system

Abstract

システムを提供する。【解決手段】データを取得する手段と、取得したデータを解析して動向を特定する手段と、投資者の目的及び許容度に基づいて戦略を生成する手段と、生成した戦略を個別化して配信する手段と、投資者からの質問に対して自然言語で回答を生成する手段と、構成要素の評価を行い、必要に応じて調整を提案する手段と、スマートフォンを用いて戦略のリアルタイムアドバイスを提供する手段と、を含むシステム。

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Individual investors are overwhelmed by an overabundance of market data information, making it difficult to make decisions based on their own judgment. Also, they cannot quickly respond to market fluctuations and have difficulty formulating an investment strategy suitable for their investment objectives and risk tolerance. Furthermore, due to a lack of knowledge about investment, there are also problems such as not being able to obtain timely and appropriate advice when having doubts or uneasiness.

Means for Solving the Problems

[0005] This invention provides a system that solves the above problems by acquiring financial market information from data collection sources, analyzing it using machine learning models, and identifying market trends. It includes means for generating an optimal investment strategy based on the investment objectives and risk tolerance of individual investors, and means for personalizing and delivering that strategy in real time. Furthermore, by integrating a function that generates answers to individual investors' questions using natural language processing technology, and further evaluates portfolio risk and proposes rebalancing, the invention enables individual investors to utilize information more effectively and make investment decisions.

[0006] "Data sources" refer to the information sources and systems used to obtain information about financial markets.

[0007] "Financial market information" refers to information that includes market-related data such as stock prices, exchange rates, economic indicators, and news.

[0008] "Analysis" refers to the computational process of verifying collected data and deriving insights such as market trends.

[0009] "Market trends" refers to information that shows the overall movement and trends of the market over a specific period.

[0010] "Individual investors" refers to ordinary investors who make individual or small-scale investments.

[0011] "Investment objectives" refer to the specific goals and results that investors aim to achieve when engaging in investment activities.

[0012] "Risk tolerance" refers to the degree of risk an individual is willing to accept in an investment.

[0013] An "investment strategy" refers to a plan or methodology adopted to achieve specific investment goals.

[0014] "Personalization" refers to adjusting general suggestions to suit the specific needs and circumstances of an individual.

[0015] "Real-time" refers to the timing at which processing and responses are performed almost immediately.

[0016] "Natural language processing technology" refers to technology for a computer to understand, interpret, and generate human language.

[0017] "Portfolio" refers to a combination of various financial assets held by an investor.

[0018] "Rebalancing" refers to the act of adjusting the asset allocation of a portfolio to maintain the original investment objectives and risk tolerance.

Brief Description of Drawings

[0019] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0023] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] This invention is a system that utilizes collected financial market information to provide effective investment strategies to individual investors. The following describes embodiments for carrying out this invention.

[0041] This system consists primarily of three elements: servers, terminals, and users.

[0042] The server acquires information about financial markets from diverse data sources, integrates it, and stores it in a database. Specific data sources include stock price information, economic indicators, and international news feeds. Furthermore, the server uses this data to analyze overall market trends and trends related to specific stocks. This involves the use of advanced machine learning models for time-series data analysis, anomaly detection, and predictive modeling.

[0043] For users, the first step is to register with the system via a terminal and provide information about their investment objectives and risk tolerance. This allows the server to create and update user profiles and prepare to propose personalized investment strategies.

[0044] The terminal acts as the interface with the user, visually displaying investment information transmitted from the server. Furthermore, the terminal allows users to input any questions or concerns they may have regarding investments, and this information is also transmitted to the server.

[0045] The server utilizes natural language processing technology to quickly generate and respond to user inquiries with appropriate advice. In this process, the natural language processing engine analyzes the user's question and creates an answer based on relevant analysis results and past market trends.

[0046] In the investment strategy proposal section, the server optimizes the portfolio and selects specific stocks based on the user's profile and market analysis results. Furthermore, from a risk management perspective, it continuously assesses the portfolio's risk and proposes rebalancing as needed. For example, if risk becomes excessively concentrated, it may recommend reinvesting in bonds or other low-risk assets.

[0047] This allows individual investors to gain clear and timely strategic insights, effectively respond to fluctuating markets, and make decisions to achieve their investment goals.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The server retrieves real-time financial market information from data sources. This includes collecting stock price data, economic indicators, and news articles via APIs.

