System

Abstract

To provide a system.SOLUTION: A system includes: means for analyzing market data in real time using an AI technology, and processing mega data at a high speed; means for generating a transaction signal indicative of a dealing timing and quantity in the market; means for using a high-speed internet communication network to collect information, and minimizing a transaction delay in the market to the minimum level; means for adjusting the transaction signal on the basis of an emotion of a user using an emotion engine recognizing the emotion of the user; and means for integrating the market data with the emotion of the user using an application to be installed to a smartphone, and providing a transaction advice in the market.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional stock trading systems require speed and accuracy in trading, but face challenges such as real-time analysis of market data, high-speed data processing, and minimizing trading delays. [Means for solving the problem]

[0005] The system of the present invention uses AI technology to analyze market data in real time and process huge amounts of data at high speed. It also generates optimal trading signals, collects information using high-speed Internet communication networks, and minimizes trading delays. This improves the speed and accuracy of trading, maximizing profits and minimizing risks. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16]FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] 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.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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 wide area network (WAN) and / or a local area network (LAN).

[0018] 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.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. 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.

[0020] 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 (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0024] 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.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, deep learning is used as the AI ​​technology to learn patterns in market data and generate optimal trading signals based on the results. In addition, a high-speed Internet communication network is used to collect information and minimize trading delays. Specifically, a fiber optic network is used to collect trading information in real time and minimize trading delays. This makes it possible to improve the speed and accuracy of trading, maximize profits, and minimize risks.

[0029] "Example 2"

[0030] The system of the present invention improves the speed and accuracy of trading by combining AI technology and a high-speed Internet communication network. Specifically, machine learning is used as AI technology to learn market data patterns and generate optimal trading signals based on the results. In addition, a high-speed Internet communication network is used to collect information and minimize trading delays. Specifically, a 5G network is used to collect trading information in real time and minimize trading delays. This makes it possible to improve the speed and accuracy of trading, maximize profits, and minimize risks.

[0031] The process flow of each embodiment will be described below.

[0032] "Example 1"

[0033] Step 1: Analyze market data in real time using deep learning as an AI technology. This analysis allows you to understand market movements and trends.

[0034] Step 2: Generate optimal trading signals based on the analysis results. These trading signals indicate the timing and amount of buying and selling.

[0035] Step 3: Use a high-speed Internet network to collect information and minimize transaction delays. Specifically, use a fiber optic network to collect transaction information in real time and minimize transaction delays.

[0036] "Example 2"

[0037] Step 1: Analyze market data in real time using machine learning as an AI technology. This analysis allows you to understand market movements and trends.

[0038] Step 2: Generate optimal trading signals based on the analysis results. These trading signals indicate the timing and amount of buying and selling.

[0039] Step 3: Use high-speed Internet networks to collect information and minimize transaction delays. Specifically, 5G networks will be used to collect transaction information in real time and minimize transaction delays.

[0040] Example 1

[0041] Next, a description will be given of Example 1 of the embodiment 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".

[0042] Traditional market data analysis systems had problems with delays in collecting and processing real-time data, reducing the speed and accuracy of trading. They also lacked the ability to process huge amounts of data quickly, making it difficult to generate optimal trading signals. Furthermore, they did not provide sufficient means to maximize profits and minimize risks.

[0043] 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.

[0044] In this invention, the server includes a means for collecting market data in real time through an optical fiber network, a means for normalizing the collected market data and imputing missing values, a means for learning patterns of the market data using a deep learning framework, a means for generating optimal trading signals based on the learning results, and a means for delivering the generated trading signals to a user's terminal, thereby improving the speed and accuracy of trading, maximizing profits, and minimizing risks.

[0045] An "optical fiber network" is a communications network that uses optical fiber cables to enable high-speed, large-capacity data communication.

[0046] "Market data" refers to data that includes information such as prices, trading volumes, and trading times in financial markets for stocks, bonds, commodities, currencies, and the like.

[0047] "Normalization" is a process of constraining each data point within a certain range in order to unify the scale of the data.

[0048] "Missing value imputation" is the process of filling in missing values ​​in a dataset in an appropriate manner.

[0049] A "deep learning framework" is a software library for building and training neural networks, and includes TENSORFLOW (registered trademark) and PyTorch.

[0050] "Pattern learning" is the process of extracting patterns and features from past data and building models to predict and classify future data.

[0051] A "trading signal" is an instruction indicating the timing of buying and selling in a financial market, and includes buying signals and selling signals.

[0052] "User terminal" means a device used by a user to receive trading signals and execute transactions, including a personal computer or smartphone.

[0053] This invention is a system that uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses deep learning to learn market data patterns and generates optimal trading signals based on the results. It also uses high-speed Internet communication networks to collect information and minimize delays in trading.

[0054] The server collects market data in real time through a fiber optic network. The collected market data is processed by a high-performance server equipped with NVIDIA GPUs. The server learns the patterns of the market data using deep learning frameworks such as TensorFlow and PyTorch. Based on the learning results, the server generates optimal trading signals.

[0055] Specifically, the server processes the data in the following steps:

[0056] 1. Market data collection: The server collects market data in real time through the fiber optic network, for example, by using APIs from financial market data providers.

[0057] 2. Data preprocessing: Normalize the collected data and impute missing values. For example, use the Pandas library to manipulate data frames and impute missing values ​​with the mean.

[0058] 3. Training the model: Build a neural network using TensorFlow or PyTorch to learn patterns from historical market data, for example using a LSTM (Long Short-Term Memory) network to train time series data.

[0059] 4. Generate trading signals: Generate optimal trading signals based on the learning results. For example, if the model judges it to be a "buy" signal, generate that signal.

[0060] 5. Signal Delivery: The generated trading signals are delivered to the users' terminals, for example, in real time using WebSocket or REST API.

[0061] Users can execute trades based on the trading signals they receive, improving the speed and accuracy of their trades, maximizing profits and minimizing risks.

[0062] As a concrete example, consider the following scenario:

[0063] A user wants to analyze stock market data in real time and receive optimal trading signals. The user's terminal receives the trading signals distributed by the server and executes the trades.

[0064] Example prompt:

[0065] "Analyze real-time stock market data and generate optimal trading signals. The data used is stock price data and trading volume data from the past year. Deliver the generated trading signals in real time."

[0066] The system enables users to improve the speed and accuracy of their transactions, maximizing profits and minimizing risks.

[0067] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0068] Step 1:

[0069] The server collects market data in real time through the fiber optic network. Specifically, it retrieves data from financial market data providers using APIs. The input is the API request and the output is the retrieved market data. For example, the server sends an API request and receives data such as stock prices and trading volumes.

[0070] Step 2:

[0071] The server preprocesses the collected market data by normalizing the data, imputing missing values, and detecting and correcting outliers. The input is the collected market data, and the output is the preprocessed data. For example, the server uses the Pandas library to manipulate data frames, impute missing values ​​with the mean, and normalize the data.

[0072] Step 3:

[0073] The server trains a deep learning model using the preprocessed data. Specifically, it uses TensorFlow or PyTorch to build a neural network and learn patterns from past market data. The input is the preprocessed data, and the output is the trained model. For example, the server uses an LSTM (long short-term memory) network to train time series data.

[0074] Step 4:

[0075] The server generates optimal trading signals based on the learning results. Specifically, it inputs real-time market data into the trained model and outputs buy and sell signals. The inputs are real-time market data and the trained model, and the output is a trading signal. For example, if the model judges it to be a "buy" signal, it generates that signal.

[0076] Step 5:

[0077] The server distributes the generated trading signals to the user's terminal. Specifically, it transmits signals in real time using WebSocket or REST API. The input is the generated trading signal, and the output is the signal distributed to the user's terminal. The user's terminal displays the received signal and executes the transaction.

[0078] (Application example 1)

[0079] Next, a description will be given of Application Example 1 of Form 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."

[0080] Conventional market data analysis systems have difficulty processing data in real time, which often results in delays in trading. They also cannot generate trading signals optimized for each user, which results in a lack of accuracy and speed in trading. Furthermore, they are not user-friendly because they do not take into account the use on mobile devices such as smartphones.

[0081] 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. In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for learning the trading patterns of users and generating individually optimized trading signals, and a means including an application installed on a smartphone. This enables real-time market data analysis and generation of trading signals, improves the speed and accuracy of trading, and makes it possible to provide trading signals optimized for each user.

[0082] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0083] "Real-time" refers to data collection and processing occurring immediately.

[0084] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[0085] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[0086] "High-speed processing" refers to analyzing data quickly and outputting results in a short period of time.

[0087] A "trading signal" refers to an instruction or sign that indicates the timing and direction of a transaction.

[0088] A "high-speed Internet network" refers to an Internet connection that can send and receive data very quickly.

[0089] "Information gathering" refers to collecting necessary data or information.

[0090] "Transaction Delay" refers to the delay in time before a transaction is executed.

[0091] "User trading patterns" refers to the user's past trading tendencies and behavioral patterns.

[0092] "Individually Optimized Trading Signals" refers to trading signals that are optimized based on the trading patterns of each individual User.

[0093] "Applications installed on a smartphone" refers to software programs that run on a smartphone.

[0094] In order to implement the present invention, the following system configuration is necessary.

[0095] First, the server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses deep learning models to learn patterns in market data and generate optimal trading signals based on the results. The server achieves this using software such as Python, Keras, Requests, Pandas, and NumPy.

[0096] The servers then collect the information over high-speed Internet connections to minimize trading delays, using fiber optic networks to collect market data in real time for immediate analysis.

[0097] In addition, the server learns the user's trading patterns and generates individually optimized trading signals, which makes it possible to provide optimal trading timing for each user. The user's trading patterns are learned by a deep learning model based on past trading data.

[0098] The application installed on a smartphone receives trading signals sent from the server and notifies the user. Through this application, users can check market data in real time and know the optimal timing for trading.

[0099] As a concrete example, a scenario is when a user opens the smartphone app "Real-time Settlement Analyst" and checks current market data in real time. The app learns the user's trading patterns and notifies them of the optimal timing to trade. For example, a notification such as "In the current market conditions, it is a good time to buy" is displayed.

[0100] An example of a prompt sentence might be:

[0101] Develop a smartphone app that analyzes market data in real time and notifies users of the best time to trade. Use deep learning to learn users' trading patterns and generate individually optimized trading signals. Use high-speed internet networks to collect trading information in real time and minimize delays.

[0102] In this way, the combination of servers, smartphone applications and high-speed internet networks allows for real-time market data analysis and generation of trading signals, improving the speed and accuracy of trading.

[0103] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0104] Step 1:

[0105] The server collects market data in real time. Specifically, it obtains market data through APIs and collects information such as prices, trading volumes, and trading history. The input is the response from the market data API, and the output is the collected market data.

[0106] Step 2:

[0107] The server pre-processes the collected market data, specifically by filtering it down to prices and volumes and converting it into numerical data. The input is the market data collected in step 1, and the output is the pre-processed data.

[0108] Step 3:

[0109] The server uses the preprocessed data to train a deep learning model, specifically, a long short-term memory (LSTM) network to learn patterns in the market data. The input is the preprocessed data, and the output is the trained deep learning model.

[0110] Step 4:

[0111] The server generates trading signals using a trained deep learning model. Specifically, the model inputs market data and outputs signals that indicate the timing and direction of trading. The input is real-time market data and the output is trading signals.

[0112] Step 5:

[0113] The server transmits the trading signals to the smartphone using a high-speed internet communication network. Specifically, the communication is performed using a fiber optic network with minimal latency. The input is the trading signal, and the output is the signal notification to the smartphone.

[0114] Step 6:

[0115] The smartphone notifies the user of the received trading signal. Specifically, it notifies the user of the timing of trading using push notifications and in-app notifications. The input is the trading signal sent from the server, and the output is the notification to the user.

[0116] Step 7:

[0117] Users check market data in real time through a smartphone app and trade based on trading signals. Specifically, they view market data within the app and execute trades according to the trading timing notified to them. The input is market data and trading signals from the smartphone app, and the output is the user's trading action.

[0118] In this way, the combination of servers, smartphone applications and high-speed internet networks allows for real-time market data analysis and generation of trading signals, improving the speed and accuracy of trading.

[0119] Example 2

[0120] Next, a second embodiment of the second embodiment will be described. 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."

[0121] Conventional trading systems lacked speed and accuracy, making it difficult to maximize profits and minimize risks. In addition, real-time information collection was insufficient, resulting in delays in trading. This often caused users to miss the optimal timing for trading, reducing trading efficiency.

[0122] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0123] In this invention, the server includes a means for collecting market data, a means for pre-processing the collected data, a means for training a machine learning model, a means for generating trading signals, a means for collecting information in real time using a high-speed Internet communication network, a means for notifying a user of the generated trading signals, and a means for the user to execute a trade, thereby improving the speed and accuracy of trading, maximizing profits, and minimizing risks.

[0124] "Market data" refers to information such as stock prices, trading volumes, and economic indicators in financial markets.

[0125] "Preprocessing" refers to the process of filling in missing values ​​in collected data, removing outliers, normalizing the data, etc.

[0126] A "machine learning model" refers to an algorithm that learns patterns from data and makes predictions and classifications.

[0127] A "trading signal" refers to information generated by a machine learning model that recommends trading at a specific time.

[0128] "High-speed Internet network" refers to infrastructure that enables high-speed and stable data communication.

[0129] "Real-time information" refers to information that immediately reflects current market conditions.

[0130] "User" refers to an individual or legal entity that conducts transactions using this system.

[0131] "Notification" refers to the act of notifying a User of a generated trading signal.

[0132] "Execution of a transaction" refers to the act of a user actually buying or selling a financial product based on a trading signal.

[0133] This invention is a system that improves the speed and accuracy of transactions by combining AI technology and high-speed Internet communication networks. Below, we will explain how to specifically implement this system.

[0134] The server first collects market data. Specifically, it uses a common data collection API to obtain past transaction data from financial markets. For example, it can use the Yahoo Finance API or Alpha Vantage API. This allows it to collect data such as stock prices, trading volumes, and economic indicators.

[0135] The server then preprocesses the collected data, using Python's Pandas library to impute missing values, remove outliers, and normalize the data, improving data quality and making it more effective for training machine learning models.

[0136] The server then trains a machine learning model, using TensorFlow or Scikit-learn to learn patterns from past market data. For example, a Long Short-Term Memory (LSTM) model can be used to forecast time series data, which can then predict future market trends and generate optimal trading signals.

[0137] After a trading signal is generated, the terminal collects information in real time using a high-speed Internet communication network, specifically using Qualcomm's 5G modem to achieve high speed and stable communication, which can minimize delays in trading.

[0138] The generated trading signals are notified to the user through the terminal. The signals are displayed in real time through the mobile app and desktop app. By receiving notifications, users can quickly execute trades.

[0139] Users execute trades based on the trading signals they receive. They access the trading platform and buy or sell stocks according to the signals generated by AI, which improves the speed and accuracy of trading, maximizing profits and minimizing risks.

[0140] As a concrete example, consider a user trading on the stock market. The server uses the Yahoo Finance API to collect stock price data for the past year. It then uses the Pandas library to impute missing values ​​in the data and remove outliers. It then uses TensorFlow to train an LSTM model to predict which stocks are likely to rise in the next week. The server generates a buy signal based on this prediction, and the terminal notifies the user of this signal in real time over the 5G network. When the user receives the notification, he or she accesses the trading platform and purchases stocks according to the signal.

[0141] Example of a prompt to be input to a generative AI model:

[0142] "Predict which stocks are likely to rise in the next week based on stock price data from the past year."

[0143] The system improves the speed and accuracy of trading, allowing users to maximize profits and minimize risks.

[0144] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0145] Step 1:

[0146] The server collects market data. Specifically, it uses a common data collection API to obtain historical trading data from financial markets. For example, it uses Yahoo Finance API or Alpha Vantage API. The input is the API request, and the output is data such as stock prices, trading volumes, and economic indicators.

[0147] Step 2:

[0148] The server preprocesses the collected data. Specifically, it uses Python's Pandas library to impute missing values ​​in the data, remove outliers, and normalize the data. The input is the collected market data, and the output is the preprocessed data.

[0149] Step 3:

[0150] The server trains a machine learning model using the preprocessed data. Specifically, it uses TensorFlow and Scikit-learn to learn patterns from past market data. For example, it uses a Long Short-Term Memory (LSTM) model to make predictions on time series data. The input is the preprocessed data, and the output is the trained machine learning model.

[0151] Step 4:

[0152] The server uses the trained model to generate trading signals, specifically, buy and sell signals based on the stock price trends predicted by the model. The inputs are the trained machine learning model and real-time market data, and the output is trading signals.

[0153] Step 5:

[0154] The terminal collects information in real time using a high-speed Internet communication network. Specifically, it uses Qualcomm's 5G modem to achieve high-speed and stable communication. The input is real-time market data, and the output is the collected real-time information.

[0155] Step 6:

[0156] The terminal notifies the user of the generated trading signals, specifically by displaying the signals in real time through a mobile or desktop app. The input is the trading signal, and the output is the notification to the user.

[0157] Step 7:

[0158] The user executes a trade based on the notified trading signal. Specifically, the user accesses the trading platform and buys or sells stocks according to the signal generated by the AI. The input is the trading signal and the trading platform, and the output is the executed trade.

[0159] (Application example 2)

[0160] Next, a description will be given of Application Example 2 of Form 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."

[0161] Traditional electronic settlement systems did not ensure sufficient speed and accuracy of transactions, making it difficult for users to settle at the optimal time. In addition, there was a lack of means to analyze market data in real time and generate optimal trading signals, making it difficult to maximize profits and minimize risks. In addition, delays in transactions were common, often impairing the user experience.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0163] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for proposing optimal timing and method for a user to make an electronic settlement, a means for analyzing market data using a machine learning model and collecting information in real time, and a means including an application to be installed on a smartphone, thereby improving the speed and accuracy of trading and enabling a user to make a settlement at an optimal timing.

[0164] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0165] "Real-time" refers to data collection and processing occurring immediately.

[0166] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[0167] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[0168] "High-speed processing" refers to analyzing data quickly and obtaining results in a short period of time.

[0169] A "trading signal" is an instruction that indicates the best time or manner to enter into a trade.

[0170] A "high-speed Internet network" refers to an Internet connection that allows for extremely fast data transmission and reception, and specifically includes 5G networks.

[0171] "Information gathering" refers to the act of collecting necessary data or information.

[0172] "Transaction Delay" refers to the delay in time before a transaction is executed.

[0173] "User" refers to any individual or legal entity that uses this system.

[0174] "Electronic payment" refers to the payment of money via the Internet.

[0175] The "optimal time" refers to the most favorable time to carry out a transaction or settlement.

[0176] "Suggesting a solution" means showing the user the optimal course of action or means.

[0177] A "machine learning model" refers to an algorithm that learns patterns from data and makes predictions and classifications.

[0178] A "smartphone" refers to a mobile phone that can connect to the Internet and run applications.

[0179] "Application" refers to a software program for achieving a specific function.

[0180] A system for implementing the present invention is configured as follows.

[0181] System Configuration

[0182] The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses machine learning models to analyze market data and generate optimal trading signals, which can improve the speed and accuracy of trading.

[0183] The terminal (smartphone) collects information using a high-speed Internet communication network (5G network) to minimize transaction delays. An application is installed that suggests the optimal timing and method for users to make electronic payments. The application collects market data in real time and uses machine learning models to notify users of the optimal timing for payment.

[0184] Hardware and Software Used

[0185] Hardware: 5G-enabled smartphone

[0186] Software: Python, Requests library, Scikit-learn library

[0187] Data processing and calculation

[0188] The server collects market data through APIs and analyzes the data using machine learning models. Specifically, the machine learning model is trained based on the collected market data to generate optimal trading signals in real time. The terminal receives market data in real time through the 5G network and notifies users of the optimal settlement timing through the application.

[0189] Examples

[0190] For example, when a user is shopping online, the application will notify them of the optimal time to make a payment, allowing them to make a payment with the most favorable exchange rate and fees.

[0191] Examples of prompt statements

[0192] "Develop an application that collects the latest market data and notifies the optimal settlement timing in real time. Analyze market data using machine learning models and collect information in real time over 5G networks."

[0193] The above is an embodiment of the present invention. This system improves the speed and accuracy of transactions, allowing users to make payments at the optimal timing.

[0194] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0195] Step 1:

[0196] The server collects market data. Specifically, it gets the latest market data through API. The input is the market data from API, and the output is the collected market data. This data includes prices, trading volumes, trading history, etc.

[0197] Step 2:

[0198] The server trains a machine learning model based on the collected market data. Specifically, it preprocesses the data, extracts features, and inputs them into the machine learning algorithm. The input is the collected market data, and the output is the trained machine learning model. This model is used to predict future market trends.

[0199] Step 3:

[0200] The server analyzes market data in real time and generates optimal trading signals. Specifically, it uses a trained machine learning model to input current market data and output trading signals. The input is real-time market data and the output is trading signals.

[0201] Step 4:

[0202] The terminal (smartphone) receives market data in real time using a high-speed Internet communication network (5G network). Specifically, it receives market data sent from the server and inputs it into the application. The input is the market data from the server, and the output is the data passed to the application.

