system

Abstract

We provide the system. [Solution] Means of obtaining information from users regarding their risk tolerance and asset goals, A means of collecting and analyzing market data in real time, A means of determining investment strategies using machine learning models based on collected data, A means for automatically adjusting a resource portfolio based on a determined investment strategy, and generating and executing buy and sell orders, A means of notifying users of the status of their asset portfolio and providing feedback via smart devices, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] Many individual investors lack expertise in asset management and thus have difficulty responding appropriately to market movements. In addition, real-time decision-making regarding investments and frequent portfolio adjustments require time and effort, and there is a problem that they are particularly likely to be influenced by emotions in unstable market situations. In such an environment, in order for individual investors to manage assets stably and effectively, a system that can respond immediately to market fluctuations without requiring specialized knowledge or effort is necessary.

Means for Solving the Problems

[0005] Note: The number in the Japanese Patent Application Publication number in the original text seems to be incorrect. It should be "82" instead of "**8**2" in the translation. You can double-check the original text for accuracy.This invention provides a system that collects and analyzes market data in real time based on user input and automatically formulates highly accurate investment strategies using machine learning models. Furthermore, this system automatically adjusts the portfolio according to the formulated strategy and appropriately executes buy and sell orders in the market. It also notifies the user of the portfolio status, makes necessary readjustments in response to market fluctuations, and automatically provides AI-generated feedback in response to questions. In this way, investors can achieve stable asset management without requiring specialized knowledge or continuous effort.

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

[0007] "Risk tolerance" is a measure that represents the degree of risk a user is willing to accept in asset management.

[0008] "Asset goals" refer to the financial objectives and targets that a user wishes to achieve through asset management.

[0009] "Market data" refers to various economic information related to asset management, such as stock prices, exchange rates, interest rates, and economic indicators.

[0010] A "machine learning model" is an algorithm that analyzes patterns based on collected data to predict future market trends and other factors.

[0011] An "investment strategy" is a plan that determines the optimal asset allocation based on market data and the user's circumstances, and outlines how to proceed with investments.

[0012] A "portfolio" refers to the entire set of assets that a user owns, combining multiple investment products.

[0013] A "buy or sell order" is an instruction issued to an exchange or broker to buy or sell an asset. [Brief explanation of the drawing]

[0014] [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 a plurality of emotions are mapped. [Figure 10] It 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 Example 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 Example 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

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] The system of this invention is designed to help users automate their asset management. The system operates primarily through a server, terminal, and user interface.

[0036] The server profiles the user's risk tolerance and asset goals based on the information provided by the user. Based on this profile, the server retrieves market data in real time from various data providers. The retrieved data is stored in a database on the server and is constantly updated to the latest state. Next, machine learning models are run to analyze the data and predict market trends.

[0037] Based on the analysis results, the server develops an optimal investment strategy for each user. This strategy includes optimizing asset allocation and adjusting the portfolio. For example, if a user specifies a moderate risk tolerance and aims for stable returns, the server will recommend a stable asset allocation of stocks and bonds and build a strategy that makes the necessary adjustments.

[0038] The device provides an interface to the user. Through the device, the user can check the status of their portfolio in real time and visually understand past performance and current asset allocation. Furthermore, when the user asks a question, the device uses AI to automatically provide an answer.

[0039] The server also automatically places buy and sell orders in the user's portfolio, ensuring that trades are executed in the market. For example, if the server predicts that a particular stock will rise, it generates an order to buy that stock. This order is executed autonomously under the user's supervision, and the timing and quantity of the buy and sell orders are determined based on a pre-configured algorithm.

[0040] In this way, a system implemented according to the embodiment of the present invention enables users to manage their assets more efficiently, control risks, and respond quickly to market changes.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user logs into the system using a terminal and enters information regarding their investment goals and risk tolerance. The terminal then sends the entered information to the server.

[0044] Step 2:

[0045] The server creates a profile based on the received user information. This profile forms the basis of the user's investment strategy and is stored in the database.

[0046] Step 3:

[0047] The server collects market data in real time from market data providers. This data includes stock prices, bond yields, exchange rates, and more. The collected data is stored in a database.

[0048] Step 4:

[0049] The server uses machine learning models to analyze collected market data. This generates investment strategy predictions based on current market conditions.

[0050] Step 5:

[0051] The server combines user profiles and market data analysis results to build a portfolio optimized for each individual user. This portfolio is automatically adjusted to maintain the optimal asset allocation for the user.

[0052] Step 6:

[0053] The server generates buy and sell orders based on the adjusted portfolio. The orders are executed at the appropriate time through the securities trading system.

[0054] Step 7:

[0055] Users can use their device to check the status of their portfolio. The device displays the latest information retrieved from the server, visually showing investment performance and current asset allocation.

[0056] Step 8:

[0057] The terminal receives questions from users and, if necessary, automatically provides answers using the server's AI capabilities. This allows users to quickly resolve their investment-related questions.

[0058] Step 9:

[0059] The server monitors market fluctuations and automatically readjusts the portfolio if necessary based on new information. The user is notified of the readjustment results.

[0060] (Example 1)

[0061] 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."

[0062] In today's complex and rapidly changing market environment, it is extremely difficult for individual investors to formulate effective investment strategies based on their own risk tolerance and asset goals, and to execute them in a timely manner. Therefore, there is a need for real-time market data analysis and automated, appropriate investment decision-making.

[0063] 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.

[0064] In this invention, the server includes means for acquiring information on risk tolerance and asset targets from users and performing profiling; means for acquiring market information in real time and storing it in a memory device; and means for predicting trends using an analytical device based on the acquired market information. This makes it possible for individual investors to easily optimize their investment strategies and accurately manage the balance between risk and return.

[0065] A "user" refers to an individual or legal entity that uses the system to manage their own assets.

[0066] "Risk tolerance" is an indicator that shows the degree to which an investor is willing to accept risk.

[0067] "Asset goals" refer to the specific financial results that a user wants to achieve through investment.

[0068] "Market information" refers to data related to financial markets, including, for example, stock prices, interest rates, and exchange rates.

[0069] "Profiling" is the process of developing an appropriate investment strategy based on a user's risk tolerance and asset goals.

[0070] "Analytical equipment" refers to hardware or software used to analyze data and predict future trends.

[0071] A "storage device" is hardware used to store data, including, for example, hard disks and SSDs.

[0072] An "investment strategy" refers to a plan for asset allocation and buying / selling, designed to achieve investment objectives.

[0073] "Means of predicting trends" refers to techniques for analyzing past and present data to predict future market changes.

[0074] The present invention is an automated platform designed to help users streamline asset management. The system primarily functions through interaction between servers, terminals, and users.

[0075] The server receives information from the user related to their risk tolerance and asset goals, and uses this information to perform profiling. Based on this profile, the server retrieves real-time market information from financial market data providers. In this data collection process, external APIs are used to store the data in storage devices. These storage devices include hard disk drives (HDDs) and solid-state drives (SSDs).

[0076] Next, the server uses machine learning frameworks, including TENSORFLOW® and PyTorch, to analyze the acquired market information. Based on the analysis results, it utilizes a generative AI model to predict future trends. Based on this prediction, the server formulates an investment strategy. This strategy includes optimal asset allocation and trading timing.

[0077] For example, if a user specifies that they "want to aim for a 10% annual growth with moderate risk," the server will use a generating AI model to calculate and recommend an appropriate combination of stocks and bonds. An example of a generated prompt message is, "Please provide the optimal asset allocation based on my risk tolerance."

[0078] The device provides users with a visual interface. Through this interface, users can check the status of their portfolio and analyze past performance. The device uses JavaScript® and React to build its dashboard, enabling interactive operation.

[0079] Furthermore, the server automatically generates and executes buy and sell orders in the market based on the devised investment strategy. The execution of trades uses highly accurate algorithms to obtain the optimal timing based on the designed algorithms. For example, if a predictive model suggests a rise in a particular stock, it generates an order to buy that stock at the appropriate time.

[0080] This invention makes it easier for users to manage their investment activities more efficiently, mitigate risks, and respond quickly to market fluctuations.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server receives input information from the user regarding their risk tolerance and asset goals. Based on this information, the server runs a profiling algorithm to generate a user profile. The user profile is output as a data structure that includes risk tolerance, target rate of return, investment period, and other information.

[0084] Step 2:

[0085] The server retrieves market information in real time from external data provider APIs. This includes stock prices, exchange rates, and interest rate information. This retrieved data is stored in storage. The input API response data is processed into a database format and updated to maintain the latest state.

[0086] Step 3:

[0087] The server uses machine learning models to analyze data based on accumulated market information. The generative AI model used here performs time-series forecasting based on pre-set parameters. As a result of this analysis, predictive data regarding future market trends is output.

[0088] Step 4:

[0089] The server develops an optimal investment strategy for each user based on the analyzed market trends. In this step, the results of the generated AI model are used to determine specific asset allocations and investment targets. As an output, a concrete action plan regarding the investment strategy is provided.

[0090] Step 5:

[0091] The server automatically generates buy and sell orders for financial instruments based on the formulated investment strategy. In this process, an algorithm determines the optimal timing and quantity for buying and selling. The generated order data is then transmitted to the market via the trading platform's API.

[0092] Step 6:

[0093] The terminal displays portfolio information provided by the server on the user interface in real time. Input information includes the current state and historical performance data of the portfolio, which are then visualized and output. Through interaction on the terminal, the user can view detailed information and apply specific filters.

[0094] (Application Example 1)

[0095] 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."

[0096] The present invention aims to improve the efficiency of automated asset management by formulating optimal investment strategies tailored to the user's risk tolerance and asset goals, as well as streamlining automated trading. Conventional methods have difficulty responding quickly to real-time market fluctuations and have limited user interfaces. Therefore, there is a need for a system that combines investment efficiency with user-friendly operation.

[0097] 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.

[0098] In this invention, the server includes means for obtaining information from the user regarding their risk tolerance and asset goals, means for collecting and analyzing market data in real time, and means for determining an investment strategy using a machine learning model based on the collected data. This enables the rapid formulation of an optimal investment strategy tailored to the user's risk tolerance and autonomous asset management in response to market fluctuations.

[0099] "Risk tolerance" is an indicator that shows the degree to which a user is willing to accept risk in asset management.

[0100] "Asset goals" refer to specific financial objectives or goals that a user hopes to achieve through asset management.

[0101] "Market data" refers to a collection of various pieces of information related to financial markets, including real-time data such as stock prices, exchange rates, and interest rates.

[0102] A "machine learning model" is an algorithm and computational model that learns patterns from data and performs prediction and classification tasks.

[0103] An "investment strategy" is a set of policies and plans for how to allocate and manage funds based on market conditions and the user's risk profile.

[0104] A "portfolio" is a collection of financial products held by a user, designed with asset allocation and risk management in mind.

[0105] "Real-time" means that information and data are processed in a way that allows them to instantly represent the current state.

[0106] "Automatic" refers to a system or device that operates independently, without requiring human intervention.

[0107] "Feedback" refers to a function in which a system automatically responds to information or questions from a user with responses or instructions.

[0108] The system implementing this invention consists of a server, a terminal, and a user interface. The server plays a central role, obtaining information from the user regarding their risk tolerance and asset goals, and profiling the user based on this information. The server obtains market data in real time from various data providers. This market data is stored in a database on the server and is constantly updated with the latest information. The server predicts market trends and develops appropriate investment strategies by running machine learning models using Python.

[0109] The terminal functions as an interface to the user, allowing them to check the status of their portfolio via their smartphone. This includes features that allow them to visually understand past performance and current asset allocation. AI running on the terminal automatically provides feedback to user questions. Furthermore, the server automatically generates buy and sell orders and executes trades based on investment strategies. This enables users to efficiently manage their assets in response to market fluctuations.

[0110] For example, if a user sets a target amount to achieve for a trip using their smartphone, the application will suggest an appropriate combination of stocks and bonds to aim for stable returns. Based on the user's set risk tolerance, the application will automatically make necessary adjustments to support asset management towards achieving the goal.

[0111] An example of a prompt to input into a generative AI model is: "The user has a moderate risk tolerance and aims for stable returns. Generate Python code that proposes the optimal investment strategy based on market data." This prompt allows the model to concretize the instructions needed to implement the logic required for the investment strategy.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server receives information about risk tolerance and asset goals transmitted from the user via a smart device. Based on this input information, the server creates a user profile and prepares the basic data necessary for asset management.

[0115] Step 2:

[0116] The server retrieves market data in real time via external data provider APIs. The retrieved data is stored in the server's database and is constantly updated to the latest state. This market data includes a variety of information related to financial markets, such as stock prices, exchange rates, and interest rates.

[0117] Step 3:

[0118] The server runs a machine learning model using Python and the scikit-learn machine learning library, based on the stored market data. This model takes market data as input and outputs market trend predictions. Data processing such as feature extraction and normalization is performed, and the model makes predictions based on the trained algorithm.

[0119] Step 4:

[0120] The server builds an investment strategy tailored to the user's profile based on predictions from machine learning models. This investment strategy determines the optimal asset allocation, taking into account predicted market trends and the user's risk tolerance.