[0051] Step 2:

[0052] The server preprocesses the acquired raw data. Specifically, it cleanses and normalizes the data and converts it into a format suitable for analysis.

[0053] Step 3:

[0054] The server analyzes pre-processed data using machine learning models to identify market trends and developments. This includes detecting outliers and predicting future price fluctuations.

[0055] Step 4:

[0056] Users input their investment objectives and risk tolerance using a terminal and send this information to the system.

[0057] Step 5:

[0058] The server creates a user profile based on information provided by the user and generates a personalized investment strategy. The user profile is stored in a database and updated as needed.

[0059] Step 6:

[0060] The server delivers the generated investment strategies to the user's device in real time. This delivery uses technologies such as WebSocket and push notifications.

[0061] Step 7:

[0062] The user enters investment-related questions via their device and sends them to the server.

[0063] Step 8:

[0064] The server uses natural language processing technology to analyze the user's question, generate an appropriate answer, and send it back to the terminal. During this process, it provides a detailed, contextual explanation, referencing past analysis results and market trends.

[0065] Step 9:

[0066] The server continuously monitors the user's portfolio and performs a risk assessment of the portfolio. It proposes rebalancing as needed and notifies the user of the proposed changes.

[0067] These steps work together to enable users to receive highly accurate investment information in real time, helping them make informed and informed investment decisions.

[0068] (Example 1)

[0069] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0070] One of the challenges individual investors face when developing effective investment strategies in financial markets is the need to properly analyze large amounts of market data and generate strategies that match their investment objectives and risk tolerance. Furthermore, obtaining real-time information and optimizing portfolios in a rapidly changing market environment is difficult. The inability to obtain investment advice in natural language also poses a communication barrier.

[0071] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0072] In this invention, the server includes means for acquiring data from information sources, means for analyzing the acquired data to identify market trends, and means for generating investment plans according to an individual's investment objectives and risk tolerance. This enables individual investors to efficiently analyze large amounts of data, receive investment strategies tailored to their needs in real time, and respond quickly to market changes through natural language support.

[0073] "Information sources" refer to a variety of sources that provide data related to financial markets. Specific examples include stock price information, economic indicators, and news feeds.

[0074] "Data analysis" refers to all techniques used to process acquired data and identify market trends and patterns. This includes statistical methods and machine learning algorithms.

[0075] A "personalized investment plan" refers to an investment strategy tailored to an individual's investment objectives and risk tolerance. This plan proposes specific actions based on market analysis results.

[0076] "Natural language processing" refers to the technology that enables computers to understand and generate human language. This makes it possible to generate appropriate answers in human language to user inquiries.

[0077] A "generative AI model" refers to a predictive model that uses machine learning algorithms to create new data and information. In particular, in language generation, it can provide natural dialogue in response to user prompts.

[0078] "Portfolio adjustment" refers to the process of reviewing and optimizing the allocation of investment assets in response to risk assessments and market fluctuations. This adjustment allows investors to pursue the minimization of risk and the maximization of returns.

[0079] This invention is a system that effectively collects and analyzes financial market information and provides personalized investment strategies to individual investors. Specific embodiments for implementing this invention are described below.

[0080] The system primarily consists of three elements: servers, terminals, and users. The servers acquire financial market data from information sources. They utilize API connections and web scraping techniques to collect data from sources such as stock prices, economic indicators, and news feeds. This allows the latest market information to be stored in the database.

[0081] The server utilizes machine learning algorithms to analyze collected data and understand overall market trends. Specifically, it employs techniques such as LSTM networks and ARIMA models to perform time-series forecasting and anomaly detection. This makes it possible to quickly capture fluctuating market trends.

[0082] The terminal acts as the interface with the user, visually displaying analysis results and investment strategies sent from the server. This includes features that present information clearly using charts and graphs. Users input their investment objectives and risk tolerance through the terminal, allowing the server to build a personalized investment plan.

[0083] Users can input questions and investment-related topics in natural language into their terminals, and the server uses a generative AI model to generate appropriate advice in response to these inquiries. Prompts can include specific questions such as, "How can I maximize profits while minimizing risk?" or "What investment strategies are you suggesting based on current market conditions?"

[0084] This system allows individual investors to adapt to fluctuating market conditions and obtain clear and timely investment strategies. Through the integration of terminals and servers, support for individual investors is enhanced, enabling risk management and optimized portfolio construction.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server retrieves data from financial market-related sources. Specifically, it uses data retrieval APIs and web scraping to collect stock price information, economic indicators, and news feeds. Inputs are source URLs and API keys, and output is financial information in raw data format. This allows the server to obtain the necessary market data in real time.

