system

Abstract

Provide a system. 【Solution means】 Means for collecting user information, Means for evaluating the user's risk tolerance, Means for obtaining market data, Means for analyzing the market data to generate an investment strategy, Means for transmitting the generated investment strategy to the user's terminal, Means for automatically optimizing asset allocation, Means for sending notifications to the user, Means for executing a financial investment simulation, Means for providing the simulation results to the user, Means for receiving feedback and improving the system, Means for evaluating surplus assets based on the user's asset situation and providing individual investment proposals, A system including the above.

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, the method 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] It is difficult for novice and intermediate investors to identify the optimal investment strategy for themselves. Especially for the young generation with insufficient investment knowledge and busy individuals, the time and effort required to select a reasonable and effective investment method are a major obstacle. In such a situation, there is a problem that it is difficult to quickly respond to fluctuations in the investment market and make an optimal decision according to individual investment goals.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a system equipped with means for collecting and analyzing user data and evaluating the user's risk tolerance. Furthermore, the system includes means for acquiring market data, analyzing it, and generating individually optimized investment strategies. It also includes means for transmitting the generated strategies to the user and automatically optimizing the portfolio, enabling the user to respond quickly to market fluctuations. In addition, it supports investment activities through reminders and alerts, runs investment simulations, and provides the user with the results, allowing even novice investors to invest with confidence.

[0006] A "user" is an individual or legal entity that uses the system to conduct investment activities.

[0007] "Data" refers to a series of numerical and textual data, including basic user information, past transaction history, asset status, and market information.

[0008] "Risk tolerance" is a criterion that indicates how much risk a user is willing to accept.

[0009] "Market data" refers to information about market trends, such as stock prices, commodity prices, and economic indicators.

[0010] "Analysis" is the process of analyzing collected data using statistical methods and machine learning algorithms.

[0011] An "investment strategy" is a plan of investment activities formulated based on the user's risk tolerance and the results of market analysis.

[0012] A "terminal" refers to a computer device or smartphone, or any other device, that a user uses to receive information.

[0013] A "portfolio" is a combination of financial assets owned by a user, and its composition is strategically optimized.

[0014] A "reminder" is a function for notifying users of the progress of investments and important timings.

[0015] An "alert" is a function for providing users with urgent notifications regarding market fluctuations and risks.

[0016] A "simulation" is a process that reproduces investment behaviors and market movements under virtual conditions and provides users with prediction results.

[0017] "Feedback" refers to evaluations and opinions on the system provided by users, and is information utilized for system improvement.

Brief Description of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0019] 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.

[0020] First, the language used in the following description will be described.

[0021] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0022] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention is a system designed to support investment activities, aiming to provide investment strategies tailored to individual user needs by utilizing collected data and market information. This system automates information provision and processes to enable users to conduct investment activities efficiently and effectively without feeling burdened.

[0040] The server can retrieve basic user information, past transaction history, and current asset status from a database and update them as needed. To assess the user's risk tolerance, the server creates a risk profile based on past investment performance and questionnaire responses.

[0041] Next, the server acquires market data in real time. The acquired data includes information such as stock prices, commodity prices, and economic indicators, and the server applies machine learning algorithms to analyze this data. Through this analysis, the server grasps current market trends and generates investment strategies based on them.

[0042] The generated investment strategy is sent to the user's device. The user can then view the proposed strategy in a visualized format on their device. For example, a user with a low risk tolerance may be shown investment options that prioritize safety, such as stable bonds or index funds.

[0043] Subsequently, the server activates its automated portfolio optimization function, determining the optimal asset allocation while considering the user's investment goals and market trends. This entire process is automated, allowing users to achieve optimized asset management without any effort on their part.

[0044] Furthermore, the server sends reminders and alerts to the user's device as needed, informing them of important market fluctuations and investment progress. For example, an alert is sent immediately when a specific price change occurs, allowing the user to respond quickly.

[0045] The system also runs investment simulations based on the user's points. This allows users to check investment results under hypothetical conditions in advance, enabling them to engage in actual investment activities with confidence. For example, the system could simulate a scenario where an annual profit of 5% is expected and report the results to the user.

[0046] Finally, users send feedback on the information and suggestions provided to the server. The server collects and analyzes this feedback and uses it to improve future suggestions and features. In this way, the system can constantly evolve and continuously improve the value provided to users.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server retrieves the user's basic information, past transaction history, and asset status from the database. This allows the server to understand the user's investment environment.

[0050] Step 2:

[0051] The server evaluates the user's risk tolerance and creates a profile based on their transaction history and survey results. This profile plays a crucial role in future recommendations.

[0052] Step 3:

[0053] The server retrieves market-related information in real time from external market data providers. This includes stock prices, commodity prices, and economic indicators.

[0054] Step 4:

[0055] The server analyzes the collected market data using machine learning algorithms. The purpose of the analysis is to predict current market trends.

[0056] Step 5:

[0057] The generating AI creates an investment strategy tailored to the user based on the analysis results and user profile. This strategy is customized.

[0058] Step 6:

[0059] The server sends the generated investment strategy to the user's terminal and notifies the user. The user can then review it on their terminal and evaluate the investment based on the strategy.

[0060] Step 7:

[0061] The server uses an automated portfolio optimization function to optimize the user's investment assets based on collected data and market trends. This process is fully automated.

[0062] Step 8:

[0063] The server sends reminders and alerts to the user's device when it detects progress toward investment targets or significant market fluctuations, in order to encourage prompt action.

[0064] Step 9:

[0065] The server runs an investment simulation based on the user's points. This simulation is conducted under hypothetical conditions and provides predictions to mitigate risk.

[0066] Step 10:

[0067] The server analyzes the simulation results and provides them to the user in report format. The user can then use this information to make actual investment decisions.

[0068] Step 11:

[0069] Users provide feedback on the information and suggestions they receive, and the server analyzes this feedback and uses it to improve the system. This feedback cycle allows the system to continuously evolve.

[0070] (Example 1)

[0071] 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."

[0072] There is a need to efficiently and effectively support users' investment activities by maximizing the effectiveness of asset management and providing investment strategies tailored to individual needs quickly and accurately. However, current systems have challenges such as the inability to analyze economic data in real time and the difficulty in providing optimal asset allocations based on users' risk tolerance.

[0073] 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.

[0074] In this invention, the server includes means for collecting user information, means for evaluating risk tolerance, and means for analyzing economic data to generate an asset utilization plan. This makes it possible to generate and provide asset management strategies tailored to individual needs in real time.

[0075] "Means of collecting user information" refers to functions that retrieve basic user profiles, transaction history, asset information, etc., from databases, etc., and update them as needed.

[0076] "Methods for evaluating risk tolerance" refer to a process of analyzing users' past performance data and survey responses to determine the level of risk they can tolerate.

[0077] "Means of acquiring economic data" refers to interfaces and APIs for obtaining relevant market information such as stock prices, commodity prices, and economic indicators in real time.

[0078] "A means of analyzing economic data to generate asset utilization plans" refers to a function that applies machine learning algorithms based on acquired market information to create optimal investment policies and strategies.

[0079] A "means for automatically optimizing asset allocation" is a mechanism that efficiently adjusts asset allocation, taking into account the user's goals and market trends.

[0080] "Means of sending notifications and warnings" refers to a system that sends messages to a device to inform it of market fluctuations or unexpected events based on conditions specified by the user.

[0081] "Means for conducting asset management trials" refers to a function that predicts results in a virtual investment environment through simulation and presents users with risk and profit scenarios.

[0082] "Means of receiving feedback and improving the system" refers to the process of collecting feedback from users and using that feedback to improve the system and enhance future services.

[0083] "Methods for conducting market analysis using learning algorithms" refers to techniques that use machine learning methods to analyze market trends and extract useful insights from the data.

[0084] A "means for providing real-time optimization feedback" is a mechanism that immediately uses the obtained analysis results to quickly provide information that influences users' investment behavior.

[0085] This invention is a system designed to support users' asset management activities, and in particular aims to provide investment strategies tailored to the individual needs of each user. The system is configured to enable users to manage their assets efficiently and effectively by automating information provision and processes.

[0086] The server retrieves basic user information, past transaction history, and current asset status from the database and updates it as needed. A common relational database management system (RDBMS) is used for the database implementation. Based on this data, the server assesses the user's risk tolerance. This assessment uses statistical methods and machine learning algorithms to analyze historical data and user questionnaire responses to create a risk profile.

[0087] Next, the server acquires economic data. It utilizes APIs (e.g., financial information services) to obtain real-time market information such as stock prices, commodity prices, and economic indicators. Based on the acquired market data, the server applies machine learning algorithms (e.g., using TENSORFLOW® or Scikit-learn) to generate an asset utilization plan. The generated plan is customized based on the user's needs to propose the optimal investment strategy.

[0088] The generated investment strategy is sent to the user's device, and the user can review the strategy in a visualized form through a GUI interface. For example, for users with low risk tolerance, stable bond and fund investments are suggested. This interface is designed to be easily understood by the user and includes interactive elements.

[0089] Furthermore, the server has a feature that automatically optimizes the portfolio. This includes calculations using efficient frontier theory to suggest the optimal asset allocation that takes into account the user's goals and market trends.

[0090] In addition, the server sends notifications and alerts in response to significant market changes and the progress of the user's portfolio. This allows users to respond to market fluctuations in real time. For example, an alert is sent when a stock exceeds a certain price, enabling a quick response.

[0091] The system also includes an investment simulation function that simulates hypothetical investment scenarios based on user settings. Users can check the investment results under hypothetical conditions in advance. For example, it could simulate a scenario assuming a 5% annual profit and provide the predicted results as a report.

[0092] Finally, users can send feedback to the server regarding the information and suggestions provided. The server collects and analyzes this feedback to help improve future suggestions and features. This feedback loop allows the system to constantly evolve and enhance the added value it provides to users.

[0093] An example of a prompt would be, "For an investor with a moderate risk tolerance, what is the optimal investment strategy you would suggest based on current market trends?"

[0094] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0095] Step 1:

[0096] The server retrieves basic user information, past transaction history, and current asset status from the database. The input is the user ID, and the output is a set of data related to the user. This data retrieval helps understand the user's investment behavior and prepares it for use in the next analysis step. The server executes database queries and stores the retrieved data in memory as objects.

[0097] Step 2:

[0098] The server assesses the user's risk tolerance. This step takes historical performance data and survey responses as input and generates a profile that quantifies risk tolerance as output. This involves analyzing the data using statistical methods and machine learning models to calculate a specific score.

[0099] Step 3:

[0100] The server retrieves economic data using an API from a financial information service. Input is an API request, and output is real-time market data such as stock prices, commodity prices, and economic indicators. The server retrieves market data and either registers it in a database or stores it in a cache.

[0101] Step 4:

[0102] The server applies machine learning algorithms based on acquired economic data to generate an asset utilization plan. The input is economic data and risk profiles, and the output is an investment strategy tailored to the user. Using TensorFlow and Scikit-learn, it performs time series analysis and predictive models to calculate the optimal investment strategy at the current time.

[0103] Step 5:

[0104] The server sends the generated investment strategy to the user's terminal. The input is the generated investment strategy information, and the output is a visualized investment strategy. For visualization, the data is converted to XML or JSON format, sent to the user's terminal over the network, and the terminal uses a GUI to visualize it.

[0105] Step 6:

[0106] The server automatically optimizes the portfolio. The input is the current asset allocation and target performance, and the output is the optimized asset allocation ratio. This involves running simulations using algorithmic methods to search for the optimal solution and adjust the asset allocation accordingly.

[0107] Step 7:

[0108] The server sends notifications and alerts to users in response to significant market fluctuations and progress. Inputs are market data and trigger conditions, and output is alert notifications to users. When the specified conditions are met, it generates a notification message and sends it to the user via push notification or email.

[0109] Step 8:

[0110] The server runs investment simulations based on the user's settings. The input is the simulation parameters, and the output is the virtual operational result. It performs Monte Carlo simulations and other methods, and provides the results to the user in a visualized form, such as text or graphs.

[0111] Step 9:

[0112] Users send feedback to the server regarding the information provided. The input is user feedback, and the output is insights useful for future suggestions and system improvements. By collecting feedback data, performing data mining analysis, and accumulating and utilizing the results, system improvements are achieved.

[0113] (Application Example 1)

[0114] 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."

[0115] Users often find it difficult to obtain appropriate investment strategies tailored to their individual asset situation and spending habits in real time, which can make asset optimization a burden. Furthermore, there is a lack of systems that allow users to consider their spending habits and effectively convert surplus funds into investments. Therefore, there is a need for convenient asset management integrated with commonly used electronic payment services, along with the provision of investment strategies tailored to individual circumstances.

[0116] 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.

[0117] In this invention, the server includes means for collecting user information, means for evaluating the user's risk tolerance, and means for acquiring market data. This makes it possible to evaluate surplus assets based on the user's spending trends and provide personalized investment suggestions in real time.

[0118] "User information" refers to data on basic information, transaction history, asset status, and spending trends of individual users who engage in investment activities.

[0119] "Risk tolerance" is an indicator that shows the range of risk a user can tolerate in their investments, and it is a factor that influences the formulation of investment strategies.

[0120] "Market data" refers to data that includes information on trends in financial markets such as stock prices, commodity prices, and economic indicators.

[0121] An "investment strategy" is a plan formulated based on market data to optimally manage a user's assets.

[0122] "Asset allocation" is the process of deciding how to allocate a user's assets across different investment targets.

[0123] A "notification" is a warning or announcement sent to a device to inform the user of important information.

[0124] A "financial investment simulation" is a simulated investment operation used to predict investment results under conditions assumed by the user and to evaluate their impact.

[0125] "Surplus assets" refer to the portion of funds available for investment after considering the user's income and expenses.

[0126] This invention is a system for supporting users' asset management and investment activities. Expenditure data obtained from users' terminals through electronic payment services is collected by a central server, and analyzed based on each user's asset status and spending trends.

[0127] The server provides investment strategies generated based on the user's basic information, risk tolerance, and market data. This system can provide users with reminders and alerts via popular devices such as smartphones, enabling them to quickly understand their surplus assets and receive optimized investment suggestions.

[0128] This system utilizes smartphones as hardware. The software environment uses React Native for application development, and Firebase for database and authentication. Python is used for analyzing spending data and generating investment strategies through machine learning.

[0129] As a concrete example, if a user's spending pattern changes and their monthly expenses decrease, a notification will be sent to the user's device suggesting that the surplus funds be automatically invested. Another example of a prompt message is, "Based on the user's spending data and market trends, please create a scenario to invest the savings from using coupons this month." This allows users to manage their assets efficiently while reducing the burden of daily asset management.

[0130] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0131] Step 1:

[0132] The server receives spending data from the user's terminal via an electronic payment service. The input is the user's spending information, and the server records this data in a database.

