System

Abstract

A system is provided.SOLUTION: A system comprising: an input means for a user to input an investment profile; a collection means for collecting market data based on the investment profile; a generation means for executing a prediction model based on economic theory to generate economic prediction data; an analysis means for integrating and analyzing the economic prediction data and the market data using artificial intelligence; and a providing means for providing an investment proposal generated by the analysis means to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's investment environment, there are individual investors interested in investment strategies based on economic theory, financial institutions seeking investment decisions that take macroeconomic trends into account, and companies and policymakers who rely on data-driven economic analysis. However, challenges faced by these targets include the complexity of integrating and analyzing economic models and real market data, the lack of investment strategies that can quickly respond to macroeconomic fluctuations, and the difficulty of making practical investment decisions using economic theory. These challenges make it difficult to formulate highly accurate investment strategies and create a situation in which accurate investment decisions are difficult to achieve. [Means for solving the problem]

[0005] To address the above-mentioned challenges, the present invention provides an AI-based investment advisory system that combines economic models and machine learning. Specifically, the system includes an input means for a user to input an investment profile, a collection means for collecting market data based on the investment profile, a generation means for generating economic forecast data by executing a prediction model based on economic theory, an analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence, and a provision means for providing the user with investment proposals generated by the analysis means. Furthermore, the system includes an interactive interface means for a user to input an investment profile through an interface, thereby improving user convenience and accuracy. Furthermore, the collection means collects market data in real time, enabling the provision of prompt and accurate investment strategies. This system enables investment advice that quickly responds to changes in the economic environment.

[0006] "User" means any person or entity that utilizes the System to enter an investment profile and receive investment offers.

[0007] An "investment profile" is a collection of information about investments, such as risk tolerance, investment period, and areas of interest, provided by a user.

[0008] "Input means" refers to an interface or device that allows a user to input an investment profile.

[0009] A "collection instrument" is an instrument that has the function of collecting market data based on an investment profile.

[0010] "Market data" refers to data such as stock prices, interest rates, exchange rates, and economic indicators collected from actual financial markets.

[0011] The "generation means" is a means having a function for generating economic forecast data by executing a forecast model based on economic theory.

[0012] "Economic forecast data" is data that predicts future economic indicators based on economic theories and models.

[0013] "Analysis tools" are tools that have the function of integrating and analyzing economic forecast data and market data using artificial intelligence.

[0014] "Artificial intelligence" is a system that uses machine learning and other AI technologies to analyze data and make judgments and predictions based on the results.

[0015] The "provision means" is a means having a function for providing the investment proposal generated by the analysis means to the user.

[0016] "Interactive interface" refers to an interactive interface such as a graphical user interface that allows users to operate intuitively.

[0017] "Real-time" means that data collection and processing occurs almost instantaneously, with minimal delay. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

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

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention realizes an AI-based investment advisory system that combines economic models and machine learning. The system is designed around a workflow in which a user inputs an investment profile, a server collects and analyzes market data, and generates investment recommendations.

[0040] System configuration

[0041] The system consists of three main components: the server, the terminal, and the user. The server acts as the hub for data analysis, while the terminal serves as the interface between the user and the server. Users input their investment profile and receive customized investment proposals.

[0042] Program processing overview

[0043] The processing of the program for realizing the present invention will be explained below in natural language.

[0044] 1. User Input

[0045] Users enter their investment profile (risk tolerance, investment horizon, investment interests, etc.) through an interactive interface on their device, using simple questions and sliders.

[0046] 2. Data transmission

[0047] The terminal sends the entered investment profile to the server, which then sends the data as an HTTP POST request and parses it.

[0048] 3. Market Data Collection

[0049] The server collects real-time market data from financial markets. This data is obtained through APIs and includes stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database.

[0050] 4. Run the predictive model

[0051] The server runs a predictive model based on economic theory to generate economic forecasts based on the collected market data. For example, the predictive model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends.

[0052] 5. Integrated analysis using AI

[0053] The server integrates the generated economic forecast data with market data and performs investment analysis using AI (artificial intelligence), which uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis.

[0054] 6. Generating and delivering investment proposals

[0055] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. These proposals are then sent back to the user via their terminal.

[0056] Specific examples

[0057] For example, a user can set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and input their investment interest as "technology." When the device sends this data to the server, the server collects market data for the technology sector in real time and runs predictive models such as GDP growth rate and interest rate trends. The AI ​​then performs an integrated analysis and recommends a portfolio of technology stocks with high risk but also high returns. This proposal is provided to the user.

[0058] In this way, the system can quickly and accurately meet the user's investment needs, allowing the user to obtain the optimal investment strategy based on their own conditions and receive support in making investment decisions.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user enters an investment profile.

[0062] The user inputs information about the investment (risk tolerance, investment period, areas of interest, etc.) through the terminal interface.

[0063] When the user has completed the input, he / she presses the "Submit" button to send the data.

[0064] Step 2:

[0065] The terminal sends the input data to the server.

[0066] The terminal retrieves the investment profile entered by the user and sends it to the server as an HTTP POST request.

[0067] The transmitted data is received by the server and prepared for analysis.

[0068] Step 3:

[0069] A server collects market data.

[0070] The server sends an API request to retrieve the latest market data from a financial market data provider.

[0071] For example, this includes data on stock prices, interest rates, exchange rates, economic indicators, etc.

[0072] The collected data is stored in a database on the server.

[0073] Step 4:

[0074] The server makes predictions based on economic models.

[0075] The server runs a predictive model based on economic theory on the collected market data.

[0076] For example, predicting future economic indicators such as GDP growth rate, inflation rate, and interest rate trends.

[0077] The generated economic forecast data is stored internally for the next analysis step.

[0078] Step 5:

[0079] The server performs integrated analysis using artificial intelligence.

[0080] The server integrates the generated economic forecast data with market data and performs analysis using AI (artificial intelligence).

[0081] AI uses machine learning algorithms to perform tasks such as risk-adjusted return calculations, portfolio optimization, and market segmentation analysis.

[0082] As a result of the integrated analysis, a customized investment recommendation is generated for the user.

[0083] Step 6:

[0084] The server generates an investment proposal and sends it to the terminal.

[0085] Based on the results of the AI ​​analysis, the server creates investment proposals that are best suited to the user's investment profile.

[0086] This includes specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition.

[0087] The generated investment proposal is transmitted to the terminal.

[0088] Step 7:

[0089] The terminal displays the investment proposal to the user.

[0090] The terminal displays the investment proposals received from the server on the interface.

[0091] The user can review the displayed customized investment proposals and use them to help make investment decisions.

[0092] This processing flow allows users to quickly and accurately obtain the optimal investment strategy based on their own investment conditions.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] Conventional investment advisory systems have the drawback of requiring extensive specialized knowledge and effort for users to individually collect market data, make economic forecasts, and formulate optimal investment strategies. Furthermore, it is difficult to collect real-time market data or perform advanced analysis using AI, and investment recommendations often lack accuracy and speed.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes input means for a user to input an investment profile, collection means for collecting market data based on the investment profile, generation means for generating economic forecast data by executing a forecast model based on economic theory, analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence, provision means for providing the user with investment proposals generated by the analysis means, communication means for transmitting the data input by the user to the server as an HTTP POST request, and interface means for the user to easily input an investment profile through an interactive interface, thereby making it possible to provide the user with investment proposals with high accuracy and speed.

[0098] "User" means any person or entity that utilizes the system to enter an investment profile and receive investment offers.

[0099] "Investment Profile" means individual information regarding investments, such as a user's risk tolerance, investment horizon, and investment interests.

[0100] "Input Means" refers to a device or mechanism for a user to input an investment profile.

[0101] "Collection Method" refers to the mechanisms and algorithms used to collect market data based on an investment profile.

[0102] "Generation means" means a system or software for executing a forecasting model based on economic theory and generating economic forecast data.

[0103] "Analysis means" refers to a mechanism for integrating economic forecast data and market data using artificial intelligence to conduct analysis.

[0104] "Providing means" refers to an interface or communication means for providing the user with the investment proposals generated by the analyzing means.

[0105] "Means of communication" refers to the protocols and technologies used to send user-entered data to a server as an HTTP POST request.

[0106] "Interface means" means a mechanism that allows a user to easily input an investment profile through an interactive interface.

[0107] "Market data" refers to data such as stock prices, interest rates, exchange rates, and economic indicators collected in real time from financial markets.

[0108] "Predictive model" means a mathematical model that analyzes market data based on economic theory and predicts future economic indicators.

[0109] "Artificial intelligence" refers to systems that include advanced data analysis techniques such as machine learning and deep learning.

[0110] The mode for implementing the invention is described as follows. The present invention realizes an AI-based investment advisory system that combines economic models and machine learning. This system is designed around a flow in which a user inputs an investment profile, and a server collects and analyzes market data to generate investment proposals. The system consists of three main components: a server, a terminal, and a user. The server functions as the center of data analysis, and the terminal serves as a link between the user and the server through an interface. The user inputs an investment profile and receives customized investment proposals.

[0111] Hardware and software used

[0112] server:

[0113] The servers consist of physical servers or virtual machines equipped with high-performance CPUs, sufficient memory, and large disk storage. They run economic forecasting models implemented in Python or R, machine learning algorithms, and database management systems.

[0114] The server has an interface to collect financial market data through APIs, using common APIs from financial data providers.

[0115] Device:

[0116] A terminal is a device used by a user, such as a smartphone, tablet, or PC. A web browser or a dedicated application is installed on the terminal, and a user interactive interface is implemented.

[0117] The terminal includes a communication module (e.g., HTTP POST request) for transmitting the input investment profile to the server.

[0118] User:

[0119] Users are individuals or legal entities who utilize the system to enter their investment profile and receive AI-based investment proposals.

[0120] Program processing overview

[0121] The processing of the program for realizing the present invention will be explained below in natural language.

[0122] User input

[0123] Users enter their investment profile (risk tolerance, investment period, investment interests, etc.) through the interactive interface of the terminal. This is done using simple questions and sliders. For example, a user might set "risk tolerance: high," "investment period: long-term (5 years or more)," and "investment interests: technology."

[0124] Sending data

[0125] The terminal sends the entered investment profile to the server, which then sends the data as an HTTP POST request and parses it.

[0126] Market Data Collection

[0127] The server collects real-time market data from financial markets through APIs, including stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database.

[0128] Running a predictive model

[0129] The server runs predictive models based on economic theory on the collected market data, which are designed to forecast economic indicators such as GDP growth, inflation, and interest rate trends.

[0130] Integrated analysis using AI

[0131] The server integrates the generated economic forecast data with market data and performs investment analysis using AI (artificial intelligence), which uses machine learning algorithms to calculate risk-adjusted returns and optimize portfolios.

[0132] Generate and deliver investment proposals

[0133] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated proposals are sent back to the user via their terminal.

[0134] Specific examples

[0135] For example, a user can set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and input their investment interest as "technology." When the device sends this data to the server, the server collects market data for the technology sector in real time and runs predictive models such as GDP growth rate and interest rate trends. The AI ​​then performs an integrated analysis and recommends a portfolio of technology stocks with high risk but also high returns. This proposal is provided to the user.

[0136] Prompt Sentence Examples

[0137] "A user has entered their risk tolerance as 'high', their investment horizon as 'long term (5+ years)', and their investment interest as 'technology'. Using this data, please gather real-time market data for the technology sector and run forecasting models on GDP growth and interest rate trends. Combine this data and suggest an optimal high-risk, high-return technology stock portfolio."

[0138] In this way, the system can quickly and accurately meet the user's investment needs, allowing the user to obtain the optimal investment strategy based on their own conditions and receive support in making investment decisions.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1:

[0141] User investment profile input

[0142] Input: The user inputs their investment profile (risk tolerance, investment horizon, investment interest areas, etc.) through an interactive interface.

[0143] How it works: The device receives the input information and organizes it into a data structure. Specifically, it saves each item as JSON format data. For example, the risk tolerance is "high," the investment period is "long-term (5 years or more)," and the investment interest area is "technology."

[0144] Output: Organized investment profile data in JSON format.

[0145] Step 2:

[0146] Submit your investment profile data

[0147] Input: Investment profile data compiled in Step 1.

[0148] How it works: The terminal sends investment profile data to the server using an HTTP POST request, which includes all profile information entered by the user.

[0149] Output: Investment profile data sent to the server.

[0150] Step 3:

[0151] Market Data Collection

[0152] Input: The investment profile data received by the server in step 2.

[0153] How it works: The server sends requests to financial data providers' APIs to gather market data, including stock prices, interest rates, exchange rates, economic indicators, etc. The server then stores the data in a database.

[0154] Output: Collected market data (stock prices, interest rates, exchange rates, economic indicators, etc.).

[0155] Step 4:

[0156] Running economic forecasting models

[0157] Input: Market data obtained in step 3.

[0158] How it works: The server initializes an economic forecasting model implemented in Python or R and feeds collected market data into the model. The model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends. The forecast results are stored in a database.

[0159] Output: Forecasted economic indicator data (GDP growth, inflation, interest rate trends, etc.).

[0160] Step 5:

[0161] Integrated analysis using AI

[0162] Input: Economic forecast and market data obtained in Step 4.

[0163] How it works: The server uses machine learning algorithms to integrate and analyze forecast data and market data, specifically to calculate risk-adjusted returns and optimize portfolios.

[0164] Output: Investment proposal data as a result of the analysis.

[0165] Step 6:

[0166] Generate and deliver investment proposals

[0167] Input: The investment proposal data generated in Step 5.

[0168] Operation: Based on the user's investment profile, the server generates an investment proposal containing a list of specific investment products (stocks, bonds, ETFs, etc.) and a recommended portfolio composition. This proposal is sent back to the terminal.

[0169] Output: Investment proposal sent to the terminal.

[0170] Specific examples

[0171] For example, a user may set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and their investment interest area as "technology." When this data is sent to the server, the server collects market data in the technology sector and runs forecasting models for GDP growth rates and interest rate trends. The AI ​​performs an integrated analysis and recommends a portfolio of high-risk, high-return technology stocks. This proposal is then sent back to the user.

[0172] Prompt Sentence Examples

[0173] "A user has entered their risk tolerance as 'high', their investment horizon as 'long term (5+ years)', and their investment interest as 'technology'. Using this data, please gather real-time market data for the technology sector and run forecasting models on GDP growth and interest rate trends. Combine this data and suggest an optimal high-risk, high-return technology stock portfolio."

[0174] (Application example 1)

[0175] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0176] Today's investment environment is becoming increasingly complex, and users need to understand and analyze a large amount of information in real time to make appropriate investment decisions. However, conventional investment advisory systems lack interactive real-time investment suggestions and intuitive information provision in virtual environments, making it difficult for users to make quick and accurate investment decisions. To solve this issue, a new system is needed that can provide investment suggestions based on user profiles in real time and display them in a virtual environment.

[0177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0178] In this invention, the server includes input means for a user to input an investment profile, collection means for collecting market data based on the investment profile, generation means for generating economic forecast data by executing a prediction model based on economic theory, analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence, and display means for displaying investment proposals in a virtual environment in real time, including provision means for providing the investment proposals generated by the analysis means to the user, thereby enabling the user to make appropriate investment decisions in an interactive real-time manner.

[0179] The "input means for a user to input an investment profile" is a means for a user to input information such as an individual's investment risk tolerance, investment period, and investment areas of interest.

[0180] The "collection means for collecting market data based on an investment profile" is a means for collecting relevant market data based on an investment profile input by a user.

[0181] The "means for generating economic forecast data by executing a forecast model based on economic theory" refers to a means for analyzing market data based on economic theory and predicting future market trends and economic indicators.

[0182] "Analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence" refers to means for using artificial intelligence technology to integrate collected market data and generated economic forecast data and perform investment analysis.

[0183] "Display means for displaying investment proposals in a virtual environment in real time, including provision means for providing the user with investment proposals generated by the analysis means" refers to means for providing the user with investment proposals generated based on the analysis results and displaying them in real time in a virtual environment.

[0184] The "interactive interface means" is a means for providing an interactive interface that allows a user to input to the system.

[0185] The "user interface means for inputting a user profile using smart glasses" refers to an interface means for a user to input profile information via smart glasses.

[0186] A "display means for collecting market data in real time and displaying it within a virtual environment" is a means for collecting market data in real time and displaying that information to a user within a virtual environment.

[0187] The present invention is an AI-based investment advisory system that combines predictive models based on economic theory with machine learning algorithms. The system is composed of three main components, including a terminal and a server. The terminal functions as a user input means, and the server functions as the center for data analysis and investment proposal generation. Specific embodiments of the present invention are described in detail below.

[0188] 1. Fill out your investment profile

[0189] The user uses the smart glasses to input their investment profile (risk tolerance, investment period, areas of interest, etc.) via voice input or gesture recognition. This input data is sent to the server via the terminal. For example, the user can input their profile as follows:

[0190] "High risk tolerance, long-term investment horizon, technology interests"

[0191] 2. Market Data Collection

[0192] The server collects market data (e.g., stock prices, exchange rates, interest rates, economic indicators) from financial markets in real time. The market data is obtained via RESTful APIs (e.g., Alpha Vantage API or Yahoo Finance API) and stored in a database. This ensures that the data always reflects the latest market trends.

[0193] 3. Implementing economic forecasting models

[0194] The server runs a predictive model based on economic theory to generate economic forecast data from the collected market data. The model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends, and uses machine learning algorithms such as random forests and neural networks.

[0195] 4. Integrated analysis using artificial intelligence

[0196] The server integrates the generated economic forecast data with real-time collected market data and performs investment analysis using artificial intelligence, which calculates risk-adjusted returns, optimizes portfolios, and performs market segmentation analysis, thereby generating investment proposals that best fit the user's investment profile.

[0197] 5. Submitting investment proposals

[0198] Finally, the server provides the user with an investment recommendation generated by the integrated analysis. This recommendation includes specific investment products (stocks, bonds, ETFs, etc.) and a recommended portfolio composition. For example, a "high-risk but high-return technology stock portfolio" may be recommended. This investment recommendation is displayed to the user in real time via the smart glasses' HUD (head-up display).

[0199] Examples and prompts

[0200] For example, if a user enters "I'm interested in long-term, high-risk technology stocks," the system will collect market data for the technology sector in real time, analyze the risks and rewards, and display appropriate investment suggestions on the HUD. An example of a prompt sentence is as follows:

[0201] "Enter your user profile: Risk tolerance is 'high,' investment horizon is 'long-term,' and area of ​​interest is 'technology.'"

[0202] In this way, the system of the present invention helps users make investment decisions intuitively and quickly, and provides an interactive investment experience in a virtual environment.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1: User enters investment profile using smart glasses

[0205] Users can input their investment profile (e.g., risk tolerance, investment horizon, areas of interest) using the smart glasses' voice input or gesture recognition functions. The input data is stored on the device as user profile data.

[0206] Input: User's investment profile (risk tolerance, investment horizon, areas of interest)

[0207] Output: User profile data

[0208] Specific actions: Type in something like "My risk tolerance is high, my investment horizon is long-term, and my area of ​​interest is technology" using your voice.

[0209] Step 2: Send user profile data to the server

[0210] The device sends the entered user profile data to the server via an HTTP POST request, which the server receives and stores in a database for analysis.

[0211] Input: User profile data

[0212] Output: HTTP POST request to the server

[0213] Specific operation: The device sends profile data to a specific URL on the server.

[0214] Step 3: The server collects market data in real time

[0215] The server collects market data in real time using an API (e.g., Alpha Vantage API), and the collected data is stored in the server's database.

[0216] Input: API request parameters (stock prices, interest rates, exchange rates, etc.)

[0217] Output: Market data

[0218] Specific operation: The server periodically calls the API to obtain the latest market data and stores it in the database.