[0203] Step 5:

[0204] The terminal notifies the user of the optimal timing for payment through the application. Specifically, the application analyzes transaction signals and sends notifications to the user. The input is the transaction signal, and the output is the notification to the user. Based on this notification, the user can make electronic payments at the optimal time.

[0205] Step 6:

[0206] The user receives a notification from the application and makes an electronic payment at the optimal time. Specifically, the user executes the payment according to the notification and completes the transaction. The input is the notification from the application, and the output is the completed electronic payment.

[0207] These are the specific processing steps of this system, which will improve the speed and accuracy of transactions and enable users to make payments at the optimal timing.

[0208] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0209] "Example 1"

[0210] In one embodiment of the present invention, a system using AI technology and a high-speed Internet communication network is combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and improves the speed and accuracy of trading based on the emotions. For example, if a user shows an emotion of joy, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates optimal trading signals taking into account the analysis results of market data and the user's emotions. This allows for more personalized trading according to the user's emotions. The emotion engine also maximizes profits and minimizes risks based on the user's emotions. For example, if a user shows an emotion of anxiety, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates trading signals to minimize risks. This allows for safer trading according to the user's emotions.

[0211] "Example 2"

[0212] In one embodiment of the present invention, a system using AI technology and a high-speed Internet communication network is combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and improves the speed and accuracy of trading based on the emotions. For example, if a user shows an emotion of joy, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates optimal trading signals taking into account the analysis results of market data and the user's emotions. This allows for more personalized trading according to the user's emotions. The emotion engine also maximizes profits and minimizes risks based on the user's emotions. For example, if a user shows an emotion of anxiety, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates trading signals to minimize risks. This allows for safer trading according to the user's emotions.

[0213] The process flow of each embodiment will be described below.

[0214] "Example 1"

[0215] Step 1: A user starts trading with the system. At this point, the emotion engine begins to recognize emotions from the user's tone of voice, facial expressions, text input, etc.

[0216] Step 2: The sentiment engine feeds the recognized sentiment back to the AI ​​technology, which combines this sentiment information with real-time market data to generate trading signals.

[0217] Step 3: The generated trading signal is sent to the exchange via high-speed internet communication networks, which increases the speed and accuracy of trading.

[0218] Step 4: After the transaction is completed, the system will provide the user with feedback on the transaction results. This feedback will be provided in a form that takes into account the user's emotions and will be optimized according to the user's emotions.

[0219] Example 1

[0220] Next, a description will be given of Example 1 of the embodiment 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".

[0221] Conventional market data analysis systems had problems with delays in trading and reduced accuracy in real-time data processing and generation of trading signals. In addition, trading signals were not generated taking into account the user's emotions, making it difficult to perform personalized trading according to the user's psychological state. This resulted in insufficient improvement in trading speed and accuracy, making it difficult to maximize profits and minimize risks.

[0222] 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.

[0223] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for recognizing user emotions, and a means for adjusting trading signals based on the recognized emotion information, thereby improving the speed and accuracy of trading and enabling personalized trading according to the user's emotions.

[0224] "AI technology" is a technology that uses artificial intelligence to analyze data and perform pattern recognition and predictions.

[0225] "Real-time" refers to data collection and processing occurring immediately, without delay.

[0226] "Market Data" refers to data relating to financial markets, such as stock prices, exchange rates, and trading volumes.

[0227] "High-speed Internet network" refers to communications infrastructure that can send and receive data at high speeds, such as optical fiber networks.

[0228] "Trading signals" refer to instructions that indicate when to buy or sell based on the analysis of market data.

[0229] "User emotion" is information that indicates the user's psychological state, and is recognized from the tone of voice, facial expression, text input, and the like.

[0230] "Emotion information" refers to data obtained as a result of analyzing a user's emotions.

[0231] "Personalized transactions" refer to transactions that are customized according to the user's individual emotions and circumstances.

[0232] This invention is a system that uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, the various elements of the server, terminal, and user work together.

[0233] The server collects market data in real time using a high-speed internet communication network. The use of a fiber optic network minimizes delays in trading information. The collected market data is input into a deep learning model on the server. The deep learning model uses frameworks such as TensorFlow and PyTorch. This allows the server to learn patterns in the market data and generate optimal trading signals.

[0234] The device is equipped with an emotion engine that recognizes emotions from the user's tone of voice, facial expressions, text input, etc. The emotion engine uses technologies such as OpenCV and Google Cloud Speech-to-Text API to analyze the user's emotions in real time. The analysis results are fed back from the device to the server.

[0235] The server re-evaluates the market data analysis results based on the feedback sentiment information and adjusts the trading signals. For example, if the user is expressing happiness, the server generates aggressive trading signals. On the other hand, if the user is expressing anxiety, the server generates trading signals that minimize risk.

[0236] As a concrete example, when a user is using a trading application, the terminal analyzes the user's tone of voice to detect the emotion of happiness. The terminal transmits this information to a server, which takes into account the analysis of market data and the user's emotion to generate aggressive trading signals. The user then makes trades based on these signals to maximize profits.

[0237] An example prompt is, "Please explain how trading signals would be adjusted if a user exhibits the emotion of happiness while using a trading application."

[0238] The system allows users to make personalized trades based on market data and their own emotions, improving the speed and accuracy of trading.

[0239] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0240] Step 1:

[0241] The server collects market data in real time using high-speed Internet communication networks. Specifically, it uses optical fiber networks to obtain data from exchanges and financial institutions. As input, it receives market data such as stock prices, exchange rates, and trading volumes, and as output, it stores these data in a database.

[0242] Step 2:

[0243] The server pre-processes the collected market data, i.e. imputing missing values, removing outliers, normalizing data, etc. It takes the stored market data as input and produces the pre-processed data as output, which improves the quality of the data.

[0244] Step 3:

[0245] The server inputs the preprocessed data into a deep learning model. Specifically, it uses TensorFlow or PyTorch to build a LSTM (long short-term memory) network or a CNN (convolutional neural network) to learn patterns in the data. It takes the preprocessed market data as input and generates a trained model as output.

[0246] Step 4:

[0247] The server uses the trained model to generate optimal trading signals, specifically, buy and sell signals based on predicted price movements and trends. It receives the trained model and real-time market data as inputs and generates trading signals as output. The signals are sent to users' trading applications in real-time.

[0248] Step 5:

[0249] The device recognizes emotions from the user's tone of voice, facial expressions, text input, etc. Specifically, it uses OpenCV to analyze facial expressions from camera footage, converts voice data to text using Google Cloud Speech-to-Text API, and analyzes emotions. It receives the user's voice and video data as input, and generates emotional information as output.

[0250] Step 6:

[0251] The device feeds back the recognized emotion information to the server. Specifically, the emotion engine sends the analyzed emotion data to the server in JSON format. The device receives emotion information as input and generates data to be sent to the server as output.

[0252] Step 7:

[0253] The server re-evaluates the analysis results of the market data based on the feedback emotion information and adjusts the trading signal. For example, if the user indicates happiness, the server generates an aggressive trading signal. On the other hand, if the user indicates anxiety, the server generates a trading signal that minimizes risk. It takes emotion information and market data as inputs and generates an adjusted trading signal as output.

[0254] (Application example 1)

[0255] Next, a description will be given of Application Example 1 of Form 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."

[0256] Conventional trading systems were unable to take into account user sentiment when analyzing market data and generating trading signals, making it difficult to provide personalized trading advice. There was also room for improvement in terms of trading delays and risk management. This resulted in a suboptimal user trading experience, and the maximization of profits and minimization of risks were not fully achieved.

[0257] 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.

[0258] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for adjusting trading signals based on user emotions using an emotion engine that recognizes user emotions, and a means for integrating market data and user emotions using an application installed on a smartphone to provide trading advice. This makes it possible to provide personalized trading advice according to the user's emotions, improve the speed and accuracy of trading, maximize profits, and minimize risks.

[0259] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0260] "Real-time" refers to data acquisition and processing occurring immediately.

[0261] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[0262] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[0263] "High-speed processing" refers to quickly analyzing data and performing calculations.

[0264] A "trading signal" is an instruction that indicates the timing and direction of buying and selling when conducting a transaction.

[0265] A "high-speed Internet communication network" is an Internet network that allows data to be sent and received at high speeds.

[0266] "Information gathering" refers to the act of collecting necessary data or information.

[0267] "Transaction Delay" means a time delay that occurs in the execution of a transaction.

[0268] An "emotion engine" is a technology that recognizes emotions from a user's tone of voice, facial expressions, text input, etc.

[0269] "User emotion" refers to the psychological state, such as joy, anxiety, anger, etc., felt by the user.

[0270] A "smartphone" is a type of mobile phone, a multi-function device that can connect to the Internet and use applications.

[0271] An "application" is a software program designed to provide a specific function or service.

[0272] "Trading Advice" is information that provides information and recommendations for making decisions when conducting transactions.

[0273] "Personalized" means customized to the characteristics and preferences of an individual user.

[0274] "Transaction speed" refers to how quickly a transaction is executed.

[0275] "Transaction accuracy" refers to a transaction being executed exactly as intended.

[0276] "Profit maximization" refers to maximizing the profits gained from a transaction.

[0277] "Minimizing risk" means minimizing the losses and dangers involved in trading.

[0278] A system for implementing the present invention is configured as follows.

[0279] The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, a Python program uses Keras (a deep learning library) to learn patterns in the market data and generate optimal trading signals. Market data is obtained from the API using Requests (an HTTP request library) and converted into a Pandas data frame.

[0280] The server also collects information through high-speed Internet communication networks to minimize transaction delays, and transmits and receives data through fiber optic networks.

[0281] In addition, the server adjusts trading signals based on the user's emotions using an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's images using OpenCV (an image processing library) and recognizes emotions using a Keras model.

[0282] An application is installed on the smartphone as a terminal. This application integrates market data obtained from a server with the user's emotions and provides trading advice. The user takes a picture of their face using the smartphone's camera, and the application uses the image to recognize emotions. Market data is obtained in real time, and optimal trading signals are generated based on the user's emotions.

[0283] For example, a user can take a picture of their face with their smartphone camera, and the app will use the image to recognize emotions. The app will then obtain market data in real time and generate optimal trading signals based on the user's emotions. This will enable the app to provide personalized trading advice based on the user's emotions.

[0284] An example of a prompt is as follows:

[0285] "Users take a picture of their face with their smartphone camera and the app uses the image to recognize emotions. It pulls market data in real time and generates optimal trading signals based on the user's emotions."

[0286] In this way, it is possible to provide personalized trading advice based on the user's emotions, improve the speed and accuracy of trading, maximize profits and minimize risks.

[0287] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0288] Step 1:

[0289] The server retrieves market data from the API using the Requests library. The input is the API endpoint and the output is a JSON response with market data. This data is converted to a Pandas dataframe.

[0290] Step 2:

[0291] The server normalizes the market data converted to a Pandas dataframe. The input is a dataframe of market data and the output is the normalized market data. The data is scaled using StandardScaler.

[0292] Step 3:

[0293] The server generates trading signals from the normalized market data using a Keras model. The input is the normalized market data and the output is the trading signal. The data is fed into the Keras model to get the prediction results.

[0294] Step 4:

[0295] A user takes a picture of their face using the smartphone camera. The input is the user's face image, and the output is an image file. This image is used for emotion recognition.

[0296] Step 5:

[0297] The device preprocesses the user's face image using OpenCV. The input is the user's face image and the output is the preprocessed image. It resizes and normalizes the image.

[0298] Step 6:

[0299] The device recognizes the user's emotion from the image preprocessed using the Keras model. The input is the preprocessed image, and the output is the user's emotion label. Input the image to the Keras model to obtain the emotion prediction result.

[0300] Step 7:

[0301] The server receives the user's emotion label and adjusts the trading signal. The input is the trading signal and the user's emotion label, and the output is the adjusted trading signal. The server increases or decreases the trading signal based on the emotion label.

[0302] Step 8:

[0303] The terminal displays the adjusted trading signals to the user. The input is the adjusted trading signals and the output is trading advice to the user. The trading advice is displayed on the smartphone screen.

[0304] In this way, it is possible to provide trading advice that is personalized according to the user's emotions.

[0305] Example 2

[0306] Next, a second embodiment of the second embodiment will be described. 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."

[0307] Conventional trading systems had insufficient means to improve the speed and accuracy of transactions, and in particular had the problem of being unable to trade while taking into account the user's emotions. There was also room for improvement in terms of transaction delays and risk management. This made it difficult to maximize users' profits and minimize their risks.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0309] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for collecting user emotion data using an emotion engine that recognizes user emotions, and a means for adjusting trading signals based on the user emotion data. This improves the speed and accuracy of trading, and enables personalized trading according to the user's emotions, maximizing profits and minimizing risks.

[0310] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0311] "Market Data" is data relating to financial markets, such as stock prices, trading volumes, and news articles.

[0312] A "trading signal" is an instruction that indicates the timing and direction of a transaction, and refers to a specific action such as "buy" or "sell."

[0313] A "high-speed Internet network" is a communications infrastructure for sending and receiving data at high speeds, specifically referring to 5G networks.

[0314] An "emotion engine" is a technology that recognizes emotions from a user's tone of voice, facial expressions, text input, etc.

[0315] "User emotion data" is data relating to the user's emotion recognized by the emotion engine.

[0316] "Personalized trading" refers to trading that is optimized according to the user's individual emotions and circumstances.

[0317] "Minimizing risk" means making the chances of loss in a transaction as low as possible.

[0318] "Maximizing profits" means making the most profit possible from a transaction.

[0319] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0320] The system of the present invention improves the speed and accuracy of transactions by combining AI technology with a high-speed Internet communication network. A specific embodiment of this system will be described below.

[0321] Hardware and Software Used

[0322] The servers use machine learning frameworks such as TensorFlow and PyTorch to run machine learning algorithms.

[0323] The terminal is equipped with a communication module for collecting market data in real time via a 5G network.

[0324] The emotion engine includes software for recognizing emotions from a user's tone of voice, facial expressions, text input, etc.

[0325] System Operation

[0326] The server receives market data sent from the terminal and analyzes it in real time using machine learning algorithms. Specifically, it compares it with past market data and learns patterns. This learning is done using techniques such as regression analysis and clustering. The server generates optimal trading signals based on the learning results and sends them to the terminal.

[0327] The terminal collects market data in real time via the 5G network and transmits it to the server, minimizing trading delays. The terminal also collects user sentiment data through an emotion engine and transmits it to the server.

[0328] When trading, users communicate their emotions to the system through the emotion engine. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc. For example, if the user expresses joy, the emotion engine feeds that information back to the server, which then generates optimal trading signals by taking into account the results of market data analysis and the user's emotions.

[0329] Examples

[0330] As a concrete example, consider a scenario in which a user makes a trade. The user initiates a trade, and the device collects market data in real time through the 5G network and sends it to the server. The server uses TensorFlow to analyze the market data and compare it with past data to learn patterns. For example, it detects a pattern of a particular stock price rising. The server generates a "buy" trading signal and sends it to the device.

[0331] If the user expresses a happy emotion, the emotion engine feeds the information back to the server, which then takes that information into account and reevaluates the trading signal, for example by generating a signal to take more risk and gain more profit. The terminal then places a "buy" order on the stock exchange based on the new trading signal received from the server. The user then checks the results of the transaction and makes the next transaction.

[0332] Examples of prompt statements

[0333] “Describe a scenario in which a user initiates a trade and collects market data in real time over a 5G network. Include how the system updates trading signals if the user expresses happiness.”

[0334] In this way, the system enables personalized trading based on the user's emotions, improving the speed and accuracy of trading.

[0335] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0336] Step 1:

[0337] The terminal collects market data in real time through the 5G network. Specifically, it collects data such as stock prices, trading volume, and news articles. The collected data is sent from the terminal to the server. The input is the market data, and the output is the data sent to the server.

[0338] Step 2:

[0339] The server analyzes the received market data using machine learning algorithms. Specifically, it uses TensorFlow and PyTorch to compare it with past market data and learn patterns. This learning uses techniques such as regression analysis and clustering. The input is the market data, and the output is the learning results.

[0340] Step 3:

[0341] The server generates optimal trading signals based on the learning results. For example, it generates "buy" and "sell" signals. The generated trading signals are sent from the server to the terminal. The input is the learning results, and the output is the trading signal.

[0342] Step 4:

[0343] When a user makes a transaction, the user communicates his / her emotions to the system through the emotion engine. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc. The recognized emotion data is sent from the terminal to the server. The input is the user's emotion, and the output is emotion data.

[0344] Step 5:

[0345] The server receives the user's emotion data and adjusts the trading signal based on it. For example, if the user is anxious, it generates a signal to minimize risk. The adjusted trading signal is sent from the server to the terminal. The input is the emotion data, and the output is the adjusted trading signal.

[0346] Step 6:

[0347] The terminal executes transactions based on the trading signals received from the server, specifically by placing "buy" or "sell" orders on the stock exchange or trading platform. The input is the trading signal, and the output is the executed transaction.

[0348] In this way, the system enables personalized trading based on the user's emotions, improving the speed and accuracy of trading.

[0349] (Application example 2)

[0350] Next, a description will be given of Application Example 2 of Form 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."

[0351] In conventional trading systems, there were insufficient means to improve the speed and accuracy of trading, making it difficult to perform personalized trading based on user emotions. In addition, there were limited means to minimize transaction delays, making it difficult to maximize profits and minimize risks.

[0352] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0353] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for generating trading signals based on the user's emotions using an emotion engine that recognizes the user's emotions, a means for capturing the user's facial expressions using a smartphone camera and recognizing the emotions, a means for proposing optimal settlement methods and timing based on the emotions, and a means for generating trading signals based on emotions using a generative AI model. This improves the speed and accuracy of trading, and enables personalized trading according to the user's emotions, maximizing profits and minimizing risks.

[0354] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0355] "Real-time" refers to data processing and information acquisition occurring immediately.

[0356] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[0357] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[0358] "High-speed processing" refers to analyzing data quickly and obtaining results in a short period of time.

[0359] A "trading signal" is an instruction that indicates when and how to enter into a trade.

[0360] A "high-speed Internet network" refers to an Internet network that can send and receive data at extremely high speeds.

[0361] "Transaction Delay" means a time delay that occurs in the execution of a transaction.

[0362] An "emotion engine" is a system for recognizing a user's emotions and processing that information.

[0363] "Smartphone camera" refers to the photographic device built into a smartphone.

[0364] "Capturing facial expressions" refers to taking pictures of the user's facial expressions using a camera and acquiring them as data.

[0365] "Emotion recognition" refers to determining the user's emotional state from acquired data.

[0366] "Payment Method" refers to the means or method of payment used in a Transaction.

[0367] "Timing" refers to the right time or moment to make a transaction or settlement.

[0368] A "generative AI model" refers to an artificial intelligence model that has been trained using machine learning or deep learning to perform a specific task.

[0369] The system for implementing this invention has the following configuration. The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses a machine learning algorithm to learn market data patterns and generates optimal trading signals based on the results. It also uses a high-speed Internet communication network (e.g., 5G network) to collect information and minimize delays in trading.

[0370] In addition, by combining an emotion engine that recognizes user emotions, trading signals are generated based on the user's emotions. The emotion engine uses the smartphone camera to capture the user's facial expressions and recognize emotions. Specifically, it detects faces using OpenCV and predicts the user's emotions using an emotion recognition model using Keras.

[0371] A generative AI model is used to suggest optimal settlement methods and timing based on emotions. The generative AI model takes user emotion data as input and generates appropriate trading signals. These trading signals are personalized according to the user's emotions, improving the speed and accuracy of trading while maximizing profits and minimizing risks.

[0372] As a concrete example, while a user is using a smartphone, the camera captures the user's facial expression and recognizes emotions. For example, if the user feels anxious, the app suggests "payment methods to minimize risk." An example of this prompt is "If the user feels anxious, please suggest the best payment method to minimize risk."

[0373] In this way, the server can combine AI technology with high-speed internet networks, emotion engines, smartphone cameras and generative AI models to improve the speed and accuracy of transactions and realize personalized transactions based on user emotions.

[0374] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0375] Step 1:

[0376] The server uses AI technology to analyze market data in real time. It takes market data as input, uses machine learning algorithms to learn patterns, and generates optimal trading signals based on the results. The output is the generated trading signals.

[0377] Step 2:

[0378] The server collects information using a high-speed Internet communication network. Specifically, it uses a 5G network to obtain market data and trading information in real time to minimize trading delays. The input is market data and trading information, and the output is trading information with low latency.

[0379] Step 3:

[0380] The device (smartphone) captures the user's facial expression using a camera. It receives the image from the camera as input, detects the face using OpenCV, and cuts out the facial area. The output is image data of the facial area.

[0381] Step 4:

[0382] The device inputs image data of the face region into an emotion recognition model to predict the user's emotion. Specifically, the device inputs image data into an emotion recognition model using Keras to obtain emotion prediction results. The output is predicted emotion data.

[0383] Step 5:

[0384] The server inputs the predicted emotion data into the generative AI model to generate emotion-based trading signals. The generative AI model generates optimal trading signals based on the emotion data and outputs them. The output is emotion-based trading signals.

