system

Abstract

We provide the system. [Solution] Means for collecting user asset information and market data, A means of analyzing collected data and generating investment proposals optimized for individual users, A means of monitoring market volatility and fluctuations in economic indicators, and recommending risk management strategies, A means of analyzing financial data for small and medium-sized enterprises and generating management strategy reports, A means of providing interactive explanations in response to user questions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to the complexity of the financial market and the sophistication of information faced by individual investors and small and medium-sized enterprises, it has become difficult to make appropriate investment decisions and financial management. In the conventional method, the advanced analysis and strategic proposals that can only be obtained by consulting a professional financial advisor are costly and also cause a prominent information gap. Therefore, it is required to provide an optimal financial strategy to users at a low cost and improve investment literacy.

Means for Solving the Problems

[0005] This invention solves the aforementioned problems by providing a system that has means for collecting user asset information and market data, and analyzes this data to generate investment proposals optimized for individual users. Furthermore, it enables real-time risk management by providing means for monitoring market volatility and fluctuations in economic indicators and recommending risk management strategies. In addition, it provides means for analyzing financial data and generating management strategy reports for small and medium-sized enterprises, thereby promoting low-cost advice on business management. Moreover, by providing means for interactively explaining to users' questions, it helps them understand financial terms and concepts and supports the improvement of investment literacy.

[0006] "User asset information" refers to data on financial assets owned by individual investors and small and medium-sized enterprises, including information on cash, stocks, bonds, etc.

[0007] "Market data" refers to a collection of various data related to asset management, such as price information, trading volume, and economic indicators in financial markets.

[0008] An "optimized investment proposal" is an investment strategy that shows the best asset allocation based on the user's financial situation and risk tolerance, taking into account expected returns and risks.

[0009] "Volatility" refers to the degree of fluctuation in market prices and is an indicator that shows how much the price of a financial product will fluctuate within a certain period of time.

[0010] A "risk management strategy" is a plan or means to minimize losses due to fluctuations in financial markets, and includes portfolio diversification and the introduction of hedging measures.

[0011] "Financial data" refers to data that shows the financial status of a company or individual, and includes information such as income, expenses, and profits.

[0012] "Interactive explanation means" refers to an interface or function that provides clear and easy-to-understand explanations of financial terms and concepts in response to user inquiries, thereby aiding understanding. [Brief explanation of the drawing]

[0013] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0025] 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.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The 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.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system that provides optimized financial consulting to individual investors and small and medium-sized enterprises. This system collects user asset information and combines it with external data such as open data and market information to propose the optimal investment strategy to the user in real time. The method of implementing this invention is described below.

[0035] First, users input information about their financial assets, risk tolerance, investment timeframe, and objectives through a dedicated application. This information is sent to the server and registered in the system.

[0036] Next, the server collects regularly updated market data and stores it in a database along with the user's asset information. The data obtained from market data providers includes stock prices, bond prices, exchange rates, and economic indicators.

[0037] Subsequently, the server uses AI and machine learning algorithms to analyze the accumulated data and create an optimal investment portfolio tailored to the user. This process considers the balance between risk and return and also analyzes individual investment options.

[0038] The generated investment proposals are presented to the user as recommendations for specific financial products and asset allocations. The device displays the generated investment strategy to the user in a visually easy-to-understand format. Furthermore, complex financial and investment terms are explained in an easy-to-understand way using an interactive assistant.

[0039] Furthermore, the server monitors market volatility and recommends hedging measures for risk management to the user as needed. This mechanism ensures that even in the event of sudden market fluctuations, strategies for mitigating risk are immediately communicated.

[0040] As a concrete example, let's assume the user is a 30-year-old company employee who wants to increase their assets over the medium to long term. We also assume they are aiming for a 20% return in three years, primarily through the stock market. In this case, the system analyzes the user's current asset allocation and proposes an investment strategy that diversifies funds into domestic and international stocks and some low-risk bonds.

[0041] Furthermore, if the market becomes unstable, risk mitigation measures such as risk hedging through derivatives and temporary increases in cash positions will be considered. This system supports the user's entire asset management process and contributes to improving their financial literacy.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user launches the application and enters asset information and investment preferences. This includes the types and quantities of financial assets held, risk tolerance, investment objectives, and target timeframe.

[0045] Step 2:

[0046] The terminal sends the information entered by the user to the server. The server stores the received information in a database and prepares it for analysis.

[0047] Step 3:

[0048] The server collects the latest market data via the internet. This data includes stock and bond prices, economic indicators, and exchange rates.

[0049] Step 4:

[0050] The server combines user data and market data for analysis. Here, it utilizes AI algorithms to generate optimal investment strategies for each user.

[0051] Step 5:

[0052] The generated investment strategy includes specific details such as the recommended purchase quantity and holding percentage for each financial product. The server transmits this information to the terminal.

[0053] Step 6:

[0054] The terminal visually displays the investment strategy received from the server to the user. This is presented clearly using graphs and charts.

[0055] Step 7:

[0056] If the user has questions about the information displayed, the device will use an interactive assistant to explain financial terms and investment strategies in detail.

[0057] Step 8:

[0058] The server continuously monitors market volatility and considers risk management strategies when risks increase. This includes suggesting portfolio adjustments and hedging measures.

[0059] Step 9:

[0060] Once a risk management strategy is decided, that information is immediately transmitted to the terminal and notified to the user.

[0061] Step 10:

[0062] Users review the notifications and, if necessary, adopt or adjust the suggested strategies.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] Traditionally, it has been difficult for individual investors and small and medium-sized enterprises (SMEs) to independently analyze market fluctuations and formulate investment and management strategies based on them, requiring specialized knowledge. Therefore, there is a need for a system that can respond to market fluctuations in real time while providing appropriate investment and risk management strategies tailored to individual needs. Furthermore, there is a lack of support for users to easily understand complex financial and investment concepts.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes means for collecting information about the user's assets and market data, means for analyzing the collected data to generate investment suggestions optimized for individual users, and means for monitoring market fluctuations and economic indicators and recommending risk management strategies. This enables real-time investment strategy suggestions and risk management tailored to individual users. Furthermore, by using an interactive assistant to explain financial terms in simple terms and aid in user understanding, it also improves financial literacy.

[0068] "Users" refer to individuals or small businesses that use the system to receive personalized financial asset management and investment strategy suggestions.

[0069] "Asset information" refers to data that shows details such as the financial assets a user holds, their risk tolerance, investment period, and investment objectives.

[0070] "Market data" refers to information that shows trends in financial markets, such as stock prices, bond prices, exchange rates, and economic indicators.

[0071] "Analysis" refers to the process of using AI and machine learning algorithms to analyze collected data in order to derive the optimal investment strategy for the user.

[0072] "Investment proposals" refer to investment strategies offered to users, including recommendations for specific financial products or asset allocations.

[0073] A "risk management strategy" refers to the methods and measures proposed to minimize the risk to a user's investment portfolio.

[0074] "Small and medium-sized enterprises" refers to small or medium-sized companies other than individual investors.

[0075] "Financial data" refers to information that indicates the financial condition of a company, such as assets, liabilities, revenues, and expenses held by a small or medium-sized enterprise (SME).

[0076] A "management strategy report" refers to a report containing analysis and recommendations created for use by small and medium-sized enterprises (SMEs) in formulating their management strategies, based on financial data.

[0077] A "conversational assistant" refers to an interface that, in response to user questions, breaks down and explains financial terminology and investment-related information within the system.

[0078] "Real-time" refers to a state in which a system can respond immediately to market fluctuations and user input.

[0079] This invention is a system that provides optimized financial consulting to individual investors and small and medium-sized enterprises. This system uses data analysis and artificial intelligence to propose customized investment and risk management strategies for individual users.

[0080] First, users input information about their financial assets, risk tolerance, investment timeframe, and objectives through a dedicated application. This input information is transmitted to a server via the internet and registered within the system.

[0081] Next, the server operates on cloud infrastructure and collects market data from market data providers. This data includes stock prices, bond prices, exchange rates, and economic indicators. The server stores this data in a database and applies machine learning and AI algorithms to generate investment strategies tailored to the user's needs.

[0082] The generated investment strategies are optimized for each user, taking into account the balance between risk and return. The device displays the proposed investment strategies in a visually easy-to-understand format on the user's device, and an interactive assistant supports user understanding. This includes using graphs and charts to illustrate portfolio composition.

[0083] Furthermore, the server monitors market volatility and recommends risk management measures to users as needed. This allows for a rapid response and provides strategies to mitigate risks even in the event of sudden market fluctuations.

[0084] As a concrete example, let's assume the user is a 30-year-old company employee who aims to increase their assets over the medium to long term, with a 20% return in three years, primarily through the stock market. In this case, the system proposes a strategy that recommends diversified investment in domestic and international stocks and low-risk bonds, based on the user's risk tolerance. Furthermore, when the market becomes unstable, strategies such as risk hedging using derivatives and temporarily increasing cash positions will be considered. This process can also be executed using the user's prompt message: "I'm a 30-year-old company employee who aims to increase my assets over the medium to long term, with a 20% return in three years, primarily through the stock market. Please propose the optimal investment strategy."

[0085] This system allows individual investors and small and medium-sized enterprises to easily formulate their own investment and risk management strategies and improve their financial literacy.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] Users enter personal information such as financial assets, risk tolerance, investment period, and investment objectives through a dedicated application. The entered information is temporarily stored on the user's device and transmitted to the server via the internet. This stage of input includes user-specific asset information.

[0089] Step 2:

[0090] The server receives information sent by users and stores that data in a secure database within the system. The data processing performed here involves standardizing and formatting the information, preparing it for analysis. This process creates user-specific profiles.

[0091] Step 3:

[0092] The server collects external data from market data providers. Input data includes stock prices, bond prices, exchange rates, and economic indicators, which are automatically retrieved via APIs. The retrieved data is stored in a database and integrated with user information.

[0093] Step 4:

[0094] The server uses AI and machine learning algorithms to analyze user information and market data. Inputs include past user behavior data and market trends, generating optimized investment strategies as output. This step involves complex data calculations to optimize the balance between risk and return.

[0095] Step 5:

[0096] The terminal receives investment proposals sent from the server and displays them visually to the user. The input data consists of details of specific investment products and strategies, and based on this, the terminal generates graphs and charts to present them in a format that is easy for the user to understand.

[0097] Step 6:

[0098] The server monitors market volatility in real time and updates investment strategies as needed. Input data includes rapid market changes, and the adjusted risk management strategy is sent to the user as output. This step includes suggestions regarding hedging with derivatives and adjusting cash positions.

[0099] (Application Example 1)

[0100] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] Users have difficulty obtaining timely and appropriate advice for efficiently managing their financial assets, and there is a particular lack of systems to respond quickly to market fluctuations and changes in economic conditions. Furthermore, limited information on asset management options based on regional economies and infrastructure investments makes it difficult for individuals and small businesses to find optimal investment strategies.

[0102] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0103] In this invention, the server includes means for collecting user capital information and market conditions, means for analyzing the collected information and generating an investment strategy optimized for each user, and means for monitoring market fluctuations and changes in economic conditions and recommending risk control strategies. This enables users to obtain the optimal investment strategy in real time and respond quickly to market fluctuations and economic conditions. Furthermore, by receiving asset management advice based on the regional economy and infrastructure investments, users can make better investment decisions.

[0104] "User" refers to an individual or group that uses a particular application or system.

[0105] "Capital information" refers to information about the assets owned by the user and their composition.

[0106] "Market conditions" is a general term for the movements of various market variables, such as stock prices, exchange rates, interest rates, and other economic indicators.

[0107] An "investment strategy" refers to a set of guidelines for selecting and allocating assets to achieve a specific objective.

[0108] "Analysis" refers to the process of processing collected data and extracting meaningful information.

[0109] A "risk control strategy" refers to policies and measures implemented to minimize risks arising from market uncertainty and volatility.

[0110] "Visually easy to understand" means that information is presented in a format that is easy to grasp intuitively through graphics and visual methods.

[0111] "Regional economy" refers to economic activities and trends within a specific geographical area.

[0112] "Infrastructure investment" refers to the investment of funds in the construction or improvement of social infrastructure such as roads, bridges, power lines, and telecommunications systems.

[0113] "Advice" refers to the act of providing options or recommendations to consider when making decisions or taking action.

[0114] The system that realizes this invention includes a user, a server, and a terminal. The user interacts with the system using a smartphone or smart glasses. The user registers information such as capital information, risk tolerance, and investment goals as input information in a dedicated application. This information is transmitted to the server via the network.

[0115] The server manages user information using a database management system (MySQL®) and integrates it with market data obtained from market data providers. A machine learning library (TENSORFLOW®) is used for analysis, performing data analysis and generating optimal investment strategies. Throughout the analysis process, the server constantly monitors market fluctuations and changes in economic conditions to formulate risk control strategies.

[0116] The device displays the generated investment strategy to the user in a visually easy-to-understand manner. The smartphone app's UI is designed using React Native to ensure users can intuitively understand the information. For example, by visually presenting investment options through the AR display of smart glasses, users can receive real-time advice based on market changes.

