4: The risk assessment unit assesses the risk of the investment strategy proposed by the investment proposal unit. The risk assessment unit may assess the risk of the investment strategy using a genetic algorithm, and may also assess the risk using cosine similarity or Jaccard coefficient. For example, the risk of the investment strategy is optimized using a genetic algorithm, the angle between the vectors of the investment strategies is calculated using cosine similarity, and the risk is assessed using the value obtained by dividing the size of the common part of the investment strategies by the total size using the Jaccard coefficient.

[0075] (Example 2) The AI ​​assistant system for financial planners according to an embodiment of the present invention is a system that collects and analyzes the financial data of a user, and performs investment proposals, risk assessments, tax-saving measures, retirement fund planning, and insurance needs analysis. This system collects the user's financial data and collects information such as income, expenditures, assets, and liabilities. Next, the collected data is analyzed to grasp the user's financial situation. Based on the analysis result, an investment strategy appropriate for the user is proposed, and the risk of the proposed investment strategy is evaluated. In addition, a tax-saving method appropriate for the user is proposed, and retirement funds are planned by predicting future income. Furthermore, insurance products are proposed based on the user's financial situation and lifestyle. For example, a collection unit that collects the user's financial data collects data on the user's income, expenditures, assets, and liabilities. Next, an analysis unit that analyzes the collected data analyzes the data using a machine learning model to grasp the user's financial situation. Based on the analysis result, an investment proposal unit proposes an investment strategy appropriate for the user. The investment proposal unit proposes an investment strategy based on the user's financial situation and market data, and ensures transparency using blockchain technology. In addition, a risk assessment unit evaluates the risk of the proposed investment strategy. The risk assessment unit performs risk assessment using a genetic algorithm. Furthermore, based on the analysis results, the tax saving proposal unit proposes tax saving methods suited to the user. The prediction unit predicts the user's future income using a machine learning model, and the retirement fund proposal unit calculates the user's living expenses after retirement and proposes retirement funds. The insurance product proposal unit proposes insurance products based on the user's financial situation and lifestyle. This allows the financial planner's AI assistant system to comprehensively analyze the user's financial situation and make investment proposals, risk assessments, tax saving measures, retirement fund planning, and insurance needs analysis. This allows the financial planner's AI assistant system to comprehensively analyze the user's financial situation and make appropriate proposals.

[0076] The AI ​​assistant system for financial planners according to the embodiment includes a collection unit, an analysis unit, an investment proposal unit, and a risk assessment unit. The collection unit collects financial data of a user. The financial data of a user includes, for example, income, expenditure, assets, and liabilities, but is not limited to such examples. The collection unit collects, for example, the income data of a user from a pay slip or a bank transaction statement. The collection unit can also collect the expenditure data of a user from a credit card statement or a household accounting application. Furthermore, the collection unit can collect the asset data of a user from the assessed value of real estate and the holding status of stocks. For example, the collection unit automatically acquires the income data of a user through an API and stores it in a database. The expenditure data can be manually entered by a user or can be automatically acquired from a credit card company. The asset data is periodically updated with the assessed value of real estate to keep the latest information. The analysis unit analyzes the financial data collected by the collection unit using a machine learning model. The analysis unit analyzes the trend of the user's income using, for example, a regression model. The analysis unit can also analyze the spending pattern of a user using a clustering model. Furthermore, the analysis unit can also evaluate the risk of the user's assets using a classification model. For example, the analysis unit uses a regression model to predict how the user's income will fluctuate in the future. The clustering model groups the user's expenditure data and analyzes which category has the most expenditure. The classification model classifies the user's asset data into high-risk assets and low-risk assets, and performs risk evaluation. The investment proposal unit proposes an investment strategy according to the user based on the analysis results obtained by the analysis unit. The investment proposal unit proposes an investment strategy such as stock investment, bond investment, real estate investment, etc., according to the user's risk tolerance. The investment proposal unit can also ensure the transparency of the investment proposal using blockchain technology. For example, the investment proposal unit evaluates the user's risk tolerance from a questionnaire survey or past investment behavior, and proposes an appropriate investment strategy. The blockchain technology records the history of the investment proposal, making it impossible to tamper with it. The risk evaluation unit evaluates the risk of the investment strategy proposed by the investment proposal unit.The risk assessment unit evaluates the risk of the investment strategy using, for example, a genetic algorithm. The risk assessment unit can also evaluate the risk of the investment strategy using cosine similarity or Jaccard coefficient. For example, the risk assessment unit optimizes the risk of the investment strategy using a genetic algorithm. Cosine similarity calculates the angle between the vectors of the investment strategy to evaluate the risk. The Jaccard coefficient is a value obtained by dividing the size of the common part of the investment strategy by the total size, and evaluates the risk. In this way, the AI ​​assistant system for financial planners according to the embodiment can collect and analyze the financial data of the user, and make investment proposals and risk assessments.

[0077] The collection unit collects the user's financial data. The user's financial data includes, but is not limited to, income, expenses, assets, and liabilities. The collection unit collects the user's income data from, for example, a pay slip or a bank statement. Specifically, the collection unit analyzes a digital copy of the pay slip provided by the user and extracts income information. From the bank statement, regular payroll transfers and other sources of income are identified and added to the income database. Furthermore, the collection unit can collect the user's expenditure data from a credit card statement or a household accounting app. The credit card statement is automatically obtained through an API and classified by expenditure category. The data from the household accounting app includes information manually entered by the user, which is also automatically collected. Furthermore, the collection unit can collect the user's asset data from the valuation of real estate and stock holdings. The valuation of real estate is obtained from a public valuation database that is updated periodically and reflected in the user's asset database. The stock holdings are updated in real time through a securities company's API and added to the user's portfolio. For example, the collection department automatically obtains a user's income data through an API and stores it in a database. Expense data can be entered manually by the user or automatically obtained from credit card companies. Asset data regularly updates the assessed value of real estate and keeps the information up to date. This allows the collection department to get a comprehensive view of the user's financial situation and organize and store the data so that other departments can use it.

[0078] The analysis unit uses a machine learning model to analyze the financial data collected by the collection unit. The analysis unit, for example, uses a regression model to analyze the user's income trends. Specifically, a regression model is constructed to predict future income fluctuations based on past income data. This model predicts increases and decreases in income by taking into account variables such as the user's occupation, age, and years of experience. The analysis unit can also analyze the user's spending patterns using a clustering model. The clustering model groups the user's spending data and analyzes which category has the most spending. For example, it classifies the data into categories such as food expenses, transportation expenses, and entertainment expenses, and calculates the spending ratio of each category. Furthermore, the analysis unit can evaluate the risk of the user's assets using a classification model. The classification model classifies the user's asset data into high-risk assets and low-risk assets, and performs a risk evaluation. For example, a risk evaluation is performed for each asset class, such as stocks, real estate, and bonds, and the risk of the user's entire portfolio is calculated. This allows the analysis unit to analyze the collected data from multiple angles and grasp the user's financial situation in detail. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term financial planning and risk management. For example, based on past expenditure data, future expenditure trends can be predicted and budget plans can be created. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term financial management and anomaly detection, improving the reliability and safety of the entire system.

[0079] The investment proposal unit proposes an investment strategy according to the user based on the analysis results obtained by the analysis unit. The investment proposal unit proposes an investment strategy such as stock investment, bond investment, real estate investment, etc., according to the user's risk tolerance. Specifically, the investment proposal unit evaluates the user's risk tolerance from a questionnaire survey and past investment behavior, and proposes an appropriate investment strategy. In the questionnaire survey, questions are asked about the user's investment experience, attitude toward risk, investment goals, etc., and the risk tolerance is quantified. For past investment behavior, the history of investments made by the user in the past is analyzed, and the risk tolerance is evaluated. In addition, the investment proposal unit can also ensure the transparency of the investment proposal using blockchain technology. Blockchain technology records the history of investment proposals and makes them impossible to tamper with. This allows the user to confirm the reliability of the proposed investment strategy. In addition, the investment proposal unit can also predict market trends using AI and propose the optimal investment timing. For example, a machine learning model is used to predict stock market trends and notify the user of the timing of buying and selling. In addition, the investment proposal unit can also periodically review the user's portfolio and propose rebalancing. This enables the investment proposal unit to provide optimal investment strategies in accordance with the user's financial goals and support long-term asset formation.