[0385] Step 6:

[0386] The terminal presents the generated trading signals to the user. Specifically, the terminal displays the trading signals on the smartphone screen and suggests optimal settlement methods and timing to the user. The input is the generated trading signals, and the output is the trading signals presented to the user.

[0387] Step 7:

[0388] The user executes a transaction based on the presented transaction signal. The user operates a smartphone and executes the transaction according to the proposed settlement method and timing. The input is the presented transaction signal, and the output is the executed transaction.

[0389] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0390] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0391] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0392] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0393] [Second embodiment]

[0394] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0395] 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.

[0396] 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 wide area network (WAN) and / or a local area network (LAN).

[0397] 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.

[0398] 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 the voice according to instructions from the processor 46.

[0399] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0400] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0401] Fig. 4 shows an example of 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.

[0402] 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.

[0403] 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.

[0404] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0405] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0406] "Example 1"

[0407] The system of the present invention uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, deep learning is used as the AI ​​technology to learn patterns in market data and generate optimal trading signals based on the results. In addition, a high-speed Internet communication network is used to collect information and minimize trading delays. Specifically, a fiber optic network is used to collect trading information in real time and minimize trading delays. This makes it possible to improve the speed and accuracy of trading, maximize profits, and minimize risks.

[0408] "Example 2"

[0409] The system of the present invention improves the speed and accuracy of trading by combining AI technology and a high-speed Internet communication network. Specifically, machine learning is used as AI technology to learn market data patterns and generate optimal trading signals based on the results. In addition, a high-speed Internet communication network is used to collect information and minimize trading delays. Specifically, a 5G network is used to collect trading information in real time and minimize trading delays. This makes it possible to improve the speed and accuracy of trading, maximize profits, and minimize risks.

[0410] The process flow of each embodiment will be described below.

[0411] "Example 1"

[0412] Step 1: Analyze market data in real time using deep learning as an AI technology. This analysis allows you to understand market movements and trends.

[0413] Step 2: Generate optimal trading signals based on the analysis results. These trading signals indicate the timing and amount of buying and selling.

[0414] Step 3: Use a high-speed Internet network to collect information and minimize transaction delays. Specifically, use a fiber optic network to collect transaction information in real time and minimize transaction delays.

[0415] "Example 2"

[0416] Step 1: Analyze market data in real time using machine learning as an AI technology. This analysis allows you to understand market movements and trends.

[0417] Step 2: Generate optimal trading signals based on the analysis results. These trading signals indicate the timing and amount of buying and selling.

[0418] Step 3: Use high-speed Internet networks to collect information and minimize transaction delays. Specifically, 5G networks will be used to collect transaction information in real time and minimize transaction delays.

[0419] Example 1

[0420] Next, a description will be given of Example 1 of Form 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".

[0421] Traditional market data analysis systems had problems with delays in collecting and processing real-time data, reducing the speed and accuracy of trading. They also lacked the ability to process huge amounts of data quickly, making it difficult to generate optimal trading signals. Furthermore, they did not provide sufficient means to maximize profits and minimize risks.

[0422] 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.

[0423] In this invention, the server includes a means for collecting market data in real time through an optical fiber network, a means for normalizing the collected market data and imputing missing values, a means for learning patterns of the market data using a deep learning framework, a means for generating optimal trading signals based on the learning results, and a means for delivering the generated trading signals to a user's terminal, thereby improving the speed and accuracy of trading, maximizing profits, and minimizing risks.

[0424] An "optical fiber network" is a communications network that uses optical fiber cables to enable high-speed, large-capacity data communication.

[0425] "Market data" refers to data that includes information such as prices, trading volumes, and trading times in financial markets for stocks, bonds, commodities, currencies, and the like.

[0426] "Normalization" is a process of constraining each data point within a certain range in order to unify the scale of the data.

[0427] "Missing value imputation" is the process of filling in missing values ​​in a dataset in an appropriate manner.

[0428] A "deep learning framework" is a software library for building and training neural networks, and includes TensorFlow and PyTorch.

[0429] "Pattern learning" is the process of extracting patterns and features from past data and building models to predict and classify future data.

[0430] A "trading signal" is an instruction indicating the timing of buying and selling in a financial market, and includes buying signals and selling signals.

[0431] "User terminal" means a device used by a user to receive trading signals and execute transactions, including a personal computer or smartphone.

[0432] This invention is a system that uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses deep learning to learn market data patterns and generates optimal trading signals based on the results. It also uses high-speed Internet communication networks to collect information and minimize delays in trading.

[0433] The server collects market data in real time through a fiber optic network. The collected market data is processed by a high-performance server equipped with NVIDIA GPUs. The server learns the patterns of the market data using deep learning frameworks such as TensorFlow and PyTorch. Based on the learning results, the server generates optimal trading signals.

[0434] Specifically, the server processes the data in the following steps:

[0435] 1. Market data collection: The server collects market data in real time through the fiber optic network, for example, by using APIs from financial market data providers.

[0436] 2. Data preprocessing: Normalize the collected data and impute missing values. For example, use the Pandas library to manipulate data frames and impute missing values ​​with the mean.

[0437] 3. Train the model: Build a neural network using TensorFlow or PyTorch to learn patterns from historical market data, for example using a LSTM (Long Short-Term Memory) network to train time series data.

[0438] 4. Generate trading signals: Generate optimal trading signals based on the learning results. For example, if the model judges it to be a "buy" signal, generate that signal.

[0439] 5. Signal Delivery: The generated trading signals are delivered to the users' terminals, for example, by sending signals in real time using WebSocket or REST API.

[0440] Users can execute trades based on the trading signals they receive, improving the speed and accuracy of their trades, maximizing profits and minimizing risks.

[0441] As a concrete example, consider the following scenario:

[0442] A user wants to analyze stock market data in real time and receive optimal trading signals. The user's terminal receives the trading signals distributed by the server and executes the trades.

[0443] Example prompt:

[0444] "Analyze real-time stock market data and generate optimal trading signals. The data used is stock price data and trading volume data from the past year. Deliver the generated trading signals in real time."

[0445] The system enables users to improve the speed and accuracy of their transactions, maximizing profits and minimizing risks.

[0446] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0447] Step 1:

[0448] The server collects market data in real time through the fiber optic network. Specifically, it retrieves data from financial market data providers using APIs. The input is the API request and the output is the retrieved market data. For example, the server sends an API request and receives data such as stock prices and trading volumes.

[0449] Step 2:

[0450] The server preprocesses the collected market data by normalizing the data, imputing missing values, and detecting and correcting outliers. The input is the collected market data, and the output is the preprocessed data. For example, the server uses the Pandas library to manipulate data frames, impute missing values ​​with the mean, and normalize the data.

[0451] Step 3:

[0452] The server trains a deep learning model using the preprocessed data. Specifically, it uses TensorFlow or PyTorch to build a neural network and learn patterns from past market data. The input is the preprocessed data, and the output is the trained model. For example, the server uses an LSTM (long short-term memory) network to train time series data.

[0453] Step 4:

[0454] The server generates optimal trading signals based on the learning results. Specifically, it inputs real-time market data into the trained model and outputs buy and sell signals. The inputs are real-time market data and the trained model, and the output is a trading signal. For example, if the model judges it to be a "buy" signal, it generates that signal.

[0455] Step 5:

[0456] The server distributes the generated trading signals to the user's terminal. Specifically, it transmits signals in real time using WebSocket or REST API. The input is the generated trading signal, and the output is the signal distributed to the user's terminal. The user's terminal displays the received signal and executes the transaction.

[0457] (Application example 1)

[0458] Next, a description will be given of Application Example 1 of Form 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".

[0459] Conventional market data analysis systems have difficulty processing data in real time, which often results in delays in trading. They also cannot generate trading signals optimized for each user, which results in a lack of accuracy and speed in trading. Furthermore, they are not user-friendly because they do not take into account the use on mobile devices such as smartphones.

[0460] 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. In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for learning the trading patterns of users and generating individually optimized trading signals, and a means including an application installed on a smartphone. This enables real-time market data analysis and generation of trading signals, improves the speed and accuracy of trading, and makes it possible to provide trading signals optimized for each user.

[0461] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0462] "Real-time" refers to data collection and processing occurring immediately.

[0463] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[0464] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[0465] "High-speed processing" refers to analyzing data quickly and outputting results in a short period of time.

[0466] A "trading signal" refers to an instruction or sign that indicates the timing and direction of a transaction.

[0467] A "high-speed Internet network" refers to an Internet connection that can send and receive data very quickly.

[0468] "Information gathering" refers to collecting necessary data or information.

[0469] "Transaction Delay" refers to the delay in time before a transaction is executed.

[0470] "User trading patterns" refers to the user's past trading tendencies and behavioral patterns.

[0471] "Individually Optimized Trading Signals" refers to trading signals that are optimized based on the trading patterns of each individual User.

[0472] "Applications installed on a smartphone" refers to software programs that run on a smartphone.

[0473] In order to implement the present invention, the following system configuration is necessary.

[0474] First, the server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses deep learning models to learn patterns in market data and generate optimal trading signals based on the results. The server achieves this using software such as Python, Keras, Requests, Pandas, and NumPy.

[0475] The servers then collect the information over high-speed Internet connections to minimize trading delays, using fiber optic networks to collect market data in real time for immediate analysis.

[0476] In addition, the server learns the user's trading patterns and generates individually optimized trading signals, which makes it possible to provide optimal trading timing for each user. The user's trading patterns are learned by a deep learning model based on past trading data.

[0477] The application installed on a smartphone receives trading signals sent from the server and notifies the user. Through this application, users can check market data in real time and know the optimal timing for trading.

[0478] As a concrete example, a scenario is when a user opens the smartphone app "Real-time Settlement Analyst" and checks current market data in real time. The app learns the user's trading patterns and notifies them of the optimal timing to trade. For example, a notification such as "In the current market conditions, it is a good time to buy" is displayed.

[0479] An example of a prompt sentence might be:

[0480] Develop a smartphone app that analyzes market data in real time and notifies users of the best time to trade. Use deep learning to learn users' trading patterns and generate individually optimized trading signals. Use high-speed internet networks to collect trading information in real time and minimize delays.

[0481] In this way, the combination of servers, smartphone applications and high-speed internet networks allows for real-time market data analysis and generation of trading signals, improving the speed and accuracy of trading.

[0482] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0483] Step 1:

[0484] The server collects market data in real time. Specifically, it obtains market data through APIs and collects information such as prices, trading volumes, and trading history. The input is the response from the market data API, and the output is the collected market data.

[0485] Step 2:

[0486] The server pre-processes the collected market data, specifically filtering it down to prices and volumes and converting it into numerical data. The input is the market data collected in step 1, and the output is the pre-processed data.

[0487] Step 3:

[0488] The server uses the preprocessed data to train a deep learning model, specifically, a long short-term memory (LSTM) network to learn patterns in the market data. The input is the preprocessed data, and the output is the trained deep learning model.

[0489] Step 4:

[0490] The server generates trading signals using a trained deep learning model. Specifically, the model inputs market data and outputs signals that indicate the timing and direction of trading. The input is real-time market data and the output is trading signals.

[0491] Step 5:

[0492] The server transmits the trading signals to the smartphone using a high-speed internet communication network. Specifically, the communication is performed using a fiber optic network with minimal latency. The input is the trading signal, and the output is the signal notification to the smartphone.

[0493] Step 6:

[0494] The smartphone notifies the user of the received trading signal. Specifically, it notifies the user of the timing of trading using push notifications and in-app notifications. The input is the trading signal sent from the server, and the output is the notification to the user.

[0495] Step 7:

[0496] Users check market data in real time through a smartphone app and trade based on trading signals. Specifically, they view market data within the app and execute trades according to the trading timing notified to them. The input is market data and trading signals from the smartphone app, and the output is the user's trading action.

[0497] In this way, the combination of servers, smartphone applications and high-speed internet networks allows for real-time market data analysis and generation of trading signals, improving the speed and accuracy of trading.

[0498] Example 2

[0499] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0500] Conventional trading systems lacked speed and accuracy, making it difficult to maximize profits and minimize risks. In addition, real-time information collection was insufficient, resulting in delays in trading. This often caused users to miss the optimal timing for trading, reducing trading efficiency.

[0501] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0502] In this invention, the server includes a means for collecting market data, a means for pre-processing the collected data, a means for training a machine learning model, a means for generating trading signals, a means for collecting information in real time using a high-speed Internet communication network, a means for notifying a user of the generated trading signals, and a means for the user to execute a trade, thereby improving the speed and accuracy of trading, maximizing profits, and minimizing risks.

[0503] "Market data" refers to information such as stock prices, trading volumes, and economic indicators in financial markets.

[0504] "Preprocessing" refers to the process of filling in missing values ​​in collected data, removing outliers, normalizing the data, etc.

[0505] A "machine learning model" refers to an algorithm that learns patterns from data and makes predictions and classifications.

[0506] A "trading signal" refers to information generated by a machine learning model that recommends trading at a specific time.

[0507] "High-speed Internet network" refers to infrastructure that enables high-speed and stable data communication.

[0508] "Real-time information" refers to information that immediately reflects current market conditions.

[0509] "User" refers to an individual or legal entity that conducts transactions using this system.

[0510] "Notification" refers to the act of informing a User of a generated trading signal.

[0511] "Execution of a transaction" refers to the act of a user actually buying or selling a financial product based on a trading signal.

[0512] This invention is a system that improves the speed and accuracy of transactions by combining AI technology and high-speed Internet communication networks. Below, we will explain how to specifically implement this system.

[0513] The server first collects market data. Specifically, it uses a common data collection API to obtain past transaction data from financial markets. For example, it can use the Yahoo Finance API or Alpha Vantage API. This allows it to collect data such as stock prices, trading volumes, and economic indicators.

[0514] The server then preprocesses the collected data, using Python's Pandas library to impute missing values, remove outliers, and normalize the data, improving data quality and making it more effective for training machine learning models.

[0515] The server then trains a machine learning model, using TensorFlow or Scikit-learn to learn patterns from past market data. For example, a Long Short-Term Memory (LSTM) model can be used to forecast time series data, which can then predict future market trends and generate optimal trading signals.

[0516] After a trading signal is generated, the terminal collects information in real time using a high-speed Internet communication network, specifically using Qualcomm's 5G modem to achieve high speed and stable communication, which can minimize delays in trading.

[0517] The generated trading signals are notified to the user through the terminal. The signals are displayed in real time through the mobile app and desktop app. By receiving notifications, users can quickly execute trades.

[0518] Users execute trades based on the trading signals they receive. They access the trading platform and buy or sell stocks according to the signals generated by AI, which improves the speed and accuracy of trading, maximizing profits and minimizing risks.

[0519] As a concrete example, consider a user trading on the stock market. The server uses the Yahoo Finance API to collect stock price data for the past year. It then uses the Pandas library to impute missing values ​​in the data and remove outliers. It then uses TensorFlow to train an LSTM model to predict which stocks are likely to rise in the next week. The server generates a buy signal based on this prediction, and the terminal notifies the user of this signal in real time over the 5G network. When the user receives the notification, he or she accesses the trading platform and purchases stocks according to the signal.

[0520] Example of a prompt to be input to a generative AI model:

[0521] "Predict which stocks are likely to rise in the next week based on stock price data from the past year."

[0522] The system improves the speed and accuracy of trading, allowing users to maximize profits and minimize risks.

[0523] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0524] Step 1:

[0525] The server collects market data. Specifically, it uses a common data collection API to obtain historical trading data from financial markets. For example, it uses Yahoo Finance API or Alpha Vantage API. The input is the API request, and the output is data such as stock prices, trading volumes, and economic indicators.

[0526] Step 2:

[0527] The server preprocesses the collected data. Specifically, it uses Python's Pandas library to impute missing values ​​in the data, remove outliers, and normalize the data. The input is the collected market data, and the output is the preprocessed data.

[0528] Step 3:

[0529] The server trains a machine learning model using the preprocessed data. Specifically, it uses TensorFlow and Scikit-learn to learn patterns from past market data. For example, it uses a Long Short-Term Memory (LSTM) model to make predictions on time series data. The input is the preprocessed data, and the output is the trained machine learning model.

[0530] Step 4:

[0531] The server uses the trained model to generate trading signals, specifically, buy and sell signals based on the stock price trends predicted by the model. The inputs are the trained machine learning model and real-time market data, and the output is trading signals.

[0532] Step 5:

[0533] The terminal collects information in real time using a high-speed Internet communication network. Specifically, it uses Qualcomm's 5G modem to achieve high-speed and stable communication. The input is real-time market data, and the output is the collected real-time information.

[0534] Step 6:

[0535] The terminal notifies the user of the generated trading signals, specifically by displaying the signals in real time through a mobile or desktop app. The input is the trading signal, and the output is the notification to the user.

[0536] Step 7:

[0537] The user executes a trade based on the notified trading signal. Specifically, the user accesses the trading platform and buys or sells stocks according to the signal generated by the AI. The input is the trading signal and the trading platform, and the output is the executed trade.

[0538] (Application example 2)

[0539] Next, a description will be given of Application Example 2 of Form 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".

[0540] Traditional electronic settlement systems did not ensure sufficient speed and accuracy of transactions, making it difficult for users to settle at the optimal time. In addition, there was a lack of means to analyze market data in real time and generate optimal trading signals, making it difficult to maximize profits and minimize risks. In addition, delays in transactions were common, often impairing the user experience.

[0541] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0542] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for proposing optimal timing and method for a user to make an electronic settlement, a means for analyzing market data using a machine learning model and collecting information in real time, and a means including an application to be installed on a smartphone, thereby improving the speed and accuracy of trading and enabling a user to make a settlement at an optimal timing.

[0543] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0544] "Real-time" refers to data collection and processing occurring immediately.

[0545] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[0546] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[0547] "High-speed processing" refers to analyzing data quickly and obtaining results in a short period of time.

[0548] A "trading signal" is an instruction that indicates the best time or manner to enter into a trade.

[0549] A "high-speed Internet network" refers to an Internet connection that allows for extremely fast data transmission and reception, and specifically includes 5G networks.

[0550] "Information gathering" refers to the act of collecting necessary data or information.

[0551] "Transaction Delay" refers to the delay in time before a transaction is executed.

[0552] "User" refers to any individual or legal entity that uses this system.

[0553] "Electronic payment" refers to the payment of money via the Internet.

[0554] The "optimal time" refers to the most favorable time to carry out a transaction or settlement.

[0555] "Suggesting a solution" means showing the user the optimal course of action or means.

[0556] A "machine learning model" refers to an algorithm that learns patterns from data and makes predictions and classifications.

[0557] A "smartphone" refers to a mobile phone that can connect to the Internet and run applications.

[0558] "Application" refers to a software program for achieving a specific function.

[0559] A system for implementing the present invention is configured as follows.

[0560] System Configuration

[0561] The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses machine learning models to analyze market data and generate optimal trading signals, which can improve the speed and accuracy of trading.

[0562] The terminal (smartphone) collects information using a high-speed Internet communication network (5G network) to minimize transaction delays. An application is installed that suggests the optimal timing and method for users to make electronic payments. The application collects market data in real time and uses machine learning models to notify users of the optimal timing for payment.

[0563] Hardware and Software Used

[0564] Hardware: 5G-enabled smartphone

[0565] Software: Python, Requests library, Scikit-learn library

[0566] Data processing and calculation

[0567] The server collects market data through APIs and analyzes the data using machine learning models. Specifically, the machine learning model is trained based on the collected market data to generate optimal trading signals in real time. The terminal receives market data in real time through the 5G network and notifies users of the optimal settlement timing through the application.

[0568] Examples

[0569] For example, when a user is shopping online, the application will notify them of the optimal time to make a payment, allowing them to make a payment with the most favorable exchange rate and fees.

[0570] Examples of prompt statements

[0571] "Develop an application that collects the latest market data and notifies the optimal settlement timing in real time. Analyze market data using machine learning models and collect information in real time over 5G networks."

[0572] The above is an embodiment of the present invention. This system improves the speed and accuracy of transactions, allowing users to make payments at the optimal timing.

[0573] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0574] Step 1:

[0575] The server collects market data. Specifically, it gets the latest market data through API. The input is the market data from API, and the output is the collected market data. This data includes prices, trading volumes, trading history, etc.

[0576] Step 2:

[0577] The server trains a machine learning model based on the collected market data. Specifically, it preprocesses the data, extracts features, and inputs them into the machine learning algorithm. The input is the collected market data, and the output is the trained machine learning model. This model is used to predict future market trends.

[0578] Step 3:

[0579] The server analyzes market data in real time and generates optimal trading signals. Specifically, it uses a trained machine learning model to input current market data and output trading signals. The input is real-time market data and the output is trading signals.

[0580] Step 4:

[0581] The terminal (smartphone) receives market data in real time using a high-speed Internet communication network (5G network). Specifically, it receives market data sent from the server and inputs it into the application. The input is the market data from the server, and the output is the data passed to the application.

[0582] Step 5:

[0583] The terminal notifies the user of the optimal timing for payment through the application. Specifically, the application analyzes transaction signals and sends notifications to the user. The input is the transaction signal, and the output is the notification to the user. Based on this notification, the user can make electronic payments at the optimal time.