[0133] Step 2:

[0134] The server analyzes collected user spending data to understand the user's asset status and spending trends. The input data consists of past spending history, which is then organized into categories for data calculation and compared with income. The output is an analyzed asset status report.

[0135] Step 3:

[0136] The server uses a machine learning model to assess the user's risk tolerance and generate a personalized investment strategy. The input is asset status reports and market data, and the output is an investment strategy tailored to the user. Specifically, it uses a generative AI model to suggest the optimal asset allocation for the user's profile.

[0137] Step 4:

[0138] The server sends the generated investment strategy to the user's terminal. The input is the generated investment strategy, and the output is the investment proposal notified to the user.

[0139] Step 5:

[0140] The user's device receives investment proposals sent from the server and displays them on the screen. The user can then review them and send feedback.

[0141] Step 6:

[0142] The server analyzes user feedback to improve the system. The input is user feedback, which is stored and analyzed as data processing, and the output is insights for system improvement.

[0143] 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.

[0144] This invention aims to provide personalized investment strategies that take into account the user's emotional state by combining an emotion engine with a system that supports investment activities. This system optimizes investment strategies for each user based on emotional data obtained from the emotion engine.

[0145] The server collects the user's basic information, past transaction history, and asset status, and stores this information in a database. Furthermore, the server assesses the user's risk tolerance and creates a profile that dynamically adjusts based on the emotional state recognized through the emotion engine. This profile also takes into account the user's current emotional tendencies.

[0146] The emotion engine recognizes the user's emotional state using user input data (e.g., text messages and voice input) and biosignals obtained from external sensors (e.g., heart rate and skin potential). This allows the system to identify the user's emotional state and prepare to provide investment strategies accordingly.

[0147] Next, the server collects market data in real time and uses machine learning algorithms to analyze market trends. This analysis is then combined with the user's profile and sentiment data to generate an investment strategy tailored to the user. This process aims to provide a safe and comfortable investment experience by considering the emotional tendencies a user exhibits under specific circumstances.

[0148] The generated investment strategy is notified to the user's device. The user then reviews the strategy in detail through their device and evaluates whether it suits them based on feedback from the sentiment engine. For example, if data indicates a preference for low-risk strategies, low-risk options will be highlighted.

[0149] Furthermore, the server activates an automatic portfolio optimization function, adjusting asset allocation based on collected market data and user sentiment data. In this way, it maintains an optimized portfolio configuration at all times and makes fine adjustments to meet the user's needs.

[0150] Furthermore, the server sends reminders and alerts to the user's device as needed, providing immediate information based on important market fluctuations and sentiment data. This feature allows users to respond quickly and ensures that emotional decisions are appropriately reflected.

[0151] Finally, the user runs an investment simulation and receives a prediction that reflects their emotional state. The simulation results are provided to the user in report format and can be used as a basis for making decisions in actual investment activities.

[0152] In this way, the system takes into account the user's emotional state and provides intuitive and efficient support for investment activities. User feedback is analyzed by the server and used to further improve the entire system.

[0153] The following describes the processing flow.

[0154] Step 1:

[0155] The server collects basic user information, past transaction history, and asset status from the database to create a dataset for initial setup. This data is important for understanding the user's investment style.

[0156] Step 2:

[0157] The server evaluates the user's risk tolerance based on their transaction history and survey data, and sets up a profile. Risk tolerance serves as the basis for determining the safety of the user's investment strategy.

[0158] Step 3:

[0159] The emotion engine receives input data from the user (e.g., text, voice) and biometric information from external devices (e.g., heart rate) to recognize the user's emotions. This allows the user's psychological state to be understood.

[0160] Step 4:

[0161] The server retrieves market information in real time from external market data providers and stores it in a database. This market information forms the basis for analysis.

[0162] Step 5:

[0163] The server analyzes collected market data using machine learning algorithms to predict trends and market changes. The analysis results are used as the basis for investment strategies.

[0164] Step 6:

[0165] The server integrates market analysis results with the user's risk profile and sentiment data to generate a personalized investment strategy. This strategy is customized to the user's investment goals and emotional state.

[0166] Step 7:

[0167] The server sends the generated investment strategy to the user's terminal and presents it in a visual format. The user can then review the strategy details and evaluate the options through their terminal.

[0168] Step 8:

[0169] The server automatically optimizes the user's investment portfolio and continuously adapts to market fluctuations. This optimization includes dynamic adjustments that take sentiment data into account.

[0170] Step 9:

[0171] The server sends reminders and alerts to user terminals when it detects progress towards investment goals or significant market fluctuations, allowing users to respond quickly.

[0172] Step 10:

[0173] The server runs investment simulations based on the user's points and provides predictive information that takes into account the user's emotional state. The simulation results can be used to assist in investment decisions.

[0174] Step 11:

[0175] Users send feedback to the server based on the information and suggestions presented. The server processes the feedback and uses it to improve the system. This allows the system to continuously evolve and improve the user experience.

[0176] (Example 2)

[0177] 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".

[0178] Current investment support systems offer uniform investment strategies without considering the user's emotional state, making it difficult to provide investment strategies optimized for individual users. Furthermore, neglecting the influence of user emotions on investment decisions can lead to high-risk decisions. This presents a challenge in providing a safe and efficient investment experience.

[0179] 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.

[0180] In this invention, the server includes means for collecting user information, means for recognizing the user's emotional state, and means for evaluating the user's risk tolerance based on the emotional state and generating a profile. This makes it possible to provide a safe and intuitive investment experience optimized for each user.

[0181] "Means of collecting user information" refers to technologies for collecting and storing basic user attribute information, past investment history, asset status, etc., in a database.

[0182] "Means for recognizing emotional states" refers to technologies that analyze text input or voice from the user, or biosignals from external sensors, to identify the user's emotional state.

[0183] "Methods for evaluating risk tolerance and generating profiles" refers to technologies that evaluate a user's tolerance for investment risk based on their emotional state and basic information, and create individual profiles.

[0184] "Methods for acquiring market data and analyzing it using machine learning algorithms" refers to technologies for analyzing numerical market data acquired in real time and predicting its trends.

[0185] "Means for generating investment strategies" refers to technologies that integrate user profiles and market data analysis results to construct investment approaches tailored to the user.

[0186] "Means of providing individual investment strategies" refers to technologies that provide customized investment strategies tailored to specific needs, based on the user's emotional state and analytical data.

[0187] "Means for automatically optimizing portfolios and adjusting asset allocation" refers to technologies that automatically adjust portfolios and mitigate risk by taking into account market conditions and user sentiment data.

[0188] "Means of sending notifications and warnings to users" refers to communication technologies that convey timely information to users based on market fluctuations or the user's emotional state.

[0189] "A means of performing investment simulations and providing results as predictions" refers to a technology that simulates future market trends based on the user's investment strategy and presents the prediction results to the user.

[0190] "Means of analyzing feedback and improving the entire system" refers to technologies for analyzing user opinions and results and improving the system based on those analyses.

[0191] This invention provides an investment support system that generates individual investment strategies that take into account the user's emotional state. The system is broadly composed of a server and a user terminal.

[0192] First, the server aggregates basic user information, past transaction history, and asset status, and stores it in a database. This data is used as the foundation for generating user profiles. In addition, to recognize the user's emotional state, text messages and voice input are collected, and the emotion engine analyzes this data. Software such as voice analysis tools and natural language processing engines are used to identify the user's emotional state.

[0193] The server then assesses the user's risk tolerance and generates a profile that reflects the user's current emotional state. This profile is used to determine the investment strategy recommended for the user.

[0194] Market data is collected in real time, and market trends are analyzed using machine learning algorithms (e.g., TensorFlow or PyTorch). This analyzed data serves as the foundation for generating investment strategies, combined with user profiles, using generative AI models.

[0195] The generated investment strategy is sent to the user's device, where they can review it. Furthermore, the server automatically optimizes the portfolio and adjusts asset allocation according to the user's risk tolerance. In addition, notifications and alerts based on significant market fluctuations and the user's emotional state are sent to support quick responses.

[0196] Users can obtain predicted investment strategies through simulations and make final investment decisions based on them. Furthermore, user feedback is analyzed to improve the system.

[0197] As a concrete example of its use, if a user inputs something like, "I'm feeling nervous today, so I want to make safe investments," the system analyzes that emotional data and proposes a low-risk investment strategy. In this way, the system provides safe and effective investment support that is tailored to the user's emotional state.

[0198] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0199] Step 1:

[0200] The server collects basic user information, past transaction history, and asset status. It receives data entered by the user from the terminal and stores it in the database. The output is a complete user profile database.

[0201] Step 2:

[0202] The server recognizes the user's emotional state. It takes text messages and voice data entered by the user into the terminal as input and analyzes them using an emotion engine. Specifically, it identifies emotions such as stress and reassurance through voice analysis and natural language processing. The output is data on the user's emotional state.

[0203] Step 3:

[0204] The server evaluates the user's risk tolerance and generates a profile. The input consists of basic information and emotional state data. As a data processing step, the correlation between emotional state and risk tolerance is evaluated, and a profile is generated. The output is the user's assigned profile.

[0205] Step 4:

[0206] The server collects and analyzes market data. The input is real-time numerical market data. Machine learning algorithms are used to perform data calculations and generate market trend predictions. The output is the analyzed market data.

[0207] Step 5:

[0208] The server generates investment strategies. The inputs are the user's profile and analyzed market data. Using a generative AI model, this data is integrated to construct an optimal investment strategy. The output is a customized investment strategy for each user.

[0209] Step 6:

[0210] The server sends the generated investment strategy to the terminal. Specifically, it delivers the information directly to the user using push notifications or email. The user can check the strategy via their terminal. The output is the investment strategy notification sent to the user.

[0211] Step 7:

[0212] The server automatically optimizes the portfolio and adjusts asset allocation. The inputs are the user's current asset status and market data. This information is compared and calculated to recalculate the optimal asset allocation. The output is the optimized portfolio.

[0213] Step 8:

[0214] The server sends notifications and warnings to the user. It generates reminders based on market fluctuations and emotional states and communicates them to the user immediately. The user receives these notifications on their device and can take appropriate action. The output consists of the reminders and alerts sent to the user.

[0215] Step 9:

[0216] The server runs an investment simulation and provides the results to the user. The input is the generated investment strategy. Using methods such as Monte Carlo simulation, it calculates predictions of risk and return. The user can view the simulation results on their terminal. The output is a prediction report presented to the user.

[0217] (Application Example 2)

[0218] 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".

[0219] In investment activities, conventional systems that provide investment strategies without considering the user's emotional state have the challenge of not always providing the optimal investment environment for the user. Furthermore, because it was difficult to appropriately recognize the user's emotional state and generate payment information and investment methods accordingly, it was difficult to realize an investment experience that matched the user's intuition.

[0220] 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.

[0221] In this invention, the server includes means for collecting user data, means for analyzing biosignals and input data using an emotion engine to recognize the user's emotional state, and means for appropriately adjusting payment information based on the user's emotional state. This makes it possible to provide investment strategies that take the user's emotional state into consideration, realizing an intuitive and secure investment experience based on emotions.

[0222] "User" refers to an individual or legal entity that conducts investment activities by using this system.

[0223] "Data" refers to all information about the user, including basic information, transaction history, asset status, and biometric signals.

[0224] "Risk tolerance" refers to a standard or indicator used to assess the degree of risk a user is willing to accept in an investment.

[0225] "Market information" refers to data on price fluctuations, commodity trends, economic indicators, etc., in financial markets.

[0226] "Investment method" refers to a strategy or technique that outlines specific investment policies and choices that users should adopt.

[0227] "Device" refers to electronic equipment used by a user to communicate with this system, and specifically includes terminals such as smartphones and computers.

[0228] "Asset allocation" refers to the act of deciding how to distribute a user's assets across different asset classes or investment destinations in what proportions.

[0229] "Notifications and warnings" refer to messages sent to users to draw their attention to important information or changes in events.

[0230] The term "emotion engine" refers to a function that recognizes a user's emotional state by analyzing biosignals and user input data.

[0231] "Biosignals" refer to physiological data obtained from the human body, such as heart rate and skin potential.

[0232] "Payment information" refers to information related to the user's payment activities, including the payment amount, payment method, and payee.

[0233] The system for implementing this invention consists of three main components: a server, a user terminal, and an emotion engine. First, the user terminal, such as a smartphone or tablet, collects user input information (text messages and voice data) and biosignals (heart rate and skin potential). This data is transmitted to the server via Bluetooth or Wi-Fi communication.

[0234] The server is built using Python and operates the emotion engine using deep learning frameworks such as TensorFlow and Keras. This emotion engine processes data sent by the user and recognizes the user's emotional state in real time. For example, by utilizing the Google® Cloud Speech-to-Text API, it can accurately convert speech data into text and analyze emotions based on that content.

[0235] Furthermore, the server collects market information and performs data analysis to generate investment strategies. This analysis constructs an optimal investment strategy that takes into account the user's current emotional state and market trends. The constructed investment strategy and payment information are sent to the user's device and displayed as notifications and alerts. This helps the user make intuitive decisions based on their emotions.

[0236] For example, if a user attempts to purchase an item from an online store and an elevated heart rate or hesitation during voice input is detected, the server will automatically reconfirm the payment amount and present relevant product information to help the user proceed with the purchase with greater confidence. An example of a prompt message generated for this process would be: "The user's heart rate is higher than normal, and they are hesitant to give instructions via voice input. Please suggest some relaxation options to provide a more comfortable experience."

[0237] In this way, by considering the user's emotional state and providing personalized investment methods and payment information tailored to their individual emotional tendencies, we support safe and secure investment activities.

[0238] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0239] Step 1:

[0240] The server receives data input from the user's device. Specifically, this includes user input data (text messages and voice data) and biosignals (heart rate and skin potential). This data is transmitted to the server via Bluetooth or Wi-Fi communication. The server temporarily stores this information in buffer memory.

[0241] Step 2:

[0242] The server uses the received data to perform sentiment analysis using an emotion engine. In this step, a deep learning model using TensorFlow or Keras analyzes the audio data, and the Google Cloud Speech-to-Text API is used to convert it to text. The input is audio data and biosignals, and the output is classification information of the user's emotional state.

[0243] Step 3:

[0244] The server automatically retrieves market information from the internet and analyzes current market trends from that data. This process utilizes machine learning algorithms to recognize patterns in the market information. The input is market information, and the output is a set of variables indicating the state of the market.

[0245] Step 4:

[0246] The server takes the emotional state classification results and a set of market state variables as input to generate an investment strategy optimized for the user. A generative AI model is used to construct a strategy that matches each user's specific situation. The output is a detailed description of the investment strategy best suited to the user.

[0247] Step 5:

[0248] The server sends the generated investment strategy to the user's terminal and displays notifications and warnings in real time. The user's terminal application displays the details of the investment strategy in the UI, and the user makes decisions based on this information. The input is the data of the generated investment strategy, and the output is the visual information provided on the user's screen.