[0219] Step 4: Run the predictive model to generate economic forecast data

[0220] The server executes a predictive model (e.g., a machine learning algorithm) based on economic theory to analyze the collected market data and generate economic forecast data, which is stored as predictive model data.

[0221] Input: Market Data

[0222] Output: Economic forecast data

[0223] How it works: The server runs predictive models using random forests and neural networks to predict GDP growth and inflation rates.

[0224] Step 5: Integrate economic forecast data and market data

[0225] The server integrates the generated economic forecast data and market data, and performs investment analysis using machine learning algorithms (e.g., risk-adjusted return calculation, portfolio optimization), generating integrated analysis data.

[0226] Input: Economic forecast data, market data

[0227] Output: Integrated analysis data

[0228] How it works: The server uses an AI model to calculate risk and return and proposes the optimal investment strategy.

[0229] Step 6: Generate investment proposals and provide them to users

[0230] The server generates optimal investment proposals based on the integrated analytical data and provides them to users, which are displayed in real time on the HUD of the smart glasses.

[0231] Input: Integrated analysis data

[0232] Output: Investment proposal

[0233] Specific operation: The server generates a proposal and displays it to the user through smart glasses as a "portfolio of technology stocks with high risk but high return."

[0234] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0235] This invention provides more accurate investment recommendations by combining an AI-based investment advisory system that combines economic models and machine learning with an emotion engine that recognizes user emotions. This system generates investment recommendations taking into account not only the user's investment profile but also the user's emotional state.

[0236] System configuration

[0237] The system consists of three main components: a server, a terminal, and a user. The server is the center of data analysis, and the terminal serves as the interface between the user and the server. The user inputs an investment profile and receives customized investment proposals. In addition, the present invention incorporates an emotion engine that acquires user emotion data and uses it for analysis.

[0238] Program processing overview

[0239] The processing of the program for realizing the present invention will be explained below in natural language.

[0240] 1. User enters investment profile

[0241] Users input investment information (risk tolerance, investment period, investment interests, etc.) through the device's interactive interface. During input, sensors such as a camera and microphone are used by the emotion engine to analyze the user's facial expressions and tone of voice to obtain emotional data.

[0242] 2. Collecting Emotional Data

[0243] The device transmits the acquired emotional data to a server, where it is analyzed along with the investment profile.

[0244] 3. Market Data Collection

[0245] The server collects real-time market data from financial markets. The data is obtained through APIs and includes stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database on the server.

[0246] 4. Run the predictive model

[0247] The server runs predictive models based on economic theory on the collected market data, predicting future economic indicators such as GDP growth rate, inflation rate, and interest rate trends, and stores the generated economic forecast data internally for the next analysis step.

[0248] 5. Integrated analysis using AI

[0249] The server integrates the generated economic forecast data, market data, and user sentiment data and performs analysis using AI (artificial intelligence). The AI ​​uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis. By using sentiment data, psychological aspects are also taken into account to allow users to invest with confidence.

[0250] 6. Generating and delivering investment proposals

[0251] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile and sentiment data. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated investment proposals are sent back to the user via their terminal.

[0252] Specific examples

[0253] For example, a user inputs their investment profile as "risk tolerance: high," "investment period: long-term (5 years or more)," and "area of ​​interest: technology," and the emotion engine recognizes from the user's facial expression that they are "excited." This data is then sent to the server, which collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and other factors. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high-risk but also high-return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[0254] In this way, the system can quickly and accurately respond to the user's investment needs and psychological state, providing optimal investment strategies. Users receive investment proposals that take into account their own conditions and emotions, allowing them to make investment decisions with peace of mind.

[0255] The processing flow will be explained below.

[0256] Step 1:

[0257] The user enters an investment profile.

[0258] The user opens the terminal interface and is presented with a form to enter information about the investment, such as risk tolerance, investment horizon, and areas of interest.

[0259] As the user types, the device's camera captures their facial expressions in real time, and the microphone analyzes their tone of voice.

[0260] As a result, emotional data of the user (for example, excitement, relief, anxiety, etc.) is acquired.

[0261] Step 2:

[0262] The device sends input data and emotion data to the server.

[0263] The terminal combines the investment profile data entered by the user into the form and the acquired emotion data and sends them to the server as an HTTP POST request.

[0264] The transmitted data is received by the server and prepared for the next stage of analysis.

[0265] Step 3:

[0266] A server collects market data.

[0267] The server sends API requests to financial market data providers to gather the latest market data.

[0268] Data collected includes stock prices, interest rates, exchange rates, economic indicators, etc.

[0269] The acquired market data is stored in a database within the server.

[0270] Step 4:

[0271] The server makes predictions based on economic models.

[0272] The server runs a predictive model based on economic theory on the collected market data.

[0273] Predictive models generate future economic indicators such as GDP growth, inflation, and interest rate trends.

[0274] The generated economic forecast data is stored internally for analysis.

[0275] Step 5:

[0276] The server performs integrated analysis using artificial intelligence.

[0277] The server integrates the generated economic forecast data, real-time market data, and user sentiment data and performs analysis using AI (artificial intelligence).

[0278] AI uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis.

[0279] Emotional data influences risk adjustment and investment product selection so that users can invest with psychological peace of mind.

[0280] Step 6:

[0281] The server generates an investment proposal and sends it to the terminal.

[0282] Based on the results of the AI ​​analysis, the server generates investment proposals that take into account the user's investment profile and emotional data.

[0283] Investment suggestions include the best investment products for the user (stocks, bonds, ETFs, etc.) and recommended portfolio composition.

[0284] The generated investment proposal is transmitted to the terminal.

[0285] Step 7:

[0286] The terminal displays the investment proposal to the user.

[0287] The terminal displays the investment proposal received from the server on the interface.

[0288] The user can check detailed investment proposals on the device screen and make investment decisions based on the proposals as needed.

[0289] This detailed processing flow allows users to quickly and accurately obtain the optimal investment strategy based on their own investment conditions and psychological state.

[0290] Example 2

[0291] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0292] Conventional investment advice systems only consider the user's investment profile and are unable to provide investment recommendations that reflect the user's emotional state. This makes it difficult to provide appropriate investment recommendations when psychological factors have a significant impact on investment decisions.

[0293] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0294] In this invention, the server includes an input means for a user to input an investment profile and acquire emotional data, a collection means for collecting market data based on the investment profile and emotional data, a generation means for executing a prediction model based on economic theory to generate economic forecast data, an analysis means for using artificial intelligence to perform an integrated analysis of the economic forecast data, market data, and emotional data, and a provision means for providing the user with investment proposals generated by the analysis means. This makes it possible to provide more accurate investment proposals that take into consideration the user's investment needs and emotional state in an integrated manner.

[0295] "User" refers to any individual or entity that utilizes the System to input their investment profile and sentiment data.

[0296] An "investment profile" is information about an investment entered by a user, including risk tolerance, investment period, areas of investment interest, and the like.

[0297] "Emotion data" is data that represents the user's emotional state, and is obtained by analyzing facial expressions and tone of voice.

[0298] "Input means" refers to a device or interface through which a user inputs investment profile and sentiment data.

[0299] "Collection Instrument" refers to a device or system for collecting market data based on investment profile and sentiment data.

[0300] "Market data" refers to data collected from financial markets, including stock prices, interest rates, exchange rates, economic indicators, etc.

[0301] A "predictive model" refers to a model that predicts future economic indicators based on economic theory.

[0302] "Generation means" refers to a device or system for executing a predictive model to generate economic forecast data.

[0303] "Economic forecast data" is data representing future economic indicators such as GDP growth rate, inflation rate, and interest rate trends, generated by a forecasting model.

[0304] "Artificial intelligence" refers to technology that uses machine learning algorithms and deep learning to analyze data and make decisions.

[0305] "Analysis means" refers to a device or system for integrating and analyzing economic forecast data, market data, and sentiment data using artificial intelligence.

[0306] "Providing means" refers to a device or interface for providing the investment proposals generated by the analyzing means to a user.

[0307] "Investment proposal" refers to information indicating specific investment products and portfolio composition generated based on a user's investment profile and sentiment data.

[0308] The present invention is a system that provides more accurate investment proposals based on a user's investment profile and emotional data. This system consists of three main components: a server, a terminal, and a user.

[0309] Hardware and Software Configuration

[0310] Terminal: The terminal provides an interface for users to input their investment profile and emotional data. It also uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice to obtain emotional data. This interface is built using front-end frameworks such as React.js and Vue.js.

[0311] Server: The server is responsible for collecting, analyzing, and synthesizing data, and generating investment proposals. Specifically, the server is built using Python's Flask or Node.js's Express, and runs machine learning algorithms using libraries such as Scikit-learn and TensorFlow. The data is stored in a database such as MySQL or MongoDB.

[0312] System Operation Overview

[0313] 1. User input of investment profile and emotional data: Users input their investment profile, such as risk tolerance, investment period, and investment interests, through an interactive interface on their device. At the same time, the camera and microphone are used to capture the user's emotional data. For example, the emotional engine can use Amazon Rekognition or IBM Watson.

[0314] 2. Collection and transmission of emotional data: The terminal temporarily stores the acquired emotional data and investment profile information and transmits it to the server in encrypted form using the HTTPS protocol.

[0315] 3. Market data collection: The server collects market data in real time from financial markets using APIs (e.g., Alpha Vantage or Yahoo Finance). The collected data includes stock prices, interest rates, exchange rates, economic indicators, etc., and is stored in a database on the server.

[0316] 4. Execution of predictive models: The server executes predictive models based on economic theory using the collected market data. These predictive models include regression analysis and time series analysis using libraries such as Scikit-learn and TensorFlow. For example, they predict future economic indicators such as GDP growth rate and inflation rate.

[0317] 5. Integrated analysis using AI: The server integrates economic forecast data generated by the predictive model, market data, and user sentiment data, and performs AI analysis. This includes calculating risk-adjusted returns and optimizing portfolios using machine learning algorithms and deep learning techniques. Psychological aspects are also taken into account, so emotional data can be used to help users invest with confidence.

[0318] 6. Generation and provision of investment proposals: Based on the results of the AI ​​analysis, the server generates investment proposals that are optimal for the user's investment profile and sentiment data. These proposals include specific investment products (e.g., stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated investment proposals are provided to the user via their terminal.

[0319] Specific examples

[0320] For example, consider the case where a user inputs an investment profile into their device, such as "Risk tolerance: High," "Investment period: Long-term (5 years or more)," and "Interest area: Technology," and the emotion engine recognizes from the user's facial expression that they are "excited." When this data is sent to the server, the server collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and so on. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high in risk but also high in return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[0321] Prompt Sentence Examples

[0322] "High risk tolerance and long investment horizon (>5 years). User's area of ​​interest is technology. Sentiment state is excited. Generate appropriate investment recommendations based on this."

[0323] In this way, the system can provide optimal investment strategies by comprehensively considering the user's investment profile and emotional data, allowing users to make investment decisions with peace of mind.

[0324] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0325] Step 1:

[0326] User input of investment profile and sentiment data

[0327] ==

[0328] Users use the device to input information about their investments, such as risk tolerance, investment horizon, and investment interests, through an interactive interface (built with React.js or Vue.js, for example). The device also uses a camera and microphone to capture the user's facial expressions and tone of voice, collecting emotional data.

[0329] Input: Investment profile information, user facial expression data, voice data

[0330] Data processing and computation: Analyzing facial and voice data to identify emotions, for example, using facial expression recognition and voice tone analysis.

[0331] Output: Investment profile and sentiment data

[0332] Step 2:

[0333] Collecting and transmitting emotional data

[0334] ==

[0335] The device temporarily stores the collected emotional data and sends it to a server using the HTTPS protocol. This communication is encrypted to protect the privacy of the data.

[0336] Input: User investment profile, sentiment data

[0337] Data processing and calculation: Integrate sentiment data and investment profiles and convert them into a suitable format.

[0338] Output: HTTPS request to the server

[0339] Step 3:

[0340] Market Data Collection

[0341] ==

[0342] The server collects real-time market data using financial APIs, such as Alpha Vantage and Yahoo Finance, to obtain stock prices, interest rates, exchange rates, economic indicators, and more.

[0343] Input: API request

[0344] Data processing and calculation: Store acquired market data in a database and convert it into the required format.

[0345] Output: Market data stored in a database

[0346] Step 4:

[0347] Running a predictive model

[0348] ==

[0349] The server uses the collected market data and investment profile data to run predictive models using Python libraries such as Scikit-learn and TensorFlow, which then predict future economic indicators (e.g., GDP growth rate, inflation rate).

[0350] Inputs: Market data, investment profile data

[0351] Data processing and calculations: Predicting future economic indicators using statistical methods and machine learning algorithms.

[0352] Output: Economic forecast data

[0353] Step 5:

[0354] Integrated analysis using AI

[0355] ==

[0356] The server performs integrated analysis using the generated economic forecast data, market data, and user sentiment data, and uses AI and machine learning algorithms (e.g., Scikit-learn and Keras) to calculate risk-adjusted returns and optimize portfolios.

[0357] Inputs: Economic forecast data, market data, sentiment data

[0358] Data processing and calculation: Apply machine learning algorithms to analyze risk and return, and take into account sentiment data to evaluate psychological aspects.

[0359] Output: Analysis result data

[0360] Step 6:

[0361] Generate and deliver investment proposals

[0362] ==

[0363] The server generates optimal investment proposals for users based on the results of the AI ​​analysis. These proposals include specific investment products and portfolio composition. The generated investment proposals are provided to users via their terminals.

[0364] Input: Analysis result data

[0365] Data processing and calculation: Formatting the analysis results in a form that is easy for users to understand.

[0366] Output: Investment proposal to user

[0367] (Application example 2)

[0368] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0369] The problem to be solved by the present invention is to enable an investment proposal system to make investment proposals that take into account not only a user's investment profile but also the user's emotional state. There is also a need for a specific means to reduce the impact of a user's psychological state on investment decisions, which is often overlooked in conventional investment proposal systems.

[0370] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input an investment profile; collection means for collecting market data based on the investment profile; generation means for generating economic forecast data by executing a prediction model based on economic theory; analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence; a sentiment analysis engine for collecting and analyzing user emotional data and reflecting it in investment proposals; and provision means for integrating the emotional data collected by the sentiment analysis engine and generating investment proposals. This makes it possible to make investment proposals that take into account the psychological aspects of the user and provide optimal investment strategies by analyzing market data in real time.

[0371] "Input means" refers to devices and interfaces through which a user inputs an investment profile.

[0372] "Collection Methods" refers to the systems and technologies used to collect market data based on an investment profile.

[0373] "Generation means" refers to a computer program or algorithm for executing a forecasting model based on economic theory and generating economic forecast data.

[0374] "Analytical Tools" refers to computing devices and software for integrating and analyzing economic forecast data and market data using artificial intelligence.

[0375] An "emotion analysis engine" refers to a machine learning model or algorithm that collects and analyzes user emotional data and reflects it in investment proposals.

[0376] "Provision means" refers to an interface or system for integrating the emotional data collected by the emotion analysis engine and generating and providing optimal investment proposals to users.

[0377] System Overview

[0378] The system for implementing this invention is mainly composed of three main components: a server, a terminal, and a user. This system provides highly accurate investment proposals by acquiring and analyzing investment profiles and sentiment data.

[0379] Program processing explanation

[0380] server

[0381] The server will be the center of data analysis and will use the following hardware and software:

[0382] Hardware: High-performance computing server

[0383] Software: Python, TensorFlow, Keras, OpenCV

[0384] The server integrates and analyzes the investment profile, sentiment data, and market data submitted by the user to generate optimal investment proposals. Machine learning algorithms and economic forecasting models are used to collect, analyze, and run data. For example, collected market data is analyzed in real time to calculate risk-adjusted returns that take sentiment data into account.

[0385] Terminal

[0386] The terminal has the role of providing an interface with the user and uses the following hardware and software.

[0387] Hardware: Camera, microphone

[0388] Software: Interactive interface, sentiment analysis engine, data transmission

[0389] When entering an investment profile, the device analyzes the user's facial expressions and tone of voice to obtain emotional data. The obtained emotional data is sent to the server in real time. The emotion analysis engine quantifies the user's psychological state and uses this information to support analysis on the server side.

[0390] crew

[0391] The user inputs the investment profile through the terminal, and provides the investment profile and sentiment data in the following steps:

[0392] 1. Enter your investment profile (risk tolerance, investment horizon, investment interests, etc.).

[0393] 2. Emotional data is collected through cameras and microphones.

[0394] 3. The entered data is sent to the server in real time.

[0395] Specific examples

[0396] For example, a user inputs their investment profile as "risk tolerance: high," "investment period: long-term (5 years or more)," and "area of ​​interest: technology," and the emotion engine recognizes from the user's facial expression that they are "excited." This data is then sent to the server, which collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and other factors. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high-risk but also high-return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[0397] Prompt Sentence Examples

[0398] "Please simulate the operation of an AI system that monitors the emotional state of factory workers in real time and makes optimal suggestions to improve work efficiency. In particular, the system will include obtaining emotional data using a facial recognition algorithm and suggesting work allocation based on that data."

[0399] This makes it possible to make investment proposals that take into account the psychological aspects of users and to analyze market data in real time to provide optimal investment strategies.

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Step 1:

[0402] Users use a terminal to input their investment profile, including information such as risk tolerance, investment period, and investment interests. After inputting the information, the system simultaneously captures emotional data such as facial expressions and tone of voice using a camera and microphone.

[0403] Input: Investment profile (risk tolerance, investment period, investment interest area), emotional data (facial expression, tone of voice)

[0404] Data processing: Collection and analysis of emotional data (facial expression recognition, voice analysis)

[0405] Output: Investment profile, sentiment data

[0406] Step 2:

[0407] The terminal transmits the acquired investment profile and sentiment data to a server in real time using a secure communication protocol.

[0408] Input: Investment profile, sentiment data

[0409] Data processing: data packing and encryption

[0410] Output: Data sent to the server

[0411] Step 3:

[0412] The server receives the submitted investment profile and sentiment data, which is then stored in an internal database.

[0413] Input: Data sent from the terminal

[0414] Data processing: receiving and decoding data, storing it in a database

[0415] Output: Investment profile and sentiment data stored in an internal database

[0416] Step 4:

[0417] The server collects market data in real time from external financial markets. Data obtained through APIs includes stock prices, exchange rates, interest rates, etc. This data is also stored in the database.

[0418] Input: Market Data API

[0419] Data Processing: Market Data Acquisition and Storage

[0420] Output: Market data stored in an internal database

[0421] Step 5:

[0422] The server runs predictive models based on economic theory to forecast future economic indicators, including GDP growth, inflation, and interest rate trends.

[0423] Input: Market Data

[0424] Data processing: Executing predictive models and generating predictive data

[0425] Output: Economic forecast data

[0426] Step 6:

[0427] The server integrates investment profiles, sentiment data, economic forecast data, and market data and analyzes them using artificial intelligence, which uses machine learning algorithms to calculate risk-adjusted returns and optimize portfolios.

[0428] Inputs: Investment profiles, sentiment data, economic forecast data, market data

[0429] Data processing: Data integration and analysis using AI

[0430] Output: Integrated analysis results

[0431] Step 7:

[0432] The server generates optimal investment proposals based on the analysis results. The proposals include specific investment products (stocks, bonds, ETFs, etc.) and portfolio composition. It also takes into account emotional data and psychological aspects to ensure users feel comfortable investing.

[0433] Input: Analysis results

[0434] Data Processing: Investment Proposal Generation

[0435] Output: Investment proposal

[0436] Step 8:

[0437] The server transmits the generated investment proposal to the terminal used by the user, and the proposal is presented to the user using an interactive interface.