[0584] Step 6:

[0585] The user receives a notification from the application and makes an electronic payment at the optimal time. Specifically, the user executes the payment according to the notification and completes the transaction. The input is the notification from the application, and the output is the completed electronic payment.

[0586] These are the specific processing steps of this system, which will improve the speed and accuracy of transactions and enable users to make payments at the optimal timing.

[0587] In addition, 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.

[0588] "Example 1"

[0589] In one embodiment of the present invention, a system using AI technology and a high-speed Internet communication network is combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and improves the speed and accuracy of trading based on the emotions. For example, if a user shows an emotion of joy, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates optimal trading signals taking into account the analysis results of market data and the user's emotions. This allows for more personalized trading according to the user's emotions. The emotion engine also maximizes profits and minimizes risks based on the user's emotions. For example, if a user shows an emotion of anxiety, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates trading signals to minimize risks. This allows for safer trading according to the user's emotions.

[0590] "Example 2"

[0591] In one embodiment of the present invention, a system using AI technology and a high-speed Internet communication network is combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and improves the speed and accuracy of trading based on the emotions. For example, if a user shows an emotion of joy, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates optimal trading signals taking into account the analysis results of market data and the user's emotions. This allows for more personalized trading according to the user's emotions. The emotion engine also maximizes profits and minimizes risks based on the user's emotions. For example, if a user shows an emotion of anxiety, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates trading signals to minimize risks. This allows for safer trading according to the user's emotions.

[0592] The process flow of each embodiment will be described below.

[0593] "Example 1"

[0594] Step 1: A user starts trading with the system. At this point, the emotion engine begins to recognize emotions from the user's tone of voice, facial expressions, text input, etc.

[0595] Step 2: The sentiment engine feeds the recognized sentiment back to the AI ​​technology, which combines this sentiment information with real-time market data to generate trading signals.

[0596] Step 3: The generated trading signal is sent to the exchange via high-speed internet communication networks, which increases the speed and accuracy of trading.

[0597] Step 4: After the transaction is completed, the system will provide the user with feedback on the transaction results. This feedback will be provided in a form that takes into account the user's emotions and will be optimized according to the user's emotions.

[0598] Example 1

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

[0600] Conventional market data analysis systems had problems with delays in trading and reduced accuracy in real-time data processing and generation of trading signals. In addition, trading signals were not generated taking into account the user's emotions, making it difficult to perform personalized trading according to the user's psychological state. This resulted in insufficient improvement in trading speed and accuracy, making it difficult to maximize profits and minimize risks.

[0601] 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.

[0602] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for recognizing user emotions, and a means for adjusting trading signals based on the recognized emotion information, thereby improving the speed and accuracy of trading and enabling personalized trading according to the user's emotions.

[0603] "AI technology" is a technology that uses artificial intelligence to analyze data and perform pattern recognition and predictions.

[0604] "Real-time" refers to data collection and processing occurring immediately, without delay.

[0605] "Market Data" refers to data relating to financial markets, such as stock prices, exchange rates, and trading volumes.

[0606] "High-speed Internet network" refers to communications infrastructure that can send and receive data at high speeds, such as optical fiber networks.

[0607] "Trading signals" refer to instructions that indicate when to buy or sell based on the analysis of market data.

[0608] "User emotion" is information that indicates the user's psychological state, and is recognized from the tone of voice, facial expression, text input, and the like.

[0609] "Emotion information" refers to data obtained as a result of analyzing a user's emotions.

[0610] "Personalized transactions" refer to transactions that are customized according to the user's individual emotions and circumstances.

[0611] This invention is a system that uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, the various elements of the server, terminal, and user work together.

[0612] The server collects market data in real time using a high-speed internet communication network. The use of a fiber optic network minimizes delays in trading information. The collected market data is input into a deep learning model on the server. The deep learning model uses frameworks such as TensorFlow and PyTorch. This allows the server to learn patterns in the market data and generate optimal trading signals.

[0613] The device is equipped with an emotion engine that recognizes emotions from the user's tone of voice, facial expressions, text input, etc. The emotion engine uses technologies such as OpenCV and Google Cloud Speech-to-Text API to analyze the user's emotions in real time. The analysis results are fed back from the device to the server.

[0614] The server re-evaluates the market data analysis results based on the feedback sentiment information and adjusts the trading signals. For example, if the user is expressing happiness, the server generates aggressive trading signals. On the other hand, if the user is expressing anxiety, the server generates trading signals that minimize risk.

[0615] As a concrete example, when a user is using a trading application, the terminal analyzes the user's tone of voice to detect the emotion of happiness. The terminal transmits this information to a server, which takes into account the analysis of market data and the user's emotion to generate aggressive trading signals. The user then makes trades based on these signals to maximize profits.

[0616] An example prompt is, "Please explain how trading signals would be adjusted if a user exhibits the emotion of happiness while using a trading application."

[0617] The system allows users to make personalized trades based on market data and their own emotions, improving the speed and accuracy of trading.

[0618] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0619] Step 1:

[0620] The server collects market data in real time using high-speed Internet communication networks. Specifically, it uses optical fiber networks to obtain data from exchanges and financial institutions. As input, it receives market data such as stock prices, exchange rates, and trading volumes, and as output, it stores these data in a database.

[0621] Step 2:

[0622] The server pre-processes the collected market data, i.e. imputing missing values, removing outliers, normalizing data, etc. It takes the stored market data as input and produces the pre-processed data as output, which improves the quality of the data.

[0623] Step 3:

[0624] The server inputs the preprocessed data into a deep learning model. Specifically, it uses TensorFlow or PyTorch to build a LSTM (long short-term memory) network or a CNN (convolutional neural network) to learn patterns in the data. It takes the preprocessed market data as input and generates a trained model as output.

[0625] Step 4:

[0626] The server uses the trained model to generate optimal trading signals, specifically, buy and sell signals based on predicted price movements and trends. It receives the trained model and real-time market data as inputs and generates trading signals as output. The signals are sent to users' trading applications in real-time.

[0627] Step 5:

[0628] The device recognizes emotions from the user's tone of voice, facial expressions, text input, etc. Specifically, it uses OpenCV to analyze facial expressions from camera footage, converts voice data to text using Google Cloud Speech-to-Text API, and analyzes emotions. It receives the user's voice and video data as input, and generates emotional information as output.

[0629] Step 6:

[0630] The device feeds back the recognized emotion information to the server. Specifically, the emotion engine sends the analyzed emotion data to the server in JSON format. The device receives emotion information as input and generates data to be sent to the server as output.

[0631] Step 7:

[0632] The server re-evaluates the analysis results of the market data based on the feedback emotion information and adjusts the trading signal. For example, if the user indicates happiness, the server generates an aggressive trading signal. On the other hand, if the user indicates anxiety, the server generates a trading signal that minimizes risk. It takes emotion information and market data as inputs and generates an adjusted trading signal as output.

[0633] (Application example 1)

[0634] Next, a description will be given of Application Example 1 of Form 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".

[0635] Conventional trading systems were unable to take into account user sentiment when analyzing market data and generating trading signals, making it difficult to provide personalized trading advice. There was also room for improvement in terms of trading delays and risk management. This resulted in a suboptimal user trading experience, and the maximization of profits and minimization of risks were not fully achieved.

[0636] 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.

[0637] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for adjusting trading signals based on user emotions using an emotion engine that recognizes user emotions, and a means for integrating market data and user emotions using an application installed on a smartphone to provide trading advice. This makes it possible to provide personalized trading advice according to the user's emotions, improve the speed and accuracy of trading, maximize profits, and minimize risks.

[0638] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0639] "Real-time" refers to data acquisition and processing occurring immediately.

[0640] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[0641] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[0642] "High-speed processing" refers to quickly analyzing data and performing calculations.

[0643] A "trading signal" is an instruction that indicates the timing and direction of buying and selling when conducting a transaction.

[0644] A "high-speed Internet communication network" is an Internet network that allows data to be sent and received at high speeds.

[0645] "Information gathering" refers to the act of collecting necessary data or information.

[0646] "Transaction Delay" means a time delay that occurs in the execution of a transaction.

[0647] An "emotion engine" is a technology that recognizes emotions from a user's tone of voice, facial expressions, text input, etc.

[0648] "User emotion" refers to the psychological state, such as joy, anxiety, anger, etc., felt by the user.

[0649] A "smartphone" is a type of mobile phone, a multi-function device that can connect to the Internet and use applications.

[0650] An "application" is a software program designed to provide a specific function or service.

[0651] "Trading Advice" is information that provides information and recommendations for making decisions when conducting transactions.

[0652] "Personalized" means customized to the characteristics and preferences of an individual user.

[0653] "Transaction speed" refers to how quickly a transaction is executed.

[0654] "Transaction accuracy" refers to a transaction being executed exactly as intended.

[0655] "Profit maximization" refers to maximizing the profits gained from a transaction.

[0656] "Minimizing risk" means minimizing the losses and dangers involved in trading.

[0657] A system for implementing the present invention is configured as follows.

[0658] The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, a Python program uses Keras (a deep learning library) to learn patterns in the market data and generate optimal trading signals. Market data is obtained from the API using Requests (an HTTP request library) and converted into a Pandas data frame.

[0659] The server also collects information through high-speed Internet communication networks to minimize transaction delays, and transmits and receives data through fiber optic networks.

[0660] In addition, the server adjusts trading signals based on the user's emotions using an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's images using OpenCV (an image processing library) and recognizes emotions using a Keras model.

[0661] An application is installed on the smartphone as a terminal. This application integrates market data obtained from a server with the user's emotions and provides trading advice. The user takes a picture of their face using the smartphone's camera, and the application uses the image to recognize emotions. Market data is obtained in real time, and optimal trading signals are generated based on the user's emotions.

[0662] For example, a user can take a picture of their face with their smartphone camera, and the app will use the image to recognize emotions. The app will then obtain market data in real time and generate optimal trading signals based on the user's emotions. This will enable the app to provide personalized trading advice based on the user's emotions.

[0663] An example of a prompt is as follows:

[0664] "Users take a picture of their face with their smartphone camera and the app uses the image to recognize emotions. It pulls market data in real time and generates optimal trading signals based on the user's emotions."

[0665] In this way, it is possible to provide personalized trading advice based on the user's emotions, improve the speed and accuracy of trading, maximize profits and minimize risks.

[0666] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0667] Step 1:

[0668] The server retrieves market data from the API using the Requests library. The input is the API endpoint and the output is a JSON response with market data. This data is converted to a Pandas dataframe.

[0669] Step 2:

[0670] The server normalizes the market data converted to a Pandas dataframe. The input is a dataframe of market data and the output is the normalized market data. The data is scaled using StandardScaler.

[0671] Step 3:

[0672] The server generates trading signals from the normalized market data using a Keras model. The input is the normalized market data and the output is the trading signal. The data is fed into the Keras model to get the prediction results.

[0673] Step 4:

[0674] A user takes a picture of their face using the smartphone camera. The input is the user's face image, and the output is an image file. This image is used for emotion recognition.

[0675] Step 5:

[0676] The device preprocesses the user's face image using OpenCV. The input is the user's face image and the output is the preprocessed image. It resizes and normalizes the image.

[0677] Step 6:

[0678] The device recognizes the user's emotion from the image preprocessed using the Keras model. The input is the preprocessed image, and the output is the user's emotion label. Input the image to the Keras model to obtain the emotion prediction result.

[0679] Step 7:

[0680] The server receives the user's emotion label and adjusts the trading signal. The input is the trading signal and the user's emotion label, and the output is the adjusted trading signal. The server increases or decreases the trading signal based on the emotion label.

[0681] Step 8:

[0682] The terminal displays the adjusted trading signals to the user. The input is the adjusted trading signals and the output is trading advice to the user. The trading advice is displayed on the smartphone screen.

[0683] In this way, it is possible to provide trading advice that is personalized according to the user's emotions.

[0684] Example 2

[0685] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0686] Conventional trading systems had insufficient means to improve the speed and accuracy of transactions, and in particular had the problem of being unable to trade while taking into account the user's emotions. There was also room for improvement in terms of transaction delays and risk management. This made it difficult to maximize users' profits and minimize their risks.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0688] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for collecting user emotion data using an emotion engine that recognizes user emotions, and a means for adjusting trading signals based on the user emotion data. This improves the speed and accuracy of trading, and enables personalized trading according to the user's emotions, maximizing profits and minimizing risks.

[0689] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0690] "Market Data" is data relating to financial markets, such as stock prices, trading volumes, and news articles.

[0691] A "trading signal" is an instruction that indicates the timing and direction of a transaction, and refers to a specific action such as "buy" or "sell."

[0692] A "high-speed Internet network" is a communications infrastructure for sending and receiving data at high speeds, specifically referring to 5G networks.

[0693] An "emotion engine" is a technology that recognizes emotions from a user's tone of voice, facial expressions, text input, etc.

[0694] "User emotion data" is data relating to the user's emotion recognized by the emotion engine.

[0695] "Personalized trading" refers to trading that is optimized according to the user's individual emotions and circumstances.

[0696] "Minimizing risk" means making the chances of loss in a transaction as low as possible.

[0697] "Maximizing profits" means making the most profit possible from a transaction.

[0698] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0699] The system of the present invention improves the speed and accuracy of transactions by combining AI technology with a high-speed Internet communication network. A specific embodiment of this system will be described below.

[0700] Hardware and Software Used

[0701] The servers use machine learning frameworks such as TensorFlow and PyTorch to run machine learning algorithms.

[0702] The terminal is equipped with a communication module for collecting market data in real time via a 5G network.

[0703] The emotion engine includes software for recognizing emotions from a user's tone of voice, facial expressions, text input, etc.

[0704] System Operation

[0705] The server receives market data sent from the terminal and analyzes it in real time using machine learning algorithms. Specifically, it compares it with past market data and learns patterns. This learning is done using techniques such as regression analysis and clustering. The server generates optimal trading signals based on the learning results and sends them to the terminal.

[0706] The terminal collects market data in real time via the 5G network and transmits it to the server, minimizing trading delays. The terminal also collects user sentiment data through an emotion engine and transmits it to the server.

[0707] When trading, users communicate their emotions to the system through the emotion engine. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc. For example, if the user expresses joy, the emotion engine feeds that information back to the server, which then generates optimal trading signals by taking into account the results of market data analysis and the user's emotions.

[0708] Examples

[0709] As a concrete example, consider a scenario in which a user makes a trade. The user initiates a trade, and the device collects market data in real time through the 5G network and sends it to the server. The server uses TensorFlow to analyze the market data and compare it with past data to learn patterns. For example, it detects a pattern of a particular stock price rising. The server generates a "buy" trading signal and sends it to the device.

[0710] If the user expresses a happy emotion, the emotion engine feeds the information back to the server, which then takes that information into account and reevaluates the trading signal, for example by generating a signal to take more risk and gain more profit. The terminal then places a "buy" order on the stock exchange based on the new trading signal received from the server. The user then checks the results of the transaction and makes the next transaction.

[0711] Examples of prompt statements

[0712] “Describe a scenario in which a user initiates a trade and collects market data in real time over a 5G network. Include how the system updates trading signals if the user expresses happiness.”

[0713] In this way, the system enables personalized trading based on the user's emotions, improving the speed and accuracy of trading.

[0714] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0715] Step 1:

[0716] The terminal collects market data in real time through the 5G network. Specifically, it collects data such as stock prices, trading volume, and news articles. The collected data is sent from the terminal to the server. The input is the market data, and the output is the data sent to the server.

[0717] Step 2:

[0718] The server analyzes the received market data using machine learning algorithms. Specifically, it uses TensorFlow and PyTorch to compare it with past market data and learn patterns. This learning uses techniques such as regression analysis and clustering. The input is the market data, and the output is the learning results.

[0719] Step 3:

[0720] The server generates optimal trading signals based on the learning results. For example, it generates "buy" and "sell" signals. The generated trading signals are sent from the server to the terminal. The input is the learning results, and the output is the trading signal.

[0721] Step 4:

[0722] When a user makes a transaction, the user communicates his / her emotions to the system through the emotion engine. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc. The recognized emotion data is sent from the terminal to the server. The input is the user's emotion, and the output is emotion data.

[0723] Step 5:

[0724] The server receives the user's emotion data and adjusts the trading signal based on it. For example, if the user is anxious, it generates a signal to minimize risk. The adjusted trading signal is sent from the server to the terminal. The input is the emotion data, and the output is the adjusted trading signal.

[0725] Step 6:

[0726] The terminal executes transactions based on the trading signals received from the server, specifically by placing "buy" or "sell" orders on the stock exchange or trading platform. The input is the trading signal, and the output is the executed transaction.

[0727] In this way, the system enables personalized trading based on the user's emotions, improving the speed and accuracy of trading.

[0728] (Application example 2)

[0729] Next, a description will be given of Application Example 2 of Form 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".

[0730] In conventional trading systems, there were insufficient means to improve the speed and accuracy of trading, making it difficult to perform personalized trading based on user emotions. In addition, there were limited means to minimize transaction delays, making it difficult to maximize profits and minimize risks.

[0731] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0732] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for generating trading signals based on the user's emotions using an emotion engine that recognizes the user's emotions, a means for capturing the user's facial expressions using a smartphone camera and recognizing the emotions, a means for proposing optimal settlement methods and timing based on the emotions, and a means for generating trading signals based on emotions using a generative AI model. This improves the speed and accuracy of trading, and enables personalized trading according to the user's emotions, maximizing profits and minimizing risks.

[0733] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0734] "Real-time" refers to data processing and information acquisition occurring immediately.

[0735] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[0736] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[0737] "High-speed processing" refers to analyzing data quickly and obtaining results in a short period of time.

[0738] A "trading signal" is an instruction that indicates when and how to enter into a trade.

[0739] A "high-speed Internet communication network" refers to an Internet network that can send and receive data at extremely high speeds.

[0740] "Transaction Delay" means a time delay that occurs in the execution of a transaction.

[0741] An "emotion engine" is a system for recognizing a user's emotions and processing that information.

[0742] "Smartphone camera" refers to the photographic device built into a smartphone.

[0743] "Capturing facial expressions" refers to capturing a picture of a user's facial expression using a camera and acquiring the data.

[0744] "Emotion recognition" refers to determining the user's emotional state from acquired data.

[0745] "Payment Method" refers to the means or method of payment used in a Transaction.

[0746] "Timing" refers to the right time or moment to make a transaction or settlement.

[0747] A "generative AI model" refers to an artificial intelligence model that has been trained using machine learning or deep learning to perform a specific task.

[0748] The system for implementing this invention has the following configuration. The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses a machine learning algorithm to learn market data patterns and generates optimal trading signals based on the results. It also uses a high-speed Internet communication network (e.g., 5G network) to collect information and minimize delays in trading.

[0749] In addition, by combining an emotion engine that recognizes user emotions, trading signals are generated based on the user's emotions. The emotion engine uses the smartphone camera to capture the user's facial expressions and recognize emotions. Specifically, it detects faces using OpenCV and predicts the user's emotions using an emotion recognition model using Keras.

[0750] A generative AI model is used to suggest optimal settlement methods and timing based on emotions. The generative AI model takes user emotion data as input and generates appropriate trading signals. These trading signals are personalized according to the user's emotions, improving the speed and accuracy of trading while maximizing profits and minimizing risks.

[0751] As a concrete example, while a user is using a smartphone, the camera captures the user's facial expression and recognizes emotions. For example, if the user feels anxious, the app suggests "payment methods to minimize risk." An example of this prompt is "If the user feels anxious, please suggest the best payment method to minimize risk."

[0752] In this way, the server can combine AI technology with high-speed internet networks, emotion engines, smartphone cameras and generative AI models to improve the speed and accuracy of transactions and realize personalized transactions based on user emotions.

[0753] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0754] Step 1:

[0755] The server uses AI technology to analyze market data in real time. It takes market data as input, uses machine learning algorithms to learn patterns, and generates optimal trading signals based on the results. The output is the generated trading signals.

[0756] Step 2:

[0757] The server collects information using a high-speed Internet communication network. Specifically, it uses a 5G network to obtain market data and trading information in real time to minimize trading delays. The input is market data and trading information, and the output is trading information with low latency.

[0758] Step 3:

[0759] The device (smartphone) captures the user's facial expression using a camera. It receives the image from the camera as input, detects the face using OpenCV, and cuts out the facial area. The output is image data of the facial area.

[0760] Step 4:

[0761] The device inputs image data of the face region into an emotion recognition model to predict the user's emotion. Specifically, the device inputs image data into an emotion recognition model using Keras to obtain emotion prediction results. The output is predicted emotion data.

[0762] Step 5:

[0763] The server inputs the predicted emotion data into the generative AI model to generate emotion-based trading signals. The generative AI model generates optimal trading signals based on the emotion data and outputs them. The output is emotion-based trading signals.

[0764] Step 6:

[0765] The terminal presents the generated trading signals to the user. Specifically, the terminal displays the trading signals on the smartphone screen and suggests optimal settlement methods and timing to the user. The input is the generated trading signals, and the output is the trading signals presented to the user.

[0766] Step 7:

[0767] The user executes a transaction based on the presented transaction signal. The user operates a smartphone and executes the transaction according to the proposed settlement method and timing. The input is the presented transaction signal, and the output is the executed transaction.