[0249] Step 6:

[0250] Users make decisions based on the information provided and send feedback back to the server via their terminal. This feedback is used to improve the system. The input is user action and feedback based on emotions, and the output is evaluation data stored in a database on the server.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] [Second Embodiment]

[0255] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0256] 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.

[0257] 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).

[0258] 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.

[0259] 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.

[0260] 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).

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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.

[0266] 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".

[0267] This invention is a system designed to support investment activities, aiming to provide investment strategies tailored to individual user needs by utilizing collected data and market information. This system automates information provision and processes to enable users to conduct investment activities efficiently and effectively without feeling burdened.

[0268] The server can retrieve basic user information, past transaction history, and current asset status from a database and update them as needed. To assess the user's risk tolerance, the server creates a risk profile based on past investment performance and questionnaire responses.

[0269] Next, the server acquires market data in real time. The acquired data includes information such as stock prices, commodity prices, and economic indicators, and the server applies machine learning algorithms to analyze this data. Through this analysis, the server grasps current market trends and generates investment strategies based on them.

[0270] The generated investment strategy is sent to the user's device. The user can then view the proposed strategy in a visualized format on their device. For example, a user with a low risk tolerance may be shown investment options that prioritize safety, such as stable bonds or index funds.

[0271] Subsequently, the server activates its automated portfolio optimization function, determining the optimal asset allocation while considering the user's investment goals and market trends. This entire process is automated, allowing users to achieve optimized asset management without any effort on their part.

[0272] Furthermore, the server sends reminders and alerts to the user's device as needed, informing them of important market fluctuations and investment progress. For example, an alert is sent immediately when a specific price change occurs, allowing the user to respond quickly.

[0273] The system also runs investment simulations based on the user's points. This allows users to check investment results under hypothetical conditions in advance, enabling them to engage in actual investment activities with confidence. For example, the system could simulate a scenario where an annual profit of 5% is expected and report the results to the user.

[0274] Finally, users send feedback on the information and suggestions provided to the server. The server collects and analyzes this feedback and uses it to improve future suggestions and features. In this way, the system can constantly evolve and continuously improve the value provided to users.

[0275] The following describes the process flow.

[0276] Step 1:

[0277] The server retrieves the user's basic information, past transaction history, and asset status from the database. Thereby, the server grasps the user's investment environment.

[0278] Step 2:

[0279] Based on the user's transaction history and questionnaire results, the server evaluates the risk tolerance and creates a profile. This profile plays an important role in future proposals.

[0280] Step 3:

[0281] The server retrieves real-time market-related information from an external market data provider service. This includes stock prices, commodity markets, and economic indicators.

[0282] Step 4:

[0283] The server analyzes the collected market data using machine learning algorithms. The purpose of the analysis is to predict the current market trend.

[0284] Step 5:

[0285] The generative AI generates an investment strategy suitable for the user based on the analysis results and the user profile. This strategy is customized.

[0286] Step 6:

[0287] The server sends the generated investment strategy to the user's terminal and notifies the user. The user can confirm this on the terminal and evaluate the investment based on the strategy.

[0288] Step 7:

[0289] The server uses an automated portfolio optimization function to optimize the user's investment assets based on collected data and market trends. This process is fully automated.

[0290] Step 8:

[0291] The server sends reminders and alerts to the user's device when it detects progress toward investment targets or significant market fluctuations, in order to encourage prompt action.

[0292] Step 9:

[0293] The server runs an investment simulation based on the user's points. This simulation is conducted under hypothetical conditions and provides predictions to mitigate risk.

[0294] Step 10:

[0295] The server analyzes the simulation results and provides them to the user in report format. The user can then use this information to make actual investment decisions.

[0296] Step 11:

[0297] Users provide feedback on the information and suggestions they receive, and the server analyzes this feedback and uses it to improve the system. This feedback cycle allows the system to continuously evolve.

[0298] (Example 1)

[0299] 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."

[0300] It is required to efficiently and effectively support the investment activities of users by maximizing the effects of asset management and quickly and accurately providing an investment strategy tailored to individual needs. However, the current system has problems such as the inability to analyze economic data in real time and the difficulty of providing an optimal asset composition based on the risk tolerance of users.

[0301] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in the first embodiment is realized by the following means.

[0302] In this invention, the server includes means for collecting user information, means for evaluating risk tolerance, and means for analyzing economic data to generate an asset utilization plan. Thereby, it becomes possible to generate and provide an asset management strategy that matches individual needs in real time.

[0303] The "means for collecting user information" is a function for obtaining the basic profile, transaction history, asset information, etc. of the user from a database or the like and updating it as necessary.

[0304] The "means for evaluating risk tolerance" is a process for analyzing the past performance data and questionnaire answers of the user to determine the degree of risk that can be tolerated.

[0305] The "means for obtaining economic data" refers to an interface or API for obtaining relevant market information such as stock prices, commodity markets, and economic indicators in real time.

[0306] The "means for analyzing economic data to generate an asset utilization plan" is a function for applying a machine learning algorithm based on the obtained market information and creating an optimal investment policy and strategy.

[0307] The "means for automatically optimizing the asset composition" is a mechanism for efficiently adjusting the asset allocation in consideration of the user's goals and market trends.

[0308] "Means of sending notifications and warnings" refers to a system that sends messages to a device to inform it of market fluctuations or unexpected events based on conditions specified by the user.

[0309] "Means for conducting asset management trials" refers to a function that predicts results in a virtual investment environment through simulation and presents users with risk and profit scenarios.

[0310] "Means of receiving feedback and improving the system" refers to the process of collecting feedback from users and using that feedback to improve the system and enhance future services.

[0311] "Methods for conducting market analysis using learning algorithms" refers to techniques that use machine learning methods to analyze market trends and extract useful insights from the data.

[0312] A "means for providing real-time optimization feedback" is a mechanism that immediately uses the obtained analysis results to quickly provide information that influences users' investment behavior.

[0313] This invention is a system designed to support users' asset management activities, and in particular aims to provide investment strategies tailored to the individual needs of each user. The system is configured to enable users to manage their assets efficiently and effectively by automating information provision and processes.

[0314] The server retrieves basic user information, past transaction history, and current asset status from the database and updates it as needed. A common relational database management system (RDBMS) is used for the database implementation. Based on this data, the server assesses the user's risk tolerance. This assessment uses statistical methods and machine learning algorithms to analyze historical data and user questionnaire responses to create a risk profile.

[0315] Next, the server acquires economic data. It utilizes APIs (e.g., financial information services) to obtain real-time market information such as stock prices, commodity prices, and economic indicators. Based on the acquired market data, the server applies machine learning algorithms (e.g., using TensorFlow or Scikit-learn) to generate an asset utilization plan. The generated plan is customized based on the user's needs to propose the optimal investment strategy.

[0316] The generated investment strategy is sent to the user's device, and the user can review the strategy in a visualized form through a GUI interface. For example, for users with low risk tolerance, stable bond and fund investments are suggested. This interface is designed to be easily understood by the user and includes interactive elements.

[0317] Furthermore, the server has a feature that automatically optimizes the portfolio. This includes calculations using efficient frontier theory to suggest the optimal asset allocation that takes into account the user's goals and market trends.

[0318] In addition, the server sends notifications and alerts in response to significant market changes and the progress of the user's portfolio. This allows users to respond to market fluctuations in real time. For example, an alert is sent when a stock exceeds a certain price, enabling a quick response.

[0319] The system also includes an investment simulation function that simulates hypothetical investment scenarios based on user settings. Users can check the investment results under hypothetical conditions in advance. For example, it could simulate a scenario assuming a 5% annual profit and provide the predicted results as a report.

[0320] Finally, users can send feedback to the server regarding the information and suggestions provided. The server collects and analyzes this feedback to help improve future suggestions and features. This feedback loop allows the system to constantly evolve and enhance the added value it provides to users.

[0321] An example of a prompt would be, "For an investor with a moderate risk tolerance, what is the optimal investment strategy you would suggest based on current market trends?"

[0322] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0323] Step 1:

[0324] The server retrieves basic user information, past transaction history, and current asset status from the database. The input is the user ID, and the output is a set of data related to the user. This data retrieval helps understand the user's investment behavior and prepares it for use in the next analysis step. The server executes database queries and stores the retrieved data in memory as objects.

[0325] Step 2:

[0326] The server assesses the user's risk tolerance. This step takes historical performance data and survey responses as input and generates a profile that quantifies risk tolerance as output. This involves analyzing the data using statistical methods and machine learning models to calculate a specific score.

[0327] Step 3:

[0328] The server retrieves economic data using an API from a financial information service. Input is an API request, and output is real-time market data such as stock prices, commodity prices, and economic indicators. The server retrieves market data and either registers it in a database or stores it in a cache.

[0329] Step 4:

[0330] The server applies machine learning algorithms based on acquired economic data to generate an asset utilization plan. The input is economic data and risk profiles, and the output is an investment strategy tailored to the user. Using TensorFlow and Scikit-learn, it performs time series analysis and predictive models to calculate the optimal investment strategy at the current time.

[0331] Step 5:

[0332] The server sends the generated investment strategy to the user's terminal. The input is the generated investment strategy information, and the output is a visualized investment strategy. For visualization, the data is converted to XML or JSON format, sent to the user's terminal over the network, and the terminal uses a GUI to visualize it.

[0333] Step 6:

[0334] The server automatically optimizes the portfolio. The input is the current asset allocation and target performance, and the output is the optimized asset allocation ratio. This involves running simulations using algorithmic methods to search for the optimal solution and adjust the asset allocation accordingly.

[0335] Step 7:

[0336] The server sends notifications and alerts to users in response to significant market fluctuations and progress. Inputs are market data and trigger conditions, and output is alert notifications to users. When the specified conditions are met, it generates a notification message and sends it to the user via push notification or email.

[0337] Step 8:

[0338] The server runs investment simulations based on the user's settings. The input is the simulation parameters, and the output is the virtual operational result. It performs Monte Carlo simulations and other methods, and provides the results to the user in a visualized form, such as text or graphs.

[0339] Step 9:

[0340] Users send feedback to the server regarding the information provided. The input is user feedback, and the output is insights useful for future suggestions and system improvements. By collecting feedback data, performing data mining analysis, and accumulating and utilizing the results, system improvements are achieved.

[0341] (Application Example 1)

[0342] 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 glasses 214 will be referred to as the "terminal."

[0343] Users often find it difficult to obtain appropriate investment strategies tailored to their individual asset situation and spending habits in real time, which can make asset optimization a burden. Furthermore, there is a lack of systems that allow users to consider their spending habits and effectively convert surplus funds into investments. Therefore, there is a need for convenient asset management integrated with commonly used electronic payment services, along with the provision of investment strategies tailored to individual circumstances.

[0344] 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.

[0345] In this invention, the server includes means for collecting user information, means for evaluating the user's risk tolerance, and means for acquiring market data. This makes it possible to evaluate surplus assets based on the user's spending trends and provide personalized investment suggestions in real time.

[0346] "User information" refers to data on basic information, transaction history, asset status, and spending trends of individual users who engage in investment activities.

[0347] "Risk tolerance" is an indicator that shows the range of risk a user can tolerate in their investments, and it is a factor that influences the formulation of investment strategies.

[0348] "Market data" refers to data that includes information on trends in financial markets such as stock prices, commodity prices, and economic indicators.

[0349] An "investment strategy" is a plan formulated based on market data to optimally manage a user's assets.

[0350] "Asset allocation" is the process of deciding how to allocate a user's assets across different investment targets.

[0351] A "notification" is a warning or announcement sent to a device to inform the user of important information.

[0352] A "financial investment simulation" is a simulated investment operation used to predict investment results under conditions assumed by the user and to evaluate their impact.

[0353] "Surplus assets" refer to the portion of funds available for investment after considering the user's income and expenses.

[0354] This invention is a system for supporting users' asset management and investment activities. Expenditure data obtained from users' terminals through electronic payment services is collected by a central server, and analyzed based on each user's asset status and spending trends.

[0355] The server provides investment strategies generated based on the user's basic information, risk tolerance, and market data. This system can provide users with reminders and alerts via popular devices such as smartphones, enabling them to quickly understand their surplus assets and receive optimized investment suggestions.

[0356] This system utilizes smartphones as hardware. The software environment uses React Native for application development, and Firebase for database and authentication. Python is used for analyzing spending data and generating investment strategies through machine learning.

[0357] As a concrete example, if a user's spending pattern changes and their monthly expenses decrease, a notification will be sent to the user's device suggesting that the surplus funds be automatically invested. Another example of a prompt message is, "Based on the user's spending data and market trends, please create a scenario to invest the savings from using coupons this month." This allows users to manage their assets efficiently while reducing the burden of daily asset management.

[0358] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0359] Step 1:

[0360] The server receives spending data from the user's terminal via an electronic payment service. The input is the user's spending information, and the server records this data in a database.

[0361] Step 2:

[0362] The server analyzes collected user spending data to understand the user's asset status and spending trends. The input data consists of past spending history, which is then organized into categories for data calculation and compared with income. The output is an analyzed asset status report.

[0363] Step 3:

[0364] The server uses a machine learning model to assess the user's risk tolerance and generate a personalized investment strategy. The input is asset status reports and market data, and the output is an investment strategy tailored to the user. Specifically, it uses a generative AI model to suggest the optimal asset allocation for the user's profile.

[0365] Step 4:

[0366] The server sends the generated investment strategy to the user's terminal. The input is the generated investment strategy, and the output is the investment proposal notified to the user.

[0367] Step 5:

[0368] The user's device receives investment proposals sent from the server and displays them on the screen. The user can then review them and send feedback.

[0369] Step 6:

[0370] The server analyzes user feedback to improve the system. The input is user feedback, which is stored and analyzed as data processing, and the output is insights for system improvement.

[0371] 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.

[0372] This invention aims to provide personalized investment strategies that take into account the user's emotional state by combining an emotion engine with a system that supports investment activities. This system optimizes investment strategies for each user based on emotional data obtained from the emotion engine.

[0373] The server collects the user's basic information, past transaction history, and asset status, and stores this information in a database. Furthermore, the server assesses the user's risk tolerance and creates a profile that dynamically adjusts based on the emotional state recognized through the emotion engine. This profile also takes into account the user's current emotional tendencies.

[0374] The emotion engine recognizes the user's emotional state using user input data (e.g., text messages and voice input) and biosignals obtained from external sensors (e.g., heart rate and skin potential). This allows the system to identify the user's emotional state and prepare to provide investment strategies accordingly.

[0375] Next, the server collects market data in real time and uses machine learning algorithms to analyze market trends. This analysis is then combined with the user's profile and sentiment data to generate an investment strategy tailored to the user. This process aims to provide a safe and comfortable investment experience by considering the emotional tendencies a user exhibits under specific circumstances.