[0121] Step 5:

[0122] The server automatically generates buy and sell orders based on the established investment strategy and executes them in the market through an intermediary. Specifically, it determines the trading volume and timing according to the set trading rules and issues orders via API. This process is performed autonomously under the user's supervision.

[0123] Step 6:

[0124] The terminal receives information from the server to display the portfolio status to the user in real time. It visually displays the portfolio's earnings and risk status, allowing the user to immediately understand the situation.

[0125] Step 7:

[0126] Users can ask the system additional questions through their device. In response to the user's questions, the on-device AI uses a generated AI model to provide automated responses based on prompts, instantly offering advice and information on asset management.

[0127] 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.

[0128] This invention is implemented as an asset management system that incorporates an emotion engine to provide more advanced support for users' investment activities. This system consists of a server, terminals, and users, and aims to optimize investment strategies, particularly by taking users' emotions into consideration.

[0129] The terminal receives input from the user and detects their emotional state regarding investments in real time. This uses technology that recognizes emotions by analyzing the user's facial expressions, tone of voice, and entered text while they are using the terminal. The emotional data is sent to a server and processed along with other investment-related data.

[0130] The server runs an emotion engine to analyze emotional data and identify the user's current emotional state. This allows it to automatically adjust investment risk tolerance if emotions such as stress or anxiety are detected. The server also uses machine learning models to generate investment strategies that consider both emotions and market data. For example, if a user's anxiety is high, the server can suggest risk-reducing options and recommend a conservative investment strategy.

[0131] Meanwhile, users can receive emotion-based investment strategies through their devices and view the current status of their portfolios in real time. For example, even if the stock market experiences a sharp decline, the emotion engine recognizes the user's anxiety and supports their calm decision-making by presenting an investment situation with a long-term outlook while lowering their risk tolerance.

[0132] The system also includes a feature where AI provides emotion-based feedback when users ask questions on their devices. For example, if a user is emotional and rushing to take profits, the AI ​​will provide advice based on a calm assessment of the situation, facilitating optimal investment decisions.

[0133] In this way, the embodiment of the present invention aims to achieve more personalized asset management by considering the influence of user emotions on investment activities. This is more user-friendly than conventional systems and is effective in pursuing optimal returns.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] Users log in to the system using a terminal and enter basic information such as investment goals, risk tolerance, and investment period. The terminal then sends the entered data to the server.

[0137] Step 2:

[0138] The device monitors the user's facial expressions and voice through a microphone and camera to recognize the user's emotional state, and analyzes the emotional data using an emotion engine. The analyzed emotional data is then sent to a server.

[0139] Step 3:

[0140] The server generates a profile based on the received user's basic information and sentiment data, and stores it in a database.

[0141] Step 4:

[0142] The server collects market data in real time from market data providers. This data includes stock price trends and exchange rates. The server stores this data in a database and updates it as needed.

[0143] Step 5:

[0144] The server analyzes collected market data and user sentiment data using machine learning models to formulate individual investment strategies. When emotions are unstable, it can design strategies that minimize risk.

[0145] Step 6:

[0146] The server automatically adjusts the portfolio based on the formulated investment strategy, taking sentiment data into consideration and dynamically modifying risk tolerance as needed.

[0147] Step 7:

[0148] The server generates buy and sell orders based on the adjusted portfolio and executes them through the appropriate securities trading platform.

[0149] Step 8:

[0150] Users can use their device to check the current status of their portfolio. The device visually displays the latest portfolio data and notifies them of any changes in their investment strategy.

[0151] Step 9:

[0152] When a user asks a question or seeks assistance on their device, the AI ​​considers the context, including emotional data, to provide appropriate feedback and advice.

[0153] Step 10:

[0154] The server continuously monitors sentiment and market data, readjusts the portfolio as needed, and notifies the user of the changes. This allows the user to continue their investment activities with peace of mind.

[0155] (Example 2)

[0156] 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 will be referred to as the "terminal."

[0157] Traditional asset management systems have limitations in mechanically generating strategies based on market data and are unable to consider the emotional state of individual users. This creates a problem where users find it difficult to make appropriate investment decisions when their emotions are running high.

[0158] 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.

[0159] In this invention, the server includes means for detecting and analyzing the user's emotional state using an emotion engine, means for determining an investment strategy using a generative model based on emotional data and market data, and means for providing the user with emotional state-based feedback using AI. This makes it possible to present a personalized investment strategy that corresponds to the user's emotional state.

[0160] "User" refers to an individual or legal entity that uses an asset management system to conduct investment activities.

[0161] "Risk tolerance" is a measure that indicates how much risk a user is willing to accept in asset management.

[0162] "Asset target" refers to the specific asset size or objective that a user aims to achieve through their investment activities.

[0163] "Market data" refers to information collected from external sources related to asset management, such as stock prices, economic indicators, and news trends.

[0164] An "emotion engine" is a software module that detects and analyzes a user's emotional state in real time.

[0165] "Emotional data" refers to information about a user's emotions obtained from their facial expressions, tone of voice, input text, etc.

[0166] A "generative model" is a mathematical model used to generate investment strategies by analyzing sentiment data and market data.

[0167] An "investment strategy" refers to a plan for adjusting a portfolio and determining asset allocation.

[0168] A "portfolio" refers to a collection of financial assets held by a user, and is structured based on an investment strategy.

[0169] "Feedback" refers to providing information and advice to users, and is based on the user's emotional state.

[0170] This invention is implemented as an asset management system that incorporates an emotion engine to improve users' investment activities. This system is centered around a server, terminals, and users, and aims to generate investment strategies that take into account the user's emotional state.

[0171] First, users access the system using a terminal and input information about their investments. The terminal is equipped with a camera and microphone, and has a mechanism to collect emotional data by analyzing the user's facial expressions and voice tone in real time. This uses facial recognition software and voice analysis tools.

[0172] The emotional data collected by the device is sent to the server. The server uses an emotional engine to analyze the emotional data and identify the user's emotional state. In addition, market data is acquired from external sources and integrated with the emotional data. Machine learning models are used for data integration, and a data analysis engine is employed.

[0173] Based on the analysis results, a generative AI model designs the optimal investment strategy. This model takes into account the user's risk tolerance and current emotional state to present specific investment options. This information is fed back to the user via their device, and suggestions for portfolio adjustments and buy / sell actions are made.

[0174] For example, if the stock market experiences a sharp decline, the emotion engine will sense the user's anxiety, and the server will generate a strategy with reduced risk tolerance, thereby supporting the user in making calm investment decisions from a long-term perspective. An example of a prompt to the generating AI model would be, "Please tell me the optimal investment strategy considering the current market conditions and my emotions."

[0175] This system design allows users to manage their assets in a personalized way that takes their emotional state into account, resulting in greater accuracy and peace of mind in their investment decisions.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The terminal accepts user input. When the user enters investment-related information, the terminal uses its camera and microphone to analyze facial expressions and voice tone in real time and collect emotional data. The input data includes the user's market information and emotional state. The emotional data is analyzed by facial recognition software and voice analysis tools. As a result, data indicating the user's emotional state is output.

[0179] Step 2:

[0180] The device sends collected emotional data to the server. The transmitted data includes numerical values ​​and indicators representing the user's emotional state. The server receives this emotional data, activates an emotion engine, and further analyzes the data. This allows for a more precise quantification and classification of the user's emotional state, and the identification of emotional changes. The output is the analyzed emotional state data.

[0181] Step 3:

[0182] The server acquires market data from external sources. This data includes stock prices, economic indicators, and news trends. The server integrates this data with already analyzed sentiment data and performs data analysis using a generative AI model. This analysis outputs information that integrates sentiment and market data.

[0183] Step 4:

[0184] The server uses a generative AI model to design investment strategies. Based on integrated sentiment and market data obtained in the analysis step, it generates investment strategies that reflect risk tolerance. Data processing includes weighting of sentiment data and risk assessment in response to market fluctuations. The output is the optimal investment strategy data.

[0185] Step 5:

[0186] The server sends the generated investment strategy to the terminal. The user receives this strategy via the terminal and reviews the strategy information presented on the screen. This information includes the level of risk, recommended asset allocation, and current portfolio status. The interactive interface provides the user with the strategy option they selected as output.

[0187] Step 6:

[0188] The terminal generates and executes buy and sell orders based on the user's selection. After the user's chosen strategy is confirmed, the terminal connects to the trading system and places the buy and sell orders. The input is the specific buy and sell details decided by the user, and the output is confirmation information for the executed orders.

[0189] Step 7:

[0190] The device provides feedback to the user. Based on information from the server, it provides feedback to the user regarding the current portfolio status and updates to investment strategies. Furthermore, the AI ​​provides emotion-based advice in response to the user's questions. The input is a question from the user, and the output is appropriate advice or alternative course of action.

[0191] (Application Example 2)

[0192] 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".

[0193] When users manage their assets, they may make inappropriate investment decisions influenced by their emotions at the time. Traditional asset management systems rely on quantitative analysis based on market data and cannot take into account the user's emotional state. As a result, users may take unfavorable investment actions driven by anxiety and fear, especially when the market is volatile. This system aims to solve this problem and support more accurate investment decisions while taking the user's emotions into consideration.

[0194] 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.

[0195] In this invention, the server includes means for detecting the user's emotions, analyzing the emotional data, and reflecting it in the investment strategy; means for detecting the user's emotions using facial recognition and voice recognition; and means for collecting and analyzing market information in real time. This makes it possible to quantitatively grasp the user's emotional state, reflect it in the investment strategy, and make stable investment decisions that are not influenced by emotions.

[0196] "User" refers to an individual or organization that uses the system to manage assets or engage in investment activities.

[0197] "Risk tolerance" refers to information indicating the limit of risk that a user can accept in asset management.

[0198] "Market information" refers to data that shows the state of financial markets, including indicators such as stock prices, interest rates, and exchange rates.

[0199] A "machine learning algorithm" refers to a computational method used to analyze collected data and predict patterns and trends.

[0200] An "investment plan" is a strategy for determining asset allocation and transaction details based on market conditions and the user's circumstances.

[0201] "Financial engineering" is a general term for the composition and management of a user's portfolio, referring to a group of assets adjusted based on an investment strategy.

[0202] A "trading order" is data that indicates instructions for executing a buy or sell transaction, and is automatically generated and executed by the system.

[0203] "Emotional data" refers to data that indicates an emotional state, obtained from non-verbal information such as a user's facial expressions and tone of voice.

[0204] "Facial recognition" refers to a technology that uses a camera to acquire facial information from users, analyzes its features, and identifies individuals and their states.

[0205] "Speech recognition" refers to a technology in which a computer captures a user's voice and analyzes the content and emotions of the language.

[0206] Embodiments of the present invention will be described in detail. This system analyzes the user's emotions in real time and uses this information to aid in asset management decision-making. The system consists of three elements: a server, a terminal, and a user.

[0207] The server receives information about the user's risk tolerance and capital targets, and collects and analyzes market information in real time. The user's device acquires emotional data in real time using facial recognition and speech recognition technologies and sends that data to the server. The technologies used include "OpenCV" for facial recognition and "Speech Analysis API" for speech recognition.

[0208] The server further analyzes sentiment data and market information using machine learning algorithms. TensorFlow is used as the machine learning algorithm. Sentiment data is analyzed in detail by the sentiment engine, and an investment plan is generated based on the user's emotional state. This plan is executed by automatically adjusting financial strategies and generating trading orders.

[0209] Meanwhile, the user's device provides emotion-based feedback, helping users understand the impact of emotions on their asset management. The AI ​​system responds appropriately to emotion-based questions entered by the user, providing feedback.

[0210] For example, if the market changes rapidly, the user's device can sense their emotions, and if it detects that anxiety is rising, the server can use a machine learning model to suggest a low-risk investment strategy. In this way, it becomes possible to suggest investment strategies based on emotions. An example of a prompt to the generative AI model would be, "Input user emotion data and generate an investment plan."

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The user begins performing routine asset management operations using the device. The device uses its camera and microphone to acquire user emotional data in real time through facial recognition and voice recognition. It acquires facial image data and voice data as input and prepares to analyze the emotional state as output.

[0214] Step 2:

[0215] Emotional data acquired by the device is transferred to the server. The server uses "OpenCV" for face recognition and a "Voice Analysis API" for speech recognition to analyze the data and identify the user's emotional state. Face image data and voice data are provided as input, and quantitative data indicating emotion is generated as output.

[0216] Step 3:

[0217] The server collects market information in real time. It uses a financial information service API to obtain market fluctuation data. It receives indicator data such as stock prices and interest rates as input, and prepares market information data as output.

[0218] Step 4:

[0219] The server uses the machine learning algorithm "TensorFlow" to analyze emotional states and market information. It takes emotional data and market information as input and generates investment strategies based on them. As output, it presents an optimal investment plan tailored to the user's risk tolerance.

[0220] Step 5:

[0221] Based on the generated investment strategy, the server automatically adjusts its financial strategy and generates trading orders. It receives an investment plan as input and prepares to generate and execute specific trading orders as output.