[0117] For example, if a user wishes to invest in a local company, the system analyzes regional economic indicators and suggests promising investment opportunities in the region. Using a generative AI model, it generates a recommended portfolio aligned with the user's investment objectives. An example of a prompt would be, "User A's investment objective is medium- to long-term asset growth. Please suggest the optimal domestic stock portfolio, taking into account the latest regional economic indicators." In this way, users can make smart investment decisions.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The user launches a smartphone application and enters capital information, risk tolerance, and investment goals. The entered data is sent from the application to the server and recorded in the database.

[0121] Step 2:

[0122] The server stores data received from users in a MySQL database and periodically retrieves market data from a market data provider. This market data includes stock prices, exchange rates, and economic indicators. The collected data is integrated and prepared as a single dataset for analysis using TensorFlow.

[0123] Step 3:

[0124] The server uses a generative AI model to begin analyzing the integrated dataset. This analysis generates an optimal investment strategy based on the user's asset status and market conditions. The input consists of user data and market data, and the output is a customized investment portfolio for each user.

[0125] Step 4:

[0126] The server prepares data to visually represent the generated investment strategy. This data is sent to the UI component of the application, which uses React Native, and displayed on the user's device. The output data includes specific investment products and recommended asset allocation information.

[0127] Step 5:

[0128] The device visually displays the received data to the user. If smart glasses are being used, the investment strategy is overlaid on the AR display. As output, the user receives investment advice in an intuitively understandable visual format.

[0129] Step 6:

[0130] Users receive alerts and notifications through their devices, including real-time risk management advice in response to market fluctuations. Based on this, users can quickly revise their investment strategies.

[0131] Step 7:

[0132] When certain conditions are met, the server generates a new investment strategy proposal based on the generated AI model and notifies the user. This allows the user to always manage their assets based on the latest information. The prompt message used is: "User A's investment objective is medium- to long-term asset growth. Please propose an optimal domestic stock portfolio considering the latest regional economic indicators."

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention provides a system for offering more personalized financial consulting to individual investors and small and medium-sized enterprises, and is particularly characterized by its ability to adjust investment strategies to take into account the user's emotions. The system generates real-time optimized investment recommendations based on the user's asset information, risk tolerance, and emotional state.

[0135] First, users input information about their financial assets, investment goals, and feedback indicating their emotional state through a dedicated application. Emotional states are collected through data such as text-based feedback, voice analysis, and even biosensors.

[0136] The server receives asset information and sentiment data submitted by users and stores it in a database along with existing market data. This allows for a comprehensive understanding of the user's current financial and emotional state.

[0137] Next, the server uses an AI algorithm to analyze the user's emotional state and adjust investment suggestions accordingly. For example, if the system detects that the user is stressed, it optimizes the investment strategy to reduce risk. Conversely, if the user is motivated, the suggestions for new investment opportunities are enhanced.

[0138] The generated investment portfolio is adjusted based on the user's risk tolerance and emotional state, with specific financial product selections and holding proportions clearly defined. The device displays this information in an easy-to-understand format, and its interactive assistant function also provides explanations of complex financial terms and strategies.

[0139] As a concrete example, suppose the user is a middle manager in their 50s with experience in stock trading, but feels anxious about rapid market changes. In this case, the system uses an emotion engine to detect the level of anxiety and suggests increasing the proportion of investments in bonds and safe assets to reduce risk. On the other hand, if the market is stable and the user prefers aggressive investment anticipating future growth, the system recommends investing in growth stocks.

[0140] Furthermore, the server continuously monitors market volatility and adjusts risk management strategies in conjunction with sentiment data. This allows users to invest with greater peace of mind, without feeling emotionally burdened.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users open the application, enter financial asset information and investment goals, and receive emotional feedback. This includes ways to express their current mood through text comments or simple slider levels.

[0144] Step 2:

[0145] The device sends the collected data to the server. The server stores the received user asset information and sentiment data in a database.

[0146] Step 3:

[0147] The server retrieves the latest financial data from external market data providers via the internet and updates its database. This includes stock prices, bond interest rates, and economic indicators.

[0148] Step 4:

[0149] The server analyzes the stored data using AI algorithms. The emotion engine analyzes the emotional feedback provided by the user to identify the current emotional state.

[0150] Step 5:

[0151] The server adjusts the investment recommendations it generates based on the user's emotional state. If the user is experiencing high levels of stress or anxiety, it constructs and proposes an investment portfolio with reduced risk.

[0152] Step 6:

[0153] The server sends investment proposals and associated risk management strategies to the terminal. The terminal visually presents this to the user, displaying actionable options in an easy-to-understand interface.

[0154] Step 7:

[0155] If a user has questions or concerns about the displayed investment proposal, they can use the interactive assistant function to ask them. The device will then provide detailed explanations of financial terms and the proposal based on the information provided by the system.

[0156] Step 8:

[0157] The server integrates market data volatility with user sentiment data, and if risks or strategic changes are necessary, it immediately readjusts the strategy and makes new recommendations.

[0158] Step 9:

[0159] Users can make final investment decisions based on their emotional state and the investment proposals presented, and then confirm and execute those decisions on their device.

[0160] (Example 2)

[0161] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0162] To enable individual investors and small and medium-sized enterprises to adopt appropriate investment strategies according to their risk tolerance and emotional state, and to manage their assets with peace of mind, dynamic investment recommendations that take real-time market information and emotional data are necessary. However, current systems do not adequately optimize investment strategies to reflect the emotional state of users, making it difficult to manage assets efficiently while reducing anxiety and stress.

[0163] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0164] In this invention, the server includes means for acquiring the user's financial asset information, investment goals, and emotional state; means for analyzing the acquired data and optimizing the investment strategy based on the emotional state; and means for generating an investment portfolio using a generative AI model. This enables the dynamic adjustment and suggestion of an appropriate investment strategy according to the user's risk tolerance and emotional state.

[0165] "Financial asset information" refers to data about assets such as cash, stocks, bonds, and real estate that a user owns.

[0166] "Investment objectives" refer to the specific goals and objectives of asset management that a user wishes to achieve, including asset growth, income generation, and risk management.

[0167] "Emotional state" refers to the psychological state a user feels regarding their investment activities, and includes data indicating stress, motivation, sense of security, and other factors.

[0168] "Means of acquisition" refers to methods and devices for accurately collecting data on financial assets, investment goals, and emotional state from users.

[0169] "Means of analysis and optimization" refers to a method or process of analyzing users' emotions and risk tolerance using acquired data, and then adjusting investment strategies based on the results.

[0170] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to create investment strategies and portfolios.

[0171] An "investment portfolio" is the allocation of investments by combining multiple financial products held by a user.

[0172] "Dynamic adjustment" refers to flexibly changing investment strategies in real time in response to market fluctuations and user sentiment.

[0173] This invention is a system that provides more personalized financial consulting to individual investors and small and medium-sized enterprises. In particular, it enables the adjustment of investment strategies that take into account the user's emotional state.

[0174] Specifically, users input data on their financial assets, investment goals, and emotional state through a dedicated application. Emotional state is captured as text feedback, voice analysis, or biosensor information.

[0175] The acquired information is received by the server and stored in database services such as AWS® RDS and MongoDB. Next, the server uses machine learning frameworks such as TensorFlow and PyTorch to analyze the data with a generative AI model and determine the user's emotional state.

[0176] Based on this analysis, the server optimizes investment strategies according to the user's emotional state. For example, if the user is stressed, the server adjusts the strategy to reduce risk. On the other hand, if the user is motivated and desires growth, it strengthens investment recommendations for growth stocks.

[0177] The generated investment portfolio is visually presented to the user via the device. The device uses graphs and charts to help the user easily understand the strategy. It also provides explanations of financial terms through an interactive assistant function.

[0178] As a concrete example, if the user is a middle manager in their 50s with experience in stock trading but is feeling anxious about rapid market changes, the system will detect this anxiety and suggest increasing the proportion of investments in bonds and safe assets to mitigate risk.

[0179] An example of a prompt for the generating AI model would be text such as, "Please tell me the investment strategy you recommend when the user's emotional state is 'stressed'."

[0180] This system allows users to receive personalized investment strategies that are adjusted in real time, enabling them to manage their assets with peace of mind while reducing emotional burden.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] Users input financial asset information, investment goals, and emotional states using a dedicated application. The input data is captured as text, voice, or biosensor information. Specifically, users enter information into forms within the application and press a submit button. As output, the data entered by the user is transferred to a server.

[0184] Step 2:

[0185] The server receives data sent from the user and stores it in a database service such as AWS RDS or MongoDB. As input, it receives data on the user's financial assets, investment goals, and sentiment state; as output, this data is stored in the database. The server then performs specific actions to verify the integrity and completeness of the data.

[0186] Step 3:

[0187] The server runs generative AI models using machine learning frameworks such as TensorFlow and PyTorch to analyze stored data. User data and market data are used as input to analyze emotional states. As output, a report on the user's emotional state is generated, and investment strategies are adjusted based on this report. Specifically, the server prompts the AI ​​model and analyzes the results.

[0188] Step 4:

[0189] Based on the reports generated by the server, the system optimizes investment strategies according to the user's emotional state. For example, using the prompt "Please tell me the recommended investment strategy when the user's emotional state is 'stressed'," the system adjusts the recommendations generated by the AI ​​model. The emotional report is used as input, and the optimized investment portfolio is obtained as output. Specifically, the server analyzes the output from the AI ​​model and calculates the appropriate proportions of financial instruments.

[0190] Step 5:

[0191] The device visually displays the generated investment portfolio to the user. The input here is optimized portfolio data, which is displayed to the user as graphs and charts. Furthermore, it includes specific actions such as an interactive assistant providing explanations of financial terms via voice and text.

[0192] Step 6:

[0193] The server continuously monitors market volatility and user sentiment, readjusting investment strategies as needed. Here, real-time data is input, and the output is an adjusted risk management strategy. Specifically, the server periodically updates market data and reruns the AI ​​model based on it.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] In individual investment activities, it is crucial to adjust investment strategies to take into account the user's emotional state and risk tolerance. However, conventional asset management systems have been insufficient in providing real-time investment suggestions that take emotional states into account, making it difficult to respond flexibly to the user's situation. Furthermore, the lack of means to comprehensively manage consumption behavior and investment strategies prevented the provision of comprehensive financial support tailored to individual needs.

[0197] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0198] In this invention, the server includes means for collecting user asset information, emotional state, and market information; means for analyzing the collected data and generating spending management and investment proposals optimized for each individual user; and means for adjusting financial activities according to the user's emotional state. This makes it possible to adjust optimal asset management and investment strategies while understanding the user's emotional state.

[0199] "User asset information" refers to data regarding the details of financial assets owned by an individual or corporation, as well as their investment goals.

[0200] "Emotional state" refers to information that indicates the user's psychological and emotional condition, and is obtained from text feedback, voice, or biosensors.

[0201] "Market information" refers to information about the current economic situation and trends in financial markets, and includes data such as price fluctuations and economic indicators.

[0202] "Means of collection" refers to the processes and methods designed to obtain necessary information from users and incorporate it into the system.

[0203] "Means of analysis and generation" refers to methods and technologies for processing and analyzing acquired data to create expenditure management and investment proposals tailored to the user.

[0204] "Means of adjusting financial activities" refer to processes and methods for optimizing spending and investment behavior in accordance with the user's emotional state.

[0205] This invention operates a system based on the user's emotional state and risk tolerance in order to optimize the user's asset management and investment recommendations. Specifically, the user inputs information about their emotional state and asset information into the application using a smartphone or wearable device. This information is collected through voice, text feedback, or biosensors.

[0206] The server processes information collected from users, analyzing their emotional state, market information, and asset information. An AI algorithm programmed in Python analyzes this data and generates spending management and investment suggestions tailored to the user's current financial situation. This allows for flexible strategic adjustments based on the user's emotional state.

[0207] The device visually displays the generated suggestions to the user and, if necessary, explains the content of the suggestions using an interactive assistant function. As a practical example, if the user is feeling stressed, the system may suggest increasing the proportion of investment in safe assets and encourage them to curb spending.

[0208] Examples of prompt statements are as follows:

[0209] "Please rate your current emotional state on a scale of 1 to 10. How do you feel about your recent spending?"

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] Users input asset information, investment goals, and emotional states using smartphones or wearable devices. This information is collected through text feedback, voice input, or biosensors. The entered data is stored in the system's database.

[0213] Step 2:

[0214] The server retrieves the user's asset information, emotional state, and market information from a database. Based on the retrieved data, it analyzes the user's psychological state and current financial situation. This uses an AI algorithm programmed in Python to perform a risk assessment tailored to the user's emotions.

[0215] Step 3:

[0216] Based on the analysis results, the server uses a generated AI model to create personalized spending management and investment suggestions for each user. Specifically, if a user is feeling stressed, it will suggest investing in safe assets, and if they are feeling uplifted, it will suggest new investment opportunities. These suggestions are generated as digital information within the system.

[0217] Step 4:

[0218] The terminal visually displays generated investment proposals and expenditure management plans to the user. The proposals are presented in an easy-to-understand format through the user interface, and an interactive assistant provides further explanations of the proposals as needed.