[0080] The risk assessment unit evaluates the risk of the investment strategy proposed by the investment proposal unit. The risk assessment unit evaluates the risk of the investment strategy by using, for example, a genetic algorithm. Specifically, the genetic algorithm optimizes the parameters of the investment strategy and evaluates the balance between risk and return. The risk assessment unit can also evaluate the risk of the investment strategy by using cosine similarity or Jaccard coefficient. Cosine similarity calculates the angle between the vectors of the investment strategy and evaluates the risk. The Jaccard coefficient is a value obtained by dividing the size of the common part of the investment strategies by the total size, and evaluates the risk. This allows the risk assessment unit to evaluate the risk of the investment strategy from multiple angles using multiple methods. Furthermore, the risk assessment unit can also use scenario analysis to evaluate the performance of the investment strategy under different market conditions. For example, scenarios such as economic growth periods, recession periods, and financial crises are set, and the risk and return of the investment strategy in each scenario are simulated. This allows the risk assessment unit to provide the user with a more realistic risk assessment. The risk assessment unit can also perform portfolio optimization for risk management. For example, the modern portfolio theory is used to optimize the balance between risk and return, and propose an optimal portfolio according to the user's risk tolerance. This enables the risk assessment unit to comprehensively assess the risks of the user's investment strategy and support appropriate risk management.

[0081] The collection unit can collect data on the user's income, expenditure, assets, and liabilities. The collection unit, for example, collects the user's income data from a pay slip or a bank transaction statement. The income data includes, for example, salary income, business income, investment income, and the like, but is not limited to such examples. The collection unit can also collect the user's expenditure data from a credit card statement or a household accounting application. The expenditure data includes, for example, living expenses, education expenses, medical expenses, and the like, but is not limited to such examples. The collection unit can also collect the user's asset data from the appraisal value of real estate and the holding status of stocks. The asset data includes, for example, real estate, stocks, deposits, and the like, but is not limited to such examples. The collection unit can also collect the user's liability data from mortgages and credit card debts. The liability data includes, for example, mortgages, credit card debts, and the like, but is not limited to such examples. As a result, the collection unit can grasp the financial situation by collecting the user's income, expenditure, assets, and liability data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's income data into the AI ​​and cause the AI ​​to collect the income data.

[0082] The investment proposal unit can propose an investment strategy based on the user's financial situation and market data. The investment proposal unit proposes an investment strategy such as stock investment, bond investment, real estate investment, etc., depending on the user's risk tolerance, for example. The user's financial situation includes, but is not limited to, income, expenditure, assets, and liabilities. The market data includes, but is not limited to, stock price data, economic indicators, and industry trends. The investment proposal unit evaluates the user's risk tolerance from a questionnaire survey and past investment behavior, and proposes an appropriate investment strategy. For example, the investment proposal unit evaluates the user's risk tolerance through a questionnaire survey, and proposes stock investment to users with high risk tolerance, and proposes bond investment to users with low risk tolerance. The investment proposal unit can also analyze the user's past investment behavior and evaluate the risk tolerance. For example, the investment proposal unit analyzes the user's past investment behavior, and proposes a high-risk investment strategy to users with high risk tolerance, and a low-risk investment strategy to users with low risk tolerance. This allows the investment proposal unit to propose an appropriate investment strategy based on the user's financial situation and market data. Some or all of the above-mentioned processing in the investment proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the investment proposal unit may input the user's financial data and market data to the AI ​​and have the AI ​​execute a proposal for an investment strategy.

[0083] The risk assessment unit can assess the risk of the proposed investment strategy. The risk assessment unit assesses the risk of the investment strategy, for example, using a genetic algorithm. The genetic algorithm is an algorithm that searches for an optimal solution by performing operations such as selection, crossover, and mutation. The risk assessment unit optimizes the risk of the investment strategy using a genetic algorithm. For example, the risk assessment unit minimizes the risk of the investment strategy using a genetic algorithm. The risk assessment unit can also assess the risk of the investment strategy using cosine similarity or Jaccard coefficient. Cosine similarity calculates the angle between two vectors in a vector space model, and determines that the smaller the angle, the higher the similarity. The Jaccard coefficient is a value obtained by dividing the size of the common part of two sets by the total size, and indicates the degree of overlap of the sets. The risk assessment unit assesses the risk of the investment strategy using cosine similarity. For example, the risk assessment unit calculates the angle between the vectors of the investment strategies and evaluates the risk. The Jaccard coefficient is a value obtained by dividing the size of the common part of the investment strategies by the total size, and evaluates the risk. This allows the risk assessment unit to evaluate the risk of the proposed investment strategy, thereby enabling risk management. A part or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit may input data on the investment strategy to AI and have the AI ​​perform risk assessment.

[0084] The analysis unit can analyze the user's financial data using a machine learning model. The analysis unit, for example, uses a regression model to analyze the user's income trend. The regression model is a model for expressing data trends with a straight line or a curve and predicting future values. The analysis unit uses the regression model to predict how the user's income will fluctuate in the future. For example, the analysis unit uses the user's past income data to predict future income. The analysis unit can also analyze the user's spending pattern using a clustering model. The clustering model is a model for dividing data into groups, and classifies similar data into the same group. The analysis unit uses the clustering model to group the user's spending data and analyze which category has the most spending. For example, the analysis unit uses the user's spending data to classify into categories such as food expenses, transportation expenses, and entertainment expenses, and grasps the spending trend. Furthermore, the analysis unit can also evaluate the risk of the user's assets using a classification model. The classification model is a model for classifying data into categories, and predicts which category the data belongs to. The analysis unit uses a classification model to classify the user's asset data into high-risk assets and low-risk assets, and performs risk assessment. For example, the analysis unit uses the user's asset data to classify into high-risk stocks and low-risk bonds, and performs risk assessment. As a result, the analysis unit improves the accuracy of analyzing financial data by using a machine learning model. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's financial data into AI, and have the AI ​​analyze the data.

[0085] The investment proposal unit can ensure the transparency of the investment proposal by using the blockchain technology. The investment proposal unit, for example, uses the blockchain technology to record the history of the investment proposal and prevent tampering. Blockchain technology is a distributed ledger technology that makes it difficult to tamper with data, and therefore transparency can be ensured. The investment proposal unit uses the blockchain technology to record the history of the investment proposal and enable the user to check the contents of the proposal. For example, the investment proposal unit records the history of the investment strategy proposed to the user in the blockchain and enables the user to check the contents of the proposal at any time. The investment proposal unit can also use the blockchain technology to build a system for ensuring the transparency of the investment proposal. For example, the investment proposal unit uses the blockchain technology to build a system for recording the history of the investment proposal and enabling the user to check the contents of the proposal. As a result, the investment proposal unit uses the blockchain technology to improve the transparency of the investment proposal. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input the history of the investment proposal into AI and have the AI ​​execute the recording using the blockchain technology.

[0086] The risk assessment unit can perform risk assessment using a genetic algorithm. The risk assessment unit, for example, uses a genetic algorithm to assess the risk of the investment strategy. The genetic algorithm is an algorithm that searches for an optimal solution by performing operations such as selection, crossover, and mutation. The risk assessment unit optimizes the risk of the investment strategy using a genetic algorithm. For example, the risk assessment unit minimizes the risk of the investment strategy using a genetic algorithm. The risk assessment unit can also assess the risk of the investment strategy using cosine similarity or Jaccard coefficient. Cosine similarity calculates the angle between two vectors in a vector space model, and determines that the smaller the angle, the higher the similarity. The Jaccard coefficient is a value obtained by dividing the size of the common part of two sets by the total size, and indicates the degree of overlap of the sets. The risk assessment unit evaluates the risk of the investment strategy using cosine similarity. For example, the risk assessment unit calculates the angle between the vectors of the investment strategies and evaluates the risk. The Jaccard coefficient is a value obtained by dividing the size of the common part of the investment strategies by the total size, and evaluates the risk. As a result, the risk assessment unit improves the accuracy of risk assessment by using a genetic algorithm. A part or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit may input data on the investment strategy to AI and have the AI ​​execute risk assessment.