[0438] Input: Investment Proposal

[0439] Data processing: data packing and encryption

[0440] Output: Data sent to the terminal

[0441] Step 9:

[0442] The user reviews the investment proposals they receive and, if necessary, provides their emotional data again, which is then sent back to the server to help improve the accuracy of the proposals.

[0443] Input: Updated emotion data

[0444] Data processing: Re-transmitting data and analyzing

[0445] Output: Updated investment proposal

[0446] These steps enable us to provide investment proposals that take into account the psychological aspects of users and analyze market data in real time to provide optimal investment strategies.

[0447] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0448] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0449] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0450] [Second embodiment]

[0451] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0452] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0453] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0454] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0455] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0456] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0457] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0458] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0459] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0460] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0461] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0462] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0463] This invention realizes an AI-based investment advisory system that combines economic models and machine learning. The system is designed around a workflow in which a user inputs an investment profile, a server collects and analyzes market data, and generates investment recommendations.

[0464] System configuration

[0465] The system consists of three main components: the server, the terminal, and the user. The server acts as the hub for data analysis, while the terminal serves as the interface between the user and the server. Users input their investment profile and receive customized investment proposals.

[0466] Program processing overview

[0467] The processing of the program for realizing the present invention will be explained below in natural language.

[0468] 1. User Input

[0469] Users enter their investment profile (risk tolerance, investment horizon, investment interests, etc.) through an interactive interface on their device, using simple questions and sliders.

[0470] 2. Data transmission

[0471] The terminal sends the entered investment profile to the server, which then sends the data as an HTTP POST request and parses it.

[0472] 3. Market Data Collection

[0473] The server collects real-time market data from financial markets. This data is obtained through APIs and includes stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database.

[0474] 4. Run the predictive model

[0475] The server runs a predictive model based on economic theory to generate economic forecasts based on the collected market data. For example, the predictive model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends.

[0476] 5. Integrated analysis using AI

[0477] The server integrates the generated economic forecast data with market data and performs investment analysis using AI (artificial intelligence), which uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis.

[0478] 6. Generating and delivering investment proposals

[0479] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. These proposals are then sent back to the user via their terminal.

[0480] Specific examples

[0481] For example, a user can set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and input their investment interest as "technology." When the device sends this data to the server, the server collects market data for the technology sector in real time and runs predictive models such as GDP growth rate and interest rate trends. The AI ​​then performs an integrated analysis and recommends a portfolio of technology stocks with high risk but also high returns. This proposal is provided to the user.

[0482] In this way, the system can quickly and accurately meet the user's investment needs, allowing the user to obtain the optimal investment strategy based on their own conditions and receive support in making investment decisions.

[0483] The processing flow will be explained below.

[0484] Step 1:

[0485] The user enters an investment profile.

[0486] The user inputs information about the investment (risk tolerance, investment period, areas of interest, etc.) through the terminal interface.

[0487] When the user has completed the input, he / she presses the "Submit" button to send the data.

[0488] Step 2:

[0489] The terminal sends the input data to the server.

[0490] The terminal retrieves the investment profile entered by the user and sends it to the server as an HTTP POST request.

[0491] The transmitted data is received by the server and prepared for analysis.

[0492] Step 3:

[0493] A server collects market data.

[0494] The server sends an API request to retrieve the latest market data from a financial market data provider.

[0495] For example, this includes data on stock prices, interest rates, exchange rates, economic indicators, etc.

[0496] The collected data is stored in a database on the server.

[0497] Step 4:

[0498] The server makes predictions based on economic models.

[0499] The server runs a predictive model based on economic theory on the collected market data.

[0500] For example, predicting future economic indicators such as GDP growth rate, inflation rate, and interest rate trends.

[0501] The generated economic forecast data is stored internally for the next analysis step.

[0502] Step 5:

[0503] The server performs integrated analysis using artificial intelligence.

[0504] The server integrates the generated economic forecast data with market data and performs analysis using AI (artificial intelligence).

[0505] AI uses machine learning algorithms to perform tasks such as risk-adjusted return calculations, portfolio optimization, and market segmentation analysis.

[0506] As a result of the integrated analysis, a customized investment recommendation is generated for the user.

[0507] Step 6:

[0508] The server generates an investment proposal and sends it to the terminal.

[0509] Based on the results of the AI ​​analysis, the server creates investment proposals that are best suited to the user's investment profile.

[0510] This includes specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition.

[0511] The generated investment proposal is transmitted to the terminal.

[0512] Step 7:

[0513] The terminal displays the investment proposal to the user.

[0514] The terminal displays the investment proposals received from the server on the interface.

[0515] The user can review the displayed customized investment proposals and use them to help make investment decisions.

[0516] This processing flow allows users to quickly and accurately obtain the optimal investment strategy based on their own investment conditions.

[0517] Example 1

[0518] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0519] Conventional investment advisory systems have the drawback of requiring extensive specialized knowledge and effort for users to individually collect market data, make economic forecasts, and formulate optimal investment strategies. Furthermore, it is difficult to collect real-time market data or perform advanced analysis using AI, and investment recommendations often lack accuracy and speed.

[0520] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0521] In this invention, the server includes input means for a user to input an investment profile, collection means for collecting market data based on the investment profile, generation means for generating economic forecast data by executing a forecast model based on economic theory, analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence, provision means for providing the user with investment proposals generated by the analysis means, communication means for transmitting the data input by the user to the server as an HTTP POST request, and interface means for the user to easily input an investment profile through an interactive interface, thereby making it possible to provide the user with investment proposals with high accuracy and speed.

[0522] "User" means any person or entity that utilizes the system to enter an investment profile and receive investment offers.

[0523] "Investment Profile" means individual information regarding investments, such as a user's risk tolerance, investment horizon, and investment interests.

[0524] "Input Means" refers to a device or mechanism for a user to input an investment profile.

[0525] "Collection Method" refers to the mechanisms and algorithms used to collect market data based on an investment profile.

[0526] "Generation means" means a system or software for executing a forecasting model based on economic theory and generating economic forecast data.

[0527] "Analysis means" refers to a mechanism for integrating economic forecast data and market data using artificial intelligence to conduct analysis.

[0528] "Providing means" refers to an interface or communication means for providing the user with the investment proposals generated by the analyzing means.

[0529] "Means of communication" refers to the protocols and technologies used to send user-entered data to a server as an HTTP POST request.

[0530] "Interface means" means a mechanism that allows a user to easily input an investment profile through an interactive interface.

[0531] "Market data" refers to data such as stock prices, interest rates, exchange rates, and economic indicators collected in real time from financial markets.

[0532] "Predictive model" means a mathematical model that analyzes market data based on economic theory and predicts future economic indicators.

[0533] "Artificial intelligence" refers to systems that include advanced data analysis techniques such as machine learning and deep learning.

[0534] The mode for implementing the invention is described as follows. The present invention realizes an AI-based investment advisory system that combines economic models and machine learning. This system is designed around a flow in which a user inputs an investment profile, and a server collects and analyzes market data to generate investment proposals. The system consists of three main components: a server, a terminal, and a user. The server functions as the center of data analysis, and the terminal serves as a link between the user and the server through an interface. The user inputs an investment profile and receives customized investment proposals.

[0535] Hardware and software used

[0536] server:

[0537] The servers consist of physical servers or virtual machines equipped with high-performance CPUs, sufficient memory, and large disk storage. They run economic forecasting models implemented in Python or R, machine learning algorithms, and database management systems.

[0538] The server has an interface to collect financial market data through APIs, using common APIs from financial data providers.

[0539] Device:

[0540] A terminal is a device used by a user, such as a smartphone, tablet, or PC. A web browser or a dedicated application is installed on the terminal, and a user interactive interface is implemented.

[0541] The terminal includes a communication module (e.g., HTTP POST request) for transmitting the input investment profile to the server.

[0542] User:

[0543] Users are individuals or legal entities who utilize the system to enter their investment profile and receive AI-based investment proposals.

[0544] Program processing overview

[0545] The processing of the program for realizing the present invention will be explained below in natural language.

[0546] User input

[0547] Users enter their investment profile (risk tolerance, investment period, investment interests, etc.) through the interactive interface of the terminal. This is done using simple questions and sliders. For example, a user might set "risk tolerance: high," "investment period: long-term (5 years or more)," and "investment interests: technology."

[0548] Sending data

[0549] The terminal sends the entered investment profile to the server, which then sends the data as an HTTP POST request and parses it.

[0550] Market Data Collection

[0551] The server collects real-time market data from financial markets through APIs, including stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database.

[0552] Running a predictive model

[0553] The server runs predictive models based on economic theory on the collected market data, which are designed to forecast economic indicators such as GDP growth, inflation, and interest rate trends.

[0554] Integrated analysis using AI

[0555] The server integrates the generated economic forecast data with market data and performs investment analysis using AI (artificial intelligence), which uses machine learning algorithms to calculate risk-adjusted returns and optimize portfolios.

[0556] Generate and deliver investment proposals

[0557] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated proposals are sent back to the user via their terminal.

[0558] Specific examples

[0559] For example, a user can set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and input their investment interest as "technology." When the device sends this data to the server, the server collects market data for the technology sector in real time and runs predictive models such as GDP growth rate and interest rate trends. The AI ​​then performs an integrated analysis and recommends a portfolio of technology stocks with high risk but also high returns. This proposal is provided to the user.

[0560] Prompt Sentence Examples

[0561] "A user has entered their risk tolerance as 'high', their investment horizon as 'long term (5+ years)', and their investment interest as 'technology'. Using this data, please gather real-time market data for the technology sector and run forecasting models on GDP growth and interest rate trends. Combine this data and suggest an optimal high-risk, high-return technology stock portfolio."

[0562] In this way, the system can quickly and accurately meet the user's investment needs, allowing the user to obtain the optimal investment strategy based on their own conditions and receive support in making investment decisions.

[0563] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0564] Step 1:

[0565] User investment profile input

[0566] Input: The user inputs their investment profile (risk tolerance, investment horizon, investment interest areas, etc.) through an interactive interface.

[0567] How it works: The device receives the input information and organizes it into a data structure. Specifically, it saves each item as JSON format data. For example, the risk tolerance is "high," the investment period is "long-term (5 years or more)," and the investment interest area is "technology."

[0568] Output: Organized investment profile data in JSON format.

[0569] Step 2:

[0570] Submit your investment profile data

[0571] Input: Investment profile data compiled in Step 1.

[0572] How it works: The terminal sends investment profile data to the server using an HTTP POST request, which includes all profile information entered by the user.

[0573] Output: Investment profile data sent to the server.

[0574] Step 3:

[0575] Market Data Collection

[0576] Input: The investment profile data received by the server in step 2.

[0577] How it works: The server sends requests to financial data providers' APIs to gather market data, including stock prices, interest rates, exchange rates, economic indicators, etc. The server then stores the data in a database.

[0578] Output: Collected market data (stock prices, interest rates, exchange rates, economic indicators, etc.).

[0579] Step 4:

[0580] Running economic forecasting models

[0581] Input: Market data obtained in step 3.

[0582] How it works: The server initializes an economic forecasting model implemented in Python or R and feeds collected market data into the model. The model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends. The forecast results are stored in a database.

[0583] Output: Forecasted economic indicator data (GDP growth, inflation, interest rate trends, etc.).

[0584] Step 5:

[0585] Integrated analysis using AI

[0586] Input: Economic forecast and market data obtained in Step 4.

[0587] How it works: The server uses machine learning algorithms to integrate and analyze forecast data and market data, specifically to calculate risk-adjusted returns and optimize portfolios.

[0588] Output: Investment proposal data as a result of the analysis.

[0589] Step 6:

[0590] Generate and deliver investment proposals

[0591] Input: The investment proposal data generated in Step 5.

[0592] Operation: Based on the user's investment profile, the server generates an investment proposal containing a list of specific investment products (stocks, bonds, ETFs, etc.) and a recommended portfolio composition. This proposal is sent back to the terminal.

[0593] Output: Investment proposal sent to the terminal.

[0594] Specific examples

[0595] For example, a user may set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and their investment interest area as "technology." When this data is sent to the server, the server collects market data in the technology sector and runs forecasting models for GDP growth rates and interest rate trends. The AI ​​performs an integrated analysis and recommends a portfolio of high-risk, high-return technology stocks. This proposal is then sent back to the user.

[0596] Prompt Sentence Examples

[0597] "A user has entered their risk tolerance as 'high', their investment horizon as 'long term (5+ years)', and their investment interest as 'technology'. Using this data, please gather real-time market data for the technology sector and run forecasting models on GDP growth and interest rate trends. Combine this data and suggest an optimal high-risk, high-return technology stock portfolio."

[0598] (Application example 1)

[0599] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0600] Today's investment environment is becoming increasingly complex, and users need to understand and analyze a large amount of information in real time to make appropriate investment decisions. However, conventional investment advisory systems lack interactive real-time investment suggestions and intuitive information provision in virtual environments, making it difficult for users to make quick and accurate investment decisions. To solve this issue, a new system is needed that can provide investment suggestions based on user profiles in real time and display them in a virtual environment.

[0601] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0602] In this invention, the server includes input means for a user to input an investment profile, collection means for collecting market data based on the investment profile, generation means for generating economic forecast data by executing a prediction model based on economic theory, analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence, and display means for displaying investment proposals in a virtual environment in real time, including provision means for providing the investment proposals generated by the analysis means to the user, thereby enabling the user to make appropriate investment decisions in an interactive real-time manner.

[0603] The "input means for a user to input an investment profile" is a means for a user to input information such as an individual's investment risk tolerance, investment period, and investment areas of interest.

[0604] The "collection means for collecting market data based on an investment profile" is a means for collecting relevant market data based on an investment profile input by a user.

[0605] The "means for generating economic forecast data by executing a forecast model based on economic theory" refers to a means for analyzing market data based on economic theory and predicting future market trends and economic indicators.

[0606] "Analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence" refers to means for using artificial intelligence technology to integrate collected market data and generated economic forecast data and perform investment analysis.

[0607] "Display means for displaying investment proposals in a virtual environment in real time, including provision means for providing the user with investment proposals generated by the analysis means" refers to means for providing the user with investment proposals generated based on the analysis results and displaying them in real time in a virtual environment.

[0608] The "interactive interface means" is a means for providing an interactive interface that allows a user to input to the system.

[0609] The "user interface means for inputting a user profile using smart glasses" refers to an interface means for a user to input profile information via smart glasses.

[0610] A "display means for collecting market data in real time and displaying it within a virtual environment" is a means for collecting market data in real time and displaying that information to a user within a virtual environment.

[0611] The present invention is an AI-based investment advisory system that combines predictive models based on economic theory with machine learning algorithms. The system is composed of three main components, including a terminal and a server. The terminal functions as a user input means, and the server functions as the center for data analysis and investment proposal generation. Specific embodiments of the present invention are described in detail below.

[0612] 1. Fill out your investment profile

[0613] The user uses the smart glasses to input their investment profile (risk tolerance, investment period, areas of interest, etc.) via voice input or gesture recognition. This input data is sent to the server via the terminal. For example, the user can input their profile as follows:

[0614] "High risk tolerance, long-term investment horizon, technology interests"

[0615] 2. Market Data Collection

[0616] The server collects market data (e.g., stock prices, exchange rates, interest rates, economic indicators) from financial markets in real time. The market data is obtained via RESTful APIs (e.g., Alpha Vantage API or Yahoo Finance API) and stored in a database. This ensures that the data always reflects the latest market trends.

[0617] 3. Implementing economic forecasting models

[0618] The server runs a predictive model based on economic theory to generate economic forecast data from the collected market data. The model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends, and uses machine learning algorithms such as random forests and neural networks.

[0619] 4. Integrated analysis using artificial intelligence

[0620] The server integrates the generated economic forecast data with real-time collected market data and performs investment analysis using artificial intelligence, which calculates risk-adjusted returns, optimizes portfolios, and performs market segmentation analysis, thereby generating investment proposals that best fit the user's investment profile.

[0621] 5. Submitting investment proposals

[0622] Finally, the server provides the user with an investment recommendation generated by the integrated analysis. This recommendation includes specific investment products (stocks, bonds, ETFs, etc.) and a recommended portfolio composition. For example, a "high-risk but high-return technology stock portfolio" may be recommended. This investment recommendation is displayed to the user in real time via the smart glasses' HUD (head-up display).

[0623] Examples and prompts

[0624] For example, if a user enters "I'm interested in long-term, high-risk technology stocks," the system will collect market data for the technology sector in real time, analyze the risks and rewards, and display appropriate investment suggestions on the HUD. An example of a prompt sentence is as follows:

[0625] "Enter your user profile: Risk tolerance is 'high,' investment horizon is 'long-term,' and area of ​​interest is 'technology.'"

[0626] In this way, the system of the present invention helps users make investment decisions intuitively and quickly, and provides an interactive investment experience in a virtual environment.

[0627] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0628] Step 1: User enters investment profile using smart glasses

[0629] Users can input their investment profile (e.g., risk tolerance, investment horizon, areas of interest) using the smart glasses' voice input or gesture recognition functions. The input data is stored on the device as user profile data.

[0630] Input: User's investment profile (risk tolerance, investment horizon, areas of interest)

[0631] Output: User profile data

[0632] Specific actions: Type in something like "My risk tolerance is high, my investment horizon is long-term, and my area of ​​interest is technology" using your voice.

[0633] Step 2: Send user profile data to the server

[0634] The device sends the entered user profile data to the server via an HTTP POST request, which the server receives and stores in a database for analysis.

[0635] Input: User profile data

[0636] Output: HTTP POST request to the server

[0637] Specific operation: The device sends profile data to a specific URL on the server.

[0638] Step 3: The server collects market data in real time

[0639] The server collects market data in real time using an API (e.g., Alpha Vantage API), and the collected data is stored in the server's database.

[0640] Input: API request parameters (stock prices, interest rates, exchange rates, etc.)

[0641] Output: Market data

[0642] Specific operation: The server periodically calls the API to obtain the latest market data and stores it in the database.

[0643] Step 4: Run the predictive model to generate economic forecast data

[0644] The server executes a predictive model (e.g., a machine learning algorithm) based on economic theory to analyze the collected market data and generate economic forecast data, which is stored as predictive model data.

[0645] Input: Market Data

[0646] Output: Economic forecast data

[0647] How it works: The server runs predictive models using random forests and neural networks to predict GDP growth and inflation rates.

[0648] Step 5: Integrate economic forecast data and market data

[0649] The server integrates the generated economic forecast data and market data, and performs investment analysis using machine learning algorithms (e.g., risk-adjusted return calculation, portfolio optimization), generating integrated analysis data.

[0650] Input: Economic forecast data, market data

[0651] Output: Integrated analysis data

[0652] How it works: The server uses an AI model to calculate risk and return and proposes the optimal investment strategy.

[0653] Step 6: Generate investment proposals and provide them to users

[0654] The server generates optimal investment proposals based on the integrated analytical data and provides them to users, which are displayed in real time on the HUD of the smart glasses.

[0655] Input: Integrated analysis data

[0656] Output: Investment proposal

[0657] Specific operation: The server generates a proposal and displays it to the user through smart glasses as a "portfolio of technology stocks with high risk but high return."

[0658] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0659] This invention provides more accurate investment recommendations by combining an AI-based investment advisory system that combines economic models and machine learning with an emotion engine that recognizes user emotions. This system generates investment recommendations taking into account not only the user's investment profile but also the user's emotional state.

[0660] System configuration

[0661] The system consists of three main components: a server, a terminal, and a user. The server is the center of data analysis, and the terminal serves as the interface between the user and the server. The user inputs an investment profile and receives customized investment proposals. In addition, the present invention incorporates an emotion engine that acquires user emotion data and uses it for analysis.

[0662] Program processing overview

[0663] The processing of the program for realizing the present invention will be explained below in natural language.

