system

Abstract

A system is provided.SOLUTION: A system comprising: a user interface means for displaying investment performance information of a user; a server means for acquiring real-time market data from an external data provider and storing the data in a database; a server means for monitoring SNS and news information in real time and inputting the acquired information to a generative AI to generate an investment decision; a server means for transmitting an automatic trade command based on the generated investment decision and executing a transaction; and a means for reflecting a transaction result on a user interface.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] Today's financial markets are diverse and complex, requiring individual investors to make investment decisions with sophisticated market analysis skills and a lot of time. However, many individual investors find it difficult to perform such market analysis due to time constraints and a lack of investment knowledge. This problem causes them to miss many investment opportunities and makes it difficult for them to achieve financial independence. In addition, there is a lack of systems that can effectively utilize real-time market data and news information to make sophisticated investment decisions. Therefore, there is a need for a system that can perform real-time market data analysis and automated trading that is easy for individual investors to use. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes a user interface for displaying a user's investment performance information, a server for acquiring real-time market data from external data providers and storing it in a database, a server for monitoring social media and news information in real time and inputting the acquired information into a generation AI to generate investment decisions, a server for sending automated trading commands and executing transactions based on the generated investment decisions, and a system for reflecting trading results in the user interface. This allows individual investors, even those without advanced market analysis capabilities, to easily make investment decisions and use automated trading based on real-time information, thereby maximizing investment opportunities. Furthermore, the system also provides functions for setting individual investment strategies based on the user's investment risk tolerance and investment goals, as well as user login and account management functions, improving convenience.

[0006] "User Interface Means" means the means by which a user can interact with the system and visually view investment performance information and other data.

[0007] "Server means" refers to a computer system that processes and manages data for the entire system, and is a means for acquiring real-time market data, managing a database, analyzing it using generated AI, and sending automatic trading commands.

[0008] "Generative AI" is an artificial intelligence model that analyzes acquired data and generates investment decisions.

[0009] "Real-time market data" refers to data that provides current trading information in financial markets in near real time.

[0010] "SNS and news information" refers to information about the latest trends and news articles collected from social media and news sites.

[0011] "Automatic trading instructions" are trading instructions for automatically executing transactions based on investment decisions generated by the generation AI.

[0012] "Data Provider" means an external data supplier that provides real-time financial market data.

[0013] "User Data" refers to data that includes information such as a system user's investment portfolio, risk tolerance, and investment goals.

[0014] "Investment risk tolerance" is an index that indicates the range of risk that a user can tolerate.

[0015] "Investment Objective" is the goal a user wishes to achieve through investment (e.g., short-term profit, long-term growth, etc.).

[0016] An "exchange" is a market facility or network where financial instruments are bought and sold.

[0017] A "broker" is a trader or platform that buys and sells financial products on behalf of investors.

[0018] "Database" means an electronic information repository for storing and managing data collected by the system.

[0019] "Login and account management functions" refers to functions that allow users to access the system and manage their account information. [Brief explanation of the drawings]

[0020] [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 showing 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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention relates to a system that enables individual investors to easily make investment decisions using real-time market data and automatically execute trades. The system includes a user interface, a server, a generating AI, and a database.

[0042] User Interface Means

[0043] It provides an interface for users to access the system. Specifically, users enter their email address and password on the login page to log in to the system. After logging in, a dashboard is displayed, displaying their current investment portfolio, investment performance, and other related information in an easy-to-read format. In addition, a settings screen allows users to enter their own risk tolerance and investment goals. This allows users to intuitively operate the system and easily manage their own investment information.

[0044] Server Means

[0045] The server plays a central role in this system. It obtains real-time market data from external financial data providers and stores it in a database. The server also monitors social media and news information in real time and supplies the data to the generation AI. The generation AI analyzes this data and generates investment decisions. The generated investment decisions are sent to exchanges and brokers via the server as automatic trading commands. Once the transaction is completed, the server stores the results in a database and reflects them in the user interface.

[0046] Generation AI

[0047] The Generator AI analyzes market data, social media, and news information provided by the server to generate investment decisions. For example, if positive news about a company spreads on social media, the Generator AI determines that the company's stock price is likely to rise and generates a buy command. This allows users to quickly invest based on the latest market trends. The Generator AI provides individually customized investment strategies based on the user's risk tolerance and investment goals.

[0048] Database Means

[0049] The database stores and manages all market data, user data, trading results, etc. acquired by the system. The server accesses the database to retrieve and analyze the necessary data, and prepares the data to be supplied to the generation AI. When a user logs into the system, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[0050] Specific examples

[0051] For example, consider the case where a user is investing in the stock market. When the user logs in and opens the dashboard, their current investment portfolio and its performance are displayed. The server constantly monitors social media and news sites, and obtains information that "Company A's stock price is expected to rise due to the huge success of its new product." This information is sent to the generation AI via the server, and the generation AI generates a decision to purchase Company A's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company A's shares. The results of the transaction are stored in a database and reflected on the dashboard.

[0052] This allows users to make real-time investments based on the latest market information without having to perform complex market analysis. This system lowers the barrier to entry for individual investors, allowing more people to efficiently take advantage of investment opportunities.

[0053] The processing flow will be explained below.

[0054] Program processing steps

[0055] User Login Process

[0056] Step 1:

[0057] The user enters their email address and password on the login page and clicks the "Login" button.

[0058] Step 2:

[0059] The terminal sends the authentication information entered by the user to the server.

[0060] Step 3:

[0061] The server checks the authentication information against a database and authenticates the user.

[0062] Step 4:

[0063] The server returns the authentication result and user data to the terminal.

[0064] Step 5:

[0065] The device will open a dashboard page displaying the user's investment portfolio, performance, and other relevant information.

[0066] View investment performance

[0067] Step 1:

[0068] The server retrieves the most recent investment performance data from the database.

[0069] Step 2:

[0070] The server transmits the acquired data to the terminal.

[0071] Step 3:

[0072] The data received by the device is converted into graphs and tables and displayed on the user's dashboard.

[0073] Market Data Collection

[0074] Step 1:

[0075] The server sends API requests to external financial data providers at configured times.

[0076] Step 2:

[0077] The server retrieves real-time market data.

[0078] Step 3:

[0079] The server saves the retrieved data in the database.

[0080] News and social media monitoring

[0081] Step 1:

[0082] The server periodically sends a request to retrieve data from the SNS API and the news API.

[0083] Step 2:

[0084] The server retrieves the latest news headlines and trending information.

[0085] Step 3:

[0086] The server stores the acquired information in a database.

[0087] Making and implementing investment decisions

[0088] Step 1:

[0089] The server sends the latest market data and news information to the generation AI.

[0090] Step 2:

[0091] Generative AI analyzes the data and generates investment decisions.

[0092] Step 3:

[0093] The generating AI sends the investment decision back to the server.

[0094] Step 4:

[0095] The server generates buy and sell instructions based on the investment decisions.

[0096] Step 5:

[0097] The server sends automated trading instructions to the exchange or broker.

[0098] Reflection of trading results

[0099] Step 1:

[0100] The server receives the trading results from the exchange or broker.

[0101] Step 2:

[0102] The server stores the transaction results in a database.

[0103] Step 3:

[0104] The server sends the transaction results to the terminal.

[0105] Step 4:

[0106] The terminal reflects the transaction results received on the dashboard and notifies the user.

[0107] Managing User Settings

[0108] Step 1:

[0109] The user enters their risk tolerance and investment goals on the settings screen and clicks the "Save" button.

[0110] Step 2:

[0111] The terminal transmits the input setting data to the server.

[0112] Step 3:

[0113] The server stores the received data in a database.

[0114] Step 4:

[0115] After the server has completed saving, it sends a notification to the device, and the device displays a confirmation message to the user to confirm that the settings have been saved.

[0116] This allows the entire system to work seamlessly together, enabling users to carry out investment activities easily and in real time.

[0117] Example 1

[0118] 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."

[0119] There is a problem in that it is difficult for individual investors to efficiently utilize real-time market data and make quick and accurate investment decisions. Traditional investment systems require advanced market analysis, which requires a great deal of time and effort for individual investors to perform. In addition, there is a lack of systems that can monitor large amounts of market data, social media information, and news information in real time and make investment decisions based on that information, which often results in timely investment opportunities being missed.

[0120] 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.

[0121] In this invention, the server includes a display means for displaying user investment performance information, an information processing means for acquiring real-time market data from external information providers and storing it in an information storage device, an information processing means for monitoring communication network information and news reports in real time and inputting the acquired information into a generative AI model to generate investment decisions, an information processing means for sending automatic trading commands based on the generated investment decisions and executing transactions, and a means for reflecting trading results on the display means. This enables individual investors to make accurate and prompt investment decisions and automatic trading in real time without performing complex market analysis.

[0122] "Display means" refers to devices or software that visually provide information to users. Specifically, it refers to a screen or interface for displaying users' investment performance information, trading results, etc.

[0123] "Information processing device means" refers to devices and software for acquiring, analyzing, storing, and transmitting data. Specifically, it includes functions for acquiring real-time market data from external information providers and storing it in an information storage device, monitoring and acquiring communication network information and news reports, feeding data to generative AI models to generate investment decisions, and executing trades based on the generated instructions.

[0124] "External Information Provider" means a third-party information service company or institution that provides market data or financial information, including financial API service and data feed providers.

[0125] "Information storage device" refers to a physical or virtual storage medium or system for storing acquired data. This includes databases, cloud storage, hard disk drives, etc.

[0126] "Communication network information" refers to information provided through social networking sites and news sites on the Internet. Specifically, it includes data such as Twitter and news site articles.

[0127] "News Information" refers to market trends and economic information provided by news articles and news organizations, including information provided by financial news sites and news organization APIs.

[0128] A "generative AI model" is a model that uses machine learning and artificial intelligence techniques to analyze data and achieve a specific purpose (in this case, investment decisions). This includes AI models built using Tensorflow (registered trademark) and PyTorch.

[0129] "Automatic trading instructions" are specific operational instructions for executing market transactions in real time based on the generated investment decisions, including information such as the type of transaction (buy or sell), name, and quantity.

[0130] "Trading results" refers to data obtained as a result of actual trading in the market, including purchase price, trading volume, trading time, etc.

[0131] MODE FOR CARRYING OUT THE INVENTION

[0132] The present invention relates to a system for enabling individual investors to efficiently utilize real-time market data and automatically make fast and accurate investment decisions. The system includes a user interface means, a server means, a generative AI model, and a database means.

[0133] User Interface Means

[0134] This is the interface through which users access the system and manage their own investment information. Users use their terminal to access the system's login page and log in by entering their email address and password. The dashboard that appears after logging in visually displays the user's investment performance information and investment portfolio. Additionally, the settings screen allows users to set their own risk tolerance and investment goals. This method allows users to operate the system intuitively.

[0135] Server Means

[0136] The server is the core of this system. First, the server obtains real-time market data from external information providers. To do this, it uses services such as the Yahoo Finance API and Alpha Vantage API. The server obtains data from these providers using an API key and stores the data in an information storage device. Next, the server monitors and obtains information from social media and news sources, such as the Twitter API and Google® News API. This data is collected in real time and fed to the generative AI model.

[0137] Generative AI Models

[0138] The generative AI model analyzes market data, social media information, and news data supplied from a server to generate investment decisions. Specifically, it uses an AI model built using machine learning frameworks such as TensorFlow and PyTorch. For example, if positive news about a company spreads on social media, the generative AI model will determine that the company's stock price is likely to rise and generate a buy command. The generative AI model provides an individually customized investment strategy based on the user's risk tolerance and investment goals.

[0139] Database Means

[0140] The database is used to store and manage market data, user data, trading results, etc. obtained by the server. For example, a database management system such as MySQL (registered trademark) or PostgreSQL is used. The server accesses the database to obtain and analyze the necessary data, and prepares data to be supplied to the generative AI model. When a user logs in, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[0141] Specific examples

[0142] For example, consider the case where a user is investing in the stock market. The user logs in using their device and opens a dashboard, which displays their current investment portfolio and its performance. The server constantly monitors social media and news sites and obtains information that "Company A's stock price is expected to rise due to the huge success of its new product." This information is sent by the server to a generative AI model, which then generates a decision to purchase Company A's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company A's shares. The results of the transaction are stored in a database and reflected on the user's dashboard.

[0143] Prompt Sentence Examples

[0144] You can input prompts like the following to the generative AI model:

[0145] "Generate appropriate investment decisions based on the latest market data and information obtained from social media and news. For example, 'Company A's stock price is likely to rise, so generate a buy command.'"

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] A user logs in.

[0149] A user accesses the system's login page using a terminal, enters their email address and password, and clicks the "Login" button. The server checks the user's email address and password against the authentication information stored in the database, and if they match, authenticates them. The input is the user's authentication information (email address, password), and the output is the authentication success or failure status. If authentication is successful, the user is redirected to the dashboard.

[0150] Step 2:

[0151] The server retrieves the market data.

[0152] The server sends requests to the API endpoints of external information providers to retrieve real-time market data. For example, it uses the Yahoo Finance API or Alpha Vantage API. The data is returned in JSON format, which is parsed and saved in information storage. The input is the API request and API key, and the output is a JSON object of market data. This data includes stock price, trading volume, historical price fluctuations, etc.

[0153] Step 3:

[0154] The server monitors social media and news information.

[0155] The server uses the Twitter API or Google News API to monitor and retrieve real-time posts and articles related to specific keywords. For example, keywords such as "Company A" and "new product" are monitored. The retrieved information is sent to the server in JSON format and saved in an information storage device. The input is the monitored keyword, and the output is a JSON object of the related social media posts and news articles.

[0156] Step 4:

[0157] The server feeds the data into the generative AI model.

[0158] The server supplies the acquired market data, social media information, and news information to the generative AI model. The data is formatted and converted into a format that the generative AI model can parse. For example, JSON data containing market data and positive social media posts about a specific company is passed to the generative AI model. The input is a JSON object of the formatted data, and the output is the status of completion of data supply to the generative AI model.

[0159] Step 5:

[0160] Generative AI models generate investment decisions.

[0161] The generative AI model analyzes the data it receives and generates an investment decision. For example, if it determines that the stock price of Company A is likely to rise due to social media information and news, the model generates a command to purchase the stock of Company A. The input is JSON data of market data, social media information, and news information, and the output is a JSON object of the investment decision command (buy or sell).

[0162] Step 6:

[0163] The server issues automatic trading commands.

[0164] The server sends automated trading commands to the exchange or broker based on the investment decisions received from the generative AI model. It sends buy or sell commands to the exchange via API. For example, send a buy command with "POST https: / / broker-api.example.com / order". The input is a JSON object of the investment decision, and the output is the status of the trade execution result.

[0165] Step 7:

[0166] The server stores the transaction results in a database and reflects them on the user interface.

[0167] Once the transaction is complete, the server saves the results (purchase price, quantity, and transaction time) in a database. The server then updates the user interface to reflect the latest investment portfolio information on the user's dashboard. The input is the transaction result data, and the output is the updated dashboard display.

[0168] (Application example 1)

[0169] 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."

[0170] Conventional investment systems require users to check market data in real time, analyze it themselves, and make investment decisions, which takes time and effort. It's also difficult to instantly grasp market data and news information and react quickly, which increases the risk of missing investment opportunities. Furthermore, most investment systems are desktop or smartphone-based, requiring users to constantly operate their devices, making them inconvenient.

[0171] 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.

[0172] In this invention, the server includes a means for displaying a user's investment performance information, a means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting it into a generation AI to generate investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, a means for reflecting trading results in a user interface, and a means for using smart glasses as a user interface to overlay the latest investment performance information and notifications on the user's field of vision, thereby enabling the user to always check the latest investment information hands-free and make investment decisions quickly and efficiently.

[0173] "User interface means" refers to means for providing an interface for a user to access the system, operate it, and view information.

[0174] The "server means" is a server that has the function of obtaining real-time market data from external data providers and storing it in a database, as well as the function of monitoring social media and news information and transmitting it to the generation AI.

[0175] "Generative AI" is artificial intelligence that generates investment decisions based on acquired market data, social media information, and news information.

[0176] "Automatic trading instructions" are trading instructions sent to exchanges and brokers based on investment decisions generated by the generation AI.

[0177] "Trading results" refer to the results of buying and selling executed based on the instructions of the generating AI, and are information reflected in the user interface.

[0178] "Smart glasses" are a wearable eyeglass-type device that can overlay information onto the user's field of vision.

[0179] System Overview

[0180] This invention is a system that allows users to check market data in real time using smart glasses and automatically make investment decisions. The system is composed of a user interface means, a server means, a generating AI, and a database means.

[0181] User Interface Means

[0182] The user interface is a pair of smart glasses. By wearing the glasses, users can see the latest investment performance information and investment-related notifications overlaid on their field of vision. This allows users to check investment information and operate the system hands-free.

[0183] Server Means

[0184] The server means has the function of acquiring real-time market data from external data providers and storing it in a database. It also monitors social media and news information in real time, inputs the acquired information into the generation AI, and generates investment decisions. Based on the generated investment decisions, the server sends automatic trading commands to exchanges and brokers to execute transactions. The trading results are stored in a database and reflected in the user interface.

[0185] Generation AI

[0186] The Generator AI analyzes real-time market data, social media information, and news information provided by the server and makes investment decisions based on the results. The Generator AI customizes investment strategies based on the user's investment risk tolerance and investment goals, providing optimal investment decisions.

[0187] Database Means

[0188] The database means stores and manages all market data, user data, and trading results acquired by the system. The server accesses the database, acquires the necessary data, and performs analysis.

[0189] Program Description

[0190] The server uses a Python program and an SDK for smart glasses (e.g., Smartech Glasses SDK) to build the system. To obtain real-time market data from external data providers, an API (e.g., api.financedata.com) is used. The generative AI model analyzes the obtained market data, social media information, and news information to make investment decisions.

[0191] Specific examples

[0192] When a user puts on the smart glasses and logs in, their current investment portfolio and its performance information are overlaid on their field of vision. The server obtains market data from external data providers and monitors social media and news sites. For example, suppose the news reports that "Company X's stock price is expected to rise due to the huge success of its new product." This information is sent to the generation AI via the server, which then generates a decision to purchase Company X's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company X's shares. The results of the transaction are stored in a database and reflected in the user interface.

[0193] Prompt Sentence Examples

[0194] Examples of prompts to be input to a generative AI model:

[0195] Prompt: "Analyze market trends about Company X from social media and news sources and make an investment decision. Your investment decision should include either buying or selling. Your investment decision should be based on your individual investor's risk tolerance and investment goals."

[0196] This enables the system to provide users with the latest market information and investment decisions in real time, supporting efficient investment.

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] The server allows a user to wear smart glasses and log in to the system. The user enters their email address and password through the user interface of the smart glasses. The server receives the entered credentials and queries an external authentication server for authentication. If authentication is successful, the server starts a user session, generates an authentication token, and sends it to the user's smart glasses. The input is the email address and password, and the output is the authentication token.

[0200] Step 2:

[0201] The server uses the authentication token to retrieve real-time market data from an external data provider and stores it in an internal database. The server sends a request to the market data API, including the authentication token, and receives real-time market data from the API as a response. The received data is stored in the database. The input is the authentication token, and the output is the real-time market data.

[0202] Step 3:

[0203] The server monitors social media and news information in real time and sends the acquired information to the generative AI model. The server accesses data sources from social media and news sites to extract important information. The extracted information is input into the generative AI model for analysis. The input is social media and news information, and the output is the analysis results.

[0204] Step 4:

[0205] The generative AI model analyzes market data, social media, and news information to make investment decisions. The server uses the input analysis results and market data and sends investment decision commands to the generative AI model based on the prompt text. The generative AI model analyzes the input data and generates the optimal investment decision (e.g., buy, sell). The input is market data and social media information, and the output is the investment decision.