[0087] The system may include a tax-saving proposal unit that proposes a tax-saving method based on the analysis result. The tax-saving proposal unit, for example, analyzes data on the user's income, expenditure, assets, and liabilities, and proposes an optimal tax-saving method. The tax-saving methods include, for example, tax deductions, tax credits, and tax preferential treatment, but are not limited to such examples. The tax-saving proposal unit analyzes the user's income data and proposes applicable tax deductions. For example, the tax-saving proposal unit analyzes the user's income data and proposes tax deductions such as medical expenses deductions and housing loan deductions. The tax-saving proposal unit can also analyze the user's expenditure data and propose applicable tax deductions. For example, the tax-saving proposal unit analyzes the user's expenditure data and proposes tax deductions such as education expenses deductions and donation deductions. Furthermore, the tax-saving proposal unit can analyze the user's asset data and propose applicable tax preferential treatment. For example, the tax-saving proposal unit analyzes the user's asset data and proposes tax preferential treatment such as investment trusts and personal pension insurance. As a result, the tax-saving proposal unit can reduce the user's tax burden by proposing a tax-saving method based on the analysis result. Some or all of the above-mentioned processing in the tax saving suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the tax saving suggestion unit can input the user's financial data into the AI ​​and have the AI ​​execute the proposal of tax saving methods.

[0088] The system may include a prediction unit that predicts the user's future income and a retirement fund proposal unit that proposes retirement funds. The prediction unit, for example, predicts the user's income using a machine learning model. The machine learning model is a model for learning past data and predicting future values. The prediction unit predicts how the user's income will fluctuate in the future using the machine learning model. For example, the prediction unit predicts the user's future income using the user's past income data. The retirement fund proposal unit calculates the user's living expenses after retirement based on the user's current financial situation and the prediction result by the prediction unit, and proposes retirement funds. Retirement funds include, for example, pensions, retirement benefits, investment income, etc., but are not limited to such examples. The retirement fund proposal unit analyzes the user's current financial situation and calculates the living expenses after retirement. For example, the retirement fund proposal unit analyzes the user's income, expenditure, assets, and liabilities data, and calculates the living expenses after retirement. In addition, the retirement fund proposal unit predicts the user's income after retirement based on the prediction result by the prediction unit and proposes retirement funds. For example, the retirement fund proposal unit predicts the user's income after retirement using the income prediction result by the prediction unit, and proposes necessary retirement funds. As a result, the prediction unit and the retirement fund proposal unit can support the user's life after retirement by predicting future income and planning retirement funds. Some or all of the above-mentioned processing in the prediction unit and the retirement fund proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's income data into the AI ​​and have the AI ​​execute an income prediction. The retirement fund proposal unit can input the income prediction results by the prediction unit into the AI ​​and have the AI ​​execute a retirement fund proposal.

[0089] The insurance system may include an insurance product proposal unit that proposes insurance products based on the financial situation and lifestyle of the user. The insurance product proposal unit, for example, analyzes data on the user's income, expenditure, assets, and liabilities, and proposes an optimal insurance product. Insurance products include, for example, life insurance, medical insurance, and non-life insurance, but are not limited to such examples. The insurance product proposal unit analyzes the user's financial situation and proposes applicable insurance products. For example, the insurance product proposal unit analyzes the user's income data and proposes income protection insurance. The insurance product proposal unit can also analyze the user's expenditure data and propose medical insurance. Furthermore, the insurance product proposal unit can analyze the user's asset data and propose non-life insurance. For example, the insurance product proposal unit analyzes the user's asset data and proposes fire insurance for real estate. This allows the insurance product proposal unit to propose an appropriate insurance product based on the user's financial situation and lifestyle. A part or all of the above-mentioned processing in the insurance product proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the insurance product proposal unit can input the user's financial data into AI and have the AI ​​execute the proposal of insurance products.

[0090] The prediction unit can predict the user's income using a machine learning model. The machine learning model is a model for learning past data and predicting future values. The prediction unit uses the machine learning model to predict how the user's income will fluctuate in the future. For example, the prediction unit predicts the future income using the user's past income data. The machine learning model includes, but is not limited to, for example, a regression model, a classification model, a clustering model, and the like. The prediction unit predicts the user's income tendency using the regression model. For example, the prediction unit predicts the future income using the user's past income data. The classification model is a model for classifying data into categories, and predicts which category the data belongs to. The prediction unit uses the classification model to classify the user's income data and predict the fluctuation of income. The clustering model is a model for dividing data into groups, and classifies similar data into the same group. The prediction unit uses the clustering model to group the user's income data and predict the income tendency. As a result, the prediction unit improves the accuracy of income prediction by using the machine learning model. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input the user's income data into the AI ​​and have the AI ​​perform income prediction.

[0091] The investment proposal unit can dynamically evaluate the risk tolerance of the user and propose an investment strategy. The investment proposal unit, for example, evaluates the risk tolerance of the user from a questionnaire survey or past investment behavior, and proposes an appropriate investment strategy. The risk tolerance is an index that indicates how much risk the user can accept. The investment proposal unit dynamically evaluates the risk tolerance of the user and proposes an investment strategy. For example, the investment proposal unit evaluates the risk tolerance of the user through a questionnaire survey, proposes stock investment to users with high risk tolerance, and proposes bond investment to users with low risk tolerance. The investment proposal unit can also analyze the past investment behavior of the user and evaluate the risk tolerance. For example, the investment proposal unit analyzes the past investment behavior of the user, proposes a high-risk investment strategy to users with high risk tolerance, and proposes a low-risk investment strategy to users with low risk tolerance. This allows the investment proposal unit to propose an investment strategy according to the risk tolerance of the user. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input a user's risk tolerance data into the AI ​​and have the AI ​​execute investment strategy proposals.

[0092] The collection unit can estimate the user's emotion and adjust the timing of collection of financial data based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit delays the collection timing and collects when the user is relaxed. The estimation of emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The collection unit estimates the user's emotion and adjusts the timing of collection of financial data based on the estimated user's emotion. For example, when the user is relaxed, the collection unit immediately collects financial data and quickly starts analysis. In addition, when the user is busy, the collection unit can also adjust the collection timing to match the user's schedule. For example, the collection unit takes into account the user's schedule and collects financial data at the optimal timing. As a result, the collection unit can reduce the burden on the user by adjusting the collection timing according to the user's emotion. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotional data into an AI and have the AI ​​adjust the collection timing.

[0093] The collection unit can analyze the user's past financial data collection history and select the optimal collection method. For example, the collection unit preferentially proposes a collection method (manual input, API integration, etc.) that the user has used in the past. The collection unit analyzes the user's past financial data collection history and selects the optimal collection method. For example, the collection unit selects the most efficient collection method from the user's past collection history. The collection unit can also analyze the user's past collection history and select a collection method with fewer errors. For example, the collection unit analyzes the user's past collection history and selects a collection method with fewer errors. In this way, the collection unit can select the optimal collection method by analyzing the past collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history data to AI and cause AI to select the optimal collection method.

[0094] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting financial data. The collection unit collects only necessary data based on the user's living situation and field of interest, for example. The collection unit collects only necessary data based on the user's living situation. For example, the collection unit collects only necessary data based on the user's living situation. The collection unit can also preferentially collect related financial data based on the user's field of interest. For example, the collection unit preferentially collects related financial data based on the user's field of interest. Furthermore, the collection unit can also filter unnecessary data based on the user's living situation and field of interest. For example, the collection unit filters unnecessary data based on the user's living situation and field of interest. This allows the collection unit to collect only necessary data based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and field of interest data to AI and cause the AI ​​to perform filtering.

[0095] The collection unit can estimate the user's emotion and determine the priority of the financial data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects only important data. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The collection unit estimates the user's emotion and determines the priority of the financial data to be collected based on the estimated user's emotion. For example, when the user is relaxed, the collection unit collects detailed data. In addition, when the user is busy, the collection unit can reduce the amount of data to be collected and preferentially collect the minimum necessary data. For example, the collection unit collects financial data at an optimal timing in consideration of the user's schedule. As a result, the collection unit determines the priority of the data to be collected according to the user's emotion, thereby enabling efficient data collection. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotional data into an AI and have the AI ​​determine the priorities of the collected data.

[0096] When collecting financial data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user. For example, if the user lives in a specific area, the collection unit preferentially collects financial data related to the area. The collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user. For example, if the user is traveling, the collection unit collects financial data related to the current location. The collection unit can also collect region-specific financial data based on the geographical location information of the user. For example, the collection unit collects region-specific financial data based on the geographical location information of the user. This allows the collection unit to preferentially collect highly relevant data based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to AI and cause the AI ​​to execute collection of highly relevant data.

