system

Abstract

The system according to this embodiment aims to provide an optimal investment strategy based on the user's investment goals and risk tolerance, and to automatically adjust the portfolio in accordance with market trends. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and an adjustment unit. The reception unit receives input from the user regarding their investment goals and risk tolerance. The analysis unit analyzes multiple data sources based on the information entered by the reception unit. The proposal unit provides an optimal investment strategy based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the portfolio according to market trends based on the investment strategy provided by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been sufficiently carried out to provide an optimal investment strategy based on the user's investment goals and risk tolerance and automatically adjust the portfolio according to market trends.

[0005] The system according to the embodiment aims to provide an optimal investment strategy based on the user's investment goals and risk tolerance and automatically adjust the portfolio according to market trends.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and an adjustment unit. The reception unit receives input from the user regarding their investment goals and risk tolerance. The analysis unit analyzes multiple data sources based on the information entered by the reception unit. The proposal unit provides an optimal investment strategy based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the portfolio according to market trends based on the investment strategy provided by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal investment strategy based on the user's investment goals and risk tolerance, and can automatically adjust the portfolio in accordance with market trends. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An investment advisor agent system according to an embodiment of the present invention is a system that provides customized investment proposals based on the user's investment goals and risk tolerance, and automatically adjusts the portfolio in accordance with market trends. This investment advisor agent system uses an AI agent to analyze multiple data sources and provide the user with the optimal investment strategy based on the latest financial theory. The investment advisor agent system is ideal for individual investors with little investment experience or busy investors who lack time. This AI agent utilizes the latest AI technology to provide investment advice tailored to each individual user, providing services with speed and accuracy unmatched by conventional investment advisors. The generating AI analyzes a large amount of market data and user profiles to provide optimal investment proposals in real time. This maximizes investment returns, minimizes risk, and speeds up investment decisions. The investment advisor agent system solves the limitations of individual investors' investment knowledge and the difficulty of responding quickly to market fluctuations. By automatically adjusting the portfolio in accordance with market fluctuations, it provides a customized investment strategy that meets the investor's needs. For example, the investment advisor agent system includes a reception unit for inputting the user's investment goals and risk tolerance. The reception unit may include AI processing and inputs the user's investment goals and risk tolerance. Next, the system includes an analysis unit that analyzes multiple data sources based on the information entered by the reception unit. The analysis unit includes AI processing to analyze the multiple data sources. Furthermore, it includes a proposal unit that provides the optimal investment strategy based on the analysis results obtained by the analysis unit. The proposal unit includes AI processing to provide the optimal investment strategy. Finally, it includes an adjustment unit that adjusts the portfolio according to market trends based on the investment strategy provided by the proposal unit. The adjustment unit includes AI processing to adjust the portfolio according to market trends. As a result, the investment advisor agent system can make customized investment proposals based on the user's investment goals and risk tolerance, and automatically adjust the portfolio according to market trends.

[0029] The investment advisor agent system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a coordination unit. The reception unit receives input from the user regarding their investment goals and risk tolerance. The reception unit provides, for example, an interface for the user to input their investment goals. The reception unit can also present options for the user to input their risk tolerance. The reception unit may include AI processing to analyze the user's input and convert it into an appropriate format. For example, the reception unit can analyze the user's input using text analysis techniques to identify the type of investment goal. The reception unit can also quantify the user's input risk tolerance and generate a risk profile. The analysis unit analyzes multiple data sources based on the information entered by the reception unit. The analysis unit includes AI processing to analyze multiple data sources. For example, the analysis unit collects market data and analyzes economic indicators. The analysis unit can also analyze corporate financial data and evaluate the financial condition of companies. The analysis unit includes AI processing to analyze data correlations and provide information that forms the basis of an investment strategy. The proposal unit provides an optimal investment strategy based on the analysis results obtained by the analysis unit. The proposal unit, including AI processing, generates an optimal investment strategy. For example, the proposal unit proposes an investment strategy aimed at maximizing returns. The proposal unit can also propose an investment strategy aimed at minimizing risk. The proposal unit, including AI processing, provides a customized investment strategy based on the user's investment goals and risk tolerance. The adjustment unit adjusts the portfolio according to market trends based on the investment strategy provided by the proposal unit. The adjustment unit, including AI processing, performs portfolio adjustments. For example, the adjustment unit changes the asset allocation in response to market fluctuations. The adjustment unit can also propose a shift from high-risk assets to low-risk assets. The adjustment unit, including AI processing, monitors market trends in real time and adjusts the portfolio. As a result, the investment advisor agent system according to this embodiment can make customized investment proposals based on the user's investment goals and risk tolerance and automatically adjust the portfolio according to market trends.

[0030] The reception desk receives the user's investment goals and risk tolerance. The reception desk provides, for example, an interface for the user to input their investment goals. Specifically, it features an intuitive graphical user interface (GUI) that provides text boxes and dropdown menus for inputting investment goals and risk tolerance. Users can input specific investment goals, such as securing retirement funds, preparing funds for home purchase, or accumulating funds for education. The reception desk can also present options for the user to input their risk tolerance. For example, it can categorize risk tolerance into "low risk," "medium risk," and "high risk," allowing the user to select their own risk tolerance. The reception desk may also include AI processing that can analyze the user's input and convert it into an appropriate format. For example, the reception desk can analyze the investment goals entered by the user using text analysis technology to identify the type of investment goal. It can also use natural language processing (NLP) technology to extract and classify investment goals from text entered by the user in free form. The reception desk can also quantify the risk tolerance entered by the user and generate a risk profile. For example, if a user selects "medium risk," this is quantified to generate a risk score, which is then used for subsequent analysis and recommendations. This allows the reception desk to accurately understand the user's investment goals and risk tolerance, and provide the information necessary for processing the entire system. Furthermore, the reception desk saves the user's input for later reference. This allows for quicker responses when the user uses the system again, based on their past input.

[0031] The analysis department analyzes multiple data sources based on information entered by the reception department. The analysis department analyzes multiple data sources, including AI processing. Specifically, the analysis department collects market data and analyzes economic indicators. For example, it collects economic indicators such as stock indices, exchange rates, interest rates, and inflation rates in real time and analyzes market trends based on this data. The analysis department can also analyze corporate financial data and evaluate the financial condition of companies. For example, it analyzes financial indicators such as corporate earnings, profit margins, debt ratios, and cash flow to evaluate the health and growth potential of companies. The analysis department analyzes data correlations, including AI processing, and provides information that forms the basis of investment strategies. For example, it uses machine learning algorithms to extract market trends and patterns from historical data and predict future market trends. Furthermore, the analysis department analyzes correlations between different data sources and provides information to optimize the balance of risk and return. For example, it analyzes correlations between specific market segments or asset classes to help construct portfolios that maximize the effects of diversification. This allows the analysis department to provide the foundational information needed to formulate optimal investment strategies based on the user's investment goals and risk tolerance. Furthermore, the analysis department updates data in real time and provides analysis results based on the latest market information. This ensures that users can always make investment decisions based on the most up-to-date information.