[0206] Step 5:

[0207] The server sends automated trading instructions to exchanges and brokers based on the investment decisions generated by the generative AI model. The server receives the generated investment decisions, generates trading instructions based on them, and sends them through the API of the corresponding exchange or broker. The input is the investment decisions, and the output is the trading instructions.

[0208] Step 6:

[0209] When the transaction is completed, the server saves the results in a database and overlays them on the user's smart glasses. The transaction results are stored in the database and are configured to be reflected in the user interface. The smart glasses display shows the transaction results in real time. The input is the transaction results, and the output is the information displayed on the user interface.

[0210] This enables the entire system to provide users with efficient, real-time investment support.

[0211] 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.

[0212] The present invention combines a system that automatically makes investment decisions based on users' real-time market data and news information with an emotion engine that recognizes users' emotions and reflects them in investment decisions. The system includes a user interface means, a server means, a generation AI, an emotion engine, and a database means.

[0213] User Interface Means

[0214] This is the interface through which users access the system to check and operate investment performance information and settings. Users log in by entering their email address and password on the login page. After logging in, a dashboard will appear where users can check their investment portfolio, investment performance, and emotional state. The settings screen also allows users to enter their risk tolerance and investment goals.

[0215] Server Means

[0216] The server plays a central role in this system. Specifically, it has the following functions:

[0217] Obtain real-time market data from external financial data providers and store it in a database.

[0218] It monitors social media and news information in real time and supplies data to the generative AI.

[0219] It receives emotion data from the emotion engine and supplies it to the generative AI.

[0220] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[0221] The transaction results are stored in a database and reflected in the user interface.

[0222] Generation AI

[0223] The Generative AI generates investment decisions by analyzing market data, social media and news information provided by the server, as well as emotional data from the Emotion Engine. For example, if a user is under stress, the Generative AI will take that into account and suggest a low-risk investment strategy. This allows the user's emotional state to be directly reflected in investment decisions.

[0224] Emotion Engine

[0225] The emotion engine is a means of recognizing a user's emotional state and recording it as data. When a user logs in, their emotions are measured using a camera or microphone, and the emotion engine sends the data to the server. The emotion data is used by the generative AI to generate investment decisions. For example, if a user expresses "joy" when logging in, the generative AI will interpret this emotion as a strong desire to invest and suggest a more aggressive investment strategy.

[0226] Database Means

[0227] The database is an electronic information repository that stores and manages all market data, user data, sentiment data, trading results, etc. acquired by the system. The server accesses the database to retrieve and analyze the necessary data and prepare the data to be supplied to the generation AI. When a user logs into the system, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[0228] Specific examples

[0229] For example, consider the case where a user is investing in the stock market. Suppose the user logs in and is recognized as "relaxed" by the emotion engine. This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotion data. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcements." Combining this information with the emotion data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, which adds Company A's shares to the user's portfolio. The trading results are stored in a database and reflected on the dashboard.

[0230] This allows users to make real-time investments based on market trends while taking into account their own emotional state. The system integrates user emotions and market data to provide optimal investment strategies, thereby more effectively supporting investment activities.

[0231] The processing flow will be explained below.

[0232] Program processing steps

[0233] User login process and sentiment data capture

[0234] Step 1:

[0235] The user enters their email address and password on the login page and clicks the "Login" button.

[0236] Step 2:

[0237] The terminal sends the authentication information entered by the user to the server.

[0238] Step 3:

[0239] The server checks the authentication information against a database and authenticates the user.

[0240] Step 4:

[0241] The device's camera and microphone recognize the user's face and voice, and an emotion engine analyzes the user's emotional state.

[0242] Step 5:

[0243] The emotion engine transmits the recognized emotion data to the server.

[0244] Step 6:

[0245] The server stores the acquired emotion data in a database.

[0246] Step 7:

[0247] The server returns the authentication result and emotion data to the device, and the device opens the dashboard page.

[0248] View investment performance

[0249] Step 1:

[0250] The server retrieves the most recent investment performance data from the database.

[0251] Step 2:

[0252] The server transmits the acquired data to the terminal.

[0253] Step 3:

[0254] The data received by the device is converted into graphs and tables and displayed on the user's dashboard.

[0255] Market Data Collection

[0256] Step 1:

[0257] The server sends API requests to external financial data providers at configured times.

[0258] Step 2:

[0259] The server retrieves real-time market data.

[0260] Step 3:

[0261] The server saves the retrieved data in the database.

[0262] News and social media monitoring

[0263] Step 1:

[0264] The server periodically sends a request to retrieve data from the SNS API and the news API.

[0265] Step 2:

[0266] The server retrieves the latest news headlines and trending information.

[0267] Step 3:

[0268] The server stores the acquired news and social media data in a database.

[0269] Making and implementing investment decisions

[0270] Step 1:

[0271] The server sends the emotion data from the emotion engine, the latest market data and news information to the generation AI.

[0272] Step 2:

[0273] Generative AI analyzes the data and generates investment decisions taking into account the user's emotional state.

[0274] Step 3:

[0275] The investment decision generated by the generation AI is sent back to the server.

[0276] Step 4:

[0277] The server generates buy and sell instructions based on the investment decisions.

[0278] Step 5:

[0279] The server sends automated trading instructions to the exchange or broker.

[0280] Reflection of trading results

[0281] Step 1:

[0282] The server receives the trading results from the exchange or broker.

[0283] Step 2:

[0284] The server stores the transaction results in a database.

[0285] Step 3:

[0286] The server sends the transaction results to the terminal.

[0287] Step 4:

[0288] The terminal reflects the transaction results received on the dashboard and notifies the user.

[0289] Managing User Settings

[0290] Step 1:

[0291] The user enters their risk tolerance and investment goals on the settings screen and clicks the "Save" button.

[0292] Step 2:

[0293] The terminal transmits the input setting data to the server.

[0294] Step 3:

[0295] The server stores the received configuration data in a database.

[0296] Step 4:

[0297] After the server has completed saving the settings, it sends a notification to the device, and the device displays a confirmation message to the user to confirm the save.

[0298] Specific examples

[0299] For example, suppose a user logs in and is recognized as "relaxed" by the emotion engine. This information is sent to the generation AI via the server. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcement." Combining this information with emotion data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, adding Company A's stock to the user's portfolio. The trading results are stored in a database and reflected on the dashboard.

[0300] In this way, users can make real-time investments based on market trends while taking their emotional state into account.The system integrates user emotions and market data to provide optimal investment strategies, thereby more effectively supporting investment activities.

[0301] Example 2

[0302] 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."

[0303] Conventional investment systems provide investment decisions based on market data and news information, but lack the ability to make investment decisions that reflect the user's emotional state. As a result, even if a user's emotional state influences investment decisions, it is unable to take this into account, potentially increasing investment risk. Furthermore, the lack of a system that integrates real-time emotion recognition and investment decisions makes it difficult to propose optimal investment strategies for users.

[0304] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a user interface means for displaying a user's investment performance information, a means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting the acquired information into a generation AI to generate investment decisions, an emotion recognition means for recognizing a user's emotional state and inputting the emotion data into the generation AI to reflect it in investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, and a means for reflecting trading results in the user interface. This integrates investment decisions that take the user's emotional state into account with investment decisions based on real-time market data, enabling more appropriate and secure investments.

[0305] "User Interface Means" means the means by which a User accesses the System through an interface to view and manipulate investment performance information and settings.

[0306] The "server means" refers to a means for obtaining real-time market data from external data providers, storing it in a database, monitoring social media and news information, and supplying data to the generation AI.

[0307] "Generative AI" is an artificial intelligence model that analyzes market data, social media information, news information, and user emotional data to generate optimal investment decisions.

[0308] "Emotion recognition means" is a means for recognizing the user's emotional state and providing that data to the generation AI.

[0309] "Automated trading instructions" are trading instructions sent to an exchange or broker based on investment decisions generated by the generation AI.

[0310] The "database means" is an electronic information repository for storing and managing market data, user data, sentiment data, trading results, etc. acquired by the system.

[0311] "Real-time market data" means timely financial data that reflects current market conditions.

[0312] "Social Media Information" is market-related information collected from social media.

[0313] "News information" refers to information that has an impact on the market and is obtained from news sites and the like.

[0314] "Investment performance information" is information that indicates the evaluation and history of investment assets held by the user.

[0315] The present invention combines a system that automatically makes investment decisions based on users' real-time market data and news information with an emotion engine that recognizes users' emotions and reflects them in investment decisions. The system includes a user interface means, a server means, a generation AI, an emotion recognition means, and a database means.

[0316] User Interface Means

[0317] Users access the system through an interface to view and manage their investment performance information and settings. At this stage, users authenticate by entering their email address and password on the login page. Once authenticated, users can access the dashboard, where they can view their investment portfolio, performance information, and emotional state. They can also enter and configure their risk tolerance and investment goals on the settings screen.

[0318] Server Means

[0319] The server plays a central role in this system and has the following functions:

[0320] Obtain real-time market data from external financial data providers and store it in a database.

[0321] It monitors social media and news information in real time and feeds data into generative AI.

[0322] Emotion data is received from the emotion recognition means and supplied to the generation AI.

[0323] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[0324] The transaction results are stored in a database and reflected in the user interface.

[0325] emotion recognition means

[0326] The emotion recognition means recognizes the user's emotional state and records it as data. When the user logs in, it measures their emotions using a camera or microphone and sends the data to the server. The generated emotion data is used by the generation AI to generate investment decisions. For example, if a user expresses "joy" when logging in, the generation AI will interpret this emotion as a high willingness to invest and suggest a more aggressive investment strategy.

[0327] Generation AI

[0328] The Generative AI analyzes market data, social media information, news information, and emotional data from emotion recognition tools provided by the server to generate investment decisions. For example, if a user is under stress, the Generative AI will take that into account and suggest a low-risk investment strategy. This allows the user's emotional state to be directly reflected in investment decisions.

[0329] Database Means

[0330] The database is an electronic information repository that stores and manages market data, user data, sentiment data, trading results, etc. acquired and generated by the system. The server accesses the database to acquire the necessary data and analyzes it. Data supplied to the generation AI is also acquired from the database. When a user logs in to the system, the server reads the user's investment portfolio information from the database and reflects it on the dashboard.

[0331] Specific examples

[0332] For example, consider a user investing in the stock market. Suppose the user logs in and is identified as "relaxed" by the emotion recognition means. This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotional data. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcement." Combining this information with the emotional data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, which adds Company A's shares to the user's portfolio. The trading results are stored in a database and reflected on the dashboard. This allows users to make real-time investments based on market trends while taking their emotional state into account.

[0333] Prompt Sentence Examples

[0334] Here are some example prompts to input to a generative AI model:

[0335] Users are relaxed, and market data shows that Company A has launched a new product. There are many positive reactions on social media, and the same goes for news. What investment decision will you make?

[0336] Examples of expected answers from generative AI:

[0337] Taking into account user sentiment and market reaction, you decide to buy Company A's stock as its stock price is likely to rise.

[0338] Through this system, users can integrate market data with their own emotional information to make more effective investments.

[0339] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0340] Step 1: User logs in

[0341] The user enters their email address and password on the login page of their device. The entered authentication information is sent to the server, which checks it against the user information stored in the database and returns the authentication result. If the authentication is successful, the user is redirected to the dashboard.

[0342] Input: Email address, Password

[0343] Output: Authentication result (success / failure)

[0344] Specific behavior:

[0345] The user clicks the login button.

[0346] The server retrieves and verifies the user information from the database.

[0347] If the authentication is successful, the dashboard data is sent to the user's terminal.

[0348] Step 2: The server retrieves market data, social media information, and news information.

[0349] The server retrieves real-time market data from external financial data providers and stores it in a database, and also uses social media APIs and news APIs to gather the latest information.

[0350] Input: API request

[0351] Output: Real-time market data, social media information, news information

[0352] Specific behavior:

[0353] The server sends a request to the financial data provider API.

[0354] The real-time market data is returned to the server and stored in a database.

[0355] The server retrieves the latest information via social media and news APIs.

[0356] Step 3: The emotion recognizer recognizes the user's emotional state.

[0357] When a user logs in, the camera and microphone are used and emotion data is acquired by the emotion recognition means. This data is sent to the server and stored in a database.

[0358] Input: Camera video, microphone audio

[0359] Output: Emotion data

[0360] Specific behavior:

[0361] The user allows use of the camera and microphone.

[0362] The emotion recognition means analyzes facial expressions and voice to generate emotion data.

[0363] The emotion data is sent to the server and stored in a database.

[0364] Step 4: The server sends market and sentiment data to the generation AI.

[0365] The server sends the collected market data, social media information, news information, and sentiment data to the generation AI, which then analyzes this data and makes investment decisions.

[0366] Input: Market data, social media information, news information, sentiment data

[0367] Output: Investment decision

[0368] Specific behavior:

[0369] The server retrieves market data, social media information, and news information from the database.

[0370] Emotional data is added and sent to the generation AI as a single dataset.

[0371] The generating AI analyzes the data and returns investment decisions to the server.

[0372] Step 5: Generative AI makes investment decisions

[0373] The generative AI analyzes the received dataset and generates optimal investment decisions, taking into account the risk tolerance and goals of the user.

[0374] Input: Market data, social media information, news information, sentiment data

[0375] Output: Investment decision

[0376] Specific behavior:

[0377] Generative AI runs analysis algorithms on market and sentiment data.

[0378] Based on the analysis results, the generation AI generates an investment decision and returns it to the server.

[0379] Step 6: The server generates the investment order and sends it to the exchange or broker.

[0380] The server generates automated trading instructions based on the investment decisions of the AI, which are then sent to the exchange or broker in real time for execution.

[0381] Input: Investment decision

[0382] Output: Buy / sell orders

[0383] Specific behavior:

[0384] The server receives investment decisions from the generation AI.

[0385] The server generates buy and sell orders and sends them to exchanges and brokers via API.

[0386] The transaction is executed and the results are returned to the server.

[0387] Step 7: The server saves the transaction results in the database and displays them on the user interface.

[0388] The trading results are returned to the server and stored in a database, which is then reflected on the user's dashboard, updating investment performance information.

[0389] Input: Trade result

[0390] Output: Updated investment performance information

[0391] Specific behavior:

[0392] The server stores the transaction results in a database.

[0393] The server sends updated investment performance information to the user's dashboard.

[0394] Users see the latest investment performance on a dashboard.

[0395] (Application example 2)

[0396] 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."

[0397] In conventional automated investment systems, investment decisions are made based on market data and news information, but the emotional state of the user is not reflected in the investment decisions, making it difficult to reflect individual investment risk tolerance and emotional decision-making.In addition, in the management of robots in factories, the emotional state of the workers is not reflected in the robot's operation, so work efficiency and safety have not been sufficiently improved.

[0398] The specific processing by the specific 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 a user interface means for displaying a user's investment performance information, a server means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting the acquired information into a generation AI to generate investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, a means for reflecting trading results in the user interface, an emotion engine means for collecting emotion data and performing emotion analysis in real time, a means for the generation AI to correct investment decisions based on the emotion data, and a means for generating robot control commands that reflect the emotional state of a person working in a factory who operates a robot. This enables more accurate investment decisions and safer, more efficient robot management by taking the user's emotional state into consideration.

[0399] "User Interface Means" means the means by which a User accesses the System through an interface to view and manipulate investment performance information and settings.

[0400] "Server means" is a central component that has the means to obtain real-time market data from external data providers and store it in a database, as well as the functionality to generate trading instructions and execute transactions.

[0401] "Generative AI" is an artificial intelligence system that analyzes market data, social media and news information, and emotional data to generate investment decisions.

[0402] The "emotion engine means" is a means for recognizing the user's emotional state, analyzing it in real time, and supplying it to the generating AI.

[0403] The "means for reflecting trading results" is a means for displaying the results of investment trading on the user interface.

[0404] The "means for generating robot control commands" refers to a means for generating commands for controlling the operation of a robot, reflecting the emotional state of a person working in a factory who is engaged in operating the robot.

[0405] "Emotion data" is data that indicates the user's emotional state, and is generated from information collected from a camera, microphone, etc.

[0406] "Investment decisions" are the results of investment decisions calculated by the generative AI based on market data, sentiment data, and news information.

[0407] MODE FOR CARRYING OUT THE INVENTION

[0408] This invention is a system that makes investment decisions based on emotion data and controls the operation of a factory robot by reflecting the emotional state of a worker who operates the robot. The system includes a user interface means, a server means, a generating AI, an emotion engine means, and a database means.

[0409] User Interface Means

[0410] The user interface means is the means by which users access the system and check and operate investment performance information and settings. Users log in by entering their email address and password on the login page. After logging in, they can access the dashboard and check their investment portfolio, investment performance, and emotional state. The settings screen allows users to enter their risk tolerance and investment goals.

[0411] Server Means

[0412] The server plays a central role in the system and has the following functions:

[0413] Obtain real-time market data from external financial data providers and store it in a database.

[0414] It monitors social media and news information in real time and supplies data to the generative AI.

[0415] It receives emotion data from the emotion engine and supplies it to the generative AI.

[0416] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[0417] The transaction results are stored in a database and reflected in the user interface.

[0418] Generative AI and Emotion Engine Methods

[0419] The Generative AI generates investment decisions by analyzing market data, social media and news information provided by the server, as well as emotional data from the Emotion Engine. The Emotion Engine recognizes the user's emotional state and records it as data. When the user logs in, they measure their emotions using a camera or microphone, and the Emotion Engine sends the data to the server.

[0420] As a concrete example, suppose a user is investing in the stock market and the emotion engine recognizes that the user is "relaxed." This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotion data. For example, the generation AI can combine news data and emotion data to determine that Company A's stock price is likely to rise and generate a buy command.

[0421] Robot control command generation

[0422] This system also uses an emotion engine to monitor the emotional state of workers operating factory robots, and the generative AI generates robot control commands based on the emotional data. For example, if a worker is recognized as "fatigued," the generative AI can generate commands to slow down or stop the robot's movements.

[0423] Examples of prompts:

[0424] "How should we adjust the robot's behavior if the worker is fatigued?"

[0425] This system enables investment decisions to be made taking into account the user's emotional state, and optimizes the robot's operating characteristics to reflect the worker's emotional state, thereby improving safety and efficiency.

[0426] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0427] Step 1:

[0428] Users access the system through the user interface and log in by entering their email address and password. The input data is sent to the server for authentication. If authentication is successful, the user's dashboard is displayed.

[0429] Step 2:

[0430] The server retrieves real-time market data from external data providers and stores it in a database. This data includes stock prices, exchange rates, commodity prices, etc. The retrieved market data is also used as input data for the generation AI.

[0431] Step 3:

[0432] The server monitors social media and news sites in real time to obtain relevant information. This information is analyzed to extract the impact of the news and trends on social media. This information is also used as input data for the generation AI.

[0433] Step 4:

[0434] When a user logs in using a camera and microphone, the emotion engine means analyzes the user's facial expressions and tone of voice to extract emotion data, which is then sent to the server and used as input data for the generation AI.

[0435] Step 5:

[0436] The server provides the acquired market data, social media and news information, and sentiment data to the generation AI. The generation AI analyzes this data and generates optimal investment decisions. For example, if the sentiment data indicates "relaxed," the generation AI generates an investment strategy with a higher risk tolerance.

[0437] Step 6:

[0438] Based on the investment decisions made by the AI, the server generates automated trading instructions and sends them to exchanges and brokers. Actual trades are carried out according to these instructions. The trading results are sent back to the server and stored in a database.

[0439] Step 7:

[0440] Through the user interface means, the server displays the latest investment portfolio information, trading results, and emotional state to the user, allowing the user to check their own investment status in real time.