[0097] The collection unit can analyze the user's social media activity and collect related data when collecting financial data. The collection unit, for example, collects financial data related to the field of interest from the user's social media activity. The collection unit analyzes the user's social media activity and collects related data. For example, the collection unit analyzes the content of the user's social media posts and collects related financial data. The collection unit can also collect related financial data with reference to the activities of the user's social media followers and friends. For example, the collection unit collects related financial data with reference to the activities of the user's social media followers and friends. This allows the collection unit to collect related data based on the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and cause AI to execute collection of related data.

[0098] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The analysis unit estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. For example, the analysis unit provides a detailed analysis result when the user is relaxed. In addition, the analysis unit can also provide an analysis result with the main points when the user is in a hurry. For example, the analysis unit provides an analysis result with the main points when the user is in a hurry. As a result, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the expression method of the analysis according to the user's emotion. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into an AI and have the AI ​​adjust the way the analysis is expressed.

[0099] The analysis unit can adjust the level of detail of the analysis based on the importance of the financial data. For example, the analysis unit performs a detailed analysis on important financial data. The analysis unit adjusts the level of detail of the analysis based on the importance of the financial data. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the financial data. For example, the analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the financial data. As a result, the analysis unit adjusts the level of detail of the analysis according to the importance of the financial data, thereby enabling efficient analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the financial data to AI and cause AI to adjust the level of detail of the analysis.

[0100] The analysis unit can apply different analysis algorithms depending on the category of financial data. For example, the analysis unit applies an income prediction algorithm to income data. The analysis unit applies different analysis algorithms depending on the category of financial data. For example, the analysis unit applies an expenditure pattern analysis algorithm to expenditure data. The analysis unit can also apply an asset evaluation algorithm to asset data. For example, the analysis unit applies an income prediction algorithm to income data. Thereby, the analysis unit applies an appropriate analysis algorithm depending on the category of financial data, thereby improving the analysis accuracy. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of financial data to AI and cause AI to apply the analysis algorithm.

[0101] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The analysis unit estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, the analysis unit provides a detailed analysis result when the user is relaxed. In addition, the analysis unit can also provide a visually stimulating analysis result when the user is excited. For example, the analysis unit provides a visually stimulating analysis result when the user is excited. In this way, the analysis unit can provide an appropriate analysis result for the user by adjusting the length of the analysis according to the user's emotion. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the length of the analysis.

[0102] The analysis unit can determine the priority of the analysis based on the submission time of the financial data. The analysis unit, for example, prioritizes the analysis of the latest financial data. The analysis unit determines the priority of the analysis based on the submission time of the financial data. For example, the analysis unit analyzes data that has been submitted earlier later. The analysis unit can also dynamically adjust the priority of the analysis based on the submission time. For example, the analysis unit dynamically adjusts the priority of the analysis based on the submission time. Thereby, the analysis unit can prioritize the analysis of the latest data by determining the priority of the analysis based on the submission time of the financial data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the financial data to AI and cause AI to execute the determination of the priority of the analysis.

[0103] The analysis unit can adjust the order of analysis based on the relevance of the financial data. For example, the analysis unit preferentially analyzes data with high relevance. The analysis unit adjusts the order of analysis based on the relevance of the financial data. For example, the analysis unit analyzes data with low relevance later. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the financial data. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the financial data. As a result, the analysis unit adjusts the order of analysis based on the relevance of the financial data, thereby enabling efficient analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the financial data to AI and cause AI to adjust the order of analysis.

[0104] The investment proposal unit can estimate the user's emotions and adjust the way of expressing the investment proposal based on the estimated user's emotions. For example, when the user is nervous, the investment proposal unit provides a simple and highly visible proposal. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The investment proposal unit estimates the user's emotions and adjusts the way of expressing the investment proposal based on the estimated user's emotions. For example, when the user is relaxed, the investment proposal unit provides a detailed proposal. In addition, when the user is in a hurry, the investment proposal unit can also provide a proposal that covers the main points. For example, when the user is in a hurry, the investment proposal unit provides a proposal that covers the main points. As a result, the investment proposal unit can provide a proposal that is easy for the user to understand by adjusting the way of expressing the investment proposal according to the user's emotions. A part or all of the above-mentioned processing in the investment proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the investment proposal unit can input a user's emotional data into an AI and have the AI ​​adjust the way the investment proposal is presented.

[0105] The investment proposal unit can adjust the level of detail of the proposal based on the importance of the investment strategy. For example, the investment proposal unit makes a detailed proposal for an important investment strategy. The investment proposal unit adjusts the level of detail of the proposal based on the importance of the investment strategy. For example, the investment proposal unit makes a simplified proposal for an investment strategy with a low importance. The investment proposal unit can also dynamically adjust the level of detail of the proposal according to the importance of the investment strategy. For example, the investment proposal unit dynamically adjusts the level of detail of the proposal according to the importance of the investment strategy. As a result, the investment proposal unit can adjust the level of detail of the proposal according to the importance of the investment strategy, thereby enabling efficient proposals. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input importance data of the investment strategy to AI and cause AI to adjust the level of detail of the proposal.

[0106] The investment proposal unit can apply different proposal algorithms depending on the category of the investment strategy. For example, the investment proposal unit applies a stock investment algorithm to a stock investment. The investment proposal unit applies different proposal algorithms depending on the category of the investment strategy. For example, the investment proposal unit applies a real estate investment algorithm to a real estate investment. The investment proposal unit can also apply a bond investment algorithm to a bond investment. For example, the investment proposal unit applies a stock investment algorithm to a stock investment. Thereby, the investment proposal unit applies an appropriate proposal algorithm depending on the category of the investment strategy, thereby improving the proposal accuracy. A part or all of the above-mentioned processing in the investment proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the investment proposal unit can input category data of the investment strategy to the AI ​​and cause the AI ​​to apply the proposal algorithm.

[0107] The investment proposal unit can estimate the user's emotion and adjust the length of the investment proposal based on the estimated user's emotion. For example, when the user is in a hurry, the investment proposal unit provides a short and to-the-point proposal. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The investment proposal unit estimates the user's emotion and adjusts the length of the investment proposal based on the estimated user's emotion. For example, the investment proposal unit provides a detailed proposal when the user is relaxed. In addition, the investment proposal unit can also provide a visually stimulating proposal when the user is excited. For example, the investment proposal unit provides a visually stimulating proposal when the user is excited. In this way, the investment proposal unit can provide a proposal appropriate for the user by adjusting the length of the investment proposal according to the user's emotion. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input a user's emotional data into the AI ​​and have the AI ​​adjust the length of the investment proposal.

[0108] The investment proposal unit can determine the priority of proposals based on the submission time of the investment strategy. For example, the investment proposal unit preferentially proposes the latest investment strategy. The investment proposal unit determines the priority of proposals based on the submission time of the investment strategy. For example, the investment proposal unit proposes an investment strategy with an older submission time later. The investment proposal unit can also dynamically adjust the priority of proposals based on the submission time. For example, the investment proposal unit dynamically adjusts the priority of proposals based on the submission time. Thereby, the investment proposal unit can preferentially propose the latest strategy by determining the priority of proposals based on the submission time of the investment strategy. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input the submission time data of the investment strategy to AI and cause AI to execute the determination of the priority of proposals.

[0109] The investment proposal unit can adjust the order of proposals based on the relevance of the investment strategies. For example, the investment proposal unit preferentially proposes highly relevant investment strategies. The investment proposal unit adjusts the order of proposals based on the relevance of the investment strategies. For example, the investment proposal unit proposes less relevant investment strategies later. The investment proposal unit can also dynamically adjust the order of proposals based on the relevance of the investment strategies. For example, the investment proposal unit dynamically adjusts the order of proposals based on the relevance of the investment strategies. As a result, the investment proposal unit adjusts the order of proposals based on the relevance of the investment strategies, thereby enabling efficient proposals. A part or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input relevance data of the investment strategies to AI and cause AI to adjust the order of proposals.