[0032] The Proposal Department provides optimal investment strategies based on the analysis results obtained by the Analysis Department. The Proposal Department generates optimal investment strategies, including AI processing. Specifically, the Proposal Department proposes investment strategies aimed at maximizing returns. For example, it selects asset classes or individual stocks predicted to have high returns based on historical data and market trends, and constructs a portfolio. The Proposal Department can also propose investment strategies aimed at minimizing risk. For example, it proposes portfolios that emphasize low-risk asset classes and diversification, aiming for stable returns while keeping risk low. The Proposal Department, including AI processing, provides customized investment strategies based on the user's investment goals and risk tolerance. For example, if the user aims to secure retirement funds and has a low risk tolerance, the Proposal Department proposes a portfolio centered on bonds and high-dividend stocks that provide stable returns. Conversely, if the user aims for high returns and has a high risk tolerance, the Proposal Department proposes an aggressive portfolio that includes growth stocks and investments in emerging markets. The Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, the user provides feedback on the proposed investment strategy, and the proposal is revised based on that feedback. Furthermore, the proposal department re-evaluates investment strategies in response to changes in the user's investment goals and risk tolerance, and adjusts them as needed. This allows the proposal department to provide users with the optimal investment strategy and support them in achieving their investment goals.

[0033] The adjustment unit adjusts the portfolio in response to market trends based on the investment strategy provided by the proposal unit. The adjustment unit incorporates AI processing to adjust the portfolio. Specifically, the adjustment unit changes the asset allocation in response to market fluctuations. For example, if the stock market plummets, the adjustment unit will reduce the proportion of stocks and increase the proportion of bonds and cash to mitigate risk. Also, if a particular asset class is overvalued, the adjustment unit will reduce the proportion of that asset class and diversify into other asset classes. The adjustment unit can also suggest a shift from high-risk assets to low-risk assets. For example, if economic uncertainty increases, the adjustment unit will suggest a shift from high-risk growth stocks to low-risk defensive stocks and bonds. The adjustment unit incorporates AI processing to monitor market trends in real time and adjust the portfolio. For example, the AI ​​analyzes market data in real time and immediately adjusts the portfolio if it detects abnormal market fluctuations or increased risk. The adjustment unit also re-evaluates the portfolio and adjusts it as needed in response to changes in the user's investment goals and risk tolerance. This allows the adjustment unit to provide customized investment recommendations based on the user's investment goals and risk tolerance, and to automatically adjust the portfolio in response to market trends. Furthermore, the adjustment unit ensures transparency by notifying the user of the adjustments. For example, when adjustments are made, the adjustment unit reports the details to the user via email or app notification, allowing the user to always be aware of the status of their portfolio. This enables the adjustment unit to maintain user trust and enhance investment transparency.

[0034] The proposal function can maximize investment returns. For example, the proposal function proposes investment strategies aimed at maximizing returns. The proposal function can also recommend investments in high-risk assets. The proposal function includes AI processing to generate investment strategies aimed at maximizing returns. For example, the proposal function analyzes historical market data to identify high-return investment targets. The proposal function can also propose the optimal investment strategy considering the balance between risk and return. The proposal function includes AI processing to monitor market trends in real time and provides investment strategies aimed at maximizing returns. This maximizes user profits by achieving maximum investment returns.

[0035] The proposal function can minimize risk. For example, the proposal function proposes investment strategies aimed at minimizing risk. The proposal function can also recommend investments in low-risk assets. The proposal function includes AI processing to generate investment strategies aimed at minimizing risk. For example, the proposal function analyzes historical market data to identify low-risk investment opportunities. The proposal function can also propose optimal investment strategies considering the balance between risk and return. The proposal function includes AI processing to monitor market trends in real time and provides investment strategies aimed at minimizing risk. This reduces the user's investment risk by minimizing risk.

[0036] The proposal department can accelerate investment decisions. For example, it proposes investment strategies aimed at rapid investment decisions. The proposal department can also make rapid decisions using real-time data. The proposal department generates investment strategies aimed at rapid investment decisions, including AI processing. For example, the proposal department collects market data in real time and makes rapid investment decisions. The proposal department can also build a rapid decision-making process and provide users with rapid investment proposals. The proposal department monitors market trends in real time, including AI processing, and makes rapid investment decisions. This accelerates investment decisions, allowing users to make investment decisions quickly.

[0037] The proposal unit can provide optimal investment recommendations in real time using generative AI. For example, the proposal unit can analyze market data in real time using generative AI and provide optimal investment recommendations. The proposal unit can also analyze user profiles using generative AI and provide individually customized investment recommendations. The proposal unit includes AI processing and provides optimal investment recommendations in real time using generative AI. For example, the proposal unit inputs market data and user profiles into the generative AI to generate optimal investment recommendations. The proposal unit can also monitor market trends in real time using generative AI and provide optimal investment recommendations. The proposal unit includes AI processing and provides optimal investment recommendations in real time using generative AI. This ensures that users always have access to the latest investment information by providing optimal investment recommendations in real time using generative AI.

[0038] The reception desk can analyze the user's past investment history and provide the optimal input format. For example, the reception desk can automatically display as suggestions investment targets and risk tolerances that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest investment targets and risk tolerances to be used for a specific time period based on the user's past investment history. The reception desk may include AI processing to analyze the user's past investment history and provide the optimal input format. This allows the reception desk to provide the optimal input format by analyzing the user's past investment history.

[0039] The input system can filter the user's current financial situation and life stage when they input their investment goals and risk tolerance. For example, the input system can suggest appropriate investment goals and risk tolerance based on the user's current income and asset situation. The input system can also suggest appropriate investment goals and risk tolerance according to the user's life stage (e.g., newly married, raising children, retired). The input system can automatically adjust the range of risk tolerance based on the user's financial situation and life stage. The input system may also include AI processing, which filters the user's current financial situation and life stage when they input their investment goals and risk tolerance. This allows the system to suggest more appropriate investment goals and risk tolerance by filtering based on the user's current financial situation and life stage.

[0040] The reception desk can prioritize inputting highly relevant information when users input their investment goals and risk tolerance, taking into account their geographical location. For example, the reception desk can suggest region-specific investment opportunities based on the user's place of residence. The reception desk can also suggest risk tolerance levels that are appropriate to the local economic conditions, based on the user's geographical location. The reception desk can suggest investment goals based on local tax systems and regulations, taking into account the user's geographical location. The reception desk may also incorporate AI processing, prioritizing the input of highly relevant information when users input their investment goals and risk tolerance, taking into account their geographical location. This allows the reception desk to suggest region-specific investment opportunities and risk tolerance levels by considering the user's geographical location.