[0441] Step 8:

[0442] In the factory, the emotion engine means analyzes the facial expressions and tone of voice of the workers to collect emotion data of the workers, which is then transmitted to the server.

[0443] Step 9:

[0444] The server provides the worker's emotional data to the AI ​​generator, which then generates optimal robot control commands. For example, if the AI ​​detects that the worker is "fatigued," it generates a command to reduce the robot's operating speed.

[0445] Step 10:

[0446] The server generates robot control commands and sends them to the robots in the factory to optimize their operation, thereby improving worker safety and work efficiency.

[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 (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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 control 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 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.

[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] The present invention relates to a system that enables individual investors to easily make investment decisions using real-time market data and automatically execute trades. The system includes a user interface, a server, a generating AI, and a database.

[0464] User Interface Means

[0465] It provides an interface for users to access the system. Specifically, users enter their email address and password on the login page to log in to the system. After logging in, a dashboard is displayed, displaying their current investment portfolio, investment performance, and other related information in an easy-to-read format. In addition, a settings screen allows users to enter their own risk tolerance and investment goals. This allows users to intuitively operate the system and easily manage their own investment information.

[0466] Server Means

[0467] The server plays a central role in this system. It obtains real-time market data from external financial data providers and stores it in a database. The server also monitors social media and news information in real time and supplies the data to the generation AI. The generation AI analyzes this data and generates investment decisions. The generated investment decisions are sent to exchanges and brokers via the server as automatic trading commands. Once the transaction is completed, the server stores the results in a database and reflects them in the user interface.

[0468] Generation AI

[0469] The Generator AI analyzes market data, social media, and news information provided by the server to generate investment decisions. For example, if positive news about a company spreads on social media, the Generator AI determines that the company's stock price is likely to rise and generates a buy command. This allows users to quickly invest based on the latest market trends. The Generator AI provides individually customized investment strategies based on the user's risk tolerance and investment goals.

[0470] Database Means

[0471] The database stores and manages all market data, user data, trading results, etc. acquired by the system. The server accesses the database to retrieve and analyze the necessary data, and prepares the data to be supplied to the generation AI. When a user logs into the system, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[0472] Specific examples

[0473] For example, consider the case where a user is investing in the stock market. When the user logs in and opens the dashboard, their current investment portfolio and its performance are displayed. The server constantly monitors social media and news sites, and obtains information that "Company A's stock price is expected to rise due to the huge success of its new product." This information is sent to the generation AI via the server, and the generation AI generates a decision to purchase Company A's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company A's shares. The results of the transaction are stored in a database and reflected on the dashboard.

[0474] This allows users to make real-time investments based on the latest market information without having to perform complex market analysis. This system lowers the barrier to entry for individual investors, allowing more people to efficiently take advantage of investment opportunities.

[0475] The processing flow will be explained below.

[0476] Program processing steps

[0477] User Login Process

[0478] Step 1:

[0479] The user enters their email address and password on the login page and clicks the "Login" button.

[0480] Step 2:

[0481] The terminal sends the authentication information entered by the user to the server.

[0482] Step 3:

[0483] The server checks the authentication information against a database and authenticates the user.

[0484] Step 4:

[0485] The server returns the authentication result and user data to the terminal.

[0486] Step 5:

[0487] The device will open a dashboard page displaying the user's investment portfolio, performance, and other relevant information.

[0488] View investment performance

[0489] Step 1:

[0490] The server retrieves the most recent investment performance data from the database.

[0491] Step 2:

[0492] The server transmits the acquired data to the terminal.

[0493] Step 3:

[0494] The data received by the device is converted into graphs and tables and displayed on the user's dashboard.

[0495] Market Data Collection

[0496] Step 1:

[0497] The server sends API requests to external financial data providers at configured times.

[0498] Step 2:

[0499] The server retrieves real-time market data.

[0500] Step 3:

[0501] The server saves the retrieved data in the database.

[0502] News and social media monitoring

[0503] Step 1:

[0504] The server periodically sends a request to retrieve data from the SNS API and the news API.

[0505] Step 2:

[0506] The server retrieves the latest news headlines and trending information.

[0507] Step 3:

[0508] The server stores the acquired information in a database.

[0509] Making and implementing investment decisions

[0510] Step 1:

[0511] The server sends the latest market data and news information to the generation AI.

[0512] Step 2:

[0513] Generative AI analyzes the data and generates investment decisions.

[0514] Step 3:

[0515] The generating AI sends the investment decision back to the server.

[0516] Step 4:

[0517] The server generates buy and sell instructions based on the investment decisions.

[0518] Step 5:

[0519] The server sends automated trading instructions to the exchange or broker.

[0520] Reflection of trading results

[0521] Step 1:

[0522] The server receives the trading results from the exchange or broker.

[0523] Step 2:

[0524] The server stores the transaction results in a database.

[0525] Step 3:

[0526] The server sends the transaction results to the terminal.

[0527] Step 4:

[0528] The terminal reflects the transaction results received on the dashboard and notifies the user.

[0529] Managing User Settings

[0530] Step 1:

[0531] The user enters their risk tolerance and investment goals on the settings screen and clicks the "Save" button.

[0532] Step 2:

[0533] The terminal transmits the input setting data to the server.

[0534] Step 3:

[0535] The server stores the received data in a database.

[0536] Step 4:

[0537] After the server has completed saving, it sends a notification to the device, and the device displays a confirmation message to the user to confirm that the settings have been saved.

[0538] This allows the entire system to work seamlessly together, enabling users to carry out investment activities easily and in real time.

[0539] Example 1

[0540] 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."

[0541] There is a problem in that it is difficult for individual investors to efficiently utilize real-time market data and make quick and accurate investment decisions. Traditional investment systems require advanced market analysis, which requires a great deal of time and effort for individual investors to perform. In addition, there is a lack of systems that can monitor large amounts of market data, social media information, and news information in real time and make investment decisions based on that information, which often results in timely investment opportunities being missed.

[0542] 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.

[0543] In this invention, the server includes a display means for displaying user investment performance information, an information processing means for acquiring real-time market data from external information providers and storing it in an information storage device, an information processing means for monitoring communication network information and news reports in real time and inputting the acquired information into a generative AI model to generate investment decisions, an information processing means for sending automatic trading commands based on the generated investment decisions and executing transactions, and a means for reflecting trading results on the display means. This enables individual investors to make accurate and prompt investment decisions and automatic trading in real time without performing complex market analysis.

[0544] "Display means" refers to devices or software that visually provide information to users. Specifically, it refers to a screen or interface for displaying users' investment performance information, trading results, etc.

[0545] "Information processing device means" refers to devices and software for acquiring, analyzing, storing, and transmitting data. Specifically, it includes functions for acquiring real-time market data from external information providers and storing it in an information storage device, monitoring and acquiring communication network information and news reports, feeding data to generative AI models to generate investment decisions, and executing trades based on the generated instructions.

[0546] "External Information Provider" means a third-party information service company or institution that provides market data or financial information, including financial API service and data feed providers.

[0547] "Information storage device" refers to a physical or virtual storage medium or system for storing acquired data. This includes databases, cloud storage, hard disk drives, etc.

[0548] "Communication network information" refers to information provided through social networking sites and news sites on the Internet. Specifically, it includes data such as Twitter and news site articles.

[0549] "News Information" refers to market trends and economic information provided by news articles and news organizations, including information provided by financial news sites and news organization APIs.

[0550] A "generative AI model" is a model that uses machine learning and artificial intelligence techniques to analyze data to achieve a specific goal (in this case, investment decisions). This includes AI models built using tools such as TensorFlow and PyTorch.

[0551] "Automatic trading instructions" are specific operational instructions for executing market transactions in real time based on the generated investment decisions, including information such as the type of transaction (buy or sell), name, and quantity.

[0552] "Trading results" refers to data obtained as a result of actual trading in the market, including purchase price, trading volume, trading time, etc.

[0553] MODE FOR CARRYING OUT THE INVENTION

[0554] The present invention relates to a system for enabling individual investors to efficiently utilize real-time market data and automatically make fast and accurate investment decisions. The system includes a user interface means, a server means, a generative AI model, and a database means.

[0555] User Interface Means

[0556] This is the interface through which users access the system and manage their own investment information. Users use their terminal to access the system's login page and log in by entering their email address and password. The dashboard that appears after logging in visually displays the user's investment performance information and investment portfolio. Additionally, the settings screen allows users to set their own risk tolerance and investment goals. This method allows users to operate the system intuitively.

[0557] Server Means

[0558] The server is the core of this system. First, the server obtains real-time market data from external information providers. To do this, it uses services such as the Yahoo Finance API and Alpha Vantage API. The server obtains data from these providers using an API key and stores the data in an information storage device. Next, the server monitors and obtains information from social media and news sources, such as the Twitter API and Google News API. This data is collected in real time and fed into the generative AI model.

[0559] Generative AI Models

[0560] The generative AI model analyzes market data, social media information, and news data supplied from a server to generate investment decisions. Specifically, it uses an AI model built using machine learning frameworks such as TensorFlow and PyTorch. For example, if positive news about a company spreads on social media, the generative AI model will determine that the company's stock price is likely to rise and generate a buy command. The generative AI model provides an individually customized investment strategy based on the user's risk tolerance and investment goals.

[0561] Database Means

[0562] The database is used to store and manage market data, user data, trading results, etc. obtained by the server. For example, a database management system such as MySQL or PostgreSQL is used. The server accesses the database to obtain the necessary data, analyze it, and prepare the data to be fed to the generative AI model. When a user logs in, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[0563] Specific examples

[0564] For example, consider the case where a user is investing in the stock market. The user logs in using their device and opens a dashboard, which displays their current investment portfolio and its performance. The server constantly monitors social media and news sites and obtains information that "Company A's stock price is expected to rise due to the huge success of its new product." This information is sent by the server to a generative AI model, which then generates a decision to purchase Company A's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company A's shares. The results of the transaction are stored in a database and reflected on the user's dashboard.

[0565] Prompt Sentence Examples

[0566] You can input prompts like the following to the generative AI model:

[0567] "Generate appropriate investment decisions based on the latest market data and information obtained from social media and news. For example, 'Company A's stock price is likely to rise, so generate a buy command.'"

[0568] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0569] Step 1:

[0570] A user logs in.

[0571] A user accesses the system's login page using a terminal, enters their email address and password, and clicks the "Login" button. The server checks the user's email address and password against the authentication information stored in the database, and if they match, authenticates them. The input is the user's authentication information (email address, password), and the output is the authentication success or failure status. If authentication is successful, the user is redirected to the dashboard.

[0572] Step 2:

[0573] The server retrieves the market data.

[0574] The server sends requests to the API endpoints of external information providers to retrieve real-time market data. For example, it uses the Yahoo Finance API or Alpha Vantage API. The data is returned in JSON format, which is parsed and saved in information storage. The input is the API request and API key, and the output is a JSON object of market data. This data includes stock price, trading volume, historical price fluctuations, etc.

[0575] Step 3:

[0576] The server monitors social media and news information.

[0577] The server uses the Twitter API or Google News API to monitor and retrieve real-time posts and articles related to specific keywords. For example, keywords such as "Company A" and "new product" are monitored. The retrieved information is sent to the server in JSON format and saved in an information storage device. The input is the monitored keyword, and the output is a JSON object of the related social media posts and news articles.

[0578] Step 4:

[0579] The server feeds the data into the generative AI model.

[0580] The server supplies the acquired market data, social media information, and news information to the generative AI model. The data is formatted and converted into a format that the generative AI model can parse. For example, JSON data containing market data and positive social media posts about a specific company is passed to the generative AI model. The input is a JSON object of the formatted data, and the output is the status of completion of data supply to the generative AI model.

[0581] Step 5:

[0582] Generative AI models generate investment decisions.

[0583] The generative AI model analyzes the data it receives and generates an investment decision. For example, if it determines that the stock price of Company A is likely to rise due to social media information and news, the model generates a command to purchase the stock of Company A. The input is JSON data of market data, social media information, and news information, and the output is a JSON object of the investment decision command (buy or sell).

[0584] Step 6:

[0585] The server issues automatic trading commands.

[0586] The server sends automated trading commands to the exchange or broker based on the investment decisions received from the generative AI model. It sends buy or sell commands to the exchange via API. For example, send a buy command with "POST https: / / broker-api.example.com / order". The input is a JSON object of the investment decision, and the output is the status of the trade execution result.

[0587] Step 7:

[0588] The server stores the transaction results in a database and reflects them on the user interface.

[0589] Once the transaction is complete, the server saves the results (purchase price, quantity, and transaction time) in a database. The server then updates the user interface to reflect the latest investment portfolio information on the user's dashboard. The input is the transaction result data, and the output is the updated dashboard display.

[0590] (Application example 1)

[0591] 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."

[0592] Conventional investment systems require users to check market data in real time, analyze it themselves, and make investment decisions, which takes time and effort. It's also difficult to instantly grasp market data and news information and react quickly, which increases the risk of missing investment opportunities. Furthermore, most investment systems are desktop or smartphone-based, requiring users to constantly operate their devices, making them inconvenient.

[0593] 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.

[0594] In this invention, the server includes a means for displaying a user's investment performance information, a means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting it into a generation AI to generate investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, a means for reflecting trading results in a user interface, and a means for using smart glasses as a user interface to overlay the latest investment performance information and notifications on the user's field of vision, thereby enabling the user to always check the latest investment information hands-free and make investment decisions quickly and efficiently.

[0595] "User interface means" refers to means for providing an interface for a user to access the system, operate it, and view information.

[0596] The "server means" is a server that has the function of obtaining real-time market data from external data providers and storing it in a database, as well as the function of monitoring social media and news information and transmitting it to the generation AI.

[0597] "Generative AI" is artificial intelligence that generates investment decisions based on acquired market data, social media information, and news information.

[0598] "Automatic trading instructions" are trading instructions sent to exchanges and brokers based on investment decisions generated by the generation AI.

[0599] "Trading results" refer to the results of buying and selling executed based on the instructions of the generating AI, and are information reflected in the user interface.

[0600] "Smart glasses" are a wearable eyeglass-type device that can overlay information onto the user's field of vision.

[0601] System Overview

[0602] This invention is a system that allows users to check market data in real time using smart glasses and automatically make investment decisions. The system is composed of a user interface means, a server means, a generating AI, and a database means.

[0603] User Interface Means

[0604] The user interface is a pair of smart glasses. By wearing the glasses, users can see the latest investment performance information and investment-related notifications overlaid on their field of vision. This allows users to check investment information and operate the system hands-free.

[0605] Server Means

[0606] The server means has the function of acquiring real-time market data from external data providers and storing it in a database. It also monitors social media and news information in real time, inputs the acquired information into the generation AI, and generates investment decisions. Based on the generated investment decisions, the server sends automatic trading commands to exchanges and brokers to execute transactions. The trading results are stored in a database and reflected in the user interface.

[0607] Generation AI

[0608] The Generator AI analyzes real-time market data, social media information, and news information provided by the server and makes investment decisions based on the results. The Generator AI customizes investment strategies based on the user's investment risk tolerance and investment goals, providing optimal investment decisions.

[0609] Database Means

[0610] The database means stores and manages all market data, user data, and trading results acquired by the system. The server accesses the database, acquires the necessary data, and performs analysis.

[0611] Program Description

[0612] The server uses a Python program and an SDK for smart glasses (e.g., Smartech Glasses SDK) to build the system. To obtain real-time market data from external data providers, an API (e.g., api.financedata.com) is used. The generative AI model analyzes the obtained market data, social media information, and news information to make investment decisions.

[0613] Specific examples

[0614] When a user puts on the smart glasses and logs in, their current investment portfolio and its performance information are overlaid on their field of vision. The server obtains market data from external data providers and monitors social media and news sites. For example, suppose the news reports that "Company X's stock price is expected to rise due to the huge success of its new product." This information is sent to the generation AI via the server, which then generates a decision to purchase Company X's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company X's shares. The results of the transaction are stored in a database and reflected in the user interface.

[0615] Prompt Sentence Examples

[0616] Examples of prompts to be input to a generative AI model:

[0617] Prompt: "Analyze market trends about Company X from social media and news sources and make an investment decision. Your investment decision should include either buying or selling. Your investment decision should be based on your individual investor's risk tolerance and investment goals."

[0618] This enables the system to provide users with the latest market information and investment decisions in real time, supporting efficient investment.

[0619] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0620] Step 1:

[0621] The server allows a user to wear smart glasses and log in to the system. The user enters their email address and password through the user interface of the smart glasses. The server receives the entered credentials and queries an external authentication server for authentication. If authentication is successful, the server starts a user session, generates an authentication token, and sends it to the user's smart glasses. The input is the email address and password, and the output is the authentication token.

[0622] Step 2:

[0623] The server uses the authentication token to retrieve real-time market data from an external data provider and stores it in an internal database. The server sends a request to the market data API, including the authentication token, and receives real-time market data from the API as a response. The received data is stored in the database. The input is the authentication token, and the output is the real-time market data.

[0624] Step 3:

[0625] The server monitors social media and news information in real time and sends the acquired information to the generative AI model. The server accesses data sources from social media and news sites to extract important information. The extracted information is input into the generative AI model for analysis. The input is social media and news information, and the output is the analysis results.

[0626] Step 4:

[0627] The generative AI model analyzes market data, social media, and news information to make investment decisions. The server uses the input analysis results and market data and sends investment decision commands to the generative AI model based on the prompt text. The generative AI model analyzes the input data and generates the optimal investment decision (e.g., buy, sell). The input is market data and social media information, and the output is the investment decision.

[0628] Step 5:

[0629] The server sends automated trading instructions to exchanges and brokers based on the investment decisions generated by the generative AI model. The server receives the generated investment decisions, generates trading instructions based on them, and sends them through the API of the corresponding exchange or broker. The input is the investment decisions, and the output is the trading instructions.

[0630] Step 6:

[0631] When the transaction is completed, the server saves the results in a database and overlays them on the user's smart glasses. The transaction results are stored in the database and are configured to be reflected in the user interface. The smart glasses display shows the transaction results in real time. The input is the transaction results, and the output is the information displayed on the user interface.

[0632] This enables the entire system to provide users with efficient, real-time investment support.

[0633] 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.

[0634] The present invention combines a system that automatically makes investment decisions based on users' real-time market data and news information with an emotion engine that recognizes users' emotions and reflects them in investment decisions. The system includes a user interface means, a server means, a generation AI, an emotion engine, and a database means.

[0635] User Interface Means

[0636] This is the interface through which users access the system to check and operate investment performance information and settings. Users log in by entering their email address and password on the login page. After logging in, a dashboard will appear where users can check their investment portfolio, investment performance, and emotional state. The settings screen also allows users to enter their risk tolerance and investment goals.

[0637] Server Means

[0638] The server plays a central role in this system. Specifically, it has the following functions:

[0639] Obtain real-time market data from external financial data providers and store it in a database.

[0640] It monitors social media and news information in real time and supplies data to the generative AI.

[0641] It receives emotion data from the emotion engine and supplies it to the generative AI.

[0642] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[0643] The transaction results are stored in a database and reflected in the user interface.

[0644] Generation AI

[0645] The Generative AI generates investment decisions by analyzing market data, social media and news information provided by the server, as well as emotional data from the Emotion Engine. For example, if a user is under stress, the Generative AI will take that into account and suggest a low-risk investment strategy. This allows the user's emotional state to be directly reflected in investment decisions.

[0646] Emotion Engine

[0647] The emotion engine is a means of recognizing a user's emotional state and recording it as data. When a user logs in, their emotions are measured using a camera or microphone, and the emotion engine sends the data to the server. The emotion data is used by the generative AI to generate investment decisions. For example, if a user expresses "joy" when logging in, the generative AI will interpret this emotion as a strong desire to invest and suggest a more aggressive investment strategy.