[0664] 1. User enters investment profile

[0665] Users input investment information (risk tolerance, investment period, investment interests, etc.) through the device's interactive interface. During input, sensors such as a camera and microphone are used by the emotion engine to analyze the user's facial expressions and tone of voice to obtain emotional data.

[0666] 2. Collecting Emotional Data

[0667] The device transmits the acquired emotional data to a server, where it is analyzed along with the investment profile.

[0668] 3. Market Data Collection

[0669] The server collects real-time market data from financial markets. The data is obtained through APIs and includes stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database on the server.

[0670] 4. Run the predictive model

[0671] The server runs predictive models based on economic theory on the collected market data, predicting future economic indicators such as GDP growth rate, inflation rate, and interest rate trends, and stores the generated economic forecast data internally for the next analysis step.

[0672] 5. Integrated analysis using AI

[0673] The server integrates the generated economic forecast data, market data, and user sentiment data and performs analysis using AI (artificial intelligence). The AI ​​uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis. By using sentiment data, psychological aspects are also taken into account to allow users to invest with confidence.

[0674] 6. Generating and delivering investment proposals

[0675] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile and sentiment data. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated investment proposals are sent back to the user via their terminal.

[0676] Specific examples

[0677] For example, a user inputs their investment profile as "risk tolerance: high," "investment period: long-term (5 years or more)," and "area of ​​interest: technology," and the emotion engine recognizes from the user's facial expression that they are "excited." This data is then sent to the server, which collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and other factors. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high-risk but also high-return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[0678] In this way, the system can quickly and accurately respond to the user's investment needs and psychological state, providing optimal investment strategies. Users receive investment proposals that take into account their own conditions and emotions, allowing them to make investment decisions with peace of mind.

[0679] The processing flow will be explained below.

[0680] Step 1:

[0681] The user enters an investment profile.

[0682] The user opens the terminal interface and is presented with a form to enter information about the investment, such as risk tolerance, investment horizon, and areas of interest.

[0683] As the user types, the device's camera captures their facial expressions in real time, and the microphone analyzes their tone of voice.

[0684] As a result, emotional data of the user (for example, excitement, relief, anxiety, etc.) is acquired.

[0685] Step 2:

[0686] The device sends input data and emotion data to the server.

[0687] The terminal combines the investment profile data entered by the user into the form and the acquired emotion data and sends them to the server as an HTTP POST request.

[0688] The transmitted data is received by the server and prepared for the next stage of analysis.

[0689] Step 3:

[0690] A server collects market data.

[0691] The server sends API requests to financial market data providers to gather the latest market data.

[0692] Data collected includes stock prices, interest rates, exchange rates, economic indicators, etc.

[0693] The acquired market data is stored in a database within the server.

[0694] Step 4:

[0695] The server makes predictions based on economic models.

[0696] The server runs a predictive model based on economic theory on the collected market data.

[0697] Predictive models generate future economic indicators such as GDP growth, inflation, and interest rate trends.

[0698] The generated economic forecast data is stored internally for analysis.

[0699] Step 5:

[0700] The server performs integrated analysis using artificial intelligence.

[0701] The server integrates the generated economic forecast data, real-time market data, and user sentiment data and performs analysis using AI (artificial intelligence).

[0702] AI uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis.

[0703] Emotional data influences risk adjustment and investment product selection so that users can invest with psychological peace of mind.

[0704] Step 6:

[0705] The server generates an investment proposal and sends it to the terminal.

[0706] Based on the results of the AI ​​analysis, the server generates investment proposals that take into account the user's investment profile and emotional data.

[0707] Investment suggestions include the best investment products for the user (stocks, bonds, ETFs, etc.) and recommended portfolio composition.

[0708] The generated investment proposal is transmitted to the terminal.

[0709] Step 7:

[0710] The terminal displays the investment proposal to the user.

[0711] The terminal displays the investment proposal received from the server on the interface.

[0712] The user can check detailed investment proposals on the device screen and make investment decisions based on the proposals as needed.

[0713] This detailed processing flow allows users to quickly and accurately obtain the optimal investment strategy based on their own investment conditions and psychological state.

[0714] Example 2

[0715] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0716] Conventional investment advice systems only consider the user's investment profile and are unable to provide investment recommendations that reflect the user's emotional state. This makes it difficult to provide appropriate investment recommendations when psychological factors have a significant impact on investment decisions.

[0717] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0718] In this invention, the server includes an input means for a user to input an investment profile and acquire emotional data, a collection means for collecting market data based on the investment profile and emotional data, a generation means for executing a prediction model based on economic theory to generate economic forecast data, an analysis means for using artificial intelligence to perform an integrated analysis of the economic forecast data, market data, and emotional data, and a provision means for providing the user with investment proposals generated by the analysis means. This makes it possible to provide more accurate investment proposals that take into consideration the user's investment needs and emotional state in an integrated manner.

[0719] "User" refers to any individual or entity that utilizes the System to input their investment profile and sentiment data.

[0720] An "investment profile" is information about an investment entered by a user, including risk tolerance, investment period, areas of investment interest, and the like.

[0721] "Emotion data" is data that represents the user's emotional state, and is obtained by analyzing facial expressions and tone of voice.

[0722] "Input means" refers to a device or interface through which a user inputs investment profile and sentiment data.

[0723] "Collection Instrument" refers to a device or system for collecting market data based on investment profile and sentiment data.

[0724] "Market data" refers to data collected from financial markets, including stock prices, interest rates, exchange rates, economic indicators, etc.

[0725] A "predictive model" refers to a model that predicts future economic indicators based on economic theory.

[0726] "Generation means" refers to a device or system for executing a predictive model to generate economic forecast data.

[0727] "Economic forecast data" is data representing future economic indicators such as GDP growth rate, inflation rate, and interest rate trends, generated by a forecasting model.

[0728] "Artificial intelligence" refers to technology that uses machine learning algorithms and deep learning to analyze data and make decisions.

[0729] "Analysis means" refers to a device or system for integrating and analyzing economic forecast data, market data, and sentiment data using artificial intelligence.

[0730] "Providing means" refers to a device or interface for providing the investment proposals generated by the analyzing means to a user.

[0731] "Investment proposal" refers to information indicating specific investment products and portfolio composition generated based on a user's investment profile and sentiment data.

[0732] The present invention is a system that provides more accurate investment proposals based on a user's investment profile and emotional data. This system consists of three main components: a server, a terminal, and a user.

[0733] Hardware and Software Configuration

[0734] Terminal: The terminal provides an interface for users to input their investment profile and emotional data. It also uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice to obtain emotional data. This interface is built using front-end frameworks such as React.js and Vue.js.

[0735] Server: The server is responsible for collecting, analyzing, and synthesizing data, and generating investment proposals. Specifically, the server is built using Python's Flask or Node.js's Express, and runs machine learning algorithms using libraries such as Scikit-learn and TensorFlow. The data is stored in a database such as MySQL or MongoDB.

[0736] System Operation Overview

[0737] 1. User input of investment profile and emotional data: Users input their investment profile, such as risk tolerance, investment period, and investment interests, through an interactive interface on their device. At the same time, the camera and microphone are used to capture the user's emotional data. For example, the emotional engine can use Amazon Rekognition or IBM Watson.

[0738] 2. Collection and transmission of emotional data: The terminal temporarily stores the acquired emotional data and investment profile information and transmits it to the server in encrypted form using the HTTPS protocol.

[0739] 3. Market data collection: The server collects market data in real time from financial markets using APIs (e.g., Alpha Vantage or Yahoo Finance). The collected data includes stock prices, interest rates, exchange rates, economic indicators, etc., and is stored in a database on the server.

[0740] 4. Execution of predictive models: The server executes predictive models based on economic theory using the collected market data. These predictive models include regression analysis and time series analysis using libraries such as Scikit-learn and TensorFlow. For example, they predict future economic indicators such as GDP growth rate and inflation rate.

[0741] 5. Integrated analysis using AI: The server integrates economic forecast data generated by the predictive model, market data, and user sentiment data, and performs AI analysis. This includes calculating risk-adjusted returns and optimizing portfolios using machine learning algorithms and deep learning techniques. Psychological aspects are also taken into account, so emotional data can be used to help users invest with confidence.

[0742] 6. Generation and provision of investment proposals: Based on the results of the AI ​​analysis, the server generates investment proposals that are optimal for the user's investment profile and sentiment data. These proposals include specific investment products (e.g., stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated investment proposals are provided to the user via their terminal.

[0743] Specific examples

[0744] For example, consider the case where a user inputs an investment profile into their device, such as "Risk tolerance: High," "Investment period: Long-term (5 years or more)," and "Interest area: Technology," and the emotion engine recognizes from the user's facial expression that they are "excited." When this data is sent to the server, the server collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and so on. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high in risk but also high in return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[0745] Prompt Sentence Examples

[0746] "High risk tolerance and long investment horizon (>5 years). User's area of ​​interest is technology. Sentiment state is excited. Generate appropriate investment recommendations based on this."

[0747] In this way, the system can provide optimal investment strategies by comprehensively considering the user's investment profile and emotional data, allowing users to make investment decisions with peace of mind.

[0748] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0749] Step 1:

[0750] User input of investment profile and sentiment data

[0751] ==

[0752] Users use the device to input information about their investments, such as risk tolerance, investment horizon, and investment interests, through an interactive interface (built with React.js or Vue.js, for example). The device also uses a camera and microphone to capture the user's facial expressions and tone of voice, collecting emotional data.

[0753] Input: Investment profile information, user facial expression data, voice data

[0754] Data processing and computation: Analyzing facial and voice data to identify emotions, for example, using facial expression recognition and voice tone analysis.

[0755] Output: Investment profile and sentiment data

[0756] Step 2:

[0757] Collecting and transmitting emotional data

[0758] ==

[0759] The device temporarily stores the collected emotional data and sends it to a server using the HTTPS protocol. This communication is encrypted to protect the privacy of the data.

[0760] Input: User investment profile, sentiment data

[0761] Data processing and calculation: Integrate sentiment data and investment profiles and convert them into a suitable format.

[0762] Output: HTTPS request to the server

[0763] Step 3:

[0764] Market Data Collection

[0765] ==

[0766] The server collects real-time market data using financial APIs, such as Alpha Vantage and Yahoo Finance, to obtain stock prices, interest rates, exchange rates, economic indicators, and more.

[0767] Input: API request

[0768] Data processing and calculation: Store acquired market data in a database and convert it into the required format.

[0769] Output: Market data stored in a database

[0770] Step 4:

[0771] Running a predictive model

[0772] ==

[0773] The server uses the collected market data and investment profile data to run predictive models using Python libraries such as Scikit-learn and TensorFlow, which then predict future economic indicators (e.g., GDP growth rate, inflation rate).

[0774] Inputs: Market data, investment profile data

[0775] Data processing and calculations: Predicting future economic indicators using statistical methods and machine learning algorithms.

[0776] Output: Economic forecast data

[0777] Step 5:

[0778] Integrated analysis using AI

[0779] ==

[0780] The server performs integrated analysis using the generated economic forecast data, market data, and user sentiment data, and uses AI and machine learning algorithms (e.g., Scikit-learn and Keras) to calculate risk-adjusted returns and optimize portfolios.

[0781] Inputs: Economic forecast data, market data, sentiment data

[0782] Data processing and calculation: Apply machine learning algorithms to analyze risk and return, and take into account sentiment data to evaluate psychological aspects.

[0783] Output: Analysis result data

[0784] Step 6:

[0785] Generate and deliver investment proposals

[0786] ==

[0787] The server generates optimal investment proposals for users based on the results of the AI ​​analysis. These proposals include specific investment products and portfolio composition. The generated investment proposals are provided to users via their terminals.

[0788] Input: Analysis result data

[0789] Data processing and calculation: Formatting the analysis results in a form that is easy for users to understand.

[0790] Output: Investment proposal to user

[0791] (Application example 2)

[0792] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0793] The problem to be solved by the present invention is to enable an investment proposal system to make investment proposals that take into account not only a user's investment profile but also the user's emotional state. There is also a need for a specific means to reduce the impact of a user's psychological state on investment decisions, which is often overlooked in conventional investment proposal systems.

[0794] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input an investment profile; collection means for collecting market data based on the investment profile; generation means for generating economic forecast data by executing a prediction model based on economic theory; analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence; a sentiment analysis engine for collecting and analyzing user emotional data and reflecting it in investment proposals; and provision means for integrating the emotional data collected by the sentiment analysis engine and generating investment proposals. This makes it possible to make investment proposals that take into account the psychological aspects of the user and provide optimal investment strategies by analyzing market data in real time.

[0795] "Input means" refers to devices and interfaces through which a user inputs an investment profile.

[0796] "Collection Methods" refers to the systems and technologies used to collect market data based on an investment profile.

[0797] "Generation means" refers to a computer program or algorithm for executing a forecasting model based on economic theory and generating economic forecast data.

[0798] "Analytical Tools" refers to computing devices and software for integrating and analyzing economic forecast data and market data using artificial intelligence.

[0799] An "emotion analysis engine" refers to a machine learning model or algorithm that collects and analyzes user emotional data and reflects it in investment proposals.

[0800] "Provision means" refers to an interface or system for integrating the emotional data collected by the emotion analysis engine and generating and providing optimal investment proposals to users.

[0801] System Overview

[0802] The system for implementing this invention is mainly composed of three main components: a server, a terminal, and a user. This system provides highly accurate investment proposals by acquiring and analyzing investment profiles and sentiment data.

[0803] Program processing explanation

[0804] server

[0805] The server will be the center of data analysis and will use the following hardware and software:

[0806] Hardware: High-performance computing server

[0807] Software: Python, TensorFlow, Keras, OpenCV

[0808] The server integrates and analyzes the investment profile, sentiment data, and market data submitted by the user to generate optimal investment proposals. Machine learning algorithms and economic forecasting models are used to collect, analyze, and run data. For example, collected market data is analyzed in real time to calculate risk-adjusted returns that take sentiment data into account.

[0809] Terminal

[0810] The terminal has the role of providing an interface with the user and uses the following hardware and software.

[0811] Hardware: Camera, microphone

[0812] Software: Interactive interface, sentiment analysis engine, data transmission

[0813] When entering an investment profile, the device analyzes the user's facial expressions and tone of voice to obtain emotional data. The obtained emotional data is sent to the server in real time. The emotion analysis engine quantifies the user's psychological state and uses this information to support analysis on the server side.

[0814] crew

[0815] The user inputs the investment profile through the terminal, and provides the investment profile and sentiment data in the following steps:

[0816] 1. Enter your investment profile (risk tolerance, investment horizon, investment interests, etc.).

[0817] 2. Emotional data is collected through cameras and microphones.

[0818] 3. The entered data is sent to the server in real time.

[0819] Specific examples

[0820] For example, a user inputs their investment profile as "risk tolerance: high," "investment period: long-term (5 years or more)," and "area of ​​interest: technology," and the emotion engine recognizes from the user's facial expression that they are "excited." This data is then sent to the server, which collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and other factors. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high-risk but also high-return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[0821] Prompt Sentence Examples

[0822] "Please simulate the operation of an AI system that monitors the emotional state of factory workers in real time and makes optimal suggestions to improve work efficiency. In particular, the system will include obtaining emotional data using a facial recognition algorithm and suggesting work allocation based on that data."

[0823] This makes it possible to make investment proposals that take into account the psychological aspects of users and to analyze market data in real time to provide optimal investment strategies.

[0824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0825] Step 1:

[0826] Users use a terminal to input their investment profile, including information such as risk tolerance, investment period, and investment interests. After inputting the information, the system simultaneously captures emotional data such as facial expressions and tone of voice using a camera and microphone.

[0827] Input: Investment profile (risk tolerance, investment period, investment interest area), emotional data (facial expression, tone of voice)

[0828] Data processing: Collection and analysis of emotional data (facial expression recognition, voice analysis)

[0829] Output: Investment profile, sentiment data

[0830] Step 2:

[0831] The terminal transmits the acquired investment profile and sentiment data to a server in real time using a secure communication protocol.

[0832] Input: Investment profile, sentiment data

[0833] Data processing: data packing and encryption

[0834] Output: Data sent to the server

[0835] Step 3:

[0836] The server receives the submitted investment profile and sentiment data, which is then stored in an internal database.

[0837] Input: Data sent from the terminal

[0838] Data processing: receiving and decoding data, storing it in a database

[0839] Output: Investment profile and sentiment data stored in an internal database

[0840] Step 4:

[0841] The server collects market data in real time from external financial markets. Data obtained through APIs includes stock prices, exchange rates, interest rates, etc. This data is also stored in the database.

[0842] Input: Market Data API

[0843] Data Processing: Market Data Acquisition and Storage

[0844] Output: Market data stored in an internal database

[0845] Step 5:

[0846] The server runs predictive models based on economic theory to forecast future economic indicators, including GDP growth, inflation, and interest rate trends.

[0847] Input: Market Data

[0848] Data processing: Executing predictive models and generating predictive data

[0849] Output: Economic forecast data

[0850] Step 6:

[0851] The server integrates investment profiles, sentiment data, economic forecast data, and market data and analyzes them using artificial intelligence, which uses machine learning algorithms to calculate risk-adjusted returns and optimize portfolios.

[0852] Inputs: Investment profiles, sentiment data, economic forecast data, market data

[0853] Data processing: Data integration and analysis using AI

[0854] Output: Integrated analysis results

[0855] Step 7:

[0856] The server generates optimal investment proposals based on the analysis results. The proposals include specific investment products (stocks, bonds, ETFs, etc.) and portfolio composition. It also takes into account emotional data and psychological aspects to ensure users feel comfortable investing.

[0857] Input: Analysis results

[0858] Data Processing: Investment Proposal Generation

[0859] Output: Investment proposal

[0860] Step 8:

[0861] The server transmits the generated investment proposal to the terminal used by the user, and the proposal is presented to the user using an interactive interface.

[0862] Input: Investment Proposal

[0863] Data processing: data packing and encryption

[0864] Output: Data sent to the terminal

[0865] Step 9:

[0866] The user reviews the investment proposals they receive and, if necessary, provides their emotional data again, which is then sent back to the server to help improve the accuracy of the proposals.

[0867] Input: Updated emotion data

[0868] Data processing: Re-transmitting data and analyzing

[0869] Output: Updated investment proposal

[0870] These steps enable us to provide investment proposals that take into account the psychological aspects of users and analyze market data in real time to provide optimal investment strategies.

[0871] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0872] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0873] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0874] [Third embodiment]

[0875] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0876] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0877] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0878] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0879] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0880] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0881] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0882] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0883] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0884] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0885] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0886] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0887] This invention realizes an AI-based investment advisory system that combines economic models and machine learning. The system is designed around a workflow in which a user inputs an investment profile, a server collects and analyzes market data, and generates investment recommendations.

[0888] System configuration

[0889] The system consists of three main components: the server, the terminal, and the user. The server acts as the hub for data analysis, while the terminal serves as the interface between the user and the server. Users input their investment profile and receive customized investment proposals.

[0890] Program processing overview

[0891] The processing of the program for realizing the present invention will be explained below in natural language.

[0892] 1. User Input

[0893] Users enter their investment profile (risk tolerance, investment horizon, investment interests, etc.) through an interactive interface on their device, using simple questions and sliders.

[0894] 2. Data transmission

[0895] The terminal sends the entered investment profile to the server, which then sends the data as an HTTP POST request and parses it.

[0896] 3. Market Data Collection

[0897] The server collects real-time market data from financial markets. This data is obtained through APIs and includes stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database.

[0898] 4. Run the predictive model

[0899] The server runs a predictive model based on economic theory to generate economic forecasts based on the collected market data. For example, the predictive model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends.

[0900] 5. Integrated analysis using AI

[0901] The server integrates the generated economic forecast data with market data and performs investment analysis using AI (artificial intelligence), which uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis.

[0902] 6. Generating and delivering investment proposals

[0903] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. These proposals are then sent back to the user via their terminal.