[0219] Step 5:

[0220] Users can provide feedback on the proposed plan, and the server will further adjust the system based on that feedback. This feedback will be used for the next data analysis, leading to improved system accuracy.

[0221] 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.

[0222] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0228] 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.

[0229] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0230] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0231] 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.

[0232] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0233] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0234] The 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.

[0235] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0236] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0237] This invention is a system that provides optimized financial consulting to individual investors and small and medium-sized enterprises. This system collects user asset information and combines it with external data such as open data and market information to propose the optimal investment strategy to the user in real time. The method of implementing this invention is described below.

[0238] First, users input information about their financial assets, risk tolerance, investment timeframe, and objectives through a dedicated application. This information is sent to the server and registered in the system.

[0239] Next, the server collects regularly updated market data and stores it in a database along with the user's asset information. The data obtained from market data providers includes stock prices, bond prices, exchange rates, and economic indicators.

[0240] Subsequently, the server uses AI and machine learning algorithms to analyze the accumulated data and create an optimal investment portfolio tailored to the user. This process considers the balance between risk and return and also analyzes individual investment options.

[0241] The generated investment proposals are presented to the user as recommendations for specific financial products and asset allocations. The device displays the generated investment strategy to the user in a visually easy-to-understand format. Furthermore, complex financial and investment terms are explained in an easy-to-understand way using an interactive assistant.

[0242] Furthermore, the server monitors market volatility and recommends hedging measures for risk management to the user as needed. This mechanism ensures that even in the event of sudden market fluctuations, strategies for mitigating risk are immediately communicated.

[0243] As a concrete example, let's assume the user is a 30-year-old company employee who wants to increase their assets over the medium to long term. We also assume they are aiming for a 20% return in three years, primarily through the stock market. In this case, the system analyzes the user's current asset allocation and proposes an investment strategy that diversifies funds into domestic and international stocks and some low-risk bonds.

[0244] Furthermore, if the market becomes unstable, risk mitigation measures such as risk hedging through derivatives and temporary increases in cash positions will be considered. This system supports the user's entire asset management process and contributes to improving their financial literacy.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user launches the application and enters asset information and investment preferences. This includes the types and quantities of financial assets held, risk tolerance, investment objectives, and target timeframe.

[0248] Step 2:

[0249] The terminal sends the information entered by the user to the server. The server stores the received information in a database and prepares it for analysis.

[0250] Step 3:

[0251] The server collects the latest market data via the internet. This data includes stock and bond prices, economic indicators, and exchange rates.

[0252] Step 4:

[0253] The server combines user data and market data for analysis. Here, it utilizes AI algorithms to generate optimal investment strategies for each user.

[0254] Step 5:

[0255] The generated investment strategy includes specific details such as the recommended purchase quantity and holding percentage for each financial product. The server transmits this information to the terminal.

[0256] Step 6:

[0257] The terminal visually displays the investment strategy received from the server to the user. This is presented clearly using graphs and charts.

[0258] Step 7:

[0259] If the user has questions about the information displayed, the device will use an interactive assistant to explain financial terms and investment strategies in detail.

[0260] Step 8:

[0261] The server continuously monitors market volatility and considers risk management strategies when risks increase. This includes suggesting portfolio adjustments and hedging measures.

[0262] Step 9:

[0263] Once a risk management strategy is decided, that information is immediately transmitted to the terminal and notified to the user.

[0264] Step 10:

[0265] Users review the notifications and, if necessary, adopt or adjust the suggested strategies.

[0266] (Example 1)

[0267] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] Traditionally, it has been difficult for individual investors and small and medium-sized enterprises (SMEs) to independently analyze market fluctuations and formulate investment and management strategies based on them, requiring specialized knowledge. Therefore, there is a need for a system that can respond to market fluctuations in real time while providing appropriate investment and risk management strategies tailored to individual needs. Furthermore, there is a lack of support for users to easily understand complex financial and investment concepts.

[0269] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0270] In this invention, the server includes means for collecting information about the user's assets and market data, means for analyzing the collected data to generate investment suggestions optimized for individual users, and means for monitoring market fluctuations and economic indicators and recommending risk management strategies. This enables real-time investment strategy suggestions and risk management tailored to individual users. Furthermore, by using an interactive assistant to explain financial terms in simple terms and aid in user understanding, it also improves financial literacy.

[0271] "Users" refer to individuals or small businesses that use the system to receive personalized financial asset management and investment strategy suggestions.

[0272] "Asset information" refers to data that shows details such as the financial assets a user holds, their risk tolerance, investment period, and investment objectives.

[0273] "Market data" refers to information that shows trends in financial markets, such as stock prices, bond prices, exchange rates, and economic indicators.

[0274] "Analysis" refers to the process of using AI and machine learning algorithms to analyze collected data in order to derive the optimal investment strategy for the user.

[0275] "Investment proposals" refer to investment strategies offered to users, including recommendations for specific financial products or asset allocations.

[0276] A "risk management strategy" refers to the methods and measures proposed to minimize the risk to a user's investment portfolio.

[0277] "Small and medium-sized enterprises" refers to small or medium-sized companies other than individual investors.

[0278] "Financial data" refers to information that indicates the financial condition of a company, such as assets, liabilities, revenues, and expenses held by a small or medium-sized enterprise (SME).

[0279] The "Business Strategy Report" refers to a report that includes analysis and recommendations created for small and medium-sized enterprises to use in formulating business strategies based on financial data.

[0280] The "Interactive Assistant" refers to an interface that breaks down and explains financial terms and investment-related information within the system in response to user questions.

[0281] "Real-time" refers to a state where the system can immediately respond to market fluctuations and user inputs.

[0282] The present invention is a system that provides optimized financial consulting for individual investors and small and medium-sized enterprises. By using data analysis and artificial intelligence, this system proposes investment and risk management strategies customized for individual users.

[0283] First, the user inputs information regarding their financial assets, risk tolerance, investment time horizon, purpose, etc. through a dedicated application. These inputted information are transmitted to the server via the Internet and registered within the system.

[0284] Next, the server functions on the cloud infrastructure and collects market data from market data providers. The data includes stock prices, bond prices, exchange rates, economic indicators, etc. The server accumulates these data in a database and applies machine learning and AI algorithms to generate an investment strategy that suits the user's needs.

[0285] The generated investment strategy takes into account the balance between risk and return and is optimized for each user. The terminal displays the proposed investment strategy in a visually understandable format on the user's device and supports the user's understanding using an interactive assistant. This also includes showing the composition of the portfolio using graphs and charts.

[0286] Furthermore, the server monitors market volatility and recommends risk management strategies to users as needed. This provides a strategy to respond quickly and reduce risks even in the event of rapid market fluctuations.

[0287] As a specific example, assume that the user is a 30-year-old company employee aiming to increase assets in the medium to long term and targeting a 20% profit in three years mainly in the stock market. In this case, this system proposes a strategy to recommend diversified investments in domestic and foreign stocks and low-risk bonds based on the user's risk tolerance. Also, when the market becomes unstable, strategies such as risk hedging using derivatives and increasing temporary cash positions are considered. This process can also be executed using the prompt sentence from the user, "I am a 30-year-old company employee aiming to increase assets in the medium to long term and targeting a 20% profit in three years mainly in the stock market. Please propose an optimal investment strategy."

[0288] With this system, individual investors and small and medium-sized enterprises can easily formulate their own investment and risk management strategies and also improve their financial literacy.

[0289] The flow of the specific process in Example 1 will be described using FIG. 11.

[0290] Step 1:

[0291] The user inputs personal information such as financial assets, risk tolerance, investment period, and investment purpose through a dedicated application. The input information is temporarily stored on the user's device and transmitted to the server via the Internet. The input at this stage includes the user's unique asset information.

[0292] Step 2:

[0293] The server receives information sent by the user and stores that data in a secure database within the system. The data processing performed here involves standardizing and formatting the information, and is done in preparation for analysis. This process creates user-specific profiles.

[0294] Step 3:

[0295] The server collects external data from market data providers. Input data includes stock prices, bond prices, exchange rates, and economic indicators, which are automatically retrieved via APIs. The retrieved data is stored in a database and integrated with user information.

[0296] Step 4:

[0297] The server uses AI and machine learning algorithms to analyze user information and market data. Inputs include past user behavior data and market trends, generating optimized investment strategies as output. This step involves complex data calculations to optimize the balance between risk and return.

[0298] Step 5:

[0299] The terminal receives investment proposals sent from the server and displays them visually to the user. The input data consists of details of specific investment products and strategies, and based on this, the terminal generates graphs and charts to present them in a format that is easy for the user to understand.

[0300] Step 6:

[0301] The server monitors market volatility in real time and updates investment strategies as needed. Input data includes rapid market changes, and the adjusted risk management strategy is sent to the user as output. This step includes suggestions regarding hedging with derivatives and adjusting cash positions.

[0302] (Application Example 1)

[0303] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0304] It is difficult for users to obtain appropriate advice for efficiently managing their financial assets in real time, and there is a lack of a system for quickly responding to market fluctuations and changes in the economic situation. In addition, since information on asset management options based on regional economies and infrastructure investment is limited, it is difficult for individuals and small and medium-sized enterprises to find an optimal investment strategy.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0306] In this invention, the server includes means for collecting the user's capital information and market situation, means for analyzing the collected information and generating an investment strategy optimized for each user, and means for monitoring market fluctuations and changes in the economic situation and recommending a risk control strategy. As a result, the user can obtain an optimal investment strategy in real time and can quickly respond to market fluctuations and changes in the economic situation. In addition, by receiving asset management advice based on regional economies and infrastructure investment, better investment decisions can be made.

[0307] The "user" refers to an individual or group using a specific application or system.

[0308] The "capital information" refers to information regarding the assets owned by the user and their composition.

[0309] The "market situation" is a general term for the movements of various variables in the market, such as stock prices, exchange rates, interest rates, and other economic indicators.

[0310] The "investment strategy" refers to the policy for the selection and allocation of assets set to achieve a specific goal.

[0311] "Analysis" refers to the process of processing collected data and extracting meaningful information.

[0312] A "risk control strategy" refers to policies and measures implemented to minimize risks arising from market uncertainty and volatility.

[0313] "Visually easy to understand" means that information is presented in a format that is easy to grasp intuitively through graphics and visual methods.

[0314] "Regional economy" refers to economic activities and trends within a specific geographical area.

[0315] "Infrastructure investment" refers to the investment of funds in the construction or improvement of social infrastructure such as roads, bridges, power lines, and telecommunications systems.

[0316] "Advice" refers to the act of providing options or recommendations to consider when making decisions or taking action.

[0317] The system that realizes this invention includes a user, a server, and a terminal. The user interacts with the system using a smartphone or smart glasses. The user registers information such as capital information, risk tolerance, and investment goals as input information in a dedicated application. This information is transmitted to the server via the network.

[0318] The server manages user information using a database management system (MySQL) and integrates it with market data obtained from market data providers. A machine learning library (TensorFlow) is used for analysis, performing data analysis and generating optimal investment strategies. Throughout the analysis process, the server constantly monitors market fluctuations and changes in economic conditions to formulate risk control strategies.

[0319] The device displays the generated investment strategy to the user in a visually easy-to-understand manner. The smartphone app's UI is designed using React Native to ensure users can intuitively understand the information. For example, by visually presenting investment options through the AR display of smart glasses, users can receive real-time advice based on market changes.

[0320] For example, if a user wishes to invest in a local company, the system analyzes regional economic indicators and suggests promising investment opportunities in the region. Using a generative AI model, it generates a recommended portfolio aligned with the user's investment objectives. An example of a prompt would be, "User A's investment objective is medium- to long-term asset growth. Please suggest the optimal domestic stock portfolio, taking into account the latest regional economic indicators." In this way, users can make smart investment decisions.

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The user launches a smartphone application and enters capital information, risk tolerance, and investment goals. The entered data is sent from the application to the server and recorded in the database.

[0324] Step 2:

[0325] The server stores data received from users in a MySQL database and periodically retrieves market data from a market data provider. This market data includes stock prices, exchange rates, and economic indicators. The collected data is integrated and prepared as a single dataset for analysis using TensorFlow.

[0326] Step 3:

[0327] The server uses a generative AI model to begin analyzing the integrated dataset. This analysis generates an optimal investment strategy based on the user's asset status and market conditions. The input consists of user data and market data, and the output is a customized investment portfolio for each user.

[0328] Step 4:

[0329] The server prepares data to visually represent the generated investment strategy. This data is sent to the UI component of the application, which uses React Native, and displayed on the user's device. The output data includes specific investment products and recommended asset allocation information.

[0330] Step 5:

[0331] The device visually displays the received data to the user. If smart glasses are being used, the investment strategy is overlaid on the AR display. As output, the user receives investment advice in an intuitively understandable visual format.

[0332] Step 6:

[0333] Users receive alerts and notifications through their devices, including real-time risk management advice in response to market fluctuations. Based on this, users can quickly revise their investment strategies.

[0334] Step 7:

[0335] When certain conditions are met, the server generates a new investment strategy proposal based on the generated AI model and notifies the user. This allows the user to always manage their assets based on the latest information. The prompt message used is: "User A's investment objective is medium- to long-term asset growth. Please propose an optimal domestic stock portfolio considering the latest regional economic indicators."