[0648] Database Means

[0649] The database is an electronic information repository that stores and manages all market data, user data, sentiment data, trading results, etc. acquired by the system. The server accesses the database to retrieve and analyze the necessary data and prepare the data to be supplied to the generation AI. When a user logs into the system, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[0650] Specific examples

[0651] For example, consider the case where a user is investing in the stock market. Suppose the user logs in and is recognized as "relaxed" by the emotion engine. This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotion data. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcements." Combining this information with the emotion data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, which adds Company A's shares to the user's portfolio. The trading results are stored in a database and reflected on the dashboard.

[0652] This allows users to make real-time investments based on market trends while taking into account their own emotional state. The system integrates user emotions and market data to provide optimal investment strategies, thereby more effectively supporting investment activities.

[0653] The processing flow will be explained below.

[0654] Program processing steps

[0655] User login process and sentiment data capture

[0656] Step 1:

[0657] The user enters their email address and password on the login page and clicks the "Login" button.

[0658] Step 2:

[0659] The terminal sends the authentication information entered by the user to the server.

[0660] Step 3:

[0661] The server checks the authentication information against a database and authenticates the user.

[0662] Step 4:

[0663] The device's camera and microphone recognize the user's face and voice, and an emotion engine analyzes the user's emotional state.

[0664] Step 5:

[0665] The emotion engine transmits the recognized emotion data to the server.

[0666] Step 6:

[0667] The server stores the acquired emotion data in a database.

[0668] Step 7:

[0669] The server returns the authentication result and emotion data to the device, and the device opens the dashboard page.

[0670] View investment performance

[0671] Step 1:

[0672] The server retrieves the most recent investment performance data from the database.

[0673] Step 2:

[0674] The server transmits the acquired data to the terminal.

[0675] Step 3:

[0676] The data received by the device is converted into graphs and tables and displayed on the user's dashboard.

[0677] Market Data Collection

[0678] Step 1:

[0679] The server sends API requests to external financial data providers at configured times.

[0680] Step 2:

[0681] The server retrieves real-time market data.

[0682] Step 3:

[0683] The server saves the retrieved data in the database.

[0684] News and social media monitoring

[0685] Step 1:

[0686] The server periodically sends a request to retrieve data from the SNS API and the news API.

[0687] Step 2:

[0688] The server retrieves the latest news headlines and trending information.

[0689] Step 3:

[0690] The server stores the acquired news and social media data in a database.

[0691] Making and implementing investment decisions

[0692] Step 1:

[0693] The server sends the emotion data from the emotion engine, the latest market data and news information to the generation AI.

[0694] Step 2:

[0695] Generative AI analyzes the data and generates investment decisions taking into account the user's emotional state.

[0696] Step 3:

[0697] The investment decision generated by the generation AI is sent back to the server.

[0698] Step 4:

[0699] The server generates buy and sell instructions based on the investment decisions.

[0700] Step 5:

[0701] The server sends automated trading instructions to the exchange or broker.

[0702] Reflection of trading results

[0703] Step 1:

[0704] The server receives the trading results from the exchange or broker.

[0705] Step 2:

[0706] The server stores the transaction results in a database.

[0707] Step 3:

[0708] The server sends the transaction results to the terminal.

[0709] Step 4:

[0710] The terminal reflects the transaction results received on the dashboard and notifies the user.

[0711] Managing User Settings

[0712] Step 1:

[0713] The user enters their risk tolerance and investment goals on the settings screen and clicks the "Save" button.

[0714] Step 2:

[0715] The terminal transmits the input setting data to the server.

[0716] Step 3:

[0717] The server stores the received configuration data in a database.

[0718] Step 4:

[0719] After the server has completed saving the settings, it sends a notification to the device, and the device displays a confirmation message to the user to confirm the save.

[0720] Specific examples

[0721] For example, suppose a user logs in and is recognized as "relaxed" by the emotion engine. This information is sent to the generation AI via the server. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcement." Combining this information with emotion data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, adding Company A's stock to the user's portfolio. The trading results are stored in a database and reflected on the dashboard.

[0722] In this way, users can make real-time investments based on market trends while taking their emotional state into account.The system integrates user emotions and market data to provide optimal investment strategies, thereby more effectively supporting investment activities.

[0723] Example 2

[0724] 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."

[0725] Conventional investment systems provide investment decisions based on market data and news information, but lack the ability to make investment decisions that reflect the user's emotional state. As a result, even if a user's emotional state influences investment decisions, it is unable to take this into account, potentially increasing investment risk. Furthermore, the lack of a system that integrates real-time emotion recognition and investment decisions makes it difficult to propose optimal investment strategies for users.

[0726] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a user interface means for displaying a user's investment performance information, a means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting the acquired information into a generation AI to generate investment decisions, an emotion recognition means for recognizing a user's emotional state and inputting the emotion data into the generation AI to reflect it in investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, and a means for reflecting trading results in the user interface. This integrates investment decisions that take the user's emotional state into account with investment decisions based on real-time market data, enabling more appropriate and secure investments.

[0727] "User Interface Means" means the means by which a User accesses the System through an interface to view and manipulate investment performance information and settings.

[0728] The "server means" refers to a means for obtaining real-time market data from external data providers, storing it in a database, monitoring social media and news information, and supplying data to the generation AI.

[0729] "Generative AI" is an artificial intelligence model that analyzes market data, social media information, news information, and user emotional data to generate optimal investment decisions.

[0730] "Emotion recognition means" is a means for recognizing the user's emotional state and providing that data to the generation AI.

[0731] "Automated trading instructions" are trading instructions sent to an exchange or broker based on investment decisions generated by the generation AI.

[0732] The "database means" is an electronic information repository for storing and managing market data, user data, sentiment data, trading results, etc. acquired by the system.

[0733] "Real-time market data" means timely financial data that reflects current market conditions.

[0734] "Social Media Information" is market-related information collected from social media.

[0735] "News information" refers to information that has an impact on the market and is obtained from news sites and the like.

[0736] "Investment performance information" is information that indicates the evaluation and history of investment assets held by the user.

[0737] The present invention combines a system that automatically makes investment decisions based on users' real-time market data and news information with an emotion engine that recognizes users' emotions and reflects them in investment decisions. The system includes a user interface means, a server means, a generation AI, an emotion recognition means, and a database means.

[0738] User Interface Means

[0739] Users access the system through an interface to view and manage their investment performance information and settings. At this stage, users authenticate by entering their email address and password on the login page. Once authenticated, users can access the dashboard, where they can view their investment portfolio, performance information, and emotional state. They can also enter and configure their risk tolerance and investment goals on the settings screen.

[0740] Server Means

[0741] The server plays a central role in this system and has the following functions:

[0742] Obtain real-time market data from external financial data providers and store it in a database.

[0743] It monitors social media and news information in real time and feeds data into generative AI.

[0744] Emotion data is received from the emotion recognition means and supplied to the generation AI.

[0745] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[0746] The transaction results are stored in a database and reflected in the user interface.

[0747] emotion recognition means

[0748] The emotion recognition means recognizes the user's emotional state and records it as data. When the user logs in, it measures their emotions using a camera or microphone and sends the data to the server. The generated emotion data is used by the generation AI to generate investment decisions. For example, if a user expresses "joy" when logging in, the generation AI will interpret this emotion as a high willingness to invest and suggest a more aggressive investment strategy.

[0749] Generation AI

[0750] The Generative AI analyzes market data, social media information, news information, and emotional data from emotion recognition tools provided by the server to generate investment decisions. For example, if a user is under stress, the Generative AI will take that into account and suggest a low-risk investment strategy. This allows the user's emotional state to be directly reflected in investment decisions.

[0751] Database Means

[0752] The database is an electronic information repository that stores and manages market data, user data, sentiment data, trading results, etc. acquired and generated by the system. The server accesses the database to acquire the necessary data and analyzes it. Data supplied to the generation AI is also acquired from the database. When a user logs in to the system, the server reads the user's investment portfolio information from the database and reflects it on the dashboard.

[0753] Specific examples

[0754] For example, consider a user investing in the stock market. Suppose the user logs in and is identified as "relaxed" by the emotion recognition means. This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotional data. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcement." Combining this information with the emotional data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, which adds Company A's shares to the user's portfolio. The trading results are stored in a database and reflected on the dashboard. This allows users to make real-time investments based on market trends while taking their emotional state into account.

[0755] Prompt Sentence Examples

[0756] Here are some example prompts to input to a generative AI model:

[0757] Users are relaxed, and market data shows that Company A has launched a new product. There are many positive reactions on social media, and the same goes for news. What investment decision will you make?

[0758] Examples of expected answers from generative AI:

[0759] Taking into account user sentiment and market reaction, you decide to buy Company A's stock as its stock price is likely to rise.

[0760] Through this system, users can integrate market data with their own emotional information to make more effective investments.

[0761] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0762] Step 1: User logs in

[0763] The user enters their email address and password on the login page of their device. The entered authentication information is sent to the server, which checks it against the user information stored in the database and returns the authentication result. If the authentication is successful, the user is redirected to the dashboard.

[0764] Input: Email address, Password

[0765] Output: Authentication result (success / failure)

[0766] Specific behavior:

[0767] The user clicks the login button.

[0768] The server retrieves and verifies the user information from the database.

[0769] If the authentication is successful, the dashboard data is sent to the user's terminal.

[0770] Step 2: The server retrieves market data, social media information, and news information.

[0771] The server retrieves real-time market data from external financial data providers and stores it in a database, and also uses social media APIs and news APIs to gather the latest information.

[0772] Input: API request

[0773] Output: Real-time market data, social media information, news information

[0774] Specific behavior:

[0775] The server sends a request to the financial data provider API.

[0776] The real-time market data is returned to the server and stored in a database.

[0777] The server retrieves the latest information via social media and news APIs.

[0778] Step 3: The emotion recognizer recognizes the user's emotional state.

[0779] When a user logs in, the camera and microphone are used and emotion data is acquired by the emotion recognition means. This data is sent to the server and stored in a database.

[0780] Input: Camera video, microphone audio

[0781] Output: Emotion data

[0782] Specific behavior:

[0783] The user allows use of the camera and microphone.

[0784] The emotion recognition means analyzes facial expressions and voice to generate emotion data.

[0785] The emotion data is sent to the server and stored in a database.

[0786] Step 4: The server sends market and sentiment data to the generation AI.

[0787] The server sends the collected market data, social media information, news information, and sentiment data to the generation AI, which then analyzes this data and makes investment decisions.

[0788] Input: Market data, social media information, news information, sentiment data

[0789] Output: Investment decision

[0790] Specific behavior:

[0791] The server retrieves market data, social media information, and news information from the database.

[0792] Emotional data is added and sent to the generation AI as a single dataset.

[0793] The generating AI analyzes the data and returns investment decisions to the server.

[0794] Step 5: Generative AI makes investment decisions

[0795] The generative AI analyzes the received dataset and generates optimal investment decisions, taking into account the risk tolerance and goals of the user.

[0796] Input: Market data, social media information, news information, sentiment data

[0797] Output: Investment decision

[0798] Specific behavior:

[0799] Generative AI runs analysis algorithms on market and sentiment data.

[0800] Based on the analysis results, the generation AI generates an investment decision and returns it to the server.

[0801] Step 6: The server generates the investment order and sends it to the exchange or broker.

[0802] The server generates automated trading instructions based on the investment decisions of the AI, which are then sent to the exchange or broker in real time for execution.

[0803] Input: Investment decision

[0804] Output: Buy / sell orders

[0805] Specific behavior:

[0806] The server receives investment decisions from the generation AI.

[0807] The server generates buy and sell orders and sends them to exchanges and brokers via API.

[0808] The transaction is executed and the results are returned to the server.

[0809] Step 7: The server saves the transaction results in the database and displays them on the user interface.

[0810] The trading results are returned to the server and stored in a database, which is then reflected on the user's dashboard, updating investment performance information.

[0811] Input: Trade result

[0812] Output: Updated investment performance information

[0813] Specific behavior:

[0814] The server stores the transaction results in a database.

[0815] The server sends updated investment performance information to the user's dashboard.

[0816] Users see the latest investment performance on a dashboard.

[0817] (Application example 2)

[0818] 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."

[0819] In conventional automated investment systems, investment decisions are made based on market data and news information, but the emotional state of the user is not reflected in the investment decisions, making it difficult to reflect individual investment risk tolerance and emotional decision-making.In addition, in the management of robots in factories, the emotional state of the workers is not reflected in the robot's operation, so work efficiency and safety have not been sufficiently improved.

[0820] The specific processing by the specific 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 a user interface means for displaying a user's investment performance information, a server means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting the acquired information into a generation AI to generate investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, a means for reflecting trading results in the user interface, an emotion engine means for collecting emotion data and performing emotion analysis in real time, a means for the generation AI to correct investment decisions based on the emotion data, and a means for generating robot control commands that reflect the emotional state of a person working in a factory who operates a robot. This enables more accurate investment decisions and safer, more efficient robot management by taking the user's emotional state into consideration.

[0821] "User Interface Means" means the means by which a User accesses the System through an interface to view and manipulate investment performance information and settings.

[0822] "Server means" is a central component that has the means to obtain real-time market data from external data providers and store it in a database, as well as the functionality to generate trading instructions and execute transactions.

[0823] "Generative AI" is an artificial intelligence system that analyzes market data, social media and news information, and emotional data to generate investment decisions.

[0824] The "emotion engine means" is a means for recognizing the user's emotional state, analyzing it in real time, and supplying it to the generating AI.

[0825] The "means for reflecting trading results" is a means for displaying the results of investment trading on the user interface.

[0826] The "means for generating robot control commands" refers to a means for generating commands for controlling the operation of a robot, reflecting the emotional state of a person working in a factory who is engaged in operating the robot.

[0827] "Emotion data" is data that indicates the user's emotional state, and is generated from information collected from a camera, microphone, etc.

[0828] "Investment decisions" are the results of investment decisions calculated by the generative AI based on market data, sentiment data, and news information.

[0829] MODE FOR CARRYING OUT THE INVENTION

[0830] This invention is a system that makes investment decisions based on emotion data and controls the operation of a factory robot by reflecting the emotional state of a worker who operates the robot. The system includes a user interface means, a server means, a generating AI, an emotion engine means, and a database means.

[0831] User Interface Means

[0832] The user interface means is the means by which users access the system and check and operate investment performance information and settings. Users log in by entering their email address and password on the login page. After logging in, they can access the dashboard and check their investment portfolio, investment performance, and emotional state. The settings screen allows users to enter their risk tolerance and investment goals.

[0833] Server Means

[0834] The server plays a central role in the system and has the following functions:

[0835] Obtain real-time market data from external financial data providers and store it in a database.

[0836] It monitors social media and news information in real time and supplies data to the generative AI.

[0837] It receives emotion data from the emotion engine and supplies it to the generative AI.

[0838] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[0839] The transaction results are stored in a database and reflected in the user interface.

[0840] Generative AI and Emotion Engine Methods

[0841] The Generative AI generates investment decisions by analyzing market data, social media and news information provided by the server, as well as emotional data from the Emotion Engine. The Emotion Engine recognizes the user's emotional state and records it as data. When the user logs in, they measure their emotions using a camera or microphone, and the Emotion Engine sends the data to the server.

[0842] As a concrete example, suppose a user is investing in the stock market and the emotion engine recognizes that the user is "relaxed." This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotion data. For example, the generation AI can combine news data and emotion data to determine that Company A's stock price is likely to rise and generate a buy command.

[0843] Robot control command generation

[0844] This system also uses an emotion engine to monitor the emotional state of workers operating factory robots, and the generative AI generates robot control commands based on the emotional data. For example, if a worker is recognized as "fatigued," the generative AI can generate commands to slow down or stop the robot's movements.

[0845] Examples of prompts:

[0846] "How should we adjust the robot's behavior if the worker is fatigued?"

[0847] This system enables investment decisions to be made taking into account the user's emotional state, and optimizes the robot's operating characteristics to reflect the worker's emotional state, thereby improving safety and efficiency.

[0848] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0849] Step 1:

[0850] Users access the system through the user interface and log in by entering their email address and password. The input data is sent to the server for authentication. If authentication is successful, the user's dashboard is displayed.

[0851] Step 2:

[0852] The server retrieves real-time market data from external data providers and stores it in a database. This data includes stock prices, exchange rates, commodity prices, etc. The retrieved market data is also used as input data for the generation AI.

[0853] Step 3:

[0854] The server monitors social media and news sites in real time to obtain relevant information. This information is analyzed to extract the impact of the news and trends on social media. This information is also used as input data for the generation AI.

[0855] Step 4:

[0856] When a user logs in using a camera and microphone, the emotion engine means analyzes the user's facial expressions and tone of voice to extract emotion data, which is then sent to the server and used as input data for the generation AI.

[0857] Step 5:

[0858] The server provides the acquired market data, social media and news information, and sentiment data to the generation AI. The generation AI analyzes this data and generates optimal investment decisions. For example, if the sentiment data indicates "relaxed," the generation AI generates an investment strategy with a higher risk tolerance.

[0859] Step 6:

[0860] Based on the investment decisions made by the AI, the server generates automated trading instructions and sends them to exchanges and brokers. Actual trades are carried out according to these instructions. The trading results are sent back to the server and stored in a database.

[0861] Step 7:

[0862] Through the user interface means, the server displays the latest investment portfolio information, trading results, and emotional state to the user, allowing the user to check their own investment status in real time.

[0863] Step 8:

[0864] In the factory, the emotion engine means analyzes the facial expressions and tone of voice of the workers to collect emotion data of the workers, which is then transmitted to the server.

[0865] Step 9:

[0866] The server provides the worker's emotional data to the AI ​​generator, which then generates optimal robot control commands. For example, if the AI ​​detects that the worker is "fatigued," it generates a command to reduce the robot's operating speed.

[0867] Step 10:

[0868] The server generates robot control commands and sends them to the robots in the factory to optimize their operation, thereby improving worker safety and work efficiency.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] [Third embodiment]

[0873] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0874] 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.

[0875] 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).

[0876] 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.

[0877] 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.

[0878] 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).

[0879] 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. 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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."

[0885] The present invention relates to a system that enables individual investors to easily make investment decisions using real-time market data and automatically execute trades. The system includes a user interface, a server, a generating AI, and a database.

[0886] User Interface Means

[0887] It provides an interface for users to access the system. Specifically, users enter their email address and password on the login page to log in to the system. After logging in, a dashboard is displayed, displaying their current investment portfolio, investment performance, and other related information in an easy-to-read format. In addition, a settings screen allows users to enter their own risk tolerance and investment goals. This allows users to intuitively operate the system and easily manage their own investment information.

[0888] Server Means

[0889] The server plays a central role in this system. It obtains real-time market data from external financial data providers and stores it in a database. The server also monitors social media and news information in real time and supplies the data to the generation AI. The generation AI analyzes this data and generates investment decisions. The generated investment decisions are sent to exchanges and brokers via the server as automatic trading commands. Once the transaction is completed, the server stores the results in a database and reflects them in the user interface.

[0890] Generation AI

[0891] The Generator AI analyzes market data, social media, and news information provided by the server to generate investment decisions. For example, if positive news about a company spreads on social media, the Generator AI determines that the company's stock price is likely to rise and generates a buy command. This allows users to quickly invest based on the latest market trends. The Generator AI provides individually customized investment strategies based on the user's risk tolerance and investment goals.

[0892] Database Means

[0893] The database stores and manages all market data, user data, trading results, etc. acquired by the system. The server accesses the database to retrieve and analyze the necessary data, and prepares the data to be supplied to the generation AI. When a user logs into the system, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[0894] Specific examples

[0895] For example, consider the case where a user is investing in the stock market. When the user logs in and opens the dashboard, their current investment portfolio and its performance are displayed. The server constantly monitors social media and news sites, and obtains information that "Company A's stock price is expected to rise due to the huge success of its new product." This information is sent to the generation AI via the server, and the generation AI generates a decision to purchase Company A's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company A's shares. The results of the transaction are stored in a database and reflected on the dashboard.