[0041] The reception desk can analyze the user's social media activity and input relevant information when the user inputs their investment goals and risk tolerance. For example, the reception desk can identify areas of interest based on the user's social media activity and suggest investment goals. The reception desk can also analyze the user's social media activity and identify factors that influence risk tolerance. The reception desk customizes the input of investment goals and risk tolerance based on the user's social media activity. The reception desk may also include AI processing, which analyzes the user's social media activity and inputs relevant information when the user inputs their investment goals and risk tolerance. This allows for the suggestion of more personalized investment goals and risk tolerance by analyzing the user's social media activity.

[0042] The analysis unit can adjust the level of detail of its analysis based on the importance of the investment objectives. For example, it will perform a detailed analysis for high-importance investment objectives. It can also perform a simplified analysis for low-importance investment objectives. The analysis unit determines the priority of the analysis according to the importance of the investment objectives. The analysis unit, including AI processing, adjusts the level of detail of its analysis based on the importance of the investment objectives. This allows for more efficient analysis by adjusting the level of detail based on the importance of the investment objectives.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the data source during analysis. For example, the analysis unit can apply a technical analysis algorithm to stock data. It can also apply a fundamental analysis algorithm to bond data. For real estate data, the analysis unit can apply an analysis algorithm using a geographic information system (GIS). The analysis unit includes AI processing and applies different analysis algorithms depending on the category of the data source during analysis. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the data source.

[0044] The analysis department can prioritize analyses based on when the data sources were submitted. For example, the analysis department will prioritize analyzing the most recent data sources. The analysis department can also prioritize older data sources based on their importance. The analysis department evaluates the reliability of the data sources based on their submission date and then prioritizes the analysis. The analysis department, including AI processing, prioritizes analyses based on when the data sources were submitted. This allows for prioritizing the analysis of the most recent information by prioritizing analyses based on when the data sources were submitted.

[0045] The analysis unit can adjust the order of analysis based on the relevance of data sources during analysis. For example, the analysis unit prioritizes analyzing data sources that are most relevant to the user's investment goals. The analysis unit can also postpone the analysis of less relevant data sources. The analysis unit dynamically adjusts the order of analysis based on the relevance of data sources. The analysis unit includes AI processing and adjusts the order of analysis based on the relevance of data sources during analysis. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of data sources.

[0046] The proposal unit can adjust the level of detail in its proposals based on the importance of the investment strategies. For example, it will provide detailed proposals for high-importance investment strategies, and simplified proposals for low-importance strategies. The proposal unit determines the priority of proposals according to the importance of the investment strategies. The proposal unit includes AI processing to adjust the level of detail in its proposals based on the importance of the investment strategies. This allows it to provide the user with the most suitable proposals by adjusting the level of detail based on the importance of the investment strategies.

[0047] The proposal function can apply different proposal algorithms depending on the investment strategy category at the time of proposal. For example, for equity investment strategies, the proposal function applies a proposal algorithm based on technical analysis. For bond investment strategies, the proposal function can also apply a proposal algorithm based on fundamental analysis. For real estate investment strategies, the proposal function applies a proposal algorithm using a geographic information system (GIS). The proposal function includes AI processing and applies different proposal algorithms depending on the investment strategy category at the time of proposal. This allows for more accurate proposals by applying different proposal algorithms depending on the investment strategy category.

[0048] The proposal department can prioritize proposals based on the submission date of the investment strategies. For example, the proposal department will prioritize the most recent investment strategies. The proposal department can also prioritize older investment strategies based on their importance. The proposal department evaluates the reliability of investment strategies based on their submission date and then prioritizes them. The proposal department, including AI processing, prioritizes proposals based on the submission date of the investment strategies. This allows for prioritizing proposals based on the submission date of investment strategies, thereby ensuring that the most up-to-date information is presented first.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the investment strategies during the proposal process. For example, the proposal unit will prioritize proposing the investment strategy most relevant to the user's investment goals. The proposal unit can also postpone proposing less relevant investment strategies. The proposal unit dynamically adjusts the order of proposals based on the relevance of the investment strategies. The proposal unit includes AI processing and adjusts the order of proposals based on the relevance of the investment strategies during the proposal process. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of the investment strategies.

[0050] The adjustment unit can analyze the user's past investment behavior during adjustment and select the optimal adjustment method. For example, the adjustment unit can propose a similar adjustment method based on the user's past successful investment behavior. The adjustment unit can also select an adjustment method that matches the user's risk tolerance based on their past investment behavior. The adjustment unit analyzes the user's past investment behavior and proposes the most efficient adjustment method. The adjustment unit includes AI processing and, during adjustment, analyzes the user's past investment behavior to select the optimal adjustment method. As a result, by analyzing the user's past investment behavior, it can propose the optimal portfolio adjustment method.

[0051] The adjustment unit can customize portfolio adjustments based on the user's current financial situation during the adjustment process. For example, the adjustment unit suggests appropriate adjustments based on the user's current income and asset situation. The adjustment unit can also automatically adjust the range of risk tolerance according to the user's financial situation. The adjustment unit selects the optimal portfolio adjustments considering the user's financial situation. The adjustment unit includes AI processing and customizes portfolio adjustments based on the user's current financial situation during the adjustment process. This allows for more appropriate adjustments by customizing portfolio adjustments based on the user's current financial situation.

[0052] The adjustment unit can select the optimal adjustment method during the adjustment process, taking into account the user's geographical location information. For example, the adjustment unit can propose an adjustment method that considers region-specific investment opportunities based on the user's place of residence. The adjustment unit can also select an adjustment method that suits the regional economic conditions based on the user's geographical location information. The adjustment unit can propose an adjustment method based on regional tax systems and regulations, taking into account the user's geographical location information. The adjustment unit includes AI processing and selects the optimal adjustment method during the adjustment process, taking into account the user's geographical location information. This allows for the proposal of region-specific investment opportunities and risk tolerance by considering the user's geographical location information.

[0053] The adjustment unit can analyze the user's social media activity during adjustment and propose portfolio adjustment measures. For example, the adjustment unit can identify investment areas of interest from the user's social media activity and propose adjustment measures. The adjustment unit can also analyze the user's social media activity and identify factors that influence risk tolerance. The adjustment unit customizes portfolio adjustment measures based on the user's social media activity. The adjustment unit includes AI processing and, during adjustment, analyzes the user's social media activity and proposes portfolio adjustment measures. This allows for the proposal of more personalized portfolio adjustment measures by analyzing the user's social media activity.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze a user's past investment history and provide the optimal input format. For example, it can automatically display investment goals and risk tolerances that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Based on the user's past investment history, it can predict and suggest investment goals and risk tolerances to be used during specific time periods. In this way, by analyzing the user's past investment history, it can provide the optimal input format.

[0056] The reception desk can filter the input of investment goals and risk tolerance based on the user's current financial situation and life stage. For example, it can suggest appropriate investment goals and risk tolerance based on the user's current income and asset situation. It can also suggest appropriate investment goals and risk tolerance according to the user's life stage (e.g., newly married, raising children, retired). The range of risk tolerance is automatically adjusted based on the user's financial situation and life stage. This allows for the suggestion of more appropriate investment goals and risk tolerance by filtering based on the user's current financial situation and life stage.