[0768] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0769] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0770] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0771] 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 smart glasses 214.

[0772] [Third embodiment]

[0773] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0774] 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.

[0775] 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 wide area network (WAN) and / or a local area network (LAN).

[0776] 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.

[0777] 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 the voice according to instructions from the processor 46.

[0778] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0779] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0780] Fig. 6 shows an example of 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.

[0781] 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.

[0782] 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.

[0783] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0784] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0785] "Example 1"

[0786] The system of the present invention uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, deep learning is used as the AI ​​technology to learn patterns in market data and generate optimal trading signals based on the results. In addition, a high-speed Internet communication network is used to collect information and minimize trading delays. Specifically, a fiber optic network is used to collect trading information in real time and minimize trading delays. This makes it possible to improve the speed and accuracy of trading, maximize profits, and minimize risks.

[0787] "Example 2"

[0788] The system of the present invention improves the speed and accuracy of trading by combining AI technology and a high-speed Internet communication network. Specifically, machine learning is used as AI technology to learn market data patterns and generate optimal trading signals based on the results. In addition, a high-speed Internet communication network is used to collect information and minimize trading delays. Specifically, a 5G network is used to collect trading information in real time and minimize trading delays. This makes it possible to improve the speed and accuracy of trading, maximize profits, and minimize risks.

[0789] The process flow of each embodiment will be described below.

[0790] "Example 1"

[0791] Step 1: Analyze market data in real time using deep learning as an AI technology. This analysis allows you to understand market movements and trends.

[0792] Step 2: Generate optimal trading signals based on the analysis results. These trading signals indicate the timing and amount of buying and selling.

[0793] Step 3: Use a high-speed Internet network to collect information and minimize transaction delays. Specifically, use a fiber optic network to collect transaction information in real time and minimize transaction delays.

[0794] "Example 2"

[0795] Step 1: Analyze market data in real time using machine learning as an AI technology. This analysis allows you to understand market movements and trends.

[0796] Step 2: Generate optimal trading signals based on the analysis results. These trading signals indicate the timing and amount of buying and selling.

[0797] Step 3: Use high-speed Internet networks to collect information and minimize transaction delays. Specifically, 5G networks will be used to collect transaction information in real time and minimize transaction delays.

[0798] Example 1

[0799] Next, a description will be given of Example 1 of the embodiment 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".

[0800] Traditional market data analysis systems had problems with delays in collecting and processing real-time data, reducing the speed and accuracy of trading. They also lacked the ability to process huge amounts of data quickly, making it difficult to generate optimal trading signals. Furthermore, they did not provide sufficient means to maximize profits and minimize risks.

[0801] 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.

[0802] In this invention, the server includes a means for collecting market data in real time through an optical fiber network, a means for normalizing the collected market data and imputing missing values, a means for learning patterns of the market data using a deep learning framework, a means for generating optimal trading signals based on the learning results, and a means for delivering the generated trading signals to a user's terminal, thereby improving the speed and accuracy of trading, maximizing profits, and minimizing risks.

[0803] An "optical fiber network" is a communications network that uses optical fiber cables to enable high-speed, large-capacity data communication.

[0804] "Market data" refers to data that includes information such as prices, trading volumes, and trading times in financial markets for stocks, bonds, commodities, currencies, and the like.

[0805] "Normalization" is a process of constraining each data point within a certain range in order to unify the scale of the data.

[0806] "Missing value imputation" is the process of filling in missing values ​​in a dataset in an appropriate manner.

[0807] A "deep learning framework" is a software library for building and training neural networks, and includes TensorFlow and PyTorch.

[0808] "Pattern learning" is the process of extracting patterns and features from past data and building models to predict and classify future data.

[0809] A "trading signal" is an instruction indicating the timing of buying and selling in a financial market, and includes buying signals and selling signals.

[0810] "User terminal" means a device used by a user to receive trading signals and execute transactions, including a personal computer or smartphone.

[0811] This invention is a system that uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses deep learning to learn market data patterns and generates optimal trading signals based on the results. It also uses high-speed Internet communication networks to collect information and minimize delays in trading.

[0812] The server collects market data in real time through a fiber optic network. The collected market data is processed by a high-performance server equipped with NVIDIA GPUs. The server learns the patterns of the market data using deep learning frameworks such as TensorFlow and PyTorch. Based on the learning results, the server generates optimal trading signals.

[0813] Specifically, the server processes the data in the following steps:

[0814] 1. Market data collection: The server collects market data in real time through the fiber optic network, for example, by using APIs from financial market data providers.

[0815] 2. Data preprocessing: Normalize the collected data and impute missing values. For example, use the Pandas library to manipulate data frames and impute missing values ​​with the mean.

[0816] 3. Training the model: Build a neural network using TensorFlow or PyTorch to learn patterns from historical market data, for example using a LSTM (Long Short-Term Memory) network to train time series data.

[0817] 4. Generate trading signals: Generate optimal trading signals based on the learning results. For example, if the model judges it to be a "buy" signal, generate that signal.

[0818] 5. Signal Delivery: The generated trading signals are delivered to the users' terminals, for example, in real time using WebSocket or REST API.

[0819] Users can execute trades based on the trading signals they receive, improving the speed and accuracy of their trades, maximizing profits and minimizing risks.

[0820] As a concrete example, consider the following scenario:

[0821] A user wants to analyze stock market data in real time and receive optimal trading signals. The user's terminal receives the trading signals distributed by the server and executes the trades.

[0822] Example prompt:

[0823] "Analyze real-time stock market data and generate optimal trading signals. The data used is stock price data and trading volume data from the past year. Deliver the generated trading signals in real time."

[0824] The system enables users to improve the speed and accuracy of their transactions, maximizing profits and minimizing risks.

[0825] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0826] Step 1:

[0827] The server collects market data in real time through the fiber optic network. Specifically, it retrieves data from financial market data providers using APIs. The input is the API request and the output is the retrieved market data. For example, the server sends an API request and receives data such as stock prices and trading volumes.

[0828] Step 2:

[0829] The server preprocesses the collected market data by normalizing the data, imputing missing values, and detecting and correcting outliers. The input is the collected market data, and the output is the preprocessed data. For example, the server uses the Pandas library to manipulate data frames, impute missing values ​​with the mean, and normalize the data.

[0830] Step 3:

[0831] The server trains a deep learning model using the preprocessed data. Specifically, it uses TensorFlow or PyTorch to build a neural network and learn patterns from past market data. The input is the preprocessed data, and the output is the trained model. For example, the server uses an LSTM (long short-term memory) network to train time series data.

[0832] Step 4:

[0833] The server generates optimal trading signals based on the learning results. Specifically, it inputs real-time market data into the trained model and outputs buy and sell signals. The inputs are real-time market data and the trained model, and the output is a trading signal. For example, if the model judges it to be a "buy" signal, it generates that signal.

[0834] Step 5:

[0835] The server distributes the generated trading signals to the user's terminal. Specifically, it transmits signals in real time using WebSocket or REST API. The input is the generated trading signal, and the output is the signal distributed to the user's terminal. The user's terminal displays the received signal and executes the transaction.

[0836] (Application example 1)

[0837] Next, a description will be given of Application Example 1 of Form 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".

[0838] Conventional market data analysis systems have difficulty processing data in real time, which often results in delays in trading. They also cannot generate trading signals optimized for each user, which results in a lack of accuracy and speed in trading. Furthermore, they are not user-friendly because they do not take into account the use on mobile devices such as smartphones.

[0839] 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. In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for learning the trading patterns of users and generating individually optimized trading signals, and a means including an application installed on a smartphone. This enables real-time market data analysis and generation of trading signals, improves the speed and accuracy of trading, and makes it possible to provide trading signals optimized for each user.

[0840] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0841] "Real-time" refers to data collection and processing occurring immediately.

[0842] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[0843] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[0844] "High-speed processing" refers to analyzing data quickly and outputting results in a short period of time.

[0845] A "trading signal" refers to an instruction or sign that indicates the timing and direction of a transaction.

[0846] A "high-speed Internet network" refers to an Internet connection that can send and receive data very quickly.

[0847] "Information gathering" refers to collecting necessary data or information.

[0848] "Transaction Delay" refers to the delay in time before a transaction is executed.

[0849] "User trading patterns" refers to the user's past trading tendencies and behavioral patterns.

[0850] "Individually Optimized Trading Signals" refers to trading signals that are optimized based on the trading patterns of each individual User.

[0851] "Applications installed on a smartphone" refers to software programs that run on a smartphone.

[0852] In order to implement the present invention, the following system configuration is necessary.

[0853] First, the server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses deep learning models to learn patterns in market data and generate optimal trading signals based on the results. The server achieves this using software such as Python, Keras, Requests, Pandas, and NumPy.

[0854] The servers then collect the information over high-speed Internet connections to minimize trading delays, using fiber optic networks to collect market data in real time for immediate analysis.

[0855] In addition, the server learns the user's trading patterns and generates individually optimized trading signals, which makes it possible to provide optimal trading timing for each user. The user's trading patterns are learned by a deep learning model based on past trading data.

[0856] The application installed on a smartphone receives trading signals sent from the server and notifies the user. Through this application, users can check market data in real time and know the optimal timing for trading.

[0857] As a concrete example, a scenario is when a user opens the smartphone app "Real-time Settlement Analyst" and checks current market data in real time. The app learns the user's trading patterns and notifies them of the optimal timing to trade. For example, a notification such as "In the current market conditions, it is a good time to buy" is displayed.

[0858] An example of a prompt sentence might be:

[0859] Develop a smartphone app that analyzes market data in real time and notifies users of the best time to trade. Use deep learning to learn users' trading patterns and generate individually optimized trading signals. Use high-speed internet networks to collect trading information in real time and minimize delays.

[0860] In this way, the combination of servers, smartphone applications and high-speed internet networks allows for real-time market data analysis and generation of trading signals, improving the speed and accuracy of trading.

[0861] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0862] Step 1:

[0863] The server collects market data in real time. Specifically, it obtains market data through APIs and collects information such as prices, trading volumes, and trading history. The input is the response from the market data API, and the output is the collected market data.

[0864] Step 2:

[0865] The server pre-processes the collected market data, specifically by filtering it down to prices and volumes and converting it into numerical data. The input is the market data collected in step 1, and the output is the pre-processed data.

[0866] Step 3:

[0867] The server uses the preprocessed data to train a deep learning model, specifically, a long short-term memory (LSTM) network to learn patterns in the market data. The input is the preprocessed data, and the output is the trained deep learning model.

[0868] Step 4:

[0869] The server generates trading signals using a trained deep learning model. Specifically, the model inputs market data and outputs signals that indicate the timing and direction of trading. The input is real-time market data and the output is trading signals.

[0870] Step 5:

[0871] The server transmits the trading signals to the smartphone using a high-speed internet communication network. Specifically, the communication is performed using a fiber optic network with minimal latency. The input is the trading signal, and the output is the signal notification to the smartphone.

[0872] Step 6:

[0873] The smartphone notifies the user of the received trading signal. Specifically, it notifies the user of the timing of trading using push notifications and in-app notifications. The input is the trading signal sent from the server, and the output is the notification to the user.

[0874] Step 7:

[0875] Users check market data in real time through a smartphone app and trade based on trading signals. Specifically, they view market data within the app and execute trades according to the trading timing notified to them. The input is market data and trading signals from the smartphone app, and the output is the user's trading action.

[0876] In this way, the combination of servers, smartphone applications and high-speed internet networks allows for real-time market data analysis and generation of trading signals, improving the speed and accuracy of trading.

[0877] Example 2

[0878] Next, a second embodiment of the second form will be described. 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".

[0879] Conventional trading systems lacked speed and accuracy, making it difficult to maximize profits and minimize risks. In addition, real-time information collection was insufficient, resulting in delays in trading. This often caused users to miss the optimal timing for trading, reducing trading efficiency.

[0880] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0881] In this invention, the server includes a means for collecting market data, a means for pre-processing the collected data, a means for training a machine learning model, a means for generating trading signals, a means for collecting information in real time using a high-speed Internet communication network, a means for notifying a user of the generated trading signals, and a means for the user to execute a trade, thereby improving the speed and accuracy of trading, maximizing profits, and minimizing risks.

[0882] "Market data" refers to information such as stock prices, trading volumes, and economic indicators in financial markets.

[0883] "Preprocessing" refers to the process of filling in missing values ​​in collected data, removing outliers, normalizing the data, etc.

[0884] A "machine learning model" refers to an algorithm that learns patterns from data and makes predictions and classifications.

[0885] A "trading signal" refers to information generated by a machine learning model that recommends trading at a specific time.

[0886] "High-speed Internet network" refers to infrastructure that enables high-speed and stable data communication.

[0887] "Real-time information" refers to information that immediately reflects current market conditions.

[0888] "User" refers to an individual or legal entity that conducts transactions using this system.

[0889] "Notification" refers to the act of informing a User of a generated trading signal.

[0890] "Execution of a transaction" refers to the act of a user actually buying or selling a financial product based on a trading signal.

[0891] This invention is a system that improves the speed and accuracy of transactions by combining AI technology and high-speed Internet communication networks. Below, we will explain how to specifically implement this system.

[0892] The server first collects market data. Specifically, it uses a common data collection API to obtain past transaction data from financial markets. For example, it can use the Yahoo Finance API or Alpha Vantage API. This allows it to collect data such as stock prices, trading volumes, and economic indicators.

[0893] The server then preprocesses the collected data, using Python's Pandas library to impute missing values, remove outliers, and normalize the data, improving data quality and making it more effective for training machine learning models.

[0894] The server then trains a machine learning model, using TensorFlow or Scikit-learn to learn patterns from past market data. For example, a Long Short-Term Memory (LSTM) model can be used to forecast time series data, which can then predict future market trends and generate optimal trading signals.

[0895] After a trading signal is generated, the terminal collects information in real time using a high-speed Internet communication network, specifically using Qualcomm's 5G modem to achieve high speed and stable communication, which can minimize delays in trading.

[0896] The generated trading signals are notified to the user through the terminal. The signals are displayed in real time through the mobile app and desktop app. By receiving notifications, users can quickly execute trades.

[0897] Users execute trades based on the trading signals they receive. They access the trading platform and buy or sell stocks according to the signals generated by AI, which improves the speed and accuracy of trading, maximizing profits and minimizing risks.

[0898] As a concrete example, consider a user trading on the stock market. The server uses the Yahoo Finance API to collect stock price data for the past year. It then uses the Pandas library to impute missing values ​​in the data and remove outliers. It then uses TensorFlow to train an LSTM model to predict which stocks are likely to rise in the next week. The server generates a buy signal based on this prediction, and the terminal notifies the user of this signal in real time over the 5G network. When the user receives the notification, he or she accesses the trading platform and purchases stocks according to the signal.

[0899] Example of a prompt to be input to a generative AI model:

[0900] "Predict which stocks are likely to rise in the next week based on stock price data from the past year."

[0901] The system improves the speed and accuracy of trading, allowing users to maximize profits and minimize risks.

[0902] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0903] Step 1:

[0904] The server collects market data. Specifically, it uses a common data collection API to obtain historical trading data from financial markets. For example, it uses Yahoo Finance API or Alpha Vantage API. The input is the API request, and the output is data such as stock prices, trading volumes, and economic indicators.

[0905] Step 2:

[0906] The server preprocesses the collected data. Specifically, it uses Python's Pandas library to impute missing values ​​in the data, remove outliers, and normalize the data. The input is the collected market data, and the output is the preprocessed data.

[0907] Step 3:

[0908] The server trains a machine learning model using the preprocessed data. Specifically, it uses TensorFlow and Scikit-learn to learn patterns from past market data. For example, it uses a Long Short-Term Memory (LSTM) model to make predictions on time series data. The input is the preprocessed data, and the output is the trained machine learning model.

[0909] Step 4:

[0910] The server uses the trained model to generate trading signals, specifically, buy and sell signals based on the stock price trends predicted by the model. The inputs are the trained machine learning model and real-time market data, and the output is trading signals.

[0911] Step 5:

[0912] The terminal collects information in real time using a high-speed Internet communication network. Specifically, it uses Qualcomm's 5G modem to achieve high-speed and stable communication. The input is real-time market data, and the output is the collected real-time information.

[0913] Step 6:

[0914] The terminal notifies the user of the generated trading signals, specifically by displaying the signals in real time through a mobile or desktop app. The input is the trading signal, and the output is the notification to the user.

[0915] Step 7:

[0916] The user executes a trade based on the notified trading signal. Specifically, the user accesses the trading platform and buys or sells stocks according to the signal generated by the AI. The input is the trading signal and the trading platform, and the output is the executed trade.

[0917] (Application example 2)

[0918] Next, a description will be given of Application Example 2 of Form 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".

[0919] Traditional electronic settlement systems did not ensure sufficient speed and accuracy of transactions, making it difficult for users to settle at the optimal time. In addition, there was a lack of means to analyze market data in real time and generate optimal trading signals, making it difficult to maximize profits and minimize risks. In addition, delays in transactions were common, often impairing the user experience.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0921] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for proposing optimal timing and method for a user to make an electronic settlement, a means for analyzing market data using a machine learning model and collecting information in real time, and a means including an application to be installed on a smartphone, thereby improving the speed and accuracy of trading and enabling a user to make a settlement at an optimal timing.

[0922] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[0923] "Real-time" refers to data collection and processing occurring immediately.

[0924] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[0925] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[0926] "High-speed processing" refers to analyzing data quickly and obtaining results in a short period of time.

[0927] A "trading signal" is an instruction that indicates the best time or manner to enter into a trade.

[0928] A "high-speed Internet network" refers to an Internet connection that allows for extremely fast data transmission and reception, and specifically includes 5G networks.

[0929] "Information gathering" refers to the act of collecting necessary data or information.

[0930] "Transaction Delay" refers to the delay in time before a transaction is executed.

[0931] "User" refers to any individual or legal entity that uses this system.

[0932] "Electronic payment" refers to the payment of money via the Internet.

[0933] The "optimal time" refers to the most favorable time to carry out a transaction or settlement.

[0934] "Suggesting a solution" means showing the user the optimal course of action or means.

[0935] A "machine learning model" refers to an algorithm that learns patterns from data and makes predictions and classifications.

[0936] A "smartphone" refers to a mobile phone that can connect to the Internet and run applications.

[0937] "Application" refers to a software program for achieving a specific function.

[0938] A system for implementing the present invention is configured as follows.

[0939] System Configuration

[0940] The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses machine learning models to analyze market data and generate optimal trading signals, which can improve the speed and accuracy of trading.

[0941] The terminal (smartphone) collects information using a high-speed Internet communication network (5G network) to minimize transaction delays. An application is installed that suggests the optimal timing and method for users to make electronic payments. The application collects market data in real time and uses machine learning models to notify users of the optimal timing for payment.

[0942] Hardware and Software Used

[0943] Hardware: 5G-enabled smartphone

[0944] Software: Python, Requests library, Scikit-learn library

[0945] Data processing and calculation

[0946] The server collects market data through APIs and analyzes the data using machine learning models. Specifically, the machine learning model is trained based on the collected market data to generate optimal trading signals in real time. The terminal receives market data in real time through the 5G network and notifies users of the optimal settlement timing through the application.

[0947] Examples

[0948] For example, when a user is shopping online, the application will notify them of the optimal time to make a payment, allowing them to make a payment with the most favorable exchange rate and fees.

[0949] Examples of prompt statements

[0950] "Develop an application that collects the latest market data and notifies the optimal settlement timing in real time. Analyze market data using machine learning models and collect information in real time over 5G networks."

[0951] The above is an embodiment of the present invention. This system improves the speed and accuracy of transactions, allowing users to make payments at the optimal timing.

[0952] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0953] Step 1:

[0954] The server collects market data. Specifically, it gets the latest market data through API. The input is the market data from API, and the output is the collected market data. This data includes prices, trading volumes, trading history, etc.

[0955] Step 2:

[0956] The server trains a machine learning model based on the collected market data. Specifically, it preprocesses the data, extracts features, and inputs them into the machine learning algorithm. The input is the collected market data, and the output is the trained machine learning model. This model is used to predict future market trends.

[0957] Step 3:

[0958] The server analyzes market data in real time and generates optimal trading signals. Specifically, it uses a trained machine learning model to input current market data and output trading signals. The input is real-time market data and the output is trading signals.

[0959] Step 4:

[0960] The terminal (smartphone) receives market data in real time using a high-speed Internet communication network (5G network). Specifically, it receives market data sent from the server and inputs it into the application. The input is the market data from the server, and the output is the data passed to the application.

[0961] Step 5:

[0962] The terminal notifies the user of the optimal timing for payment through the application. Specifically, the application analyzes transaction signals and sends notifications to the user. The input is the transaction signal, and the output is the notification to the user. Based on this notification, the user can make electronic payments at the optimal time.

[0963] Step 6:

[0964] The user receives a notification from the application and makes an electronic payment at the optimal time. Specifically, the user executes the payment according to the notification and completes the transaction. The input is the notification from the application, and the output is the completed electronic payment.

[0965] These are the specific processing steps of this system, which will improve the speed and accuracy of transactions and enable users to make payments at the optimal timing.

[0966] In addition, 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.