[0896] This allows users to make real-time investments based on the latest market information without having to perform complex market analysis. This system lowers the barrier to entry for individual investors, allowing more people to efficiently take advantage of investment opportunities.

[0897] The processing flow will be explained below.

[0898] Program processing steps

[0899] User Login Process

[0900] Step 1:

[0901] The user enters their email address and password on the login page and clicks the "Login" button.

[0902] Step 2:

[0903] The terminal sends the authentication information entered by the user to the server.

[0904] Step 3:

[0905] The server checks the authentication information against a database and authenticates the user.

[0906] Step 4:

[0907] The server returns the authentication result and user data to the terminal.

[0908] Step 5:

[0909] The device will open a dashboard page displaying the user's investment portfolio, performance, and other relevant information.

[0910] View investment performance

[0911] Step 1:

[0912] The server retrieves the most recent investment performance data from the database.

[0913] Step 2:

[0914] The server transmits the acquired data to the terminal.

[0915] Step 3:

[0916] The data received by the device is converted into graphs and tables and displayed on the user's dashboard.

[0917] Market Data Collection

[0918] Step 1:

[0919] The server sends API requests to external financial data providers at configured times.

[0920] Step 2:

[0921] The server retrieves real-time market data.

[0922] Step 3:

[0923] The server saves the retrieved data in the database.

[0924] News and social media monitoring

[0925] Step 1:

[0926] The server periodically sends a request to retrieve data from the SNS API and the news API.

[0927] Step 2:

[0928] The server retrieves the latest news headlines and trending information.

[0929] Step 3:

[0930] The server stores the acquired information in a database.

[0931] Making and implementing investment decisions

[0932] Step 1:

[0933] The server sends the latest market data and news information to the generation AI.

[0934] Step 2:

[0935] Generative AI analyzes the data and generates investment decisions.

[0936] Step 3:

[0937] The generating AI sends the investment decision back to the server.

[0938] Step 4:

[0939] The server generates buy and sell instructions based on the investment decisions.

[0940] Step 5:

[0941] The server sends automated trading instructions to the exchange or broker.

[0942] Reflection of trading results

[0943] Step 1:

[0944] The server receives the trading results from the exchange or broker.

[0945] Step 2:

[0946] The server stores the transaction results in a database.

[0947] Step 3:

[0948] The server sends the transaction results to the terminal.

[0949] Step 4:

[0950] The terminal reflects the transaction results received on the dashboard and notifies the user.

[0951] Managing User Settings

[0952] Step 1:

[0953] The user enters their risk tolerance and investment goals on the settings screen and clicks the "Save" button.

[0954] Step 2:

[0955] The terminal transmits the input setting data to the server.

[0956] Step 3:

[0957] The server stores the received data in a database.

[0958] Step 4:

[0959] After the server has completed saving, it sends a notification to the device, and the device displays a confirmation message to the user to confirm that the settings have been saved.

[0960] This allows the entire system to work seamlessly together, enabling users to carry out investment activities easily and in real time.

[0961] Example 1

[0962] 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."

[0963] There is a problem in that it is difficult for individual investors to efficiently utilize real-time market data and make quick and accurate investment decisions. Traditional investment systems require advanced market analysis, which requires a great deal of time and effort for individual investors to perform. In addition, there is a lack of systems that can monitor large amounts of market data, social media information, and news information in real time and make investment decisions based on that information, which often results in timely investment opportunities being missed.

[0964] 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.

[0965] In this invention, the server includes a display means for displaying user investment performance information, an information processing means for acquiring real-time market data from external information providers and storing it in an information storage device, an information processing means for monitoring communication network information and news reports in real time and inputting the acquired information into a generative AI model to generate investment decisions, an information processing means for sending automatic trading commands based on the generated investment decisions and executing transactions, and a means for reflecting trading results on the display means. This enables individual investors to make accurate and prompt investment decisions and automatic trading in real time without performing complex market analysis.

[0966] "Display means" refers to devices or software that visually provide information to users. Specifically, it refers to a screen or interface for displaying users' investment performance information, trading results, etc.

[0967] "Information processing device means" refers to devices and software for acquiring, analyzing, storing, and transmitting data. Specifically, it includes functions for acquiring real-time market data from external information providers and storing it in an information storage device, monitoring and acquiring communication network information and news reports, feeding data to generative AI models to generate investment decisions, and executing trades based on the generated instructions.

[0968] "External Information Provider" means a third-party information service company or institution that provides market data or financial information, including financial API service and data feed providers.

[0969] "Information storage device" refers to a physical or virtual storage medium or system for storing acquired data. This includes databases, cloud storage, hard disk drives, etc.

[0970] "Communication network information" refers to information provided through social networking sites and news sites on the Internet. Specifically, it includes data such as Twitter and news site articles.

[0971] "News Information" refers to market trends and economic information provided by news articles and news organizations, including information provided by financial news sites and news organization APIs.

[0972] A "generative AI model" is a model that uses machine learning and artificial intelligence techniques to analyze data to achieve a specific goal (in this case, investment decisions). This includes AI models built using tools such as TensorFlow and PyTorch.

[0973] "Automatic trading instructions" are specific operational instructions for executing market transactions in real time based on the generated investment decisions, including information such as the type of transaction (buy or sell), name, and quantity.

[0974] "Trading results" refers to data obtained as a result of actual trading in the market, including purchase price, trading volume, trading time, etc.

[0975] MODE FOR CARRYING OUT THE INVENTION

[0976] The present invention relates to a system for enabling individual investors to efficiently utilize real-time market data and automatically make fast and accurate investment decisions. The system includes a user interface means, a server means, a generative AI model, and a database means.

[0977] User Interface Means

[0978] This is the interface through which users access the system and manage their own investment information. Users use their terminal to access the system's login page and log in by entering their email address and password. The dashboard that appears after logging in visually displays the user's investment performance information and investment portfolio. Additionally, the settings screen allows users to set their own risk tolerance and investment goals. This method allows users to operate the system intuitively.

[0979] Server Means

[0980] The server is the core of this system. First, the server obtains real-time market data from external information providers. To do this, it uses services such as the Yahoo Finance API and Alpha Vantage API. The server obtains data from these providers using an API key and stores the data in an information storage device. Next, the server monitors and obtains information from social media and news sources, such as the Twitter API and Google News API. This data is collected in real time and fed into the generative AI model.

[0981] Generative AI Models

[0982] The generative AI model analyzes market data, social media information, and news data supplied from a server to generate investment decisions. Specifically, it uses an AI model built using machine learning frameworks such as TensorFlow and PyTorch. For example, if positive news about a company spreads on social media, the generative AI model will determine that the company's stock price is likely to rise and generate a buy command. The generative AI model provides an individually customized investment strategy based on the user's risk tolerance and investment goals.

[0983] Database Means

[0984] The database is used to store and manage market data, user data, trading results, etc. obtained by the server. For example, a database management system such as MySQL or PostgreSQL is used. The server accesses the database to obtain the necessary data, analyze it, and prepare the data to be fed to the generative AI model. When a user logs in, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[0985] Specific examples

[0986] For example, consider the case where a user is investing in the stock market. The user logs in using their device and opens a dashboard, which displays their current investment portfolio and its performance. The server constantly monitors social media and news sites and obtains information that "Company A's stock price is expected to rise due to the huge success of its new product." This information is sent by the server to a generative AI model, which then generates a decision to purchase Company A's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company A's shares. The results of the transaction are stored in a database and reflected on the user's dashboard.

[0987] Prompt Sentence Examples

[0988] You can input prompts like the following to the generative AI model:

[0989] "Generate appropriate investment decisions based on the latest market data and information obtained from social media and news. For example, 'Company A's stock price is likely to rise, so generate a buy command.'"

[0990] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0991] Step 1:

[0992] A user logs in.

[0993] A user accesses the system's login page using a terminal, enters their email address and password, and clicks the "Login" button. The server checks the user's email address and password against the authentication information stored in the database, and if they match, authenticates them. The input is the user's authentication information (email address, password), and the output is the authentication success or failure status. If authentication is successful, the user is redirected to the dashboard.

[0994] Step 2:

[0995] The server retrieves the market data.

[0996] The server sends requests to the API endpoints of external information providers to retrieve real-time market data. For example, it uses the Yahoo Finance API or Alpha Vantage API. The data is returned in JSON format, which is parsed and saved in information storage. The input is the API request and API key, and the output is a JSON object of market data. This data includes stock price, trading volume, historical price fluctuations, etc.

[0997] Step 3:

[0998] The server monitors social media and news information.

[0999] The server uses the Twitter API or Google News API to monitor and retrieve real-time posts and articles related to specific keywords. For example, keywords such as "Company A" and "new product" are monitored. The retrieved information is sent to the server in JSON format and saved in an information storage device. The input is the monitored keyword, and the output is a JSON object of the related social media posts and news articles.

[1000] Step 4:

[1001] The server feeds the data into the generative AI model.

[1002] The server supplies the acquired market data, social media information, and news information to the generative AI model. The data is formatted and converted into a format that the generative AI model can parse. For example, JSON data containing market data and positive social media posts about a specific company is passed to the generative AI model. The input is a JSON object of the formatted data, and the output is the status of completion of data supply to the generative AI model.

[1003] Step 5:

[1004] Generative AI models generate investment decisions.

[1005] The generative AI model analyzes the data it receives and generates an investment decision. For example, if it determines that the stock price of Company A is likely to rise due to social media information and news, the model generates a command to purchase the stock of Company A. The input is JSON data of market data, social media information, and news information, and the output is a JSON object of the investment decision command (buy or sell).

[1006] Step 6:

[1007] The server issues automatic trading commands.

[1008] The server sends automated trading commands to the exchange or broker based on the investment decisions received from the generative AI model. It sends buy or sell commands to the exchange via API. For example, send a buy command with "POST https: / / broker-api.example.com / order". The input is a JSON object of the investment decision, and the output is the status of the trade execution result.

[1009] Step 7:

[1010] The server stores the transaction results in a database and reflects them on the user interface.

[1011] Once the transaction is complete, the server saves the results (purchase price, quantity, and transaction time) in a database. The server then updates the user interface to reflect the latest investment portfolio information on the user's dashboard. The input is the transaction result data, and the output is the updated dashboard display.

[1012] (Application example 1)

[1013] 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."

[1014] Conventional investment systems require users to check market data in real time, analyze it themselves, and make investment decisions, which takes time and effort. It's also difficult to instantly grasp market data and news information and react quickly, which increases the risk of missing investment opportunities. Furthermore, most investment systems are desktop or smartphone-based, requiring users to constantly operate their devices, making them inconvenient.

[1015] 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.

[1016] In this invention, the server includes a means for displaying a user's investment performance information, a means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting it into a generation AI to generate investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, a means for reflecting trading results in a user interface, and a means for using smart glasses as a user interface to overlay the latest investment performance information and notifications on the user's field of vision, thereby enabling the user to always check the latest investment information hands-free and make investment decisions quickly and efficiently.

[1017] "User interface means" refers to means for providing an interface for a user to access the system, operate it, and view information.

[1018] The "server means" is a server that has the function of obtaining real-time market data from external data providers and storing it in a database, as well as the function of monitoring social media and news information and transmitting it to the generation AI.

[1019] "Generative AI" is artificial intelligence that generates investment decisions based on acquired market data, social media information, and news information.

[1020] "Automatic trading instructions" are trading instructions sent to exchanges and brokers based on investment decisions generated by the generation AI.

[1021] "Trading results" refer to the results of buying and selling executed based on the instructions of the generating AI, and are information reflected in the user interface.

[1022] "Smart glasses" are a wearable eyeglass-type device that can overlay information onto the user's field of vision.

[1023] System Overview

[1024] This invention is a system that allows users to check market data in real time using smart glasses and automatically make investment decisions. The system is composed of a user interface means, a server means, a generating AI, and a database means.

[1025] User Interface Means

[1026] The user interface is a pair of smart glasses. By wearing the glasses, users can see the latest investment performance information and investment-related notifications overlaid on their field of vision. This allows users to check investment information and operate the system hands-free.

[1027] Server Means

[1028] The server means has the function of acquiring real-time market data from external data providers and storing it in a database. It also monitors social media and news information in real time, inputs the acquired information into the generation AI, and generates investment decisions. Based on the generated investment decisions, the server sends automatic trading commands to exchanges and brokers to execute transactions. The trading results are stored in a database and reflected in the user interface.

[1029] Generation AI

[1030] The Generator AI analyzes real-time market data, social media information, and news information provided by the server and makes investment decisions based on the results. The Generator AI customizes investment strategies based on the user's investment risk tolerance and investment goals, providing optimal investment decisions.

[1031] Database Means

[1032] The database means stores and manages all market data, user data, and trading results acquired by the system. The server accesses the database, acquires the necessary data, and performs analysis.

[1033] Program Description

[1034] The server uses a Python program and an SDK for smart glasses (e.g., Smartech Glasses SDK) to build the system. To obtain real-time market data from external data providers, an API (e.g., api.financedata.com) is used. The generative AI model analyzes the obtained market data, social media information, and news information to make investment decisions.

[1035] Specific examples

[1036] When a user puts on the smart glasses and logs in, their current investment portfolio and its performance information are overlaid on their field of vision. The server obtains market data from external data providers and monitors social media and news sites. For example, suppose the news reports that "Company X's stock price is expected to rise due to the huge success of its new product." This information is sent to the generation AI via the server, which then generates a decision to purchase Company X's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company X's shares. The results of the transaction are stored in a database and reflected in the user interface.

[1037] Prompt Sentence Examples

[1038] Examples of prompts to be input to a generative AI model:

[1039] Prompt: "Analyze market trends about Company X from social media and news sources and make an investment decision. Your investment decision should include either buying or selling. Your investment decision should be based on your individual investor's risk tolerance and investment goals."

[1040] This enables the system to provide users with the latest market information and investment decisions in real time, supporting efficient investment.

[1041] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1042] Step 1:

[1043] The server allows a user to wear smart glasses and log in to the system. The user enters their email address and password through the user interface of the smart glasses. The server receives the entered credentials and queries an external authentication server for authentication. If authentication is successful, the server starts a user session, generates an authentication token, and sends it to the user's smart glasses. The input is the email address and password, and the output is the authentication token.

[1044] Step 2:

[1045] The server uses the authentication token to retrieve real-time market data from an external data provider and stores it in an internal database. The server sends a request to the market data API, including the authentication token, and receives real-time market data from the API as a response. The received data is stored in the database. The input is the authentication token, and the output is the real-time market data.

[1046] Step 3:

[1047] The server monitors social media and news information in real time and sends the acquired information to the generative AI model. The server accesses data sources from social media and news sites to extract important information. The extracted information is input into the generative AI model for analysis. The input is social media and news information, and the output is the analysis results.

[1048] Step 4:

[1049] The generative AI model analyzes market data, social media, and news information to make investment decisions. The server uses the input analysis results and market data and sends investment decision commands to the generative AI model based on the prompt text. The generative AI model analyzes the input data and generates the optimal investment decision (e.g., buy, sell). The input is market data and social media information, and the output is the investment decision.

[1050] Step 5:

[1051] The server sends automated trading instructions to exchanges and brokers based on the investment decisions generated by the generative AI model. The server receives the generated investment decisions, generates trading instructions based on them, and sends them through the API of the corresponding exchange or broker. The input is the investment decisions, and the output is the trading instructions.

[1052] Step 6:

[1053] When the transaction is completed, the server saves the results in a database and overlays them on the user's smart glasses. The transaction results are stored in the database and are configured to be reflected in the user interface. The smart glasses display shows the transaction results in real time. The input is the transaction results, and the output is the information displayed on the user interface.

[1054] This enables the entire system to provide users with efficient, real-time investment support.

[1055] 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.

[1056] The present invention combines a system that automatically makes investment decisions based on users' real-time market data and news information with an emotion engine that recognizes users' emotions and reflects them in investment decisions. The system includes a user interface means, a server means, a generation AI, an emotion engine, and a database means.

[1057] User Interface Means

[1058] This is the interface through which users access the system to check and operate investment performance information and settings. Users log in by entering their email address and password on the login page. After logging in, a dashboard will appear where users can check their investment portfolio, investment performance, and emotional state. The settings screen also allows users to enter their risk tolerance and investment goals.

[1059] Server Means

[1060] The server plays a central role in this system. Specifically, it has the following functions:

[1061] Obtain real-time market data from external financial data providers and store it in a database.

[1062] It monitors social media and news information in real time and supplies data to the generative AI.

[1063] It receives emotion data from the emotion engine and supplies it to the generative AI.

[1064] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[1065] The transaction results are stored in a database and reflected in the user interface.

[1066] Generation AI

[1067] The Generative AI generates investment decisions by analyzing market data, social media and news information provided by the server, as well as emotional data from the Emotion Engine. For example, if a user is under stress, the Generative AI will take that into account and suggest a low-risk investment strategy. This allows the user's emotional state to be directly reflected in investment decisions.

[1068] Emotion Engine

[1069] The emotion engine is a means of recognizing a user's emotional state and recording it as data. When a user logs in, their emotions are measured using a camera or microphone, and the emotion engine sends the data to the server. The emotion data is used by the generative AI to generate investment decisions. For example, if a user expresses "joy" when logging in, the generative AI will interpret this emotion as a strong desire to invest and suggest a more aggressive investment strategy.

[1070] Database Means

[1071] The database is an electronic information repository that stores and manages all market data, user data, sentiment data, trading results, etc. acquired by the system. The server accesses the database to retrieve and analyze the necessary data and prepare the data to be supplied to the generation AI. When a user logs into the system, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[1072] Specific examples

[1073] For example, consider the case where a user is investing in the stock market. Suppose the user logs in and is recognized as "relaxed" by the emotion engine. This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotion data. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcements." Combining this information with the emotion data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, which adds Company A's shares to the user's portfolio. The trading results are stored in a database and reflected on the dashboard.

[1074] This allows users to make real-time investments based on market trends while taking into account their own emotional state. The system integrates user emotions and market data to provide optimal investment strategies, thereby more effectively supporting investment activities.

[1075] The processing flow will be explained below.

[1076] Program processing steps

[1077] User login process and sentiment data capture

[1078] Step 1:

[1079] The user enters their email address and password on the login page and clicks the "Login" button.

[1080] Step 2:

[1081] The terminal sends the authentication information entered by the user to the server.

[1082] Step 3:

[1083] The server checks the authentication information against a database and authenticates the user.

[1084] Step 4:

[1085] The device's camera and microphone recognize the user's face and voice, and an emotion engine analyzes the user's emotional state.

[1086] Step 5:

[1087] The emotion engine transmits the recognized emotion data to the server.

[1088] Step 6:

[1089] The server stores the acquired emotion data in a database.

[1090] Step 7:

[1091] The server returns the authentication result and emotion data to the device, and the device opens the dashboard page.

[1092] View investment performance

[1093] Step 1:

[1094] The server retrieves the most recent investment performance data from the database.

[1095] Step 2:

[1096] The server transmits the acquired data to the terminal.

[1097] Step 3:

[1098] The data received by the device is converted into graphs and tables and displayed on the user's dashboard.

[1099] Market Data Collection

[1100] Step 1:

[1101] The server sends API requests to external financial data providers at configured times.

[1102] Step 2:

[1103] The server retrieves real-time market data.

[1104] Step 3:

[1105] The server saves the retrieved data in the database.

[1106] News and social media monitoring

[1107] Step 1:

[1108] The server periodically sends a request to retrieve data from the SNS API and the news API.