[0110] The risk assessment unit can estimate the user's emotion and adjust the risk assessment criteria based on the estimated user's emotion. For example, when the user is nervous, the risk assessment unit tightens the risk assessment criteria. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The risk assessment unit estimates the user's emotion and adjusts the risk assessment criteria based on the estimated user's emotion. For example, the risk assessment unit relaxes the risk assessment criteria when the user is relaxed. In addition, the risk assessment unit can also perform risk assessment quickly when the user is in a hurry. For example, the risk assessment unit performs risk assessment quickly when the user is in a hurry. As a result, the risk assessment unit can provide an appropriate risk assessment for the user by adjusting the risk assessment criteria according to the user's emotion. A part or all of the above-mentioned processing in the risk assessment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the risk assessment unit can input a user's emotional data into the AI ​​and have the AI ​​adjust the risk assessment criteria.

[0111] The risk assessment unit can improve the accuracy of the assessment by taking into account the interrelationships between the investment strategies. The risk assessment unit, for example, analyzes the interrelationships between a plurality of investment strategies and improves the accuracy of the risk assessment. The risk assessment unit improves the accuracy of the assessment by taking into account the interrelationships between the investment strategies. For example, the risk assessment unit performs risk assessment by taking into account the interdependence between the investment strategies. The risk assessment unit can also adjust the result of the risk assessment by taking into account the interrelationships between the investment strategies. For example, the risk assessment unit adjusts the result of the risk assessment by taking into account the interrelationships between the investment strategies. As a result, the risk assessment unit improves the accuracy of the risk assessment by taking into account the interrelationships between the investment strategies. A part or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input interrelationship data between the investment strategies into AI and cause the AI ​​to execute the improvement of the accuracy of the assessment.

[0112] The risk assessment unit can perform the assessment by taking into consideration the attribute information of the person who submitted the investment strategy. The risk assessment unit performs the risk assessment by taking into consideration, for example, the past performance of the person who submitted the investment strategy. The risk assessment unit performs the assessment by taking into consideration the attribute information of the person who submitted the investment strategy. For example, the risk assessment unit performs the risk assessment by taking into consideration the expertise and experience of the person who submitted the investment strategy. The risk assessment unit can also adjust the result of the risk assessment based on the attribute information of the person who submitted the investment strategy. For example, the risk assessment unit adjusts the result of the risk assessment based on the attribute information of the person who submitted the investment strategy. As a result, the risk assessment unit improves the accuracy of the risk assessment by taking into consideration the attribute information of the person who submitted the investment strategy. A part or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input attribute information data of the person who submitted the investment strategy into AI and have the AI ​​execute the assessment.

[0113] The risk assessment unit can estimate the user's emotion and adjust the order of displaying the risk assessment results based on the estimated user's emotion. For example, when the user is nervous, the risk assessment unit displays the evaluation results with a low risk first. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The risk assessment unit estimates the user's emotion and adjusts the order of displaying the risk assessment results based on the estimated user's emotion. For example, when the user is relaxed, the risk assessment unit displays a detailed risk assessment result. In addition, when the user is in a hurry, the risk assessment unit can also display a risk assessment result with a main point first. For example, when the user is in a hurry, the risk assessment unit displays a risk assessment result with a main point first. In this way, the risk assessment unit can provide a result that is easy for the user to understand by adjusting the order of displaying the risk assessment results according to the user's emotion. A part or all of the above-mentioned processing in the risk assessment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the risk assessment unit can input a user's emotional data into an AI and have the AI ​​adjust the display order of the risk assessment results.

[0114] The risk assessment unit can perform the assessment taking into account the geographical distribution of the investment strategy. For example, the risk assessment unit analyzes the geographical distribution of the investment strategy and performs the risk assessment. The risk assessment unit performs the assessment taking into account the geographical distribution of the investment strategy. For example, the risk assessment unit adjusts the result of the risk assessment taking into account the geographical risk. The risk assessment unit can also improve the accuracy of the risk assessment based on the geographical distribution of the investment strategy. For example, the risk assessment unit improves the accuracy of the risk assessment based on the geographical distribution of the investment strategy. As a result, the risk assessment unit improves the accuracy of the risk assessment by taking into account the geographical distribution of the investment strategy. A part or all of the above-mentioned processing in the risk assessment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the risk assessment unit can input the geographical distribution data of the investment strategy to the AI ​​and cause the AI ​​to execute the assessment.

[0115] The risk assessment unit can improve the accuracy of the assessment by referring to literature related to the investment strategy. The risk assessment unit, for example, improves the accuracy of the risk assessment by referring to literature related to the investment strategy. The risk assessment unit improves the accuracy of the assessment by referring to literature related to the investment strategy. For example, the risk assessment unit adjusts the result of the risk assessment based on information on the related literature. The risk assessment unit can also analyze literature related to the investment strategy and improve the accuracy of the risk assessment. For example, the risk assessment unit analyzes literature related to the investment strategy and improves the accuracy of the risk assessment. As a result, the risk assessment unit improves the accuracy of the risk assessment by referring to literature related to the investment strategy. A part or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI, or may be performed without using AI. For example, the risk assessment unit can input literature data related to the investment strategy into AI and cause AI to execute the improvement of the accuracy of the assessment.

[0116] The tax saving suggestion unit can estimate the user's emotions and adjust the method of tax saving suggestions based on the estimated user's emotions. For example, when the user is nervous, the tax saving suggestion unit provides a simple and highly visible tax saving suggestion. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The tax saving suggestion unit estimates the user's emotions and adjusts the method of tax saving suggestions based on the estimated user's emotions. For example, when the user is relaxed, the tax saving suggestion unit provides a detailed tax saving suggestion. In addition, when the user is in a hurry, the tax saving suggestion unit can also provide a tax saving suggestion that covers the main points. For example, when the user is in a hurry, the tax saving suggestion unit provides a tax saving suggestion that covers the main points. As a result, the tax saving suggestion unit can provide a suggestion that is easy for the user to understand by adjusting the method of tax saving suggestions according to the user's emotions. A part or all of the above-mentioned processing in the tax saving suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the tax saving suggestion unit may input the user's emotion data to AI and cause AI to adjust the method of tax saving suggestions.

[0117] The tax saving suggestion unit can select the optimal suggested method by analyzing the user's past tax history. The tax saving suggestion unit, for example, analyzes the user's past tax history and makes an optimal tax saving suggestion. The tax saving suggestion unit analyzes the user's past tax history and selects the optimal suggested method. For example, the tax saving suggestion unit adjusts the detail level of the tax saving suggestion based on the user's past tax history. The tax saving suggestion unit can also suggest the optimal tax saving method by referring to the user's past tax history. For example, the tax saving suggestion unit suggests the optimal tax saving method by referring to the user's past tax history. As a result, the tax saving suggestion unit can analyze the user's past tax history and make the optimal tax saving suggestion. A part or all of the above-mentioned processing in the tax saving suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the tax saving suggestion unit can input the user's past tax history data into AI and cause AI to select the optimal suggested method.

[0118] The tax saving suggestion unit can estimate the user's emotions and determine the priority of tax saving suggestions based on the estimated user's emotions. For example, when the user is nervous, the tax saving suggestion unit provides important tax saving suggestions with priority. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The tax saving suggestion unit estimates the user's emotions and determines the priority of tax saving suggestions based on the estimated user's emotions. For example, when the user is relaxed, the tax saving suggestion unit provides detailed tax saving suggestions. In addition, when the user is in a hurry, the tax saving suggestion unit can also provide tax saving suggestions with a focus on the main points with priority. For example, when the user is in a hurry, the tax saving suggestion unit provides tax saving suggestions with a focus on the main points with priority. As a result, the tax saving suggestion unit can provide important suggestions for the user with priority by determining the priority of tax saving suggestions according to the user's emotions. A part or all of the above-mentioned processing in the tax saving suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the tax saving suggestion unit may input the user's emotion data to AI and cause AI to determine the priority order of tax saving suggestions.

[0119] The tax saving proposal unit can select the optimal proposed method in consideration of the geographical location information of the user. For example, the tax saving proposal unit adjusts the tax saving proposal in consideration of differences in geographical tax systems. The tax saving proposal unit can also propose the optimal tax saving method with reference to the geographical location information of the user. For example, the tax saving proposal unit proposes the optimal tax saving method with reference to the geographical location information of the user. This enables the tax saving proposal unit to propose the optimal tax saving method based on the geographical location information of the user. A part or all of the above-mentioned processing in the tax saving proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the tax saving proposal unit can input the geographical location information data of the user to AI and cause the AI ​​to select the optimal proposed method.