[0222] Step 6:

[0223] The user's device notifies them of the generated investment strategy and real-time feedback. It displays sentiment-based feedback to aid user understanding. It takes investment plans and sentiment feedback data as input and provides information that promotes the user's well-being as output.

[0224] Step 7:

[0225] When a user asks a question through their device, the AI ​​automatically processes the prompt using a generated AI model and responds with specific investment advice. It receives the user's question as input and provides appropriate feedback as output.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] The system of this invention is designed to help users automate their asset management. The system operates primarily through a server, terminal, and user interface.

[0243] The server profiles the user's risk tolerance and asset goals based on the information provided by the user. Based on this profile, the server retrieves market data in real time from various data providers. The retrieved data is stored in a database on the server and is constantly updated to the latest state. Next, machine learning models are run to analyze the data and predict market trends.

[0244] Based on the analysis results, the server develops an optimal investment strategy for each user. This strategy includes optimizing asset allocation and adjusting the portfolio. For example, if a user specifies a moderate risk tolerance and aims for stable returns, the server will recommend a stable asset allocation of stocks and bonds and build a strategy that makes the necessary adjustments.

[0245] The device provides an interface to the user. Through the device, the user can check the status of their portfolio in real time and visually understand past performance and current asset allocation. Furthermore, when the user asks a question, the device uses AI to automatically provide an answer.

[0246] The server also automatically places buy and sell orders in the user's portfolio, ensuring that trades are executed in the market. For example, if the server predicts that a particular stock will rise, it generates an order to buy that stock. This order is executed autonomously under the user's supervision, and the timing and quantity of the buy and sell orders are determined based on a pre-configured algorithm.

[0247] In this way, a system implemented according to the embodiment of the present invention enables users to manage their assets more efficiently, control risks, and respond quickly to market changes.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The user logs into the system using a terminal and enters information regarding their investment goals and risk tolerance. The terminal then sends the entered information to the server.

[0251] Step 2:

[0252] The server creates a profile based on the received user information. This profile forms the basis of the user's investment strategy and is stored in the database.

[0253] Step 3:

[0254] The server collects market data in real time from market data providers. This data includes stock prices, bond yields, exchange rates, and more. The collected data is stored in a database.

[0255] Step 4:

[0256] The server uses machine learning models to analyze collected market data. This generates investment strategy predictions based on current market conditions.

[0257] Step 5:

[0258] The server combines user profiles and market data analysis results to build a portfolio optimized for each individual user. This portfolio is automatically adjusted to maintain the optimal asset allocation for the user.

[0259] Step 6:

[0260] The server generates buy and sell orders based on the adjusted portfolio. The orders are executed at the appropriate time through the securities trading system.

[0261] Step 7:

[0262] Users can use their device to check the status of their portfolio. The device displays the latest information retrieved from the server, visually showing investment performance and current asset allocation.

[0263] Step 8:

[0264] The terminal receives questions from users and, if necessary, automatically provides answers using the server's AI capabilities. This allows users to quickly resolve their investment-related questions.

[0265] Step 9:

[0266] The server monitors market fluctuations and automatically readjusts the portfolio if necessary based on new information. The user is notified of the readjustment results.

[0267] (Example 1)

[0268] 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."

[0269] In today's complex and rapidly changing market environment, it is extremely difficult for individual investors to formulate effective investment strategies based on their own risk tolerance and asset goals, and to execute them in a timely manner. Therefore, there is a need for real-time market data analysis and automated, appropriate investment decision-making.

[0270] 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.

[0271] In this invention, the server includes means for acquiring information on risk tolerance and asset targets from users and performing profiling; means for acquiring market information in real time and storing it in a memory device; and means for predicting trends using an analytical device based on the acquired market information. This makes it possible for individual investors to easily optimize their investment strategies and accurately manage the balance between risk and return.

[0272] A "user" refers to an individual or legal entity that uses the system to manage their own assets.

[0273] "Risk tolerance" is an indicator that shows the degree to which an investor is willing to accept risk.

[0274] "Asset goals" refer to the specific financial results that a user wants to achieve through investment.

[0275] "Market information" refers to data related to financial markets, including, for example, stock prices, interest rates, and exchange rates.

[0276] "Profiling" is the process of developing an appropriate investment strategy based on a user's risk tolerance and asset goals.

[0277] "Analytical equipment" refers to hardware or software used to analyze data and predict future trends.

[0278] A "storage device" is hardware used to store data, including, for example, hard disks and SSDs.

[0279] An "investment strategy" refers to a plan for asset allocation and buying / selling, designed to achieve investment objectives.

[0280] "Means of predicting trends" refers to techniques for analyzing past and present data to predict future market changes.

[0281] The present invention is an automated platform designed to help users streamline asset management. The system primarily functions through interaction between servers, terminals, and users.

[0282] The server receives information from the user related to their risk tolerance and asset goals, and uses this information to perform profiling. Based on this profile, the server retrieves real-time market information from financial market data providers. In this data collection process, external APIs are used to store the data in storage devices. These storage devices include hard disk drives (HDDs) and solid-state drives (SSDs).

[0283] Next, the server uses a machine learning framework such as TensorFlow or PyTorch to analyze the acquired market information. Based on the analysis results, it utilizes the generated AI model to predict future trends. Based on this prediction, the server formulates an investment strategy. This strategy includes the optimal allocation of assets and the timing of transactions, etc.

[0284] For example, when the user specifies "want to aim for 10% annual growth with medium risk", the server uses the generated AI model to calculate and recommend an appropriate combination of stocks and bonds. An example of the generated prompt sentence is "Please provide the optimal asset allocation based on the risk tolerance".

[0285] The terminal provides a visual interface to the user. Through this interface, the user can check the status of their portfolio and analyze past performance. The terminal constructs a dashboard using JavaScript or React and enables interactive operations.

[0286] Furthermore, based on the formulated investment strategy, the server automatically generates buy and sell orders and executes them in the market. To obtain the optimal timing for the execution of trades based on the designed algorithm, a highly accurate algorithm is used. As a specific example, when the prediction model suggests an increase in a specific stock, an order to purchase that stock at an appropriate timing is generated.

[0287] With this invention, it becomes easier for users to manage their investment activities more efficiently, avoid risks, and respond promptly to market fluctuations.

[0288] The flow of the specific process in Example 1 will be described using FIG. 11.

[0289] Step 1:

[0290] The server receives input information from the user regarding their risk tolerance and asset goals. Based on this information, the server runs a profiling algorithm to generate a user profile. The user profile is output as a data structure that includes risk tolerance, target rate of return, investment period, and other information.

[0291] Step 2:

[0292] The server retrieves market information in real time from external data provider APIs. This includes stock prices, exchange rates, and interest rate information. This retrieved data is stored in storage. The input API response data is processed into a database format and updated to maintain the latest state.

[0293] Step 3:

[0294] The server uses machine learning models to analyze data based on accumulated market information. The generative AI model used here performs time-series forecasting based on pre-set parameters. As a result of this analysis, predictive data regarding future market trends is output.

[0295] Step 4:

[0296] The server develops an optimal investment strategy for each user based on the analyzed market trends. In this step, the results of the generated AI model are used to determine specific asset allocations and investment targets. As an output, a concrete action plan regarding the investment strategy is provided.

[0297] Step 5:

[0298] The server automatically generates buy and sell orders for financial instruments based on the formulated investment strategy. In this process, an algorithm determines the optimal timing and quantity for buying and selling. The generated order data is then transmitted to the market via the trading platform's API.

[0299] Step 6:

[0300] The terminal displays portfolio information provided by the server on the user interface in real time. Input information includes the current state and historical performance data of the portfolio, which are then visualized and output. Through interaction on the terminal, the user can view detailed information and apply specific filters.

[0301] (Application Example 1)

[0302] 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."

[0303] The present invention aims to improve the efficiency of automated asset management by formulating optimal investment strategies tailored to the user's risk tolerance and asset goals, as well as streamlining automated trading. Conventional methods have difficulty responding quickly to real-time market fluctuations and have limited user interfaces. Therefore, there is a need for a system that combines investment efficiency with user-friendly operation.

[0304] 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.

[0305] In this invention, the server includes means for obtaining information from the user regarding their risk tolerance and asset goals, means for collecting and analyzing market data in real time, and means for determining an investment strategy using a machine learning model based on the collected data. This enables the rapid formulation of an optimal investment strategy tailored to the user's risk tolerance and autonomous asset management in response to market fluctuations.

[0306] "Risk tolerance" is an indicator that shows the degree to which a user is willing to accept risk in asset management.

[0307] The "asset goal" is a specific financial goal or objective that a user wishes to achieve through asset management.

[0308] "Market data" is a collection of various information related to the financial market, including real-time data such as stock prices, exchange rates, interest rates, etc.

[0309] A "machine learning model" is an algorithm and computational model that learns patterns from data and performs tasks such as prediction and classification.

[0310] An "investment strategy" is a policy or plan on how to allocate and manage funds based on market conditions and the user's risk profile.

[0311] A "portfolio" is a collection of financial products held by a user, designed considering asset allocation and risk management.

[0312] "Real-time" means that information and data are processed at a timing when they can immediately represent the current state.

[0313] "Automatic" refers to the state where a program or device operates independently without the need for human operation.

[0314] "Feedback" is a function where the system automatically returns responses or instructions to information and questions from the user.

[0315] The system for implementing this invention is composed of a server, a terminal, and a user interface. The server plays a central role, obtains information regarding the risk tolerance and asset goals from the user, and profiles the user based on it. The server obtains real-time market data from various data providers. This market data is stored in a database on the server and is constantly updated with the latest information. The server predicts market trends by executing a machine learning model using Python and formulates an appropriate investment strategy.

[0316] The terminal functions as an interface to the user, allowing them to check the status of their portfolio via their smartphone. This includes features that allow them to visually understand past performance and current asset allocation. AI running on the terminal automatically provides feedback to user questions. Furthermore, the server automatically generates buy and sell orders and executes trades based on investment strategies. This enables users to efficiently manage their assets in response to market fluctuations.

[0317] For example, if a user sets a target amount to achieve for a trip using their smartphone, the application will suggest an appropriate combination of stocks and bonds to aim for stable returns. Based on the user's set risk tolerance, the application will automatically make necessary adjustments to support asset management towards achieving the goal.

[0318] An example of a prompt to input into a generative AI model is: "The user has a moderate risk tolerance and aims for stable returns. Generate Python code that proposes the optimal investment strategy based on market data." This prompt allows the model to concretize the instructions needed to implement the logic required for the investment strategy.

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The server receives information about risk tolerance and asset goals transmitted from the user via a smart device. Based on this input information, the server creates a user profile and prepares the basic data necessary for asset management.

[0322] Step 2:

[0323] The server retrieves market data in real time via external data provider APIs. The retrieved data is stored in the server's database and is constantly updated to the latest state. This market data includes a variety of information related to financial markets, such as stock prices, exchange rates, and interest rates.

[0324] Step 3:

[0325] The server runs a machine learning model using Python and the scikit-learn machine learning library, based on the stored market data. This model takes market data as input and outputs market trend predictions. Data processing such as feature extraction and normalization is performed, and the model makes predictions based on the trained algorithm.

[0326] Step 4:

[0327] The server builds an investment strategy tailored to the user's profile based on predictions from machine learning models. This investment strategy determines the optimal asset allocation, taking into account predicted market trends and the user's risk tolerance.

[0328] Step 5:

[0329] The server automatically generates buy and sell orders based on the established investment strategy and executes them in the market through an intermediary. Specifically, it determines the trading volume and timing according to the set trading rules and issues orders via API. This process is performed autonomously under the user's supervision.

[0330] Step 6:

[0331] The terminal receives information from the server to display the portfolio status to the user in real time. It visually displays the portfolio's earnings and risk status, allowing the user to immediately understand the situation.

[0332] Step 7:

[0333] Users can ask the system additional questions through their device. In response to the user's questions, the on-device AI uses a generated AI model to provide automated responses based on prompts, instantly offering advice and information on asset management.

[0334] 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.

[0335] This invention is implemented as an asset management system that incorporates an emotion engine to provide more advanced support for users' investment activities. This system consists of a server, terminals, and users, and aims to optimize investment strategies, particularly by taking users' emotions into consideration.

[0336] The terminal receives input from the user and detects their emotional state regarding investments in real time. This uses technology that recognizes emotions by analyzing the user's facial expressions, tone of voice, and entered text while they are using the terminal. The emotional data is sent to a server and processed along with other investment-related data.

[0337] The server runs an emotion engine to analyze emotional data and identify the user's current emotional state. This allows it to automatically adjust investment risk tolerance if emotions such as stress or anxiety are detected. The server also uses machine learning models to generate investment strategies that consider both emotions and market data. For example, if a user's anxiety is high, the server can suggest risk-reducing options and recommend a conservative investment strategy.

[0338] Meanwhile, users can receive emotion-based investment strategies through their devices and view the current status of their portfolios in real time. For example, even if the stock market experiences a sharp decline, the emotion engine recognizes the user's anxiety and supports their calm decision-making by presenting an investment situation with a long-term outlook while lowering their risk tolerance.