[0967] "Example 1"

[0968] In one embodiment of the present invention, a system using AI technology and a high-speed Internet communication network is combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and improves the speed and accuracy of trading based on the emotions. For example, if a user shows an emotion of joy, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates optimal trading signals taking into account the analysis results of market data and the user's emotions. This allows for more personalized trading according to the user's emotions. The emotion engine also maximizes profits and minimizes risks based on the user's emotions. For example, if a user shows an emotion of anxiety, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates trading signals to minimize risks. This allows for safer trading according to the user's emotions.

[0969] "Example 2"

[0970] In one embodiment of the present invention, a system using AI technology and a high-speed Internet communication network is combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and improves the speed and accuracy of trading based on the emotions. For example, if a user shows an emotion of joy, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates optimal trading signals taking into account the analysis results of market data and the user's emotions. This allows for more personalized trading according to the user's emotions. The emotion engine also maximizes profits and minimizes risks based on the user's emotions. For example, if a user shows an emotion of anxiety, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates trading signals to minimize risks. This allows for safer trading according to the user's emotions.

[0971] The process flow of each embodiment will be described below.

[0972] "Example 1"

[0973] Step 1: A user starts trading with the system. At this point, the emotion engine begins to recognize emotions from the user's tone of voice, facial expressions, text input, etc.

[0974] Step 2: The sentiment engine feeds the recognized sentiment back to the AI ​​technology, which combines this sentiment information with real-time market data to generate trading signals.

[0975] Step 3: The generated trading signal is sent to the exchange via high-speed internet communication networks, which increases the speed and accuracy of trading.

[0976] Step 4: After the transaction is completed, the system will provide the user with feedback on the transaction results. This feedback will be provided in a form that takes into account the user's emotions and will be optimized according to the user's emotions.

[0977] Example 1

[0978] Next, a description will be given of Example 1 of the embodiment 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".

[0979] Conventional market data analysis systems had problems with delays in trading and reduced accuracy in real-time data processing and generation of trading signals. In addition, trading signals were not generated taking into account the user's emotions, making it difficult to perform personalized trading according to the user's psychological state. This resulted in insufficient improvement in trading speed and accuracy, making it difficult to maximize profits and minimize risks.

[0980] 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.

[0981] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for recognizing user emotions, and a means for adjusting trading signals based on the recognized emotion information, thereby improving the speed and accuracy of trading and enabling personalized trading according to the user's emotions.

[0982] "AI technology" is a technology that uses artificial intelligence to analyze data and perform pattern recognition and predictions.

[0983] "Real-time" refers to data collection and processing occurring immediately, without delay.

[0984] "Market Data" refers to data relating to financial markets, such as stock prices, exchange rates, and trading volumes.

[0985] "High-speed Internet network" refers to communications infrastructure that can send and receive data at high speeds, such as optical fiber networks.

[0986] "Trading signals" refer to instructions that indicate when to buy or sell based on the analysis of market data.

[0987] "User emotion" is information that indicates the user's psychological state, and is recognized from the tone of voice, facial expression, text input, and the like.

[0988] "Emotion information" refers to data obtained as a result of analyzing a user's emotions.

[0989] "Personalized transactions" refer to transactions that are customized according to the user's individual emotions and circumstances.

[0990] This invention is a system that uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, the various elements of the server, terminal, and user work together.

[0991] The server collects market data in real time using a high-speed internet communication network. The use of a fiber optic network minimizes delays in trading information. The collected market data is input into a deep learning model on the server. The deep learning model uses frameworks such as TensorFlow and PyTorch. This allows the server to learn patterns in the market data and generate optimal trading signals.

[0992] The device is equipped with an emotion engine that recognizes emotions from the user's tone of voice, facial expressions, text input, etc. The emotion engine uses technologies such as OpenCV and Google Cloud Speech-to-Text API to analyze the user's emotions in real time. The analysis results are fed back from the device to the server.

[0993] The server re-evaluates the market data analysis results based on the feedback sentiment information and adjusts the trading signals. For example, if the user is expressing happiness, the server generates aggressive trading signals. On the other hand, if the user is expressing anxiety, the server generates trading signals that minimize risk.

[0994] As a concrete example, when a user is using a trading application, the terminal analyzes the user's tone of voice to detect the emotion of happiness. The terminal transmits this information to a server, which takes into account the analysis of market data and the user's emotion to generate aggressive trading signals. The user then makes trades based on these signals to maximize profits.

[0995] An example prompt is, "Please explain how trading signals would be adjusted if a user exhibits the emotion of happiness while using a trading application."

[0996] The system allows users to make personalized trades based on market data and their own emotions, improving the speed and accuracy of trading.

[0997] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0998] Step 1:

[0999] The server collects market data in real time using high-speed Internet communication networks. Specifically, it uses optical fiber networks to obtain data from exchanges and financial institutions. As input, it receives market data such as stock prices, exchange rates, and trading volumes, and as output, it stores these data in a database.

[1000] Step 2:

[1001] The server pre-processes the collected market data, i.e. imputing missing values, removing outliers, normalizing data, etc. It takes the stored market data as input and produces the pre-processed data as output, which improves the quality of the data.

[1002] Step 3:

[1003] The server inputs the preprocessed data into a deep learning model. Specifically, it uses TensorFlow or PyTorch to build a LSTM (long short-term memory) network or a CNN (convolutional neural network) to learn patterns in the data. It takes the preprocessed market data as input and generates a trained model as output.

[1004] Step 4:

[1005] The server uses the trained model to generate optimal trading signals, specifically, buy and sell signals based on predicted price movements and trends. It receives the trained model and real-time market data as inputs and generates trading signals as output. The signals are sent to users' trading applications in real-time.

[1006] Step 5:

[1007] The device recognizes emotions from the user's tone of voice, facial expressions, text input, etc. Specifically, it uses OpenCV to analyze facial expressions from camera footage, converts voice data to text using Google Cloud Speech-to-Text API, and analyzes emotions. It receives the user's voice and video data as input, and generates emotional information as output.

[1008] Step 6:

[1009] The device feeds back the recognized emotion information to the server. Specifically, the emotion engine sends the analyzed emotion data to the server in JSON format. The device receives emotion information as input and generates data to be sent to the server as output.

[1010] Step 7:

[1011] The server re-evaluates the analysis results of the market data based on the feedback emotion information and adjusts the trading signal. For example, if the user indicates happiness, the server generates an aggressive trading signal. On the other hand, if the user indicates anxiety, the server generates a trading signal that minimizes risk. It takes emotion information and market data as inputs and generates an adjusted trading signal as output.

[1012] (Application example 1)

[1013] Next, a description will be given of Application Example 1 of Form 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 trading systems were unable to take into account user sentiment when analyzing market data and generating trading signals, making it difficult to provide personalized trading advice. There was also room for improvement in terms of trading delays and risk management. This resulted in a suboptimal user trading experience, and the maximization of profits and minimization of risks were not fully achieved.

[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 analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for adjusting trading signals based on user emotions using an emotion engine that recognizes user emotions, and a means for integrating market data and user emotions using an application installed on a smartphone to provide trading advice. This makes it possible to provide personalized trading advice according to the user's emotions, improve the speed and accuracy of trading, maximize profits, and minimize risks.

[1017] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[1018] "Real-time" refers to data acquisition and processing occurring immediately.

[1019] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[1020] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[1021] "High-speed processing" refers to quickly analyzing data and performing calculations.

[1022] A "trading signal" is an instruction that indicates the timing and direction of buying and selling when conducting a transaction.

[1023] A "high-speed Internet communication network" is an Internet network that allows data to be sent and received at high speeds.

[1024] "Information gathering" refers to the act of collecting necessary data or information.

[1025] "Transaction Delay" means a time delay that occurs in the execution of a transaction.

[1026] An "emotion engine" is a technology that recognizes emotions from a user's tone of voice, facial expressions, text input, etc.

[1027] "User emotion" refers to the psychological state, such as joy, anxiety, anger, etc., felt by the user.

[1028] A "smartphone" is a type of mobile phone, a multi-function device that can connect to the Internet and use applications.

[1029] An "application" is a software program designed to provide a specific function or service.

[1030] "Trading Advice" is information that provides information and recommendations for making decisions when conducting transactions.

[1031] "Personalized" means customized to the characteristics and preferences of an individual user.

[1032] "Transaction speed" refers to how quickly a transaction is executed.

[1033] "Transaction accuracy" refers to a transaction being executed exactly as intended.

[1034] "Profit maximization" refers to maximizing the profits gained from a transaction.

[1035] "Minimizing risk" means minimizing the losses and dangers involved in trading.

[1036] A system for implementing the present invention is configured as follows.

[1037] The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, a Python program uses Keras (a deep learning library) to learn patterns in the market data and generate optimal trading signals. Market data is obtained from the API using Requests (an HTTP request library) and converted into a Pandas data frame.

[1038] The server also collects information via high-speed Internet communication networks to minimize transaction delays, and transmits and receives data via fiber optic networks.

[1039] In addition, the server adjusts trading signals based on the user's emotions using an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's images using OpenCV (an image processing library) and recognizes emotions using a Keras model.

[1040] An application is installed on the smartphone as a terminal. This application integrates market data obtained from a server with the user's emotions and provides trading advice. The user takes a picture of their face using the smartphone's camera, and the application uses the image to recognize emotions. Market data is obtained in real time, and optimal trading signals are generated based on the user's emotions.

[1041] For example, a user can take a picture of their face with their smartphone camera, and the app will use the image to recognize emotions. The app will then obtain market data in real time and generate optimal trading signals based on the user's emotions. This will enable the app to provide personalized trading advice based on the user's emotions.

[1042] An example of a prompt is as follows:

[1043] "Users take a picture of their face with their smartphone camera and the app uses the image to recognize emotions. It pulls market data in real time and generates optimal trading signals based on the user's emotions."

[1044] In this way, it is possible to provide personalized trading advice based on the user's emotions, improve the speed and accuracy of trading, maximize profits and minimize risks.

[1045] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1046] Step 1:

[1047] The server retrieves market data from the API using the Requests library. The input is the API endpoint and the output is a JSON response with market data. This data is converted to a Pandas dataframe.

[1048] Step 2:

[1049] The server normalizes the market data converted to a Pandas dataframe. The input is a dataframe of market data and the output is the normalized market data. The data is scaled using StandardScaler.

[1050] Step 3:

[1051] The server generates trading signals from the normalized market data using a Keras model. The input is the normalized market data and the output is the trading signal. The data is fed into the Keras model and prediction results are obtained.

[1052] Step 4:

[1053] A user takes a picture of their face using the smartphone camera. The input is the user's face image, and the output is an image file. This image is used for emotion recognition.

[1054] Step 5:

[1055] The device preprocesses the user's face image using OpenCV. The input is the user's face image and the output is the preprocessed image. It resizes and normalizes the image.

[1056] Step 6:

[1057] The device recognizes the user's emotion from the image preprocessed using the Keras model. The input is the preprocessed image, and the output is the user's emotion label. Input the image to the Keras model to obtain the emotion prediction result.

[1058] Step 7:

[1059] The server receives the user's emotion label and adjusts the trading signal. The input is the trading signal and the user's emotion label, and the output is the adjusted trading signal. The server increases or decreases the trading signal based on the emotion label.

[1060] Step 8:

[1061] The terminal displays the adjusted trading signals to the user. The input is the adjusted trading signals and the output is trading advice to the user. The trading advice is displayed on the smartphone screen.

[1062] In this way, it is possible to provide trading advice that is personalized according to the user's emotions.

[1063] Example 2

[1064] Next, a second embodiment of the second form will be described. 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".

[1065] Conventional trading systems had insufficient means to improve the speed and accuracy of transactions, and in particular had the problem of being unable to trade while taking into account the user's emotions. There was also room for improvement in terms of transaction delays and risk management. This made it difficult to maximize users' profits and minimize their risks.

[1066] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1067] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for collecting user emotion data using an emotion engine that recognizes user emotions, and a means for adjusting trading signals based on the user emotion data. This improves the speed and accuracy of trading, and enables personalized trading according to the user's emotions, maximizing profits and minimizing risks.

[1068] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[1069] "Market Data" is data relating to financial markets, such as stock prices, trading volumes, and news articles.

[1070] A "trading signal" is an instruction that indicates the timing and direction of a transaction, and refers to a specific action such as "buy" or "sell."

[1071] A "high-speed Internet network" is a communications infrastructure for sending and receiving data at high speeds, specifically referring to 5G networks.

[1072] An "emotion engine" is a technology that recognizes emotions from a user's tone of voice, facial expressions, text input, etc.

[1073] "User emotion data" is data relating to the user's emotion recognized by the emotion engine.

[1074] "Personalized trading" refers to trading that is optimized according to the user's individual emotions and circumstances.

[1075] "Minimizing risk" means making the chances of loss in a transaction as low as possible.

[1076] "Maximizing profits" means making the most profit possible from a transaction.

[1077] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1078] The system of the present invention improves the speed and accuracy of transactions by combining AI technology with a high-speed Internet communication network. A specific embodiment of this system will be described below.

[1079] Hardware and Software Used

[1080] The servers use machine learning frameworks such as TensorFlow and PyTorch to run machine learning algorithms.

[1081] The terminal is equipped with a communication module for collecting market data in real time via a 5G network.

[1082] The emotion engine includes software for recognizing emotions from a user's tone of voice, facial expressions, text input, etc.

[1083] System Operation

[1084] The server receives market data sent from the terminal and analyzes it in real time using machine learning algorithms. Specifically, it compares it with past market data and learns patterns. This learning is done using techniques such as regression analysis and clustering. The server generates optimal trading signals based on the learning results and sends them to the terminal.

[1085] The terminal collects market data in real time via the 5G network and transmits it to the server, minimizing trading delays. The terminal also collects user sentiment data through an emotion engine and transmits it to the server.

[1086] When trading, users communicate their emotions to the system through the emotion engine. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc. For example, if the user expresses joy, the emotion engine feeds that information back to the server, which then generates optimal trading signals by taking into account the results of market data analysis and the user's emotions.

[1087] Examples

[1088] As a concrete example, consider a scenario in which a user makes a trade. The user initiates a trade, and the device collects market data in real time through the 5G network and sends it to the server. The server uses TensorFlow to analyze the market data and compare it with past data to learn patterns. For example, it detects a pattern of a particular stock price rising. The server generates a "buy" trading signal and sends it to the device.

[1089] If the user expresses a happy emotion, the emotion engine feeds the information back to the server, which then takes that information into account and reevaluates the trading signal, for example by generating a signal to take more risk and gain more profit. The terminal then places a "buy" order on the stock exchange based on the new trading signal received from the server. The user then checks the results of the transaction and makes the next transaction.

[1090] Examples of prompt statements

[1091] “Describe a scenario in which a user initiates a trade and collects market data in real time over a 5G network. Include how the system updates trading signals if the user expresses happiness.”

[1092] In this way, the system enables personalized trading based on the user's emotions, improving the speed and accuracy of trading.

[1093] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1094] Step 1:

[1095] The terminal collects market data in real time through the 5G network. Specifically, it collects data such as stock prices, trading volume, and news articles. The collected data is sent from the terminal to the server. The input is the market data, and the output is the data sent to the server.

[1096] Step 2:

[1097] The server analyzes the received market data using machine learning algorithms. Specifically, it uses TensorFlow and PyTorch to compare it with past market data and learn patterns. This learning uses techniques such as regression analysis and clustering. The input is the market data, and the output is the learning results.

[1098] Step 3:

[1099] The server generates optimal trading signals based on the learning results. For example, it generates "buy" and "sell" signals. The generated trading signals are sent from the server to the terminal. The input is the learning results, and the output is the trading signal.

[1100] Step 4:

[1101] When a user makes a transaction, the user communicates his / her emotions to the system through the emotion engine. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc. The recognized emotion data is sent from the terminal to the server. The input is the user's emotion, and the output is emotion data.

[1102] Step 5:

[1103] The server receives the user's emotion data and adjusts the trading signal based on it. For example, if the user is anxious, it generates a signal to minimize risk. The adjusted trading signal is sent from the server to the terminal. The input is the emotion data, and the output is the adjusted trading signal.

[1104] Step 6:

[1105] The terminal executes transactions based on the trading signals received from the server, specifically by placing "buy" or "sell" orders on the stock exchange or trading platform. The input is the trading signal, and the output is the executed transaction.

[1106] In this way, the system enables personalized trading based on the user's emotions, improving the speed and accuracy of trading.

[1107] (Application example 2)

[1108] Next, a description will be given of Application Example 2 of Form 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".

[1109] In conventional trading systems, there were insufficient means to improve the speed and accuracy of trading, making it difficult to perform personalized trading based on user emotions. In addition, there were limited means to minimize transaction delays, making it difficult to maximize profits and minimize risks.

[1110] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1111] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for generating trading signals based on the user's emotions using an emotion engine that recognizes the user's emotions, a means for capturing the user's facial expressions using a smartphone camera and recognizing the emotions, a means for proposing optimal settlement methods and timing based on the emotions, and a means for generating trading signals based on emotions using a generative AI model. This improves the speed and accuracy of trading, and enables personalized trading according to the user's emotions, maximizing profits and minimizing risks.

[1112] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[1113] "Real-time" refers to data processing and information acquisition occurring immediately.

[1114] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[1115] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[1116] "High-speed processing" refers to analyzing data quickly and obtaining results in a short period of time.

[1117] A "trading signal" is an instruction that indicates when and how to enter into a trade.

[1118] A "high-speed Internet network" refers to an Internet network that can send and receive data at extremely high speeds.

[1119] "Transaction Delay" means a time delay that occurs in the execution of a transaction.

[1120] An "emotion engine" is a system for recognizing a user's emotions and processing that information.

[1121] "Smartphone camera" refers to the photographic device built into a smartphone.

[1122] "Capturing facial expressions" refers to taking pictures of the user's facial expressions using a camera and acquiring them as data.

[1123] "Emotion recognition" refers to determining the user's emotional state from acquired data.

[1124] "Payment Method" refers to the means or method of payment used in a Transaction.

[1125] "Timing" refers to the right time or moment to make a transaction or settlement.

[1126] A "generative AI model" refers to an artificial intelligence model that has been trained using machine learning or deep learning to perform a specific task.

[1127] The system for implementing this invention has the following configuration. The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses a machine learning algorithm to learn market data patterns and generates optimal trading signals based on the results. It also uses a high-speed Internet communication network (e.g., 5G network) to collect information and minimize delays in trading.

[1128] In addition, by combining an emotion engine that recognizes user emotions, trading signals are generated based on the user's emotions. The emotion engine uses the smartphone camera to capture the user's facial expressions and recognize emotions. Specifically, it detects faces using OpenCV and predicts the user's emotions using an emotion recognition model using Keras.

[1129] A generative AI model is used to suggest optimal settlement methods and timing based on emotions. The generative AI model takes user emotion data as input and generates appropriate trading signals. These trading signals are personalized according to the user's emotions, improving the speed and accuracy of trading while maximizing profits and minimizing risks.

[1130] As a concrete example, while a user is using a smartphone, the camera captures the user's facial expression and recognizes emotions. For example, if the user feels anxious, the app suggests "payment methods to minimize risk." An example of this prompt is "If the user feels anxious, please suggest the best payment method to minimize risk."

[1131] In this way, the server can combine AI technology with high-speed internet networks, emotion engines, smartphone cameras and generative AI models to improve the speed and accuracy of transactions and realize personalized transactions based on user emotions.

[1132] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1133] Step 1:

[1134] The server uses AI technology to analyze market data in real time. It takes market data as input, uses machine learning algorithms to learn patterns, and generates optimal trading signals based on the results. The output is the generated trading signals.

[1135] Step 2:

[1136] The server collects information using a high-speed Internet communication network. Specifically, it uses a 5G network to obtain market data and trading information in real time to minimize trading delays. The input is market data and trading information, and the output is trading information with low latency.

[1137] Step 3:

[1138] The device (smartphone) captures the user's facial expression using a camera. It receives the image from the camera as input, detects the face using OpenCV, and cuts out the facial area. The output is image data of the facial area.

[1139] Step 4:

[1140] The device inputs image data of the face region into an emotion recognition model to predict the user's emotion. Specifically, the device inputs image data into an emotion recognition model using Keras to obtain emotion prediction results. The output is predicted emotion data.

[1141] Step 5:

[1142] The server inputs the predicted emotion data into the generative AI model to generate emotion-based trading signals. The generative AI model generates optimal trading signals based on the emotion data and outputs them. The output is emotion-based trading signals.

[1143] Step 6:

[1144] The terminal presents the generated trading signals to the user. Specifically, the terminal displays the trading signals on the smartphone screen and suggests optimal settlement methods and timing to the user. The input is the generated trading signals, and the output is the trading signals presented to the user.

[1145] Step 7:

[1146] The user executes a transaction based on the presented transaction signal. The user operates a smartphone and executes the transaction according to the proposed settlement method and timing. The input is the presented transaction signal, and the output is the executed transaction.

[1147] 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 voice indicating a user input for 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.

[1148] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[1149] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1150] 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.

[1151] [Fourth embodiment]

[1152] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1153] 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.

[1154] 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 wide area network (WAN) and / or a local area network (LAN).