[0376] The generated investment strategy is notified to the user's device. The user then reviews the strategy in detail through their device and evaluates whether it suits them based on feedback from the sentiment engine. For example, if data indicates a preference for low-risk strategies, low-risk options will be highlighted.

[0377] Furthermore, the server activates an automatic portfolio optimization function, adjusting asset allocation based on collected market data and user sentiment data. In this way, it maintains an optimized portfolio configuration at all times and makes fine-tuned adjustments to meet user needs.

[0378] Furthermore, the server sends reminders and alerts to the user's device as needed, providing immediate information based on important market fluctuations and sentiment data. This feature allows users to respond quickly and ensures that emotional decisions are appropriately reflected.

[0379] Finally, the user runs an investment simulation and receives a prediction that reflects their emotional state. The simulation results are provided to the user in report format and can be used as a basis for making decisions in actual investment activities.

[0380] In this way, the system takes into account the user's emotional state and provides intuitive and efficient support for investment activities. User feedback is analyzed by the server and used to further improve the entire system.

[0381] The following describes the processing flow.

[0382] Step 1:

[0383] The server collects basic user information, past transaction history, and asset status from the database to create a dataset for initial setup. This data is important for understanding the user's investment style.

[0384] Step 2:

[0385] The server evaluates the user's risk tolerance based on their transaction history and survey data, and sets up a profile. Risk tolerance serves as the basis for determining the safety of the user's investment strategy.

[0386] Step 3:

[0387] The emotion engine receives input data from the user (e.g., text, voice) and biometric information from external devices (e.g., heart rate) to recognize the user's emotions. This allows the user's psychological state to be understood.

[0388] Step 4:

[0389] The server retrieves market information in real time from external market data providers and stores it in a database. This market information forms the basis for analysis.

[0390] Step 5:

[0391] The server analyzes collected market data using machine learning algorithms to predict trends and market changes. The analysis results are used as the basis for investment strategies.

[0392] Step 6:

[0393] The server integrates market analysis results with the user's risk profile and sentiment data to generate a personalized investment strategy. This strategy is customized to the user's investment goals and emotional state.

[0394] Step 7:

[0395] The server sends the generated investment strategy to the user's terminal and presents it in a visual format. The user can then review the strategy details and evaluate the options through their terminal.

[0396] Step 8:

[0397] The server automatically optimizes the user's investment portfolio and continuously adapts to market fluctuations. This optimization includes dynamic adjustments that take sentiment data into account.

[0398] Step 9:

[0399] The server sends reminders and alerts to user terminals when it detects progress towards investment goals or significant market fluctuations, allowing users to respond quickly.

[0400] Step 10:

[0401] The server runs investment simulations based on the user's points and provides predictive information that takes into account the user's emotional state. The simulation results can be used to assist in investment decisions.

[0402] Step 11:

[0403] Users send feedback to the server based on the information and suggestions presented. The server processes the feedback and uses it to improve the system. This allows the system to continuously evolve and improve the user experience.

[0404] (Example 2)

[0405] 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".

[0406] Current investment support systems offer uniform investment strategies without considering the user's emotional state, making it difficult to provide investment strategies optimized for individual users. Furthermore, neglecting the influence of user emotions on investment decisions can lead to high-risk decisions. This presents a challenge in providing a safe and efficient investment experience.

[0407] 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.

[0408] In this invention, the server includes means for collecting user information, means for recognizing the user's emotional state, and means for evaluating the user's risk tolerance based on the emotional state and generating a profile. This makes it possible to provide a safe and intuitive investment experience optimized for each user.

[0409] "Means of collecting user information" refers to technologies for collecting and storing basic user attribute information, past investment history, asset status, etc., in a database.

[0410] "Means for recognizing emotional states" refers to technologies that analyze text input or voice from the user, or biosignals from external sensors, to identify the user's emotional state.

[0411] "Methods for evaluating risk tolerance and generating profiles" refers to technologies that evaluate a user's tolerance for investment risk based on their emotional state and basic information, and create individual profiles.

[0412] "Methods for acquiring market data and analyzing it using machine learning algorithms" refers to technologies for analyzing numerical market data acquired in real time and predicting its trends.

[0413] "Means for generating investment strategies" refers to technologies that integrate user profiles and market data analysis results to construct investment approaches tailored to the user.

[0414] "Means of providing individual investment strategies" refers to technologies that provide customized investment strategies tailored to specific needs, based on the user's emotional state and analytical data.

[0415] "Means for automatically optimizing portfolios and adjusting asset allocation" refers to technologies that automatically adjust portfolios and mitigate risk by taking into account market conditions and user sentiment data.

[0416] "Means of sending notifications and warnings to users" refers to communication technologies that convey timely information to users based on market fluctuations or the user's emotional state.

[0417] "A means of performing investment simulations and providing results as predictions" refers to a technology that simulates future market trends based on the user's investment strategy and presents the prediction results to the user.

[0418] "Means of analyzing feedback and improving the entire system" refers to technologies for analyzing user opinions and results and improving the system based on those analyses.

[0419] This invention provides an investment support system that generates individual investment strategies that take into account the user's emotional state. The system is broadly composed of a server and a user terminal.

[0420] First, the server aggregates basic user information, past transaction history, and asset status, and stores it in a database. This data is used as the foundation for generating user profiles. In addition, to recognize the user's emotional state, text messages and voice input are collected, and the emotion engine analyzes this data. Software such as voice analysis tools and natural language processing engines are used to identify the user's emotional state.

[0421] The server then assesses the user's risk tolerance and generates a profile that reflects the user's current emotional state. This profile is used to determine the investment strategy recommended for the user.

[0422] Market data is collected in real time, and market trends are analyzed using machine learning algorithms (e.g., TensorFlow or PyTorch). This analyzed data serves as the foundation for generating investment strategies, combined with user profiles, using generative AI models.

[0423] The generated investment strategy is sent to the user's device, where they can review it. Furthermore, the server automatically optimizes the portfolio and adjusts asset allocation according to the user's risk tolerance. In addition, notifications and alerts based on significant market fluctuations and the user's emotional state are sent to support quick responses.

[0424] Users can obtain predicted investment strategies through simulations and make final investment decisions based on them. Furthermore, user feedback is analyzed to improve the system.

[0425] As a concrete example of its use, if a user inputs something like, "I'm feeling nervous today, so I want to make safe investments," the system analyzes that emotional data and proposes a low-risk investment strategy. In this way, the system provides safe and effective investment support that is tailored to the user's emotional state.

[0426] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0427] Step 1:

[0428] The server collects basic user information, past transaction history, and asset status. It receives data entered by the user from the terminal and stores it in the database. The output is a complete user profile database.

[0429] Step 2:

[0430] The server recognizes the user's emotional state. It takes text messages and voice data entered by the user into the terminal as input and analyzes them using an emotion engine. Specifically, it identifies emotions such as stress and reassurance through voice analysis and natural language processing. The output is data on the user's emotional state.

[0431] Step 3:

[0432] The server evaluates the user's risk tolerance and generates a profile. The input consists of basic information and emotional state data. As a data processing step, the correlation between emotional state and risk tolerance is evaluated, and a profile is generated. The output is the user's assigned profile.

[0433] Step 4:

[0434] The server collects and analyzes market data. The input is real-time numerical market data. Machine learning algorithms are used to perform data calculations and generate market trend predictions. The output is the analyzed market data.

[0435] Step 5:

[0436] The server generates investment strategies. The inputs are the user's profile and analyzed market data. Using a generative AI model, this data is integrated to construct an optimal investment strategy. The output is a customized investment strategy for each user.

[0437] Step 6:

[0438] The server sends the generated investment strategy to the terminal. Specifically, it delivers the information directly to the user using push notifications or email. The user can check the strategy via their terminal. The output is the investment strategy notification sent to the user.

[0439] Step 7:

[0440] The server automatically optimizes the portfolio and adjusts asset allocation. The inputs are the user's current asset status and market data. This information is compared and calculated to recalculate the optimal asset allocation. The output is the optimized portfolio.

[0441] Step 8:

[0442] The server sends notifications and warnings to the user. It generates reminders based on market fluctuations and emotional states and communicates them to the user immediately. The user receives these notifications on their device and can take appropriate action. The output consists of the reminders and alerts sent to the user.

[0443] Step 9:

[0444] The server runs an investment simulation and provides the results to the user. The input is the generated investment strategy. Using methods such as Monte Carlo simulation, it calculates predictions of risk and return. The user can view the simulation results on their terminal. The output is a prediction report presented to the user.

[0445] (Application Example 2)

[0446] 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."

[0447] In investment activities, conventional systems that provide investment strategies without considering the user's emotional state have the challenge of not always providing the optimal investment environment for the user. Furthermore, because it was difficult to appropriately recognize the user's emotional state and generate payment information and investment methods accordingly, it was difficult to realize an investment experience that matched the user's intuition.

[0448] 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.

[0449] In this invention, the server includes means for collecting user data, means for analyzing biosignals and input data using an emotion engine to recognize the user's emotional state, and means for appropriately adjusting payment information based on the user's emotional state. This makes it possible to provide investment strategies that take the user's emotional state into consideration, realizing an intuitive and secure investment experience based on emotions.

[0450] "User" refers to an individual or legal entity that conducts investment activities by using this system.

[0451] "Data" refers to all information about the user, including basic information, transaction history, asset status, and biometric signals.

[0452] "Risk tolerance" refers to a standard or indicator used to assess the degree of risk a user is willing to accept in an investment.

[0453] "Market information" refers to data on price fluctuations, commodity trends, economic indicators, etc., in financial markets.

[0454] "Investment method" refers to a strategy or technique that outlines specific investment policies and choices that users should adopt.

[0455] "Device" refers to electronic equipment used by a user to communicate with this system, and specifically includes terminals such as smartphones and computers.

[0456] "Asset allocation" refers to the act of deciding how to distribute a user's assets across different asset classes or investment destinations in what proportions.

[0457] "Notifications and warnings" refer to messages sent to users to draw their attention to important information or changes in events.

[0458] The term "emotion engine" refers to a function that recognizes a user's emotional state by analyzing biosignals and user input data.

[0459] "Biosignals" refer to physiological data obtained from the human body, such as heart rate and skin potential.

[0460] "Payment information" refers to information related to the user's payment activities, including the payment amount, payment method, and payee.

[0461] The system for implementing this invention consists of three main components: a server, a user terminal, and an emotion engine. First, the user terminal, such as a smartphone or tablet, collects user input information (text messages and voice data) and biosignals (heart rate and skin potential). This data is transmitted to the server via Bluetooth or Wi-Fi communication.

[0462] The server is built using Python and operates an emotion engine using deep learning frameworks such as TensorFlow and Keras. This emotion engine processes data sent by the user and recognizes the user's emotional state in real time. For example, by utilizing the Google Cloud Speech-to-Text API, it can accurately convert speech data into text and analyze emotions based on that content.

[0463] Furthermore, the server collects market information and performs data analysis to generate investment strategies. This analysis constructs an optimal investment strategy that takes into account the user's current emotional state and market trends. The constructed investment strategy and payment information are sent to the user's device and displayed as notifications and alerts. This helps the user make intuitive decisions based on their emotions.

[0464] For example, if a user attempts to purchase an item from an online store and an elevated heart rate or hesitation during voice input is detected, the server will automatically reconfirm the payment amount and present relevant product information to help the user proceed with the purchase with greater confidence. An example of a prompt message generated for this process would be: "The user's heart rate is higher than normal, and they are hesitant to give instructions via voice input. Please suggest some relaxation options to provide a more comfortable experience."

[0465] In this way, by considering the user's emotional state and providing personalized investment methods and payment information tailored to their individual emotional tendencies, we support safe and secure investment activities.

[0466] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0467] Step 1:

[0468] The server receives data input from the user's device. Specifically, this includes user input data (text messages and voice data) and biosignals (heart rate and skin potential). This data is transmitted to the server via Bluetooth or Wi-Fi communication. The server temporarily stores this information in buffer memory.

[0469] Step 2:

[0470] The server uses the received data to perform sentiment analysis using an emotion engine. In this step, a deep learning model using TensorFlow or Keras analyzes the audio data, and the Google Cloud Speech-to-Text API is used to convert it to text. The input is audio data and biosignals, and the output is classification information of the user's emotional state.

[0471] Step 3:

[0472] The server automatically retrieves market information from the internet and analyzes current market trends from that data. This process utilizes machine learning algorithms to recognize patterns in the market information. The input is market information, and the output is a set of variables indicating the state of the market.

[0473] Step 4:

[0474] The server takes the emotional state classification results and a set of market state variables as input to generate an investment strategy optimized for the user. A generative AI model is used to construct a strategy that matches each user's specific situation. The output is a detailed description of the investment strategy best suited to the user.

[0475] Step 5:

[0476] The server sends the generated investment strategy to the user's terminal and displays notifications and warnings in real time. The user's terminal application displays the details of the investment strategy in the UI, and the user makes decisions based on this information. The input is the data of the generated investment strategy, and the output is the visual information provided on the user's screen.

[0477] Step 6:

[0478] Users make decisions based on the information provided and send feedback back to the server via their terminal. This feedback is used to improve the system. The input is user action and feedback based on emotions, and the output is evaluation data stored in a database on the server.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] [Third Embodiment]

[0483] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0484] 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.

[0485] 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).

[0486] 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.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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".

[0495] This invention is a system designed to support investment activities, aiming to provide investment strategies tailored to individual user needs by utilizing collected data and market information. This system automates information provision and processes to enable users to conduct investment activities efficiently and effectively without feeling burdened.

[0496] The server can retrieve basic user information, past transaction history, and current asset status from a database and update them as needed. To assess the user's risk tolerance, the server creates a risk profile based on past investment performance and questionnaire responses.

[0497] Next, the server acquires market data in real time. The acquired data includes information such as stock prices, commodity prices, and economic indicators, and the server applies machine learning algorithms to analyze this data. Through this analysis, the server grasps current market trends and generates investment strategies based on them.

[0498] The generated investment strategy is sent to the user's device. The user can then view the proposed strategy in a visualized format on their device. For example, a user with a low risk tolerance may be shown investment options that prioritize safety, such as stable bonds or index funds.

[0499] Subsequently, the server activates its automated portfolio optimization function, determining the optimal asset allocation while considering the user's investment goals and market trends. This entire process is automated, allowing users to achieve optimized asset management without any effort on their part.

[0500] Furthermore, the server sends reminders and alerts to the user's device as needed, informing them of important market fluctuations and investment progress. For example, an alert is sent immediately when a specific price change occurs, allowing the user to respond quickly.

[0501] The system also runs investment simulations based on the user's points. This allows users to check investment results under hypothetical conditions in advance, enabling them to engage in actual investment activities with confidence. For example, the system could simulate a scenario where an annual profit of 5% is expected and report the results to the user.