[0339] The system also includes a feature where AI provides emotion-based feedback when users ask questions on their devices. For example, if a user is emotional and rushing to take profits, the AI ​​will provide advice based on a calm assessment of the situation, facilitating optimal investment decisions.

[0340] In this way, the embodiment of the present invention aims to achieve more personalized asset management by considering the influence of user emotions on investment activities. This is more user-friendly than conventional systems and is effective in pursuing optimal returns.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] Users log in to the system using a terminal and enter basic information such as investment goals, risk tolerance, and investment period. The terminal then sends the entered data to the server.

[0344] Step 2:

[0345] The device monitors the user's facial expressions and voice through a microphone and camera to recognize the user's emotional state, and analyzes the emotional data using an emotion engine. The analyzed emotional data is then sent to a server.

[0346] Step 3:

[0347] The server generates a profile based on the received user's basic information and sentiment data, and stores it in a database.

[0348] Step 4:

[0349] The server collects market data in real time from market data providers. This data includes stock price trends and exchange rates. The server stores this data in a database and updates it as needed.

[0350] Step 5:

[0351] The server analyzes collected market data and user sentiment data using machine learning models to formulate individual investment strategies. When emotions are unstable, it can design strategies that minimize risk.

[0352] Step 6:

[0353] The server automatically adjusts the portfolio based on the formulated investment strategy, taking sentiment data into consideration and dynamically modifying risk tolerance as needed.

[0354] Step 7:

[0355] The server generates buy and sell orders based on the adjusted portfolio and executes them through the appropriate securities trading platform.

[0356] Step 8:

[0357] Users can use their device to check the current status of their portfolio. The device visually displays the latest portfolio data and notifies them of any changes in their investment strategy.

[0358] Step 9:

[0359] When a user asks a question or seeks assistance on their device, the AI ​​considers the context, including emotional data, to provide appropriate feedback and advice.

[0360] Step 10:

[0361] The server continuously monitors sentiment and market data, readjusts the portfolio as needed, and notifies the user of the changes. This allows the user to continue their investment activities with peace of mind.

[0362] (Example 2)

[0363] 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".

[0364] Traditional asset management systems have limitations in mechanically generating strategies based on market data and are unable to consider the emotional state of individual users. This creates a problem where users find it difficult to make appropriate investment decisions when their emotions are running high.

[0365] 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.

[0366] In this invention, the server includes means for detecting and analyzing the user's emotional state using an emotion engine, means for determining an investment strategy using a generative model based on emotional data and market data, and means for providing the user with emotional state-based feedback using AI. This makes it possible to present a personalized investment strategy that corresponds to the user's emotional state.

[0367] "User" refers to an individual or legal entity that uses an asset management system to conduct investment activities.

[0368] "Risk tolerance" is a measure that indicates how much risk a user is willing to accept in asset management.

[0369] "Asset target" refers to the specific asset size or objective that a user aims to achieve through their investment activities.

[0370] "Market data" refers to information collected from external sources related to asset management, such as stock prices, economic indicators, and news trends.

[0371] An "emotion engine" is a software module that detects and analyzes a user's emotional state in real time.

[0372] "Emotional data" refers to information about a user's emotions obtained from their facial expressions, tone of voice, input text, etc.

[0373] A "generative model" is a mathematical model used to generate investment strategies by analyzing sentiment data and market data.

[0374] An "investment strategy" refers to a plan for adjusting a portfolio and determining asset allocation.

[0375] A "portfolio" refers to a collection of financial assets held by a user, and is structured based on an investment strategy.

[0376] "Feedback" refers to providing information and advice to users, and is based on the user's emotional state.

[0377] This invention is implemented as an asset management system that incorporates an emotion engine to improve users' investment activities. This system is centered around a server, terminals, and users, and aims to generate investment strategies that take into account the user's emotional state.

[0378] First, users access the system using a terminal and input information about their investments. The terminal is equipped with a camera and microphone, and has a mechanism to collect emotional data by analyzing the user's facial expressions and voice tone in real time. This uses facial recognition software and voice analysis tools.

[0379] The emotional data collected by the device is sent to the server. The server uses an emotional engine to analyze the emotional data and identify the user's emotional state. In addition, market data is acquired from external sources and integrated with the emotional data. Machine learning models are used for data integration, and a data analysis engine is employed.

[0380] Based on the analysis results, a generative AI model designs the optimal investment strategy. This model takes into account the user's risk tolerance and current emotional state to present specific investment options. This information is fed back to the user via their device, and suggestions for portfolio adjustments and buy / sell actions are made.

[0381] For example, if the stock market experiences a sharp decline, the emotion engine will sense the user's anxiety, and the server will generate a strategy with reduced risk tolerance, thereby supporting the user in making calm investment decisions from a long-term perspective. An example of a prompt to the generating AI model would be, "Please tell me the optimal investment strategy considering the current market conditions and my emotions."

[0382] This system design allows users to manage their assets in a personalized way that takes their emotional state into account, resulting in greater accuracy and peace of mind in their investment decisions.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The terminal accepts user input. When the user enters investment-related information, the terminal uses its camera and microphone to analyze facial expressions and voice tone in real time and collect emotional data. The input data includes the user's market information and emotional state. The emotional data is analyzed by facial recognition software and voice analysis tools. As a result, data indicating the user's emotional state is output.

[0386] Step 2:

[0387] The device sends collected emotional data to the server. The transmitted data includes numerical values ​​and indicators representing the user's emotional state. The server receives this emotional data, activates an emotion engine, and further analyzes the data. This allows for a more precise quantification and classification of the user's emotional state, and the identification of emotional changes. The output is the analyzed emotional state data.

[0388] Step 3:

[0389] The server acquires market data from external sources. This data includes stock prices, economic indicators, and news trends. The server integrates this data with already analyzed sentiment data and performs data analysis using a generative AI model. This analysis outputs information that integrates sentiment and market data.

[0390] Step 4:

[0391] The server uses a generative AI model to design investment strategies. Based on integrated sentiment and market data obtained in the analysis step, it generates investment strategies that reflect risk tolerance. Data processing includes weighting of sentiment data and risk assessment in response to market fluctuations. The output is the optimal investment strategy data.

[0392] Step 5:

[0393] The server sends the generated investment strategy to the terminal. The user receives this strategy via the terminal and reviews the strategy information presented on the screen. This information includes the level of risk, recommended asset allocation, and current portfolio status. The interactive interface provides the user with the strategy option they selected as output.

[0394] Step 6:

[0395] The terminal generates and executes buy and sell orders based on the user's selection. After the user's chosen strategy is confirmed, the terminal connects to the trading system and places the buy and sell orders. The input is the specific buy and sell details decided by the user, and the output is confirmation information for the executed orders.

[0396] Step 7:

[0397] The device provides feedback to the user. Based on information from the server, it provides feedback to the user regarding the current portfolio status and updates to investment strategies. Furthermore, the AI ​​provides emotion-based advice in response to the user's questions. The input is a question from the user, and the output is appropriate advice or alternative course of action.

[0398] (Application Example 2)

[0399] 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."

[0400] When users manage their assets, they may make inappropriate investment decisions influenced by their emotions at the time. Traditional asset management systems rely on quantitative analysis based on market data and cannot take into account the user's emotional state. As a result, users may take unfavorable investment actions driven by anxiety and fear, especially when the market is volatile. This system aims to solve this problem and support more accurate investment decisions while taking the user's emotions into consideration.

[0401] 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.

[0402] In this invention, the server includes means for detecting the user's emotions, analyzing the emotional data, and reflecting it in the investment strategy; means for detecting the user's emotions using facial recognition and voice recognition; and means for collecting and analyzing market information in real time. This makes it possible to quantitatively grasp the user's emotional state, reflect it in the investment strategy, and make stable investment decisions that are not influenced by emotions.

[0403] "User" refers to an individual or organization that uses the system to manage assets or engage in investment activities.

[0404] "Risk tolerance" refers to information indicating the limit of risk that a user can accept in asset management.

[0405] "Market information" refers to data that shows the state of financial markets, including indicators such as stock prices, interest rates, and exchange rates.

[0406] A "machine learning algorithm" refers to a computational method used to analyze collected data and predict patterns and trends.

[0407] An "investment plan" is a strategy for determining asset allocation and transaction details based on market conditions and the user's circumstances.

[0408] "Financial engineering" is a general term for the composition and management of a user's portfolio, referring to a group of assets adjusted based on an investment strategy.

[0409] A "trading order" is data that indicates instructions for executing a buy or sell transaction, and is automatically generated and executed by the system.

[0410] "Emotional data" refers to data that indicates an emotional state, obtained from non-verbal information such as a user's facial expressions and tone of voice.

[0411] "Facial recognition" refers to a technology that uses a camera to acquire facial information from users, analyzes its features, and identifies individuals and their states.

[0412] "Speech recognition" refers to a technology in which a computer captures a user's voice and analyzes the content and emotions of the language.

[0413] Embodiments of the present invention will be described in detail. This system analyzes the user's emotions in real time and uses this information to aid in asset management decision-making. The system consists of three elements: a server, a terminal, and a user.

[0414] The server receives information about the user's risk tolerance and capital targets, and collects and analyzes market information in real time. The user's device acquires emotional data in real time using facial recognition and speech recognition technologies and sends that data to the server. The technologies used include "OpenCV" for facial recognition and "Speech Analysis API" for speech recognition.

[0415] The server further analyzes sentiment data and market information using machine learning algorithms. TensorFlow is used as the machine learning algorithm. Sentiment data is analyzed in detail by the sentiment engine, and an investment plan is generated based on the user's emotional state. This plan is executed by automatically adjusting financial strategies and generating trading orders.

[0416] Meanwhile, the user's device provides emotion-based feedback, helping users understand the impact of emotions on their asset management. The AI ​​system responds appropriately to emotion-based questions entered by the user, providing feedback.

[0417] For example, if the market changes rapidly, the user's device can sense their emotions, and if it detects that anxiety is rising, the server can use a machine learning model to suggest a low-risk investment strategy. In this way, it becomes possible to suggest investment strategies based on emotions. An example of a prompt to the generative AI model would be, "Input user emotion data and generate an investment plan."

[0418] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0419] Step 1:

[0420] The user begins performing routine asset management operations using the device. The device uses its camera and microphone to acquire user emotional data in real time through facial recognition and voice recognition. It acquires facial image data and voice data as input and prepares to analyze the emotional state as output.

[0421] Step 2:

[0422] Emotional data acquired by the device is transferred to the server. The server uses "OpenCV" for face recognition and a "Voice Analysis API" for speech recognition to analyze the data and identify the user's emotional state. Face image data and voice data are provided as input, and quantitative data indicating emotion is generated as output.

[0423] Step 3:

[0424] The server collects market information in real time. It uses a financial information service API to obtain market fluctuation data. It receives indicator data such as stock prices and interest rates as input, and prepares market information data as output.

[0425] Step 4:

[0426] The server uses the machine learning algorithm "TensorFlow" to analyze emotional states and market information. It takes emotional data and market information as input and generates investment strategies based on them. As output, it presents an optimal investment plan tailored to the user's risk tolerance.

[0427] Step 5:

[0428] Based on the generated investment strategy, the server automatically adjusts its financial strategy and generates trading orders. It receives an investment plan as input and prepares to generate and execute specific trading orders as output.

[0429] Step 6:

[0430] The user's device notifies them of the generated investment strategy and real-time feedback. It displays sentiment-based feedback to aid user understanding. It takes investment plans and sentiment feedback data as input and provides information that promotes the user's well-being as output.

[0431] Step 7:

[0432] When a user asks a question through their device, the AI ​​automatically processes the prompt using a generated AI model and responds with specific investment advice. It receives the user's question as input and provides appropriate feedback as output.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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".

[0449] The system of this invention is designed to help users automate their asset management. The system operates primarily through a server, terminal, and user interface.

[0450] The server profiles the user's risk tolerance and asset goals based on the information provided by the user. Based on this profile, the server retrieves market data in real time from various data providers. The retrieved data is stored in a database on the server and is constantly updated to the latest state. Next, machine learning models are run to analyze the data and predict market trends.

[0451] Based on the analysis results, the server develops an optimal investment strategy for each user. This strategy includes optimizing asset allocation and adjusting the portfolio. For example, if a user specifies a moderate risk tolerance and aims for stable returns, the server will recommend a stable asset allocation of stocks and bonds and build a strategy that makes the necessary adjustments.

[0452] The device provides an interface to the user. Through the device, the user can check the status of their portfolio in real time and visually understand past performance and current asset allocation. Furthermore, when the user asks a question, the device uses AI to automatically provide an answer.

[0453] The server also automatically places buy and sell orders in the user's portfolio, ensuring that trades are executed in the market. For example, if the server predicts that a particular stock will rise, it generates an order to buy that stock. This order is executed autonomously under the user's supervision, and the timing and quantity of the buy and sell orders are determined based on a pre-configured algorithm.

[0454] In this way, a system implemented according to the embodiment of the present invention enables users to manage their assets more efficiently, control risks, and respond quickly to market changes.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The user logs into the system using a terminal and enters information regarding their investment goals and risk tolerance. The terminal then sends the entered information to the server.