[1155] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1156] 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 the voice according to instructions from the processor 46.

[1157] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1159] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. 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.

[1160] Fig. 8 shows an example of 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.

[1161] 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.

[1162] 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.

[1163] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1164] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1165] "Example 1"

[1166] The system of the present invention uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, deep learning is used as the AI ​​technology to learn patterns in market data and generate optimal trading signals based on the results. In addition, a high-speed Internet communication network is used to collect information and minimize trading delays. Specifically, a fiber optic network is used to collect trading information in real time and minimize trading delays. This makes it possible to improve the speed and accuracy of trading, maximize profits, and minimize risks.

[1167] "Example 2"

[1168] The system of the present invention improves the speed and accuracy of trading by combining AI technology and a high-speed Internet communication network. Specifically, machine learning is used as AI technology to learn market data patterns and generate optimal trading signals based on the results. In addition, a high-speed Internet communication network is used to collect information and minimize trading delays. Specifically, a 5G network is used to collect trading information in real time and minimize trading delays. This makes it possible to improve the speed and accuracy of trading, maximize profits, and minimize risks.

[1169] The process flow of each embodiment will be described below.

[1170] "Example 1"

[1171] Step 1: Analyze market data in real time using deep learning as an AI technology. This analysis allows you to understand market movements and trends.

[1172] Step 2: Generate optimal trading signals based on the analysis results. These trading signals indicate the timing and amount of buying and selling.

[1173] Step 3: Use a high-speed Internet network to collect information and minimize transaction delays. Specifically, use a fiber optic network to collect transaction information in real time and minimize transaction delays.

[1174] "Example 2"

[1175] Step 1: Analyze market data in real time using machine learning as an AI technology. This analysis allows you to understand market movements and trends.

[1176] Step 2: Generate optimal trading signals based on the analysis results. These trading signals indicate the timing and amount of buying and selling.

[1177] Step 3: Use high-speed Internet networks to collect information and minimize transaction delays. Specifically, 5G networks will be used to collect transaction information in real time and minimize transaction delays.

[1178] Example 1

[1179] Next, a description will be given of Example 1 of the embodiment 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".

[1180] Traditional market data analysis systems had problems with delays in collecting and processing real-time data, reducing the speed and accuracy of trading. They also lacked the ability to process huge amounts of data quickly, making it difficult to generate optimal trading signals. Furthermore, they did not provide sufficient means to maximize profits and minimize risks.

[1181] 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.

[1182] In this invention, the server includes a means for collecting market data in real time through an optical fiber network, a means for normalizing the collected market data and imputing missing values, a means for learning patterns of the market data using a deep learning framework, a means for generating optimal trading signals based on the learning results, and a means for delivering the generated trading signals to a user's terminal, thereby improving the speed and accuracy of trading, maximizing profits, and minimizing risks.

[1183] An "optical fiber network" is a communications network that uses optical fiber cables to enable high-speed, large-capacity data communication.

[1184] "Market data" refers to data that includes information such as prices, trading volumes, and trading times in financial markets for stocks, bonds, commodities, currencies, and the like.

[1185] "Normalization" is a process of constraining each data point within a certain range in order to unify the scale of the data.

[1186] "Missing value imputation" is the process of filling in missing values ​​in a dataset in an appropriate manner.

[1187] A "deep learning framework" is a software library for building and training neural networks, and includes TensorFlow and PyTorch.

[1188] "Pattern learning" is the process of extracting patterns and features from past data and building models to predict and classify future data.

[1189] A "trading signal" is an instruction indicating the timing of buying and selling in a financial market, and includes buying signals and selling signals.

[1190] "User terminal" means a device used by a user to receive trading signals and execute transactions, including a personal computer or smartphone.

[1191] This invention is a system that uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses deep learning to learn market data patterns and generates optimal trading signals based on the results. It also uses high-speed Internet communication networks to collect information and minimize delays in trading.

[1192] The server collects market data in real time through a fiber optic network. The collected market data is processed by a high-performance server equipped with NVIDIA GPUs. The server learns the patterns of the market data using deep learning frameworks such as TensorFlow and PyTorch. Based on the learning results, the server generates optimal trading signals.

[1193] Specifically, the server processes the data in the following steps:

[1194] 1. Market data collection: The server collects market data in real time through the fiber optic network, for example, by using APIs from financial market data providers.

[1195] 2. Data preprocessing: Normalize the collected data and impute missing values. For example, use the Pandas library to manipulate data frames and impute missing values ​​with the mean.

[1196] 3. Train the model: Build a neural network using TensorFlow or PyTorch to learn patterns from historical market data, for example using a LSTM (Long Short-Term Memory) network to train time series data.

[1197] 4. Generate trading signals: Generate optimal trading signals based on the learning results. For example, if the model judges it to be a "buy" signal, generate that signal.

[1198] 5. Signal Delivery: The generated trading signals are delivered to the users' terminals, for example, by sending signals in real time using WebSocket or REST API.

[1199] Users can execute trades based on the trading signals they receive, improving the speed and accuracy of their trades, maximizing profits and minimizing risks.

[1200] As a concrete example, consider the following scenario:

[1201] A user wants to analyze stock market data in real time and receive optimal trading signals. The user's terminal receives the trading signals distributed by the server and executes the trades.

[1202] Example prompt:

[1203] "Analyze real-time stock market data and generate optimal trading signals. The data used is stock price data and trading volume data from the past year. Deliver the generated trading signals in real time."

[1204] The system enables users to improve the speed and accuracy of their transactions, maximizing profits and minimizing risks.

[1205] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1206] Step 1:

[1207] The server collects market data in real time through the fiber optic network. Specifically, it retrieves data from financial market data providers using APIs. The input is the API request and the output is the retrieved market data. For example, the server sends an API request and receives data such as stock prices and trading volumes.

[1208] Step 2:

[1209] The server preprocesses the collected market data by normalizing the data, imputing missing values, and detecting and correcting outliers. The input is the collected market data, and the output is the preprocessed data. For example, the server uses the Pandas library to manipulate data frames, impute missing values ​​with the mean, and normalize the data.

[1210] Step 3:

[1211] The server trains a deep learning model using the preprocessed data. Specifically, it uses TensorFlow or PyTorch to build a neural network and learn patterns from past market data. The input is the preprocessed data, and the output is the trained model. For example, the server uses an LSTM (long short-term memory) network to train time series data.

[1212] Step 4:

[1213] The server generates optimal trading signals based on the learning results. Specifically, it inputs real-time market data into the trained model and outputs buy and sell signals. The inputs are real-time market data and the trained model, and the output is a trading signal. For example, if the model judges it to be a "buy" signal, it generates that signal.

[1214] Step 5:

[1215] The server distributes the generated trading signals to the user's terminal. Specifically, it transmits signals in real time using WebSocket or REST API. The input is the generated trading signal, and the output is the signal distributed to the user's terminal. The user's terminal displays the received signal and executes the transaction.

[1216] (Application example 1)

[1217] Next, a description will be given of Application Example 1 of Form 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".

[1218] Conventional market data analysis systems have difficulty processing data in real time, which often results in delays in trading. They also cannot generate trading signals optimized for each user, which results in a lack of accuracy and speed in trading. Furthermore, they are not user-friendly because they do not take into account the use on mobile devices such as smartphones.

[1219] 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. In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for learning the trading patterns of users and generating individually optimized trading signals, and a means including an application installed on a smartphone. This enables real-time market data analysis and generation of trading signals, improves the speed and accuracy of trading, and makes it possible to provide trading signals optimized for each user.

[1220] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[1221] "Real-time" refers to data collection and processing occurring immediately.

[1222] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[1223] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[1224] "High-speed processing" refers to analyzing data quickly and outputting results in a short period of time.

[1225] A "trading signal" refers to an instruction or sign that indicates the timing and direction of a transaction.

[1226] A "high-speed Internet network" refers to an Internet connection that can send and receive data very quickly.

[1227] "Information gathering" refers to collecting necessary data or information.

[1228] "Transaction Delay" refers to the delay in time before a transaction is executed.

[1229] "User trading patterns" refers to the user's past trading tendencies and behavioral patterns.

[1230] "Individually Optimized Trading Signals" refers to trading signals that are optimized based on the trading patterns of each individual User.

[1231] "Applications installed on a smartphone" refers to software programs that run on a smartphone.

[1232] In order to implement the present invention, the following system configuration is necessary.

[1233] First, the server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses deep learning models to learn patterns in market data and generate optimal trading signals based on the results. The server achieves this using software such as Python, Keras, Requests, Pandas, and NumPy.

[1234] The servers then collect the information over high-speed Internet connections to minimize trading delays, using fiber optic networks to collect market data in real time for immediate analysis.

[1235] In addition, the server learns the user's trading patterns and generates individually optimized trading signals, which makes it possible to provide optimal trading timing for each user. The user's trading patterns are learned by a deep learning model based on past trading data.

[1236] The application installed on a smartphone receives trading signals sent from the server and notifies the user. Through this application, users can check market data in real time and know the optimal timing for trading.

[1237] As a concrete example, a scenario is when a user opens the smartphone app "Real-time Settlement Analyst" and checks current market data in real time. The app learns the user's trading patterns and notifies them of the optimal timing to trade. For example, a notification such as "In the current market conditions, it is a good time to buy" is displayed.

[1238] An example of a prompt sentence might be:

[1239] Develop a smartphone app that analyzes market data in real time and notifies users of the best time to trade. Use deep learning to learn users' trading patterns and generate individually optimized trading signals. Use high-speed internet networks to collect trading information in real time and minimize delays.

[1240] In this way, the combination of servers, smartphone applications and high-speed internet networks allows for real-time market data analysis and generation of trading signals, improving the speed and accuracy of trading.

[1241] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1242] Step 1:

[1243] The server collects market data in real time. Specifically, it obtains market data through APIs and collects information such as prices, trading volumes, and trading history. The input is the response from the market data API, and the output is the collected market data.

[1244] Step 2:

[1245] The server pre-processes the collected market data, specifically by filtering it down to prices and volumes and converting it into numerical data. The input is the market data collected in step 1, and the output is the pre-processed data.

[1246] Step 3:

[1247] The server uses the preprocessed data to train a deep learning model, specifically, a long short-term memory (LSTM) network to learn patterns in the market data. The input is the preprocessed data, and the output is the trained deep learning model.

[1248] Step 4:

[1249] The server generates trading signals using a trained deep learning model. Specifically, the model inputs market data and outputs signals that indicate the timing and direction of trading. The input is real-time market data and the output is trading signals.

[1250] Step 5:

[1251] The server transmits the trading signals to the smartphone using a high-speed internet communication network. Specifically, the communication is performed using a fiber optic network with minimal latency. The input is the trading signal, and the output is the signal notification to the smartphone.

[1252] Step 6:

[1253] The smartphone notifies the user of the received trading signal. Specifically, it notifies the user of the timing of trading using push notifications and in-app notifications. The input is the trading signal sent from the server, and the output is the notification to the user.

[1254] Step 7:

[1255] Users check market data in real time through a smartphone app and trade based on trading signals. Specifically, they view market data within the app and execute trades according to the trading timing notified to them. The input is market data and trading signals from the smartphone app, and the output is the user's trading action.

[1256] In this way, the combination of servers, smartphone applications and high-speed internet networks allows for real-time market data analysis and generation of trading signals, improving the speed and accuracy of trading.

[1257] Example 2

[1258] Next, a second embodiment of the second embodiment will be described. 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".

[1259] Conventional trading systems lacked speed and accuracy, making it difficult to maximize profits and minimize risks. In addition, real-time information collection was insufficient, resulting in delays in trading. This often caused users to miss the optimal timing for trading, reducing trading efficiency.

[1260] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1261] In this invention, the server includes a means for collecting market data, a means for pre-processing the collected data, a means for training a machine learning model, a means for generating trading signals, a means for collecting information in real time using a high-speed Internet communication network, a means for notifying a user of the generated trading signals, and a means for the user to execute a trade, thereby improving the speed and accuracy of trading, maximizing profits, and minimizing risks.

[1262] "Market data" refers to information such as stock prices, trading volumes, and economic indicators in financial markets.

[1263] "Preprocessing" refers to the process of filling in missing values ​​in collected data, removing outliers, normalizing the data, etc.

[1264] A "machine learning model" refers to an algorithm that learns patterns from data and makes predictions and classifications.

[1265] A "trading signal" refers to information generated by a machine learning model that recommends trading at a specific time.

[1266] "High-speed Internet network" refers to infrastructure that enables high-speed and stable data communication.

[1267] "Real-time information" refers to information that immediately reflects current market conditions.

[1268] "User" refers to an individual or legal entity that conducts transactions using this system.

[1269] "Notification" refers to the act of informing a User of a generated trading signal.

[1270] "Execution of a transaction" refers to the act of a user actually buying or selling a financial product based on a trading signal.

[1271] This invention is a system that improves the speed and accuracy of transactions by combining AI technology and high-speed Internet communication networks. Below, we will explain how to specifically implement this system.

[1272] The server first collects market data. Specifically, it uses a common data collection API to obtain past transaction data from financial markets. For example, it can use the Yahoo Finance API or Alpha Vantage API. This allows it to collect data such as stock prices, trading volumes, and economic indicators.

[1273] The server then preprocesses the collected data, using Python's Pandas library to impute missing values, remove outliers, and normalize the data, improving data quality and making it more effective for training machine learning models.

[1274] The server then trains a machine learning model, using TensorFlow or Scikit-learn to learn patterns from past market data. For example, a Long Short-Term Memory (LSTM) model can be used to forecast time series data, which can then predict future market trends and generate optimal trading signals.

[1275] After a trading signal is generated, the terminal collects information in real time using a high-speed Internet communication network, specifically using Qualcomm's 5G modem to achieve high speed and stable communication, which can minimize delays in trading.

[1276] The generated trading signals are notified to the user through the terminal. The signals are displayed in real time through the mobile app and desktop app. By receiving notifications, users can quickly execute trades.

[1277] Users execute trades based on the trading signals they receive. They access the trading platform and buy or sell stocks according to the signals generated by AI, which improves the speed and accuracy of trading, maximizing profits and minimizing risks.

[1278] As a concrete example, consider a user trading on the stock market. The server uses the Yahoo Finance API to collect stock price data for the past year. It then uses the Pandas library to impute missing values ​​in the data and remove outliers. It then uses TensorFlow to train an LSTM model to predict which stocks are likely to rise in the next week. The server generates a buy signal based on this prediction, and the terminal notifies the user of this signal in real time over the 5G network. When the user receives the notification, he or she accesses the trading platform and purchases stocks according to the signal.

[1279] Example of a prompt to be input to a generative AI model:

[1280] "Predict which stocks are likely to rise in the next week based on stock price data from the past year."

[1281] The system improves the speed and accuracy of trading, allowing users to maximize profits and minimize risks.

[1282] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1283] Step 1:

[1284] The server collects market data. Specifically, it uses a common data collection API to obtain historical trading data from financial markets. For example, it uses Yahoo Finance API or Alpha Vantage API. The input is the API request, and the output is data such as stock prices, trading volumes, and economic indicators.

[1285] Step 2:

[1286] The server preprocesses the collected data. Specifically, it uses Python's Pandas library to impute missing values ​​in the data, remove outliers, and normalize the data. The input is the collected market data, and the output is the preprocessed data.

[1287] Step 3:

[1288] The server trains a machine learning model using the preprocessed data. Specifically, it uses TensorFlow and Scikit-learn to learn patterns from past market data. For example, it uses a Long Short-Term Memory (LSTM) model to make predictions on time series data. The input is the preprocessed data, and the output is the trained machine learning model.

[1289] Step 4:

[1290] The server uses the trained model to generate trading signals, specifically, buy and sell signals based on the stock price trends predicted by the model. The inputs are the trained machine learning model and real-time market data, and the output is trading signals.

[1291] Step 5:

[1292] The terminal collects information in real time using a high-speed Internet communication network. Specifically, it uses Qualcomm's 5G modem to achieve high-speed and stable communication. The input is real-time market data, and the output is the collected real-time information.

[1293] Step 6:

[1294] The terminal notifies the user of the generated trading signals, specifically by displaying the signals in real time through a mobile or desktop app. The input is the trading signal, and the output is the notification to the user.

[1295] Step 7:

[1296] The user executes a trade based on the notified trading signal. Specifically, the user accesses the trading platform and buys or sells stocks according to the signal generated by the AI. The input is the trading signal and the trading platform, and the output is the executed trade.

[1297] (Application example 2)

[1298] Next, a description will be given of Application Example 2 of Form 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".

[1299] Traditional electronic settlement systems did not ensure sufficient speed and accuracy of transactions, making it difficult for users to settle at the optimal time. In addition, there was a lack of means to analyze market data in real time and generate optimal trading signals, making it difficult to maximize profits and minimize risks. In addition, delays in transactions were common, often impairing the user experience.

[1300] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1301] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for proposing optimal timing and method for a user to make an electronic settlement, a means for analyzing market data using a machine learning model and collecting information in real time, and a means including an application to be installed on a smartphone, thereby improving the speed and accuracy of trading and enabling a user to make a settlement at an optimal timing.

[1302] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[1303] "Real-time" refers to data collection and processing occurring immediately.

[1304] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[1305] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[1306] "High-speed processing" refers to analyzing data quickly and obtaining results in a short period of time.

[1307] A "trading signal" is an instruction that indicates the best time or manner to enter into a trade.

[1308] A "high-speed Internet network" refers to an Internet connection that allows for extremely fast data transmission and reception, and specifically includes 5G networks.

[1309] "Information gathering" refers to the act of collecting necessary data or information.

[1310] "Transaction Delay" refers to the delay in time before a transaction is executed.

[1311] "User" refers to any individual or legal entity that uses this system.

[1312] "Electronic payment" refers to the payment of money via the Internet.

[1313] The "optimal time" refers to the most favorable time to carry out a transaction or settlement.

[1314] "Suggesting a solution" means showing the user the optimal course of action or means.

[1315] A "machine learning model" refers to an algorithm that learns patterns from data and makes predictions and classifications.

[1316] A "smartphone" refers to a mobile phone that can connect to the Internet and run applications.

[1317] "Application" refers to a software program for achieving a specific function.

[1318] A system for implementing the present invention is configured as follows.

[1319] System Configuration

[1320] The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses machine learning models to analyze market data and generate optimal trading signals, which can improve the speed and accuracy of trading.

[1321] The terminal (smartphone) collects information using a high-speed Internet communication network (5G network) to minimize transaction delays. An application is installed that suggests the optimal timing and method for users to make electronic payments. The application collects market data in real time and uses machine learning models to notify users of the optimal timing for payment.

[1322] Hardware and Software Used

[1323] Hardware: 5G-enabled smartphone

[1324] Software: Python, Requests library, Scikit-learn library

[1325] Data processing and calculation

[1326] The server collects market data through APIs and analyzes the data using machine learning models. Specifically, the machine learning model is trained based on the collected market data to generate optimal trading signals in real time. The terminal receives market data in real time through the 5G network and notifies users of the optimal settlement timing through the application.

[1327] Examples

[1328] For example, when a user is shopping online, the application will notify them of the optimal time to make a payment, allowing them to make a payment with the most favorable exchange rate and fees.

[1329] Examples of prompt statements

[1330] "Develop an application that collects the latest market data and notifies the optimal settlement timing in real time. Analyze market data using machine learning models and collect information in real time over 5G networks."

[1331] The above is an embodiment of the present invention. This system improves the speed and accuracy of transactions, allowing users to make payments at the optimal timing.

[1332] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1333] Step 1:

[1334] The server collects market data. Specifically, it gets the latest market data through API. The input is the market data from API, and the output is the collected market data. This data includes prices, trading volumes, trading history, etc.

[1335] Step 2:

[1336] The server trains a machine learning model based on the collected market data. Specifically, it preprocesses the data, extracts features, and inputs them into the machine learning algorithm. The input is the collected market data, and the output is the trained machine learning model. This model is used to predict future market trends.

[1337] Step 3:

[1338] The server analyzes market data in real time and generates optimal trading signals. Specifically, it uses a trained machine learning model to input current market data and output trading signals. The input is real-time market data and the output is trading signals.

[1339] Step 4:

[1340] The terminal (smartphone) receives market data in real time using a high-speed Internet communication network (5G network). Specifically, it receives market data sent from the server and inputs it into the application. The input is the market data from the server, and the output is the data passed to the application.

[1341] Step 5:

[1342] The terminal notifies the user of the optimal timing for payment through the application. Specifically, the application analyzes transaction signals and sends notifications to the user. The input is the transaction signal, and the output is the notification to the user. Based on this notification, the user can make electronic payments at the optimal time.

[1343] Step 6:

[1344] The user receives a notification from the application and makes an electronic payment at the optimal time. Specifically, the user executes the payment according to the notification and completes the transaction. The input is the notification from the application, and the output is the completed electronic payment.

[1345] These are the specific processing steps of this system, which will improve the speed and accuracy of transactions and enable users to make payments at the optimal timing.

[1346] In addition, 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.