[0502] Finally, users send feedback on the information and suggestions provided to the server. The server collects and analyzes this feedback and uses it to improve future suggestions and features. In this way, the system can constantly evolve and continuously improve the value provided to users.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] The server retrieves the user's basic information, past transaction history, and asset status from the database. This allows the server to understand the user's investment environment.

[0506] Step 2:

[0507] The server evaluates the user's risk tolerance and creates a profile based on their transaction history and survey results. This profile plays a crucial role in future recommendations.

[0508] Step 3:

[0509] The server retrieves market-related information in real time from external market data providers. This includes stock prices, commodity prices, and economic indicators.

[0510] Step 4:

[0511] The server analyzes the collected market data using machine learning algorithms. The purpose of the analysis is to predict current market trends.

[0512] Step 5:

[0513] The generating AI creates an investment strategy tailored to the user based on the analysis results and user profile. This strategy is customized.

[0514] Step 6:

[0515] The server sends the generated investment strategy to the user's terminal and notifies the user. The user can then review it on their terminal and evaluate the investment based on the strategy.

[0516] Step 7:

[0517] The server uses an automated portfolio optimization function to optimize the user's investment assets based on collected data and market trends. This process is fully automated.

[0518] Step 8:

[0519] The server sends reminders and alerts to the user's device when it detects progress toward investment targets or significant market fluctuations, in order to encourage prompt action.

[0520] Step 9:

[0521] The server runs an investment simulation based on the user's points. This simulation is conducted under hypothetical conditions and provides predictions to mitigate risk.

[0522] Step 10:

[0523] The server analyzes the simulation results and provides them to the user in report format. The user can then use this information to make actual investment decisions.

[0524] Step 11:

[0525] Users provide feedback on the information and suggestions they receive, and the server analyzes this feedback and uses it to improve the system. This feedback cycle allows the system to continuously evolve.

[0526] (Example 1)

[0527] 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."

[0528] There is a need to efficiently and effectively support users' investment activities by maximizing the effectiveness of asset management and providing investment strategies tailored to individual needs quickly and accurately. However, current systems have challenges such as the inability to analyze economic data in real time and the difficulty in providing optimal asset allocations based on users' risk tolerance.

[0529] 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.

[0530] In this invention, the server includes means for collecting user information, means for evaluating risk tolerance, and means for analyzing economic data to generate an asset utilization plan. This makes it possible to generate and provide asset management strategies tailored to individual needs in real time.

[0531] "Means of collecting user information" refers to functions that retrieve basic user profiles, transaction history, asset information, etc., from databases, etc., and update them as needed.

[0532] "Methods for evaluating risk tolerance" refer to a process of analyzing users' past performance data and survey responses to determine the level of risk they can tolerate.

[0533] "Means of acquiring economic data" refers to interfaces and APIs for obtaining relevant market information such as stock prices, commodity prices, and economic indicators in real time.

[0534] "A means of analyzing economic data to generate asset utilization plans" refers to a function that applies machine learning algorithms based on acquired market information to create optimal investment policies and strategies.

[0535] A "means for automatically optimizing asset allocation" is a mechanism that efficiently adjusts asset allocation, taking into account the user's goals and market trends.

[0536] "Means of sending notifications and warnings" refers to a system that sends messages to a device to inform it of market fluctuations or unexpected events based on conditions specified by the user.

[0537] "Means for conducting asset management trials" refers to a function that predicts results in a virtual investment environment through simulation and presents users with risk and profit scenarios.

[0538] "Means of receiving feedback and improving the system" refers to the process of collecting feedback from users and using that feedback to improve the system and enhance future services.

[0539] "Methods for conducting market analysis using learning algorithms" refers to techniques that use machine learning methods to analyze market trends and extract useful insights from the data.

[0540] A "means for providing real-time optimization feedback" is a mechanism that immediately uses the obtained analysis results to quickly provide information that influences users' investment behavior.

[0541] This invention is a system designed to support users' asset management activities, and in particular aims to provide investment strategies tailored to the individual needs of each user. The system is configured to enable users to manage their assets efficiently and effectively by automating information provision and processes.

[0542] The server retrieves basic user information, past transaction history, and current asset status from the database and updates it as needed. A common relational database management system (RDBMS) is used for the database implementation. Based on this data, the server assesses the user's risk tolerance. This assessment uses statistical methods and machine learning algorithms to analyze historical data and user questionnaire responses to create a risk profile.

[0543] Next, the server acquires economic data. It utilizes APIs (e.g., financial information services) to obtain real-time market information such as stock prices, commodity prices, and economic indicators. Based on the acquired market data, the server applies machine learning algorithms (e.g., using TensorFlow or Scikit-learn) to generate an asset utilization plan. The generated plan is customized based on the user's needs to propose the optimal investment strategy.

[0544] The generated investment strategy is sent to the user's device, and the user can review the strategy in a visualized form through a GUI interface. For example, for users with low risk tolerance, stable bond and fund investments are suggested. This interface is designed to be easily understood by the user and includes interactive elements.

[0545] Furthermore, the server has a feature that automatically optimizes the portfolio. This includes calculations using efficient frontier theory to suggest the optimal asset allocation that takes into account the user's goals and market trends.

[0546] In addition, the server sends notifications and alerts in response to significant market changes and the progress of the user's portfolio. This allows users to respond to market fluctuations in real time. For example, an alert is sent when a stock exceeds a certain price, enabling a quick response.

[0547] The system also includes an investment simulation function that simulates hypothetical investment scenarios based on user settings. Users can check the investment results under hypothetical conditions in advance. For example, it could simulate a scenario assuming a 5% annual profit and provide the predicted results as a report.

[0548] Finally, users can send feedback to the server regarding the information and suggestions provided. The server collects and analyzes this feedback to help improve future suggestions and features. This feedback loop allows the system to constantly evolve and enhance the added value it provides to users.

[0549] An example of a prompt would be, "For an investor with a moderate risk tolerance, what is the optimal investment strategy you would suggest based on current market trends?"

[0550] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0551] Step 1:

[0552] The server retrieves basic user information, past transaction history, and current asset status from the database. The input is the user ID, and the output is a set of data related to the user. This data retrieval helps understand the user's investment behavior and prepares it for use in the next analysis step. The server executes database queries and stores the retrieved data in memory as objects.

[0553] Step 2:

[0554] The server assesses the user's risk tolerance. This step takes historical performance data and survey responses as input and generates a profile that quantifies risk tolerance as output. This involves analyzing the data using statistical methods and machine learning models to calculate a specific score.

[0555] Step 3:

[0556] The server retrieves economic data using an API from a financial information service. Input is an API request, and output is real-time market data such as stock prices, commodity prices, and economic indicators. The server retrieves market data and either registers it in a database or stores it in a cache.

[0557] Step 4:

[0558] The server applies machine learning algorithms based on acquired economic data to generate an asset utilization plan. The input is economic data and risk profiles, and the output is an investment strategy tailored to the user. Using TensorFlow and Scikit-learn, it performs time series analysis and predictive models to calculate the optimal investment strategy at the current time.

[0559] Step 5:

[0560] The server sends the generated investment strategy to the user's terminal. The input is the generated investment strategy information, and the output is a visualized investment strategy. For visualization, the data is converted to XML or JSON format, sent to the user's terminal over the network, and the terminal uses a GUI to visualize it.

[0561] Step 6:

[0562] The server automatically optimizes the portfolio. The input is the current asset allocation and target performance, and the output is the optimized asset allocation ratio. This involves running simulations using algorithmic methods to search for the optimal solution and adjust the asset allocation accordingly.

[0563] Step 7:

[0564] The server sends notifications and alerts to users in response to significant market fluctuations and progress. Inputs are market data and trigger conditions, and output is alert notifications to users. When the specified conditions are met, it generates a notification message and sends it to the user via push notification or email.

[0565] Step 8:

[0566] The server runs investment simulations based on the user's settings. The input is the simulation parameters, and the output is the virtual operational result. It performs Monte Carlo simulations and other methods, and provides the results to the user in a visualized form, such as text or graphs.

[0567] Step 9:

[0568] Users send feedback to the server regarding the information provided. The input is user feedback, and the output is insights useful for future suggestions and system improvements. By collecting feedback data, performing data mining analysis, and accumulating and utilizing the results, system improvements are achieved.

[0569] (Application Example 1)

[0570] 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."

[0571] Users often find it difficult to obtain appropriate investment strategies tailored to their individual asset situation and spending habits in real time, which can make asset optimization a burden. Furthermore, there is a lack of systems that allow users to consider their spending habits and effectively convert surplus funds into investments. Therefore, there is a need for convenient asset management integrated with commonly used electronic payment services, along with the provision of investment strategies tailored to individual circumstances.

[0572] 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.

[0573] In this invention, the server includes means for collecting user information, means for evaluating the user's risk tolerance, and means for acquiring market data. This makes it possible to evaluate surplus assets based on the user's spending trends and provide personalized investment suggestions in real time.

[0574] "User information" refers to data on basic information, transaction history, asset status, and spending trends of individual users who engage in investment activities.

[0575] "Risk tolerance" is an indicator that shows the range of risk a user can tolerate in their investments, and it is a factor that influences the formulation of investment strategies.

[0576] "Market data" refers to data that includes information on trends in financial markets such as stock prices, commodity prices, and economic indicators.

[0577] An "investment strategy" is a plan formulated based on market data to optimally manage a user's assets.

[0578] "Asset allocation" is the process of deciding how to allocate a user's assets across different investment targets.

[0579] A "notification" is a warning or announcement sent to a device to inform the user of important information.

[0580] A "financial investment simulation" is a simulated investment operation used to predict investment results under conditions assumed by the user and to evaluate their impact.

[0581] "Surplus assets" refer to the portion of funds available for investment after considering the user's income and expenses.

[0582] This invention is a system for supporting users' asset management and investment activities. Expenditure data obtained from users' terminals through electronic payment services is collected by a central server, and analyzed based on each user's asset status and spending trends.

[0583] The server provides investment strategies generated based on the user's basic information, risk tolerance, and market data. This system can provide users with reminders and alerts via popular devices such as smartphones, enabling them to quickly understand their surplus assets and receive optimized investment suggestions.

[0584] This system utilizes smartphones as hardware. The software environment uses React Native for application development, and Firebase for database and authentication. Python is used for analyzing spending data and generating investment strategies through machine learning.

[0585] As a concrete example, if a user's spending pattern changes and their monthly expenses decrease, a notification will be sent to the user's device suggesting that the surplus funds be automatically invested. Another example of a prompt message is, "Based on the user's spending data and market trends, please create a scenario to invest the savings from using coupons this month." This allows users to manage their assets efficiently while reducing the burden of daily asset management.

[0586] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0587] Step 1:

[0588] The server receives spending data from the user's terminal via an electronic payment service. The input is the user's spending information, and the server records this data in a database.

[0589] Step 2:

[0590] The server analyzes collected user spending data to understand the user's asset status and spending trends. The input data consists of past spending history, which is then organized into categories for data calculation and compared with income. The output is an analyzed asset status report.

[0591] Step 3:

[0592] The server uses a machine learning model to assess the user's risk tolerance and generate a personalized investment strategy. The input is asset status reports and market data, and the output is an investment strategy tailored to the user. Specifically, it uses a generative AI model to suggest the optimal asset allocation for the user's profile.

[0593] Step 4:

[0594] The server sends the generated investment strategy to the user's terminal. The input is the generated investment strategy, and the output is the investment proposal notified to the user.

[0595] Step 5:

[0596] The user's device receives investment proposals sent from the server and displays them on the screen. The user can then review them and send feedback.

[0597] Step 6:

[0598] The server analyzes user feedback to improve the system. The input is user feedback, which is stored and analyzed as data processing, and the output is insights for system improvement.

[0599] 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.

[0600] This invention aims to provide personalized investment strategies that take into account the user's emotional state by combining an emotion engine with a system that supports investment activities. This system optimizes investment strategies for each user based on emotional data obtained from the emotion engine.

[0601] The server collects the user's basic information, past transaction history, and asset status, and stores this information in a database. Furthermore, the server assesses the user's risk tolerance and creates a profile that dynamically adjusts based on the emotional state recognized through the emotion engine. This profile also takes into account the user's current emotional tendencies.

[0602] The emotion engine recognizes the user's emotional state using user input data (e.g., text messages and voice input) and biosignals obtained from external sensors (e.g., heart rate and skin potential). This allows the system to identify the user's emotional state and prepare to provide investment strategies accordingly.

[0603] Next, the server collects market data in real time and uses machine learning algorithms to analyze market trends. This analysis is then combined with the user's profile and sentiment data to generate an investment strategy tailored to the user. This process aims to provide a safe and comfortable investment experience by considering the emotional tendencies a user exhibits under specific circumstances.

[0604] The generated investment strategy is notified to the user's device. The user then reviews the strategy in detail through their device and evaluates whether it suits them based on feedback from the sentiment engine. For example, if data indicates a preference for low-risk strategies, low-risk options will be highlighted.

[0605] Furthermore, the server activates an automatic portfolio optimization function, adjusting asset allocation based on collected market data and user sentiment data. In this way, it maintains an optimized portfolio configuration at all times and makes fine adjustments to meet the user's needs.

[0606] Furthermore, the server sends reminders and alerts to the user's device as needed, providing immediate information based on important market fluctuations and sentiment data. This feature allows users to respond quickly and ensures that emotional decisions are appropriately reflected.

[0607] Finally, the user runs an investment simulation and receives a prediction that reflects their emotional state. The simulation results are provided to the user in report format and can be used as a basis for making decisions in actual investment activities.

[0608] In this way, the system takes into account the user's emotional state and provides intuitive and efficient support for investment activities. User feedback is analyzed by the server and used to further improve the entire system.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] The server collects basic user information, past transaction history, and asset status from the database to create a dataset for initial setup. This data is important for understanding the user's investment style.

[0612] Step 2:

[0613] The server evaluates the user's risk tolerance based on their transaction history and survey data, and sets up a profile. Risk tolerance serves as the basis for determining the safety of the user's investment strategy.

[0614] Step 3:

[0615] The emotion engine receives input data from the user (e.g., text, voice) and biometric information from external devices (e.g., heart rate) to recognize the user's emotions. This allows the user's psychological state to be understood.

[0616] Step 4:

[0617] The server retrieves market information in real time from external market data providers and stores it in a database. This market information forms the basis for analysis.

[0618] Step 5:

[0619] The server analyzes collected market data using machine learning algorithms to predict trends and market changes. The analysis results are used as the basis for investment strategies.

[0620] Step 6:

[0621] The server integrates market analysis results with the user's risk profile and sentiment data to generate a personalized investment strategy. This strategy is customized to the user's investment goals and emotional state.

[0622] Step 7:

[0623] The server sends the generated investment strategy to the user's terminal and presents it in a visual format. The user can then review the strategy details and evaluate the options through their terminal.