[0088] Step 2:

[0089] The server analyzes the acquired raw data. It uses machine learning algorithms, specifically LSTM and ARIMA, to analyze time-series patterns. The input is raw data, and the output is analysis results showing market trends. This process extracts trends for specific stocks or market segments and predicts future market trends.

[0090] Step 3:

[0091] Users input their investment objectives and risk tolerance via a terminal. This input is in the form of numerical or multiple-choice information. The server then creates an investment profile for the individual investor. The output is the user's profile data, which is used later for developing investment strategies.

[0092] Step 4:

[0093] The server matches user profiles with analysis results to generate personalized investment strategies. The input is the user's profile and market analysis results, and the output is an optimized investment strategy. Specifically, the AI ​​model constructs a recommended portfolio and creates investment scenarios from a risk management perspective.

[0094] Step 5:

[0095] The terminal visually displays investment strategies received from the server. The input is the recommended investment strategy from the server, and the output is visual information (graphs, charts) for the user. This allows the user to intuitively understand the received strategy.

[0096] Step 6:

[0097] Users input investment-related questions into a terminal, and the server uses a generative AI model to generate answers in natural language. The input is the user's question, and the output is the answer in natural language. This allows individual investors to receive appropriate feedback regarding their doubts and concerns.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] For individual investors, making appropriate investment decisions in a fluctuating market environment is a challenging task. In particular, quickly analyzing diverse data and interpreting the results requires specialized knowledge, placing a significant burden on individual investors. Therefore, it is necessary to provide real-time, personalized investment strategies and risk management to enable individual investors to make better investment decisions.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for acquiring data, means for analyzing the acquired data to identify trends, and means for generating strategies based on the investor's objectives and tolerance. This enables users to obtain the latest trends in financial markets and personalized strategies in real time, and to make optimal investment decisions quickly.

[0103] "Means of acquiring data" refers to a function that collects necessary information from external data sources and incorporates it into the system.

[0104] "Means of analyzing acquired data to identify trends" refers to the ability to analyze collected data and perform processing to clarify current market and economic trends.

[0105] "Means for generating strategies based on investor objectives and tolerance" refers to a function for creating customized investment strategies that take into account the investment objectives and risk tolerance of individual investors.

[0106] "Personalized delivery methods" refer to methods for adjusting the generated investment strategies to each investor's profile and delivering them directly.

[0107] A "means for generating answers in natural language" refers to a mechanism for receiving questions from investors and generating answers in natural language.

[0108] "A means of evaluating components and proposing adjustments as needed" refers to a function that evaluates each element within a portfolio and proposes adjustments if the risk increases.

[0109] "A means of providing real-time strategic advice using smartphones" refers to a mechanism that provides users with real-time investment strategies and advice through mobile devices.

[0110] This invention is a system that provides individual investors with real-time investment strategies utilizing financial market information. The system mainly consists of three elements: a server, a terminal, and a user.

[0111] The server acts as the central hub for information. Regarding data acquisition, it obtains financial market data from diverse data sources. This involves using APIs via the internet to aggregate time-series data such as stock prices and economic indicators. Furthermore, the server analyzes this data and utilizes machine learning frameworks such as TENSORFLOW® and PyTorch to perform time-series data modeling and anomaly detection in order to identify market trends.

[0112] The terminal acts as the interface between the user and the server. Using a portable information terminal such as a smartphone, it provides an application for users to input their investment objectives and risk tolerance. This application visualizes data and provides real-time notifications of investment strategies. Natural language processing, including Hugging Face Transformers, is used to receive instructions from the server and display responses to the user in natural language.

[0113] Users can access this system through their devices to obtain personalized investment strategies tailored to their individual profiles. For example, if a user enters a question via a smartphone application such as, "What is the recommended asset allocation based on current market conditions?", the server analyzes the latest market data and presents an appropriate portfolio. This process is rapid, allowing for timely investment decisions without delay.

[0114] By providing new investment insights using generative AI models, users can respond efficiently and effectively to fluctuating markets. An example of how the generative AI model supports effective strategic recommendations in this system is the prompt, "Based on the user's profile, please create a recommended portfolio based on market trends over the past month."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server retrieves financial market data from data sources. It receives stock price and economic indicator data via APIs as input and stores it in its internal database. The data is managed as time-series data and used for future analysis.