[0904] Specific examples

[0905] For example, a user can set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and input their investment interest as "technology." When the device sends this data to the server, the server collects market data for the technology sector in real time and runs predictive models such as GDP growth rate and interest rate trends. The AI ​​then performs an integrated analysis and recommends a portfolio of technology stocks with high risk but also high returns. This proposal is provided to the user.

[0906] In this way, the system can quickly and accurately meet the user's investment needs, allowing the user to obtain the optimal investment strategy based on their own conditions and receive support in making investment decisions.

[0907] The processing flow will be explained below.

[0908] Step 1:

[0909] The user enters an investment profile.

[0910] The user inputs information about the investment (risk tolerance, investment period, areas of interest, etc.) through the terminal interface.

[0911] When the user has completed the input, he / she presses the "Submit" button to send the data.

[0912] Step 2:

[0913] The terminal sends the input data to the server.

[0914] The terminal retrieves the investment profile entered by the user and sends it to the server as an HTTP POST request.

[0915] The transmitted data is received by the server and prepared for analysis.

[0916] Step 3:

[0917] A server collects market data.

[0918] The server sends an API request to retrieve the latest market data from a financial market data provider.

[0919] For example, this includes data on stock prices, interest rates, exchange rates, economic indicators, etc.

[0920] The collected data is stored in a database on the server.

[0921] Step 4:

[0922] The server makes predictions based on economic models.

[0923] The server runs a predictive model based on economic theory on the collected market data.

[0924] For example, predicting future economic indicators such as GDP growth rate, inflation rate, and interest rate trends.

[0925] The generated economic forecast data is stored internally for the next analysis step.

[0926] Step 5:

[0927] The server performs integrated analysis using artificial intelligence.

[0928] The server integrates the generated economic forecast data with market data and performs analysis using AI (artificial intelligence).

[0929] AI uses machine learning algorithms to perform tasks such as risk-adjusted return calculations, portfolio optimization, and market segmentation analysis.

[0930] As a result of the integrated analysis, a customized investment recommendation is generated for the user.

[0931] Step 6:

[0932] The server generates an investment proposal and sends it to the terminal.

[0933] Based on the results of the AI ​​analysis, the server creates investment proposals that are best suited to the user's investment profile.

[0934] This includes specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition.

[0935] The generated investment proposal is transmitted to the terminal.

[0936] Step 7:

[0937] The terminal displays the investment proposal to the user.

[0938] The terminal displays the investment proposals received from the server on the interface.

[0939] The user can review the displayed customized investment proposals and use them to help make investment decisions.

[0940] This processing flow allows users to quickly and accurately obtain the optimal investment strategy based on their own investment conditions.

[0941] Example 1

[0942] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0943] Conventional investment advisory systems have the drawback of requiring extensive specialized knowledge and effort for users to individually collect market data, make economic forecasts, and formulate optimal investment strategies. Furthermore, it is difficult to collect real-time market data or perform advanced analysis using AI, and investment recommendations often lack accuracy and speed.

[0944] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0945] In this invention, the server includes input means for a user to input an investment profile, collection means for collecting market data based on the investment profile, generation means for generating economic forecast data by executing a forecast model based on economic theory, analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence, provision means for providing the user with investment proposals generated by the analysis means, communication means for transmitting the data input by the user to the server as an HTTP POST request, and interface means for the user to easily input an investment profile through an interactive interface, thereby making it possible to provide the user with investment proposals with high accuracy and speed.

[0946] "User" means any person or entity that utilizes the system to enter an investment profile and receive investment offers.

[0947] "Investment Profile" means individual information regarding investments, such as a user's risk tolerance, investment horizon, and investment interests.

[0948] "Input Means" refers to a device or mechanism for a user to input an investment profile.

[0949] "Collection Method" refers to the mechanisms and algorithms used to collect market data based on an investment profile.

[0950] "Generation means" means a system or software for executing a forecasting model based on economic theory and generating economic forecast data.

[0951] "Analysis means" refers to a mechanism for integrating economic forecast data and market data using artificial intelligence to conduct analysis.

[0952] "Providing means" refers to an interface or communication means for providing the user with the investment proposals generated by the analyzing means.

[0953] "Means of communication" refers to the protocols and technologies used to send user-entered data to a server as an HTTP POST request.

[0954] "Interface means" means a mechanism that allows a user to easily input an investment profile through an interactive interface.

[0955] "Market data" refers to data such as stock prices, interest rates, exchange rates, and economic indicators collected in real time from financial markets.

[0956] "Predictive model" means a mathematical model that analyzes market data based on economic theory and predicts future economic indicators.

[0957] "Artificial intelligence" refers to systems that include advanced data analysis techniques such as machine learning and deep learning.

[0958] The mode for implementing the invention is described as follows. The present invention realizes an AI-based investment advisory system that combines economic models and machine learning. This system is designed around a flow in which a user inputs an investment profile, and a server collects and analyzes market data to generate investment proposals. The system consists of three main components: a server, a terminal, and a user. The server functions as the center of data analysis, and the terminal serves as a link between the user and the server through an interface. The user inputs an investment profile and receives customized investment proposals.

[0959] Hardware and software used

[0960] server:

[0961] The servers consist of physical servers or virtual machines equipped with high-performance CPUs, sufficient memory, and large disk storage. They run economic forecasting models implemented in Python or R, machine learning algorithms, and database management systems.

[0962] The server has an interface to collect financial market data through APIs, using common APIs from financial data providers.

[0963] Device:

[0964] A terminal is a device used by a user, such as a smartphone, tablet, or PC. A web browser or a dedicated application is installed on the terminal, and a user interactive interface is implemented.

[0965] The terminal includes a communication module (e.g., HTTP POST request) for transmitting the input investment profile to the server.

[0966] User:

[0967] Users are individuals or legal entities who utilize the system to enter their investment profile and receive AI-based investment proposals.

[0968] Program processing overview

[0969] The processing of the program for realizing the present invention will be explained below in natural language.

[0970] User input

[0971] Users enter their investment profile (risk tolerance, investment period, investment interests, etc.) through the interactive interface of the terminal. This is done using simple questions and sliders. For example, a user might set "risk tolerance: high," "investment period: long-term (5 years or more)," and "investment interests: technology."

[0972] Sending data

[0973] The terminal sends the entered investment profile to the server, which then sends the data as an HTTP POST request and parses it.

[0974] Market Data Collection

[0975] The server collects real-time market data from financial markets through APIs, including stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database.

[0976] Running a predictive model

[0977] The server runs predictive models based on economic theory on the collected market data, which are designed to forecast economic indicators such as GDP growth, inflation, and interest rate trends.

[0978] Integrated analysis using AI

[0979] The server integrates the generated economic forecast data with market data and performs investment analysis using AI (artificial intelligence), which uses machine learning algorithms to calculate risk-adjusted returns and optimize portfolios.

[0980] Generate and deliver investment proposals

[0981] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated proposals are sent back to the user via their terminal.

[0982] Specific examples

[0983] For example, a user can set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and input their investment interest as "technology." When the device sends this data to the server, the server collects market data for the technology sector in real time and runs predictive models such as GDP growth rate and interest rate trends. The AI ​​then performs an integrated analysis and recommends a portfolio of technology stocks with high risk but also high returns. This proposal is provided to the user.

[0984] Prompt Sentence Examples

[0985] "A user has entered their risk tolerance as 'high', their investment horizon as 'long term (5+ years)', and their investment interest as 'technology'. Using this data, please gather real-time market data for the technology sector and run forecasting models on GDP growth and interest rate trends. Combine this data and suggest an optimal high-risk, high-return technology stock portfolio."

[0986] In this way, the system can quickly and accurately meet the user's investment needs, allowing the user to obtain the optimal investment strategy based on their own conditions and receive support in making investment decisions.

[0987] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0988] Step 1:

[0989] User investment profile input

[0990] Input: The user inputs their investment profile (risk tolerance, investment horizon, investment interest areas, etc.) through an interactive interface.

[0991] How it works: The device receives the input information and organizes it into a data structure. Specifically, it saves each item as JSON format data. For example, the risk tolerance is "high," the investment period is "long-term (5 years or more)," and the investment interest area is "technology."

[0992] Output: Organized investment profile data in JSON format.

[0993] Step 2:

[0994] Submit your investment profile data

[0995] Input: Investment profile data compiled in Step 1.

[0996] How it works: The terminal sends investment profile data to the server using an HTTP POST request, which includes all profile information entered by the user.

[0997] Output: Investment profile data sent to the server.

[0998] Step 3:

[0999] Market Data Collection

[1000] Input: The investment profile data received by the server in step 2.

[1001] How it works: The server sends requests to financial data providers' APIs to gather market data, including stock prices, interest rates, exchange rates, economic indicators, etc. The server then stores the data in a database.

[1002] Output: Collected market data (stock prices, interest rates, exchange rates, economic indicators, etc.).

[1003] Step 4:

[1004] Running economic forecasting models

[1005] Input: Market data obtained in step 3.

[1006] How it works: The server initializes an economic forecasting model implemented in Python or R and feeds collected market data into the model. The model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends. The forecast results are stored in a database.

[1007] Output: Forecasted economic indicator data (GDP growth, inflation, interest rate trends, etc.).

[1008] Step 5:

[1009] Integrated analysis using AI

[1010] Input: Economic forecast and market data obtained in Step 4.

[1011] How it works: The server uses machine learning algorithms to integrate and analyze forecast data and market data, specifically to calculate risk-adjusted returns and optimize portfolios.

[1012] Output: Investment proposal data as a result of the analysis.

[1013] Step 6:

[1014] Generate and deliver investment proposals

[1015] Input: The investment proposal data generated in Step 5.

[1016] Operation: Based on the user's investment profile, the server generates an investment proposal containing a list of specific investment products (stocks, bonds, ETFs, etc.) and a recommended portfolio composition. This proposal is sent back to the terminal.

[1017] Output: Investment proposal sent to the terminal.

[1018] Specific examples

[1019] For example, a user may set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and their investment interest area as "technology." When this data is sent to the server, the server collects market data in the technology sector and runs forecasting models for GDP growth rates and interest rate trends. The AI ​​performs an integrated analysis and recommends a portfolio of high-risk, high-return technology stocks. This proposal is then sent back to the user.

[1020] Prompt Sentence Examples

[1021] "A user has entered their risk tolerance as 'high', their investment horizon as 'long term (5+ years)', and their investment interest as 'technology'. Using this data, please gather real-time market data for the technology sector and run forecasting models on GDP growth and interest rate trends. Combine this data and suggest an optimal high-risk, high-return technology stock portfolio."

[1022] (Application example 1)

[1023] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1024] Today's investment environment is becoming increasingly complex, and users need to understand and analyze a large amount of information in real time to make appropriate investment decisions. However, conventional investment advisory systems lack interactive real-time investment suggestions and intuitive information provision in virtual environments, making it difficult for users to make quick and accurate investment decisions. To solve this issue, a new system is needed that can provide investment suggestions based on user profiles in real time and display them in a virtual environment.

[1025] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1026] In this invention, the server includes input means for a user to input an investment profile, collection means for collecting market data based on the investment profile, generation means for generating economic forecast data by executing a prediction model based on economic theory, analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence, and display means for displaying investment proposals in a virtual environment in real time, including provision means for providing the investment proposals generated by the analysis means to the user, thereby enabling the user to make appropriate investment decisions in an interactive real-time manner.

[1027] The "input means for a user to input an investment profile" is a means for a user to input information such as an individual's investment risk tolerance, investment period, and investment areas of interest.

[1028] The "collection means for collecting market data based on an investment profile" is a means for collecting relevant market data based on an investment profile input by a user.

[1029] The "means for generating economic forecast data by executing a forecast model based on economic theory" refers to a means for analyzing market data based on economic theory and predicting future market trends and economic indicators.

[1030] "Analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence" refers to means for using artificial intelligence technology to integrate collected market data and generated economic forecast data and perform investment analysis.

[1031] "Display means for displaying investment proposals in a virtual environment in real time, including provision means for providing the user with investment proposals generated by the analysis means" refers to means for providing the user with investment proposals generated based on the analysis results and displaying them in real time in a virtual environment.

[1032] The "interactive interface means" is a means for providing an interactive interface that allows a user to input to the system.

[1033] The "user interface means for inputting a user profile using smart glasses" refers to an interface means for a user to input profile information via smart glasses.

[1034] A "display means for collecting market data in real time and displaying it within a virtual environment" is a means for collecting market data in real time and displaying that information to a user within a virtual environment.

[1035] The present invention is an AI-based investment advisory system that combines predictive models based on economic theory with machine learning algorithms. The system is composed of three main components, including a terminal and a server. The terminal functions as a user input means, and the server functions as the center for data analysis and investment proposal generation. Specific embodiments of the present invention are described in detail below.

[1036] 1. Fill out your investment profile

[1037] The user uses the smart glasses to input their investment profile (risk tolerance, investment period, areas of interest, etc.) via voice input or gesture recognition. This input data is sent to the server via the terminal. For example, the user can input their profile as follows:

[1038] "High risk tolerance, long-term investment horizon, technology interests"

[1039] 2. Market Data Collection

[1040] The server collects market data (e.g., stock prices, exchange rates, interest rates, economic indicators) from financial markets in real time. The market data is obtained via RESTful APIs (e.g., Alpha Vantage API or Yahoo Finance API) and stored in a database. This ensures that the data always reflects the latest market trends.

[1041] 3. Implementing economic forecasting models

[1042] The server runs a predictive model based on economic theory to generate economic forecast data from the collected market data. The model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends, and uses machine learning algorithms such as random forests and neural networks.

[1043] 4. Integrated analysis using artificial intelligence

[1044] The server integrates the generated economic forecast data with real-time collected market data and performs investment analysis using artificial intelligence, which calculates risk-adjusted returns, optimizes portfolios, and performs market segmentation analysis, thereby generating investment proposals that best fit the user's investment profile.

[1045] 5. Submitting investment proposals

[1046] Finally, the server provides the user with an investment recommendation generated by the integrated analysis. This recommendation includes specific investment products (stocks, bonds, ETFs, etc.) and a recommended portfolio composition. For example, a "high-risk but high-return technology stock portfolio" may be recommended. This investment recommendation is displayed to the user in real time via the smart glasses' HUD (head-up display).

[1047] Examples and prompts

[1048] For example, if a user enters "I'm interested in long-term, high-risk technology stocks," the system will collect market data for the technology sector in real time, analyze the risks and rewards, and display appropriate investment suggestions on the HUD. An example of a prompt sentence is as follows:

[1049] "Enter your user profile: Risk tolerance is 'high,' investment horizon is 'long-term,' and area of ​​interest is 'technology.'"

[1050] In this way, the system of the present invention helps users make investment decisions intuitively and quickly, and provides an interactive investment experience in a virtual environment.

[1051] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1052] Step 1: User enters investment profile using smart glasses

[1053] Users can input their investment profile (e.g., risk tolerance, investment horizon, areas of interest) using the smart glasses' voice input or gesture recognition functions. The input data is stored on the device as user profile data.

[1054] Input: User's investment profile (risk tolerance, investment horizon, areas of interest)

[1055] Output: User profile data

[1056] Specific actions: Type in something like "My risk tolerance is high, my investment horizon is long-term, and my area of ​​interest is technology" using your voice.

[1057] Step 2: Send user profile data to the server

[1058] The device sends the entered user profile data to the server via an HTTP POST request, which the server receives and stores in a database for analysis.

[1059] Input: User profile data

[1060] Output: HTTP POST request to the server

[1061] Specific operation: The device sends profile data to a specific URL on the server.

[1062] Step 3: The server collects market data in real time

[1063] The server collects market data in real time using an API (e.g., Alpha Vantage API), and the collected data is stored in the server's database.

[1064] Input: API request parameters (stock prices, interest rates, exchange rates, etc.)

[1065] Output: Market data

[1066] Specific operation: The server periodically calls the API to obtain the latest market data and stores it in the database.

[1067] Step 4: Run the predictive model to generate economic forecast data

[1068] The server executes a predictive model (e.g., a machine learning algorithm) based on economic theory to analyze the collected market data and generate economic forecast data, which is stored as predictive model data.

[1069] Input: Market Data

[1070] Output: Economic forecast data

[1071] How it works: The server runs predictive models using random forests and neural networks to predict GDP growth and inflation rates.

[1072] Step 5: Integrate economic forecast data and market data

[1073] The server integrates the generated economic forecast data and market data, and performs investment analysis using machine learning algorithms (e.g., risk-adjusted return calculation, portfolio optimization), generating integrated analysis data.

[1074] Input: Economic forecast data, market data

[1075] Output: Integrated analysis data

[1076] How it works: The server uses an AI model to calculate risk and return and proposes the optimal investment strategy.

[1077] Step 6: Generate investment proposals and provide them to users

[1078] The server generates optimal investment proposals based on the integrated analytical data and provides them to users, which are displayed in real time on the HUD of the smart glasses.

[1079] Input: Integrated analysis data

[1080] Output: Investment proposal

[1081] Specific operation: The server generates a proposal and displays it to the user through smart glasses as a "portfolio of technology stocks with high risk but high return."

[1082] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1083] This invention provides more accurate investment recommendations by combining an AI-based investment advisory system that combines economic models and machine learning with an emotion engine that recognizes user emotions. This system generates investment recommendations taking into account not only the user's investment profile but also the user's emotional state.

[1084] System configuration

[1085] The system consists of three main components: a server, a terminal, and a user. The server is the center of data analysis, and the terminal serves as the interface between the user and the server. The user inputs an investment profile and receives customized investment proposals. In addition, the present invention incorporates an emotion engine that acquires user emotion data and uses it for analysis.

[1086] Program processing overview

[1087] The processing of the program for realizing the present invention will be explained below in natural language.

[1088] 1. User enters investment profile

[1089] Users input investment information (risk tolerance, investment period, investment interests, etc.) through the device's interactive interface. During input, sensors such as a camera and microphone are used by the emotion engine to analyze the user's facial expressions and tone of voice to obtain emotional data.

[1090] 2. Collecting Emotional Data

[1091] The device transmits the acquired emotional data to a server, where it is analyzed along with the investment profile.

[1092] 3. Market Data Collection

[1093] The server collects real-time market data from financial markets. The data is obtained through APIs and includes stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database on the server.

[1094] 4. Run the predictive model

[1095] The server runs predictive models based on economic theory on the collected market data, predicting future economic indicators such as GDP growth rate, inflation rate, and interest rate trends, and stores the generated economic forecast data internally for the next analysis step.

[1096] 5. Integrated analysis using AI

[1097] The server integrates the generated economic forecast data, market data, and user sentiment data and performs analysis using AI (artificial intelligence). The AI ​​uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis. By using sentiment data, psychological aspects are also taken into account to allow users to invest with confidence.

[1098] 6. Generating and delivering investment proposals

[1099] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile and sentiment data. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated investment proposals are sent back to the user via their terminal.

[1100] Specific examples

[1101] For example, a user inputs their investment profile as "risk tolerance: high," "investment period: long-term (5 years or more)," and "area of ​​interest: technology," and the emotion engine recognizes from the user's facial expression that they are "excited." This data is then sent to the server, which collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and other factors. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high-risk but also high-return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[1102] In this way, the system can quickly and accurately respond to the user's investment needs and psychological state, providing optimal investment strategies. Users receive investment proposals that take into account their own conditions and emotions, allowing them to make investment decisions with peace of mind.

[1103] The processing flow will be explained below.

[1104] Step 1:

[1105] The user enters an investment profile.

[1106] The user opens the terminal interface and is presented with a form to enter information about the investment, such as risk tolerance, investment horizon, and areas of interest.

[1107] As the user types, the device's camera captures their facial expressions in real time, and the microphone analyzes their tone of voice.