[0458] Step 2:

[0459] The server creates a profile based on the received user information. This profile forms the basis of the user's investment strategy and is stored in the database.

[0460] Step 3:

[0461] The server collects market data in real time from market data providers. This data includes stock prices, bond yields, exchange rates, and more. The collected data is stored in a database.

[0462] Step 4:

[0463] The server uses machine learning models to analyze collected market data. This generates investment strategy predictions based on current market conditions.

[0464] Step 5:

[0465] The server combines user profiles and market data analysis results to build a portfolio optimized for each individual user. This portfolio is automatically adjusted to maintain the optimal asset allocation for the user.

[0466] Step 6:

[0467] The server generates buy and sell orders based on the adjusted portfolio. The orders are executed at the appropriate time through the securities trading system.

[0468] Step 7:

[0469] Users can use their device to check the status of their portfolio. The device displays the latest information retrieved from the server, visually showing investment performance and current asset allocation.

[0470] Step 8:

[0471] The terminal receives questions from users and, if necessary, automatically provides answers using the server's AI capabilities. This allows users to quickly resolve their investment-related questions.

[0472] Step 9:

[0473] The server monitors market fluctuations and automatically readjusts the portfolio if necessary based on new information. The user is notified of the readjustment results.

[0474] (Example 1)

[0475] 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."

[0476] In today's complex and rapidly changing market environment, it is extremely difficult for individual investors to formulate effective investment strategies based on their own risk tolerance and asset goals, and to execute them in a timely manner. Therefore, there is a need for real-time market data analysis and automated, appropriate investment decision-making.

[0477] 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.

[0478] In this invention, the server includes means for acquiring information on risk tolerance and asset targets from users and performing profiling; means for acquiring market information in real time and storing it in a memory device; and means for predicting trends using an analytical device based on the acquired market information. This makes it possible for individual investors to easily optimize their investment strategies and accurately manage the balance between risk and return.

[0479] A "user" refers to an individual or legal entity that uses the system to manage their own assets.

[0480] "Risk tolerance" is an indicator that shows the degree to which an investor is willing to accept risk.

[0481] "Asset goals" refer to the specific financial results that a user wants to achieve through investment.

[0482] "Market information" refers to data related to financial markets, including, for example, stock prices, interest rates, and exchange rates.

[0483] "Profiling" is the process of developing an appropriate investment strategy based on a user's risk tolerance and asset goals.

[0484] "Analytical equipment" refers to hardware or software used to analyze data and predict future trends.

[0485] A "storage device" is hardware used to store data, including, for example, hard disks and SSDs.

[0486] An "investment strategy" refers to a plan for asset allocation and buying / selling, designed to achieve investment objectives.

[0487] "Means of predicting trends" refers to techniques for analyzing past and present data to predict future market changes.

[0488] The present invention is an automated platform designed to help users streamline asset management. The system primarily functions through interaction between servers, terminals, and users.

[0489] The server receives information from the user related to their risk tolerance and asset goals, and uses this information to perform profiling. Based on this profile, the server retrieves real-time market information from financial market data providers. In this data collection process, external APIs are used to store the data in storage devices. These storage devices include hard disk drives (HDDs) and solid-state drives (SSDs).

[0490] Next, the server uses machine learning frameworks such as TensorFlow and PyTorch to analyze the acquired market information. Based on the analysis results, it utilizes a generative AI model to predict future trends. Based on this prediction, the server formulates an investment strategy. This strategy includes optimal asset allocation and timing of transactions.

[0491] For example, if a user specifies that they "want to aim for a 10% annual growth with moderate risk," the server will use a generating AI model to calculate and recommend an appropriate combination of stocks and bonds. An example of a generated prompt message is, "Please provide the optimal asset allocation based on my risk tolerance."

[0492] The device provides users with a visual interface. Through this interface, users can check the status of their portfolio and analyze past performance. The device uses JavaScript and React to build its dashboard, enabling interactive operation.

[0493] Furthermore, the server automatically generates and executes buy and sell orders in the market based on the devised investment strategy. The execution of trades uses highly accurate algorithms to obtain the optimal timing based on the designed algorithms. For example, if a predictive model suggests a rise in a particular stock, it generates an order to buy that stock at the appropriate time.

[0494] This invention makes it easier for users to manage their investment activities more efficiently, mitigate risks, and respond quickly to market fluctuations.

[0495] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0496] Step 1:

[0497] The server receives input information from the user regarding their risk tolerance and asset goals. Based on this information, the server runs a profiling algorithm to generate a user profile. The user profile is output as a data structure that includes risk tolerance, target rate of return, investment period, and other information.

[0498] Step 2:

[0499] The server retrieves market information in real time from external data provider APIs. This includes stock prices, exchange rates, and interest rate information. This retrieved data is stored in storage. The input API response data is processed into a database format and updated to maintain the latest state.

[0500] Step 3:

[0501] The server uses machine learning models to analyze data based on accumulated market information. The generative AI model used here performs time-series forecasting based on pre-set parameters. As a result of this analysis, predictive data regarding future market trends is output.

[0502] Step 4:

[0503] The server develops an optimal investment strategy for each user based on the analyzed market trends. In this step, the results of the generated AI model are used to determine specific asset allocations and investment targets. As an output, a concrete action plan regarding the investment strategy is provided.

[0504] Step 5:

[0505] The server automatically generates buy and sell orders for financial instruments based on the formulated investment strategy. In this process, an algorithm determines the optimal timing and quantity for buying and selling. The generated order data is then transmitted to the market via the trading platform's API.

[0506] Step 6:

[0507] The terminal displays portfolio information provided by the server on the user interface in real time. Input information includes the current state and historical performance data of the portfolio, which are then visualized and output. Through interaction on the terminal, the user can view detailed information and apply specific filters.

[0508] (Application Example 1)

[0509] 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."

[0510] The present invention aims to improve the efficiency of automated asset management by formulating optimal investment strategies tailored to the user's risk tolerance and asset goals, as well as streamlining automated trading. Conventional methods have difficulty responding quickly to real-time market fluctuations and have limited user interfaces. Therefore, there is a need for a system that combines investment efficiency with user-friendly operation.

[0511] 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.

[0512] In this invention, the server includes means for obtaining information from the user regarding their risk tolerance and asset goals, means for collecting and analyzing market data in real time, and means for determining an investment strategy using a machine learning model based on the collected data. This enables the rapid formulation of an optimal investment strategy tailored to the user's risk tolerance and autonomous asset management in response to market fluctuations.

[0513] "Risk tolerance" is an indicator that shows the degree to which a user is willing to accept risk in asset management.

[0514] "Asset goals" refer to specific financial objectives or goals that a user hopes to achieve through asset management.

[0515] "Market data" refers to a collection of various pieces of information related to financial markets, including real-time data such as stock prices, exchange rates, and interest rates.

[0516] A "machine learning model" is an algorithm and computational model that learns patterns from data and performs prediction and classification tasks.

[0517] An "investment strategy" is a set of policies and plans for how to allocate and manage funds based on market conditions and the user's risk profile.

[0518] A "portfolio" is a collection of financial products held by a user, designed with asset allocation and risk management in mind.

[0519] "Real-time" means that information and data are processed in a way that allows them to instantly represent the current state.

[0520] "Automatic" refers to a system or device that operates independently, without requiring human intervention.

[0521] "Feedback" refers to a function in which a system automatically responds to information or questions from a user with responses or instructions.

[0522] The system implementing this invention consists of a server, a terminal, and a user interface. The server plays a central role, obtaining information from the user regarding their risk tolerance and asset goals, and profiling the user based on this information. The server obtains market data in real time from various data providers. This market data is stored in a database on the server and is constantly updated with the latest information. The server predicts market trends and develops appropriate investment strategies by running machine learning models using Python.

[0523] The terminal functions as an interface to the user, allowing them to check the status of their portfolio via their smartphone. This includes features that allow them to visually understand past performance and current asset allocation. AI running on the terminal automatically provides feedback to user questions. Furthermore, the server automatically generates buy and sell orders and executes trades based on investment strategies. This enables users to efficiently manage their assets in response to market fluctuations.

[0524] For example, if a user sets a target amount to achieve for a trip using their smartphone, the application will suggest an appropriate combination of stocks and bonds to aim for stable returns. Based on the user's set risk tolerance, the application will automatically make necessary adjustments to support asset management towards achieving the goal.

[0525] An example of a prompt to input into a generative AI model is: "The user has a moderate risk tolerance and aims for stable returns. Generate Python code that proposes the optimal investment strategy based on market data." This prompt allows the model to concretize the instructions needed to implement the logic required for the investment strategy.

[0526] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0527] Step 1:

[0528] The server receives information about risk tolerance and asset goals transmitted from the user via a smart device. Based on this input information, the server creates a user profile and prepares the basic data necessary for asset management.

[0529] Step 2:

[0530] The server retrieves market data in real time via external data provider APIs. The retrieved data is stored in the server's database and is constantly updated to the latest state. This market data includes a variety of information related to financial markets, such as stock prices, exchange rates, and interest rates.

[0531] Step 3:

[0532] The server runs a machine learning model using Python and the scikit-learn machine learning library, based on the stored market data. This model takes market data as input and outputs market trend predictions. Data processing such as feature extraction and normalization is performed, and the model makes predictions based on the trained algorithm.

[0533] Step 4:

[0534] The server builds an investment strategy tailored to the user's profile based on predictions from machine learning models. This investment strategy determines the optimal asset allocation, taking into account predicted market trends and the user's risk tolerance.

[0535] Step 5:

[0536] The server automatically generates buy and sell orders based on the established investment strategy and executes them in the market through an intermediary. Specifically, it determines the trading volume and timing according to the set trading rules and issues orders via API. This process is performed autonomously under the user's supervision.

[0537] Step 6:

[0538] The terminal receives information from the server to display the portfolio status to the user in real time. It visually displays the portfolio's earnings and risk status, allowing the user to immediately understand the situation.

[0539] Step 7:

[0540] Users can ask the system additional questions through their device. In response to the user's questions, the on-device AI uses a generated AI model to provide automated responses based on prompts, instantly offering advice and information on asset management.

[0541] 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.

[0542] This invention is implemented as an asset management system that incorporates an emotion engine to provide more advanced support for users' investment activities. This system consists of a server, terminals, and users, and aims to optimize investment strategies, particularly by taking users' emotions into consideration.

[0543] The terminal receives input from the user and detects their emotional state regarding investments in real time. This uses technology that recognizes emotions by analyzing the user's facial expressions, tone of voice, and entered text while they are using the terminal. The emotional data is sent to a server and processed along with other investment-related data.

[0544] The server runs an emotion engine to analyze emotional data and identify the user's current emotional state. This allows it to automatically adjust investment risk tolerance if emotions such as stress or anxiety are detected. The server also uses machine learning models to generate investment strategies that consider both emotions and market data. For example, if a user's anxiety is high, the server can suggest risk-reducing options and recommend a conservative investment strategy.

[0545] Meanwhile, users can receive emotion-based investment strategies through their devices and view the current status of their portfolios in real time. For example, even if the stock market experiences a sharp decline, the emotion engine recognizes the user's anxiety and supports their calm decision-making by presenting an investment situation with a long-term outlook while lowering their risk tolerance.

[0546] The system also includes a feature where AI provides emotion-based feedback when users ask questions on their devices. For example, if a user is emotional and rushing to take profits, the AI ​​will provide advice based on a calm assessment of the situation, facilitating optimal investment decisions.

[0547] In this way, the embodiment of the present invention aims to achieve more personalized asset management by considering the influence of user emotions on investment activities. This is more user-friendly than conventional systems and is effective in pursuing optimal returns.

[0548] The following describes the processing flow.

[0549] Step 1:

[0550] Users log in to the system using a terminal and enter basic information such as investment goals, risk tolerance, and investment period. The terminal then sends the entered data to the server.

[0551] Step 2:

[0552] The device monitors the user's facial expressions and voice through a microphone and camera to recognize the user's emotional state, and analyzes the emotional data using an emotion engine. The analyzed emotional data is then sent to a server.

[0553] Step 3:

[0554] The server generates a profile based on the received user's basic information and sentiment data, and stores it in a database.

[0555] Step 4:

[0556] The server collects market data in real time from market data providers. This data includes stock price trends and exchange rates. The server stores this data in a database and updates it as needed.

[0557] Step 5:

[0558] The server analyzes collected market data and user sentiment data using machine learning models to formulate individual investment strategies. When emotions are unstable, it can design strategies that minimize risk.

[0559] Step 6:

[0560] The server automatically adjusts the portfolio based on the formulated investment strategy, taking sentiment data into consideration and dynamically modifying risk tolerance as needed.

[0561] Step 7:

[0562] The server generates buy and sell orders based on the adjusted portfolio and executes them through the appropriate securities trading platform.

[0563] Step 8:

[0564] Users can use their device to check the current status of their portfolio. The device visually displays the latest portfolio data and notifies them of any changes in their investment strategy.