[1109] Step 2:

[1110] The server retrieves the latest news headlines and trending information.

[1111] Step 3:

[1112] The server stores the acquired news and social media data in a database.

[1113] Making and implementing investment decisions

[1114] Step 1:

[1115] The server sends the emotion data from the emotion engine, the latest market data and news information to the generation AI.

[1116] Step 2:

[1117] Generative AI analyzes the data and generates investment decisions taking into account the user's emotional state.

[1118] Step 3:

[1119] The investment decision generated by the generation AI is sent back to the server.

[1120] Step 4:

[1121] The server generates buy and sell instructions based on the investment decisions.

[1122] Step 5:

[1123] The server sends automated trading instructions to the exchange or broker.

[1124] Reflection of trading results

[1125] Step 1:

[1126] The server receives the trading results from the exchange or broker.

[1127] Step 2:

[1128] The server stores the transaction results in a database.

[1129] Step 3:

[1130] The server sends the transaction results to the terminal.

[1131] Step 4:

[1132] The terminal reflects the transaction results received on the dashboard and notifies the user.

[1133] Managing User Settings

[1134] Step 1:

[1135] The user enters their risk tolerance and investment goals on the settings screen and clicks the "Save" button.

[1136] Step 2:

[1137] The terminal transmits the input setting data to the server.

[1138] Step 3:

[1139] The server stores the received configuration data in a database.

[1140] Step 4:

[1141] After the server has completed saving the settings, it sends a notification to the device, and the device displays a confirmation message to the user to confirm the save.

[1142] Specific examples

[1143] For example, suppose a user logs in and is recognized as "relaxed" by the emotion engine. This information is sent to the generation AI via the server. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcement." Combining this information with emotion data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, adding Company A's stock to the user's portfolio. The trading results are stored in a database and reflected on the dashboard.

[1144] In this way, users can make real-time investments based on market trends while taking their emotional state into account.The system integrates user emotions and market data to provide optimal investment strategies, thereby more effectively supporting investment activities.

[1145] Example 2

[1146] 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."

[1147] Conventional investment systems provide investment decisions based on market data and news information, but lack the ability to make investment decisions that reflect the user's emotional state. As a result, even if a user's emotional state influences investment decisions, it is unable to take this into account, potentially increasing investment risk. Furthermore, the lack of a system that integrates real-time emotion recognition and investment decisions makes it difficult to propose optimal investment strategies for users.

[1148] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a user interface means for displaying a user's investment performance information, a means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting the acquired information into a generation AI to generate investment decisions, an emotion recognition means for recognizing a user's emotional state and inputting the emotion data into the generation AI to reflect it in investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, and a means for reflecting trading results in the user interface. This integrates investment decisions that take the user's emotional state into account with investment decisions based on real-time market data, enabling more appropriate and secure investments.

[1149] "User Interface Means" means the means by which a User accesses the System through an interface to view and manipulate investment performance information and settings.

[1150] The "server means" refers to a means for obtaining real-time market data from external data providers, storing it in a database, monitoring social media and news information, and supplying data to the generation AI.

[1151] "Generative AI" is an artificial intelligence model that analyzes market data, social media information, news information, and user emotional data to generate optimal investment decisions.

[1152] "Emotion recognition means" is a means for recognizing the user's emotional state and providing that data to the generation AI.

[1153] "Automated trading instructions" are trading instructions sent to an exchange or broker based on investment decisions generated by the generation AI.

[1154] The "database means" is an electronic information repository for storing and managing market data, user data, sentiment data, trading results, etc. acquired by the system.

[1155] "Real-time market data" means timely financial data that reflects current market conditions.

[1156] "Social Media Information" is market-related information collected from social media.

[1157] "News information" refers to information that has an impact on the market and is obtained from news sites and the like.

[1158] "Investment performance information" is information that indicates the evaluation and history of investment assets held by the user.

[1159] The present invention combines a system that automatically makes investment decisions based on users' real-time market data and news information with an emotion engine that recognizes users' emotions and reflects them in investment decisions. The system includes a user interface means, a server means, a generation AI, an emotion recognition means, and a database means.

[1160] User Interface Means

[1161] Users access the system through an interface to view and manage their investment performance information and settings. At this stage, users authenticate by entering their email address and password on the login page. Once authenticated, users can access the dashboard, where they can view their investment portfolio, performance information, and emotional state. They can also enter and configure their risk tolerance and investment goals on the settings screen.

[1162] Server Means

[1163] The server plays a central role in this system and has the following functions:

[1164] Obtain real-time market data from external financial data providers and store it in a database.

[1165] It monitors social media and news information in real time and feeds data into generative AI.

[1166] Emotion data is received from the emotion recognition means and supplied to the generation AI.

[1167] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[1168] The transaction results are stored in a database and reflected in the user interface.

[1169] emotion recognition means

[1170] The emotion recognition means recognizes the user's emotional state and records it as data. When the user logs in, it measures their emotions using a camera or microphone and sends the data to the server. The generated emotion data is used by the generation AI to generate investment decisions. For example, if a user expresses "joy" when logging in, the generation AI will interpret this emotion as a high willingness to invest and suggest a more aggressive investment strategy.

[1171] Generation AI

[1172] The Generative AI analyzes market data, social media information, news information, and emotional data from emotion recognition tools provided by the server to generate investment decisions. For example, if a user is under stress, the Generative AI will take that into account and suggest a low-risk investment strategy. This allows the user's emotional state to be directly reflected in investment decisions.

[1173] Database Means

[1174] The database is an electronic information repository that stores and manages market data, user data, sentiment data, trading results, etc. acquired and generated by the system. The server accesses the database to acquire the necessary data and analyzes it. Data supplied to the generation AI is also acquired from the database. When a user logs in to the system, the server reads the user's investment portfolio information from the database and reflects it on the dashboard.

[1175] Specific examples

[1176] For example, consider a user investing in the stock market. Suppose the user logs in and is identified as "relaxed" by the emotion recognition means. This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotional data. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcement." Combining this information with the emotional data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, which adds Company A's shares to the user's portfolio. The trading results are stored in a database and reflected on the dashboard. This allows users to make real-time investments based on market trends while taking their emotional state into account.

[1177] Prompt Sentence Examples

[1178] Here are some example prompts to input to a generative AI model:

[1179] Users are relaxed, and market data shows that Company A has launched a new product. There are many positive reactions on social media, and the same goes for news. What investment decision will you make?

[1180] Examples of expected answers from generative AI:

[1181] Taking into account user sentiment and market reaction, you decide to buy Company A's stock as its stock price is likely to rise.

[1182] Through this system, users can integrate market data with their own emotional information to make more effective investments.

[1183] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1184] Step 1: User logs in

[1185] The user enters their email address and password on the login page of their device. The entered authentication information is sent to the server, which checks it against the user information stored in the database and returns the authentication result. If the authentication is successful, the user is redirected to the dashboard.

[1186] Input: Email address, Password

[1187] Output: Authentication result (success / failure)

[1188] Specific behavior:

[1189] The user clicks the login button.

[1190] The server retrieves and verifies the user information from the database.

[1191] If the authentication is successful, the dashboard data is sent to the user's terminal.

[1192] Step 2: The server retrieves market data, social media information, and news information.

[1193] The server retrieves real-time market data from external financial data providers and stores it in a database, and also uses social media APIs and news APIs to gather the latest information.

[1194] Input: API request

[1195] Output: Real-time market data, social media information, news information

[1196] Specific behavior:

[1197] The server sends a request to the financial data provider API.

[1198] The real-time market data is returned to the server and stored in a database.

[1199] The server retrieves the latest information via social media and news APIs.

[1200] Step 3: The emotion recognizer recognizes the user's emotional state.

[1201] When a user logs in, the camera and microphone are used and emotion data is acquired by the emotion recognition means. This data is sent to the server and stored in a database.

[1202] Input: Camera video, microphone audio

[1203] Output: Emotion data

[1204] Specific behavior:

[1205] The user allows use of the camera and microphone.

[1206] The emotion recognition means analyzes facial expressions and voice to generate emotion data.

[1207] The emotion data is sent to the server and stored in a database.

[1208] Step 4: The server sends market and sentiment data to the generation AI.

[1209] The server sends the collected market data, social media information, news information, and sentiment data to the generation AI, which then analyzes this data and makes investment decisions.

[1210] Input: Market data, social media information, news information, sentiment data

[1211] Output: Investment decision

[1212] Specific behavior:

[1213] The server retrieves market data, social media information, and news information from the database.

[1214] Emotional data is added and sent to the generation AI as a single dataset.

[1215] The generating AI analyzes the data and returns investment decisions to the server.

[1216] Step 5: Generative AI makes investment decisions

[1217] The generative AI analyzes the received dataset and generates optimal investment decisions, taking into account the risk tolerance and goals of the user.

[1218] Input: Market data, social media information, news information, sentiment data

[1219] Output: Investment decision

[1220] Specific behavior:

[1221] Generative AI runs analysis algorithms on market and sentiment data.

[1222] Based on the analysis results, the generation AI generates an investment decision and returns it to the server.

[1223] Step 6: The server generates the investment order and sends it to the exchange or broker.

[1224] The server generates automated trading instructions based on the investment decisions of the AI, which are then sent to the exchange or broker in real time for execution.

[1225] Input: Investment decision

[1226] Output: Buy / sell orders

[1227] Specific behavior:

[1228] The server receives investment decisions from the generation AI.

[1229] The server generates buy and sell orders and sends them to exchanges and brokers via API.

[1230] The transaction is executed and the results are returned to the server.

[1231] Step 7: The server saves the transaction results in the database and displays them on the user interface.

[1232] The trading results are returned to the server and stored in a database, which is then reflected on the user's dashboard, updating investment performance information.

[1233] Input: Trade result

[1234] Output: Updated investment performance information

[1235] Specific behavior:

[1236] The server stores the transaction results in a database.

[1237] The server sends updated investment performance information to the user's dashboard.

[1238] Users see the latest investment performance on a dashboard.

[1239] (Application example 2)

[1240] 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."

[1241] In conventional automated investment systems, investment decisions are made based on market data and news information, but the emotional state of the user is not reflected in the investment decisions, making it difficult to reflect individual investment risk tolerance and emotional decision-making.In addition, in the management of robots in factories, the emotional state of the workers is not reflected in the robot's operation, so work efficiency and safety have not been sufficiently improved.

[1242] The specific processing by the specific 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 a user interface means for displaying a user's investment performance information, a server means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting the acquired information into a generation AI to generate investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, a means for reflecting trading results in the user interface, an emotion engine means for collecting emotion data and performing emotion analysis in real time, a means for the generation AI to correct investment decisions based on the emotion data, and a means for generating robot control commands that reflect the emotional state of a person working in a factory who operates a robot. This enables more accurate investment decisions and safer, more efficient robot management by taking the user's emotional state into consideration.

[1243] "User Interface Means" means the means by which a User accesses the System through an interface to view and manipulate investment performance information and settings.

[1244] "Server means" is a central component that has the means to obtain real-time market data from external data providers and store it in a database, as well as the functionality to generate trading instructions and execute transactions.

[1245] "Generative AI" is an artificial intelligence system that analyzes market data, social media and news information, and emotional data to generate investment decisions.

[1246] The "emotion engine means" is a means for recognizing the user's emotional state, analyzing it in real time, and supplying it to the generating AI.

[1247] The "means for reflecting trading results" is a means for displaying the results of investment trading on the user interface.

[1248] The "means for generating robot control commands" refers to a means for generating commands for controlling the operation of a robot, reflecting the emotional state of a person working in a factory who is engaged in operating the robot.

[1249] "Emotion data" is data that indicates the user's emotional state, and is generated from information collected from a camera, microphone, etc.

[1250] "Investment decisions" are the results of investment decisions calculated by the generative AI based on market data, sentiment data, and news information.

[1251] MODE FOR CARRYING OUT THE INVENTION

[1252] This invention is a system that makes investment decisions based on emotion data and controls the operation of a factory robot by reflecting the emotional state of a worker who operates the robot. The system includes a user interface means, a server means, a generating AI, an emotion engine means, and a database means.

[1253] User Interface Means

[1254] The user interface means is the means by which users access the system and check and operate investment performance information and settings. Users log in by entering their email address and password on the login page. After logging in, they can access the dashboard and check their investment portfolio, investment performance, and emotional state. The settings screen allows users to enter their risk tolerance and investment goals.

[1255] Server Means

[1256] The server plays a central role in the system and has the following functions:

[1257] Obtain real-time market data from external financial data providers and store it in a database.

[1258] It monitors social media and news information in real time and supplies data to the generative AI.

[1259] It receives emotion data from the emotion engine and supplies it to the generative AI.

[1260] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[1261] The transaction results are stored in a database and reflected in the user interface.

[1262] Generative AI and Emotion Engine Methods

[1263] The Generative AI generates investment decisions by analyzing market data, social media and news information provided by the server, as well as emotional data from the Emotion Engine. The Emotion Engine recognizes the user's emotional state and records it as data. When the user logs in, they measure their emotions using a camera or microphone, and the Emotion Engine sends the data to the server.

[1264] As a concrete example, suppose a user is investing in the stock market and the emotion engine recognizes that the user is "relaxed." This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotion data. For example, the generation AI can combine news data and emotion data to determine that Company A's stock price is likely to rise and generate a buy command.

[1265] Robot control command generation

[1266] This system also uses an emotion engine to monitor the emotional state of workers operating factory robots, and the generative AI generates robot control commands based on the emotional data. For example, if a worker is recognized as "fatigued," the generative AI can generate commands to slow down or stop the robot's movements.

[1267] Examples of prompts:

[1268] "How should we adjust the robot's behavior if the worker is fatigued?"

[1269] This system enables investment decisions to be made taking into account the user's emotional state, and optimizes the robot's operating characteristics to reflect the worker's emotional state, thereby improving safety and efficiency.

[1270] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1271] Step 1:

[1272] Users access the system through the user interface and log in by entering their email address and password. The input data is sent to the server for authentication. If authentication is successful, the user's dashboard is displayed.

[1273] Step 2:

[1274] The server retrieves real-time market data from external data providers and stores it in a database. This data includes stock prices, exchange rates, commodity prices, etc. The retrieved market data is also used as input data for the generation AI.

[1275] Step 3:

[1276] The server monitors social media and news sites in real time to obtain relevant information. This information is analyzed to extract the impact of the news and trends on social media. This information is also used as input data for the generation AI.

[1277] Step 4:

[1278] When a user logs in using a camera and microphone, the emotion engine means analyzes the user's facial expressions and tone of voice to extract emotion data, which is then sent to the server and used as input data for the generation AI.

[1279] Step 5:

[1280] The server provides the acquired market data, social media and news information, and sentiment data to the generation AI. The generation AI analyzes this data and generates optimal investment decisions. For example, if the sentiment data indicates "relaxed," the generation AI generates an investment strategy with a higher risk tolerance.

[1281] Step 6:

[1282] Based on the investment decisions made by the AI, the server generates automated trading instructions and sends them to exchanges and brokers. Actual trades are carried out according to these instructions. The trading results are sent back to the server and stored in a database.

[1283] Step 7:

[1284] Through the user interface means, the server displays the latest investment portfolio information, trading results, and emotional state to the user, allowing the user to check their own investment status in real time.

[1285] Step 8:

[1286] In the factory, the emotion engine means analyzes the facial expressions and tone of voice of the workers to collect emotion data of the workers, which is then transmitted to the server.

[1287] Step 9:

[1288] The server provides the worker's emotional data to the AI ​​generator, which then generates optimal robot control commands. For example, if the AI ​​detects that the worker is "fatigued," it generates a command to reduce the robot's operating speed.

[1289] Step 10:

[1290] The server generates robot control commands and sends them to the robots in the factory to optimize their operation, thereby improving worker safety and work efficiency.

[1291] 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.

[1292] 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.

[1293] 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.

[1294] [Fourth embodiment]

[1295] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1296] 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.

[1297] 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).

[1298] 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.

[1299] 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.

[1300] 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).

[1301] 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. 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.

[1302] 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.

[1303] 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.

[1304] 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.

[1305] 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.

[1306] 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.

[1307] 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."

[1308] The present invention relates to a system that enables individual investors to easily make investment decisions using real-time market data and automatically execute trades. The system includes a user interface, a server, a generating AI, and a database.

[1309] User Interface Means

[1310] It provides an interface for users to access the system. Specifically, users enter their email address and password on the login page to log in to the system. After logging in, a dashboard is displayed, displaying their current investment portfolio, investment performance, and other related information in an easy-to-read format. In addition, a settings screen allows users to enter their own risk tolerance and investment goals. This allows users to intuitively operate the system and easily manage their own investment information.

[1311] Server Means

[1312] The server plays a central role in this system. It obtains real-time market data from external financial data providers and stores it in a database. The server also monitors social media and news information in real time and supplies the data to the generation AI. The generation AI analyzes this data and generates investment decisions. The generated investment decisions are sent to exchanges and brokers via the server as automatic trading commands. Once the transaction is completed, the server stores the results in a database and reflects them in the user interface.

[1313] Generation AI

[1314] The Generator AI analyzes market data, social media, and news information provided by the server to generate investment decisions. For example, if positive news about a company spreads on social media, the Generator AI determines that the company's stock price is likely to rise and generates a buy command. This allows users to quickly invest based on the latest market trends. The Generator AI provides individually customized investment strategies based on the user's risk tolerance and investment goals.

[1315] Database Means

[1316] The database stores and manages all market data, user data, trading results, etc. acquired by the system. The server accesses the database to retrieve and analyze the necessary data, and prepares the data to be supplied to the generation AI. When a user logs into the system, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[1317] Specific examples

[1318] For example, consider the case where a user is investing in the stock market. When the user logs in and opens the dashboard, their current investment portfolio and its performance are displayed. The server constantly monitors social media and news sites, and obtains information that "Company A's stock price is expected to rise due to the huge success of its new product." This information is sent to the generation AI via the server, and the generation AI generates a decision to purchase Company A's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company A's shares. The results of the transaction are stored in a database and reflected on the dashboard.

[1319] This allows users to make real-time investments based on the latest market information without having to perform complex market analysis. This system lowers the barrier to entry for individual investors, allowing more people to efficiently take advantage of investment opportunities.

[1320] The processing flow will be explained below.

[1321] Program processing steps

[1322] User Login Process

[1323] Step 1:

[1324] The user enters their email address and password on the login page and clicks the "Login" button.

[1325] Step 2:

[1326] The terminal sends the authentication information entered by the user to the server.

[1327] Step 3:

[1328] The server checks the authentication information against a database and authenticates the user.

[1329] Step 4:

[1330] The server returns the authentication result and user data to the terminal.

[1331] Step 5:

[1332] The device will open a dashboard page displaying the user's investment portfolio, performance, and other relevant information.

[1333] View investment performance

[1334] Step 1:

[1335] The server retrieves the most recent investment performance data from the database.

[1336] Step 2:

[1337] The server transmits the acquired data to the terminal.

[1338] Step 3:

[1339] The data received by the device is converted into graphs and tables and displayed on the user's dashboard.

[1340] Market Data Collection

[1341] Step 1:

[1342] The server sends API requests to external financial data providers at configured times.

[1343] Step 2:

[1344] The server retrieves real-time market data.

[1345] Step 3:

[1346] The server saves the retrieved data in the database.

[1347] News and social media monitoring

[1348] Step 1:

[1349] The server periodically sends a request to retrieve data from the SNS API and the news API.

[1350] Step 2:

[1351] The server retrieves the latest news headlines and trending information.

[1352] Step 3:

[1353] The server stores the acquired information in a database.

[1354] Making and implementing investment decisions

[1355] Step 1:

[1356] The server sends the latest market data and news information to the generation AI.