[1108] As a result, emotional data of the user (for example, excitement, relief, anxiety, etc.) is acquired.

[1109] Step 2:

[1110] The device sends input data and emotion data to the server.

[1111] The terminal combines the investment profile data entered by the user into the form and the acquired emotion data and sends them to the server as an HTTP POST request.

[1112] The transmitted data is received by the server and prepared for the next stage of analysis.

[1113] Step 3:

[1114] A server collects market data.

[1115] The server sends API requests to financial market data providers to gather the latest market data.

[1116] Data collected includes stock prices, interest rates, exchange rates, economic indicators, etc.

[1117] The acquired market data is stored in a database within the server.

[1118] Step 4:

[1119] The server makes predictions based on economic models.

[1120] The server runs a predictive model based on economic theory on the collected market data.

[1121] Predictive models generate future economic indicators such as GDP growth, inflation, and interest rate trends.

[1122] The generated economic forecast data is stored internally for analysis.

[1123] Step 5:

[1124] The server performs integrated analysis using artificial intelligence.

[1125] The server integrates the generated economic forecast data, real-time market data, and user sentiment data and performs analysis using AI (artificial intelligence).

[1126] AI uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis.

[1127] Emotional data influences risk adjustment and investment product selection so that users can invest with psychological peace of mind.

[1128] Step 6:

[1129] The server generates an investment proposal and sends it to the terminal.

[1130] Based on the results of the AI ​​analysis, the server generates investment proposals that take into account the user's investment profile and emotional data.

[1131] Investment suggestions include the best investment products for the user (stocks, bonds, ETFs, etc.) and recommended portfolio composition.

[1132] The generated investment proposal is transmitted to the terminal.

[1133] Step 7:

[1134] The terminal displays the investment proposal to the user.

[1135] The terminal displays the investment proposal received from the server on the interface.

[1136] The user can check detailed investment proposals on the device screen and make investment decisions based on the proposals as needed.

[1137] This detailed processing flow allows users to quickly and accurately obtain the optimal investment strategy based on their own investment conditions and psychological state.

[1138] Example 2

[1139] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1140] Conventional investment advice systems only consider the user's investment profile and are unable to provide investment recommendations that reflect the user's emotional state. This makes it difficult to provide appropriate investment recommendations when psychological factors have a significant impact on investment decisions.

[1141] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1142] In this invention, the server includes an input means for a user to input an investment profile and acquire emotional data, a collection means for collecting market data based on the investment profile and emotional data, a generation means for executing a prediction model based on economic theory to generate economic forecast data, an analysis means for using artificial intelligence to perform an integrated analysis of the economic forecast data, market data, and emotional data, and a provision means for providing the user with investment proposals generated by the analysis means. This makes it possible to provide more accurate investment proposals that take into consideration the user's investment needs and emotional state in an integrated manner.

[1143] "User" refers to any individual or entity that utilizes the System to input their investment profile and sentiment data.

[1144] An "investment profile" is information about an investment entered by a user, including risk tolerance, investment period, areas of investment interest, and the like.

[1145] "Emotion data" is data that represents the user's emotional state, and is obtained by analyzing facial expressions and tone of voice.

[1146] "Input means" refers to a device or interface through which a user inputs investment profile and sentiment data.

[1147] "Collection Instrument" refers to a device or system for collecting market data based on investment profile and sentiment data.

[1148] "Market data" refers to data collected from financial markets, including stock prices, interest rates, exchange rates, economic indicators, etc.

[1149] A "predictive model" refers to a model that predicts future economic indicators based on economic theory.

[1150] "Generation means" refers to a device or system for executing a predictive model to generate economic forecast data.

[1151] "Economic forecast data" is data representing future economic indicators such as GDP growth rate, inflation rate, and interest rate trends, generated by a forecasting model.

[1152] "Artificial intelligence" refers to technology that uses machine learning algorithms and deep learning to analyze data and make decisions.

[1153] "Analysis means" refers to a device or system for integrating and analyzing economic forecast data, market data, and sentiment data using artificial intelligence.

[1154] "Providing means" refers to a device or interface for providing the investment proposals generated by the analyzing means to a user.

[1155] "Investment proposal" refers to information indicating specific investment products and portfolio composition generated based on a user's investment profile and sentiment data.

[1156] The present invention is a system that provides more accurate investment proposals based on a user's investment profile and emotional data. This system consists of three main components: a server, a terminal, and a user.

[1157] Hardware and Software Configuration

[1158] Terminal: The terminal provides an interface for users to input their investment profile and emotional data. It also uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice to obtain emotional data. This interface is built using front-end frameworks such as React.js and Vue.js.

[1159] Server: The server is responsible for collecting, analyzing, and synthesizing data, and generating investment proposals. Specifically, the server is built using Python's Flask or Node.js's Express, and runs machine learning algorithms using libraries such as Scikit-learn and TensorFlow. The data is stored in a database such as MySQL or MongoDB.

[1160] System Operation Overview

[1161] 1. User input of investment profile and emotional data: Users input their investment profile, such as risk tolerance, investment period, and investment interests, through an interactive interface on their device. At the same time, the camera and microphone are used to capture the user's emotional data. For example, the emotional engine can use Amazon Rekognition or IBM Watson.

[1162] 2. Collection and transmission of emotional data: The terminal temporarily stores the acquired emotional data and investment profile information and transmits it to the server in encrypted form using the HTTPS protocol.

[1163] 3. Market data collection: The server collects market data in real time from financial markets using APIs (e.g., Alpha Vantage or Yahoo Finance). The collected data includes stock prices, interest rates, exchange rates, economic indicators, etc., and is stored in a database on the server.

[1164] 4. Execution of predictive models: The server executes predictive models based on economic theory using the collected market data. These predictive models include regression analysis and time series analysis using libraries such as Scikit-learn and TensorFlow. For example, they predict future economic indicators such as GDP growth rate and inflation rate.

[1165] 5. Integrated analysis using AI: The server integrates economic forecast data generated by the predictive model, market data, and user sentiment data, and performs AI analysis. This includes calculating risk-adjusted returns and optimizing portfolios using machine learning algorithms and deep learning techniques. Psychological aspects are also taken into account, so emotional data can be used to help users invest with confidence.

[1166] 6. Generation and provision of investment proposals: Based on the results of the AI ​​analysis, the server generates investment proposals that are optimal for the user's investment profile and sentiment data. These proposals include specific investment products (e.g., stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated investment proposals are provided to the user via their terminal.

[1167] Specific examples

[1168] For example, consider the case where a user inputs an investment profile into their device, such as "Risk tolerance: High," "Investment period: Long-term (5 years or more)," and "Interest area: Technology," and the emotion engine recognizes from the user's facial expression that they are "excited." When this data is sent to the server, the server collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and so on. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high in risk but also high in return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[1169] Prompt Sentence Examples

[1170] "High risk tolerance and long investment horizon (>5 years). User's area of ​​interest is technology. Sentiment state is excited. Generate appropriate investment recommendations based on this."

[1171] In this way, the system can provide optimal investment strategies by comprehensively considering the user's investment profile and emotional data, allowing users to make investment decisions with peace of mind.

[1172] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1173] Step 1:

[1174] User input of investment profile and sentiment data

[1175] ==

[1176] Users use the device to input information about their investments, such as risk tolerance, investment horizon, and investment interests, through an interactive interface (built with React.js or Vue.js, for example). The device also uses a camera and microphone to capture the user's facial expressions and tone of voice, collecting emotional data.

[1177] Input: Investment profile information, user facial expression data, voice data

[1178] Data processing and computation: Analyzing facial and voice data to identify emotions, for example, using facial expression recognition and voice tone analysis.

[1179] Output: Investment profile and sentiment data

[1180] Step 2:

[1181] Collecting and transmitting emotional data

[1182] ==

[1183] The device temporarily stores the collected emotional data and sends it to a server using the HTTPS protocol. This communication is encrypted to protect the privacy of the data.

[1184] Input: User investment profile, sentiment data

[1185] Data processing and calculation: Integrate sentiment data and investment profiles and convert them into a suitable format.

[1186] Output: HTTPS request to the server

[1187] Step 3:

[1188] Market Data Collection

[1189] ==

[1190] The server collects real-time market data using financial APIs, such as Alpha Vantage and Yahoo Finance, to obtain stock prices, interest rates, exchange rates, economic indicators, and more.

[1191] Input: API request

[1192] Data processing and calculation: Store acquired market data in a database and convert it into the required format.

[1193] Output: Market data stored in a database

[1194] Step 4:

[1195] Running a predictive model

[1196] ==

[1197] The server uses the collected market data and investment profile data to run predictive models using Python libraries such as Scikit-learn and TensorFlow, which then predict future economic indicators (e.g., GDP growth rate, inflation rate).

[1198] Inputs: Market data, investment profile data

[1199] Data processing and calculations: Predicting future economic indicators using statistical methods and machine learning algorithms.

[1200] Output: Economic forecast data

[1201] Step 5:

[1202] Integrated analysis using AI

[1203] ==

[1204] The server performs integrated analysis using the generated economic forecast data, market data, and user sentiment data, and uses AI and machine learning algorithms (e.g., Scikit-learn and Keras) to calculate risk-adjusted returns and optimize portfolios.

[1205] Inputs: Economic forecast data, market data, sentiment data

[1206] Data processing and calculation: Apply machine learning algorithms to analyze risk and return, and take into account sentiment data to evaluate psychological aspects.

[1207] Output: Analysis result data

[1208] Step 6:

[1209] Generate and deliver investment proposals

[1210] ==

[1211] The server generates optimal investment proposals for users based on the results of the AI ​​analysis. These proposals include specific investment products and portfolio composition. The generated investment proposals are provided to users via their terminals.

[1212] Input: Analysis result data

[1213] Data processing and calculation: Formatting the analysis results in a form that is easy for users to understand.

[1214] Output: Investment proposal to user

[1215] (Application example 2)

[1216] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1217] The problem to be solved by the present invention is to enable an investment proposal system to make investment proposals that take into account not only a user's investment profile but also the user's emotional state. There is also a need for a specific means to reduce the impact of a user's psychological state on investment decisions, which is often overlooked in conventional investment proposal systems.

[1218] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input an investment profile; collection means for collecting market data based on the investment profile; generation means for generating economic forecast data by executing a prediction model based on economic theory; analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence; a sentiment analysis engine for collecting and analyzing user emotional data and reflecting it in investment proposals; and provision means for integrating the emotional data collected by the sentiment analysis engine and generating investment proposals. This makes it possible to make investment proposals that take into account the psychological aspects of the user and provide optimal investment strategies by analyzing market data in real time.

[1219] "Input means" refers to devices and interfaces through which a user inputs an investment profile.

[1220] "Collection Methods" refers to the systems and technologies used to collect market data based on an investment profile.

[1221] "Generation means" refers to a computer program or algorithm for executing a forecasting model based on economic theory and generating economic forecast data.

[1222] "Analytical Tools" refers to computing devices and software for integrating and analyzing economic forecast data and market data using artificial intelligence.

[1223] An "emotion analysis engine" refers to a machine learning model or algorithm that collects and analyzes user emotional data and reflects it in investment proposals.

[1224] "Provision means" refers to an interface or system for integrating the emotional data collected by the emotion analysis engine and generating and providing optimal investment proposals to users.

[1225] System Overview

[1226] The system for implementing this invention is mainly composed of three main components: a server, a terminal, and a user. This system provides highly accurate investment proposals by acquiring and analyzing investment profiles and sentiment data.

[1227] Program processing explanation

[1228] server

[1229] The server will be the center of data analysis and will use the following hardware and software:

[1230] Hardware: High-performance computing server

[1231] Software: Python, TensorFlow, Keras, OpenCV

[1232] The server integrates and analyzes the investment profile, sentiment data, and market data submitted by the user to generate optimal investment proposals. Machine learning algorithms and economic forecasting models are used to collect, analyze, and run data. For example, collected market data is analyzed in real time to calculate risk-adjusted returns that take sentiment data into account.

[1233] Terminal

[1234] The terminal has the role of providing an interface with the user and uses the following hardware and software.

[1235] Hardware: Camera, microphone

[1236] Software: Interactive interface, sentiment analysis engine, data transmission

[1237] When entering an investment profile, the device analyzes the user's facial expressions and tone of voice to obtain emotional data. The obtained emotional data is sent to the server in real time. The emotion analysis engine quantifies the user's psychological state and uses this information to support analysis on the server side.

[1238] crew

[1239] The user inputs the investment profile through the terminal, and provides the investment profile and sentiment data in the following steps:

[1240] 1. Enter your investment profile (risk tolerance, investment horizon, investment interests, etc.).

[1241] 2. Emotional data is collected through cameras and microphones.

[1242] 3. The entered data is sent to the server in real time.

[1243] Specific examples

[1244] For example, a user inputs their investment profile as "risk tolerance: high," "investment period: long-term (5 years or more)," and "area of ​​interest: technology," and the emotion engine recognizes from the user's facial expression that they are "excited." This data is then sent to the server, which collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and other factors. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high-risk but also high-return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[1245] Prompt Sentence Examples

[1246] "Please simulate the operation of an AI system that monitors the emotional state of factory workers in real time and makes optimal suggestions to improve work efficiency. In particular, the system will include obtaining emotional data using a facial recognition algorithm and suggesting work allocation based on that data."

[1247] This makes it possible to make investment proposals that take into account the psychological aspects of users and to analyze market data in real time to provide optimal investment strategies.

[1248] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1249] Step 1:

[1250] Users use a terminal to input their investment profile, including information such as risk tolerance, investment period, and investment interests. After inputting the information, the system simultaneously captures emotional data such as facial expressions and tone of voice using a camera and microphone.

[1251] Input: Investment profile (risk tolerance, investment period, investment interest area), emotional data (facial expression, tone of voice)

[1252] Data processing: Collection and analysis of emotional data (facial expression recognition, voice analysis)

[1253] Output: Investment profile, sentiment data

[1254] Step 2:

[1255] The terminal transmits the acquired investment profile and sentiment data to a server in real time using a secure communication protocol.

[1256] Input: Investment profile, sentiment data

[1257] Data processing: data packing and encryption

[1258] Output: Data sent to the server

[1259] Step 3:

[1260] The server receives the submitted investment profile and sentiment data, which is then stored in an internal database.

[1261] Input: Data sent from the terminal

[1262] Data processing: receiving and decoding data, storing it in a database

[1263] Output: Investment profile and sentiment data stored in an internal database

[1264] Step 4:

[1265] The server collects market data in real time from external financial markets. Data obtained through APIs includes stock prices, exchange rates, interest rates, etc. This data is also stored in the database.

[1266] Input: Market Data API

[1267] Data Processing: Market Data Acquisition and Storage

[1268] Output: Market data stored in an internal database

[1269] Step 5:

[1270] The server runs predictive models based on economic theory to forecast future economic indicators, including GDP growth, inflation, and interest rate trends.

[1271] Input: Market Data

[1272] Data processing: Executing predictive models and generating predictive data

[1273] Output: Economic forecast data

[1274] Step 6:

[1275] The server integrates investment profiles, sentiment data, economic forecast data, and market data and analyzes them using artificial intelligence, which uses machine learning algorithms to calculate risk-adjusted returns and optimize portfolios.

[1276] Inputs: Investment profiles, sentiment data, economic forecast data, market data

[1277] Data processing: Data integration and analysis using AI

[1278] Output: Integrated analysis results

[1279] Step 7:

[1280] The server generates optimal investment proposals based on the analysis results. The proposals include specific investment products (stocks, bonds, ETFs, etc.) and portfolio composition. It also takes into account emotional data and psychological aspects to ensure users feel comfortable investing.

[1281] Input: Analysis results

[1282] Data Processing: Investment Proposal Generation

[1283] Output: Investment proposal

[1284] Step 8:

[1285] The server transmits the generated investment proposal to the terminal used by the user, and the proposal is presented to the user using an interactive interface.

[1286] Input: Investment Proposal

[1287] Data processing: data packing and encryption

[1288] Output: Data sent to the terminal

[1289] Step 9:

[1290] The user reviews the investment proposals they receive and, if necessary, provides their emotional data again, which is then sent back to the server to help improve the accuracy of the proposals.

[1291] Input: Updated emotion data

[1292] Data processing: Re-transmitting data and analyzing

[1293] Output: Updated investment proposal

[1294] These steps enable us to provide investment proposals that take into account the psychological aspects of users and analyze market data in real time to provide optimal investment strategies.

[1295] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1296] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1297] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1298] [Fourth embodiment]

[1299] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1300] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1301] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1302] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1303] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1304] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1305] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1306] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1307] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1308] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1309] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1310] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1311] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1312] This invention realizes an AI-based investment advisory system that combines economic models and machine learning. The system is designed around a workflow in which a user inputs an investment profile, a server collects and analyzes market data, and generates investment recommendations.

[1313] System configuration

[1314] The system consists of three main components: the server, the terminal, and the user. The server acts as the hub for data analysis, while the terminal serves as the interface between the user and the server. Users input their investment profile and receive customized investment proposals.

[1315] Program processing overview

[1316] The processing of the program for realizing the present invention will be explained below in natural language.

[1317] 1. User Input

[1318] Users enter their investment profile (risk tolerance, investment horizon, investment interests, etc.) through an interactive interface on their device, using simple questions and sliders.

[1319] 2. Data transmission

[1320] The terminal sends the entered investment profile to the server, which then sends the data as an HTTP POST request and parses it.

[1321] 3. Market Data Collection

[1322] The server collects real-time market data from financial markets. This data is obtained through APIs and includes stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database.

[1323] 4. Run the predictive model

[1324] The server runs a predictive model based on economic theory to generate economic forecasts based on the collected market data. For example, the predictive model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends.

[1325] 5. Integrated analysis using AI

[1326] The server integrates the generated economic forecast data with market data and performs investment analysis using AI (artificial intelligence), which uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis.

[1327] 6. Generating and delivering investment proposals

[1328] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. These proposals are then sent back to the user via their terminal.

[1329] Specific examples

[1330] For example, a user can set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and input their investment interest as "technology." When the device sends this data to the server, the server collects market data for the technology sector in real time and runs predictive models such as GDP growth rate and interest rate trends. The AI ​​then performs an integrated analysis and recommends a portfolio of technology stocks with high risk but also high returns. This proposal is provided to the user.

[1331] In this way, the system can quickly and accurately meet the user's investment needs, allowing the user to obtain the optimal investment strategy based on their own conditions and receive support in making investment decisions.

[1332] The processing flow will be explained below.

[1333] Step 1:

[1334] The user enters an investment profile.

[1335] The user inputs information about the investment (risk tolerance, investment period, areas of interest, etc.) through the terminal interface.

[1336] When the user has completed the input, he / she presses the "Submit" button to send the data.

[1337] Step 2:

[1338] The terminal sends the input data to the server.

[1339] The terminal retrieves the investment profile entered by the user and sends it to the server as an HTTP POST request.

[1340] The transmitted data is received by the server and prepared for analysis.

[1341] Step 3:

[1342] A server collects market data.

[1343] The server sends an API request to retrieve the latest market data from a financial market data provider.

[1344] For example, this includes data on stock prices, interest rates, exchange rates, economic indicators, etc.

[1345] The collected data is stored in a database on the server.

[1346] Step 4:

[1347] The server makes predictions based on economic models.

[1348] The server runs a predictive model based on economic theory on the collected market data.

[1349] For example, predicting future economic indicators such as GDP growth rate, inflation rate, and interest rate trends.

[1350] The generated economic forecast data is stored internally for the next analysis step.

[1351] Step 5:

[1352] The server performs integrated analysis using artificial intelligence.

[1353] The server integrates the generated economic forecast data with market data and performs analysis using AI (artificial intelligence).

[1354] AI uses machine learning algorithms to perform tasks such as risk-adjusted return calculations, portfolio optimization, and market segmentation analysis.