[0336] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0337] This invention provides a system for offering more personalized financial consulting to individual investors and small and medium-sized enterprises, and is particularly characterized by its ability to adjust investment strategies to take into account the user's emotions. The system generates real-time optimized investment recommendations based on the user's asset information, risk tolerance, and emotional state.

[0338] First, users input information about their financial assets, investment goals, and feedback indicating their emotional state through a dedicated application. Emotional states are collected through data such as text-based feedback, voice analysis, and even biosensors.

[0339] The server receives asset information and sentiment data submitted by users and stores it in a database along with existing market data. This allows for a comprehensive understanding of the user's current financial and emotional state.

[0340] Next, the server uses an AI algorithm to analyze the user's emotional state and adjust investment suggestions accordingly. For example, if the system detects that the user is stressed, it optimizes the investment strategy to reduce risk. Conversely, if the user is motivated, the suggestions for new investment opportunities are enhanced.

[0341] The generated investment portfolio is adjusted based on the user's risk tolerance and emotional state, with specific financial product selections and holding proportions clearly defined. The device displays this information in an easy-to-understand format, and its interactive assistant function also provides explanations of complex financial terms and strategies.

[0342] As a concrete example, suppose the user is a middle manager in their 50s with experience in stock trading, but feels anxious about rapid market changes. In this case, the system uses an emotion engine to detect the level of anxiety and suggests increasing the proportion of investments in bonds and safe assets to reduce risk. On the other hand, if the market is stable and the user prefers aggressive investment anticipating future growth, the system recommends investing in growth stocks.

[0343] Furthermore, the server continuously monitors market volatility and adjusts risk management strategies in conjunction with sentiment data. This allows users to invest with greater peace of mind, without feeling emotionally burdened.

[0344] The following describes the processing flow.

[0345] Step 1:

[0346] Users open the application, enter financial asset information and investment goals, and receive emotional feedback. This includes ways to express their current mood through text comments or simple slider levels.

[0347] Step 2:

[0348] The device sends the collected data to the server. The server stores the received user asset information and sentiment data in a database.

[0349] Step 3:

[0350] The server retrieves the latest financial data from external market data providers via the internet and updates its database. This includes stock prices, bond interest rates, and economic indicators.

[0351] Step 4:

[0352] The server analyzes the stored data using AI algorithms. The emotion engine analyzes the emotional feedback provided by the user to identify the current emotional state.

[0353] Step 5:

[0354] The server adjusts the investment recommendations it generates based on the user's emotional state. If the user is experiencing high levels of stress or anxiety, it constructs and proposes an investment portfolio with reduced risk.

[0355] Step 6:

[0356] The server sends investment proposals and associated risk management strategies to the terminal. The terminal visually presents this to the user, displaying actionable options in an easy-to-understand interface.

[0357] Step 7:

[0358] If a user has questions or concerns about the displayed investment proposal, they can use the interactive assistant function to ask them. The device will then provide detailed explanations of financial terms and the proposal based on the information provided by the system.

[0359] Step 8:

[0360] The server integrates market data volatility with user sentiment data, and if risks or strategic changes are necessary, it immediately readjusts the strategy and makes new recommendations.

[0361] Step 9:

[0362] Users can make final investment decisions based on their emotional state and the investment proposals presented, and then confirm and execute those decisions on their device.

[0363] (Example 2)

[0364] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0365] To enable individual investors and small and medium-sized enterprises to adopt appropriate investment strategies according to their risk tolerance and emotional state, and to manage their assets with peace of mind, dynamic investment recommendations that take real-time market information and emotional data are necessary. However, current systems do not adequately optimize investment strategies to reflect the emotional state of users, making it difficult to manage assets efficiently while reducing anxiety and stress.

[0366] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0367] In this invention, the server includes means for acquiring the user's financial asset information, investment goals, and emotional state; means for analyzing the acquired data and optimizing the investment strategy based on the emotional state; and means for generating an investment portfolio using a generative AI model. This enables the dynamic adjustment and suggestion of an appropriate investment strategy according to the user's risk tolerance and emotional state.

[0368] "Financial asset information" refers to data about assets such as cash, stocks, bonds, and real estate that a user owns.

[0369] "Investment objectives" refer to the specific goals and objectives of asset management that a user wishes to achieve, including asset growth, income generation, and risk management.

[0370] "Emotional state" refers to the psychological state a user feels regarding their investment activities, and includes data indicating stress, motivation, sense of security, and other factors.

[0371] "Means of acquisition" refers to methods and devices for accurately collecting data on financial assets, investment goals, and emotional state from users.

[0372] "Means of analysis and optimization" refers to a method or process of analyzing users' emotions and risk tolerance using acquired data, and then adjusting investment strategies based on the results.

[0373] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to create investment strategies and portfolios.

[0374] An "investment portfolio" is the allocation of investments by combining multiple financial products held by a user.

[0375] "Dynamic adjustment" refers to flexibly changing investment strategies in real time in response to market fluctuations and user sentiment.

[0376] This invention is a system that provides more personalized financial consulting to individual investors and small and medium-sized enterprises. In particular, it enables the adjustment of investment strategies that take into account the user's emotional state.

[0377] Specifically, users input data on their financial assets, investment goals, and emotional state through a dedicated application. Emotional state is captured as text feedback, voice analysis, or biosensor information.

[0378] The acquired information is received by the server and stored in a database service such as AWS RDS or MongoDB. The server then uses machine learning frameworks such as TensorFlow or PyTorch to analyze the data with a generative AI model and determine the user's emotional state.

[0379] Based on this analysis, the server optimizes investment strategies according to the user's emotional state. For example, if the user is stressed, the server adjusts the strategy to reduce risk. On the other hand, if the user is motivated and desires growth, it strengthens investment recommendations for growth stocks.

[0380] The generated investment portfolio is visually presented to the user via the device. The device uses graphs and charts to help the user easily understand the strategy. It also provides explanations of financial terms through an interactive assistant function.

[0381] As a concrete example, if the user is a middle manager in their 50s with experience in stock trading but is feeling anxious about rapid market changes, the system will detect this anxiety and suggest increasing the proportion of investments in bonds and safe assets to mitigate risk.

[0382] An example of a prompt for the generating AI model would be a text input such as, "Please tell me the investment strategy you would recommend when the user's emotional state is 'stressed'."

[0383] This system allows users to receive personalized investment strategies that are adjusted in real time, enabling them to manage their assets with peace of mind while reducing emotional burden.

[0384] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0385] Step 1:

[0386] Users input financial asset information, investment goals, and emotional states using a dedicated application. The input data is captured as text, voice, or biosensor information. Specifically, users enter information into forms within the application and press a submit button. As output, the data entered by the user is transferred to a server.

[0387] Step 2:

[0388] The server receives data sent from the user and stores it in a database service such as AWS RDS or MongoDB. As input, it receives data on the user's financial assets, investment goals, and sentiment state; as output, this data is stored in the database. The server then performs specific actions to verify the integrity and completeness of the data.

[0389] Step 3:

[0390] The server runs generative AI models using machine learning frameworks such as TensorFlow and PyTorch to analyze stored data. User data and market data are used as input to analyze emotional states. As output, a report on the user's emotional state is generated, and investment strategies are adjusted based on this report. Specifically, the server prompts the AI ​​model and analyzes the results.

[0391] Step 4:

[0392] The server optimizes investment strategies based on the user's emotional state, using reports generated by the server. For example, using the prompt "What investment strategy would you recommend if the user's emotional state is 'stressed'?", the server adjusts the recommendations generated by the AI ​​model. The emotional report is used as input, and the optimized investment portfolio is obtained as output. Specifically, the server analyzes the output from the AI ​​model and calculates the appropriate proportions of financial instruments.

[0393] Step 5:

[0394] The device visually displays the generated investment portfolio to the user. The input here is optimized portfolio data, which is displayed to the user as graphs and charts. Furthermore, it includes specific actions such as an interactive assistant providing explanations of financial terms via voice and text.

[0395] Step 6:

[0396] The server continuously monitors market volatility and user sentiment, readjusting investment strategies as needed. Here, real-time data is input, and the output is an adjusted risk management strategy. Specifically, the server periodically updates market data and reruns the AI ​​model based on it.

[0397] (Application Example 2)

[0398] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0399] In individual investment activities, it is crucial to adjust investment strategies to take into account the user's emotional state and risk tolerance. However, conventional asset management systems have been insufficient in providing real-time investment suggestions that take emotional states into account, making it difficult to respond flexibly to the user's situation. Furthermore, the lack of means to comprehensively manage consumption behavior and investment strategies prevented the provision of comprehensive financial support tailored to individual needs.

[0400] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0401] In this invention, the server includes means for collecting user asset information, emotional state, and market information; means for analyzing the collected data and generating spending management and investment proposals optimized for each individual user; and means for adjusting financial activities according to the user's emotional state. This makes it possible to adjust optimal asset management and investment strategies while understanding the user's emotional state.

[0402] "User asset information" refers to data regarding the details of financial assets owned by an individual or corporation, as well as their investment goals.

[0403] "Emotional state" refers to information that indicates the user's psychological and emotional condition, and is obtained from text feedback, voice, or biosensors.

[0404] "Market information" refers to information about the current economic situation and trends in financial markets, and includes data such as price fluctuations and economic indicators.

[0405] "Means of collection" refers to the processes and methods designed to obtain necessary information from users and incorporate it into the system.

[0406] "Means of analysis and generation" refers to methods and technologies for processing and analyzing acquired data to create expenditure management and investment proposals tailored to the user.

[0407] "Means of adjusting financial activities" refer to processes and methods for optimizing spending and investment behavior in accordance with the user's emotional state.

[0408] This invention operates a system based on the user's emotional state and risk tolerance in order to optimize the user's asset management and investment recommendations. Specifically, the user inputs information about their emotional state and asset information into the application using a smartphone or wearable device. This information is collected through voice, text feedback, or biosensors.

[0409] The server processes information collected from users, analyzing their emotional state, market information, and asset information. An AI algorithm programmed in Python analyzes this data and generates spending management and investment suggestions tailored to the user's current financial situation. This allows for flexible strategic adjustments based on the user's emotional state.

[0410] The device visually displays the generated suggestions to the user and, if necessary, explains the content of the suggestions using an interactive assistant function. As a practical example, if the user is feeling stressed, the system may suggest increasing the proportion of investment in safe assets and encourage them to curb spending.

[0411] Examples of prompt statements are as follows:

[0412] "Please rate your current emotional state on a scale of 1 to 10. How do you feel about your recent spending?"

[0413] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0414] Step 1:

[0415] Users input asset information, investment goals, and emotional states using smartphones or wearable devices. This information is collected through text feedback, voice input, or biosensors. The entered data is stored in the system's database.

[0416] Step 2:

[0417] The server retrieves the user's asset information, emotional state, and market information from a database. Based on the retrieved data, it analyzes the user's psychological state and current financial situation. This uses an AI algorithm programmed in Python to perform a risk assessment tailored to the user's emotions.

[0418] Step 3:

[0419] Based on the analysis results, the server uses a generated AI model to create personalized spending management and investment suggestions for each user. Specifically, if a user is feeling stressed, it will suggest investing in safe assets, and if they are feeling uplifted, it will suggest new investment opportunities. These suggestions are generated as digital information within the system.

[0420] Step 4:

[0421] The terminal visually displays generated investment proposals and expenditure management plans to the user. The proposals are presented in an easy-to-understand format through the user interface, and an interactive assistant provides further explanations of the proposals as needed.

[0422] Step 5:

[0423] Users can provide feedback on the proposed plan, and the server will further adjust the system based on that feedback. This feedback will be used for the next data analysis, leading to improved system accuracy.

[0424] 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.

[0425] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0426] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] 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.

[0430] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0431] 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.

[0432] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0433] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0434] 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.

[0435] 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.

[0436] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0437] The 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.

[0438] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0439] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0440] This invention is a system that provides optimized financial consulting to individual investors and small and medium-sized enterprises. This system collects user asset information and combines it with external data such as open data and market information to propose the optimal investment strategy to the user in real time. The method of implementing this invention is described below.

[0441] First, users input information about their financial assets, risk tolerance, investment timeframe, and objectives through a dedicated application. This information is sent to the server and registered in the system.

[0442] Next, the server collects regularly updated market data and stores it in a database along with the user's asset information. The data obtained from market data providers includes stock prices, bond prices, exchange rates, and economic indicators.

[0443] Subsequently, the server uses AI and machine learning algorithms to analyze the accumulated data and create an optimal investment portfolio tailored to the user. This process considers the balance between risk and return and also analyzes individual investment options.

[0444] The generated investment proposals are presented to the user as recommendations for specific financial products and asset allocations. The device displays the generated investment strategy to the user in a visually easy-to-understand format. Furthermore, complex financial and investment terms are explained in an easy-to-understand way using an interactive assistant.

[0445] Furthermore, the server monitors market volatility and recommends hedging measures for risk management to the user as needed. This mechanism ensures that even in the event of sudden market fluctuations, strategies for mitigating risk are immediately communicated.