[0118] Step 2:

[0119] The server uses machine learning models to analyze the acquired data. It takes time-series data as input and uses TensorFlow or PyTorch to detect data trends and anomalies. This analysis reveals trends in specific markets and generates predictions from the model. The output provides the analyzed market trend information.

[0120] Step 3:

[0121] Users use a terminal to input their investment objectives and risk tolerance. This becomes input data, and the terminal sends this information to the server. This input information is a crucial element when the server generates personalized strategies.

[0122] Step 4:

[0123] The server generates personalized investment strategies based on the user's investment objectives and risk tolerance. It receives analyzed market information and user profiles as input and uses a generating AI model to create strategies. As output, personalized investment strategies are generated and prepared for later distribution.

[0124] Step 5:

[0125] The terminal delivers investment strategies received from the server to the user. The terminal receives personalized strategies as input and presents them visually through the user interface. It also employs a real-time notification function, providing immediate notifications when important strategy updates occur.

[0126] Step 6:

[0127] The user enters investment-related questions through a terminal. The terminal sends the entered questions to a server, which generates appropriate answers using natural language processing.

[0128] Step 7:

[0129] The server uses a natural language processing engine to generate answers to user questions. It analyzes the user's question as input and uses a generative AI model to create answers based on market trend information. The output is the answer expressed in natural language, which is then provided to the user again via the terminal.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention is a system that uses an emotion engine to analyze a user's emotions and provide more effective investment strategies and advice. The following describes in detail the embodiments for carrying out this invention.

[0132] This invention involves three main elements: a server, a terminal, and a user. First, the server acquires financial market information from data sources and analyzes market trends based on this information. Machine learning models are used for the analysis to predict market trends and price fluctuations. Simultaneously, an emotion engine analyzes input from the terminal and estimates the user's emotions in real time.

[0133] Users input their investment goals and risk tolerance into the system via their device. Furthermore, through interaction with the user, the emotion engine identifies emotional states from text-based communication and recognizes emotional fluctuations that may influence investment decisions.

[0134] The server combines the results of sentiment analysis by an emotion engine with general market trend analysis to generate personalized investment strategies for each user. If stress or anxiety is detected in the user, the server will suggest investment options with relatively low risk, thus providing emotionally sensitive strategies.

[0135] The device displays personalized investment strategies delivered from the server to the user in real time. Furthermore, when the user enters investment-related questions or concerns, an emotion engine considers the tone and content to generate appropriate answers, which are then displayed on the device.

[0136] For example, if a user is anxious about fluctuations in the stock market, the emotion engine can identify that emotion, and the server may suggest investing in low-risk bonds after considering market analysis and the user's emotional state. Furthermore, when answering questions, the system can use language that alleviates the user's anxiety.

[0137] By implementing the invention in this way, users will be able to receive information and advice tailored to their individual emotional state, allowing them to make investment decisions with greater confidence.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The server retrieves financial market information in real time from data sources. This includes collecting economic data and news via APIs.

[0141] Step 2:

[0142] The server preprocesses the acquired data and performs analysis to identify market trends and movements. Machine learning models are used in the analysis to derive unique movements and predictions.

[0143] Step 3:

[0144] Users input information about their investment objectives and risk tolerance using their devices and send it to the system.

[0145] Step 4:

[0146] The device transmits user input to an emotion engine, which analyzes the user's emotional state from their text and voice. This allows the system to determine whether the user is feeling safe or anxious.

[0147] Step 5:

[0148] The server combines analyzed market trends with the user's emotional state to generate personalized investment strategies. If stress or anxiety is detected, the system prioritizes strategies with lower risk.

[0149] Step 6:

[0150] The server delivers the generated personalized investment strategy to the user's device using real-time notification technology. This notification is made immediate so that the user can check it in a timely manner.

[0151] Step 7:

[0152] The terminal displays investment information delivered from the server to the user. The user makes investment decisions based on this information.

[0153] Step 8:

[0154] Users enter their investment-related questions and concerns into their device and send them to the server.

[0155] Step 9:

[0156] The server uses natural language processing technology to analyze the user's question and, taking into account the emotional state generated by the emotion engine, generates an appropriate response. This response is crafted in a tone that provides a sense of calm and reassurance.