[0624] Step 8:

[0625] The server automatically optimizes the user's investment portfolio and continuously adapts to market fluctuations. This optimization includes dynamic adjustments that take sentiment data into account.

[0626] Step 9:

[0627] The server sends reminders and alerts to user terminals when it detects progress towards investment goals or significant market fluctuations, allowing users to respond quickly.

[0628] Step 10:

[0629] The server runs investment simulations based on the user's points and provides predictive information that takes into account the user's emotional state. The simulation results can be used to assist in investment decisions.

[0630] Step 11:

[0631] Users send feedback to the server based on the information and suggestions presented. The server processes the feedback and uses it to improve the system. This allows the system to continuously evolve and improve the user experience.

[0632] (Example 2)

[0633] 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."

[0634] Current investment support systems offer uniform investment strategies without considering the user's emotional state, making it difficult to provide investment strategies optimized for individual users. Furthermore, neglecting the influence of user emotions on investment decisions can lead to high-risk decisions. This presents a challenge in providing a safe and efficient investment experience.

[0635] 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.

[0636] In this invention, the server includes means for collecting user information, means for recognizing the user's emotional state, and means for evaluating the user's risk tolerance based on the emotional state and generating a profile. This makes it possible to provide a safe and intuitive investment experience optimized for each user.

[0637] "Means of collecting user information" refers to technologies for collecting and storing basic user attribute information, past investment history, asset status, etc., in a database.

[0638] "Means for recognizing emotional states" refers to technologies that analyze text input or voice from the user, or biosignals from external sensors, to identify the user's emotional state.

[0639] "Methods for evaluating risk tolerance and generating profiles" refers to technologies that evaluate a user's tolerance for investment risk based on their emotional state and basic information, and create individual profiles.

[0640] "Methods for acquiring market data and analyzing it using machine learning algorithms" refers to technologies for analyzing numerical market data acquired in real time and predicting its trends.

[0641] "Means for generating investment strategies" refers to technologies that integrate user profiles and market data analysis results to construct investment approaches tailored to the user.

[0642] "Means of providing individual investment strategies" refers to technologies that provide customized investment strategies tailored to specific needs, based on the user's emotional state and analytical data.

[0643] "Means for automatically optimizing portfolios and adjusting asset allocation" refers to technologies that automatically adjust portfolios and mitigate risk by taking into account market conditions and user sentiment data.

[0644] "Means of sending notifications and warnings to users" refers to communication technologies that convey timely information to users based on market fluctuations or the user's emotional state.

[0645] "A means of performing investment simulations and providing results as predictions" refers to a technology that simulates future market trends based on the user's investment strategy and presents the prediction results to the user.

[0646] "Means of analyzing feedback and improving the entire system" refers to technologies for analyzing user opinions and results and improving the system based on those analyses.

[0647] This invention provides an investment support system that generates individual investment strategies that take into account the user's emotional state. The system is broadly composed of a server and a user terminal.

[0648] First, the server aggregates basic user information, past transaction history, and asset status, and stores it in a database. This data is used as the foundation for generating user profiles. In addition, to recognize the user's emotional state, text messages and voice input are collected, and the emotion engine analyzes this data. Software such as voice analysis tools and natural language processing engines are used to identify the user's emotional state.

[0649] The server then assesses the user's risk tolerance and generates a profile that reflects the user's current emotional state. This profile is used to determine the investment strategy recommended for the user.

[0650] Market data is collected in real time, and market trends are analyzed using machine learning algorithms (e.g., TensorFlow or PyTorch). This analyzed data serves as the foundation for generating investment strategies, combined with user profiles, using generative AI models.

[0651] The generated investment strategy is sent to the user's device, where they can review it. Furthermore, the server automatically optimizes the portfolio and adjusts asset allocation according to the user's risk tolerance. In addition, notifications and alerts based on significant market fluctuations and the user's emotional state are sent to support quick responses.

[0652] Users can obtain predicted investment strategies through simulations and make final investment decisions based on them. Furthermore, user feedback is analyzed to improve the system.

[0653] As a concrete example of its use, if a user inputs something like, "I'm feeling nervous today, so I want to make safe investments," the system analyzes that emotional data and proposes a low-risk investment strategy. In this way, the system provides safe and effective investment support that is tailored to the user's emotional state.

[0654] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0655] Step 1:

[0656] The server collects basic user information, past transaction history, and asset status. It receives data entered by the user from the terminal and stores it in the database. The output is a complete user profile database.

[0657] Step 2:

[0658] The server recognizes the user's emotional state. It takes text messages and voice data entered by the user into the terminal as input and analyzes them using an emotion engine. Specifically, it identifies emotions such as stress and reassurance through voice analysis and natural language processing. The output is data on the user's emotional state.

[0659] Step 3:

[0660] The server evaluates the user's risk tolerance and generates a profile. The input consists of basic information and emotional state data. As a data processing step, the correlation between emotional state and risk tolerance is evaluated, and a profile is generated. The output is the user's assigned profile.

[0661] Step 4:

[0662] The server collects and analyzes market data. The input is real-time numerical market data. Machine learning algorithms are used to perform data calculations and generate market trend predictions. The output is the analyzed market data.

[0663] Step 5:

[0664] The server generates investment strategies. The inputs are the user's profile and analyzed market data. Using a generative AI model, this data is integrated to construct an optimal investment strategy. The output is a customized investment strategy for each user.

[0665] Step 6:

[0666] The server sends the generated investment strategy to the terminal. Specifically, it delivers the information directly to the user using push notifications or email. The user can check the strategy via their terminal. The output is the investment strategy notification sent to the user.

[0667] Step 7:

[0668] The server automatically optimizes the portfolio and adjusts asset allocation. The inputs are the user's current asset status and market data. This information is compared and calculated to recalculate the optimal asset allocation. The output is the optimized portfolio.

[0669] Step 8:

[0670] The server sends notifications and warnings to the user. It generates reminders based on market fluctuations and emotional states and communicates them to the user immediately. The user receives these notifications on their device and can take appropriate action. The output consists of the reminders and alerts sent to the user.

[0671] Step 9:

[0672] The server runs an investment simulation and provides the results to the user. The input is the generated investment strategy. Using methods such as Monte Carlo simulation, it calculates predictions of risk and return. The user can view the simulation results on their terminal. The output is a prediction report presented to the user.

[0673] (Application Example 2)

[0674] 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."

[0675] In investment activities, conventional systems that provide investment strategies without considering the user's emotional state have the challenge of not always providing the optimal investment environment for the user. Furthermore, because it was difficult to appropriately recognize the user's emotional state and generate payment information and investment methods accordingly, it was difficult to realize an investment experience that matched the user's intuition.

[0676] 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.

[0677] In this invention, the server includes means for collecting user data, means for analyzing biosignals and input data using an emotion engine to recognize the user's emotional state, and means for appropriately adjusting payment information based on the user's emotional state. This makes it possible to provide investment strategies that take the user's emotional state into consideration, realizing an intuitive and secure investment experience based on emotions.

[0678] "User" refers to an individual or legal entity that conducts investment activities by using this system.

[0679] "Data" refers to all information about the user, including basic information, transaction history, asset status, and biometric signals.

[0680] "Risk tolerance" refers to a standard or indicator used to assess the degree of risk a user is willing to accept in an investment.

[0681] "Market information" refers to data on price fluctuations, commodity trends, economic indicators, etc., in financial markets.

[0682] "Investment method" refers to a strategy or technique that outlines specific investment policies and choices that users should adopt.

[0683] "Device" refers to electronic equipment used by a user to communicate with this system, and specifically includes terminals such as smartphones and computers.

[0684] "Asset allocation" refers to the act of deciding how to distribute a user's assets across different asset classes or investment destinations in what proportions.

[0685] "Notifications and warnings" refer to messages sent to users to draw their attention to important information or changes in events.

[0686] The term "emotion engine" refers to a function that recognizes a user's emotional state by analyzing biosignals and user input data.

[0687] "Biosignals" refer to physiological data obtained from the human body, such as heart rate and skin potential.

[0688] "Payment information" refers to information related to the user's payment activities, including the payment amount, payment method, and payee.

[0689] The system for implementing this invention consists of three main components: a server, a user terminal, and an emotion engine. First, the user terminal, such as a smartphone or tablet, collects user input information (text messages and voice data) and biosignals (heart rate and skin potential). This data is transmitted to the server via Bluetooth or Wi-Fi communication.

[0690] The server is built using Python and operates an emotion engine using deep learning frameworks such as TensorFlow and Keras. This emotion engine processes data sent by the user and recognizes the user's emotional state in real time. For example, by utilizing the Google Cloud Speech-to-Text API, it can accurately convert speech data into text and analyze emotions based on that content.

[0691] Furthermore, the server collects market information and performs data analysis to generate investment strategies. This analysis constructs an optimal investment strategy that takes into account the user's current emotional state and market trends. The constructed investment strategy and payment information are sent to the user's device and displayed as notifications and alerts. This helps the user make intuitive decisions based on their emotions.

[0692] For example, if a user attempts to purchase an item from an online store and an elevated heart rate or hesitation during voice input is detected, the server will automatically reconfirm the payment amount and present relevant product information to help the user proceed with the purchase with greater confidence. An example of a prompt message generated for this process would be: "The user's heart rate is higher than normal, and they are hesitant to give instructions via voice input. Please suggest some relaxation options to provide a more comfortable experience."

[0693] In this way, by considering the user's emotional state and providing personalized investment methods and payment information tailored to their individual emotional tendencies, we support safe and secure investment activities.

[0694] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0695] Step 1:

[0696] The server receives data input from the user's device. Specifically, this includes user input data (text messages and voice data) and biosignals (heart rate and skin potential). This data is transmitted to the server via Bluetooth or Wi-Fi communication. The server temporarily stores this information in buffer memory.

[0697] Step 2:

[0698] The server uses the received data to perform sentiment analysis using an emotion engine. In this step, a deep learning model using TensorFlow or Keras analyzes the audio data, and the Google Cloud Speech-to-Text API is used to convert it to text. The input is audio data and biosignals, and the output is classification information of the user's emotional state.

[0699] Step 3:

[0700] The server automatically retrieves market information from the internet and analyzes current market trends from that data. This process utilizes machine learning algorithms to recognize patterns in the market information. The input is market information, and the output is a set of variables indicating the state of the market.

[0701] Step 4:

[0702] The server takes the emotional state classification results and a set of market state variables as input to generate an investment strategy optimized for the user. A generative AI model is used to construct a strategy that matches each user's specific situation. The output is a detailed description of the investment strategy best suited to the user.

[0703] Step 5:

[0704] The server sends the generated investment strategy to the user's terminal and displays notifications and warnings in real time. The user's terminal application displays the details of the investment strategy in the UI, and the user makes decisions based on this information. The input is the data of the generated investment strategy, and the output is the visual information provided on the user's screen.

[0705] Step 6:

[0706] Users make decisions based on the information provided and send feedback back to the server via their terminal. This feedback is used to improve the system. The input is user action and feedback based on emotions, and the output is evaluation data stored in a database on the server.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] [Fourth Embodiment]

[0711] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0712] 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.

[0713] 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).

[0714] 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.

[0715] 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.

[0716] 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).

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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".

[0724] This invention is a system designed to support investment activities, aiming to provide investment strategies tailored to individual user needs by utilizing collected data and market information. This system automates information provision and processes to enable users to conduct investment activities efficiently and effectively without feeling burdened.

[0725] The server can retrieve basic user information, past transaction history, and current asset status from a database and update them as needed. To assess the user's risk tolerance, the server creates a risk profile based on past investment performance and questionnaire responses.

[0726] Next, the server acquires market data in real time. The acquired data includes information such as stock prices, commodity prices, and economic indicators, and the server applies machine learning algorithms to analyze this data. Through this analysis, the server grasps current market trends and generates investment strategies based on them.

[0727] The generated investment strategy is sent to the user's device. The user can then view the proposed strategy in a visualized format on their device. For example, a user with a low risk tolerance may be shown investment options that prioritize safety, such as stable bonds or index funds.

[0728] Subsequently, the server activates its automated portfolio optimization function, determining the optimal asset allocation while considering the user's investment goals and market trends. This entire process is automated, allowing users to achieve optimized asset management without any effort on their part.

[0729] Furthermore, the server sends reminders and alerts to the user's device as needed, informing them of important market fluctuations and investment progress. For example, an alert is sent immediately when a specific price change occurs, allowing the user to respond quickly.

[0730] The system also runs investment simulations based on the user's points. This allows users to check investment results under hypothetical conditions in advance, enabling them to engage in actual investment activities with confidence. For example, the system could simulate a scenario where an annual profit of 5% is expected and report the results to the user.

[0731] Finally, users send feedback on the information and suggestions provided to the server. The server collects and analyzes this feedback and uses it to improve future suggestions and features. In this way, the system can constantly evolve and continuously improve the value provided to users.

[0732] The following describes the processing flow.

[0733] Step 1:

[0734] The server retrieves the user's basic information, past transaction history, and asset status from the database. This allows the server to understand the user's investment environment.

[0735] Step 2:

[0736] The server evaluates the user's risk tolerance and creates a profile based on their transaction history and survey results. This profile plays a crucial role in future recommendations.

[0737] Step 3:

[0738] The server retrieves market-related information in real time from external market data providers. This includes stock prices, commodity prices, and economic indicators.

[0739] Step 4:

[0740] The server analyzes the collected market data using machine learning algorithms. The purpose of the analysis is to predict current market trends.

[0741] Step 5:

[0742] The generating AI creates an investment strategy tailored to the user based on the analysis results and user profile. This strategy is customized.

[0743] Step 6:

[0744] The server sends the generated investment strategy to the user's terminal and notifies the user. The user can then review it on their terminal and evaluate the investment based on the strategy.

[0745] Step 7:

[0746] The server uses an automated portfolio optimization function to optimize the user's investment assets based on collected data and market trends. This process is fully automated.

[0747] Step 8:

[0748] The server sends reminders and alerts to the user's device when it detects progress toward investment targets or significant market fluctuations, in order to encourage prompt action.

[0749] Step 9:

[0750] The server runs an investment simulation based on the user's points. This simulation is conducted under hypothetical conditions and provides predictions to mitigate risk.

[0751] Step 10:

[0752] The server analyzes the simulation results and provides them to the user in report format. The user can then use this information to make actual investment decisions.

[0753] Step 11:

[0754] Users provide feedback on the information and suggestions they receive, and the server analyzes this feedback and uses it to improve the system. This feedback cycle allows the system to continuously evolve.

[0755] (Example 1)

[0756] 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".

[0757] There is a need to efficiently and effectively support users' investment activities by maximizing the effectiveness of asset management and providing investment strategies tailored to individual needs quickly and accurately. However, current systems have challenges such as the inability to analyze economic data in real time and the difficulty in providing optimal asset allocations based on users' risk tolerance.