[0565] Step 9:

[0566] When a user asks a question or seeks assistance on their device, the AI ​​considers the context, including emotional data, to provide appropriate feedback and advice.

[0567] Step 10:

[0568] The server continuously monitors sentiment and market data, readjusts the portfolio as needed, and notifies the user of the changes. This allows the user to continue their investment activities with peace of mind.

[0569] (Example 2)

[0570] 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."

[0571] Traditional asset management systems have limitations in mechanically generating strategies based on market data and are unable to consider the emotional state of individual users. This creates a problem where users find it difficult to make appropriate investment decisions when their emotions are running high.

[0572] 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.

[0573] In this invention, the server includes means for detecting and analyzing the user's emotional state using an emotion engine, means for determining an investment strategy using a generative model based on emotional data and market data, and means for providing the user with emotional state-based feedback using AI. This makes it possible to present a personalized investment strategy that corresponds to the user's emotional state.

[0574] "User" refers to an individual or legal entity that uses an asset management system to conduct investment activities.

[0575] "Risk tolerance" is a measure that indicates how much risk a user is willing to accept in asset management.

[0576] "Asset target" refers to the specific asset size or objective that a user aims to achieve through their investment activities.

[0577] "Market data" refers to information collected from external sources related to asset management, such as stock prices, economic indicators, and news trends.

[0578] An "emotion engine" is a software module that detects and analyzes a user's emotional state in real time.

[0579] "Emotional data" refers to information about a user's emotions obtained from their facial expressions, tone of voice, input text, etc.

[0580] A "generative model" is a mathematical model used to generate investment strategies by analyzing sentiment data and market data.

[0581] An "investment strategy" refers to a plan for adjusting a portfolio and determining asset allocation.

[0582] A "portfolio" refers to a collection of financial assets held by a user, and is structured based on an investment strategy.

[0583] "Feedback" refers to providing information and advice to users, and is based on the user's emotional state.

[0584] This invention is implemented as an asset management system that incorporates an emotion engine to improve users' investment activities. This system is centered around a server, terminals, and users, and aims to generate investment strategies that take into account the user's emotional state.

[0585] First, users access the system using a terminal and input information about their investments. The terminal is equipped with a camera and microphone, and has a mechanism to collect emotional data by analyzing the user's facial expressions and voice tone in real time. This uses facial recognition software and voice analysis tools.

[0586] The emotional data collected by the device is sent to the server. The server uses an emotional engine to analyze the emotional data and identify the user's emotional state. In addition, market data is acquired from external sources and integrated with the emotional data. Machine learning models are used for data integration, and a data analysis engine is employed.

[0587] Based on the analysis results, a generative AI model designs the optimal investment strategy. This model takes into account the user's risk tolerance and current emotional state to present specific investment options. This information is fed back to the user via their device, and suggestions for portfolio adjustments and buy / sell actions are made.

[0588] For example, if the stock market experiences a sharp decline, the emotion engine will sense the user's anxiety, and the server will generate a strategy with reduced risk tolerance, thereby supporting the user in making calm investment decisions from a long-term perspective. An example of a prompt to the generating AI model would be, "Please tell me the optimal investment strategy considering the current market conditions and my emotions."

[0589] This system design allows users to manage their assets in a personalized way that takes their emotional state into account, resulting in greater accuracy and peace of mind in their investment decisions.

[0590] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0591] Step 1:

[0592] The terminal accepts user input. When the user enters investment-related information, the terminal uses its camera and microphone to analyze facial expressions and voice tone in real time and collect emotional data. The input data includes the user's market information and emotional state. The emotional data is analyzed by facial recognition software and voice analysis tools. As a result, data indicating the user's emotional state is output.

[0593] Step 2:

[0594] The device sends collected emotional data to the server. The transmitted data includes numerical values ​​and indicators representing the user's emotional state. The server receives this emotional data, activates an emotion engine, and further analyzes the data. This allows for a more precise quantification and classification of the user's emotional state, and the identification of emotional changes. The output is the analyzed emotional state data.

[0595] Step 3:

[0596] The server acquires market data from external sources. This data includes stock prices, economic indicators, and news trends. The server integrates this data with already analyzed sentiment data and performs data analysis using a generative AI model. This analysis outputs information that integrates sentiment and market data.

[0597] Step 4:

[0598] The server uses a generative AI model to design investment strategies. Based on integrated sentiment and market data obtained in the analysis step, it generates investment strategies that reflect risk tolerance. Data processing includes weighting of sentiment data and risk assessment in response to market fluctuations. The output is the optimal investment strategy data.

[0599] Step 5:

[0600] The server sends the generated investment strategy to the terminal. The user receives this strategy via the terminal and reviews the strategy information presented on the screen. This information includes the level of risk, recommended asset allocation, and current portfolio status. The interactive interface provides the user with the strategy option they selected as output.

[0601] Step 6:

[0602] The terminal generates and executes buy and sell orders based on the user's selection. After the user's chosen strategy is confirmed, the terminal connects to the trading system and places the buy and sell orders. The input is the specific buy and sell details decided by the user, and the output is confirmation information for the executed orders.

[0603] Step 7:

[0604] The device provides feedback to the user. Based on information from the server, it provides feedback to the user regarding the current portfolio status and updates to investment strategies. Furthermore, the AI ​​provides emotion-based advice in response to the user's questions. The input is a question from the user, and the output is appropriate advice or alternative course of action.

[0605] (Application Example 2)

[0606] 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."

[0607] When users manage their assets, they may make inappropriate investment decisions influenced by their emotions at the time. Traditional asset management systems rely on quantitative analysis based on market data and cannot take into account the user's emotional state. As a result, users may take unfavorable investment actions driven by anxiety and fear, especially when the market is volatile. This system aims to solve this problem and support more accurate investment decisions while taking the user's emotions into consideration.

[0608] 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.

[0609] In this invention, the server includes means for detecting the user's emotions, analyzing the emotional data, and reflecting it in the investment strategy; means for detecting the user's emotions using facial recognition and voice recognition; and means for collecting and analyzing market information in real time. This makes it possible to quantitatively grasp the user's emotional state, reflect it in the investment strategy, and make stable investment decisions that are not influenced by emotions.

[0610] "User" refers to an individual or organization that uses the system to manage assets or engage in investment activities.

[0611] "Risk tolerance" refers to information indicating the limit of risk that a user can accept in asset management.

[0612] "Market information" refers to data that shows the state of financial markets, including indicators such as stock prices, interest rates, and exchange rates.

[0613] A "machine learning algorithm" refers to a computational method used to analyze collected data and predict patterns and trends.

[0614] An "investment plan" is a strategy for determining asset allocation and transaction details based on market conditions and the user's circumstances.

[0615] "Financial engineering" is a general term for the composition and management of a user's portfolio, referring to a group of assets adjusted based on an investment strategy.

[0616] A "trading order" is data that indicates instructions for executing a buy or sell transaction, and is automatically generated and executed by the system.

[0617] "Emotional data" refers to data that indicates an emotional state, obtained from non-verbal information such as a user's facial expressions and tone of voice.

[0618] "Facial recognition" refers to a technology that uses a camera to acquire facial information from users, analyzes its features, and identifies individuals and their states.

[0619] "Speech recognition" refers to a technology in which a computer captures a user's voice and analyzes the content and emotions of the language.

[0620] Embodiments of the present invention will be described in detail. This system analyzes the user's emotions in real time and uses this information to aid in asset management decision-making. The system consists of three elements: a server, a terminal, and a user.

[0621] The server receives information about the user's risk tolerance and capital targets, and collects and analyzes market information in real time. The user's device acquires emotional data in real time using facial recognition and speech recognition technologies and sends that data to the server. The technologies used include "OpenCV" for facial recognition and "Speech Analysis API" for speech recognition.

[0622] The server further analyzes sentiment data and market information using machine learning algorithms. TensorFlow is used as the machine learning algorithm. Sentiment data is analyzed in detail by the sentiment engine, and an investment plan is generated based on the user's emotional state. This plan is executed by automatically adjusting financial strategies and generating trading orders.

[0623] Meanwhile, the user's device provides emotion-based feedback, helping users understand the impact of emotions on their asset management. The AI ​​system responds appropriately to emotion-based questions entered by the user, providing feedback.

[0624] For example, if the market changes rapidly, the user's device can sense their emotions, and if it detects that anxiety is rising, the server can use a machine learning model to suggest a low-risk investment strategy. In this way, it becomes possible to suggest investment strategies based on emotions. An example of a prompt to the generative AI model would be, "Input user emotion data and generate an investment plan."

[0625] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0626] Step 1:

[0627] The user begins performing routine asset management operations using the device. The device uses its camera and microphone to acquire user emotional data in real time through facial recognition and voice recognition. It acquires facial image data and voice data as input and prepares to analyze the emotional state as output.

[0628] Step 2:

[0629] Emotional data acquired by the device is transferred to the server. The server uses "OpenCV" for face recognition and a "Voice Analysis API" for speech recognition to analyze the data and identify the user's emotional state. Face image data and voice data are provided as input, and quantitative data indicating emotion is generated as output.

[0630] Step 3:

[0631] The server collects market information in real time. It uses a financial information service API to obtain market fluctuation data. It receives indicator data such as stock prices and interest rates as input, and prepares market information data as output.

[0632] Step 4:

[0633] The server uses the machine learning algorithm "TensorFlow" to analyze emotional states and market information. It takes emotional data and market information as input and generates investment strategies based on them. As output, it presents an optimal investment plan tailored to the user's risk tolerance.

[0634] Step 5:

[0635] Based on the generated investment strategy, the server automatically adjusts its financial strategy and generates trading orders. It receives an investment plan as input and prepares to generate and execute specific trading orders as output.

[0636] Step 6:

[0637] The user's device notifies them of the generated investment strategy and real-time feedback. It displays sentiment-based feedback to aid user understanding. It takes investment plans and sentiment feedback data as input and provides information that promotes the user's well-being as output.

[0638] Step 7:

[0639] When a user asks a question through their device, the AI ​​automatically processes the prompt using a generated AI model and responds with specific investment advice. It receives the user's question as input and provides appropriate feedback as output.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] [Fourth Embodiment]

[0644] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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).

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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".

[0657] The system of this invention is designed to help users automate their asset management. The system operates primarily through a server, terminal, and user interface.

[0658] The server profiles the user's risk tolerance and asset goals based on the information provided by the user. Based on this profile, the server retrieves market data in real time from various data providers. The retrieved data is stored in a database on the server and is constantly updated to the latest state. Next, machine learning models are run to analyze the data and predict market trends.

[0659] Based on the analysis results, the server develops an optimal investment strategy for each user. This strategy includes optimizing asset allocation and adjusting the portfolio. For example, if a user specifies a moderate risk tolerance and aims for stable returns, the server will recommend a stable asset allocation of stocks and bonds and build a strategy that makes the necessary adjustments.

[0660] The device provides an interface to the user. Through the device, the user can check the status of their portfolio in real time and visually understand past performance and current asset allocation. Furthermore, when the user asks a question, the device uses AI to automatically provide an answer.

[0661] The server also automatically places buy and sell orders in the user's portfolio, ensuring that trades are executed in the market. For example, if the server predicts that a particular stock will rise, it generates an order to buy that stock. This order is executed autonomously under the user's supervision, and the timing and quantity of the buy and sell orders are determined based on a pre-configured algorithm.

[0662] In this way, a system implemented according to the embodiment of the present invention enables users to manage their assets more efficiently, control risks, and respond quickly to market changes.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The user logs into the system using a terminal and enters information regarding their investment goals and risk tolerance. The terminal then sends the entered information to the server.

[0666] Step 2:

[0667] The server creates a profile based on the received user information. This profile forms the basis of the user's investment strategy and is stored in the database.

[0668] Step 3:

[0669] The server collects market data in real time from market data providers. This data includes stock prices, bond yields, exchange rates, and more. The collected data is stored in a database.

[0670] Step 4:

[0671] The server uses machine learning models to analyze collected market data. This generates investment strategy predictions based on current market conditions.

[0672] Step 5:

[0673] The server combines user profiles and market data analysis results to build a portfolio optimized for each individual user. This portfolio is automatically adjusted to maintain the optimal asset allocation for the user.

[0674] Step 6:

[0675] The server generates buy and sell orders based on the adjusted portfolio. The orders are executed at the appropriate time through the securities trading system.

[0676] Step 7:

[0677] Users can use their device to check the status of their portfolio. The device displays the latest information retrieved from the server, visually showing investment performance and current asset allocation.

[0678] Step 8:

[0679] The terminal receives questions from users and, if necessary, automatically provides answers using the server's AI capabilities. This allows users to quickly resolve their investment-related questions.

[0680] Step 9:

[0681] The server monitors market fluctuations and automatically readjusts the portfolio if necessary based on new information. The user is notified of the readjustment results.

[0682] (Example 1)

[0683] 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".

[0684] In today's complex and rapidly changing market environment, it is extremely difficult for individual investors to formulate effective investment strategies based on their own risk tolerance and asset goals, and to execute them in a timely manner. Therefore, there is a need for real-time market data analysis and automated, appropriate investment decision-making.