[1355] As a result of the integrated analysis, a customized investment recommendation is generated for the user.

[1356] Step 6:

[1357] The server generates an investment proposal and sends it to the terminal.

[1358] Based on the results of the AI ​​analysis, the server creates investment proposals that are best suited to the user's investment profile.

[1359] This includes specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition.

[1360] The generated investment proposal is transmitted to the terminal.

[1361] Step 7:

[1362] The terminal displays the investment proposal to the user.

[1363] The terminal displays the investment proposals received from the server on the interface.

[1364] The user can review the displayed customized investment proposals and use them to help make investment decisions.

[1365] This processing flow allows users to quickly and accurately obtain the optimal investment strategy based on their own investment conditions.

[1366] Example 1

[1367] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1368] Conventional investment advisory systems have the drawback of requiring extensive specialized knowledge and effort for users to individually collect market data, make economic forecasts, and formulate optimal investment strategies. Furthermore, it is difficult to collect real-time market data or perform advanced analysis using AI, and investment recommendations often lack accuracy and speed.

[1369] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1370] In this invention, the server includes input means for a user to input an investment profile, collection means for collecting market data based on the investment profile, generation means for generating economic forecast data by executing a forecast model based on economic theory, analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence, provision means for providing the user with investment proposals generated by the analysis means, communication means for transmitting the data input by the user to the server as an HTTP POST request, and interface means for the user to easily input an investment profile through an interactive interface, thereby making it possible to provide the user with investment proposals with high accuracy and speed.

[1371] "User" means any person or entity that utilizes the system to enter an investment profile and receive investment offers.

[1372] "Investment Profile" means individual information regarding investments, such as a user's risk tolerance, investment horizon, and investment interests.

[1373] "Input Means" refers to a device or mechanism for a user to input an investment profile.

[1374] "Collection Method" refers to the mechanisms and algorithms used to collect market data based on an investment profile.

[1375] "Generation means" means a system or software for executing a forecasting model based on economic theory and generating economic forecast data.

[1376] "Analysis means" refers to a mechanism for integrating economic forecast data and market data using artificial intelligence to conduct analysis.

[1377] "Providing means" refers to an interface or communication means for providing the user with the investment proposals generated by the analyzing means.

[1378] "Means of communication" refers to the protocols and technologies used to send user-entered data to a server as an HTTP POST request.

[1379] "Interface means" means a mechanism that allows a user to easily input an investment profile through an interactive interface.

[1380] "Market data" refers to data such as stock prices, interest rates, exchange rates, and economic indicators collected in real time from financial markets.

[1381] "Predictive model" means a mathematical model that analyzes market data based on economic theory and predicts future economic indicators.

[1382] "Artificial intelligence" refers to systems that include advanced data analysis techniques such as machine learning and deep learning.

[1383] The mode for implementing the invention is described as follows. The present invention realizes an AI-based investment advisory system that combines economic models and machine learning. This system is designed around a flow in which a user inputs an investment profile, and a server collects and analyzes market data to generate investment proposals. The system consists of three main components: a server, a terminal, and a user. The server functions as the center of data analysis, and the terminal serves as a link between the user and the server through an interface. The user inputs an investment profile and receives customized investment proposals.

[1384] Hardware and software used

[1385] server:

[1386] The servers consist of physical servers or virtual machines equipped with high-performance CPUs, sufficient memory, and large disk storage. They run economic forecasting models implemented in Python or R, machine learning algorithms, and database management systems.

[1387] The server has an interface to collect financial market data through APIs, using common APIs from financial data providers.

[1388] Device:

[1389] A terminal is a device used by a user, such as a smartphone, tablet, or PC. A web browser or a dedicated application is installed on the terminal, and a user interactive interface is implemented.

[1390] The terminal includes a communication module (e.g., HTTP POST request) for transmitting the input investment profile to the server.

[1391] User:

[1392] Users are individuals or legal entities who utilize the system to enter their investment profile and receive AI-based investment proposals.

[1393] Program processing overview

[1394] The processing of the program for realizing the present invention will be explained below in natural language.

[1395] User input

[1396] Users enter their investment profile (risk tolerance, investment period, investment interests, etc.) through the interactive interface of the terminal. This is done using simple questions and sliders. For example, a user might set "risk tolerance: high," "investment period: long-term (5 years or more)," and "investment interests: technology."

[1397] Sending data

[1398] The terminal sends the entered investment profile to the server, which then sends the data as an HTTP POST request and parses it.

[1399] Market Data Collection

[1400] The server collects real-time market data from financial markets through APIs, including stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database.

[1401] Running a predictive model

[1402] The server runs predictive models based on economic theory on the collected market data, which are designed to forecast economic indicators such as GDP growth, inflation, and interest rate trends.

[1403] Integrated analysis using AI

[1404] The server integrates the generated economic forecast data with market data and performs investment analysis using AI (artificial intelligence), which uses machine learning algorithms to calculate risk-adjusted returns and optimize portfolios.

[1405] Generate and deliver investment proposals

[1406] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated proposals are sent back to the user via their terminal.

[1407] Specific examples

[1408] For example, a user can set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and input their investment interest as "technology." When the device sends this data to the server, the server collects market data for the technology sector in real time and runs predictive models such as GDP growth rate and interest rate trends. The AI ​​then performs an integrated analysis and recommends a portfolio of technology stocks with high risk but also high returns. This proposal is provided to the user.

[1409] Prompt Sentence Examples

[1410] "A user has entered their risk tolerance as 'high', their investment horizon as 'long term (5+ years)', and their investment interest as 'technology'. Using this data, please gather real-time market data for the technology sector and run forecasting models on GDP growth and interest rate trends. Combine this data and suggest an optimal high-risk, high-return technology stock portfolio."

[1411] In this way, the system can quickly and accurately meet the user's investment needs, allowing the user to obtain the optimal investment strategy based on their own conditions and receive support in making investment decisions.

[1412] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1413] Step 1:

[1414] User investment profile input

[1415] Input: The user inputs their investment profile (risk tolerance, investment horizon, investment interest areas, etc.) through an interactive interface.

[1416] How it works: The device receives the input information and organizes it into a data structure. Specifically, it saves each item as JSON format data. For example, the risk tolerance is "high," the investment period is "long-term (5 years or more)," and the investment interest area is "technology."

[1417] Output: Organized investment profile data in JSON format.

[1418] Step 2:

[1419] Submit your investment profile data

[1420] Input: Investment profile data compiled in Step 1.

[1421] How it works: The terminal sends investment profile data to the server using an HTTP POST request, which includes all profile information entered by the user.

[1422] Output: Investment profile data sent to the server.

[1423] Step 3:

[1424] Market Data Collection

[1425] Input: The investment profile data received by the server in step 2.

[1426] How it works: The server sends requests to financial data providers' APIs to gather market data, including stock prices, interest rates, exchange rates, economic indicators, etc. The server then stores the data in a database.

[1427] Output: Collected market data (stock prices, interest rates, exchange rates, economic indicators, etc.).

[1428] Step 4:

[1429] Running economic forecasting models

[1430] Input: Market data obtained in step 3.

[1431] How it works: The server initializes an economic forecasting model implemented in Python or R and feeds collected market data into the model. The model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends. The forecast results are stored in a database.

[1432] Output: Forecasted economic indicator data (GDP growth, inflation, interest rate trends, etc.).

[1433] Step 5:

[1434] Integrated analysis using AI

[1435] Input: Economic forecast and market data obtained in Step 4.

[1436] How it works: The server uses machine learning algorithms to integrate and analyze forecast data and market data, specifically to calculate risk-adjusted returns and optimize portfolios.

[1437] Output: Investment proposal data as a result of the analysis.

[1438] Step 6:

[1439] Generate and deliver investment proposals

[1440] Input: The investment proposal data generated in Step 5.

[1441] Operation: Based on the user's investment profile, the server generates an investment proposal containing a list of specific investment products (stocks, bonds, ETFs, etc.) and a recommended portfolio composition. This proposal is sent back to the terminal.

[1442] Output: Investment proposal sent to the terminal.

[1443] Specific examples

[1444] For example, a user may set their risk tolerance as "high," their investment period as "long-term (5 years or more)," and their investment interest area as "technology." When this data is sent to the server, the server collects market data in the technology sector and runs forecasting models for GDP growth rates and interest rate trends. The AI ​​performs an integrated analysis and recommends a portfolio of high-risk, high-return technology stocks. This proposal is then sent back to the user.

[1445] Prompt Sentence Examples

[1446] "A user has entered their risk tolerance as 'high', their investment horizon as 'long term (5+ years)', and their investment interest as 'technology'. Using this data, please gather real-time market data for the technology sector and run forecasting models on GDP growth and interest rate trends. Combine this data and suggest an optimal high-risk, high-return technology stock portfolio."

[1447] (Application example 1)

[1448] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1449] Today's investment environment is becoming increasingly complex, and users need to understand and analyze a large amount of information in real time to make appropriate investment decisions. However, conventional investment advisory systems lack interactive real-time investment suggestions and intuitive information provision in virtual environments, making it difficult for users to make quick and accurate investment decisions. To solve this issue, a new system is needed that can provide investment suggestions based on user profiles in real time and display them in a virtual environment.

[1450] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1451] In this invention, the server includes input means for a user to input an investment profile, collection means for collecting market data based on the investment profile, generation means for generating economic forecast data by executing a prediction model based on economic theory, analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence, and display means for displaying investment proposals in a virtual environment in real time, including provision means for providing the investment proposals generated by the analysis means to the user, thereby enabling the user to make appropriate investment decisions in an interactive real-time manner.

[1452] The "input means for a user to input an investment profile" is a means for a user to input information such as an individual's investment risk tolerance, investment period, and investment areas of interest.

[1453] The "collection means for collecting market data based on an investment profile" is a means for collecting relevant market data based on an investment profile input by a user.

[1454] The "means for generating economic forecast data by executing a forecast model based on economic theory" refers to a means for analyzing market data based on economic theory and predicting future market trends and economic indicators.

[1455] "Analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence" refers to means for using artificial intelligence technology to integrate collected market data and generated economic forecast data and perform investment analysis.

[1456] "Display means for displaying investment proposals in a virtual environment in real time, including provision means for providing the user with investment proposals generated by the analysis means" refers to means for providing the user with investment proposals generated based on the analysis results and displaying them in real time in a virtual environment.

[1457] The "interactive interface means" is a means for providing an interactive interface that allows a user to input to the system.

[1458] The "user interface means for inputting a user profile using smart glasses" refers to an interface means for a user to input profile information via smart glasses.

[1459] A "display means for collecting market data in real time and displaying it within a virtual environment" is a means for collecting market data in real time and displaying that information to a user within a virtual environment.

[1460] The present invention is an AI-based investment advisory system that combines predictive models based on economic theory with machine learning algorithms. The system is composed of three main components, including a terminal and a server. The terminal functions as a user input means, and the server functions as the center for data analysis and investment proposal generation. Specific embodiments of the present invention are described in detail below.

[1461] 1. Fill out your investment profile

[1462] The user uses the smart glasses to input their investment profile (risk tolerance, investment period, areas of interest, etc.) via voice input or gesture recognition. This input data is sent to the server via the terminal. For example, the user can input their profile as follows:

[1463] "High risk tolerance, long-term investment horizon, technology interests"

[1464] 2. Market Data Collection

[1465] The server collects market data (e.g., stock prices, exchange rates, interest rates, economic indicators) from financial markets in real time. The market data is obtained via RESTful APIs (e.g., Alpha Vantage API or Yahoo Finance API) and stored in a database. This ensures that the data always reflects the latest market trends.

[1466] 3. Implementing economic forecasting models

[1467] The server runs a predictive model based on economic theory to generate economic forecast data from the collected market data. The model predicts economic indicators such as GDP growth rate, inflation rate, and interest rate trends, and uses machine learning algorithms such as random forests and neural networks.

[1468] 4. Integrated analysis using artificial intelligence

[1469] The server integrates the generated economic forecast data with real-time collected market data and performs investment analysis using artificial intelligence, which calculates risk-adjusted returns, optimizes portfolios, and performs market segmentation analysis, thereby generating investment proposals that best fit the user's investment profile.

[1470] 5. Submitting investment proposals

[1471] Finally, the server provides the user with an investment recommendation generated by the integrated analysis. This recommendation includes specific investment products (stocks, bonds, ETFs, etc.) and a recommended portfolio composition. For example, a "high-risk but high-return technology stock portfolio" may be recommended. This investment recommendation is displayed to the user in real time via the smart glasses' HUD (head-up display).

[1472] Examples and prompts

[1473] For example, if a user enters "I'm interested in long-term, high-risk technology stocks," the system will collect market data for the technology sector in real time, analyze the risks and rewards, and display appropriate investment suggestions on the HUD. An example of a prompt sentence is as follows:

[1474] "Enter your user profile: Risk tolerance is 'high,' investment horizon is 'long-term,' and area of ​​interest is 'technology.'"

[1475] In this way, the system of the present invention helps users make investment decisions intuitively and quickly, and provides an interactive investment experience in a virtual environment.

[1476] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1477] Step 1: User enters investment profile using smart glasses

[1478] Users can input their investment profile (e.g., risk tolerance, investment horizon, areas of interest) using the smart glasses' voice input or gesture recognition functions. The input data is stored on the device as user profile data.

[1479] Input: User's investment profile (risk tolerance, investment horizon, areas of interest)

[1480] Output: User profile data

[1481] Specific actions: Type in something like "My risk tolerance is high, my investment horizon is long-term, and my area of ​​interest is technology" using your voice.

[1482] Step 2: Send user profile data to the server

[1483] The device sends the entered user profile data to the server via an HTTP POST request, which the server receives and stores in a database for analysis.

[1484] Input: User profile data

[1485] Output: HTTP POST request to the server

[1486] Specific operation: The device sends profile data to a specific URL on the server.

[1487] Step 3: The server collects market data in real time

[1488] The server collects market data in real time using an API (e.g., Alpha Vantage API), and the collected data is stored in the server's database.

[1489] Input: API request parameters (stock prices, interest rates, exchange rates, etc.)

[1490] Output: Market data

[1491] Specific operation: The server periodically calls the API to obtain the latest market data and stores it in the database.

[1492] Step 4: Run the predictive model to generate economic forecast data

[1493] The server executes a predictive model (e.g., a machine learning algorithm) based on economic theory to analyze the collected market data and generate economic forecast data, which is stored as predictive model data.

[1494] Input: Market Data

[1495] Output: Economic forecast data

[1496] How it works: The server runs predictive models using random forests and neural networks to predict GDP growth and inflation rates.

[1497] Step 5: Integrate economic forecast data and market data

[1498] The server integrates the generated economic forecast data and market data, and performs investment analysis using machine learning algorithms (e.g., risk-adjusted return calculation, portfolio optimization), generating integrated analysis data.

[1499] Input: Economic forecast data, market data

[1500] Output: Integrated analysis data

[1501] How it works: The server uses an AI model to calculate risk and return and proposes the optimal investment strategy.

[1502] Step 6: Generate investment proposals and provide them to users

[1503] The server generates optimal investment proposals based on the integrated analytical data and provides them to users, which are displayed in real time on the HUD of the smart glasses.

[1504] Input: Integrated analysis data

[1505] Output: Investment proposal

[1506] Specific operation: The server generates a proposal and displays it to the user through smart glasses as a "portfolio of technology stocks with high risk but high return."

[1507] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1508] This invention provides more accurate investment recommendations by combining an AI-based investment advisory system that combines economic models and machine learning with an emotion engine that recognizes user emotions. This system generates investment recommendations taking into account not only the user's investment profile but also the user's emotional state.

[1509] System configuration

[1510] The system consists of three main components: a server, a terminal, and a user. The server is the center of data analysis, and the terminal serves as the interface between the user and the server. The user inputs an investment profile and receives customized investment proposals. In addition, the present invention incorporates an emotion engine that acquires user emotion data and uses it for analysis.

[1511] Program processing overview

[1512] The processing of the program for realizing the present invention will be explained below in natural language.

[1513] 1. User enters investment profile

[1514] Users input investment information (risk tolerance, investment period, investment interests, etc.) through the device's interactive interface. During input, sensors such as a camera and microphone are used by the emotion engine to analyze the user's facial expressions and tone of voice to obtain emotional data.

[1515] 2. Collecting Emotional Data

[1516] The device transmits the acquired emotional data to a server, where it is analyzed along with the investment profile.

[1517] 3. Market Data Collection

[1518] The server collects real-time market data from financial markets. The data is obtained through APIs and includes stock prices, interest rates, exchange rates, economic indicators, etc. The collected data is stored in a database on the server.

[1519] 4. Run the predictive model

[1520] The server runs predictive models based on economic theory on the collected market data, predicting future economic indicators such as GDP growth rate, inflation rate, and interest rate trends, and stores the generated economic forecast data internally for the next analysis step.

[1521] 5. Integrated analysis using AI

[1522] The server integrates the generated economic forecast data, market data, and user sentiment data and performs analysis using AI (artificial intelligence). The AI ​​uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis. By using sentiment data, psychological aspects are also taken into account to allow users to invest with confidence.

[1523] 6. Generating and delivering investment proposals

[1524] Based on the AI ​​analysis results, the server generates investment proposals that are optimal for the user's investment profile and sentiment data. These proposals include specific investment products (stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated investment proposals are sent back to the user via their terminal.

[1525] Specific examples

[1526] For example, a user inputs their investment profile as "risk tolerance: high," "investment period: long-term (5 years or more)," and "area of ​​interest: technology," and the emotion engine recognizes from the user's facial expression that they are "excited." This data is then sent to the server, which collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and other factors. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high-risk but also high-return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[1527] In this way, the system can quickly and accurately respond to the user's investment needs and psychological state, providing optimal investment strategies. Users receive investment proposals that take into account their own conditions and emotions, allowing them to make investment decisions with peace of mind.

[1528] The processing flow will be explained below.

[1529] Step 1:

[1530] The user enters an investment profile.

[1531] The user opens the terminal interface and is presented with a form to enter information about the investment, such as risk tolerance, investment horizon, and areas of interest.

[1532] As the user types, the device's camera captures their facial expressions in real time, and the microphone analyzes their tone of voice.

[1533] As a result, emotional data of the user (for example, excitement, relief, anxiety, etc.) is acquired.

[1534] Step 2:

[1535] The device sends input data and emotion data to the server.

[1536] The terminal combines the investment profile data entered by the user into the form and the acquired emotion data and sends them to the server as an HTTP POST request.

[1537] The transmitted data is received by the server and prepared for the next stage of analysis.

[1538] Step 3:

[1539] A server collects market data.

[1540] The server sends API requests to financial market data providers to gather the latest market data.

[1541] Data collected includes stock prices, interest rates, exchange rates, economic indicators, etc.

[1542] The acquired market data is stored in a database within the server.

[1543] Step 4:

[1544] The server makes predictions based on economic models.

[1545] The server runs a predictive model based on economic theory on the collected market data.

[1546] Predictive models generate future economic indicators such as GDP growth, inflation, and interest rate trends.

[1547] The generated economic forecast data is stored internally for analysis.

[1548] Step 5:

[1549] The server performs integrated analysis using artificial intelligence.

[1550] The server integrates the generated economic forecast data, real-time market data, and user sentiment data and performs analysis using AI (artificial intelligence).

[1551] AI uses machine learning algorithms to calculate risk-adjusted returns, optimize portfolios, and perform market segmentation analysis.

[1552] Emotional data influences risk adjustment and investment product selection so that users can invest with psychological peace of mind.

[1553] Step 6:

[1554] The server generates an investment proposal and sends it to the terminal.

[1555] Based on the results of the AI ​​analysis, the server generates investment proposals that take into account the user's investment profile and emotional data.