[0057] The reception desk can prioritize inputting highly relevant information when users enter their investment goals and risk tolerance, taking into account their geographical location. For example, it can suggest region-specific investment opportunities based on the user's place of residence. It can also suggest risk tolerance levels that are appropriate to the local economic conditions based on the user's geographical location. It can also suggest investment goals based on local tax systems and regulations, taking into account the user's geographical location. In this way, by considering the user's geographical location, it can suggest region-specific investment opportunities and risk tolerance levels.

[0058] The analysis department can adjust the level of detail in its analysis based on the importance of the investment objectives. For example, it can perform a detailed analysis for high-importance investment objectives and a simplified analysis for low-importance ones. The priority of the analysis is determined according to the importance of the investment objectives. This allows for more efficient analysis by adjusting the level of detail based on the importance of the investment objectives.

[0059] The analysis department can apply different analysis algorithms depending on the category of the data source during analysis. For example, technical analysis algorithms can be applied to stock data, fundamental analysis algorithms to bond data, and geographic information system (GIS)-based analysis algorithms to real estate data. By applying different analysis algorithms depending on the category of the data source, more accurate analysis becomes possible.

[0060] The proposal department can adjust the level of detail in proposals based on the importance of the investment strategies. For example, it can provide detailed proposals for high-importance investment strategies and simplified proposals for low-importance ones. The priority of proposals is determined according to the importance of the investment strategies. This allows the department to provide the user with the most suitable proposal by adjusting the level of detail based on the importance of the investment strategies.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives the user's investment goals and risk tolerance. The reception desk provides an interface for the user to enter their investment goals and presents options for entering their risk tolerance. Furthermore, the reception desk incorporates AI processing to analyze the user's input and convert it into an appropriate format. For example, it can analyze investment goals using text analysis technology to identify the type of investment goal. It can also quantify risk tolerance and generate a risk profile. Step 2: The analysis department analyzes multiple data sources based on the information entered by the reception department. The analysis department analyzes multiple data sources, including AI processing. For example, it collects market data and analyzes economic indicators. Furthermore, it can also analyze corporate financial data and evaluate the financial condition of companies. The analysis department analyzes the correlation between data and provides information that forms the basis of investment strategies. Step 3: The proposal unit provides the optimal investment strategy based on the analysis results obtained by the analysis unit. The proposal unit generates the optimal investment strategy, including AI processing. For example, it proposes investment strategies aimed at maximizing returns or strategies aimed at minimizing risk. The proposal unit provides a customized investment strategy based on the user's investment goals and risk tolerance. Step 4: The adjustment unit adjusts the portfolio in accordance with market trends based on the investment strategy provided by the proposal unit. The adjustment unit performs portfolio adjustments, including AI processing. For example, it changes the asset allocation in response to market fluctuations. Furthermore, it can also suggest shifts from high-risk assets to low-risk assets. The adjustment unit monitors market trends in real time and adjusts the portfolio accordingly.

[0063] (Example of form 2) An investment advisor agent system according to an embodiment of the present invention is a system that provides customized investment proposals based on the user's investment goals and risk tolerance, and automatically adjusts the portfolio in accordance with market trends. This investment advisor agent system uses an AI agent to analyze multiple data sources and provide the user with the optimal investment strategy based on the latest financial theory. The investment advisor agent system is ideal for individual investors with little investment experience or busy investors who lack time. This AI agent utilizes the latest AI technology to provide investment advice tailored to each individual user, providing services with speed and accuracy unmatched by conventional investment advisors. The generating AI analyzes a large amount of market data and user profiles to provide optimal investment proposals in real time. This maximizes investment returns, minimizes risk, and speeds up investment decisions. The investment advisor agent system solves the limitations of individual investors' investment knowledge and the difficulty of responding quickly to market fluctuations. By automatically adjusting the portfolio in accordance with market fluctuations, it provides a customized investment strategy that meets the investor's needs. For example, the investment advisor agent system includes a reception unit for inputting the user's investment goals and risk tolerance. The reception unit may include AI processing and inputs the user's investment goals and risk tolerance. Next, the system includes an analysis unit that analyzes multiple data sources based on the information entered by the reception unit. The analysis unit includes AI processing to analyze the multiple data sources. Furthermore, it includes a proposal unit that provides the optimal investment strategy based on the analysis results obtained by the analysis unit. The proposal unit includes AI processing to provide the optimal investment strategy. Finally, it includes an adjustment unit that adjusts the portfolio according to market trends based on the investment strategy provided by the proposal unit. The adjustment unit includes AI processing to adjust the portfolio according to market trends. As a result, the investment advisor agent system can make customized investment proposals based on the user's investment goals and risk tolerance, and automatically adjust the portfolio according to market trends.

[0064] The investment advisor agent system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a coordination unit. The reception unit receives input from the user regarding their investment goals and risk tolerance. The reception unit provides, for example, an interface for the user to input their investment goals. The reception unit can also present options for the user to input their risk tolerance. The reception unit may include AI processing to analyze the user's input and convert it into an appropriate format. For example, the reception unit can analyze the user's input using text analysis techniques to identify the type of investment goal. The reception unit can also quantify the user's input risk tolerance and generate a risk profile. The analysis unit analyzes multiple data sources based on the information entered by the reception unit. The analysis unit includes AI processing to analyze multiple data sources. For example, the analysis unit collects market data and analyzes economic indicators. The analysis unit can also analyze corporate financial data and evaluate the financial condition of companies. The analysis unit includes AI processing to analyze data correlations and provide information that forms the basis of an investment strategy. The proposal unit provides an optimal investment strategy based on the analysis results obtained by the analysis unit. The proposal unit, including AI processing, generates an optimal investment strategy. For example, the proposal unit proposes an investment strategy aimed at maximizing returns. The proposal unit can also propose an investment strategy aimed at minimizing risk. The proposal unit, including AI processing, provides a customized investment strategy based on the user's investment goals and risk tolerance. The adjustment unit adjusts the portfolio according to market trends based on the investment strategy provided by the proposal unit. The adjustment unit, including AI processing, performs portfolio adjustments. For example, the adjustment unit changes the asset allocation in response to market fluctuations. The adjustment unit can also propose a shift from high-risk assets to low-risk assets. The adjustment unit, including AI processing, monitors market trends in real time and adjusts the portfolio. As a result, the investment advisor agent system according to this embodiment can make customized investment proposals based on the user's investment goals and risk tolerance and automatically adjust the portfolio according to market trends.