[0685] 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.

[0686] In this invention, the server includes means for acquiring information on risk tolerance and asset targets from users and performing profiling; means for acquiring market information in real time and storing it in a memory device; and means for predicting trends using an analytical device based on the acquired market information. This makes it possible for individual investors to easily optimize their investment strategies and accurately manage the balance between risk and return.

[0687] A "user" refers to an individual or legal entity that uses the system to manage their own assets.

[0688] "Risk tolerance" is an indicator that shows the degree to which an investor is willing to accept risk.

[0689] "Asset goals" refer to the specific financial results that a user wants to achieve through investment.

[0690] "Market information" refers to data related to financial markets, including, for example, stock prices, interest rates, and exchange rates.

[0691] "Profiling" is the process of developing an appropriate investment strategy based on a user's risk tolerance and asset goals.

[0692] "Analytical equipment" refers to hardware or software used to analyze data and predict future trends.

[0693] A "storage device" is hardware used to store data, including, for example, hard disks and SSDs.

[0694] An "investment strategy" refers to a plan for asset allocation and buying / selling, designed to achieve investment objectives.

[0695] "Means of predicting trends" refers to techniques for analyzing past and present data to predict future market changes.

[0696] The present invention is an automated platform designed to help users streamline asset management. The system primarily functions through interaction between servers, terminals, and users.

[0697] The server receives information from the user related to their risk tolerance and asset goals, and uses this information to perform profiling. Based on this profile, the server retrieves real-time market information from financial market data providers. In this data collection process, external APIs are used to store the data in storage devices. These storage devices include hard disk drives (HDDs) and solid-state drives (SSDs).

[0698] Next, the server uses machine learning frameworks such as TensorFlow and PyTorch to analyze the acquired market information. Based on the analysis results, it utilizes a generative AI model to predict future trends. Based on this prediction, the server formulates an investment strategy. This strategy includes optimal asset allocation and timing of transactions.

[0699] For example, if a user specifies that they "want to aim for a 10% annual growth with moderate risk," the server will use a generating AI model to calculate and recommend an appropriate combination of stocks and bonds. An example of a generated prompt message is, "Please provide the optimal asset allocation based on my risk tolerance."

[0700] The device provides users with a visual interface. Through this interface, users can check the status of their portfolio and analyze past performance. The device uses JavaScript and React to build its dashboard, enabling interactive operation.

[0701] Furthermore, the server automatically generates and executes buy and sell orders in the market based on the devised investment strategy. The execution of trades uses highly accurate algorithms to obtain the optimal timing based on the designed algorithms. For example, if a predictive model suggests a rise in a particular stock, it generates an order to buy that stock at the appropriate time.

[0702] This invention makes it easier for users to manage their investment activities more efficiently, mitigate risks, and respond quickly to market fluctuations.

[0703] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0704] Step 1:

[0705] The server receives input information from the user regarding their risk tolerance and asset goals. Based on this information, the server runs a profiling algorithm to generate a user profile. The user profile is output as a data structure that includes risk tolerance, target rate of return, investment period, and other information.

[0706] Step 2:

[0707] The server retrieves market information in real time from external data provider APIs. This includes stock prices, exchange rates, and interest rate information. This retrieved data is stored in storage. The input API response data is processed into a database format and updated to maintain the latest state.

[0708] Step 3:

[0709] The server uses machine learning models to analyze data based on accumulated market information. The generative AI model used here performs time-series forecasting based on pre-set parameters. As a result of this analysis, predictive data regarding future market trends is output.

[0710] Step 4:

[0711] The server develops an optimal investment strategy for each user based on the analyzed market trends. In this step, the results of the generated AI model are used to determine specific asset allocations and investment targets. As an output, a concrete action plan regarding the investment strategy is provided.

[0712] Step 5:

[0713] The server automatically generates buy and sell orders for financial instruments based on the formulated investment strategy. In this process, an algorithm determines the optimal timing and quantity for buying and selling. The generated order data is then transmitted to the market via the trading platform's API.

[0714] Step 6:

[0715] The terminal displays portfolio information provided by the server on the user interface in real time. Input information includes the current state and historical performance data of the portfolio, which are then visualized and output. Through interaction on the terminal, the user can view detailed information and apply specific filters.

[0716] (Application Example 1)

[0717] 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".

[0718] The present invention aims to improve the efficiency of automated asset management by formulating optimal investment strategies tailored to the user's risk tolerance and asset goals, as well as streamlining automated trading. Conventional methods have difficulty responding quickly to real-time market fluctuations and have limited user interfaces. Therefore, there is a need for a system that combines investment efficiency with user-friendly operation.

[0719] 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.

[0720] In this invention, the server includes means for obtaining information from the user regarding their risk tolerance and asset goals, means for collecting and analyzing market data in real time, and means for determining an investment strategy using a machine learning model based on the collected data. This enables the rapid formulation of an optimal investment strategy tailored to the user's risk tolerance and autonomous asset management in response to market fluctuations.

[0721] "Risk tolerance" is an indicator that shows the degree to which a user is willing to accept risk in asset management.

[0722] "Asset goals" refer to specific financial objectives or goals that a user hopes to achieve through asset management.

[0723] "Market data" refers to a collection of various pieces of information related to financial markets, including real-time data such as stock prices, exchange rates, and interest rates.

[0724] A "machine learning model" is an algorithm and computational model that learns patterns from data and performs prediction and classification tasks.

[0725] An "investment strategy" is a set of policies and plans for how to allocate and manage funds based on market conditions and the user's risk profile.

[0726] A "portfolio" is a collection of financial products held by a user, designed with asset allocation and risk management in mind.

[0727] "Real-time" means that information and data are processed in a way that allows them to instantly represent the current state.

[0728] "Automatic" refers to a system or device that operates independently, without requiring human intervention.

[0729] "Feedback" refers to a function in which a system automatically responds to information or questions from a user with responses or instructions.

[0730] The system implementing this invention consists of a server, a terminal, and a user interface. The server plays a central role, obtaining information from the user regarding their risk tolerance and asset goals, and profiling the user based on this information. The server obtains market data in real time from various data providers. This market data is stored in a database on the server and is constantly updated with the latest information. The server predicts market trends and develops appropriate investment strategies by running machine learning models using Python.

[0731] The terminal functions as an interface to the user, allowing them to check the status of their portfolio via their smartphone. This includes features that allow them to visually understand past performance and current asset allocation. AI running on the terminal automatically provides feedback to user questions. Furthermore, the server automatically generates buy and sell orders and executes trades based on investment strategies. This enables users to efficiently manage their assets in response to market fluctuations.

[0732] For example, if a user sets a target amount to achieve for a trip using their smartphone, the application will suggest an appropriate combination of stocks and bonds to aim for stable returns. Based on the user's set risk tolerance, the application will automatically make necessary adjustments to support asset management towards achieving the goal.

[0733] An example of a prompt to input into a generative AI model is: "The user has a moderate risk tolerance and aims for stable returns. Generate Python code that proposes the optimal investment strategy based on market data." This prompt allows the model to concretize the instructions needed to implement the logic required for the investment strategy.

[0734] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0735] Step 1:

[0736] The server receives information about risk tolerance and asset goals transmitted from the user via a smart device. Based on this input information, the server creates a user profile and prepares the basic data necessary for asset management.

[0737] Step 2:

[0738] The server retrieves market data in real time via external data provider APIs. The retrieved data is stored in the server's database and is constantly updated to the latest state. This market data includes a variety of information related to financial markets, such as stock prices, exchange rates, and interest rates.

[0739] Step 3:

[0740] The server runs a machine learning model using Python and the scikit-learn machine learning library, based on the stored market data. This model takes market data as input and outputs market trend predictions. Data processing such as feature extraction and normalization is performed, and the model makes predictions based on the trained algorithm.

[0741] Step 4:

[0742] The server builds an investment strategy tailored to the user's profile based on predictions from machine learning models. This investment strategy determines the optimal asset allocation, taking into account predicted market trends and the user's risk tolerance.

[0743] Step 5:

[0744] The server automatically generates buy and sell orders based on the established investment strategy and executes them in the market through an intermediary. Specifically, it determines the trading volume and timing according to the set trading rules and issues orders via API. This process is performed autonomously under the user's supervision.

[0745] Step 6:

[0746] The terminal receives information from the server to display the portfolio status to the user in real time. It visually displays the portfolio's earnings and risk status, allowing the user to immediately understand the situation.

[0747] Step 7:

[0748] Users can ask the system additional questions through their device. In response to the user's questions, the on-device AI uses a generated AI model to provide automated responses based on prompts, instantly offering advice and information on asset management.

[0749] 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.

[0750] This invention is implemented as an asset management system that incorporates an emotion engine to provide more advanced support for users' investment activities. This system consists of a server, terminals, and users, and aims to optimize investment strategies, particularly by taking users' emotions into consideration.

[0751] The terminal receives input from the user and detects their emotional state regarding investments in real time. This uses technology that recognizes emotions by analyzing the user's facial expressions, tone of voice, and entered text while they are using the terminal. The emotional data is sent to a server and processed along with other investment-related data.

[0752] The server runs an emotion engine to analyze emotional data and identify the user's current emotional state. This allows it to automatically adjust investment risk tolerance if emotions such as stress or anxiety are detected. The server also uses machine learning models to generate investment strategies that consider both emotions and market data. For example, if a user's anxiety is high, the server can suggest risk-reducing options and recommend a conservative investment strategy.

[0753] Meanwhile, users can receive emotion-based investment strategies through their devices and view the current status of their portfolios in real time. For example, even if the stock market experiences a sharp decline, the emotion engine recognizes the user's anxiety and supports their calm decision-making by presenting an investment situation with a long-term outlook while lowering their risk tolerance.

[0754] The system also includes a feature where AI provides emotion-based feedback when users ask questions on their devices. For example, if a user is emotional and rushing to take profits, the AI ​​will provide advice based on a calm assessment of the situation, facilitating optimal investment decisions.

[0755] In this way, the embodiment of the present invention aims to achieve more personalized asset management by considering the influence of user emotions on investment activities. This is more user-friendly than conventional systems and is effective in pursuing optimal returns.

[0756] The following describes the processing flow.

[0757] Step 1:

[0758] Users log in to the system using a terminal and enter basic information such as investment goals, risk tolerance, and investment period. The terminal then sends the entered data to the server.

[0759] Step 2:

[0760] The device monitors the user's facial expressions and voice through a microphone and camera to recognize the user's emotional state, and analyzes the emotional data using an emotion engine. The analyzed emotional data is then sent to a server.

[0761] Step 3:

[0762] The server generates a profile based on the received user's basic information and sentiment data, and stores it in a database.

[0763] Step 4:

[0764] The server collects market data in real time from market data providers. This data includes stock price trends and exchange rates. The server stores this data in a database and updates it as needed.

[0765] Step 5:

[0766] The server analyzes collected market data and user sentiment data using machine learning models to formulate individual investment strategies. When emotions are unstable, it can design strategies that minimize risk.

[0767] Step 6:

[0768] The server automatically adjusts the portfolio based on the formulated investment strategy, taking sentiment data into consideration and dynamically modifying risk tolerance as needed.

[0769] Step 7:

[0770] The server generates buy and sell orders based on the adjusted portfolio and executes them through the appropriate securities trading platform.

[0771] Step 8:

[0772] Users can use their device to check the current status of their portfolio. The device visually displays the latest portfolio data and notifies them of any changes in their investment strategy.

[0773] Step 9:

[0774] When a user asks a question or seeks assistance on their device, the AI ​​considers the context, including emotional data, to provide appropriate feedback and advice.

[0775] Step 10:

[0776] The server continuously monitors sentiment and market data, readjusts the portfolio as needed, and notifies the user of the changes. This allows the user to continue their investment activities with peace of mind.

[0777] (Example 2)

[0778] 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".

[0779] Traditional asset management systems have limitations in mechanically generating strategies based on market data and are unable to consider the emotional state of individual users. This creates a problem where users find it difficult to make appropriate investment decisions when their emotions are running high.

[0780] 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.

[0781] In this invention, the server includes means for detecting and analyzing the user's emotional state using an emotion engine, means for determining an investment strategy using a generative model based on emotional data and market data, and means for providing the user with emotional state-based feedback using AI. This makes it possible to present a personalized investment strategy that corresponds to the user's emotional state.

[0782] "User" refers to an individual or legal entity that uses an asset management system to conduct investment activities.

[0783] "Risk tolerance" is a measure that indicates how much risk a user is willing to accept in asset management.

[0784] "Asset target" refers to the specific asset size or objective that a user aims to achieve through their investment activities.

[0785] "Market data" refers to information collected from external sources related to asset management, such as stock prices, economic indicators, and news trends.

[0786] An "emotion engine" is a software module that detects and analyzes a user's emotional state in real time.

[0787] "Emotional data" refers to information about a user's emotions obtained from their facial expressions, tone of voice, input text, etc.

[0788] A "generative model" is a mathematical model used to generate investment strategies by analyzing sentiment data and market data.