[0157] Step 10:

[0158] The server sends the generated response to the terminal in real time and displays it to the user. Based on this response, the user can decide on their next course of action after making an informed decision.

[0159] Through the process described above, this system links user emotions with market information, providing more flexible and accurate support in investment activities.

[0160] (Example 2)

[0161] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0162] Individual investors often find it difficult to grasp the complexities of the market and make appropriate investment decisions while considering their own emotional state. Furthermore, providing customized investment strategies that take into account emotional fluctuations and individual risk tolerances in real time is also challenging. This can lead to the risk of making inappropriate decisions in specific market conditions.

[0163] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0164] In this invention, the server includes means for acquiring market data from information sources, means for analyzing the acquired market data and identifying trends, and means for predicting market trends using machine learning algorithms. This makes it possible to analyze the sentiment of individual investors in real time and provide strategies that meet their individual needs.

[0165] "Information sources" refer to external data providers or services accessed to obtain market data.

[0166] "Market data" refers to information related to financial markets, such as prices, trading volume, and economic indicators.

[0167] "Analysis" refers to the process of analyzing acquired market data to derive specific patterns and trends.

[0168] "Trends" refer to the general trends in changes in prices and demand in the market.

[0169] A "machine learning algorithm" refers to a computational method that uses historical data to train a model and then makes predictions about unknown data.

[0170] A "trend" refers to the direction of change over time indicated by prices and other variables in the market.

[0171] An "investor" refers to an individual or group that puts capital into the market with the aim of making a profit.

[0172] An "emotion analysis engine" refers to a technology that analyzes an individual's text and behavior to identify their emotional state.

[0173] "Natural language" refers to the language that humans use on a daily basis, and it is used in technologies that allow computers to understand and process it.

[0174] "Real-time notification technology" refers to communication methods and technologies for instantly transmitting information to users.

[0175] This system is primarily composed of interactions between servers, terminals, and users. The specific functions of each element are described below.

[0176] server

[0177] The server functions as a center for collecting market data. Specifically, it regularly acquires market data from multiple sources. The platforms used include financial data providers, and the data obtained from these is stored in a database. The server then analyzes the data using machine learning algorithms to predict market trends and developments. This process utilizes machine learning frameworks such as TensorFlow and PyTorch. The server also uses a sentiment analysis engine to analyze the sentiment from user text input.

[0178] terminal

[0179] The terminal provides an interface for users to interact with the server. Through the terminal, users can input their investment goals, risk tolerance, and opinions on the market. This information is transmitted to the server in real time. The terminal immediately displays analysis results and personalized investment strategies provided by the server to the user, helping them to make quick investment decisions.

[0180] User

[0181] Users input their financial situation and opinions on the market into the terminal. This allows users to clearly communicate their needs and concerns to the system. For example, they can enter prompts such as, "I'm concerned about the recent market volatility, so please suggest some safe investment options." Based on this, the system detects the user's emotional state and provides investment advice accordingly.

[0182] For example, if a user inputs "I feel anxious about the sharp decline in stock prices," the sentiment analysis engine identifies that emotion, and the server uses that data to suggest investing in high-safety bonds. This allows the user to make investment decisions with confidence, taking risk into consideration.

[0183] This system effectively integrates these components to provide users with timely and appropriate investment advice tailored to their individual emotions and market trends.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] The server retrieves market data from multiple sources. It receives price information and economic indicators from financial data providers as input and stores this data in its database. After saving, the server performs data formatting processing so that it can be used in subsequent analysis processes.

[0187] Step 2:

[0188] Users input information about their investment goals and risk tolerance through their device. The device collects this data from the user in text format and immediately sends it to the server. The entered information serves as the basis for generating investment strategies.

[0189] Step 3:

[0190] The server uses an emotion analysis engine to analyze text data sent by the user. It receives the user's text message as input and extracts emotional characteristics using natural language processing techniques. As a result of the emotion analysis, the user's emotional state is output as "reassured," "anxious," "stressed," etc.

[0191] Step 4:

[0192] The server uses machine learning algorithms to analyze acquired market data and predict future market trends. Using the market data formatted in Step 1 as input data, the machine learning model is executed to obtain prediction results. The output of the prediction will provide information on future price fluctuations and trends.

[0193] Step 5:

[0194] The server combines sentiment analysis results with market trend forecasts to generate personalized investment strategies. Inputs include the user's emotional state and market forecast data, and an algorithm is used to select options suitable for the user. The resulting strategies include recommended investment options ranging from low-risk to high-risk.