[0758] 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.

[0759] In this invention, the server includes means for collecting user information, means for evaluating risk tolerance, and means for analyzing economic data to generate an asset utilization plan. This makes it possible to generate and provide asset management strategies tailored to individual needs in real time.

[0760] "Means of collecting user information" refers to functions that retrieve basic user profiles, transaction history, asset information, etc., from databases, etc., and update them as needed.

[0761] "Methods for evaluating risk tolerance" refer to a process of analyzing users' past performance data and survey responses to determine the level of risk they can tolerate.

[0762] "Means of acquiring economic data" refers to interfaces and APIs for obtaining relevant market information such as stock prices, commodity prices, and economic indicators in real time.

[0763] "A means of analyzing economic data to generate asset utilization plans" refers to a function that applies machine learning algorithms based on acquired market information to create optimal investment policies and strategies.

[0764] A "means for automatically optimizing asset allocation" is a mechanism that efficiently adjusts asset allocation, taking into account the user's goals and market trends.

[0765] "Means of sending notifications and warnings" refers to a system that sends messages to a device to inform it of market fluctuations or unexpected events based on conditions specified by the user.

[0766] "Means for conducting asset management trials" refers to a function that predicts results in a virtual investment environment through simulation and presents users with risk and profit scenarios.

[0767] "Means of receiving feedback and improving the system" refers to the process of collecting feedback from users and using that feedback to improve the system and enhance future services.

[0768] "Methods for conducting market analysis using learning algorithms" refers to techniques that use machine learning methods to analyze market trends and extract useful insights from the data.

[0769] A "means for providing real-time optimization feedback" is a mechanism that immediately uses the obtained analysis results to quickly provide information that influences users' investment behavior.

[0770] This invention is a system designed to support users' asset management activities, and in particular aims to provide investment strategies tailored to the individual needs of each user. The system is configured to enable users to manage their assets efficiently and effectively by automating information provision and processes.

[0771] The server retrieves basic user information, past transaction history, and current asset status from the database and updates it as needed. A common relational database management system (RDBMS) is used for the database implementation. Based on this data, the server assesses the user's risk tolerance. This assessment uses statistical methods and machine learning algorithms to analyze historical data and user questionnaire responses to create a risk profile.

[0772] Next, the server acquires economic data. It utilizes APIs (e.g., financial information services) to obtain real-time market information such as stock prices, commodity prices, and economic indicators. Based on the acquired market data, the server applies machine learning algorithms (e.g., using TensorFlow or Scikit-learn) to generate an asset utilization plan. The generated plan is customized based on the user's needs to propose the optimal investment strategy.

[0773] The generated investment strategy is sent to the user's device, and the user can review the strategy in a visualized form through a GUI interface. For example, for users with low risk tolerance, stable bond and fund investments are suggested. This interface is designed to be easily understood by the user and includes interactive elements.

[0774] Furthermore, the server has a feature that automatically optimizes the portfolio. This includes calculations using efficient frontier theory to suggest the optimal asset allocation that takes into account the user's goals and market trends.

[0775] In addition, the server sends notifications and alerts in response to significant market changes and the progress of the user's portfolio. This allows users to respond to market fluctuations in real time. For example, an alert is sent when a stock exceeds a certain price, enabling a quick response.

[0776] The system also includes an investment simulation function that simulates hypothetical investment scenarios based on user settings. Users can check the investment results under hypothetical conditions in advance. For example, it could simulate a scenario assuming a 5% annual profit and provide the predicted results as a report.

[0777] Finally, users can send feedback to the server regarding the information and suggestions provided. The server collects and analyzes this feedback to help improve future suggestions and features. This feedback loop allows the system to constantly evolve and enhance the added value it provides to users.

[0778] An example of a prompt would be, "For an investor with a moderate risk tolerance, what is the optimal investment strategy you would suggest based on current market trends?"

[0779] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0780] Step 1:

[0781] The server retrieves basic user information, past transaction history, and current asset status from the database. The input is the user ID, and the output is a set of data related to the user. This data retrieval helps understand the user's investment behavior and prepares it for use in the next analysis step. The server executes database queries and stores the retrieved data in memory as objects.

[0782] Step 2:

[0783] The server assesses the user's risk tolerance. This step takes historical performance data and survey responses as input and generates a profile that quantifies risk tolerance as output. This involves analyzing the data using statistical methods and machine learning models to calculate a specific score.

[0784] Step 3:

[0785] The server retrieves economic data using an API from a financial information service. Input is an API request, and output is real-time market data such as stock prices, commodity prices, and economic indicators. The server retrieves market data and either registers it in a database or stores it in a cache.

[0786] Step 4:

[0787] The server applies machine learning algorithms based on acquired economic data to generate an asset utilization plan. The input is economic data and risk profiles, and the output is an investment strategy tailored to the user. Using TensorFlow and Scikit-learn, it performs time series analysis and predictive models to calculate the optimal investment strategy at the current time.

[0788] Step 5:

[0789] The server sends the generated investment strategy to the user's terminal. The input is the generated investment strategy information, and the output is a visualized investment strategy. For visualization, the data is converted to XML or JSON format, sent to the user's terminal over the network, and the terminal uses a GUI to visualize it.

[0790] Step 6:

[0791] The server automatically optimizes the portfolio. The input is the current asset allocation and target performance, and the output is the optimized asset allocation ratio. This involves running simulations using algorithmic methods to search for the optimal solution and adjust the asset allocation accordingly.

[0792] Step 7:

[0793] The server sends notifications and alerts to users in response to significant market fluctuations and progress. Inputs are market data and trigger conditions, and output is alert notifications to users. When the specified conditions are met, it generates a notification message and sends it to the user via push notification or email.

[0794] Step 8:

[0795] The server runs investment simulations based on the user's settings. The input is the simulation parameters, and the output is the virtual operational result. It performs Monte Carlo simulations and other methods, and provides the results to the user in a visualized form, such as text or graphs.

[0796] Step 9:

[0797] Users send feedback to the server regarding the information provided. The input is user feedback, and the output is insights useful for future suggestions and system improvements. By collecting feedback data, performing data mining analysis, and accumulating and utilizing the results, system improvements are achieved.

[0798] (Application Example 1)

[0799] 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".

[0800] Users often find it difficult to obtain appropriate investment strategies tailored to their individual asset situation and spending habits in real time, which can make asset optimization a burden. Furthermore, there is a lack of systems that allow users to consider their spending habits and effectively convert surplus funds into investments. Therefore, there is a need for convenient asset management integrated with commonly used electronic payment services, along with the provision of investment strategies tailored to individual circumstances.

[0801] 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.

[0802] In this invention, the server includes means for collecting user information, means for evaluating the user's risk tolerance, and means for acquiring market data. This makes it possible to evaluate surplus assets based on the user's spending trends and provide personalized investment suggestions in real time.

[0803] "User information" refers to data on basic information, transaction history, asset status, and spending trends of individual users who engage in investment activities.

[0804] "Risk tolerance" is an indicator that shows the range of risk a user can tolerate in their investments, and it is a factor that influences the formulation of investment strategies.

[0805] "Market data" refers to data that includes information on trends in financial markets such as stock prices, commodity prices, and economic indicators.

[0806] An "investment strategy" is a plan formulated based on market data to optimally manage a user's assets.

[0807] "Asset allocation" is the process of deciding how to allocate a user's assets across different investment targets.

[0808] A "notification" is a warning or announcement sent to a device to inform the user of important information.

[0809] A "financial investment simulation" is a simulated investment operation used to predict investment results under conditions assumed by the user and to evaluate their impact.

[0810] "Surplus assets" refer to the portion of funds available for investment after considering the user's income and expenses.

[0811] This invention is a system for supporting users' asset management and investment activities. Expenditure data obtained from users' terminals through electronic payment services is collected by a central server, and analyzed based on each user's asset status and spending trends.

[0812] The server provides investment strategies generated based on the user's basic information, risk tolerance, and market data. This system can provide users with reminders and alerts via popular devices such as smartphones, enabling them to quickly understand their surplus assets and receive optimized investment suggestions.

[0813] This system utilizes smartphones as hardware. The software environment uses React Native for application development, and Firebase for database and authentication. Python is used for analyzing spending data and generating investment strategies through machine learning.

[0814] As a concrete example, if a user's spending pattern changes and their monthly expenses decrease, a notification will be sent to the user's device suggesting that the surplus funds be automatically invested. Another example of a prompt message is, "Based on the user's spending data and market trends, please create a scenario to invest the savings from using coupons this month." This allows users to manage their assets efficiently while reducing the burden of daily asset management.

[0815] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0816] Step 1:

[0817] The server receives spending data from the user's terminal via an electronic payment service. The input is the user's spending information, and the server records this data in a database.

[0818] Step 2:

[0819] The server analyzes collected user spending data to understand the user's asset status and spending trends. The input data consists of past spending history, which is then organized into categories for data calculation and compared with income. The output is an analyzed asset status report.

[0820] Step 3:

[0821] The server uses a machine learning model to assess the user's risk tolerance and generate a personalized investment strategy. The input is asset status reports and market data, and the output is an investment strategy tailored to the user. Specifically, it uses a generative AI model to suggest the optimal asset allocation for the user's profile.

[0822] Step 4:

[0823] The server sends the generated investment strategy to the user's terminal. The input is the generated investment strategy, and the output is the investment proposal notified to the user.

[0824] Step 5:

[0825] The user's device receives investment proposals sent from the server and displays them on the screen. The user can then review them and send feedback.

[0826] Step 6:

[0827] The server analyzes user feedback to improve the system. The input is user feedback, which is stored and analyzed as data processing, and the output is insights for system improvement.

[0828] 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.

[0829] This invention aims to provide personalized investment strategies that take into account the user's emotional state by combining an emotion engine with a system that supports investment activities. This system optimizes investment strategies for each user based on emotional data obtained from the emotion engine.

[0830] The server collects the user's basic information, past transaction history, and asset status, and stores this information in a database. Furthermore, the server assesses the user's risk tolerance and creates a profile that dynamically adjusts based on the emotional state recognized through the emotion engine. This profile also takes into account the user's current emotional tendencies.

[0831] The emotion engine recognizes the user's emotional state using user input data (e.g., text messages and voice input) and biosignals obtained from external sensors (e.g., heart rate and skin potential). This allows the system to identify the user's emotional state and prepare to provide investment strategies accordingly.

[0832] Next, the server collects market data in real time and uses machine learning algorithms to analyze market trends. This analysis is then combined with the user's profile and sentiment data to generate an investment strategy tailored to the user. This process aims to provide a safe and comfortable investment experience by considering the emotional tendencies a user exhibits under specific circumstances.

[0833] The generated investment strategy is notified to the user's device. The user then reviews the strategy in detail through their device and evaluates whether it suits them based on feedback from the sentiment engine. For example, if data indicates a preference for low-risk strategies, low-risk options will be highlighted.

[0834] Furthermore, the server activates an automatic portfolio optimization function, adjusting asset allocation based on collected market data and user sentiment data. In this way, it maintains an optimized portfolio configuration at all times and makes fine adjustments to meet the user's needs.

[0835] Furthermore, the server sends reminders and alerts to the user's device as needed, providing immediate information based on important market fluctuations and sentiment data. This feature allows users to respond quickly and ensures that emotional decisions are appropriately reflected.

[0836] Finally, the user runs an investment simulation and receives a prediction that reflects their emotional state. The simulation results are provided to the user in report format and can be used as a basis for making decisions in actual investment activities.

[0837] In this way, the system takes into account the user's emotional state and provides intuitive and efficient support for investment activities. User feedback is analyzed by the server and used to further improve the entire system.

[0838] The following describes the processing flow.

[0839] Step 1:

[0840] The server collects basic user information, past transaction history, and asset status from the database to create a dataset for initial setup. This data is important for understanding the user's investment style.

[0841] Step 2:

[0842] The server evaluates the user's risk tolerance based on their transaction history and survey data, and sets up a profile. Risk tolerance serves as the basis for determining the safety of the user's investment strategy.

[0843] Step 3:

[0844] The emotion engine receives input data from the user (e.g., text, voice) and biometric information from external devices (e.g., heart rate) to recognize the user's emotions. This allows the user's psychological state to be understood.

[0845] Step 4:

[0846] The server retrieves market information in real time from external market data providers and stores it in a database. This market information forms the basis for analysis.

[0847] Step 5:

[0848] The server analyzes collected market data using machine learning algorithms to predict trends and market changes. The analysis results are used as the basis for investment strategies.

[0849] Step 6:

[0850] The server integrates market analysis results with the user's risk profile and sentiment data to generate a personalized investment strategy. This strategy is customized to the user's investment goals and emotional state.

[0851] Step 7:

[0852] The server sends the generated investment strategy to the user's terminal and presents it in a visual format. The user can then review the strategy details and evaluate the options through their terminal.

[0853] Step 8:

[0854] The server automatically optimizes the user's investment portfolio and continuously adapts to market fluctuations. This optimization includes dynamic adjustments that take sentiment data into account.

[0855] Step 9:

[0856] The server sends reminders and alerts to user terminals when it detects progress towards investment goals or significant market fluctuations, allowing users to respond quickly.

[0857] Step 10:

[0858] The server runs investment simulations based on the user's points and provides predictive information that takes into account the user's emotional state. The simulation results can be used to assist in investment decisions.

[0859] Step 11:

[0860] Users send feedback to the server based on the information and suggestions presented. The server processes the feedback and uses it to improve the system. This allows the system to continuously evolve and improve the user experience.

[0861] (Example 2)

[0862] 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".

[0863] Current investment support systems offer uniform investment strategies without considering the user's emotional state, making it difficult to provide investment strategies optimized for individual users. Furthermore, neglecting the influence of user emotions on investment decisions can lead to high-risk decisions. This presents a challenge in providing a safe and efficient investment experience.

[0864] 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.

[0865] In this invention, the server includes means for collecting user information, means for recognizing the user's emotional state, and means for evaluating the user's risk tolerance based on the emotional state and generating a profile. This makes it possible to provide a safe and intuitive investment experience optimized for each user.

[0866] "Means of collecting user information" refers to technologies for collecting and storing basic user attribute information, past investment history, asset status, etc., in a database.

[0867] "Means for recognizing emotional states" refers to technologies that analyze text input or voice from the user, or biosignals from external sensors, to identify the user's emotional state.

[0868] "Methods for evaluating risk tolerance and generating profiles" refers to technologies that evaluate a user's tolerance for investment risk based on their emotional state and basic information, and create individual profiles.

[0869] "Methods for acquiring market data and analyzing it using machine learning algorithms" refers to technologies for analyzing numerical market data acquired in real time and predicting its trends.

[0870] "Means for generating investment strategies" refers to technologies that integrate user profiles and market data analysis results to construct investment approaches tailored to the user.