[0446] As a concrete example, let's assume the user is a 30-year-old company employee who wants to increase their assets over the medium to long term. We also assume they are aiming for a 20% return in three years, primarily through the stock market. In this case, the system analyzes the user's current asset allocation and proposes an investment strategy that diversifies funds into domestic and international stocks and some low-risk bonds.

[0447] Furthermore, if the market becomes unstable, risk mitigation measures such as risk hedging through derivatives and temporary increases in cash positions will be considered. This system supports the user's entire asset management process and contributes to improving their financial literacy.

[0448] The following describes the processing flow.

[0449] Step 1:

[0450] The user launches the application and enters asset information and investment preferences. This includes the types and quantities of financial assets held, risk tolerance, investment objectives, and target timeframe.

[0451] Step 2:

[0452] The terminal sends the information entered by the user to the server. The server stores the received information in a database and prepares it for analysis.

[0453] Step 3:

[0454] The server collects the latest market data via the internet. This data includes stock and bond prices, economic indicators, and exchange rates.

[0455] Step 4:

[0456] The server combines user data and market data for analysis. Here, it utilizes AI algorithms to generate optimal investment strategies for each user.

[0457] Step 5:

[0458] The generated investment strategy includes specific details such as the recommended purchase quantity and holding percentage for each financial product. The server transmits this information to the terminal.

[0459] Step 6:

[0460] The terminal visually displays the investment strategy received from the server to the user. This is presented clearly using graphs and charts.

[0461] Step 7:

[0462] If the user has questions about the information displayed, the device will use an interactive assistant to explain financial terms and investment strategies in detail.

[0463] Step 8:

[0464] The server continuously monitors market volatility and considers risk management strategies when risks increase. This includes suggesting portfolio adjustments and hedging measures.

[0465] Step 9:

[0466] Once a risk management strategy is decided, that information is immediately transmitted to the terminal and notified to the user.

[0467] Step 10:

[0468] Users review the notifications and, if necessary, adopt or adjust the suggested strategies.

[0469] (Example 1)

[0470] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0471] Traditionally, it has been difficult for individual investors and small and medium-sized enterprises (SMEs) to independently analyze market fluctuations and formulate investment and management strategies based on them, requiring specialized knowledge. Therefore, there is a need for a system that can respond to market fluctuations in real time while providing appropriate investment and risk management strategies tailored to individual needs. Furthermore, there is a lack of support for users to easily understand complex financial and investment concepts.

[0472] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0473] In this invention, the server includes means for collecting information about the user's assets and market data, means for analyzing the collected data to generate investment suggestions optimized for individual users, and means for monitoring market fluctuations and economic indicators and recommending risk management strategies. This enables real-time investment strategy suggestions and risk management tailored to individual users. Furthermore, by using an interactive assistant to explain financial terms in simple terms and aid in user understanding, it also improves financial literacy.

[0474] "Users" refer to individuals or small businesses that use the system to receive personalized financial asset management and investment strategy suggestions.

[0475] "Asset information" refers to data that shows details such as the financial assets a user holds, their risk tolerance, investment period, and investment objectives.

[0476] "Market data" refers to information that shows trends in financial markets, such as stock prices, bond prices, exchange rates, and economic indicators.

[0477] "Analysis" refers to the process of using AI and machine learning algorithms to analyze collected data in order to derive the optimal investment strategy for the user.

[0478] "Investment proposals" refer to investment strategies offered to users, including recommendations for specific financial products or asset allocations.

[0479] A "risk management strategy" refers to the methods and measures proposed to minimize the risk to a user's investment portfolio.

[0480] "Small and medium-sized enterprises" refers to small or medium-sized companies other than individual investors.

[0481] "Financial data" refers to information that indicates the financial condition of a company, such as assets, liabilities, revenues, and expenses held by a small or medium-sized enterprise (SME).

[0482] A "management strategy report" refers to a report containing analysis and recommendations created based on financial data for use by small and medium-sized enterprises in formulating their management strategies.

[0483] A "conversational assistant" refers to an interface that, in response to user questions, breaks down and explains financial terminology and investment-related information within the system.

[0484] "Real-time" refers to a state in which a system can respond immediately to market fluctuations and user input.

[0485] This invention is a system that provides optimized financial consulting to individual investors and small and medium-sized enterprises. This system uses data analysis and artificial intelligence to propose customized investment and risk management strategies for individual users.

[0486] First, users input information about their financial assets, risk tolerance, investment timeframe, and objectives through a dedicated application. This input information is transmitted to a server via the internet and registered within the system.

[0487] Next, the server operates on cloud infrastructure and collects market data from market data providers. This data includes stock prices, bond prices, exchange rates, and economic indicators. The server stores this data in a database and applies machine learning and AI algorithms to generate investment strategies tailored to the user's needs.

[0488] The generated investment strategies are optimized for each user, taking into account the balance between risk and return. The device displays the proposed investment strategies in a visually easy-to-understand format on the user's device, and an interactive assistant supports user understanding. This includes using graphs and charts to illustrate portfolio composition.

[0489] Furthermore, the server monitors market volatility and recommends risk management measures to users as needed. This allows for a rapid response and provides strategies to mitigate risks even in the event of sudden market fluctuations.

[0490] As a concrete example, let's assume the user is a 30-year-old company employee who aims to increase their assets over the medium to long term, with a 20% return in three years, primarily through the stock market. In this case, the system proposes a strategy that recommends diversified investment in domestic and international stocks and low-risk bonds, based on the user's risk tolerance. Furthermore, when the market becomes unstable, strategies such as risk hedging using derivatives and temporarily increasing cash positions will be considered. This process can also be executed using the user's prompt message: "I'm a 30-year-old company employee who aims to increase my assets over the medium to long term, with a 20% return in three years, primarily through the stock market. Please propose the optimal investment strategy."

[0491] This system allows individual investors and small and medium-sized enterprises to easily formulate their own investment and risk management strategies and improve their financial literacy.

[0492] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0493] Step 1:

[0494] Users enter personal information such as financial assets, risk tolerance, investment period, and investment objectives through a dedicated application. This information is temporarily stored on the user's device and transmitted to a server via the internet. This stage of input includes user-specific asset information.

[0495] Step 2:

[0496] The server receives information sent by the user and stores that data in a secure database within the system. The data processing performed here involves standardizing and formatting the information, and is done in preparation for analysis. This process creates user-specific profiles.

[0497] Step 3:

[0498] The server collects external data from market data providers. Input data includes stock prices, bond prices, exchange rates, and economic indicators, which are automatically retrieved via APIs. The retrieved data is stored in a database and integrated with user information.

[0499] Step 4:

[0500] The server uses AI and machine learning algorithms to analyze user information and market data. Inputs include past user behavior data and market trends, generating optimized investment strategies as output. This step involves complex data calculations to optimize the balance between risk and return.

[0501] Step 5:

[0502] The terminal receives investment proposals sent from the server and displays them visually to the user. The input data consists of details of specific investment products and strategies, and based on this, the terminal generates graphs and charts to present them in a format that is easy for the user to understand.

[0503] Step 6:

[0504] The server monitors market volatility in real time and updates investment strategies as needed. Input data includes rapid market changes, and the adjusted risk management strategy is sent to the user as output. This step includes suggestions regarding hedging with derivatives and adjusting cash positions.

[0505] (Application Example 1)

[0506] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0507] Users have difficulty obtaining timely and appropriate advice for efficiently managing their financial assets, and there is a particular lack of systems to respond quickly to market fluctuations and changes in economic conditions. Furthermore, limited information on asset management options based on regional economies and infrastructure investments makes it difficult for individuals and small businesses to find optimal investment strategies.

[0508] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0509] In this invention, the server includes means for collecting user capital information and market conditions, means for analyzing the collected information and generating an investment strategy optimized for each user, and means for monitoring market fluctuations and changes in economic conditions and recommending risk control strategies. This enables users to obtain the optimal investment strategy in real time and respond quickly to market fluctuations and economic conditions. Furthermore, by receiving asset management advice based on the regional economy and infrastructure investments, users can make better investment decisions.

[0510] "User" refers to an individual or group that uses a particular application or system.

[0511] "Capital information" refers to information about the assets owned by the user and their composition.

[0512] "Market conditions" is a general term for the movements of various market variables, such as stock prices, exchange rates, interest rates, and other economic indicators.

[0513] An "investment strategy" refers to a set of guidelines for selecting and allocating assets to achieve a specific objective.

[0514] "Analysis" refers to the process of processing collected data and extracting meaningful information.

[0515] A "risk control strategy" refers to policies and measures implemented to minimize risks arising from market uncertainty and volatility.

[0516] "Visually easy to understand" means that information is presented in a format that is easy to grasp intuitively through graphics and visual methods.

[0517] "Regional economy" refers to economic activities and trends within a specific geographical area.

[0518] "Infrastructure investment" refers to the investment of funds in the construction or improvement of social infrastructure such as roads, bridges, power lines, and telecommunications systems.

[0519] "Advice" refers to the act of providing options or recommendations to consider when making decisions or taking action.

[0520] The system that realizes this invention includes a user, a server, and a terminal. The user interacts with the system using a smartphone or smart glasses. The user registers information such as capital information, risk tolerance, and investment goals as input information in a dedicated application. This information is transmitted to the server via the network.

[0521] The server manages user information using a database management system (MySQL) and integrates it with market data obtained from market data providers. A machine learning library (TensorFlow) is used for analysis, performing data analysis and generating optimal investment strategies. Throughout the analysis process, the server constantly monitors market fluctuations and changes in economic conditions to formulate risk control strategies.

[0522] The device displays the generated investment strategy to the user in a visually easy-to-understand manner. The smartphone app's UI is designed using React Native to ensure users can intuitively understand the information. For example, by visually presenting investment options through the AR display of smart glasses, users can receive real-time advice based on market changes.

[0523] For example, if a user wishes to invest in a local company, the system analyzes regional economic indicators and suggests promising investment opportunities in the region. Using a generative AI model, it generates a recommended portfolio aligned with the user's investment objectives. An example of a prompt would be, "User A's investment objective is medium- to long-term asset growth. Please suggest the optimal domestic stock portfolio, taking into account the latest regional economic indicators." In this way, users can make smart investment decisions.

[0524] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0525] Step 1:

[0526] The user launches a smartphone application and enters capital information, risk tolerance, and investment goals. The entered data is sent from the application to the server and recorded in a database.

[0527] Step 2:

[0528] The server stores data received from users in a MySQL database and periodically retrieves market data from a market data provider. This market data includes stock prices, exchange rates, and economic indicators. The collected data is integrated and prepared as a single dataset for analysis using TensorFlow.

[0529] Step 3:

[0530] The server uses a generative AI model to begin analyzing the integrated dataset. This analysis generates an optimal investment strategy based on the user's asset status and market conditions. The input consists of user data and market data, and the output is a customized investment portfolio for each user.

[0531] Step 4:

[0532] The server prepares data to visually represent the generated investment strategy. This data is sent to the UI component of the application, which uses React Native, and displayed on the user's device. The output data includes specific investment products and recommended asset allocation information.

[0533] Step 5:

[0534] The device visually displays the received data to the user. If smart glasses are being used, the investment strategy is overlaid on the AR display. As output, the user receives investment advice in an intuitively understandable visual format.

[0535] Step 6:

[0536] Users receive alerts and notifications through their devices, including real-time risk management advice in response to market fluctuations. Based on this, users can quickly revise their investment strategies.

[0537] Step 7:

[0538] When certain conditions are met, the server generates a new investment strategy proposal based on the generated AI model and notifies the user. This allows the user to always manage their assets based on the latest information. The prompt message used is: "User A's investment objective is medium- to long-term asset growth. Please propose an optimal domestic stock portfolio considering the latest regional economic indicators."

[0539] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0540] This invention provides a system for offering more personalized financial consulting to individual investors and small and medium-sized enterprises, and is particularly characterized by its ability to adjust investment strategies to take into account the user's emotions. The system generates real-time optimized investment recommendations based on the user's asset information, risk tolerance, and emotional state.

[0541] First, users input information about their financial assets, investment goals, and feedback indicating their emotional state through a dedicated application. Emotional states are collected through data such as text-based feedback, voice analysis, and even biosensors.

[0542] The server receives asset information and sentiment data submitted by users and stores it in a database along with existing market data. This allows for a comprehensive understanding of the user's current financial and emotional state.

[0543] Next, the server uses an AI algorithm to analyze the user's emotional state and adjust investment suggestions accordingly. For example, if the system detects that the user is stressed, it optimizes the investment strategy to reduce risk. Conversely, if the user is motivated, the suggestions for new investment opportunities are enhanced.

[0544] The generated investment portfolio is adjusted based on the user's risk tolerance and emotional state, with specific financial product selections and holding proportions clearly defined. The device displays this information in an easy-to-understand format, and its interactive assistant function also provides explanations of complex financial terms and strategies.

[0545] As a concrete example, suppose the user is a middle manager in their 50s with experience in stock trading, but feels anxious about rapid market changes. In this case, the system uses an emotion engine to detect the level of anxiety and suggests increasing the proportion of investments in bonds and safe assets to reduce risk. On the other hand, if the market is stable and the user prefers aggressive investment anticipating future growth, the system recommends investing in growth stocks.