[1347] "Example 1"

[1348] In one embodiment of the present invention, a system using AI technology and a high-speed Internet communication network is combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and improves the speed and accuracy of trading based on the emotions. For example, if a user shows an emotion of joy, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates optimal trading signals taking into account the analysis results of market data and the user's emotions. This allows for more personalized trading according to the user's emotions. The emotion engine also maximizes profits and minimizes risks based on the user's emotions. For example, if a user shows an emotion of anxiety, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates trading signals to minimize risks. This allows for safer trading according to the user's emotions.

[1349] "Example 2"

[1350] In one embodiment of the present invention, a system using AI technology and a high-speed Internet communication network is combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and improves the speed and accuracy of trading based on the emotions. For example, if a user shows an emotion of joy, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates optimal trading signals taking into account the analysis results of market data and the user's emotions. This allows for more personalized trading according to the user's emotions. The emotion engine also maximizes profits and minimizes risks based on the user's emotions. For example, if a user shows an emotion of anxiety, the emotion engine feeds the information back to the AI ​​technology, and the AI ​​generates trading signals to minimize risks. This allows for safer trading according to the user's emotions.

[1351] The process flow of each embodiment will be described below.

[1352] "Example 1"

[1353] Step 1: A user starts trading with the system. At this point, the emotion engine begins to recognize emotions from the user's tone of voice, facial expressions, text input, etc.

[1354] Step 2: The sentiment engine feeds the recognized sentiment back to the AI ​​technology, which combines this sentiment information with real-time market data to generate trading signals.

[1355] Step 3: The generated trading signal is sent to the exchange via high-speed internet communication networks, which increases the speed and accuracy of trading.

[1356] Step 4: After the transaction is completed, the system will provide the user with feedback on the transaction results. This feedback will be provided in a form that takes into account the user's emotions and will be optimized according to the user's emotions.

[1357] Example 1

[1358] Next, a description will be given of Example 1 of the embodiment 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".

[1359] Conventional market data analysis systems had problems with delays in trading and reduced accuracy in real-time data processing and generation of trading signals. In addition, trading signals were not generated taking into account the user's emotions, making it difficult to perform personalized trading according to the user's psychological state. This resulted in insufficient improvement in trading speed and accuracy, making it difficult to maximize profits and minimize risks.

[1360] 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.

[1361] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for recognizing user emotions, and a means for adjusting trading signals based on the recognized emotion information, thereby improving the speed and accuracy of trading and enabling personalized trading according to the user's emotions.

[1362] "AI technology" is a technology that uses artificial intelligence to analyze data and perform pattern recognition and predictions.

[1363] "Real-time" refers to data collection and processing occurring immediately, without delay.

[1364] "Market Data" refers to data relating to financial markets, such as stock prices, exchange rates, and trading volumes.

[1365] "High-speed Internet network" refers to communications infrastructure that can send and receive data at high speeds, such as optical fiber networks.

[1366] "Trading signals" refer to instructions that indicate when to buy or sell based on the analysis of market data.

[1367] "User emotion" is information that indicates the user's psychological state, and is recognized from the tone of voice, facial expression, text input, and the like.

[1368] "Emotion information" refers to data obtained as a result of analyzing a user's emotions.

[1369] "Personalized transactions" refer to transactions that are customized according to the user's individual emotions and circumstances.

[1370] This invention is a system that uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, the various elements of the server, terminal, and user work together.

[1371] The server collects market data in real time using a high-speed internet communication network. The use of a fiber optic network minimizes delays in trading information. The collected market data is input into a deep learning model on the server. The deep learning model uses frameworks such as TensorFlow and PyTorch. This allows the server to learn patterns in the market data and generate optimal trading signals.

[1372] The device is equipped with an emotion engine that recognizes emotions from the user's tone of voice, facial expressions, text input, etc. The emotion engine uses technologies such as OpenCV and Google Cloud Speech-to-Text API to analyze the user's emotions in real time. The analysis results are fed back from the device to the server.

[1373] The server re-evaluates the market data analysis results based on the feedback sentiment information and adjusts the trading signals. For example, if the user is expressing happiness, the server generates aggressive trading signals. On the other hand, if the user is expressing anxiety, the server generates trading signals that minimize risk.

[1374] As a concrete example, when a user is using a trading application, the terminal analyzes the user's tone of voice to detect the emotion of happiness. The terminal transmits this information to a server, which takes into account the analysis of market data and the user's emotion to generate aggressive trading signals. The user then makes trades based on these signals to maximize profits.

[1375] An example prompt is, "Please explain how trading signals would be adjusted if a user exhibits the emotion of happiness while using a trading application."

[1376] The system allows users to make personalized trades based on market data and their own emotions, improving the speed and accuracy of trading.

[1377] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1378] Step 1:

[1379] The server collects market data in real time using high-speed Internet communication networks. Specifically, it uses optical fiber networks to obtain data from exchanges and financial institutions. As input, it receives market data such as stock prices, exchange rates, and trading volumes, and as output, it stores these data in a database.

[1380] Step 2:

[1381] The server pre-processes the collected market data, i.e. imputing missing values, removing outliers, normalizing data, etc. It takes the stored market data as input and produces the pre-processed data as output, which improves the quality of the data.

[1382] Step 3:

[1383] The server inputs the preprocessed data into a deep learning model. Specifically, it uses TensorFlow or PyTorch to build a LSTM (long short-term memory) network or a CNN (convolutional neural network) to learn patterns in the data. It takes the preprocessed market data as input and generates a trained model as output.

[1384] Step 4:

[1385] The server uses the trained model to generate optimal trading signals, specifically, buy and sell signals based on predicted price movements and trends. It receives the trained model and real-time market data as inputs and generates trading signals as output. The signals are sent to users' trading applications in real-time.

[1386] Step 5:

[1387] The device recognizes emotions from the user's tone of voice, facial expressions, text input, etc. Specifically, it uses OpenCV to analyze facial expressions from camera footage, converts voice data to text using Google Cloud Speech-to-Text API, and analyzes emotions. It receives the user's voice and video data as input, and generates emotional information as output.

[1388] Step 6:

[1389] The device feeds back the recognized emotion information to the server. Specifically, the emotion engine sends the analyzed emotion data to the server in JSON format. The device receives emotion information as input and generates data to be sent to the server as output.

[1390] Step 7:

[1391] The server re-evaluates the analysis results of the market data based on the feedback emotion information and adjusts the trading signal. For example, if the user indicates happiness, the server generates an aggressive trading signal. On the other hand, if the user indicates anxiety, the server generates a trading signal that minimizes risk. It takes emotion information and market data as inputs and generates an adjusted trading signal as output.

[1392] (Application example 1)

[1393] Next, a description will be given of Application Example 1 of Form 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".

[1394] Conventional trading systems were unable to take into account user sentiment when analyzing market data and generating trading signals, making it difficult to provide personalized trading advice. There was also room for improvement in terms of trading delays and risk management. This resulted in a suboptimal user trading experience, and the maximization of profits and minimization of risks were not fully achieved.

[1395] 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.

[1396] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for adjusting trading signals based on user emotions using an emotion engine that recognizes user emotions, and a means for integrating market data and user emotions using an application installed on a smartphone to provide trading advice. This makes it possible to provide personalized trading advice according to the user's emotions, improve the speed and accuracy of trading, maximize profits, and minimize risks.

[1397] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[1398] "Real-time" refers to data acquisition and processing occurring immediately.

[1399] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[1400] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[1401] "High-speed processing" refers to quickly analyzing data and performing calculations.

[1402] A "trading signal" is an instruction that indicates the timing and direction of buying and selling when conducting a transaction.

[1403] A "high-speed Internet communication network" is an Internet network that allows data to be sent and received at high speeds.

[1404] "Information gathering" refers to the act of collecting necessary data or information.

[1405] "Transaction Delay" means a time delay that occurs in the execution of a transaction.

[1406] An "emotion engine" is a technology that recognizes emotions from a user's tone of voice, facial expressions, text input, etc.

[1407] "User emotion" refers to the psychological state, such as joy, anxiety, anger, etc., felt by the user.

[1408] A "smartphone" is a type of mobile phone, a multi-function device that can connect to the Internet and use applications.

[1409] An "application" is a software program designed to provide a specific function or service.

[1410] "Trading Advice" is information that provides information and recommendations for making decisions when conducting transactions.

[1411] "Personalized" means customized to the characteristics and preferences of an individual user.

[1412] "Transaction speed" refers to how quickly a transaction is executed.

[1413] "Transaction accuracy" refers to a transaction being executed exactly as intended.

[1414] "Profit maximization" refers to maximizing the profits gained from a transaction.

[1415] "Minimizing risk" means minimizing the losses and dangers involved in trading.

[1416] A system for implementing the present invention is configured as follows.

[1417] The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, a Python program uses Keras (a deep learning library) to learn patterns in the market data and generate optimal trading signals. Market data is obtained from the API using Requests (an HTTP request library) and converted into a Pandas data frame.

[1418] The server also collects information through high-speed Internet communication networks to minimize transaction delays, and transmits and receives data through fiber optic networks.

[1419] In addition, the server adjusts trading signals based on the user's emotions using an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's images using OpenCV (an image processing library) and recognizes emotions using a Keras model.

[1420] An application is installed on the smartphone as a terminal. This application integrates market data obtained from a server with the user's emotions and provides trading advice. The user takes a picture of their face using the smartphone's camera, and the application uses the image to recognize emotions. Market data is obtained in real time, and optimal trading signals are generated based on the user's emotions.

[1421] For example, a user can take a picture of their face with their smartphone camera, and the app will use the image to recognize emotions. The app will then obtain market data in real time and generate optimal trading signals based on the user's emotions. This will enable the app to provide personalized trading advice based on the user's emotions.

[1422] The following are examples of prompt sentences:

[1423] "Users take a picture of their face with their smartphone camera and the app uses the image to recognize emotions. It pulls market data in real time and generates optimal trading signals based on the user's emotions."

[1424] In this way, it is possible to provide personalized trading advice based on the user's emotions, improve the speed and accuracy of trading, maximize profits and minimize risks.

[1425] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1426] Step 1:

[1427] The server retrieves market data from the API using the Requests library. The input is the API endpoint and the output is a JSON response with market data. This data is converted to a Pandas dataframe.

[1428] Step 2:

[1429] The server normalizes the market data converted to a Pandas dataframe. The input is a dataframe of market data and the output is the normalized market data. The data is scaled using StandardScaler.

[1430] Step 3:

[1431] The server generates trading signals from the normalized market data using a Keras model. The input is the normalized market data and the output is the trading signal. The data is fed into the Keras model and prediction results are obtained.

[1432] Step 4:

[1433] A user takes a picture of their face using the smartphone camera. The input is the user's face image, and the output is an image file. This image is used for emotion recognition.

[1434] Step 5:

[1435] The device preprocesses the user's face image using OpenCV. The input is the user's face image and the output is the preprocessed image. It resizes and normalizes the image.

[1436] Step 6:

[1437] The device recognizes the user's emotion from the image preprocessed using the Keras model. The input is the preprocessed image, and the output is the user's emotion label. Input the image to the Keras model to obtain the emotion prediction result.

[1438] Step 7:

[1439] The server receives the user's emotion label and adjusts the trading signal. The input is the trading signal and the user's emotion label, and the output is the adjusted trading signal. The server increases or decreases the trading signal based on the emotion label.

[1440] Step 8:

[1441] The terminal displays the adjusted trading signals to the user. The input is the adjusted trading signals and the output is trading advice to the user. The trading advice is displayed on the smartphone screen.

[1442] In this way, it is possible to provide trading advice that is personalized according to the user's emotions.

[1443] Example 2

[1444] Next, a second embodiment of the second embodiment will be described. 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".

[1445] Conventional trading systems had insufficient means to improve the speed and accuracy of transactions, and in particular had the problem of being unable to trade while taking into account the user's emotions. There was also room for improvement in terms of transaction delays and risk management. This made it difficult to maximize users' profits and minimize their risks.

[1446] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1447] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for collecting user emotion data using an emotion engine that recognizes user emotions, and a means for adjusting trading signals based on the user emotion data. This improves the speed and accuracy of trading, and enables personalized trading according to the user's emotions, maximizing profits and minimizing risks.

[1448] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[1449] "Market Data" is data relating to financial markets, such as stock prices, trading volumes, and news articles.

[1450] A "trading signal" is an instruction that indicates the timing and direction of a transaction, and refers to a specific action such as "buy" or "sell."

[1451] A "high-speed Internet network" is a communications infrastructure for sending and receiving data at high speeds, specifically referring to 5G networks.

[1452] An "emotion engine" is a technology that recognizes emotions from a user's tone of voice, facial expressions, text input, etc.

[1453] "User emotion data" is data relating to the user's emotion recognized by the emotion engine.

[1454] "Personalized trading" refers to trading that is optimized according to the user's individual emotions and circumstances.

[1455] "Minimizing risk" means making the chances of loss in a transaction as low as possible.

[1456] "Maximizing profits" means making the most profit possible from a transaction.

[1457] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1458] The system of the present invention improves the speed and accuracy of transactions by combining AI technology with a high-speed Internet communication network. A specific embodiment of this system will be described below.

[1459] Hardware and Software Used

[1460] The servers use machine learning frameworks such as TensorFlow and PyTorch to run machine learning algorithms.

[1461] The terminal is equipped with a communication module for collecting market data in real time via a 5G network.

[1462] The emotion engine includes software for recognizing emotions from a user's tone of voice, facial expressions, text input, etc.

[1463] System Operation

[1464] The server receives market data sent from the terminal and analyzes it in real time using machine learning algorithms. Specifically, it compares it with past market data and learns patterns. This learning is done using techniques such as regression analysis and clustering. The server generates optimal trading signals based on the learning results and sends them to the terminal.

[1465] The terminal collects market data in real time via the 5G network and transmits it to the server, minimizing trading delays. The terminal also collects user sentiment data through an emotion engine and transmits it to the server.

[1466] When trading, users communicate their emotions to the system through the emotion engine. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc. For example, if the user expresses joy, the emotion engine feeds that information back to the server, which then generates optimal trading signals by taking into account the results of market data analysis and the user's emotions.

[1467] Examples

[1468] As a concrete example, consider a scenario in which a user makes a trade. The user initiates a trade, and the device collects market data in real time through the 5G network and sends it to the server. The server uses TensorFlow to analyze the market data and compare it with past data to learn patterns. For example, it detects a pattern of a particular stock price rising. The server generates a "buy" trading signal and sends it to the device.

[1469] If the user expresses a happy emotion, the emotion engine feeds the information back to the server, which then takes that information into account and reevaluates the trading signal, for example by generating a signal to take more risk and gain more profit. The terminal then places a "buy" order on the stock exchange based on the new trading signal received from the server. The user then checks the results of the transaction and makes the next transaction.

[1470] Examples of prompt statements

[1471] “Describe a scenario in which a user initiates a trade and collects market data in real time over a 5G network. Include how the system updates trading signals if the user expresses happiness.”

[1472] In this way, the system enables personalized trading based on the user's emotions, improving the speed and accuracy of trading.

[1473] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1474] Step 1:

[1475] The terminal collects market data in real time through the 5G network. Specifically, it collects data such as stock prices, trading volume, and news articles. The collected data is sent from the terminal to the server. The input is the market data, and the output is the data sent to the server.

[1476] Step 2:

[1477] The server analyzes the received market data using machine learning algorithms. Specifically, it uses TensorFlow and PyTorch to compare it with past market data and learn patterns. This learning uses techniques such as regression analysis and clustering. The input is the market data, and the output is the learning results.

[1478] Step 3:

[1479] The server generates optimal trading signals based on the learning results. For example, it generates "buy" and "sell" signals. The generated trading signals are sent from the server to the terminal. The input is the learning results, and the output is the trading signal.

[1480] Step 4:

[1481] When a user makes a transaction, the user communicates his / her emotions to the system through the emotion engine. The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc. The recognized emotion data is sent from the terminal to the server. The input is the user's emotion, and the output is emotion data.

[1482] Step 5:

[1483] The server receives the user's emotion data and adjusts the trading signal based on it. For example, if the user is anxious, it generates a signal to minimize risk. The adjusted trading signal is sent from the server to the terminal. The input is the emotion data, and the output is the adjusted trading signal.

[1484] Step 6:

[1485] The terminal executes transactions based on the trading signals received from the server, specifically by placing "buy" or "sell" orders on the stock exchange or trading platform. The input is the trading signal, and the output is the executed transaction.

[1486] In this way, the system enables personalized trading based on the user's emotions, improving the speed and accuracy of trading.

[1487] (Application example 2)

[1488] Next, a description will be given of Application Example 2 of Form 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".

[1489] In conventional trading systems, there were insufficient means to improve the speed and accuracy of trading, making it difficult to perform personalized trading based on user emotions. In addition, there were limited means to minimize transaction delays, making it difficult to maximize profits and minimize risks.

[1490] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1491] In this invention, the server includes a means for analyzing market data in real time using AI technology and processing a huge amount of data at high speed, a means for generating optimal trading signals, a means for collecting information using a high-speed Internet communication network and minimizing delays in trading, a means for generating trading signals based on the user's emotions using an emotion engine that recognizes the user's emotions, a means for capturing the user's facial expressions using a smartphone camera and recognizing the emotions, a means for proposing optimal settlement methods and timing based on the emotions, and a means for generating trading signals based on emotions using a generative AI model. This improves the speed and accuracy of trading, and enables personalized trading according to the user's emotions, maximizing profits and minimizing risks.

[1492] "AI technology" is a technology that uses artificial intelligence to analyze data and learn patterns.

[1493] "Real-time" refers to data processing and information acquisition occurring immediately.

[1494] "Market data" refers to data that includes information such as prices, trading volumes, and trading histories in financial markets.

[1495] "Massive data" refers to an extremely large amount of data, on a scale that is difficult to process using conventional methods.

[1496] "High-speed processing" refers to analyzing data quickly and obtaining results in a short period of time.

[1497] A "trading signal" is an instruction that indicates when and how to enter into a trade.

[1498] A "high-speed Internet communication network" refers to an Internet network that can send and receive data at extremely high speeds.

[1499] "Transaction Delay" means a time delay that occurs in the execution of a transaction.

[1500] An "emotion engine" is a system for recognizing a user's emotions and processing that information.

[1501] "Smartphone camera" refers to the photographic device built into a smartphone.

[1502] "Capturing facial expressions" refers to capturing a picture of a user's facial expression using a camera and acquiring the data.

[1503] "Emotion recognition" refers to determining the user's emotional state from acquired data.

[1504] "Payment Method" refers to the means or method of payment used in a Transaction.

[1505] "Timing" refers to the right time or moment to make a transaction or settlement.

[1506] A "generative AI model" refers to an artificial intelligence model that has been trained using machine learning or deep learning to perform a specific task.

[1507] The system for implementing this invention has the following configuration. The server uses AI technology to analyze market data in real time and process huge amounts of data at high speed. Specifically, it uses a machine learning algorithm to learn market data patterns and generates optimal trading signals based on the results. It also uses a high-speed Internet communication network (e.g., 5G network) to collect information and minimize delays in trading.

[1508] In addition, by combining an emotion engine that recognizes user emotions, trading signals are generated based on the user's emotions. The emotion engine uses the smartphone camera to capture the user's facial expressions and recognize emotions. Specifically, it detects faces using OpenCV and predicts the user's emotions using an emotion recognition model using Keras.

[1509] A generative AI model is used to suggest optimal settlement methods and timing based on emotions. The generative AI model takes user emotion data as input and generates appropriate trading signals. These trading signals are personalized according to the user's emotions, improving the speed and accuracy of trading while maximizing profits and minimizing risks.

[1510] As a concrete example, while a user is using a smartphone, the camera captures the user's facial expression and recognizes emotions. For example, if the user feels anxious, the app suggests "payment methods to minimize risk." An example of this prompt is "If the user feels anxious, please suggest the best payment method to minimize risk."

[1511] In this way, the server can combine AI technology with high-speed internet networks, emotion engines, smartphone cameras and generative AI models to improve the speed and accuracy of transactions and realize personalized transactions based on user emotions.

[1512] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1513] Step 1:

[1514] The server uses AI technology to analyze market data in real time. It takes market data as input, uses machine learning algorithms to learn patterns, and generates optimal trading signals based on the results. The output is the generated trading signals.

[1515] Step 2:

[1516] The server collects information using a high-speed Internet communication network. Specifically, it uses a 5G network to obtain market data and trading information in real time to minimize trading delays. The input is market data and trading information, and the output is trading information with low latency.

[1517] Step 3:

[1518] The device (smartphone) captures the user's facial expression using a camera. It receives the image from the camera as input, detects the face using OpenCV, and cuts out the facial area. The output is image data of t...

Claims

1. A means to analyze market data in real time using AI technology and process huge amounts of data at high speed; means for generating trading signals indicating optimal timing and amounts to buy and sell in the market; A means for collecting information using a high-speed Internet communication network and minimizing delays in market transactions; means for adjusting the trading signals based on a user's emotion using an emotion engine that recognizes a user's emotion; A means for integrating market data and the user's sentiments using an application installed on a smartphone to provide trading advice in the market; A system including:

2. 10. The system of claim 1, wherein the AI ​​technology includes means for improving speed and accuracy of transactions.

3. The system of claim 1 further comprising means for maximizing benefits and minimizing risks for said user.

Images