[0120] The prediction unit can estimate the user's emotion and adjust the income prediction method based on the estimated user's emotion. For example, when the user is nervous, the prediction unit provides a simple and highly visible income prediction. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The prediction unit estimates the user's emotion and adjusts the income prediction method based on the estimated user's emotion. For example, the prediction unit provides a detailed income prediction when the user is relaxed. In addition, the prediction unit can also provide an income prediction with the main points when the user is in a hurry. For example, the prediction unit provides an income prediction with the main points when the user is in a hurry. As a result, the prediction unit can provide a prediction that is easy for the user to understand by adjusting the income prediction method according to the user's emotion. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input a user's emotional data into the AI ​​and have the AI ​​adjust the income prediction method.

[0121] The prediction unit can select the optimal prediction method by analyzing the user's past income history when predicting income. For example, the prediction unit adjusts the level of detail of the income prediction based on the user's past income history. The prediction unit can also select the optimal income prediction method by referring to the user's past income history. For example, the prediction unit selects the optimal income prediction method by referring to the user's past income history. As a result, the prediction unit can analyze the user's past income history to perform optimal income prediction. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the user's past income history data into AI and cause AI to select the optimal prediction method.

[0122] The prediction unit can estimate the user's emotion and determine the priority of the income prediction based on the estimated user's emotion. For example, when the user is nervous, the prediction unit provides important income predictions with priority. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The prediction unit estimates the user's emotion and determines the priority of the income prediction based on the estimated user's emotion. For example, the prediction unit provides a detailed income prediction when the user is relaxed. In addition, the prediction unit can also provide a basic income prediction with priority when the user is in a hurry. For example, the prediction unit provides a basic income prediction with priority when the user is in a hurry. As a result, the prediction unit can provide important predictions for the user with priority by determining the priority of the income predictions according to the user's emotion. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input a user's emotional data into the AI ​​and have the AI ​​determine the priorities of income predictions.

[0123] The prediction unit can select the optimal prediction method in consideration of the geographical location information of the user when predicting income. For example, the prediction unit adjusts the income prediction in consideration of the geographical economic situation. The prediction unit can also select the optimal income prediction method with reference to the geographical location information of the user. For example, the prediction unit selects the optimal income prediction method with reference to the geographical location information of the user. This enables the prediction unit to perform optimal income prediction based on the geographical location information of the user. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the geographical location information data of the user to the AI ​​and cause the AI ​​to select the optimal prediction method.

[0124] The retirement fund proposal unit can estimate the user's emotions and adjust the method of retirement fund proposal based on the estimated user's emotions. For example, when the user is nervous, the retirement fund proposal unit provides a simple and highly visible retirement fund proposal. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The retirement fund proposal unit estimates the user's emotions and adjusts the method of retirement fund proposal based on the estimated user's emotions. For example, when the user is relaxed, the retirement fund proposal unit provides a detailed retirement fund proposal. In addition, when the user is in a hurry, the retirement fund proposal unit can also provide a retirement fund proposal that covers the main points. For example, when the user is in a hurry, the retirement fund proposal unit provides a retirement fund proposal that covers the main points. In this way, the retirement fund proposal unit can provide a proposal that is easy for the user to understand by adjusting the method of retirement fund proposal according to the user's emotions. Some or all of the above-mentioned processing in the retirement fund proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the retirement fund proposal unit may input the user's emotion data to the AI ​​and have the AI ​​adjust the method of retirement fund proposal.

[0125] The retirement fund proposal unit can select the optimal proposal method by analyzing the user's past financial history when proposing retirement funds. For example, the retirement fund proposal unit adjusts the level of detail of the retirement fund proposal based on the user's past financial history. The retirement fund proposal unit can also select the optimal retirement fund proposal method by referring to the user's past financial history. For example, the retirement fund proposal unit selects the optimal retirement fund proposal method by referring to the user's past financial history. As a result, the retirement fund proposal unit can analyze the user's past financial history to propose optimal retirement funds. Some or all of the above-mentioned processing in the retirement fund proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the retirement fund proposal unit can input the user's past financial history data into AI and have the AI ​​select the optimal proposal method.

[0126] The retirement fund proposal unit can estimate the user's emotions and determine the priority of retirement fund proposals based on the estimated user's emotions. For example, when the user is nervous, the retirement fund proposal unit provides important retirement fund proposals with priority. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The retirement fund proposal unit estimates the user's emotions and determines the priority of retirement fund proposals based on the estimated user's emotions. For example, when the user is relaxed, the retirement fund proposal unit provides detailed retirement fund proposals. In addition, when the user is in a hurry, the retirement fund proposal unit can also provide retirement fund proposals with a focus on the main points with priority. For example, when the user is in a hurry, the retirement fund proposal unit provides retirement fund proposals with a focus on the main points with priority. In this way, the retirement fund proposal unit can provide proposals that are important to the user with priority by determining the priority of retirement fund proposals according to the user's emotions. Some or all of the above-mentioned processing in the retirement fund proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the retirement fund proposal unit may input the user's emotion data into AI and have the AI ​​determine the priority order of retirement fund proposals.

[0127] The retirement fund proposal unit can select the optimal proposal method by taking into consideration the geographic location information of the user when proposing retirement funds. For example, the retirement fund proposal unit adjusts the retirement fund proposal by taking into consideration the geographical difference in living expenses. The retirement fund proposal unit can also select the optimal retirement fund proposal method by referring to the geographic location information of the user. For example, the retirement fund proposal unit selects the optimal retirement fund proposal method by referring to the geographic location information of the user. This enables the retirement fund proposal unit to propose the optimal retirement fund based on the geographic location information of the user. Some or all of the above-mentioned processing in the retirement fund proposal unit may be performed, for example, using AI or may be performed without using AI. For example, the retirement fund proposal unit can input the geographic location information data of the user to the AI ​​and have the AI ​​select the optimal proposal method.

[0128] The insurance product proposal unit can estimate the user's emotion and adjust the method of insurance product proposal based on the estimated user's emotion. For example, when the user is nervous, the insurance product proposal unit provides a simple and highly visible insurance product proposal. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The insurance product proposal unit estimates the user's emotion and adjusts the method of insurance product proposal based on the estimated user's emotion. For example, when the user is relaxed, the insurance product proposal unit provides a detailed insurance product proposal. In addition, when the user is in a hurry, the insurance product proposal unit can also provide an insurance product proposal that covers the main points. For example, when the user is in a hurry, the insurance product proposal unit provides an insurance product proposal that covers the main points. As a result, the insurance product proposal unit can provide a proposal that is easy for the user to understand by adjusting the method of insurance product proposal according to the user's emotion. A part or all of the above-mentioned processing in the insurance product proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the insurance product proposal unit may input the user's emotion data to the AI ​​and cause the AI ​​to adjust the method of proposing insurance products.

[0129] The insurance product proposal unit can select the optimal proposal method by analyzing the user's past insurance history when proposing an insurance product. For example, the insurance product proposal unit adjusts the level of detail of the insurance product proposal based on the user's past insurance history. The insurance product proposal unit can also select the optimal insurance product proposal method by referring to the user's past insurance history. For example, the insurance product proposal unit selects the optimal insurance product proposal method by referring to the user's past insurance history. As a result, the insurance product proposal unit can propose the optimal insurance product by analyzing the user's past insurance history. A part or all of the above-mentioned processing in the insurance product proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the insurance product proposal unit can input the user's past insurance history data into AI and cause AI to select the optimal proposal method.

[0130] The insurance product proposal unit can estimate the user's emotion and determine the priority of insurance product proposals based on the estimated user's emotion. For example, when the user is nervous, the insurance product proposal unit preferentially provides important insurance product proposals. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. The insurance product proposal unit estimates the user's emotion and determines the priority of insurance product proposals based on the estimated user's emotion. For example, when the user is relaxed, the insurance product proposal unit provides a detailed insurance product proposal. In addition, when the user is in a hurry, the insurance product proposal unit can also preferentially provide an insurance product proposal that covers the main points. For example, when the user is in a hurry, the insurance product proposal unit preferentially provides an insurance product proposal that covers the main points. As a result, the insurance product proposal unit can preferentially provide proposals that are important to the user by determining the priority of insurance product proposals according to the user's emotion. A part or all of the above-mentioned processing in the insurance product proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the insurance product proposal unit may input the user's emotion data to AI and have the AI ​​execute the determination of the priority order of insurance product proposals.