[0195] Step 6:

[0196] The terminal presents personalized investment strategies provided by the server to the user in real time. It receives strategy data from the server as input and visualizes and displays it through a graphical interface. The user can then review the strategy details on the screen and decide on their next action.

[0197] Step 7:

[0198] If a user has additional questions or concerns regarding the strategy, they can inquire again through their device. The device sends the user's questions as text data to the server. The server reuses its sentiment analysis engine to generate appropriate answers and returns them to the device. This allows the user to continue making investment decisions with confidence.

[0199] (Application Example 2)

[0200] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0201] The aim is to solve the problem of difficulty in providing investment strategies that take into account the influence of emotions on individuals' financial activities, and to support individuals in making investment decisions with confidence. Furthermore, it aims to improve financial management skills by suggesting optimal payment methods tailored to each individual's emotional state.

[0202] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0203] In this invention, the server includes means for acquiring financial market information from data collection sources, means for analyzing the acquired financial market information and identifying market trends, means for generating investment strategies based on an individual's investment objectives and risk tolerance, means for personalizing the generated investment strategies and delivering them to the individual, means for generating responses in natural language to inquiries from the individual, means for evaluating the risk of the portfolio and proposing restructuring as necessary, and means for identifying an individual's emotional state using sentiment analysis technology and proposing payment methods appropriate to that emotional state. This makes it possible to provide investment strategies and payment methods that take into account an individual's emotional state.

[0204] A "data source" is an external or internal source of information used to collect information on financial markets.

[0205] "Financial market information" refers to data on conditions in financial markets, such as prices, trading volume, and trends.

[0206] "Market trends" is a concept that refers to price fluctuations, trends, and other market movements in financial markets.

[0207] "Individual" refers to an investor with specific investment objectives and risk tolerance.

[0208] "Investment objectives" refer to the goals that an individual aims to achieve when engaging in investment activities.

[0209] "Risk tolerance" is a measure that represents the range of risk an individual is willing to accept in investments.

[0210] An "investment strategy" is a plan or policy for conducting investment activities, formulated based on an individual's investment objectives and risk tolerance.

[0211] "Emotional analysis technology" is a method of identifying an individual's emotional state from their text, voice, and actions.

[0212] "Personalization" refers to customizing general information or strategies to suit the specific circumstances of an individual.

[0213] "Real-time notification technology" is a communication technology that transmits information to users instantly.

[0214] "Restructuring" refers to the act of changing the composition of a portfolio and adjusting its risk and profitability.

[0215] "Emotional state" refers to an individual's current psychological and emotional state.

[0216] "Payment method" refers to the means or process of settlement used by an individual when conducting financial transactions.

[0217] The system for realizing this invention mainly consists of three main elements: a server, a terminal, and a user.

[0218] The server acquires financial market information from data sources and analyzes market trends based on that information. Machine learning models are used for the analysis to predict market trends and price fluctuations. Furthermore, the server uses sentiment analysis technology to analyze text and voice input from users to provide a real-time estimation of the user's emotional state. For this purpose, it utilizes natural language processing libraries such as Transformers and the BERT model.

[0219] The terminal functions as an interface for the user to interact with the system, receiving the user's investment objectives and risk tolerance. It also displays personalized investment strategies and emotionally responsive payment method suggestions in real time. The server provides natural language answers to questions and concerns entered by the user through the terminal, assisting the user.

[0220] Users input information related to their investments into their device and receive suggestions from the server for the optimal investment strategy and payment method based on their emotional state. For example, if a user is feeling stressed, the server can suggest a low-risk payment plan.

[0221] Using a generative AI model, the server will provide personalized suggestions based on prompts such as: "The user is feeling anxious and needs low-risk financial advice. Please generate a secure payment plan to suggest to him."

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The server retrieves financial market information from data sources. Specifically, it collects price, trading volume, and trend data using APIs such as the Yahoo Finance API. This data is used as input for subsequent market trend analysis.

[0225] Step 2:

[0226] The server analyzes acquired financial market information using a machine learning model to identify market trends. This analysis outputs market trends and price fluctuation predictions. Specifically, it uses the scikit-learn library in Python to perform regression analysis on historical data.