[0065] The reception desk receives the user's investment goals and risk tolerance. The reception desk provides, for example, an interface for the user to input their investment goals. Specifically, it features an intuitive graphical user interface (GUI) that provides text boxes and dropdown menus for inputting investment goals and risk tolerance. Users can input specific investment goals, such as securing retirement funds, preparing funds for home purchase, or accumulating funds for education. The reception desk can also present options for the user to input their risk tolerance. For example, it can categorize risk tolerance into "low risk," "medium risk," and "high risk," allowing the user to select their own risk tolerance. The reception desk may also include AI processing that can analyze the user's input and convert it into an appropriate format. For example, the reception desk can analyze the investment goals entered by the user using text analysis technology to identify the type of investment goal. It can also use natural language processing (NLP) technology to extract and classify investment goals from text entered by the user in free form. The reception desk can also quantify the risk tolerance entered by the user and generate a risk profile. For example, if a user selects "medium risk," this is quantified to generate a risk score, which is then used for subsequent analysis and recommendations. This allows the reception desk to accurately understand the user's investment goals and risk tolerance, and provide the information necessary for processing the entire system. Furthermore, the reception desk saves the user's input for later reference. This allows for quicker responses when the user uses the system again, based on their past input.

[0066] The analysis department analyzes multiple data sources based on information entered by the reception department. The analysis department analyzes multiple data sources, including AI processing. Specifically, the analysis department collects market data and analyzes economic indicators. For example, it collects economic indicators such as stock indices, exchange rates, interest rates, and inflation rates in real time and analyzes market trends based on this data. The analysis department can also analyze corporate financial data and evaluate the financial condition of companies. For example, it analyzes financial indicators such as corporate earnings, profit margins, debt ratios, and cash flow to evaluate the health and growth potential of companies. The analysis department analyzes data correlations, including AI processing, and provides information that forms the basis of investment strategies. For example, it uses machine learning algorithms to extract market trends and patterns from historical data and predict future market trends. Furthermore, the analysis department analyzes correlations between different data sources and provides information to optimize the balance of risk and return. For example, it analyzes correlations between specific market segments or asset classes to help construct portfolios that maximize the effects of diversification. This allows the analysis department to provide the foundational information needed to formulate optimal investment strategies based on the user's investment goals and risk tolerance. Furthermore, the analysis department updates data in real time and provides analysis results based on the latest market information. This ensures that users can always make investment decisions based on the most up-to-date information.

[0067] The Proposal Department provides optimal investment strategies based on the analysis results obtained by the Analysis Department. The Proposal Department generates optimal investment strategies, including AI processing. Specifically, the Proposal Department proposes investment strategies aimed at maximizing returns. For example, it selects asset classes or individual stocks predicted to have high returns based on historical data and market trends, and constructs a portfolio. The Proposal Department can also propose investment strategies aimed at minimizing risk. For example, it proposes portfolios that emphasize low-risk asset classes and diversification, aiming for stable returns while keeping risk low. The Proposal Department, including AI processing, provides customized investment strategies based on the user's investment goals and risk tolerance. For example, if the user aims to secure retirement funds and has a low risk tolerance, the Proposal Department proposes a portfolio centered on bonds and high-dividend stocks that provide stable returns. Conversely, if the user aims for high returns and has a high risk tolerance, the Proposal Department proposes an aggressive portfolio that includes growth stocks and investments in emerging markets. The Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, the user provides feedback on the proposed investment strategy, and the proposal is revised based on that feedback. Furthermore, the proposal department re-evaluates investment strategies in response to changes in the user's investment goals and risk tolerance, and adjusts them as needed. This allows the proposal department to provide users with the optimal investment strategy and support them in achieving their investment goals.

[0068] The adjustment unit adjusts the portfolio in response to market trends based on the investment strategy provided by the proposal unit. The adjustment unit incorporates AI processing to adjust the portfolio. Specifically, the adjustment unit changes the asset allocation in response to market fluctuations. For example, if the stock market plummets, the adjustment unit will reduce the proportion of stocks and increase the proportion of bonds and cash to mitigate risk. Also, if a particular asset class is overvalued, the adjustment unit will reduce the proportion of that asset class and diversify into other asset classes. The adjustment unit can also suggest a shift from high-risk assets to low-risk assets. For example, if economic uncertainty increases, the adjustment unit will suggest a shift from high-risk growth stocks to low-risk defensive stocks and bonds. The adjustment unit incorporates AI processing to monitor market trends in real time and adjust the portfolio. For example, the AI ​​analyzes market data in real time and immediately adjusts the portfolio if it detects abnormal market fluctuations or increased risk. The adjustment unit also re-evaluates the portfolio and adjusts it as needed in response to changes in the user's investment goals and risk tolerance. This allows the adjustment unit to provide customized investment recommendations based on the user's investment goals and risk tolerance, and to automatically adjust the portfolio in response to market trends. Furthermore, the adjustment unit ensures transparency by notifying the user of the adjustments. For example, when adjustments are made, the adjustment unit reports the details to the user via email or app notification, allowing the user to always be aware of the status of their portfolio. This enables the adjustment unit to maintain user trust and enhance investment transparency.

[0069] The proposal function can maximize investment returns. For example, the proposal function proposes investment strategies aimed at maximizing returns. The proposal function can also recommend investments in high-risk assets. The proposal function includes AI processing to generate investment strategies aimed at maximizing returns. For example, the proposal function analyzes historical market data to identify high-return investment targets. The proposal function can also propose the optimal investment strategy considering the balance between risk and return. The proposal function includes AI processing to monitor market trends in real time and provides investment strategies aimed at maximizing returns. This maximizes user profits by achieving maximum investment returns.

[0070] The proposal function can minimize risk. For example, the proposal function proposes investment strategies aimed at minimizing risk. The proposal function can also recommend investments in low-risk assets. The proposal function includes AI processing to generate investment strategies aimed at minimizing risk. For example, the proposal function analyzes historical market data to identify low-risk investment opportunities. The proposal function can also propose optimal investment strategies considering the balance between risk and return. The proposal function includes AI processing to monitor market trends in real time and provides investment strategies aimed at minimizing risk. This reduces the user's investment risk by minimizing risk.

[0071] The proposal department can accelerate investment decisions. For example, it proposes investment strategies aimed at rapid investment decisions. The proposal department can also make rapid decisions using real-time data. The proposal department generates investment strategies aimed at rapid investment decisions, including AI processing. For example, the proposal department collects market data in real time and makes rapid investment decisions. The proposal department can also build a rapid decision-making process and provide users with rapid investment proposals. The proposal department monitors market trends in real time, including AI processing, and makes rapid investment decisions. This accelerates investment decisions, allowing users to make investment decisions quickly.

[0072] The proposal unit can provide optimal investment recommendations in real time using generative AI. For example, the proposal unit can analyze market data in real time using generative AI and provide optimal investment recommendations. The proposal unit can also analyze user profiles using generative AI and provide individually customized investment recommendations. The proposal unit includes AI processing and provides optimal investment recommendations in real time using generative AI. For example, the proposal unit inputs market data and user profiles into the generative AI to generate optimal investment recommendations. The proposal unit can also monitor market trends in real time using generative AI and provide optimal investment recommendations. The proposal unit includes AI processing and provides optimal investment recommendations in real time using generative AI. This ensures that users always have access to the latest investment information by providing optimal investment recommendations in real time using generative AI.