[1556] Investment suggestions include the best investment products for the user (stocks, bonds, ETFs, etc.) and recommended portfolio composition.

[1557] The generated investment proposal is transmitted to the terminal.

[1558] Step 7:

[1559] The terminal displays the investment proposal to the user.

[1560] The terminal displays the investment proposal received from the server on the interface.

[1561] The user can check detailed investment proposals on the device screen and make investment decisions based on the proposals as needed.

[1562] This detailed processing flow allows users to quickly and accurately obtain the optimal investment strategy based on their own investment conditions and psychological state.

[1563] Example 2

[1564] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1565] Conventional investment advice systems only consider the user's investment profile and are unable to provide investment recommendations that reflect the user's emotional state. This makes it difficult to provide appropriate investment recommendations when psychological factors have a significant impact on investment decisions.

[1566] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1567] In this invention, the server includes an input means for a user to input an investment profile and acquire emotional data, a collection means for collecting market data based on the investment profile and emotional data, a generation means for executing a prediction model based on economic theory to generate economic forecast data, an analysis means for using artificial intelligence to perform an integrated analysis of the economic forecast data, market data, and emotional data, and a provision means for providing the user with investment proposals generated by the analysis means. This makes it possible to provide more accurate investment proposals that take into consideration the user's investment needs and emotional state in an integrated manner.

[1568] "User" refers to any individual or entity that utilizes the System to input their investment profile and sentiment data.

[1569] An "investment profile" is information about an investment entered by a user, including risk tolerance, investment period, areas of investment interest, and the like.

[1570] "Emotion data" is data that represents the user's emotional state, and is obtained by analyzing facial expressions and tone of voice.

[1571] "Input means" refers to a device or interface through which a user inputs investment profile and sentiment data.

[1572] "Collection Instrument" refers to a device or system for collecting market data based on investment profile and sentiment data.

[1573] "Market data" refers to data collected from financial markets, including stock prices, interest rates, exchange rates, economic indicators, etc.

[1574] A "predictive model" refers to a model that predicts future economic indicators based on economic theory.

[1575] "Generation means" refers to a device or system for executing a predictive model to generate economic forecast data.

[1576] "Economic forecast data" is data representing future economic indicators such as GDP growth rate, inflation rate, and interest rate trends, generated by a forecasting model.

[1577] "Artificial intelligence" refers to technology that uses machine learning algorithms and deep learning to analyze data and make decisions.

[1578] "Analysis means" refers to a device or system for integrating and analyzing economic forecast data, market data, and sentiment data using artificial intelligence.

[1579] "Providing means" refers to a device or interface for providing the investment proposals generated by the analyzing means to a user.

[1580] "Investment proposal" refers to information indicating specific investment products and portfolio composition generated based on a user's investment profile and sentiment data.

[1581] The present invention is a system that provides more accurate investment proposals based on a user's investment profile and emotional data. This system consists of three main components: a server, a terminal, and a user.

[1582] Hardware and Software Configuration

[1583] Terminal: The terminal provides an interface for users to input their investment profile and emotional data. It also uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice to obtain emotional data. This interface is built using front-end frameworks such as React.js and Vue.js.

[1584] Server: The server is responsible for collecting, analyzing, and synthesizing data, and generating investment proposals. Specifically, the server is built using Python's Flask or Node.js's Express, and runs machine learning algorithms using libraries such as Scikit-learn and TensorFlow. The data is stored in a database such as MySQL or MongoDB.

[1585] System Operation Overview

[1586] 1. User input of investment profile and emotional data: Users input their investment profile, such as risk tolerance, investment period, and investment interests, through an interactive interface on their device. At the same time, the camera and microphone are used to capture the user's emotional data. For example, the emotional engine can use Amazon Rekognition or IBM Watson.

[1587] 2. Collection and transmission of emotional data: The terminal temporarily stores the acquired emotional data and investment profile information and transmits it to the server in encrypted form using the HTTPS protocol.

[1588] 3. Market data collection: The server collects market data in real time from financial markets using APIs (e.g., Alpha Vantage or Yahoo Finance). The collected data includes stock prices, interest rates, exchange rates, economic indicators, etc., and is stored in a database on the server.

[1589] 4. Execution of predictive models: The server executes predictive models based on economic theory using the collected market data. These predictive models include regression analysis and time series analysis using libraries such as Scikit-learn and TensorFlow. For example, they predict future economic indicators such as GDP growth rate and inflation rate.

[1590] 5. Integrated analysis using AI: The server integrates economic forecast data generated by the predictive model, market data, and user sentiment data, and performs AI analysis. This includes calculating risk-adjusted returns and optimizing portfolios using machine learning algorithms and deep learning techniques. Psychological aspects are also taken into account, so emotional data can be used to help users invest with confidence.

[1591] 6. Generation and provision of investment proposals: Based on the results of the AI ​​analysis, the server generates investment proposals that are optimal for the user's investment profile and sentiment data. These proposals include specific investment products (e.g., stocks, bonds, ETFs, etc.) and recommended portfolio composition. The generated investment proposals are provided to the user via their terminal.

[1592] Specific examples

[1593] For example, consider the case where a user inputs an investment profile into their device, such as "Risk tolerance: High," "Investment period: Long-term (5 years or more)," and "Interest area: Technology," and the emotion engine recognizes from the user's facial expression that they are "excited." When this data is sent to the server, the server collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and so on. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high in risk but also high in return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[1594] Prompt Sentence Examples

[1595] "High risk tolerance and long investment horizon (>5 years). User's area of ​​interest is technology. Sentiment state is excited. Generate appropriate investment recommendations based on this."

[1596] In this way, the system can provide optimal investment strategies by comprehensively considering the user's investment profile and emotional data, allowing users to make investment decisions with peace of mind.

[1597] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1598] Step 1:

[1599] User input of investment profile and sentiment data

[1600] ==

[1601] Users use the device to input information about their investments, such as risk tolerance, investment horizon, and investment interests, through an interactive interface (built with React.js or Vue.js, for example). The device also uses a camera and microphone to capture the user's facial expressions and tone of voice, collecting emotional data.

[1602] Input: Investment profile information, user facial expression data, voice data

[1603] Data processing and computation: Analyzing facial and voice data to identify emotions, for example, using facial expression recognition and voice tone analysis.

[1604] Output: Investment profile and sentiment data

[1605] Step 2:

[1606] Collecting and transmitting emotional data

[1607] ==

[1608] The device temporarily stores the collected emotional data and sends it to a server using the HTTPS protocol. This communication is encrypted to protect the privacy of the data.

[1609] Input: User investment profile, sentiment data

[1610] Data processing and calculation: Integrate sentiment data and investment profiles and convert them into a suitable format.

[1611] Output: HTTPS request to the server

[1612] Step 3:

[1613] Market Data Collection

[1614] ==

[1615] The server collects real-time market data using financial APIs, such as Alpha Vantage and Yahoo Finance, to obtain stock prices, interest rates, exchange rates, economic indicators, and more.

[1616] Input: API request

[1617] Data processing and calculation: Store acquired market data in a database and convert it into the required format.

[1618] Output: Market data stored in a database

[1619] Step 4:

[1620] Running a predictive model

[1621] ==

[1622] The server uses the collected market data and investment profile data to run predictive models using Python libraries such as Scikit-learn and TensorFlow, which then predict future economic indicators (e.g., GDP growth rate, inflation rate).

[1623] Inputs: Market data, investment profile data

[1624] Data processing and calculations: Predicting future economic indicators using statistical methods and machine learning algorithms.

[1625] Output: Economic forecast data

[1626] Step 5:

[1627] Integrated analysis using AI

[1628] ==

[1629] The server performs integrated analysis using the generated economic forecast data, market data, and user sentiment data, and uses AI and machine learning algorithms (e.g., Scikit-learn and Keras) to calculate risk-adjusted returns and optimize portfolios.

[1630] Inputs: Economic forecast data, market data, sentiment data

[1631] Data processing and calculation: Apply machine learning algorithms to analyze risk and return, and take into account sentiment data to evaluate psychological aspects.

[1632] Output: Analysis result data

[1633] Step 6:

[1634] Generate and deliver investment proposals

[1635] ==

[1636] The server generates optimal investment proposals for users based on the results of the AI ​​analysis. These proposals include specific investment products and portfolio composition. The generated investment proposals are provided to users via their terminals.

[1637] Input: Analysis result data

[1638] Data processing and calculation: Formatting the analysis results in a form that is easy for users to understand.

[1639] Output: Investment proposal to user

[1640] (Application example 2)

[1641] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1642] The problem to be solved by the present invention is to enable an investment proposal system to make investment proposals that take into account not only a user's investment profile but also the user's emotional state. There is also a need for a specific means to reduce the impact of a user's psychological state on investment decisions, which is often overlooked in conventional investment proposal systems.

[1643] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input an investment profile; collection means for collecting market data based on the investment profile; generation means for generating economic forecast data by executing a prediction model based on economic theory; analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence; a sentiment analysis engine for collecting and analyzing user emotional data and reflecting it in investment proposals; and provision means for integrating the emotional data collected by the sentiment analysis engine and generating investment proposals. This makes it possible to make investment proposals that take into account the psychological aspects of the user and provide optimal investment strategies by analyzing market data in real time.

[1644] "Input means" refers to devices and interfaces through which a user inputs an investment profile.

[1645] "Collection Methods" refers to the systems and technologies used to collect market data based on an investment profile.

[1646] "Generation means" refers to a computer program or algorithm for executing a forecasting model based on economic theory and generating economic forecast data.

[1647] "Analytical Tools" refers to computing devices and software for integrating and analyzing economic forecast data and market data using artificial intelligence.

[1648] An "emotion analysis engine" refers to a machine learning model or algorithm that collects and analyzes user emotional data and reflects it in investment proposals.

[1649] "Provision means" refers to an interface or system for integrating the emotional data collected by the emotion analysis engine and generating and providing optimal investment proposals to users.

[1650] System Overview

[1651] The system for implementing this invention is mainly composed of three main components: a server, a terminal, and a user. This system provides highly accurate investment proposals by acquiring and analyzing investment profiles and sentiment data.

[1652] Program processing explanation

[1653] server

[1654] The server will be the center of data analysis and will use the following hardware and software:

[1655] Hardware: High-performance computing server

[1656] Software: Python, TensorFlow, Keras, OpenCV

[1657] The server integrates and analyzes the investment profile, sentiment data, and market data submitted by the user to generate optimal investment proposals. Machine learning algorithms and economic forecasting models are used to collect, analyze, and run data. For example, collected market data is analyzed in real time to calculate risk-adjusted returns that take sentiment data into account.

[1658] Terminal

[1659] The terminal has the role of providing an interface with the user and uses the following hardware and software.

[1660] Hardware: Camera, microphone

[1661] Software: Interactive interface, sentiment analysis engine, data transmission

[1662] When entering an investment profile, the device analyzes the user's facial expressions and tone of voice to obtain emotional data. The obtained emotional data is sent to the server in real time. The emotion analysis engine quantifies the user's psychological state and uses this information to support analysis on the server side.

[1663] crew

[1664] The user inputs the investment profile through the terminal, and provides the investment profile and sentiment data in the following steps:

[1665] 1. Enter your investment profile (risk tolerance, investment horizon, investment interests, etc.).

[1666] 2. Emotional data is collected through cameras and microphones.

[1667] 3. The entered data is sent to the server in real time.

[1668] Specific examples

[1669] For example, a user inputs their investment profile as "risk tolerance: high," "investment period: long-term (5 years or more)," and "area of ​​interest: technology," and the emotion engine recognizes from the user's facial expression that they are "excited." This data is then sent to the server, which collects market data on the technology sector in real time and predicts GDP growth rates, interest rate trends, and other factors. The AI ​​integrates this data and generates investment proposals that take the user's emotional state into account. As a result, it recommends a portfolio of technology stocks that are high-risk but also high-return, but also adjusts the risk to prevent excessive influence from emotions. This proposal is then provided to the user.

[1670] Prompt Sentence Examples

[1671] "Please simulate the operation of an AI system that monitors the emotional state of factory workers in real time and makes optimal suggestions to improve work efficiency. In particular, the system will include obtaining emotional data using a facial recognition algorithm and suggesting work allocation based on that data."

[1672] This makes it possible to make investment proposals that take into account the psychological aspects of users and to analyze market data in real time to provide optimal investment strategies.

[1673] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1674] Step 1:

[1675] Users use a terminal to input their investment profile, including information such as risk tolerance, investment period, and investment interests. After inputting the information, the system simultaneously captures emotional data such as facial expressions and tone of voice using a camera and microphone.

[1676] Input: Investment profile (risk tolerance, investment period, investment interest area), emotional data (facial expression, tone of voice)

[1677] Data processing: Collection and analysis of emotional data (facial expression recognition, voice analysis)

[1678] Output: Investment profile, sentiment data

[1679] Step 2:

[1680] The terminal transmits the acquired investment profile and sentiment data to a server in real time using a secure communication protocol.

[1681] Input: Investment profile, sentiment data

[1682] Data processing: data packing and encryption

[1683] Output: Data sent to the server

[1684] Step 3:

[1685] The server receives the submitted investment profile and sentiment data, which is then stored in an internal database.

[1686] Input: Data sent from the terminal

[1687] Data processing: receiving and decoding data, storing it in a database

[1688] Output: Investment profile and sentiment data stored in an internal database

[1689] Step 4:

[1690] The server collects market data in real time from external financial markets. Data obtained through APIs includes stock prices, exchange rates, interest rates, etc. This data is also stored in the database.

[1691] Input: Market Data API

[1692] Data Processing: Market Data Acquisition and Storage

[1693] Output: Market data stored in an internal database

[1694] Step 5:

[1695] The server runs predictive models based on economic theory to forecast future economic indicators, including GDP growth, inflation, and interest rate trends.

[1696] Input: Market Data

[1697] Data processing: Executing predictive models and generating predictive data

[1698] Output: Economic forecast data

[1699] Step 6:

[1700] The server integrates investment profiles, sentiment data, economic forecast data, and market data and analyzes them using artificial intelligence, which uses machine learning algorithms to calculate risk-adjusted returns and optimize portfolios.

[1701] Inputs: Investment profiles, sentiment data, economic forecast data, market data

[1702] Data processing: Data integration and analysis using AI

[1703] Output: Integrated analysis results

[1704] Step 7:

[1705] The server generates optimal investment proposals based on the analysis results. The proposals include specific investment products (stocks, bonds, ETFs, etc.) and portfolio composition. It also takes into account emotional data and psychological aspects to ensure users feel comfortable investing.

[1706] Input: Analysis results

[1707] Data Processing: Investment Proposal Generation

[1708] Output: Investment proposal

[1709] Step 8:

[1710] The server transmits the generated investment proposal to the terminal used by the user, and the proposal is presented to the user using an interactive interface.

[1711] Input: Investment Proposal

[1712] Data processing: data packing and encryption

[1713] Output: Data sent to the terminal

[1714] Step 9:

[1715] The user reviews the investment proposals they receive and, if necessary, provides their emotional data again, which is then sent back to the server to help improve the accuracy of the proposals.

[1716] Input: Updated emotion data

[1717] Data processing: Re-transmitting data and analyzing

[1718] Output: Updated investment proposal

[1719] These steps enable us to provide investment proposals that take into account the psychological aspects of users and analyze market data in real time to provide optimal investment strategies.

[1720] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1721] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1722] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1723] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1724] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1725] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1726] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1727] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1728] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1729] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1730] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1731] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1732] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1733] 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.

[1734] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1735] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1736] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1737] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1738] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1739] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1740] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1741] The following is further disclosed regarding the above embodiment.

[1742] (Claim 1)

[1743] [input means for a user to input an investment profile];

[1744] a collection means for collecting market data based on said investment profile;

[1745] [a generating means for executing a predictive model based on economic theory to generate economic forecast data];

[1746] [Analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence] means;

[1747] [a providing means for providing a user with an investment proposal generated by the analysis means];

[1748] A system including:

[1749] (Claim 2)

[1750] 2. The system of claim 1, further comprising an interactive interface for the user to input an investment profile through the interface.

[1751] (Claim 3)

[1752] 2. The system of claim 1, wherein said collecting means collects market data in real time.

[1753] "Example 1"

[1754] (Claim 1)

[1755] [input means for a user to input an investment profile];

[1756] a collection means for collecting market data based on said investment profile;

[1757] [a generating means for executing a predictive model based on economic theory to generate economic forecast data];

[1758] [Analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence] means;

[1759] [a providing means for providing a user with an investment proposal generated by the analysis means];

[1760] A communication means for sending user-entered data to the server as an HTTP POST request;

[1761] [Interface means for allowing a user to easily input an investment profile through an interactive interface] means;

[1762] A system including:

[1763] (Claim 2)

[1764] 2. The system of claim 1, wherein said collecting means collects market data in real time.

[1765] (Claim 3)

[1766] 2. The system of claim 1, wherein the generating means uses a machine learning algorithm to perform risk-adjusted return calculations and portfolio optimization.

[1767] "Application Example 1"

[1768] (Claim 1)

[1769] [input means for a user to input an investment profile];

[1770] a collection means for collecting market data based on said investment profile;

[1771] [a generating means for executing a predictive model based on economic theory to generate economic forecast data];

[1772] [Analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence] means;

[1773] [Display means for displaying real-time investment proposals in a virtual environment, including providing means for providing the investment proposals generated by said analysis means to a user];

[1774] A system including:

[1775] (Claim 2)

[1776] 10. The system of claim 1, further comprising: interactive interface means for the user to input an investment profile through an interface; and user interface means for inputting a user profile using smart glasses.

[1777] (Claim 3)

[1778] 10. The system of claim 1, further comprising display means for displaying market data collected by said collecting means in real time within a virtual environment.

[1779] "Example 2: Combining Emotion Engines"

[1780] (Claim 1)

[1781] [input means for a user to input an investment profile and obtain sentiment data];

[1782] a collection means for collecting market data based on said investment profile and sentiment data;

[1783] [a generating means for executing a predictive model based on economic theory to generate economic forecast data];

[1784] [analysis means for integrating and analyzing the economic forecast data, market data, and sentiment data using artificial intelligence] means;

[1785] [a providing means for providing a user with an investment proposal generated by the analysis means];

[1786] A system including:

[1787] (Claim 2)

[1788] 10. The system of claim 1, further comprising an interactive interface for the user to input investment profile and sentiment data through the interface.

[1789] (Claim 3)

[1790] 2. The system of claim 1, wherein said collecting means collects market data in real time.

[1791] "Application example 2 when combining emotion engines"

[1792] New Claims

[1793] (Claim 1)

[1794] [input means for a user to input an investment profile];

[1795] a collection means for collecting market data based on said investment profile;

[1796] [a generating means for executing a predictive model based on economic theory to generate economic forecast data];

[1797] [Analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence] means;

[1798] A sentiment analysis engine for collecting and analyzing user sentiment data and reflecting it in investment proposals;

[1799] [a providing means for generating investment proposals by integrating the emotion data collected by the emotion analysis engine];

[1800] A system including:

[1801] (Claim 2)

[1802] 2. The system of claim 1, further comprising an interactive interface for the user to input an investment profile through the interface.

[1803] (Claim 3)

[1804] 2. The system of claim 1, wherein said collecting means collects market data in real time. [Explanation of symbols]

[1805] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. input means for a user to input an investment profile; a collection means for collecting market data based on said investment profile; generating means for executing a prediction model based on economic theory to generate economic prediction data; an analysis means for integrating and analyzing the economic forecast data and market data using artificial intelligence; providing means for providing the investment proposal generated by the analysis means to a user; A system including:

2. 10. The system of claim 1, further comprising an interactive interface means for the user to input an investment profile through an interface.

3. 2. The system of claim 1, wherein said collecting means collects market data in real time.

Images