[0227] Step 3:

[0228] Users input their investment objectives and risk tolerance via a terminal. This input information is used as basic data for the server to generate appropriate investment strategies.

[0229] Step 4:

[0230] The server processes data extracted from text and audio using sentiment analysis technology to analyze the user's emotional state. Specifically, it uses the Transformers library and the BERT model to estimate the emotional state. This process outputs data related to the user's emotional state.

[0231] Step 5:

[0232] The server combines the market trend analysis results obtained in Step 2, the user's input information in Step 3, and their emotional state in Step 4 to generate a personalized investment strategy and payment method. As output, it presents low-risk or high-risk investment plans based on the user's emotional state.

[0233] Step 6:

[0234] The device displays the generated investment strategy and suggested payment methods to the user in real time. If the user asks questions about the presented plan, the device collects those questions.

[0235] Step 7:

[0236] The server analyzes the user's question using natural language processing techniques and generates an appropriate answer. This process also utilizes the BERT model to output answers that take the user's emotions into account.

[0237] Step 8:

[0238] The terminal presents the user with a response from the server. The user can then decide on further actions based on this response. The system functions in a loop, repeating this process.

[0239] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0240] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0241] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0242] [Second Embodiment]

[0243] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0244] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0245] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0246] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0247] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0248] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0249] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0250] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0251] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0252] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0253] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0254] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0255] This invention is a system that utilizes collected financial market information to provide effective investment strategies to individual investors. The following describes embodiments for carrying out this invention.

[0256] This system consists primarily of three elements: servers, terminals, and users.

[0257] The server acquires information about financial markets from diverse data sources, integrates it, and stores it in a database. Specific data sources include stock price information, economic indicators, and international news feeds. Furthermore, the server uses this data to analyze overall market trends and trends related to specific stocks. This involves the use of advanced machine learning models for time-series data analysis, anomaly detection, and predictive modeling.

[0258] For users, the first step is to register with the system via a terminal and provide information about their investment objectives and risk tolerance. This allows the server to create and update user profiles and prepare to propose personalized investment strategies.

[0259] The terminal acts as the interface with the user, visually displaying investment information transmitted from the server. Furthermore, the terminal allows users to input any questions or concerns they may have regarding investments, and this information is then sent to the server.

[0260] The server utilizes natural language processing technology to quickly generate and respond to user inquiries with appropriate advice. In this process, the natural language processing engine analyzes the user's question and creates an answer based on relevant analysis results and past market trends.

[0261] In the investment strategy proposal section, the server optimizes the portfolio and selects specific stocks based on the user's profile and market analysis results. Furthermore, from a risk management perspective, it continuously assesses the portfolio's risk and proposes rebalancing as needed. For example, if risk becomes excessively concentrated, it may recommend reinvesting in bonds or other low-risk assets.

[0262] This allows individual investors to gain clear and timely strategic insights, effectively respond to fluctuating markets, and make decisions to achieve their investment goals.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] The server retrieves real-time financial market information from data sources. This includes collecting stock price data, economic indicators, and news articles via APIs.

[0266] Step 2:

[0267] The server preprocesses the acquired raw data. Specifically, it cleanses and normalizes the data and converts it into a format suitable for analysis.

[0268] Step 3:

[0269] The server analyzes pre-processed data using machine learning models to identify market trends and developments. This includes detecting outliers and predicting future price fluctuations.

[0270] Step 4:

[0271] Users input their investment objectives and risk tolerance using a terminal and send this information to the system.

[0272] Step 5:

[0273] The server creates a user profile based on information provided by the user and generates a personalized investment strategy. The user profile is stored in a database and updated as needed.

[0274] Step 6:

[0275] The server delivers the generated investment strategies to the user's device in real time. This delivery uses technologies such as WebSocket and push notifications.

[0276] Step 7:

[0277] The user inputs investment-related questions through the terminal and sends them to the server.

[0278] Step 8:

[0279] The server analyzes the user's questions using natural language processing technology, generates appropriate answers, and returns them to the terminal. At this time, while referring to past analysis results and market trends, a detailed explanation according to the context is provided.

[0280] Step 9:

[0281] The server continuously monitors the user's portfolio and conducts a risk assessment of the portfolio. If necessary, a proposal for rebalancing is made and the content of the proposal is notified to the terminal.

[0282] By the coordinated operation of these steps, the user can receive highly accurate investment information in real time, which helps to make wise investment decisions based on the information.