[0073] The reception desk can estimate the user's emotions and adjust the input method for investment goals and risk tolerance based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of investment goals and risk tolerance. The reception desk may include AI processing to estimate the user's emotions and adjust the input method based on the estimated emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows users to input investment goals and risk tolerance more comfortably by adjusting the input method according to their emotions.

[0074] The reception desk can analyze the user's past investment history and provide the optimal input format. For example, the reception desk can automatically display as suggestions investment targets and risk tolerances that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest investment targets and risk tolerances to be used for a specific time period based on the user's past investment history. The reception desk may include AI processing to analyze the user's past investment history and provide the optimal input format. This allows the reception desk to provide the optimal input format by analyzing the user's past investment history.

[0075] The input system can filter the user's current financial situation and life stage when they input their investment goals and risk tolerance. For example, the input system can suggest appropriate investment goals and risk tolerance based on the user's current income and asset situation. The input system can also suggest appropriate investment goals and risk tolerance according to the user's life stage (e.g., newly married, raising children, retired). The input system can automatically adjust the range of risk tolerance based on the user's financial situation and life stage. The input system may also include AI processing, which filters the user's current financial situation and life stage when they input their investment goals and risk tolerance. This allows the system to suggest more appropriate investment goals and risk tolerance by filtering based on the user's current financial situation and life stage.

[0076] The reception desk can estimate the user's emotions and, based on those emotions, prioritize the investment goals and risk tolerances to be entered. For example, if the user is feeling anxious, the reception desk might prioritize the input of risk tolerances to provide reassurance. If the user is relaxed, the reception desk might prioritize the input of investment goals, allowing for more detailed goal setting. If the user is in a hurry, the reception desk might prioritize inputting only the most important items to allow for quick completion. The reception desk may also include AI processing to estimate the user's emotions and, based on those emotions, prioritize the investment goals and risk tolerances to be entered. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows users to input investment goals and risk tolerances more efficiently by prioritizing inputs according to their emotions.

[0077] The reception desk can prioritize inputting highly relevant information when users input their investment goals and risk tolerance, taking into account their geographical location. For example, the reception desk can suggest region-specific investment opportunities based on the user's place of residence. The reception desk can also suggest risk tolerance levels that are appropriate to the local economic conditions, based on the user's geographical location. The reception desk can suggest investment goals based on local tax systems and regulations, taking into account the user's geographical location. The reception desk may also incorporate AI processing, prioritizing the input of highly relevant information when users input their investment goals and risk tolerance, taking into account their geographical location. This allows the reception desk to suggest region-specific investment opportunities and risk tolerance levels by considering the user's geographical location.

[0078] The reception desk can analyze the user's social media activity and input relevant information when the user inputs their investment goals and risk tolerance. For example, the reception desk can identify areas of interest based on the user's social media activity and suggest investment goals. The reception desk can also analyze the user's social media activity and identify factors that influence risk tolerance. The reception desk customizes the input of investment goals and risk tolerance based on the user's social media activity. The reception desk may also include AI processing, which analyzes the user's social media activity and inputs relevant information when the user inputs their investment goals and risk tolerance. This allows for the suggestion of more personalized investment goals and risk tolerance by analyzing the user's social media activity.

[0079] The analytics department can estimate user emotions and adjust the analysis method of data sources based on the estimated user emotions. For example, if the user is feeling anxious, the analytics department will prioritize analyzing low-risk data sources. If the user is relaxed, the analytics department can also analyze a wide range of data sources and provide a detailed investment strategy. If the user is in a hurry, the analytics department will quickly analyze only the most important data sources. The analytics department includes AI processing to estimate user emotions and adjust the analysis method of data sources based on the estimated user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate investment strategies by adjusting the analysis method of data sources according to the user's emotions.

[0080] The analysis unit can adjust the level of detail of its analysis based on the importance of the investment objectives. For example, it will perform a detailed analysis for high-importance investment objectives. It can also perform a simplified analysis for low-importance investment objectives. The analysis unit determines the priority of the analysis according to the importance of the investment objectives. The analysis unit, including AI processing, adjusts the level of detail of its analysis based on the importance of the investment objectives. This allows for more efficient analysis by adjusting the level of detail based on the importance of the investment objectives.

[0081] The analysis unit can apply different analysis algorithms depending on the category of the data source during analysis. For example, the analysis unit can apply a technical analysis algorithm to stock data. It can also apply a fundamental analysis algorithm to bond data. For real estate data, the analysis unit can apply an analysis algorithm using a geographic information system (GIS). The analysis unit includes AI processing and applies different analysis algorithms depending on the category of the data source during analysis. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the data source.

[0082] The analytics unit can estimate the user's emotions and prioritize analysis based on those emotions. For example, if the user is feeling anxious, the analytics unit will prioritize analyzing low-risk investments. If the user is relaxed, the analytics unit can analyze a wide range of investments. If the user is in a hurry, the analytics unit will quickly analyze important investments. The analytics unit includes AI processing to estimate the user's emotions and prioritize analysis based on those emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate investment strategies by prioritizing analysis according to the user's emotions.

[0083] The analysis department can prioritize analyses based on when the data sources were submitted. For example, the analysis department will prioritize analyzing the most recent data sources. The analysis department can also prioritize older data sources based on their importance. The analysis department evaluates the reliability of the data sources based on their submission date and then prioritizes the analysis. The analysis department, including AI processing, prioritizes analyses based on when the data sources were submitted. This allows for prioritizing the analysis of the most recent information by prioritizing analyses based on when the data sources were submitted.

[0084] The analysis unit can adjust the order of analysis based on the relevance of data sources during analysis. For example, the analysis unit prioritizes analyzing data sources that are most relevant to the user's investment goals. The analysis unit can also postpone the analysis of less relevant data sources. The analysis unit dynamically adjusts the order of analysis based on the relevance of data sources. The analysis unit includes AI processing and adjusts the order of analysis based on the relevance of data sources during analysis. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of data sources.

[0085] The suggestion function can estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. For example, if the user is feeling anxious, the suggestion function can provide a simple and reassuring presentation. If the user is relaxed, the suggestion function can also provide a presentation that includes detailed information. If the user is in a hurry, the suggestion function can provide a concise presentation that gets straight to the point. The suggestion function includes AI processing to estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the presentation of suggestions to be adjusted according to the user's emotions, making them easier for the user to understand.

[0086] The proposal unit can adjust the level of detail in its proposals based on the importance of the investment strategies. For example, it will provide detailed proposals for high-importance investment strategies, and simplified proposals for low-importance strategies. The proposal unit determines the priority of proposals according to the importance of the investment strategies. The proposal unit includes AI processing to adjust the level of detail in its proposals based on the importance of the investment strategies. This allows it to provide the user with the most suitable proposals by adjusting the level of detail based on the importance of the investment strategies.