[0131] The insurance product proposal unit can select the optimal proposal method in consideration of the geographical location information of the user when proposing an insurance product. For example, the insurance product proposal unit adjusts the insurance product proposal in consideration of differences in geographical insurance systems. The insurance product proposal unit can also select the optimal insurance product proposal method with reference to the geographical location information of the user. For example, the insurance product proposal unit selects the optimal insurance product proposal method with reference to the geographical location information of the user. This enables the insurance product proposal unit to propose the optimal insurance product based on the geographical location information of the user. A part or all of the above-mentioned processing in the insurance product proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the insurance product proposal unit can input the geographical location information data of the user to AI and cause the AI ​​to select the optimal proposal method.

[0132] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0133] When collecting the user's financial data, the financial planner AI assistant system can analyze the user's social media activities and collect related data. For example, the collection unit analyzes the content of the user's social media posts and collects financial data related to the user's field of interest. The collection unit can also collect related financial data by referring to the activities of the user's social media followers and friends. This allows the collection unit to collect related data based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the AI ​​and cause the AI ​​to collect related data.

[0134] When collecting financial data of a user, the AI ​​assistant system for financial planners can preferentially collect highly relevant data taking into account the geographical location information of the user. For example, if the user lives in a particular area, the collection unit preferentially collects financial data related to that area. In addition, if the user is traveling, the collection unit can also collect financial data related to the current location. This allows the collection unit to preferentially collect highly relevant data based on the geographical location information of the user. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to the AI ​​and cause the AI ​​to collect highly relevant data.

[0135] The financial planner AI assistant system can estimate the user's emotions and determine the priority of financial data to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting only important data. Also, when the user is relaxed, the collection unit can collect detailed data. This allows the collection unit to determine the priority of data to be collected according to the user's emotions, thereby enabling efficient data collection. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data to AI and have the AI ​​execute the determination of the priority of collected data.

[0136] The financial planner's AI assistant system can estimate the user's emotions and adjust the way the analysis is expressed based on the estimated user's emotions. For example, when the user is nervous, the analysis unit can provide a simple and highly visible analysis result. Also, when the user is relaxed, the analysis unit can provide a detailed analysis result. In this way, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the way the analysis is expressed according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data into AI and cause AI to adjust the way the analysis is expressed.

[0137] The financial planner's AI assistant system can estimate the user's emotions and adjust the way in which the investment proposal is expressed based on the estimated user's emotions. For example, when the user is nervous, the investment proposal unit can provide a simple and highly visible proposal. Also, when the user is relaxed, the investment proposal unit can provide a detailed proposal. In this way, the investment proposal unit can provide a proposal that is easy for the user to understand by adjusting the way in which the investment proposal is expressed according to the user's emotions. Some or all of the above-mentioned processing in the investment proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the investment proposal unit can input the user's emotion data into AI and cause the AI ​​to adjust the way in which the investment proposal is expressed.

[0138] The financial planner's AI assistant system can analyze the user's past financial data collection history and select the optimal collection method. For example, the collection unit preferentially suggests collection methods (manual input, API integration, etc.) that the user has used in the past. The collection unit can also select the most efficient collection method from the user's past collection history. This allows the collection unit to select the optimal collection method by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history data into AI and have the AI ​​select the optimal collection method.

[0139] The financial planner's AI assistant system can estimate the user's emotions and adjust the risk assessment criteria based on the estimated user's emotions. For example, the risk assessment unit can tighten the risk assessment criteria when the user is nervous. Also, the risk assessment unit can relax the risk assessment criteria when the user is relaxed. In this way, the risk assessment unit can provide an appropriate risk assessment for the user by adjusting the risk assessment criteria according to the user's emotions. Some or all of the above-mentioned processing in the risk assessment unit may be performed, for example, using AI or may be performed without using AI. For example, the risk assessment unit can input the user's emotion data into the AI ​​and cause the AI ​​to adjust the risk assessment criteria.

[0140] The financial planner's AI assistant system can estimate the user's emotions and adjust the tax-saving proposal method based on the estimated user's emotions. For example, the tax-saving proposal unit can provide a simple and highly visible tax-saving proposal when the user is nervous. Also, the tax-saving proposal unit can provide a detailed tax-saving proposal when the user is relaxed. In this way, the tax-saving proposal unit can provide a proposal that is easy for the user to understand by adjusting the tax-saving proposal method according to the user's emotions. A part or all of the above-mentioned processing in the tax-saving proposal unit may be performed, for example, using AI or may be performed without using AI. For example, the tax-saving proposal unit can input the user's emotion data into AI and cause AI to adjust the tax-saving proposal method.

[0141] The financial planner's AI assistant system can estimate the user's emotions and adjust the income prediction method based on the estimated user's emotions. For example, the prediction unit can provide a simple and highly visible income prediction when the user is nervous. Also, the prediction unit can provide a detailed income prediction when the user is relaxed. In this way, the prediction unit can provide a prediction that is easy for the user to understand by adjusting the income prediction method according to the user's emotions. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's emotion data into the AI ​​and cause the AI ​​to adjust the income prediction method.

[0142] The financial planner's AI assistant system can estimate the user's emotions and adjust the method of insurance product proposal based on the estimated user's emotions. For example, when the user is nervous, the insurance product proposal unit can provide a simple and highly visible insurance product proposal. Also, when the user is relaxed, the insurance product proposal unit can provide a detailed insurance product proposal. In this way, the insurance product proposal unit can provide a proposal that is easy for the user to understand by adjusting the method of insurance product proposal according to the user's emotions. A part or all of the above-mentioned processing in the insurance product proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the insurance product proposal unit can input the user's emotion data into AI and cause AI to adjust the method of insurance product proposal.

[0143] The process flow of the second embodiment will be briefly described below.

[0144] Step 1: The collection unit collects the user's financial data. The user's financial data includes income, expenses, assets, and liabilities. For example, the collection unit collects income data from pay slips and bank transaction statements, and collects expense data from credit card statements and a household accounting app. It can also collect asset data from real estate appraisals and stock holdings. The collection unit can automatically obtain data through the API and store it in a database. Step 2: The analysis unit uses a machine learning model to analyze the financial data collected by the collection unit. The analysis unit uses a regression model to analyze the user's income trends and a clustering model to analyze the user's spending patterns. Furthermore, the analysis unit uses a classification model to evaluate the risk of the user's assets. For example, the regression model is used to predict future fluctuations in income, the clustering model is used to group spending data, and the classification model is used to classify asset data into high-risk and low-risk assets. Step 3: The investment proposal unit proposes an investment strategy appropriate to the user based on the analysis results obtained by the analysis unit. The investment proposal unit proposes investment strategies such as stock investment, bond investment, and real estate investment according to the user's risk tolerance. In addition, blockchain technology is used to ensure the transparency of investment proposals. For example, the user's risk tolerance is evaluated based on questionnaire surveys and past investment behavior, and an appropriate investment strategy is proposed. Blockchain technology records the history of investment proposals, preventing tampering. Step 4: The risk assessment unit assesses the risk of the investment strategy proposed by the investment proposal unit. The risk assessment unit may assess the risk of the investment strategy using a genetic algorithm, and may also assess the risk using cosine similarity or Jaccard coefficient. For example, the risk of the investment strategy is optimized using a genetic algorithm, the angle between the vectors of the investment strategies is calculated using cosine similarity, and the risk is assessed using the value obtained by dividing the size of the common part of the investment strategies by the total size using the Jaccard coefficient.

[0145] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0147] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] For example, the collection unit can collect financial data of the user using the reception device 38 and the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The investment proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an investment strategy based on the analysis result. The risk assessment unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the risk of the proposed investment strategy. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0150] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0151] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0164] For example, the collection unit can collect the user's financial data using the microphone 238 and the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The investment proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an investment strategy based on the analysis result. The risk assessment unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the risk of the proposed investment strategy. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0166] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0167] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0172] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0175] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0179] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0180] For example, the collection unit can collect financial data of the user using the microphone 238 and camera 42 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The investment proposal unit is realized by the specific processing unit 290 of the data processing device 12, and proposes an investment strategy based on the analysis results. The risk assessment unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the risk of the proposed investment strategy. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0181] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0182] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0183] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0184] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0185] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0187] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0188] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0189] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0190] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0191] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0192] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0193] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0194] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0195] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0196] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0197] For example, the collection unit can collect financial data of the user using the microphone 238 and camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The investment proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an investment strategy based on the analysis results. The risk assessment unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the risk of the proposed investment strategy. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0198] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0199] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0200] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0201] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0202] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0203] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0204] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0205] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0206] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0207] In addition, 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 upon request from the data processing device 12.