[0546] Furthermore, the server continuously monitors market volatility and adjusts risk management strategies in conjunction with sentiment data. This allows users to invest with greater peace of mind, without feeling emotionally burdened.

[0547] The following describes the processing flow.

[0548] Step 1:

[0549] Users open the application, enter financial asset information and investment goals, and receive emotional feedback. This includes ways to express their current mood through text comments or simple slider levels.

[0550] Step 2:

[0551] The device sends the collected data to the server. The server stores the received user asset information and sentiment data in a database.

[0552] Step 3:

[0553] The server retrieves the latest financial data from external market data providers via the internet and updates its database. This includes stock prices, bond interest rates, and economic indicators.

[0554] Step 4:

[0555] The server analyzes the stored data using AI algorithms. The emotion engine analyzes the emotional feedback provided by the user to identify the current emotional state.

[0556] Step 5:

[0557] The server adjusts the investment recommendations it generates based on the user's emotional state. If the user is experiencing high levels of stress or anxiety, it constructs and proposes an investment portfolio with reduced risk.

[0558] Step 6:

[0559] The server sends investment proposals and associated risk management strategies to the terminal. The terminal visually presents this to the user, displaying actionable options in an easy-to-understand interface.

[0560] Step 7:

[0561] If a user has questions or concerns about the displayed investment proposal, they can use the interactive assistant function to ask them. The device will then provide detailed explanations of financial terms and the proposal based on the information provided by the system.

[0562] Step 8:

[0563] The server integrates market data volatility with user sentiment data, and if risks or strategic changes are necessary, it immediately readjusts the strategy and makes new recommendations.

[0564] Step 9:

[0565] Users can make final investment decisions based on their emotional state and the investment proposals presented, and then confirm and execute those decisions on their device.

[0566] (Example 2)

[0567] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0568] To enable individual investors and small and medium-sized enterprises to adopt appropriate investment strategies according to their risk tolerance and emotional state, and to manage their assets with peace of mind, dynamic investment recommendations that take real-time market information and emotional data are necessary. However, current systems do not adequately optimize investment strategies to reflect the emotional state of users, making it difficult to manage assets efficiently while reducing anxiety and stress.

[0569] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0570] In this invention, the server includes means for acquiring the user's financial asset information, investment goals, and emotional state; means for analyzing the acquired data and optimizing the investment strategy based on the emotional state; and means for generating an investment portfolio using a generative AI model. This enables the dynamic adjustment and suggestion of an appropriate investment strategy according to the user's risk tolerance and emotional state.

[0571] "Financial asset information" refers to data about assets such as cash, stocks, bonds, and real estate that a user owns.

[0572] "Investment objectives" refer to the specific goals and objectives of asset management that a user wishes to achieve, including asset growth, income generation, and risk management.

[0573] "Emotional state" refers to the psychological state a user feels regarding their investment activities, and includes data indicating stress, motivation, sense of security, and other factors.

[0574] "Means of acquisition" refers to methods and devices for accurately collecting data on financial assets, investment goals, and emotional state from users.

[0575] "Means of analysis and optimization" refers to a method or process of analyzing users' emotions and risk tolerance using acquired data, and then adjusting investment strategies based on the results.

[0576] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to create investment strategies and portfolios.

[0577] An "investment portfolio" is the allocation of investments by combining multiple financial products held by a user.

[0578] "Dynamic adjustment" refers to flexibly changing investment strategies in real time in response to market fluctuations and user sentiment.

[0579] This invention is a system that provides more personalized financial consulting to individual investors and small and medium-sized enterprises. In particular, it enables the adjustment of investment strategies that take into account the user's emotional state.

[0580] Specifically, users input data on their financial assets, investment goals, and emotional state through a dedicated application. Emotional state is captured as text feedback, voice analysis, or biosensor information.

[0581] The acquired information is received by the server and stored in a database service such as AWS RDS or MongoDB. The server then uses machine learning frameworks such as TensorFlow or PyTorch to analyze the data with a generative AI model and determine the user's emotional state.

[0582] Based on this analysis, the server optimizes investment strategies according to the user's emotional state. For example, if the user is stressed, the server adjusts the strategy to reduce risk. On the other hand, if the user is motivated and desires growth, it strengthens investment recommendations for growth stocks.

[0583] The generated investment portfolio is visually presented to the user via the device. The device uses graphs and charts to help the user easily understand the strategy. It also provides explanations of financial terms through an interactive assistant function.

[0584] As a concrete example, if the user is a middle manager in their 50s with experience in stock trading but is feeling anxious about rapid market changes, the system will detect this anxiety and suggest increasing the proportion of investments in bonds and safe assets to mitigate risk.

[0585] An example of a prompt for the generating AI model would be a text input such as, "Please tell me the investment strategy you would recommend when the user's emotional state is 'stressed'."

[0586] This system allows users to receive personalized investment strategies that are adjusted in real time, enabling them to manage their assets with peace of mind while reducing emotional burden.

[0587] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0588] Step 1:

[0589] Users input financial asset information, investment goals, and emotional states using a dedicated application. The input data is captured as text, voice, or biosensor information. Specifically, users enter information into forms within the application and press a submit button. As output, the data entered by the user is transferred to a server.

[0590] Step 2:

[0591] The server receives data sent from the user and stores it in a database service such as AWS RDS or MongoDB. As input, it receives data on the user's financial assets, investment goals, and sentiment state; as output, this data is stored in the database. The server then performs specific actions to verify the integrity and completeness of the data.

[0592] Step 3:

[0593] The server runs generative AI models using machine learning frameworks such as TensorFlow and PyTorch to analyze stored data. User data and market data are used as input to analyze emotional states. As output, a report on the user's emotional state is generated, and investment strategies are adjusted based on this report. Specifically, the server prompts the AI ​​model and analyzes the results.

[0594] Step 4:

[0595] The server optimizes investment strategies based on the user's emotional state, using reports generated by the server. For example, using the prompt "What investment strategy would you recommend if the user's emotional state is 'stressed'?", the server adjusts the recommendations generated by the AI ​​model. The emotional report is used as input, and the optimized investment portfolio is obtained as output. Specifically, the server analyzes the output from the AI ​​model and calculates the appropriate proportions of financial instruments.

[0596] Step 5:

[0597] The device visually displays the generated investment portfolio to the user. The input here is optimized portfolio data, which is displayed to the user as graphs and charts. Furthermore, it includes specific actions such as an interactive assistant providing explanations of financial terms via voice and text.

[0598] Step 6:

[0599] The server continuously monitors market volatility and user sentiment, readjusting investment strategies as needed. Here, real-time data is input, and the output is an adjusted risk management strategy. Specifically, the server periodically updates market data and reruns the AI ​​model based on it.

[0600] (Application Example 2)

[0601] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0602] In individual investment activities, it is crucial to adjust investment strategies to take into account the user's emotional state and risk tolerance. However, conventional asset management systems have been insufficient in providing real-time investment suggestions that take emotional states into account, making it difficult to respond flexibly to the user's situation. Furthermore, the lack of means to comprehensively manage consumption behavior and investment strategies prevented the provision of comprehensive financial support tailored to individual needs.

[0603] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0604] In this invention, the server includes means for collecting user asset information, emotional state, and market information; means for analyzing the collected data and generating spending management and investment proposals optimized for each individual user; and means for adjusting financial activities according to the user's emotional state. This makes it possible to adjust optimal asset management and investment strategies while understanding the user's emotional state.

[0605] "User asset information" refers to data regarding the details of financial assets owned by an individual or corporation, as well as their investment goals.

[0606] "Emotional state" refers to information that indicates the user's psychological and emotional condition, and is obtained from text feedback, voice, or biosensors.

[0607] "Market information" refers to information about the current economic situation and trends in financial markets, and includes data such as price fluctuations and economic indicators.

[0608] "Means of collection" refers to the processes and methods designed to obtain necessary information from users and incorporate it into the system.

[0609] "Means of analysis and generation" refers to methods and technologies for processing and analyzing acquired data to create expenditure management and investment proposals tailored to the user.

[0610] "Means of adjusting financial activities" refer to processes and methods for optimizing spending and investment behavior in accordance with the user's emotional state.

[0611] This invention operates a system based on the user's emotional state and risk tolerance in order to optimize the user's asset management and investment recommendations. Specifically, the user inputs information about their emotional state and asset information into the application using a smartphone or wearable device. This information is collected through voice, text feedback, or biosensors.

[0612] The server processes information collected from users, analyzing their emotional state, market information, and asset information. An AI algorithm programmed in Python analyzes this data and generates spending management and investment suggestions tailored to the user's current financial situation. This allows for flexible strategic adjustments based on the user's emotional state.

[0613] The device visually displays the generated suggestions to the user and, if necessary, explains the content of the suggestions using an interactive assistant function. As a practical example, if the user is feeling stressed, the system may suggest increasing the proportion of investment in safe assets and encourage them to curb spending.

[0614] Examples of prompt statements are as follows:

[0615] "Please rate your current emotional state on a scale of 1 to 10. How do you feel about your recent spending?"

[0616] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0617] Step 1:

[0618] Users input asset information, investment goals, and emotional states using smartphones or wearable devices. This information is collected through text feedback, voice input, or biosensors. The entered data is stored in the system's database.

[0619] Step 2:

[0620] The server retrieves the user's asset information, emotional state, and market information from a database. Based on the retrieved data, it analyzes the user's psychological state and current financial situation. This uses an AI algorithm programmed in Python to perform a risk assessment tailored to the user's emotions.

[0621] Step 3:

[0622] Based on the analysis results, the server uses a generated AI model to create personalized spending management and investment suggestions for each user. Specifically, if a user is feeling stressed, it will suggest investing in safe assets, and if they are feeling uplifted, it will suggest new investment opportunities. These suggestions are generated as digital information within the system.

[0623] Step 4:

[0624] The terminal visually displays generated investment proposals and expenditure management plans to the user. The proposals are presented in an easy-to-understand format through the user interface, and an interactive assistant provides further explanations of the proposals as needed.

[0625] Step 5:

[0626] Users can provide feedback on the proposed plan, and the server will further adjust the system based on that feedback. This feedback will be used for the next data analysis, leading to improved system accuracy.

[0627] 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.

[0628] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0629] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] 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.

[0633] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0634] 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.

[0635] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0636] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0637] 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.

[0638] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0639] 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.

[0640] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0641] The 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.

[0642] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0643] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] This invention is a system that provides optimized financial consulting to individual investors and small and medium-sized enterprises. This system collects user asset information and combines it with external data such as open data and market information to propose the optimal investment strategy to the user in real time. The method of implementing this invention is described below.

[0645] First, users input information about their financial assets, risk tolerance, investment timeframe, and objectives through a dedicated application. This information is sent to the server and registered in the system.

[0646] Next, the server collects regularly updated market data and stores it in a database along with the user's asset information. The data obtained from market data providers includes stock prices, bond prices, exchange rates, and economic indicators.

[0647] Subsequently, the server uses AI and machine learning algorithms to analyze the accumulated data and create an optimal investment portfolio tailored to the user. This process considers the balance between risk and return and also analyzes individual investment options.

[0648] The generated investment proposals are presented to the user as recommendations for specific financial products and asset allocations. The device displays the generated investment strategy to the user in a visually easy-to-understand format. Furthermore, complex financial and investment terms are explained in an easy-to-understand way using an interactive assistant.

[0649] Furthermore, the server monitors market volatility and recommends hedging measures for risk management to the user as needed. This mechanism ensures that even in the event of sudden market fluctuations, strategies for mitigating risk are immediately communicated.

[0650] As a concrete example, let's assume the user is a 30-year-old company employee who wants to increase their assets over the medium to long term. We also assume they are aiming for a 20% return in three years, primarily through the stock market. In this case, the system analyzes the user's current asset allocation and proposes an investment strategy that diversifies funds into domestic and international stocks and some low-risk bonds.

[0651] Furthermore, if the market becomes unstable, risk mitigation measures such as risk hedging through derivatives and temporary increases in cash positions will be considered. This system supports the user's entire asset management process and contributes to improving their financial literacy.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] The user launches the application and enters asset information and investment preferences. This includes the types and quantities of financial assets held, risk tolerance, investment objectives, and target timeframe.

[0655] Step 2:

[0656] The terminal sends the information entered by the user to the server. The server stores the received information in a database and prepares it for analysis.

[0657] Step 3:

[0658] The server collects the latest market data via the internet. This data includes stock and bond prices, economic indicators, and exchange rates.

[0659] Step 4:

[0660] The server combines user data and market data for analysis. Here, it utilizes AI algorithms to generate optimal investment strategies for each user.

[0661] Step 5:

[0662] The generated investment strategy includes specific details such as the recommended purchase quantity and holding percentage for each financial product. The server transmits this information to the terminal.

[0663] Step 6:

[0664] The terminal visually displays the investment strategy received from the server to the user. This is presented clearly using graphs and charts.

[0665] Step 7:

[0666] If the user has questions about the information displayed, the device will use an interactive assistant to explain financial terms and investment strategies in detail.