[0871] "Means of providing individual investment strategies" refers to technologies that provide customized investment strategies tailored to specific needs, based on the user's emotional state and analytical data.

[0872] "Means for automatically optimizing portfolios and adjusting asset allocation" refers to technologies that automatically adjust portfolios and mitigate risk by taking into account market conditions and user sentiment data.

[0873] "Means of sending notifications and warnings to users" refers to communication technologies that convey timely information to users based on market fluctuations or the user's emotional state.

[0874] "A means of performing investment simulations and providing results as predictions" refers to a technology that simulates future market trends based on the user's investment strategy and presents the prediction results to the user.

[0875] "Means of analyzing feedback and improving the entire system" refers to technologies for analyzing user opinions and results and improving the system based on those analyses.

[0876] This invention provides an investment support system that generates individual investment strategies that take into account the user's emotional state. The system is broadly composed of a server and a user terminal.

[0877] First, the server aggregates basic user information, past transaction history, and asset status, and stores it in a database. This data is used as the foundation for generating user profiles. In addition, to recognize the user's emotional state, text messages and voice input are collected, and the emotion engine analyzes this data. Software such as voice analysis tools and natural language processing engines are used to identify the user's emotional state.

[0878] The server then assesses the user's risk tolerance and generates a profile that reflects the user's current emotional state. This profile is used to determine the investment strategy recommended for the user.

[0879] Market data is collected in real time, and market trends are analyzed using machine learning algorithms (e.g., TensorFlow or PyTorch). This analyzed data serves as the foundation for generating investment strategies, combined with user profiles, using generative AI models.

[0880] The generated investment strategy is sent to the user's device, where they can review it. Furthermore, the server automatically optimizes the portfolio and adjusts asset allocation according to the user's risk tolerance. In addition, notifications and alerts based on significant market fluctuations and the user's emotional state are sent to support quick responses.

[0881] Users can obtain predicted investment strategies through simulations and make final investment decisions based on them. Furthermore, user feedback is analyzed to improve the system.

[0882] As a concrete example of its use, if a user inputs something like, "I'm feeling nervous today, so I want to make safe investments," the system analyzes that emotional data and proposes a low-risk investment strategy. In this way, the system provides safe and effective investment support that is tailored to the user's emotional state.

[0883] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0884] Step 1:

[0885] The server collects basic user information, past transaction history, and asset status. It receives data entered by the user from the terminal and stores it in the database. The output is a complete user profile database.

[0886] Step 2:

[0887] The server recognizes the user's emotional state. It takes text messages and voice data entered by the user into the terminal as input and analyzes them using an emotion engine. Specifically, it identifies emotions such as stress and reassurance through voice analysis and natural language processing. The output is data on the user's emotional state.

[0888] Step 3:

[0889] The server evaluates the user's risk tolerance and generates a profile. The input consists of basic information and emotional state data. As a data processing step, the correlation between emotional state and risk tolerance is evaluated, and a profile is generated. The output is the user's assigned profile.

[0890] Step 4:

[0891] The server collects and analyzes market data. The input is real-time numerical market data. Machine learning algorithms are used to perform data calculations and generate market trend predictions. The output is the analyzed market data.

[0892] Step 5:

[0893] The server generates investment strategies. The inputs are the user's profile and analyzed market data. Using a generative AI model, this data is integrated to construct an optimal investment strategy. The output is a customized investment strategy for each user.

[0894] Step 6:

[0895] The server sends the generated investment strategy to the terminal. Specifically, it delivers the information directly to the user using push notifications or email. The user can check the strategy via their terminal. The output is the investment strategy notification sent to the user.

[0896] Step 7:

[0897] The server automatically optimizes the portfolio and adjusts asset allocation. The inputs are the user's current asset status and market data. This information is compared and calculated to recalculate the optimal asset allocation. The output is the optimized portfolio.

[0898] Step 8:

[0899] The server sends notifications and warnings to the user. It generates reminders based on market fluctuations and emotional states and communicates them to the user immediately. The user receives these notifications on their device and can take appropriate action. The output consists of the reminders and alerts sent to the user.

[0900] Step 9:

[0901] The server runs an investment simulation and provides the results to the user. The input is the generated investment strategy. Using methods such as Monte Carlo simulation, it calculates predictions of risk and return. The user can view the simulation results on their terminal. The output is a prediction report presented to the user.

[0902] (Application Example 2)

[0903] 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".

[0904] In investment activities, conventional systems that provide investment strategies without considering the user's emotional state have the challenge of not always providing the optimal investment environment for the user. Furthermore, because it was difficult to appropriately recognize the user's emotional state and generate payment information and investment methods accordingly, it was difficult to realize an investment experience that matched the user's intuition.

[0905] 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.

[0906] In this invention, the server includes means for collecting user data, means for analyzing biosignals and input data using an emotion engine to recognize the user's emotional state, and means for appropriately adjusting payment information based on the user's emotional state. This makes it possible to provide investment strategies that take the user's emotional state into consideration, realizing an intuitive and secure investment experience based on emotions.

[0907] "User" refers to an individual or legal entity that conducts investment activities by using this system.

[0908] "Data" refers to all information about the user, including basic information, transaction history, asset status, and biometric signals.

[0909] "Risk tolerance" refers to a standard or indicator used to assess the degree of risk a user is willing to accept in an investment.

[0910] "Market information" refers to data on price fluctuations, commodity trends, economic indicators, etc., in financial markets.

[0911] "Investment method" refers to a strategy or technique that outlines specific investment policies and choices that users should adopt.

[0912] "Device" refers to electronic equipment used by a user to communicate with this system, and specifically includes terminals such as smartphones and computers.

[0913] "Asset allocation" refers to the act of deciding how to distribute a user's assets across different asset classes or investment destinations in what proportions.

[0914] "Notifications and warnings" refer to messages sent to users to draw their attention to important information or changes in events.

[0915] The term "emotion engine" refers to a function that recognizes a user's emotional state by analyzing biosignals and user input data.

[0916] "Biosignals" refer to physiological data obtained from the human body, such as heart rate and skin potential.

[0917] "Payment information" refers to information related to the user's payment activities, including the payment amount, payment method, and payee.

[0918] The system for implementing this invention consists of three main components: a server, a user terminal, and an emotion engine. First, the user terminal, such as a smartphone or tablet, collects user input information (text messages and voice data) and biosignals (heart rate and skin potential). This data is transmitted to the server via Bluetooth or Wi-Fi communication.

[0919] The server is built using Python and operates an emotion engine using deep learning frameworks such as TensorFlow and Keras. This emotion engine processes data sent by the user and recognizes the user's emotional state in real time. For example, by utilizing the Google Cloud Speech-to-Text API, it can accurately convert speech data into text and analyze emotions based on that content.

[0920] Furthermore, the server collects market information and performs data analysis to generate investment strategies. This analysis constructs an optimal investment strategy that takes into account the user's current emotional state and market trends. The constructed investment strategy and payment information are sent to the user's device and displayed as notifications and alerts. This helps the user make intuitive decisions based on their emotions.

[0921] For example, if a user attempts to purchase an item from an online store and an elevated heart rate or hesitation during voice input is detected, the server will automatically reconfirm the payment amount and present relevant product information to help the user proceed with the purchase with greater confidence. An example of a prompt message generated for this process would be: "The user's heart rate is higher than normal, and they are hesitant to give instructions via voice input. Please suggest some relaxation options to provide a more comfortable experience."

[0922] In this way, by considering the user's emotional state and providing personalized investment methods and payment information tailored to their individual emotional tendencies, we support safe and secure investment activities.

[0923] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0924] Step 1:

[0925] The server receives data input from the user's device. Specifically, this includes user input data (text messages and voice data) and biosignals (heart rate and skin potential). This data is transmitted to the server via Bluetooth or Wi-Fi communication. The server temporarily stores this information in buffer memory.

[0926] Step 2:

[0927] The server uses the received data to perform sentiment analysis using an emotion engine. In this step, a deep learning model using TensorFlow or Keras analyzes the audio data, and the Google Cloud Speech-to-Text API is used to convert it to text. The input is audio data and biosignals, and the output is classification information of the user's emotional state.

[0928] Step 3:

[0929] The server automatically retrieves market information from the internet and analyzes current market trends from that data. This process utilizes machine learning algorithms to recognize patterns in the market information. The input is market information, and the output is a set of variables indicating the state of the market.

[0930] Step 4:

[0931] The server takes the emotional state classification results and a set of market state variables as input to generate an investment strategy optimized for the user. A generative AI model is used to construct a strategy that matches each user's specific situation. The output is a detailed description of the investment strategy best suited to the user.

[0932] Step 5:

[0933] The server sends the generated investment strategy to the user's terminal and displays notifications and warnings in real time. The user's terminal application displays the details of the investment strategy in the UI, and the user makes decisions based on this information. The input is the data of the generated investment strategy, and the output is the visual information provided on the user's screen.

[0934] Step 6:

[0935] Users make decisions based on the information provided and send feedback back to the server via their terminal. This feedback is used to improve the system. The input is user action and feedback based on emotions, and the output is evaluation data stored in a database on the server.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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."

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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.

[0955] 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.

[0956] 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.

[0957] The following is further disclosed regarding the embodiments described above.

[0958] (Claim 1)

[0959] Means of collecting user data,

[0960] A means for evaluating the user's risk tolerance,

[0961] Means of obtaining market data,

[0962] A means for analyzing the aforementioned market data to generate an investment strategy,

[0963] A means for sending the generated investment strategy to the user's terminal,

[0964] A means to automatically optimize the portfolio,

[0965] A means of sending reminders and alerts to users,

[0966] Means for performing investment simulations,

[0967] Means for providing the aforementioned simulation results to the user,

[0968] A means of receiving feedback and improving the system,

[0969] A system that includes this.

[0970] (Claim 2)

[0971] The system according to claim 1, further comprising means for analyzing the user's data to generate individual investment strategies.

[0972] (Claim 3)

[0973] The system according to claim 1, characterized in that the aforementioned investment strategy is based on market trends.

[0974] "Example 1"

[0975] (Claim 1)

[0976] Means of collecting user information,

[0977] A means for evaluating the risk tolerance of the user,

[0978] Means of obtaining economic data,

[0979] A means for analyzing the aforementioned economic data to generate an asset utilization plan,

[0980] A means for transmitting the generated asset utilization plan to the user's computer,

[0981] A means to automatically optimize asset allocation,

[0982] A means of sending notifications and warnings to users,

[0983] Means for conducting asset management trials,

[0984] Means for providing the aforementioned trial results to the user,

[0985] A means of receiving feedback and improving the system,

[0986] Methods for conducting market analysis using learning algorithms,

[0987] A means for providing real-time optimization feedback based on the aforementioned analysis results,

[0988] A system that includes this.

[0989] (Claim 2)

[0990] The system according to claim 1, further comprising means for analyzing user information to generate individual asset utilization plans.

[0991] (Claim 3)

[0992] The system according to claim 1, characterized in that the asset utilization plan is based on market trends.

[0993] "Application Example 1"

[0994] (Claim 1)

[0995] Means of collecting user information,

[0996] A means for evaluating the user's risk tolerance,

[0997] Means of obtaining market data,

[0998] A means for analyzing the aforementioned market data to generate an investment strategy,

[0999] A means for sending the generated investment strategy to the user's terminal,

[1000] A means to automatically optimize asset allocation,

[1001] A means of sending notifications to users,

[1002] Methods for performing financial investment simulations,

[1003] Means for providing the aforementioned simulation results to the user,

[1004] A means of receiving feedback and improving the system,

[1005] A means of evaluating surplus assets based on the user's asset status and providing individual investment proposals,

[1006] A system that includes this.

[1007] (Claim 2)

[1008] The system according to claim 1, further comprising means for analyzing the user's information to generate individual investment strategies.

[1009] (Claim 3)

[1010] The system according to claim 1, characterized in that the aforementioned investment strategy is based on market trends.

[1011] "Example 2 of combining an emotion engine"

[1012] (Claim 1)

[1013] Means of collecting user information,

[1014] Means for recognizing the emotional state of the user,

[1015] A means for evaluating the user's risk tolerance based on their emotional state and generating a profile,

[1016] A method for acquiring market data and analyzing it using machine learning algorithms,

[1017] A means for generating an investment strategy based on the aforementioned analysis results,

[1018] A means of integrating the generated investment strategy with the user profile and providing individualized investment strategies,

[1019] Means for transmitting the generated investment strategy to the user's device,

[1020] A means to automatically optimize the portfolio and adjust asset allocation according to the user's needs,

[1021] A means of sending notifications and warnings to users based on important market fluctuations and sentiment data,

[1022] A means of running investment simulations and providing the results as predictions,

[1023] The means of analyzing the aforementioned feedback and improving the entire system,

[1024] A system that includes this.

[1025] (Claim 2)

[1026] The system according to claim 1, further comprising means for generating dynamically adjusted individual investment strategies based on the recognition of the aforementioned emotional state and the results of market data analysis.

[1027] (Claim 3)

[1028] The system according to claim 1, characterized in that the investment strategy provides a safe and intuitive investment experience by taking into account the user's emotional state.

[1029] "Application example 2 when combining with an emotional engine"

[1030] (Claim 1)

[1031] Means of collecting user data,

[1032] A means for evaluating the user's risk tolerance,

[1033] Means of obtaining market information,

[1034] A means for analyzing the aforementioned market information to generate an investment method,

[1035] A means for transmitting the generated investment method to the user's device,

[1036] A means to automatically optimize asset allocation,

[1037] A means of sending notifications and warnings to users,

[1038] Means for performing investment simulations,

[1039] Means for providing the aforementioned simulation results to the user,

[1040] A means of receiving feedback and improving the system,

[1041] A means of recognizing a user's emotional state by analyzing biosignals and input data using an emotion engine,

[1042] A means of appropriately adjusting payment information based on the user's emotional state,

[1043] A system that includes this.

[1044] (Claim 2)

[1045] The system according to claim 1, further comprising means for analyzing the aforementioned user data to generate individual investment methods, and utilizing emotional data provided by an emotional engine.

[1046] (Claim 3)

[1047] The system according to claim 1, characterized in that the investment method is based on market trends and the emotional state of the user. [Explanation of Symbols]

[1048] 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 of collecting user information, A means for evaluating the user's risk tolerance, Means of obtaining market data, A means for analyzing the aforementioned market data to generate an investment strategy, A means for sending the generated investment strategy to the user's terminal, A means to automatically optimize asset allocation, A means of sending notifications to users, Methods for performing financial investment simulations, Means for providing the aforementioned simulation results to the user, A means of receiving feedback and improving the system, A means of evaluating surplus assets based on the user's asset status and providing individual investment proposals, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing the user's information to generate individual investment strategies.

3. The system according to claim 1, characterized in that the aforementioned investment strategy is based on market trends.

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