[1357] Step 2:

[1358] Generative AI analyzes the data and generates investment decisions.

[1359] Step 3:

[1360] The generating AI sends the investment decision back to the server.

[1361] Step 4:

[1362] The server generates buy and sell instructions based on the investment decisions.

[1363] Step 5:

[1364] The server sends automated trading instructions to the exchange or broker.

[1365] Reflection of trading results

[1366] Step 1:

[1367] The server receives the trading results from the exchange or broker.

[1368] Step 2:

[1369] The server stores the transaction results in a database.

[1370] Step 3:

[1371] The server sends the transaction results to the terminal.

[1372] Step 4:

[1373] The terminal reflects the transaction results received on the dashboard and notifies the user.

[1374] Managing User Settings

[1375] Step 1:

[1376] The user enters their risk tolerance and investment goals on the settings screen and clicks the "Save" button.

[1377] Step 2:

[1378] The terminal transmits the input setting data to the server.

[1379] Step 3:

[1380] The server stores the received data in a database.

[1381] Step 4:

[1382] After the server has completed saving, it sends a notification to the device, and the device displays a confirmation message to the user to confirm that the settings have been saved.

[1383] This allows the entire system to work seamlessly together, enabling users to carry out investment activities easily and in real time.

[1384] Example 1

[1385] 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."

[1386] There is a problem in that it is difficult for individual investors to efficiently utilize real-time market data and make quick and accurate investment decisions. Traditional investment systems require advanced market analysis, which requires a great deal of time and effort for individual investors to perform. In addition, there is a lack of systems that can monitor large amounts of market data, social media information, and news information in real time and make investment decisions based on that information, which often results in timely investment opportunities being missed.

[1387] 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.

[1388] In this invention, the server includes a display means for displaying user investment performance information, an information processing means for acquiring real-time market data from external information providers and storing it in an information storage device, an information processing means for monitoring communication network information and news reports in real time and inputting the acquired information into a generative AI model to generate investment decisions, an information processing means for sending automatic trading commands based on the generated investment decisions and executing transactions, and a means for reflecting trading results on the display means. This enables individual investors to make accurate and prompt investment decisions and automatic trading in real time without performing complex market analysis.

[1389] "Display means" refers to devices or software that visually provide information to users. Specifically, it refers to a screen or interface for displaying users' investment performance information, trading results, etc.

[1390] "Information processing device means" refers to devices and software for acquiring, analyzing, storing, and transmitting data. Specifically, it includes functions for acquiring real-time market data from external information providers and storing it in an information storage device, monitoring and acquiring communication network information and news reports, feeding data to generative AI models to generate investment decisions, and executing trades based on the generated instructions.

[1391] "External Information Provider" means a third-party information service company or institution that provides market data or financial information, including financial API service and data feed providers.

[1392] "Information storage device" refers to a physical or virtual storage medium or system for storing acquired data. This includes databases, cloud storage, hard disk drives, etc.

[1393] "Communication network information" refers to information provided through social networking sites and news sites on the Internet. Specifically, it includes data such as Twitter and news site articles.

[1394] "News Information" refers to market trends and economic information provided by news articles and news organizations, including information provided by financial news sites and news organization APIs.

[1395] A "generative AI model" is a model that uses machine learning and artificial intelligence techniques to analyze data to achieve a specific goal (in this case, investment decisions). This includes AI models built using tools such as TensorFlow and PyTorch.

[1396] "Automatic trading instructions" are specific operational instructions for executing market transactions in real time based on the generated investment decisions, including information such as the type of transaction (buy or sell), name, and quantity.

[1397] "Trading results" refers to data obtained as a result of actual trading in the market, including purchase price, trading volume, trading time, etc.

[1398] MODE FOR CARRYING OUT THE INVENTION

[1399] The present invention relates to a system for enabling individual investors to efficiently utilize real-time market data and automatically make fast and accurate investment decisions. The system includes a user interface means, a server means, a generative AI model, and a database means.

[1400] User Interface Means

[1401] This is the interface through which users access the system and manage their own investment information. Users use their terminal to access the system's login page and log in by entering their email address and password. The dashboard that appears after logging in visually displays the user's investment performance information and investment portfolio. Additionally, the settings screen allows users to set their own risk tolerance and investment goals. This method allows users to operate the system intuitively.

[1402] Server Means

[1403] The server is the core of this system. First, the server obtains real-time market data from external information providers. To do this, it uses services such as the Yahoo Finance API and Alpha Vantage API. The server obtains data from these providers using an API key and stores the data in an information storage device. Next, the server monitors and obtains information from social media and news sources, such as the Twitter API and Google News API. This data is collected in real time and fed into the generative AI model.

[1404] Generative AI Models

[1405] The generative AI model analyzes market data, social media information, and news data supplied from a server to generate investment decisions. Specifically, it uses an AI model built using machine learning frameworks such as TensorFlow and PyTorch. For example, if positive news about a company spreads on social media, the generative AI model will determine that the company's stock price is likely to rise and generate a buy command. The generative AI model provides an individually customized investment strategy based on the user's risk tolerance and investment goals.

[1406] Database Means

[1407] The database is used to store and manage market data, user data, trading results, etc. obtained by the server. For example, a database management system such as MySQL or PostgreSQL is used. The server accesses the database to obtain the necessary data, analyze it, and prepare the data to be fed to the generative AI model. When a user logs in, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[1408] Specific examples

[1409] For example, consider the case where a user is investing in the stock market. The user logs in using their device and opens a dashboard, which displays their current investment portfolio and its performance. The server constantly monitors social media and news sites and obtains information that "Company A's stock price is expected to rise due to the huge success of its new product." This information is sent by the server to a generative AI model, which then generates a decision to purchase Company A's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company A's shares. The results of the transaction are stored in a database and reflected on the user's dashboard.

[1410] Prompt Sentence Examples

[1411] You can input prompts like the following to the generative AI model:

[1412] "Generate appropriate investment decisions based on the latest market data and information obtained from social media and news. For example, 'Company A's stock price is likely to rise, so generate a buy command.'"

[1413] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1414] Step 1:

[1415] A user logs in.

[1416] A user accesses the system's login page using a terminal, enters their email address and password, and clicks the "Login" button. The server checks the user's email address and password against the authentication information stored in the database, and if they match, authenticates them. The input is the user's authentication information (email address, password), and the output is the authentication success or failure status. If authentication is successful, the user is redirected to the dashboard.

[1417] Step 2:

[1418] The server retrieves the market data.

[1419] The server sends requests to the API endpoints of external information providers to retrieve real-time market data. For example, it uses the Yahoo Finance API or Alpha Vantage API. The data is returned in JSON format, which is parsed and saved in information storage. The input is the API request and API key, and the output is a JSON object of market data. This data includes stock price, trading volume, historical price fluctuations, etc.

[1420] Step 3:

[1421] The server monitors social media and news information.

[1422] The server uses the Twitter API or Google News API to monitor and retrieve real-time posts and articles related to specific keywords. For example, keywords such as "Company A" and "new product" are monitored. The retrieved information is sent to the server in JSON format and saved in an information storage device. The input is the monitored keyword, and the output is a JSON object of the related social media posts and news articles.

[1423] Step 4:

[1424] The server feeds the data into the generative AI model.

[1425] The server supplies the acquired market data, social media information, and news information to the generative AI model. The data is formatted and converted into a format that the generative AI model can parse. For example, JSON data containing market data and positive social media posts about a specific company is passed to the generative AI model. The input is a JSON object of the formatted data, and the output is the status of completion of data supply to the generative AI model.

[1426] Step 5:

[1427] Generative AI models generate investment decisions.

[1428] The generative AI model analyzes the data it receives and generates an investment decision. For example, if it determines that the stock price of Company A is likely to rise due to social media information and news, the model generates a command to purchase the stock of Company A. The input is JSON data of market data, social media information, and news information, and the output is a JSON object of the investment decision command (buy or sell).

[1429] Step 6:

[1430] The server issues automatic trading commands.

[1431] The server sends automated trading commands to the exchange or broker based on the investment decisions received from the generative AI model. It sends buy or sell commands to the exchange via API. For example, send a buy command with "POST https: / / broker-api.example.com / order". The input is a JSON object of the investment decision, and the output is the status of the trade execution result.

[1432] Step 7:

[1433] The server stores the transaction results in a database and reflects them on the user interface.

[1434] Once the transaction is complete, the server saves the results (purchase price, quantity, and transaction time) in a database. The server then updates the user interface to reflect the latest investment portfolio information on the user's dashboard. The input is the transaction result data, and the output is the updated dashboard display.

[1435] (Application example 1)

[1436] 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."

[1437] Conventional investment systems require users to check market data in real time, analyze it themselves, and make investment decisions, which takes time and effort. It's also difficult to instantly grasp market data and news information and react quickly, which increases the risk of missing investment opportunities. Furthermore, most investment systems are desktop or smartphone-based, requiring users to constantly operate their devices, making them inconvenient.

[1438] 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.

[1439] In this invention, the server includes a means for displaying a user's investment performance information, a means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting it into a generation AI to generate investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, a means for reflecting trading results in a user interface, and a means for using smart glasses as a user interface to overlay the latest investment performance information and notifications on the user's field of vision, thereby enabling the user to always check the latest investment information hands-free and make investment decisions quickly and efficiently.

[1440] "User interface means" refers to means for providing an interface for a user to access the system, operate it, and view information.

[1441] The "server means" is a server that has the function of obtaining real-time market data from external data providers and storing it in a database, as well as the function of monitoring social media and news information and transmitting it to the generation AI.

[1442] "Generative AI" is artificial intelligence that generates investment decisions based on acquired market data, social media information, and news information.

[1443] "Automatic trading instructions" are trading instructions sent to exchanges and brokers based on investment decisions generated by the generation AI.

[1444] "Trading results" refer to the results of buying and selling executed based on the instructions of the generating AI, and are information reflected in the user interface.

[1445] "Smart glasses" are a wearable eyeglass-type device that can overlay information onto the user's field of vision.

[1446] System Overview

[1447] This invention is a system that allows users to check market data in real time using smart glasses and automatically make investment decisions. The system is composed of a user interface means, a server means, a generating AI, and a database means.

[1448] User Interface Means

[1449] The user interface is a pair of smart glasses. By wearing the glasses, users can see the latest investment performance information and investment-related notifications overlaid on their field of vision. This allows users to check investment information and operate the system hands-free.

[1450] Server Means

[1451] The server means has the function of acquiring real-time market data from external data providers and storing it in a database. It also monitors social media and news information in real time, inputs the acquired information into the generation AI, and generates investment decisions. Based on the generated investment decisions, the server sends automatic trading commands to exchanges and brokers to execute transactions. The trading results are stored in a database and reflected in the user interface.

[1452] Generation AI

[1453] The Generator AI analyzes real-time market data, social media information, and news information provided by the server and makes investment decisions based on the results. The Generator AI customizes investment strategies based on the user's investment risk tolerance and investment goals, providing optimal investment decisions.

[1454] Database Means

[1455] The database means stores and manages all market data, user data, and trading results acquired by the system. The server accesses the database, acquires the necessary data, and performs analysis.

[1456] Program Description

[1457] The server uses a Python program and an SDK for smart glasses (e.g., Smartech Glasses SDK) to build the system. To obtain real-time market data from external data providers, an API (e.g., api.financedata.com) is used. The generative AI model analyzes the obtained market data, social media information, and news information to make investment decisions.

[1458] Specific examples

[1459] When a user puts on the smart glasses and logs in, their current investment portfolio and its performance information are overlaid on their field of vision. The server obtains market data from external data providers and monitors social media and news sites. For example, suppose the news reports that "Company X's stock price is expected to rise due to the huge success of its new product." This information is sent to the generation AI via the server, which then generates a decision to purchase Company X's shares. Based on this decision, the server sends a buy command to the exchange, automatically purchasing Company X's shares. The results of the transaction are stored in a database and reflected in the user interface.

[1460] Prompt Sentence Examples

[1461] Examples of prompts to be input to a generative AI model:

[1462] Prompt: "Analyze market trends about Company X from social media and news sources and make an investment decision. Your investment decision should include either buying or selling. Your investment decision should be based on your individual investor's risk tolerance and investment goals."

[1463] This enables the system to provide users with the latest market information and investment decisions in real time, supporting efficient investment.

[1464] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1465] Step 1:

[1466] The server allows a user to wear smart glasses and log in to the system. The user enters their email address and password through the user interface of the smart glasses. The server receives the entered credentials and queries an external authentication server for authentication. If authentication is successful, the server starts a user session, generates an authentication token, and sends it to the user's smart glasses. The input is the email address and password, and the output is the authentication token.

[1467] Step 2:

[1468] The server uses the authentication token to retrieve real-time market data from an external data provider and stores it in an internal database. The server sends a request to the market data API, including the authentication token, and receives real-time market data from the API as a response. The received data is stored in the database. The input is the authentication token, and the output is the real-time market data.

[1469] Step 3:

[1470] The server monitors social media and news information in real time and sends the acquired information to the generative AI model. The server accesses data sources from social media and news sites to extract important information. The extracted information is input into the generative AI model for analysis. The input is social media and news information, and the output is the analysis results.

[1471] Step 4:

[1472] The generative AI model analyzes market data, social media, and news information to make investment decisions. The server uses the input analysis results and market data and sends investment decision commands to the generative AI model based on the prompt text. The generative AI model analyzes the input data and generates the optimal investment decision (e.g., buy, sell). The input is market data and social media information, and the output is the investment decision.

[1473] Step 5:

[1474] The server sends automated trading instructions to exchanges and brokers based on the investment decisions generated by the generative AI model. The server receives the generated investment decisions, generates trading instructions based on them, and sends them through the API of the corresponding exchange or broker. The input is the investment decisions, and the output is the trading instructions.

[1475] Step 6:

[1476] When the transaction is completed, the server saves the results in a database and overlays them on the user's smart glasses. The transaction results are stored in the database and are configured to be reflected in the user interface. The smart glasses display shows the transaction results in real time. The input is the transaction results, and the output is the information displayed on the user interface.

[1477] This enables the entire system to provide users with efficient, real-time investment support.

[1478] 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.

[1479] The present invention combines a system that automatically makes investment decisions based on users' real-time market data and news information with an emotion engine that recognizes users' emotions and reflects them in investment decisions. The system includes a user interface means, a server means, a generation AI, an emotion engine, and a database means.

[1480] User Interface Means

[1481] This is the interface through which users access the system to check and operate investment performance information and settings. Users log in by entering their email address and password on the login page. After logging in, a dashboard will appear where users can check their investment portfolio, investment performance, and emotional state. The settings screen also allows users to enter their risk tolerance and investment goals.

[1482] Server Means

[1483] The server plays a central role in this system. Specifically, it has the following functions:

[1484] Obtain real-time market data from external financial data providers and store it in a database.

[1485] It monitors social media and news information in real time and supplies data to the generative AI.

[1486] It receives emotion data from the emotion engine and supplies it to the generative AI.

[1487] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[1488] The transaction results are stored in a database and reflected in the user interface.

[1489] Generation AI

[1490] The Generative AI generates investment decisions by analyzing market data, social media and news information provided by the server, as well as emotional data from the Emotion Engine. For example, if a user is under stress, the Generative AI will take that into account and suggest a low-risk investment strategy. This allows the user's emotional state to be directly reflected in investment decisions.

[1491] Emotion Engine

[1492] The emotion engine is a means of recognizing a user's emotional state and recording it as data. When a user logs in, their emotions are measured using a camera or microphone, and the emotion engine sends the data to the server. The emotion data is used by the generative AI to generate investment decisions. For example, if a user expresses "joy" when logging in, the generative AI will interpret this emotion as a strong desire to invest and suggest a more aggressive investment strategy.

[1493] Database Means

[1494] The database is an electronic information repository that stores and manages all market data, user data, sentiment data, trading results, etc. acquired by the system. The server accesses the database to retrieve and analyze the necessary data and prepare the data to be supplied to the generation AI. When a user logs into the system, the server reads the user's investment portfolio information from the database and displays it on the dashboard.

[1495] Specific examples

[1496] For example, consider the case where a user is investing in the stock market. Suppose the user logs in and is recognized as "relaxed" by the emotion engine. This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotion data. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcements." Combining this information with the emotion data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, which adds Company A's shares to the user's portfolio. The trading results are stored in a database and reflected on the dashboard.

[1497] This allows users to make real-time investments based on market trends while taking into account their own emotional state. The system integrates user emotions and market data to provide optimal investment strategies, thereby more effectively supporting investment activities.

[1498] The processing flow will be explained below.

[1499] Program processing steps

[1500] User login process and sentiment data capture

[1501] Step 1:

[1502] The user enters their email address and password on the login page and clicks the "Login" button.

[1503] Step 2:

[1504] The terminal sends the authentication information entered by the user to the server.

[1505] Step 3:

[1506] The server checks the authentication information against a database and authenticates the user.

[1507] Step 4:

[1508] The device's camera and microphone recognize the user's face and voice, and an emotion engine analyzes the user's emotional state.

[1509] Step 5:

[1510] The emotion engine transmits the recognized emotion data to the server.

[1511] Step 6:

[1512] The server stores the acquired emotion data in a database.

[1513] Step 7:

[1514] The server returns the authentication result and emotion data to the device, and the device opens the dashboard page.

[1515] View investment performance

[1516] Step 1:

[1517] The server retrieves the most recent investment performance data from the database.

[1518] Step 2:

[1519] The server transmits the acquired data to the terminal.

[1520] Step 3:

[1521] The data received by the device is converted into graphs and tables and displayed on the user's dashboard.

[1522] Market Data Collection

[1523] Step 1:

[1524] The server sends API requests to external financial data providers at configured times.

[1525] Step 2:

[1526] The server retrieves real-time market data.

[1527] Step 3:

[1528] The server saves the retrieved data in the database.

[1529] News and social media monitoring

[1530] Step 1:

[1531] The server periodically sends a request to retrieve data from the SNS API and the news API.

[1532] Step 2:

[1533] The server retrieves the latest news headlines and trending information.

[1534] Step 3:

[1535] The server stores the acquired news and social media data in a database.

[1536] Making and implementing investment decisions

[1537] Step 1:

[1538] The server sends the emotion data from the emotion engine, the latest market data and news information to the generation AI.

[1539] Step 2:

[1540] Generative AI analyzes the data and generates investment decisions taking into account the user's emotional state.

[1541] Step 3:

[1542] The investment decision generated by the generation AI is sent back to the server.

[1543] Step 4:

[1544] The server generates buy and sell instructions based on the investment decisions.

[1545] Step 5:

[1546] The server sends automated trading instructions to the exchange or broker.

[1547] Reflection of trading results

[1548] Step 1:

[1549] The server receives the trading results from the exchange or broker.

[1550] Step 2:

[1551] The server stores the transaction results in a database.

[1552] Step 3:

[1553] The server sends the transaction results to the terminal.

[1554] Step 4:

[1555] The terminal reflects the transaction results received on the dashboard and notifies the user.

[1556] Managing User Settings

[1557] Step 1:

[1558] The user enters their risk tolerance and investment goals on the settings screen and clicks the "Save" button.

[1559] Step 2:

[1560] The terminal transmits the input setting data to the server.

[1561] Step 3:

[1562] The server stores the received configuration data in a database.

[1563] Step 4:

[1564] After the server has completed saving the settings, it sends a notification to the device, and the device displays a confirmation message to the user to confirm the save.

[1565] Specific examples

[1566] For example, suppose a user logs in and is recognized as "relaxed" by the emotion engine. This information is sent to the generation AI via the server. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcement." Combining this information with emotion data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, adding Company A's stock to the user's portfolio. The trading results are stored in a database and reflected on the dashboard.

[1567] In this way, users can make real-time investments based on market trends while taking their emotional state into account.The system integrates user emotions and market data to provide optimal investment strategies, thereby more effectively supporting investment activities.