[0087] The proposal function can apply different proposal algorithms depending on the investment strategy category at the time of proposal. For example, for equity investment strategies, the proposal function applies a proposal algorithm based on technical analysis. For bond investment strategies, the proposal function can also apply a proposal algorithm based on fundamental analysis. For real estate investment strategies, the proposal function applies a proposal algorithm using a geographic information system (GIS). The proposal function includes AI processing and applies different proposal algorithms depending on the investment strategy category at the time of proposal. This allows for more accurate proposals by applying different proposal algorithms depending on the investment strategy category.

[0088] The suggestion section can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, if the user is feeling anxious, the suggestion section will provide a short, to-the-point suggestion. If the user is relaxed, the suggestion section may provide a longer suggestion with more detailed explanations. If the user is in a hurry, the suggestion section will provide a short, easily understandable suggestion. The suggestion section includes AI processing to estimate the user's emotions and adjust the length of the suggestion based on those emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of suggestions that are easier for the user to understand by adjusting the length of the suggestion according to the user's emotions.

[0089] The proposal department can prioritize proposals based on the submission date of the investment strategies. For example, the proposal department will prioritize the most recent investment strategies. The proposal department can also prioritize older investment strategies based on their importance. The proposal department evaluates the reliability of investment strategies based on their submission date and then prioritizes them. The proposal department, including AI processing, prioritizes proposals based on the submission date of the investment strategies. This allows for prioritizing proposals based on the submission date of investment strategies, thereby ensuring that the most up-to-date information is presented first.

[0090] The proposal unit can adjust the order of proposals based on the relevance of the investment strategies during the proposal process. For example, the proposal unit will prioritize proposing the investment strategy most relevant to the user's investment goals. The proposal unit can also postpone proposing less relevant investment strategies. The proposal unit dynamically adjusts the order of proposals based on the relevance of the investment strategies. The proposal unit includes AI processing and adjusts the order of proposals based on the relevance of the investment strategies during the proposal process. This allows for more efficient proposals by adjusting the order of proposals based on the relevance of the investment strategies.

[0091] The adjustment unit can estimate the user's emotions and adjust the portfolio adjustment method based on the estimated emotions. For example, if the user is feeling anxious, the adjustment unit will prioritize shifting to lower-risk assets. If the user is relaxed, the adjustment unit may also suggest investing in higher-risk assets. If the user is in a hurry, the adjustment unit will select a method that completes the adjustment quickly. The adjustment unit includes AI processing to estimate the user's emotions and adjust the portfolio adjustment method based on the estimated emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to optimize the portfolio adjustment for the user by adjusting the portfolio adjustment method according to the user's emotions.

[0092] The adjustment unit can analyze the user's past investment behavior during adjustment and select the optimal adjustment method. For example, the adjustment unit can propose a similar adjustment method based on the user's past successful investment behavior. The adjustment unit can also select an adjustment method that matches the user's risk tolerance based on their past investment behavior. The adjustment unit analyzes the user's past investment behavior and proposes the most efficient adjustment method. The adjustment unit includes AI processing and, during adjustment, analyzes the user's past investment behavior to select the optimal adjustment method. As a result, by analyzing the user's past investment behavior, it can propose the optimal portfolio adjustment method.

[0093] The adjustment unit can customize portfolio adjustments based on the user's current financial situation during the adjustment process. For example, the adjustment unit suggests appropriate adjustments based on the user's current income and asset situation. The adjustment unit can also automatically adjust the range of risk tolerance according to the user's financial situation. The adjustment unit selects the optimal portfolio adjustments considering the user's financial situation. The adjustment unit includes AI processing and customizes portfolio adjustments based on the user's current financial situation during the adjustment process. This allows for more appropriate adjustments by customizing portfolio adjustments based on the user's current financial situation.

[0094] The adjustment unit can estimate the user's emotions and determine portfolio adjustment priorities based on those estimated emotions. For example, if the user is feeling anxious, the adjustment unit will prioritize shifting to lower-risk assets. If the user is relaxed, the adjustment unit may also prioritize investing in higher-risk assets. If the user is in a hurry, the adjustment unit will prioritize methods that complete the adjustment quickly. The adjustment unit includes AI processing to estimate the user's emotions and determine portfolio adjustment priorities based on those estimated emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate adjustments by determining portfolio adjustment priorities according to the user's emotions.

[0095] The adjustment unit can select the optimal adjustment method during the adjustment process, taking into account the user's geographical location information. For example, the adjustment unit can propose an adjustment method that considers region-specific investment opportunities based on the user's place of residence. The adjustment unit can also select an adjustment method that suits the regional economic conditions based on the user's geographical location information. The adjustment unit can propose an adjustment method based on regional tax systems and regulations, taking into account the user's geographical location information. The adjustment unit includes AI processing and selects the optimal adjustment method during the adjustment process, taking into account the user's geographical location information. This allows for the proposal of region-specific investment opportunities and risk tolerance by considering the user's geographical location information.

[0096] The adjustment unit can analyze the user's social media activity during adjustment and propose portfolio adjustment measures. For example, the adjustment unit can identify investment areas of interest from the user's social media activity and propose adjustment measures. The adjustment unit can also analyze the user's social media activity and identify factors that influence risk tolerance. The adjustment unit customizes portfolio adjustment measures based on the user's social media activity. The adjustment unit includes AI processing and, during adjustment, analyzes the user's social media activity and proposes portfolio adjustment measures. This allows for the proposal of more personalized portfolio adjustment measures by analyzing the user's social media activity.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can estimate the user's emotions and adjust the input method for investment goals and risk tolerance based on those emotions. For example, if the user is feeling anxious, it can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow for quick input of investment goals and risk tolerance. In this way, by adjusting the input method according to the user's emotions, the user can input their investment goals and risk tolerance more comfortably.

[0099] The reception desk can analyze a user's past investment history and provide the optimal input format. For example, it can automatically display investment goals and risk tolerances that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Based on the user's past investment history, it can predict and suggest investment goals and risk tolerances to be used during specific time periods. In this way, by analyzing the user's past investment history, it can provide the optimal input format.

[0100] The reception desk can filter the input of investment goals and risk tolerance based on the user's current financial situation and life stage. For example, it can suggest appropriate investment goals and risk tolerance based on the user's current income and asset situation. It can also suggest appropriate investment goals and risk tolerance according to the user's life stage (e.g., newly married, raising children, retired). The range of risk tolerance is automatically adjusted based on the user's financial situation and life stage. This allows for the suggestion of more appropriate investment goals and risk tolerance by filtering based on the user's current financial situation and life stage.