[0208] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0209] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0210] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0211] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0212] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0213] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0214] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0215] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0216] (Appendix 1) A collection unit for collecting financial data of users; an analysis unit that analyzes the financial data collected by the collection unit; an investment proposal unit that makes an investment proposal based on the analysis result obtained by the analysis unit; A risk assessment unit that assesses the risk of the investment strategy proposed by the investment proposal unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect data about your income, expenses, assets, and liabilities 2. The system of claim 1, (Appendix 3) The investment proposal department: Suggest investment strategies based on your financial situation and market data 2. The system of claim 1, (Appendix 4) The risk assessment unit includes: Evaluate the risks of proposed investment strategies 2. The system of claim 1, (Appendix 5) The analysis unit is Analyze your financial data using machine learning models 2. The system of claim 1, (Appendix 6) The investment proposal department: Using blockchain technology to ensure transparency in investment proposals 2. The system of claim 1, (Appendix 7) The risk assessment unit includes: Use genetic algorithms to assess risk 2. The system of claim 1, (Appendix 8) Equipped with a tax saving proposal section that proposes tax saving methods based on the analysis results 2. The system of claim 1, (Appendix 9) A prediction unit for predicting a user's future income; A retirement fund proposal unit proposes retirement funds based on the current financial situation of the user and the prediction result by the prediction unit. 2. The system of claim 1, (Appendix 10) An insurance product proposal unit is provided that proposes insurance products based on the user's financial situation and lifestyle. 2. The system of claim 1, (Appendix 11) The prediction unit is Predicting user income using machine learning models 2. The system of claim 1, (Appendix 12) The investment proposal department: Dynamically assess users' risk tolerance and suggest investment strategies 2. The system of claim 1, (Appendix 13) The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting financial data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The collecting unit includes: Analyze the user's past financial data collection history and select the optimal collection method 2. The system of claim 1, (Appendix 15) The collecting unit includes: When collecting financial data, filter it based on the user's current life situation and interests 2. The system of claim 1, (Appendix 16) The collecting unit includes: To estimate user sentiment and prioritize financial data to be collected based on the estimated user sentiment. 2. The system of claim 1, (Appendix 17) The collecting unit includes: When collecting financial data, prioritize collection of relevant data by considering the user's geographic location information 2. The system of claim 1, (Appendix 18) The collecting unit includes: Analyze your social media activity and collect relevant data when collecting financial data 2. The system of claim 1, (Appendix 19) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The analysis unit is Adjust the level of analysis detail based on the importance of financial data 2. The system of claim 1, (Appendix 21) The analysis unit is Apply different analysis algorithms to different categories of financial data 2. The system of claim 1, (Appendix 22) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The analysis unit is Prioritize analyses based on financial data submission timing 2. The system of claim 1, (Appendix 24) The analysis unit is Adjust the order of analysis based on financial data relevance 2. The system of claim 1, (Appendix 25) The investment proposal department: Inferring user sentiment and adjusting the presentation of investment proposals based on the inferred user sentiment 2. The system of claim 1, (Appendix 26) The investment proposal department: Adjust the level of detail in your proposal based on the importance of your investment strategy 2. The system of claim 1, (Appendix 27) The investment proposal department: Apply different suggestion algorithms depending on the category of investment strategies 2. The system of claim 1, (Appendix 28) The investment proposal department: Inferring user sentiment and adjusting the length of the investment proposal based on the inferred user sentiment 2. The system of claim 1, (Appendix 29) The investment proposal department: Prioritize proposals based on the timing of investment strategy submissions 2. The system of claim 1, (Appendix 30) The investment proposal department: Adjust the order of suggestions based on their relevance to your investment strategy 2. The system of claim 1, (Appendix 31) The risk assessment unit includes: Inferring user emotions and adjusting risk assessment criteria based on the inferred user emotions 2. The system of claim 1, (Appendix 32) The risk assessment unit includes: Improve valuation accuracy by considering the interrelationships between investment strategies 2. The system of claim 1, (Appendix 33) The risk assessment unit includes: Evaluation is carried out taking into account the attributes of the person submitting the investment strategy 2. The system of claim 1, (Appendix 34) The risk assessment unit includes: Inferring user sentiment and adjusting the order in which risk assessment results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 35) The risk assessment unit includes: Evaluation will take into account the geographical distribution of investment strategies 2. The system of claim 1, (Appendix 36) The risk assessment unit includes: Improve the accuracy of your evaluation by referring to relevant literature on investment strategies 2. The system of claim 1, (Appendix 37) The tax saving suggestion section includes: Infer user sentiment and adjust tax saving suggestions based on the inferred user sentiment 2. The system of claim 1, (Appendix 38) The tax saving suggestion section includes: Analyze the user's past tax history to select the most appropriate proposal method 2. The system of claim 1, (Appendix 39) The tax saving suggestion section includes: Infer user sentiment and prioritize tax-saving suggestions based on the inferred user sentiment 2. The system of claim 1, (Appendix 40) The tax saving suggestion section includes: Select the optimal suggestion method taking into account the user's geographic location information 2. The system of claim 1, (Appendix 41) The prediction unit is Inferring user sentiment and adjusting a revenue forecasting methodology based on the inferred user sentiment 2. The system of claim 1, (Appendix 42) The prediction unit is When predicting income, analyze the user's past income history and select the optimal prediction method. 2. The system of claim 1, (Appendix 43) The prediction unit is Inferring user sentiment and prioritizing revenue forecasts based on the inferred user sentiment 2. The system of claim 1, (Appendix 44) The prediction unit is When predicting revenue, choose the best forecasting method considering the user's geographic location information 2. The system of claim 1, (Appendix 45) The retirement fund proposal unit includes: Inferring user sentiment and adjusting retirement fund proposal methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 46) The retirement fund proposal unit includes: When proposing retirement funds, analyze the user's past financial history to select the most appropriate proposal method 2. The system of claim 1, (Appendix 47) The retirement fund proposal unit includes: Inferring user sentiment and prioritizing retirement fund proposals based on the inferred user sentiment 2. The system of claim 1, (Appendix 48) The retirement fund proposal unit includes: When proposing retirement funds, the system selects the optimal proposal method taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 49) The insurance product proposal unit To estimate a user's emotions and adjust a method of proposing insurance products based on the estimated user's emotions 2. The system of claim 1, (Appendix 50) The insurance product proposal unit When proposing insurance products, analyze the user's past insurance history and select the optimal proposal method. 2. The system of claim 1, (Appendix 51) The insurance product proposal unit To estimate a user's emotions and prioritize insurance product proposals based on the estimated user's emotions. 2. The system of claim 1, (Appendix 52) The insurance product proposal unit When proposing insurance products, the system selects the optimal proposal method taking into account the user's geographic location information. 2. The system of claim 1, [Explanation of symbols]

[0217] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit for collecting financial data of users; an analysis unit that analyzes the financial data collected by the collection unit; an investment proposal unit that makes an investment proposal based on the analysis result obtained by the analysis unit; A risk assessment unit that assesses the risk of the investment strategy proposed by the investment proposal unit. A system characterized by:

2. The collecting unit includes: Collecting data about said users' income, expenses, assets, and liabilities 2. The system of claim 1 .

3. The investment proposal department: Suggest investment strategies based on said user's financial situation and market data 2. The system of claim 1 .

4. The risk assessment unit includes: Evaluate the risks of proposed investment strategies 2. The system of claim 1 .

5. The analysis unit is Analyzing the user's financial data using a machine learning model 2. The system of claim 1 .

6. The investment proposal department: Using blockchain technology to ensure transparency in investment proposals 2. The system of claim 1 .

7. The risk assessment unit includes: Use genetic algorithms to assess risk 2. The system of claim 1 .

8. Equipped with a tax saving proposal section that proposes tax saving methods based on the analysis results 2. The system of claim 1 .

9. A prediction unit for predicting the user's future income; A retirement fund proposal unit proposes retirement funds based on the current financial situation of the user and the prediction result by the prediction unit.

2. The system of claim 1 .

10. An insurance product proposal unit that proposes insurance products based on the financial situation and lifestyle of the user 2. The system of claim 1 .

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