[1568] Example 2

[1569] 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."

[1570] Conventional investment systems provide investment decisions based on market data and news information, but lack the ability to make investment decisions that reflect the user's emotional state. As a result, even if a user's emotional state influences investment decisions, it is unable to take this into account, potentially increasing investment risk. Furthermore, the lack of a system that integrates real-time emotion recognition and investment decisions makes it difficult to propose optimal investment strategies for users.

[1571] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a user interface means for displaying a user's investment performance information, a means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting the acquired information into a generation AI to generate investment decisions, an emotion recognition means for recognizing a user's emotional state and inputting the emotion data into the generation AI to reflect it in investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, and a means for reflecting trading results in the user interface. This integrates investment decisions that take the user's emotional state into account with investment decisions based on real-time market data, enabling more appropriate and secure investments.

[1572] "User Interface Means" means the means by which a User accesses the System through an interface to view and manipulate investment performance information and settings.

[1573] The "server means" refers to a means for obtaining real-time market data from external data providers, storing it in a database, monitoring social media and news information, and supplying data to the generation AI.

[1574] "Generative AI" is an artificial intelligence model that analyzes market data, social media information, news information, and user emotional data to generate optimal investment decisions.

[1575] "Emotion recognition means" is a means for recognizing the user's emotional state and providing that data to the generation AI.

[1576] "Automated trading instructions" are trading instructions sent to an exchange or broker based on investment decisions generated by the generation AI.

[1577] The "database means" is an electronic information repository for storing and managing market data, user data, sentiment data, trading results, etc. acquired by the system.

[1578] "Real-time market data" means timely financial data that reflects current market conditions.

[1579] "Social Media Information" is market-related information collected from social media.

[1580] "News information" refers to information that has an impact on the market and is obtained from news sites and the like.

[1581] "Investment performance information" is information that indicates the evaluation and history of investment assets held by the user.

[1582] The present invention combines a system that automatically makes investment decisions based on users' real-time market data and news information with an emotion engine that recognizes users' emotions and reflects them in investment decisions. The system includes a user interface means, a server means, a generation AI, an emotion recognition means, and a database means.

[1583] User Interface Means

[1584] Users access the system through an interface to view and manage their investment performance information and settings. At this stage, users authenticate by entering their email address and password on the login page. Once authenticated, users can access the dashboard, where they can view their investment portfolio, performance information, and emotional state. They can also enter and configure their risk tolerance and investment goals on the settings screen.

[1585] Server Means

[1586] The server plays a central role in this system and has the following functions:

[1587] Obtain real-time market data from external financial data providers and store it in a database.

[1588] It monitors social media and news information in real time and feeds data into generative AI.

[1589] Emotion data is received from the emotion recognition means and supplied to the generation AI.

[1590] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[1591] The transaction results are stored in a database and reflected in the user interface.

[1592] emotion recognition means

[1593] The emotion recognition means recognizes the user's emotional state and records it as data. When the user logs in, it measures their emotions using a camera or microphone and sends the data to the server. The generated emotion data is used by the generation AI to generate investment decisions. For example, if a user expresses "joy" when logging in, the generation AI will interpret this emotion as a high willingness to invest and suggest a more aggressive investment strategy.

[1594] Generation AI

[1595] The Generative AI analyzes market data, social media information, news information, and emotional data from emotion recognition tools provided by the server to generate investment decisions. For example, if a user is under stress, the Generative AI will take that into account and suggest a low-risk investment strategy. This allows the user's emotional state to be directly reflected in investment decisions.

[1596] Database Means

[1597] The database is an electronic information repository that stores and manages market data, user data, sentiment data, trading results, etc. acquired and generated by the system. The server accesses the database to acquire the necessary data and analyzes it. Data supplied to the generation AI is also acquired from the database. When a user logs in to the system, the server reads the user's investment portfolio information from the database and reflects it on the dashboard.

[1598] Specific examples

[1599] For example, consider a user investing in the stock market. Suppose the user logs in and is identified as "relaxed" by the emotion recognition means. This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotional data. At the same time, the server monitors social media and news sites to obtain information about "Company A's new product announcement." Combining this information with the emotional data, the generation AI determines that Company A's stock price is likely to rise and generates a buy command. The server sends this command to the exchange, which adds Company A's shares to the user's portfolio. The trading results are stored in a database and reflected on the dashboard. This allows users to make real-time investments based on market trends while taking their emotional state into account.

[1600] Prompt Sentence Examples

[1601] Here are some example prompts to input to a generative AI model:

[1602] Users are relaxed, and market data shows that Company A has launched a new product. There are many positive reactions on social media, and the same goes for news. What investment decision will you make?

[1603] Examples of expected answers from generative AI:

[1604] Taking into account user sentiment and market reaction, you decide to buy Company A's stock as its stock price is likely to rise.

[1605] Through this system, users can integrate market data with their own emotional information to make more effective investments.

[1606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1607] Step 1: User logs in

[1608] The user enters their email address and password on the login page of their device. The entered authentication information is sent to the server, which checks it against the user information stored in the database and returns the authentication result. If the authentication is successful, the user is redirected to the dashboard.

[1609] Input: Email address, Password

[1610] Output: Authentication result (success / failure)

[1611] Specific behavior:

[1612] The user clicks the login button.

[1613] The server retrieves and verifies the user information from the database.

[1614] If the authentication is successful, the dashboard data is sent to the user's terminal.

[1615] Step 2: The server retrieves market data, social media information, and news information.

[1616] The server retrieves real-time market data from external financial data providers and stores it in a database, and also uses social media APIs and news APIs to gather the latest information.

[1617] Input: API request

[1618] Output: Real-time market data, social media information, news information

[1619] Specific behavior:

[1620] The server sends a request to the financial data provider API.

[1621] The real-time market data is returned to the server and stored in a database.

[1622] The server retrieves the latest information via social media and news APIs.

[1623] Step 3: The emotion recognizer recognizes the user's emotional state.

[1624] When a user logs in, the camera and microphone are used and emotion data is acquired by the emotion recognition means. This data is sent to the server and stored in a database.

[1625] Input: Camera video, microphone audio

[1626] Output: Emotion data

[1627] Specific behavior:

[1628] The user allows use of the camera and microphone.

[1629] The emotion recognition means analyzes facial expressions and voice to generate emotion data.

[1630] The emotion data is sent to the server and stored in a database.

[1631] Step 4: The server sends market and sentiment data to the generation AI.

[1632] The server sends the collected market data, social media information, news information, and sentiment data to the generation AI, which then analyzes this data and makes investment decisions.

[1633] Input: Market data, social media information, news information, sentiment data

[1634] Output: Investment decision

[1635] Specific behavior:

[1636] The server retrieves market data, social media information, and news information from the database.

[1637] Emotional data is added and sent to the generation AI as a single dataset.

[1638] The generating AI analyzes the data and returns investment decisions to the server.

[1639] Step 5: Generative AI makes investment decisions

[1640] The generative AI analyzes the received dataset and generates optimal investment decisions, taking into account the risk tolerance and goals of the user.

[1641] Input: Market data, social media information, news information, sentiment data

[1642] Output: Investment decision

[1643] Specific behavior:

[1644] Generative AI runs analysis algorithms on market and sentiment data.

[1645] Based on the analysis results, the generation AI generates an investment decision and returns it to the server.

[1646] Step 6: The server generates the investment order and sends it to the exchange or broker.

[1647] The server generates automated trading instructions based on the investment decisions of the AI, which are then sent to the exchange or broker in real time for execution.

[1648] Input: Investment decision

[1649] Output: Buy / sell orders

[1650] Specific behavior:

[1651] The server receives investment decisions from the generation AI.

[1652] The server generates buy and sell orders and sends them to exchanges and brokers via API.

[1653] The transaction is executed and the results are returned to the server.

[1654] Step 7: The server saves the transaction results in the database and displays them on the user interface.

[1655] The trading results are returned to the server and stored in a database, which is then reflected on the user's dashboard, updating investment performance information.

[1656] Input: Trade result

[1657] Output: Updated investment performance information

[1658] Specific behavior:

[1659] The server stores the transaction results in a database.

[1660] The server sends updated investment performance information to the user's dashboard.

[1661] Users see the latest investment performance on a dashboard.

[1662] (Application example 2)

[1663] 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."

[1664] In conventional automated investment systems, investment decisions are made based on market data and news information, but the emotional state of the user is not reflected in the investment decisions, making it difficult to reflect individual investment risk tolerance and emotional decision-making.In addition, in the management of robots in factories, the emotional state of the workers is not reflected in the robot's operation, so work efficiency and safety have not been sufficiently improved.

[1665] The specific processing by the specific 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 a user interface means for displaying a user's investment performance information, a server means for acquiring real-time market data from an external data provider and storing it in a database, a means for monitoring social media and news information in real time and inputting the acquired information into a generation AI to generate investment decisions, a means for sending automatic trading commands and executing transactions based on the generated investment decisions, a means for reflecting trading results in the user interface, an emotion engine means for collecting emotion data and performing emotion analysis in real time, a means for the generation AI to correct investment decisions based on the emotion data, and a means for generating robot control commands that reflect the emotional state of a person working in a factory who operates a robot. This enables more accurate investment decisions and safer, more efficient robot management by taking the user's emotional state into consideration.

[1666] "User Interface Means" means the means by which a User accesses the System through an interface to view and manipulate investment performance information and settings.

[1667] "Server means" is a central component that has the means to obtain real-time market data from external data providers and store it in a database, as well as the functionality to generate trading instructions and execute transactions.

[1668] "Generative AI" is an artificial intelligence system that analyzes market data, social media and news information, and emotional data to generate investment decisions.

[1669] The "emotion engine means" is a means for recognizing the user's emotional state, analyzing it in real time, and supplying it to the generating AI.

[1670] The "means for reflecting trading results" is a means for displaying the results of investment trading on the user interface.

[1671] The "means for generating robot control commands" refers to a means for generating commands for controlling the operation of a robot, reflecting the emotional state of a person working in a factory who is engaged in operating the robot.

[1672] "Emotion data" is data that indicates the user's emotional state, and is generated from information collected from a camera, microphone, etc.

[1673] "Investment decisions" are the results of investment decisions calculated by the generative AI based on market data, sentiment data, and news information.

[1674] MODE FOR CARRYING OUT THE INVENTION

[1675] This invention is a system that makes investment decisions based on emotion data and controls the operation of a factory robot by reflecting the emotional state of a worker who operates the robot. The system includes a user interface means, a server means, a generating AI, an emotion engine means, and a database means.

[1676] User Interface Means

[1677] The user interface means is the means by which users access the system and check and operate investment performance information and settings. Users log in by entering their email address and password on the login page. After logging in, they can access the dashboard and check their investment portfolio, investment performance, and emotional state. The settings screen allows users to enter their risk tolerance and investment goals.

[1678] Server Means

[1679] The server plays a central role in the system and has the following functions:

[1680] Obtain real-time market data from external financial data providers and store it in a database.

[1681] It monitors social media and news information in real time and supplies data to the generative AI.

[1682] It receives emotion data from the emotion engine and supplies it to the generative AI.

[1683] Based on the investment decisions made by the generation AI, automated trading instructions are generated and sent to exchanges and brokers.

[1684] The transaction results are stored in a database and reflected in the user interface.

[1685] Generative AI and Emotion Engine Methods

[1686] The Generative AI generates investment decisions by analyzing market data, social media and news information provided by the server, as well as emotional data from the Emotion Engine. The Emotion Engine recognizes the user's emotional state and records it as data. When the user logs in, they measure their emotions using a camera or microphone, and the Emotion Engine sends the data to the server.

[1687] As a concrete example, suppose a user is investing in the stock market and the emotion engine recognizes that the user is "relaxed." This information is sent to the generation AI via the server, and the generation AI makes investment decisions based on this emotion data. For example, the generation AI can combine news data and emotion data to determine that Company A's stock price is likely to rise and generate a buy command.

[1688] Robot control command generation

[1689] This system also uses an emotion engine to monitor the emotional state of workers operating factory robots, and the generative AI generates robot control commands based on the emotional data. For example, if a worker is recognized as "fatigued," the generative AI can generate commands to slow down or stop the robot's movements.

[1690] Examples of prompts:

[1691] "How should we adjust the robot's behavior if the worker is fatigued?"

[1692] This system enables investment decisions to be made taking into account the user's emotional state, and optimizes the robot's operating characteristics to reflect the worker's emotional state, thereby improving safety and efficiency.

[1693] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1694] Step 1:

[1695] Users access the system through the user interface and log in by entering their email address and password. The input data is sent to the server for authentication. If authentication is successful, the user's dashboard is displayed.

[1696] Step 2:

[1697] The server retrieves real-time market data from external data providers and stores it in a database. This data includes stock prices, exchange rates, commodity prices, etc. The retrieved market data is also used as input data for the generation AI.

[1698] Step 3:

[1699] The server monitors social media and news sites in real time to obtain relevant information. This information is analyzed to extract the impact of the news and trends on social media. This information is also used as input data for the generation AI.

[1700] Step 4:

[1701] When a user logs in using a camera and microphone, the emotion engine means analyzes the user's facial expressions and tone of voice to extract emotion data, which is then sent to the server and used as input data for the generation AI.

[1702] Step 5:

[1703] The server provides the acquired market data, social media and news information, and sentiment data to the generation AI. The generation AI analyzes this data and generates optimal investment decisions. For example, if the sentiment data indicates "relaxed," the generation AI generates an investment strategy with a higher risk tolerance.

[1704] Step 6:

[1705] Based on the investment decisions made by the AI, the server generates automated trading instructions and sends them to exchanges and brokers. Actual trades are carried out according to these instructions. The trading results are sent back to the server and stored in a database.

[1706] Step 7:

[1707] Through the user interface means, the server displays the latest investment portfolio information, trading results, and emotional state to the user, allowing the user to check their own investment status in real time.

[1708] Step 8:

[1709] In the factory, the emotion engine means analyzes the facial expressions and tone of voice of the workers to collect emotion data of the workers, which is then transmitted to the server.

[1710] Step 9:

[1711] The server provides the worker's emotional data to the AI ​​generator, which then generates optimal robot control commands. For example, if the AI ​​detects that the worker is "fatigued," it generates a command to reduce the robot's operating speed.

[1712] Step 10:

[1713] The server generates robot control commands and sends them to the robots in the factory to optimize their operation, thereby improving worker safety and work efficiency.

[1714] 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.

[1715] 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.

[1716] 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.

[1717] 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.

[1718] 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.

[1719] 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.

[1720] 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).

[1721] 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.

[1722] 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."

[1723] 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.

[1724] 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).

[1725] 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.

[1726] 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.

[1727] 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.

[1728] 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.

[1729] 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.

[1730] 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.

[1731] 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.

[1732] 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.

[1733] 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.

[1734] 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.

[1735] The following is further disclosed regarding the above embodiment.

[1736] (Claim 1)

[1737] a user interface means for displaying a user's investment performance information;

[1738] server means for obtaining real-time market data from external data providers and storing the data in a database;

[1739] A server means for monitoring SNS and news information in real time, inputting the acquired information into a generating AI to generate investment decisions;

[1740] a server means for transmitting automatic trading instructions and executing transactions based on the generated investment decisions;

[1741] A means for reflecting the transaction results on a user interface;

[1742] A system including:

[1743] (Claim 2)

[1744] 10. The system of claim 1, further comprising server means for individually setting and storing an investment strategy based on a user's investment risk tolerance and investment goals.

[1745] (Claim 3)

[1746] 10. The system of claim 1, further comprising means for providing user login and account management functions.

[1747] "Example 1"

[1748] (Claim 1)

[1749] a display means for displaying the user's investment performance information;

[1750] an information processing device for acquiring real-time market data from an external information provider and storing the data in an information storage device;

[1751] an information processing device that monitors communication network information and news reports in real time, inputs the acquired information into a generating AI model, and generates investment decisions;

[1752] an information processing device that transmits an automatic trading command based on the generated investment decision and executes the transaction;

[1753] A means for reflecting the transaction results on the display means;

[1754] A system including:

[1755] (Claim 2)

[1756] 10. The system according to claim 1, further comprising information processing means for individually setting and saving an investment strategy based on a user's investment risk tolerance and investment goals.

[1757] (Claim 3)

[1758] 10. The system of claim 1, further comprising means for providing user login and account management functions.

[1759] "Application Example 1"

[1760] (Claim 1)

[1761] a user interface means for displaying a user's investment performance information;

[1762] server means for obtaining real-time market data from external data providers and storing the data in a database;

[1763] A server means for monitoring SNS and news information in real time, inputting the acquired information into a generating AI to generate investment decisions;

[1764] a server means for transmitting automatic trading instructions and executing transactions based on the generated investment decisions;

[1765] A means for reflecting the transaction results on a user interface;

[1766] a means for overlaying up-to-date investment performance information and notifications onto the field of view using smart glasses as a user interface;

[1767] A system including:

[1768] (Claim 2)

[1769] 10. The system of claim 1, further comprising server means for individually setting and storing an investment strategy based on a user's investment risk tolerance and investment goals.

[1770] (Claim 3)

[1771] 10. The system of claim 1, further comprising means for providing user login and account management functions.

[1772] "Example 2: Combining Emotion Engines"

[1773] (Claim 1)

[1774] a user interface means for displaying a user's investment performance information;

[1775] server means for obtaining real-time market data from external data providers and storing the data in a database;

[1776] a server means for monitoring social media and news information in real time and inputting the acquired information into a generation AI to generate investment decisions;

[1777] An emotion recognition means that recognizes the user's emotional state and inputs the emotion data into the AI ​​generator to reflect it in investment decisions;

[1778] a server means for transmitting automatic trading instructions and executing transactions based on the generated investment decisions;

[1779] A means for reflecting the transaction results on a user interface;

[1780] A system including:

[1781] (Claim 2)

[1782] 10. The system of claim 1, further comprising server means for individually setting and storing an investment strategy based on a user's investment risk tolerance and investment goals.

[1783] (Claim 3)

[1784] 10. The system of claim 1, further comprising means for providing user login and account management functions.

[1785] That's all.

[1786] "Application example 2 when combining emotion engines"

[1787] (Claim 1)

[1788] a user interface means for displaying a user's investment performance information;

[1789] server means for obtaining real-time market data from external data providers and storing the data in a database;

[1790] A server means for monitoring SNS and news information in real time, inputting the acquired information into a generating AI to generate investment decisions;

[1791] a server means for transmitting automatic trading instructions and executing transactions based on the generated investment decisions;

[1792] A means for reflecting the transaction results on a user interface;

[1793] an emotion engine means for collecting emotion data and performing emotion analysis in real time;

[1794] A means for the generative AI to correct investment decisions based on emotional data;

[1795] A means for generating a robot control command that reflects the emotional state of a person who operates a robot working in a factory;

[1796] A system including:

[1797] (Claim 2)

[1798] 10. The system of claim 1, further comprising server means for individually setting and storing an investment strategy based on a user's investment risk tolerance and investment goals.

[1799] (Claim 3)

[1800] 10. The system of claim 1, further comprising means for providing user login and account management functions. [Explanation of symbols]

[1801] 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. a user interface means for displaying a user's investment performance information; server means for obtaining real-time market data from external data providers and storing the data in a database; A server means for monitoring SNS and news information in real time, inputting the acquired information into a generating AI to generate investment decisions; a server means for transmitting automatic trading instructions and executing transactions based on the generated investment decisions; A means for reflecting the transaction results on a user interface; A system including:

2. 2. The system of claim 1, further comprising server means for individually setting and storing an investment strategy based on a user's investment risk tolerance and investment goals.

3. 10. The system of claim 1, further comprising means for providing user login and account management functions.

Images