[0667] Step 8:

[0668] The server continuously monitors market volatility and considers risk management strategies when risks increase. This includes suggesting portfolio adjustments and hedging measures.

[0669] Step 9:

[0670] Once a risk management strategy is decided, that information is immediately transmitted to the terminal and notified to the user.

[0671] Step 10:

[0672] Users review the notifications and, if necessary, adopt or adjust the suggested strategies.

[0673] (Example 1)

[0674] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0675] Traditionally, it has been difficult for individual investors and small and medium-sized enterprises (SMEs) to independently analyze market fluctuations and formulate investment and management strategies based on them, requiring specialized knowledge. Therefore, there is a need for a system that can respond to market fluctuations in real time while providing appropriate investment and risk management strategies tailored to individual needs. Furthermore, there is a lack of support for users to easily understand complex financial and investment concepts.

[0676] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0677] In this invention, the server includes means for collecting information about the user's assets and market data, means for analyzing the collected data to generate investment suggestions optimized for individual users, and means for monitoring market fluctuations and economic indicators and recommending risk management strategies. This enables real-time investment strategy suggestions and risk management tailored to individual users. Furthermore, by using an interactive assistant to explain financial terms in simple terms and aid in user understanding, it also improves financial literacy.

[0678] "Users" refer to individuals or small businesses that use the system to receive personalized financial asset management and investment strategy suggestions.

[0679] "Asset information" refers to data that shows details such as the financial assets a user holds, their risk tolerance, investment period, and investment objectives.

[0680] "Market data" refers to information that shows trends in financial markets, such as stock prices, bond prices, exchange rates, and economic indicators.

[0681] "Analysis" refers to the process of using AI and machine learning algorithms to analyze collected data in order to derive the optimal investment strategy for the user.

[0682] "Investment proposals" refer to investment strategies offered to users, including recommendations for specific financial products or asset allocations.

[0683] A "risk management strategy" refers to the methods and measures proposed to minimize the risk to a user's investment portfolio.

[0684] "Small and medium-sized enterprises" refers to small or medium-sized companies other than individual investors.

[0685] "Financial data" refers to information that indicates the financial condition of a company, such as assets, liabilities, revenues, and expenses held by a small or medium-sized enterprise (SME).

[0686] A "management strategy report" refers to a report containing analysis and recommendations created based on financial data for use by small and medium-sized enterprises in formulating their management strategies.

[0687] A "conversational assistant" refers to an interface that, in response to user questions, breaks down and explains financial terminology and investment-related information within the system.

[0688] "Real-time" refers to a state in which a system can respond immediately to market fluctuations and user input.

[0689] This invention is a system that provides optimized financial consulting to individual investors and small and medium-sized enterprises. This system uses data analysis and artificial intelligence to propose customized investment and risk management strategies for individual users.

[0690] First, users input information about their financial assets, risk tolerance, investment timeframe, and objectives through a dedicated application. This input information is transmitted to a server via the internet and registered within the system.

[0691] Next, the server operates on cloud infrastructure and collects market data from market data providers. This data includes stock prices, bond prices, exchange rates, and economic indicators. The server stores this data in a database and applies machine learning and AI algorithms to generate investment strategies tailored to the user's needs.

[0692] The generated investment strategies are optimized for each user, taking into account the balance between risk and return. The device displays the proposed investment strategies in a visually easy-to-understand format on the user's device, and an interactive assistant supports user understanding. This includes using graphs and charts to illustrate portfolio composition.

[0693] Furthermore, the server monitors market volatility and recommends risk management measures to users as needed. This allows for a rapid response and provides strategies to mitigate risks even in the event of sudden market fluctuations.

[0694] As a concrete example, let's assume the user is a 30-year-old company employee who aims to increase their assets over the medium to long term, with a 20% return in three years, primarily through the stock market. In this case, the system proposes a strategy that recommends diversified investment in domestic and international stocks and low-risk bonds, based on the user's risk tolerance. Furthermore, when the market becomes unstable, strategies such as risk hedging using derivatives and temporarily increasing cash positions will be considered. This process can also be executed using the user's prompt message: "I'm a 30-year-old company employee who aims to increase my assets over the medium to long term, with a 20% return in three years, primarily through the stock market. Please propose the optimal investment strategy."

[0695] This system allows individual investors and small and medium-sized enterprises to easily formulate their own investment and risk management strategies and improve their financial literacy.

[0696] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0697] Step 1:

[0698] Users enter personal information such as financial assets, risk tolerance, investment period, and investment objectives through a dedicated application. This information is temporarily stored on the user's device and transmitted to a server via the internet. This stage of input includes user-specific asset information.

[0699] Step 2:

[0700] The server receives information sent by the user and stores that data in a secure database within the system. The data processing performed here involves standardizing and formatting the information, and is done in preparation for analysis. This process creates user-specific profiles.

[0701] Step 3:

[0702] The server collects external data from market data providers. Input data includes stock prices, bond prices, exchange rates, and economic indicators, which are automatically retrieved via APIs. The retrieved data is stored in a database and integrated with user information.

[0703] Step 4:

[0704] The server uses AI and machine learning algorithms to analyze user information and market data. Inputs include past user behavior data and market trends, generating optimized investment strategies as output. This step involves complex data calculations to optimize the balance between risk and return.

[0705] Step 5:

[0706] The terminal receives investment proposals sent from the server and displays them visually to the user. The input data consists of details of specific investment products and strategies, and based on this, the terminal generates graphs and charts to present them in a format that is easy for the user to understand.

[0707] Step 6:

[0708] The server monitors market volatility in real time and updates investment strategies as needed. Input data includes rapid market changes, and the adjusted risk management strategy is sent to the user as output. This step includes suggestions regarding hedging with derivatives and adjusting cash positions.

[0709] (Application Example 1)

[0710] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0711] Users have difficulty obtaining timely and appropriate advice for efficiently managing their financial assets, and there is a particular lack of systems to respond quickly to market fluctuations and changes in economic conditions. Furthermore, limited information on asset management options based on regional economies and infrastructure investments makes it difficult for individuals and small businesses to find optimal investment strategies.

[0712] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0713] In this invention, the server includes means for collecting user capital information and market conditions, means for analyzing the collected information and generating an investment strategy optimized for each user, and means for monitoring market fluctuations and changes in economic conditions and recommending risk control strategies. This enables users to obtain the optimal investment strategy in real time and respond quickly to market fluctuations and economic conditions. Furthermore, by receiving asset management advice based on the regional economy and infrastructure investments, users can make better investment decisions.

[0714] "User" refers to an individual or group that uses a particular application or system.

[0715] "Capital information" refers to information about the assets owned by the user and their composition.

[0716] "Market conditions" is a general term for the movements of various market variables, such as stock prices, exchange rates, interest rates, and other economic indicators.

[0717] An "investment strategy" refers to a set of guidelines for selecting and allocating assets to achieve a specific objective.

[0718] "Analysis" refers to the process of processing collected data and extracting meaningful information.

[0719] A "risk control strategy" refers to policies and measures implemented to minimize risks arising from market uncertainty and volatility.

[0720] "Visually easy to understand" means that information is presented in a format that is easy to grasp intuitively through graphics and visual methods.

[0721] "Regional economy" refers to economic activities and trends within a specific geographical area.

[0722] "Infrastructure investment" refers to the investment of funds in the construction or improvement of social infrastructure such as roads, bridges, power lines, and telecommunications systems.

[0723] "Advice" refers to the act of providing options or recommendations to consider when making decisions or taking action.

[0724] The system that realizes this invention includes a user, a server, and a terminal. The user interacts with the system using a smartphone or smart glasses. The user registers information such as capital information, risk tolerance, and investment goals as input information in a dedicated application. This information is transmitted to the server via the network.

[0725] The server manages user information using a database management system (MySQL) and integrates it with market data obtained from market data providers. A machine learning library (TensorFlow) is used for analysis, performing data analysis and generating optimal investment strategies. Throughout the analysis process, the server constantly monitors market fluctuations and changes in economic conditions to formulate risk control strategies.

[0726] The device displays the generated investment strategy to the user in a visually easy-to-understand manner. The smartphone app's UI is designed using React Native to ensure users can intuitively understand the information. For example, by visually presenting investment options through the AR display of smart glasses, users can receive real-time advice based on market changes.

[0727] For example, if a user wishes to invest in a local company, the system analyzes regional economic indicators and suggests promising investment opportunities in the region. Using a generative AI model, it generates a recommended portfolio aligned with the user's investment objectives. An example of a prompt would be, "User A's investment objective is medium- to long-term asset growth. Please suggest the optimal domestic stock portfolio, taking into account the latest regional economic indicators." In this way, users can make smart investment decisions.

[0728] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0729] Step 1:

[0730] The user launches a smartphone application and enters capital information, risk tolerance, and investment goals. The entered data is sent from the application to the server and recorded in a database.

[0731] Step 2:

[0732] The server stores data received from users in a MySQL database and periodically retrieves market data from a market data provider. This market data includes stock prices, exchange rates, and economic indicators. The collected data is integrated and prepared as a single dataset for analysis using TensorFlow.

[0733] Step 3:

[0734] The server uses a generative AI model to begin analyzing the integrated dataset. This analysis generates an optimal investment strategy based on the user's asset status and market conditions. The input consists of user data and market data, and the output is a customized investment portfolio for each user.

[0735] Step 4:

[0736] The server prepares data to visually represent the generated investment strategy. This data is sent to the UI component of the application, which uses React Native, and displayed on the user's device. The output data includes specific investment products and recommended asset allocation information.

[0737] Step 5:

[0738] The device visually displays the received data to the user. If smart glasses are being used, the investment strategy is overlaid on the AR display. As output, the user receives investment advice in an intuitively understandable visual format.

[0739] Step 6:

[0740] Users receive alerts and notifications through their devices, including real-time risk management advice in response to market fluctuations. Based on this, users can quickly revise their investment strategies.

[0741] Step 7:

[0742] When certain conditions are met, the server generates a new investment strategy proposal based on the generated AI model and notifies the user. This allows the user to always manage their assets based on the latest information. The prompt message used is: "User A's investment objective is medium- to long-term asset growth. Please propose an optimal domestic stock portfolio considering the latest regional economic indicators."

[0743] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0744] This invention provides a system for offering more personalized financial consulting to individual investors and small and medium-sized enterprises, and is particularly characterized by its ability to adjust investment strategies to take into account the user's emotions. The system generates real-time optimized investment recommendations based on the user's asset information, risk tolerance, and emotional state.

[0745] First, users input information about their financial assets, investment goals, and feedback indicating their emotional state through a dedicated application. Emotional states are collected through data such as text-based feedback, voice analysis, and even biosensors.

[0746] The server receives asset information and sentiment data submitted by users and stores it in a database along with existing market data. This allows for a comprehensive understanding of the user's current financial and emotional state.

[0747] Next, the server uses an AI algorithm to analyze the user's emotional state and adjust investment suggestions accordingly. For example, if the system detects that the user is stressed, it optimizes the investment strategy to reduce risk. Conversely, if the user is motivated, the suggestions for new investment opportunities are enhanced.

[0748] The generated investment portfolio is adjusted based on the user's risk tolerance and emotional state, with specific financial product selections and holding proportions clearly defined. The device displays this information in an easy-to-understand format, and its interactive assistant function also provides explanations of complex financial terms and strategies.

[0749] As a concrete example, suppose the user is a middle manager in their 50s with experience in stock trading, but feels anxious about rapid market changes. In this case, the system uses an emotion engine to detect the level of anxiety and suggests increasing the proportion of investments in bonds and safe assets to reduce risk. On the other hand, if the market is stable and the user prefers aggressive investment anticipating future growth, the system recommends investing in growth stocks.

[0750] Furthermore, the server continuously monitors market volatility and adjusts risk management strategies in conjunction with sentiment data. This allows users to invest with greater peace of mind, without feeling emotionally burdened.

[0751] The following describes the processing flow.

[0752] Step 1:

[0753] Users open the application, enter financial asset information and investment goals, and receive emotional feedback. This includes ways to express their current mood through text comments or simple slider levels.

[0754] Step 2:

[0755] The device sends the collected data to the server. The server stores the received user asset information and sentiment data in a database.

[0756] Step 3:

[0757] The server retrieves the latest financial data from external market data providers via the internet and updates its database. This includes stock prices, bond interest rates, and economic indicators.

[0758] Step 4:

[0759] The server analyzes the stored data using AI algorithms. The emotion engine analyzes the emotional feedback provided by the user to identify the current emotional state.

[0760] Step 5:

[0761] The server adjusts the investment recommendations it generates based on the user's emotional state. If the user is experiencing high levels of stress or anxiety, it constructs and proposes an investment portfolio with reduced risk.

[0762] Step 6:

[0763] The server sends investment proposals and associated risk management strategies to the terminal. The terminal visually presents this to the user, displaying actionable options in an easy-to-understand interface.

[0764] Step 7:

[0765] If a user has questions or concerns about the displayed investment proposal, they can use the interactive assistant function to ask them. The device will then provide detailed explanations of financial terms and the proposal based on the information provided by the system.

[0766] Step 8:

[0767] The server integrates market data volatility with user sentiment data, and if risks or strategic changes are necessary, it immediately readjusts the strategy and makes new recommendations.