[0789] An "investment strategy" refers to a plan for adjusting a portfolio and determining asset allocation.

[0790] A "portfolio" refers to a collection of financial assets held by a user, and is structured based on an investment strategy.

[0791] "Feedback" refers to providing information and advice to users, and is based on the user's emotional state.

[0792] This invention is implemented as an asset management system that incorporates an emotion engine to improve users' investment activities. This system is centered around a server, terminals, and users, and aims to generate investment strategies that take into account the user's emotional state.

[0793] First, users access the system using a terminal and input information about their investments. The terminal is equipped with a camera and microphone, and has a mechanism to collect emotional data by analyzing the user's facial expressions and voice tone in real time. This uses facial recognition software and voice analysis tools.

[0794] The emotional data collected by the device is sent to the server. The server uses an emotional engine to analyze the emotional data and identify the user's emotional state. In addition, market data is acquired from external sources and integrated with the emotional data. Machine learning models are used for data integration, and a data analysis engine is employed.

[0795] Based on the analysis results, a generative AI model designs the optimal investment strategy. This model takes into account the user's risk tolerance and current emotional state to present specific investment options. This information is fed back to the user via their device, and suggestions for portfolio adjustments and buy / sell actions are made.

[0796] For example, if the stock market experiences a sharp decline, the emotion engine will sense the user's anxiety, and the server will generate a strategy with reduced risk tolerance, thereby supporting the user in making calm investment decisions from a long-term perspective. An example of a prompt to the generating AI model would be, "Please tell me the optimal investment strategy considering the current market conditions and my emotions."

[0797] This system design allows users to manage their assets in a personalized way that takes their emotional state into account, resulting in greater accuracy and peace of mind in their investment decisions.

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1:

[0800] The terminal accepts user input. When the user enters investment-related information, the terminal uses its camera and microphone to analyze facial expressions and voice tone in real time and collect emotional data. The input data includes the user's market information and emotional state. The emotional data is analyzed by facial recognition software and voice analysis tools. As a result, data indicating the user's emotional state is output.

[0801] Step 2:

[0802] The device sends collected emotional data to the server. The transmitted data includes numerical values ​​and indicators representing the user's emotional state. The server receives this emotional data, activates an emotion engine, and further analyzes the data. This allows for a more precise quantification and classification of the user's emotional state, and the identification of emotional changes. The output is the analyzed emotional state data.

[0803] Step 3:

[0804] The server acquires market data from external sources. This data includes stock prices, economic indicators, and news trends. The server integrates this data with already analyzed sentiment data and performs data analysis using a generative AI model. This analysis outputs information that integrates sentiment and market data.

[0805] Step 4:

[0806] The server uses a generative AI model to design investment strategies. Based on integrated sentiment and market data obtained in the analysis step, it generates investment strategies that reflect risk tolerance. Data processing includes weighting of sentiment data and risk assessment in response to market fluctuations. The output is the optimal investment strategy data.

[0807] Step 5:

[0808] The server sends the generated investment strategy to the terminal. The user receives this strategy via the terminal and reviews the strategy information presented on the screen. This information includes the level of risk, recommended asset allocation, and current portfolio status. The interactive interface provides the user with the strategy option they selected as output.

[0809] Step 6:

[0810] The terminal generates and executes buy and sell orders based on the user's selection. After the user's chosen strategy is confirmed, the terminal connects to the trading system and places the buy and sell orders. The input is the specific buy and sell details decided by the user, and the output is confirmation information for the executed orders.

[0811] Step 7:

[0812] The device provides feedback to the user. Based on information from the server, it provides feedback to the user regarding the current portfolio status and updates to investment strategies. Furthermore, the AI ​​provides emotion-based advice in response to the user's questions. The input is a question from the user, and the output is appropriate advice or alternative course of action.

[0813] (Application Example 2)

[0814] 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".

[0815] When users manage their assets, they may make inappropriate investment decisions influenced by their emotions at the time. Traditional asset management systems rely on quantitative analysis based on market data and cannot take into account the user's emotional state. As a result, users may take unfavorable investment actions driven by anxiety and fear, especially when the market is volatile. This system aims to solve this problem and support more accurate investment decisions while taking the user's emotions into consideration.

[0816] 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.

[0817] In this invention, the server includes means for detecting the user's emotions, analyzing the emotional data, and reflecting it in the investment strategy; means for detecting the user's emotions using facial recognition and voice recognition; and means for collecting and analyzing market information in real time. This makes it possible to quantitatively grasp the user's emotional state, reflect it in the investment strategy, and make stable investment decisions that are not influenced by emotions.

[0818] "User" refers to an individual or organization that uses the system to manage assets or engage in investment activities.

[0819] "Risk tolerance" refers to information indicating the limit of risk that a user can accept in asset management.

[0820] "Market information" refers to data that shows the state of financial markets, including indicators such as stock prices, interest rates, and exchange rates.

[0821] A "machine learning algorithm" refers to a computational method used to analyze collected data and predict patterns and trends.

[0822] An "investment plan" is a strategy for determining asset allocation and transaction details based on market conditions and the user's circumstances.

[0823] "Financial engineering" is a general term for the composition and management of a user's portfolio, referring to a group of assets adjusted based on an investment strategy.

[0824] A "trading order" is data that indicates instructions for executing a buy or sell transaction, and is automatically generated and executed by the system.

[0825] "Emotional data" refers to data that indicates an emotional state, obtained from non-verbal information such as a user's facial expressions and tone of voice.

[0826] "Facial recognition" refers to a technology that uses a camera to acquire facial information from users, analyzes its features, and identifies individuals and their states.

[0827] "Speech recognition" refers to a technology in which a computer captures a user's voice and analyzes the content and emotions of the language.

[0828] Embodiments of the present invention will be described in detail. This system analyzes the user's emotions in real time and uses this information to aid in asset management decision-making. The system consists of three elements: a server, a terminal, and a user.

[0829] The server receives information about the user's risk tolerance and capital targets, and collects and analyzes market information in real time. The user's device acquires emotional data in real time using facial recognition and speech recognition technologies and sends that data to the server. The technologies used include "OpenCV" for facial recognition and "Speech Analysis API" for speech recognition.

[0830] The server further analyzes sentiment data and market information using machine learning algorithms. TensorFlow is used as the machine learning algorithm. Sentiment data is analyzed in detail by the sentiment engine, and an investment plan is generated based on the user's emotional state. This plan is executed by automatically adjusting financial strategies and generating trading orders.

[0831] Meanwhile, the user's device provides emotion-based feedback, helping users understand the impact of emotions on their asset management. The AI ​​system responds appropriately to emotion-based questions entered by the user, providing feedback.

[0832] For example, if the market changes rapidly, the user's device can sense their emotions, and if it detects that anxiety is rising, the server can use a machine learning model to suggest a low-risk investment strategy. In this way, it becomes possible to suggest investment strategies based on emotions. An example of a prompt to the generative AI model would be, "Input user emotion data and generate an investment plan."

[0833] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0834] Step 1:

[0835] The user begins performing routine asset management operations using the device. The device uses its camera and microphone to acquire user emotional data in real time through facial recognition and voice recognition. It acquires facial image data and voice data as input and prepares to analyze the emotional state as output.

[0836] Step 2:

[0837] Emotional data acquired by the device is transferred to the server. The server uses "OpenCV" for face recognition and a "Voice Analysis API" for speech recognition to analyze the data and identify the user's emotional state. Face image data and voice data are provided as input, and quantitative data indicating emotion is generated as output.

[0838] Step 3:

[0839] The server collects market information in real time. It uses a financial information service API to obtain market fluctuation data. It receives indicator data such as stock prices and interest rates as input, and prepares market information data as output.

[0840] Step 4:

[0841] The server uses the machine learning algorithm "TensorFlow" to analyze emotional states and market information. It takes emotional data and market information as input and generates investment strategies based on them. As output, it presents an optimal investment plan tailored to the user's risk tolerance.

[0842] Step 5:

[0843] Based on the generated investment strategy, the server automatically adjusts its financial strategy and generates trading orders. It receives an investment plan as input and prepares to generate and execute specific trading orders as output.

[0844] Step 6:

[0845] The user's device notifies them of the generated investment strategy and real-time feedback. It displays sentiment-based feedback to aid user understanding. It takes investment plans and sentiment feedback data as input and provides information that promotes the user's well-being as output.

[0846] Step 7:

[0847] When a user asks a question through their device, the AI ​​automatically processes the prompt using a generated AI model and responds with specific investment advice. It receives the user's question as input and provides appropriate feedback as output.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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."

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] The following is further disclosed regarding the embodiments described above.

[0870] (Claim 1)

[0871] Means of obtaining information from users regarding their risk tolerance and asset goals,

[0872] A means of collecting and analyzing market data in real time,

[0873] A means of determining investment strategies using machine learning models based on collected data,

[0874] A means of automatically adjusting the portfolio based on the determined investment strategy,

[0875] A means of generating and executing buy and sell orders based on the adjusted portfolio,

[0876] A means of notifying users of the status of their portfolio,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, comprising means for monitoring market fluctuations and readjusting the portfolio as necessary.

[0880] (Claim 3)

[0881] The system according to claim 1, comprising means for automatically providing feedback by AI in response to questions from users.

[0882] "Example 1"

[0883] (Claim 1)

[0884] A means of obtaining information from users regarding their risk tolerance and asset goals, and performing profiling.

[0885] A means of acquiring market information in real time and storing it in a storage device,

[0886] A means of predicting trends using analytical equipment based on acquired market information,

[0887] A means of formulating an optimal investment strategy using digital computing circuits based on prediction results,

[0888] A means of automatically trading financial instruments according to a devised investment strategy,

[0889] A means of communicating the status of the portfolio to the user through a visual display device,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, comprising means for readjusting a portfolio by programmed instructions in response to market fluctuations.

[0893] (Claim 3)

[0894] The system according to claim 1, further comprising means for automatically responding to user inquiries using an artificial intelligence system.

[0895] "Application Example 1"

[0896] (Claim 1)

[0897] Means of obtaining information from users regarding their risk tolerance and asset goals,

[0898] A means of collecting and analyzing market data in real time,

[0899] A means of determining investment strategies using machine learning models based on collected data,

[0900] A means for automatically adjusting a resource portfolio based on a determined investment strategy, and generating and executing buy and sell orders,

[0901] A means of notifying users of the status of their asset portfolio and providing feedback via smart devices,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, comprising means for monitoring market fluctuations, readjusting the asset portfolio as necessary, and executing trading strategies based on the user's target profit in real time.

[0905] (Claim 3)

[0906] The system according to claim 1, comprising means for automatically providing responses to user questions using artificial intelligence and assisting the user with financial goals via a smartphone.

[0907] "Example 2 of combining an emotion engine"

[0908] (Claim 1)

[0909] Means of obtaining information from users regarding their risk tolerance and asset goals,

[0910] A means of collecting and analyzing market data in real time,

[0911] A means of detecting and analyzing the user's emotional state using an emotion engine,

[0912] A means of determining an investment strategy using a generative model based on collected and analyzed sentiment data and market data,

[0913] A means of automatically adjusting the portfolio based on the determined investment strategy,

[0914] A means of generating and executing buy and sell orders based on the adjusted portfolio,

[0915] A means of providing users with emotional state-based feedback using AI,

[0916] A means of notifying users of the status of their portfolio,

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The system according to claim 1, comprising means for monitoring market fluctuations and the emotional state of users and readjusting the portfolio as necessary.

[0920] (Claim 3)

[0921] The system according to claim 1, comprising means for automatically providing emotionally-based feedback in response to a user's question.

[0922] "Application example 2 when combining with an emotional engine"

[0923] (Claim 1)

[0924] Means for obtaining information from users regarding their risk tolerance and capital targets,

[0925] A means of collecting and analyzing market information in real time,

[0926] A means of determining an investment plan using a machine learning algorithm based on collected information,

[0927] A means of automatically adjusting financial management based on the decided investment plan,

[0928] A means of generating and executing trading orders based on adjusted financial strategies,

[0929] A means of notifying users of the status of their financial investments,

[0930] A means to detect users' emotions, analyze emotional data, and reflect it in investment strategies,

[0931] A method for detecting a user's emotions using facial recognition and voice recognition,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, comprising means for monitoring market fluctuations and readjusting financial strategies as necessary.

[0935] (Claim 3)

[0936] The system according to claim 1, comprising means for automatically providing feedback by AI in response to questions from users. [Explanation of Symbols]

[0937] 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 obtaining information from users regarding their risk tolerance and asset goals, A means of collecting and analyzing market data in real time, A means of determining investment strategies using machine learning models based on collected data, A means for automatically adjusting a resource portfolio based on a determined investment strategy, and generating and executing buy and sell orders, A means of notifying users of the status of their asset portfolio and providing feedback via smart devices, A system that includes this.

2. The system according to claim 1, comprising means for monitoring market fluctuations, readjusting the asset portfolio as necessary, and executing a trading strategy based on the user's target profit in real time.

3. The system according to claim 1, comprising means for automatically providing responses to user questions using artificial intelligence and assisting the user with financial goals via a smartphone.

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