[0101] The reception system can estimate the user's emotions and, based on those emotions, prioritize the investment goals and risk tolerance to be entered. For example, if the user is feeling anxious, it will prioritize entering their risk tolerance to provide reassurance. If the user is relaxed, it may prioritize entering investment goals to allow for more detailed goal setting. If the user is in a hurry, it will prioritize entering only the most important items to allow for quick completion. In this way, by prioritizing input according to the user's emotions, users can enter their investment goals and risk tolerance more efficiently.

[0102] The reception desk can prioritize inputting highly relevant information when users enter their investment goals and risk tolerance, taking into account their geographical location. For example, it can suggest region-specific investment opportunities based on the user's place of residence. It can also suggest risk tolerance levels that are appropriate to the local economic conditions based on the user's geographical location. It can also suggest investment goals based on local tax systems and regulations, taking into account the user's geographical location. In this way, by considering the user's geographical location, it can suggest region-specific investment opportunities and risk tolerance levels.

[0103] The analytics department can estimate user emotions and adjust the data source analysis method based on those estimated emotions. For example, if a user is feeling anxious, it can prioritize analyzing low-risk data sources. If a user is relaxed, it can analyze a wide range of data sources and provide a detailed investment strategy. If a user is in a hurry, it can quickly analyze only the most important data sources. By adjusting the data source analysis method according to the user's emotions, it can provide a more appropriate investment strategy.

[0104] The analysis department can adjust the level of detail in its analysis based on the importance of the investment objectives. For example, it can perform a detailed analysis for high-importance investment objectives and a simplified analysis for low-importance ones. The priority of the analysis is determined according to the importance of the investment objectives. This allows for more efficient analysis by adjusting the level of detail based on the importance of the investment objectives.

[0105] The analysis department can apply different analysis algorithms depending on the category of the data source during analysis. For example, technical analysis algorithms can be applied to stock data, fundamental analysis algorithms to bond data, and geographic information system (GIS)-based analysis algorithms to real estate data. By applying different analysis algorithms depending on the category of the data source, more accurate analysis becomes possible.

[0106] The proposal function can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is feeling anxious, it can provide a simple and reassuring presentation. If the user is relaxed, it can provide a presentation that includes detailed information. If the user is in a hurry, it can provide a concise presentation that gets straight to the point. By adjusting the presentation of the proposal according to the user's emotions, it is possible to provide a proposal that is easier for the user to understand.

[0107] The proposal department can adjust the level of detail in proposals based on the importance of the investment strategies. For example, it can provide detailed proposals for high-importance investment strategies and simplified proposals for low-importance ones. The priority of proposals is determined according to the importance of the investment strategies. This allows the department to provide the user with the most suitable proposal by adjusting the level of detail based on the importance of the investment strategies.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives the user's investment goals and risk tolerance. The reception desk provides an interface for the user to enter their investment goals and presents options for entering their risk tolerance. Furthermore, the reception desk incorporates AI processing to analyze the user's input and convert it into an appropriate format. For example, it can analyze investment goals using text analysis technology to identify the type of investment goal. It can also quantify risk tolerance and generate a risk profile. Step 2: The analysis department analyzes multiple data sources based on the information entered by the reception department. The analysis department analyzes multiple data sources, including AI processing. For example, it collects market data and analyzes economic indicators. Furthermore, it can also analyze corporate financial data and evaluate the financial condition of companies. The analysis department analyzes the correlation between data and provides information that forms the basis of investment strategies. Step 3: The proposal unit provides the optimal investment strategy based on the analysis results obtained by the analysis unit. The proposal unit generates the optimal investment strategy, including AI processing. For example, it proposes investment strategies aimed at maximizing returns or strategies aimed at minimizing risk. The proposal unit provides a customized investment strategy based on the user's investment goals and risk tolerance. Step 4: The adjustment unit adjusts the portfolio in accordance with market trends based on the investment strategy provided by the proposal unit. The adjustment unit performs portfolio adjustments, including AI processing. For example, it changes the asset allocation in response to market fluctuations. Furthermore, it can also suggest shifts from high-risk assets to low-risk assets. The adjustment unit monitors market trends in real time and adjusts the portfolio accordingly.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for inputting the user's investment goals and risk tolerance. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes multiple data sources. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an optimal investment strategy. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the portfolio according to market trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for inputting the user's investment goals and risk tolerance. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes multiple data sources. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides an optimal investment strategy. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adjusts the portfolio according to market trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for inputting the user's investment goals and risk tolerance. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes multiple data sources. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides an optimal investment strategy. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the portfolio according to market trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and adjustment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for inputting the user's investment goals and risk tolerance. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes multiple data sources. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides an optimal investment strategy. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adjusts the portfolio according to market trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A reception area where users input their investment goals and risk tolerance, An analysis unit analyzes multiple data sources based on the information entered by the reception unit, A proposal unit provides an optimal investment strategy based on the analysis results obtained by the aforementioned analysis unit, The system includes an adjustment unit that adjusts the portfolio in accordance with market trends based on the investment strategy provided by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Achieve maximum investment returns The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Achieve risk minimization. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, To enable faster investment decisions The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, The AI ​​generates optimal investment suggestions in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for investment goals and risk tolerance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past investment history and provides the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users input their investment goals and risk tolerance, the system filters the results based on their current financial situation and life stage. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and, based on those emotions, determines the priority of the investment goals and risk tolerance to be entered. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input their investment goals and risk tolerance, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their investment goals and risk tolerance, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis method of the data source based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the investment objectives. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of the data source. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data sources were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data sources. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the investment strategy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the investment strategy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, we prioritize them based on when the investment strategy is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the investment strategy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, It estimates the user's emotions and adjusts the portfolio adjustment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, During adjustments, the system analyzes the user's past investment behavior to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, During adjustments, the portfolio adjustment method is customized based on the user's current financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, It estimates user sentiment and determines portfolio adjustment priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, During the adjustment process, the optimal adjustment method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, During the adjustment process, we analyze the user's social media activity and propose methods for adjusting the portfolio. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their investment goals and risk tolerance, An analysis unit analyzes multiple data sources based on the information entered by the reception unit, A proposal unit provides an optimal investment strategy based on the analysis results obtained by the aforementioned analysis unit, The system includes an adjustment unit that adjusts the portfolio in accordance with market trends based on the investment strategy provided by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, Achieve maximum investment returns The system according to feature 1.

3. The aforementioned proposal section is, Achieve risk minimization. The system according to feature 1.

4. The aforementioned proposal section is, To enable faster investment decisions The system according to feature 1.

5. The aforementioned proposal section is, The AI ​​generates optimal investment suggestions in real time. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for investment goals and risk tolerance based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past investment history and provides the optimal input format. The system according to feature 1.

8. The aforementioned reception unit is When users input their investment goals and risk tolerance, the system filters the results based on their current financial situation and life stage. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and, based on those emotions, determines the priority of the investment goals and risk tolerance to be entered. The system according to feature 1.

10. The aforementioned reception unit is When users input their investment goals and risk tolerance, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system according to feature 1.

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