[0768] Step 9:

[0769] Users can make final investment decisions based on their emotional state and the investment proposals presented, and then confirm and execute those decisions on their device.

[0770] (Example 2)

[0771] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0772] To enable individual investors and small and medium-sized enterprises to adopt appropriate investment strategies according to their risk tolerance and emotional state, and to manage their assets with peace of mind, dynamic investment recommendations that take real-time market information and emotional data are necessary. However, current systems do not adequately optimize investment strategies to reflect the emotional state of users, making it difficult to manage assets efficiently while reducing anxiety and stress.

[0773] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0774] In this invention, the server includes means for acquiring the user's financial asset information, investment goals, and emotional state; means for analyzing the acquired data and optimizing the investment strategy based on the emotional state; and means for generating an investment portfolio using a generative AI model. This enables the dynamic adjustment and suggestion of an appropriate investment strategy according to the user's risk tolerance and emotional state.

[0775] "Financial asset information" refers to data about assets such as cash, stocks, bonds, and real estate that a user owns.

[0776] "Investment objectives" refer to the specific goals and objectives of asset management that a user wishes to achieve, including asset growth, income generation, and risk management.

[0777] "Emotional state" refers to the psychological state a user feels regarding their investment activities, and includes data indicating stress, motivation, sense of security, and other factors.

[0778] "Means of acquisition" refers to methods and devices for accurately collecting data on financial assets, investment goals, and emotional state from users.

[0779] "Means of analysis and optimization" refers to a method or process of analyzing users' emotions and risk tolerance using acquired data, and then adjusting investment strategies based on the results.

[0780] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to create investment strategies and portfolios.

[0781] An "investment portfolio" is the allocation of investments by combining multiple financial products held by a user.

[0782] "Dynamic adjustment" refers to flexibly changing investment strategies in real time in response to market fluctuations and user sentiment.

[0783] This invention is a system that provides more personalized financial consulting to individual investors and small and medium-sized enterprises. In particular, it enables the adjustment of investment strategies that take into account the user's emotional state.

[0784] Specifically, users input data on their financial assets, investment goals, and emotional state through a dedicated application. Emotional state is captured as text feedback, voice analysis, or biosensor information.

[0785] The acquired information is received by the server and stored in a database service such as AWS RDS or MongoDB. The server then uses machine learning frameworks such as TensorFlow or PyTorch to analyze the data with a generative AI model and determine the user's emotional state.

[0786] Based on this analysis, the server optimizes investment strategies according to the user's emotional state. For example, if the user is stressed, the server adjusts the strategy to reduce risk. On the other hand, if the user is motivated and desires growth, it strengthens investment recommendations for growth stocks.

[0787] The generated investment portfolio is visually presented to the user via the device. The device uses graphs and charts to help the user easily understand the strategy. It also provides explanations of financial terms through an interactive assistant function.

[0788] As a concrete example, if the user is a middle manager in their 50s with experience in stock trading but is feeling anxious about rapid market changes, the system will detect this anxiety and suggest increasing the proportion of investments in bonds and safe assets to mitigate risk.

[0789] An example of a prompt for the generating AI model would be a text input such as, "Please tell me the investment strategy you would recommend when the user's emotional state is 'stressed'."

[0790] This system allows users to receive personalized investment strategies that are adjusted in real time, enabling them to manage their assets with peace of mind while reducing emotional burden.

[0791] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0792] Step 1:

[0793] Users input financial asset information, investment goals, and emotional states using a dedicated application. The input data is captured as text, voice, or biosensor information. Specifically, users enter information into forms within the application and press a submit button. As output, the data entered by the user is transferred to a server.

[0794] Step 2:

[0795] The server receives data sent from the user and stores it in a database service such as AWS RDS or MongoDB. As input, it receives data on the user's financial assets, investment goals, and sentiment state; as output, this data is stored in the database. The server then performs specific actions to verify the integrity and completeness of the data.

[0796] Step 3:

[0797] The server runs generative AI models using machine learning frameworks such as TensorFlow and PyTorch to analyze stored data. User data and market data are used as input to analyze emotional states. As output, a report on the user's emotional state is generated, and investment strategies are adjusted based on this report. Specifically, the server prompts the AI ​​model and analyzes the results.

[0798] Step 4:

[0799] The server optimizes investment strategies based on the user's emotional state, using reports generated by the server. For example, using the prompt "What investment strategy would you recommend if the user's emotional state is 'stressed'?", the server adjusts the recommendations generated by the AI ​​model. The emotional report is used as input, and the optimized investment portfolio is obtained as output. Specifically, the server analyzes the output from the AI ​​model and calculates the appropriate proportions of financial instruments.

[0800] Step 5:

[0801] The device visually displays the generated investment portfolio to the user. The input here is optimized portfolio data, which is displayed to the user as graphs and charts. Furthermore, it includes specific actions such as an interactive assistant providing explanations of financial terms via voice and text.

[0802] Step 6:

[0803] The server continuously monitors market volatility and user sentiment, readjusting investment strategies as needed. Here, real-time data is input, and the output is an adjusted risk management strategy. Specifically, the server periodically updates market data and reruns the AI ​​model based on it.

[0804] (Application Example 2)

[0805] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0806] In individual investment activities, it is crucial to adjust investment strategies to take into account the user's emotional state and risk tolerance. However, conventional asset management systems have been insufficient in providing real-time investment suggestions that take emotional states into account, making it difficult to respond flexibly to the user's situation. Furthermore, the lack of means to comprehensively manage consumption behavior and investment strategies prevented the provision of comprehensive financial support tailored to individual needs.

[0807] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0808] In this invention, the server includes means for collecting user asset information, emotional state, and market information; means for analyzing the collected data and generating spending management and investment proposals optimized for each individual user; and means for adjusting financial activities according to the user's emotional state. This makes it possible to adjust optimal asset management and investment strategies while understanding the user's emotional state.

[0809] "User asset information" refers to data regarding the details of financial assets owned by an individual or corporation, as well as their investment goals.

[0810] "Emotional state" refers to information that indicates the user's psychological and emotional condition, and is obtained from text feedback, voice, or biosensors.

[0811] "Market information" refers to information about the current economic situation and trends in financial markets, and includes data such as price fluctuations and economic indicators.

[0812] "Means of collection" refers to the processes and methods designed to obtain necessary information from users and incorporate it into the system.

[0813] "Means of analysis and generation" refers to methods and technologies for processing and analyzing acquired data to create expenditure management and investment proposals tailored to the user.

[0814] "Means of adjusting financial activities" refer to processes and methods for optimizing spending and investment behavior in accordance with the user's emotional state.

[0815] This invention operates a system based on the user's emotional state and risk tolerance in order to optimize the user's asset management and investment recommendations. Specifically, the user inputs information about their emotional state and asset information into the application using a smartphone or wearable device. This information is collected through voice, text feedback, or biosensors.

[0816] The server processes information collected from users, analyzing their emotional state, market information, and asset information. An AI algorithm programmed in Python analyzes this data and generates spending management and investment suggestions tailored to the user's current financial situation. This allows for flexible strategic adjustments based on the user's emotional state.

[0817] The device visually displays the generated suggestions to the user and, if necessary, explains the content of the suggestions using an interactive assistant function. As a practical example, if the user is feeling stressed, the system may suggest increasing the proportion of investment in safe assets and encourage them to curb spending.

[0818] Examples of prompt statements are as follows:

[0819] "Please rate your current emotional state on a scale of 1 to 10. How do you feel about your recent spending?"

[0820] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0821] Step 1:

[0822] Users input asset information, investment goals, and emotional states using smartphones or wearable devices. This information is collected through text feedback, voice input, or biosensors. The entered data is stored in the system's database.

[0823] Step 2:

[0824] The server retrieves the user's asset information, emotional state, and market information from a database. Based on the retrieved data, it analyzes the user's psychological state and current financial situation. This uses an AI algorithm programmed in Python to perform a risk assessment tailored to the user's emotions.

[0825] Step 3:

[0826] Based on the analysis results, the server uses a generated AI model to create personalized spending management and investment suggestions for each user. Specifically, if a user is feeling stressed, it will suggest investing in safe assets, and if they are feeling uplifted, it will suggest new investment opportunities. These suggestions are generated as digital information within the system.

[0827] Step 4:

[0828] The terminal visually displays generated investment proposals and expenditure management plans to the user. The proposals are presented in an easy-to-understand format through the user interface, and an interactive assistant provides further explanations of the proposals as needed.

[0829] Step 5:

[0830] Users can provide feedback on the proposed plan, and the server will further adjust the system based on that feedback. This feedback will be used for the next data analysis, leading to improved system accuracy.

[0831] 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.

[0832] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0833] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0834] 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.

[0835] Figure 9 shows an 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.

[0836] 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.

[0837] 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.

[0838] 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, motorcycles, etc., 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, for example, based 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.

[0839] 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."

[0840] 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.

[0841] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0842] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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 the like 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.

[0851] 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.

[0852] The following is further disclosed regarding the embodiments described above.

[0853] (Claim 1)

[0854] Means for collecting user asset information and market data,

[0855] A means of analyzing collected data and generating investment proposals optimized for individual users,

[0856] A means of monitoring market volatility and fluctuations in economic indicators, and recommending risk management strategies,

[0857] A means of analyzing financial data for small and medium-sized enterprises and generating management strategy reports,

[0858] A means of providing interactive explanations in response to user questions,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, further comprising means for generating an investment portfolio based on the user's risk tolerance.

[0862] (Claim 3)

[0863] The system according to claim 1, further comprising means for updating investment proposals in real time based on market fluctuation information.

[0864] "Example 1"

[0865] (Claim 1)

[0866] Means for collecting information about users' assets and market data,

[0867] A means of analyzing collected data to generate optimized investment proposals for individual users,

[0868] A means of monitoring market fluctuations and economic indicators, and recommending risk management strategies,

[0869] A means of analyzing financial data for small and medium-sized businesses and generating strategic management reports,

[0870] A means of providing explanations to users' questions using an interactive assistant,

[0871] A means of updating investment proposals in real time during market fluctuations and displaying them visually to users,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, further comprising means for generating an investment portfolio based on the user's risk tolerance and adjusting the proposed portfolio in real time.

[0875] (Claim 3)

[0876] The system according to claim 1, further comprising means to help the user understand by translating complex financial terms into simpler explanations using an interactive assistant.

[0877] "Application Example 1"

[0878] (Claim 1)

[0879] Means for collecting user capital information and market conditions,

[0880] A means of analyzing collected information and generating an investment strategy optimized for each user,

[0881] A means of monitoring market fluctuations and changes in economic conditions, and recommending risk control strategies,

[0882] A means of analyzing financial information for organizations and generating management plan reports,

[0883] A means of providing interactive explanations in response to user questions,

[0884] A display method for presenting investment strategies to users in a visually easy-to-understand manner,

[0885] A means of providing asset management advice based on regional economics and infrastructure investment,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, further comprising means for generating investment allocations based on the user's risk tolerance.

[0889] (Claim 3)

[0890] The system according to claim 1, further comprising means for updating investment proposals in real time based on market fluctuation information and notifying users.

[0891] "Example 2 of combining an emotion engine"

[0892] (Claim 1)

[0893] Means for obtaining users' financial asset information, investment goals, and emotional state,

[0894] A means of analyzing acquired data and optimizing investment strategies based on emotional states,

[0895] A means of generating an investment portfolio using a generative AI model,

[0896] A means of continuously monitoring market volatility and user sentiment, and dynamically adjusting risk management strategies,

[0897] A means of visualizing investment proposals to users and explaining them through an interactive assistant,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, further comprising means for adjusting the investment portfolio based on the user's risk tolerance and emotional state.

[0901] (Claim 3)

[0902] The system according to claim 1, further comprising means for updating investment recommendations in real time based on market fluctuation information and user sentiment analysis results.

[0903] "Application example 2 when combining with an emotional engine"

[0904] (Claim 1)

[0905] Means for collecting user asset information, emotional state, and market information,

[0906] A means of analyzing collected data and generating spending management and investment suggestions optimized for individual users,

[0907] A means of monitoring market price fluctuations and changes in economic indicators, and recommending risk management strategies,

[0908] A means of managing assets in real time based on user purchasing behavior,

[0909] A means of adjusting financial activities according to the user's emotional state,

[0910] A means of providing interactive explanations in response to user inquiries,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, further comprising means for generating an investment portfolio based on the user's risk tolerance and emotional state.

[0914] (Claim 3)

[0915] The system according to claim 1, further comprising means for updating investment recommendations in real time based on market fluctuation information and emotional states. [Explanation of Symbols]

[0916] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting user asset information and market data, A means of analyzing collected data and generating investment proposals optimized for individual users, A means of monitoring market volatility and fluctuations in economic indicators, and recommending risk management strategies, A means of analyzing financial data for small and medium-sized enterprises and generating management strategy reports, A means of providing interactive explanations in response to user questions, A system that includes this.

2. The system according to claim 1, further comprising means for generating an investment portfolio based on the user's risk tolerance.

3. The system according to claim 1, further comprising means for updating investment proposals in real time based on market fluctuation information.

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