[0283] (Example 1)

[0284] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] When individual investors formulate effective investment strategies in the financial market, issues they face include appropriately analyzing a large amount of market data and generating strategies that match their investment objectives and risk tolerance. Furthermore, in a rapidly changing market environment, it is difficult to obtain information in real time and optimize the portfolio. The inability to obtain investment advice in natural language also poses a communication barrier.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0287] In this invention, the server includes means for acquiring data from information sources, means for analyzing the acquired data to identify market trends, and means for generating investment plans according to an individual's investment objectives and risk tolerance. This enables individual investors to efficiently analyze large amounts of data, receive investment strategies tailored to their needs in real time, and respond quickly to market changes through natural language support.

[0288] "Information sources" refer to a variety of sources that provide data related to financial markets. Specific examples include stock price information, economic indicators, and news feeds.

[0289] "Data analysis" refers to all techniques used to process acquired data and identify market trends and patterns. This includes statistical methods and machine learning algorithms.

[0290] A "personalized investment plan" refers to an investment strategy tailored to an individual's investment objectives and risk tolerance. This plan proposes specific actions based on market analysis results.

[0291] "Natural language processing" refers to the technology that enables computers to understand and generate human language. This makes it possible to generate appropriate answers in human language to user inquiries.

[0292] A "generative AI model" refers to a predictive model that uses machine learning algorithms to create new data and information. In particular, in language generation, it can provide natural dialogue in response to user prompts.

[0293] "Portfolio adjustment" refers to the process of reviewing and optimizing the allocation of investment assets in response to risk assessments and market fluctuations. This adjustment allows investors to pursue the minimization of risk and the maximization of returns.

[0294] This invention is a system that effectively collects and analyzes financial market information and provides personalized investment strategies to individual investors. Specific embodiments for implementing this invention are described below.

[0295] The system primarily consists of three elements: servers, terminals, and users. The servers acquire financial market data from information sources. They utilize API connections and web scraping techniques to collect data from sources such as stock prices, economic indicators, and news feeds. This allows the latest market information to be stored in the database.

[0296] The server utilizes machine learning algorithms to analyze collected data and understand overall market trends. Specifically, it employs techniques such as LSTM networks and ARIMA models to perform time-series forecasting and anomaly detection. This makes it possible to quickly capture fluctuating market trends.

[0297] The terminal acts as the interface with the user, visually displaying analysis results and investment strategies sent from the server. This includes features that present information clearly using charts and graphs. Users input their investment objectives and risk tolerance through the terminal, allowing the server to build a personalized investment plan.

[0298] Users can input questions and investment-related topics in natural language into their terminals, and the server uses a generative AI model to generate appropriate advice in response to these inquiries. Prompts can include specific questions such as, "How can I maximize profits while minimizing risk?" or "What investment strategies are you suggesting based on current market conditions?"

[0299] This system allows individual investors to adapt to fluctuating market conditions and obtain clear and timely investment strategies. Through the integration of terminals and servers, support for individual investors is enhanced, enabling risk management and optimized portfolio construction.

[0300] The flow of the specific process in Example 1 will be described using FIG. 11.

[0301] Step 1:

[0302] The server obtains data from information sources related to the financial market. Specifically, it collects stock price information, economic indicators, and news feeds using data acquisition APIs or web scraping. The input is the information source URL or API key, and the output is financial information in raw data format. Thereby, the necessary market data is acquired in real time.

[0303] Step 2:

[0304] The server analyzes the acquired raw data. It utilizes machine learning algorithms, specifically LSTM or ARIMA, to analyze time series patterns. The input is the raw data, and the output is the analysis result indicating market trends. In this step, trends of specific stocks or market segments are extracted to predict the future trends of the market.

[0305] Step 3:

[0306] The user inputs investment objectives and risk tolerance via a terminal. The input is information in numerical or option form. Thereby, the server creates an investment profile for individual investors. The output is the user's profile data, which is used for subsequent investment strategy formulation.

[0307] Step 4:

[0308] The server matches the user profile and the analysis result to generate an individualized investment strategy. The input is the user's profile and the market analysis result, and the output is an optimized investment strategy. Specifically, an AI model constructs a recommended portfolio and creates investment scenarios from the perspective of risk management.

[0309] Step 5:

[0310] The terminal visually displays investment strategies received from the server. The input is the recommended investment strategy from the server, and the output is visual information (graphs, charts) for the user. This allows the user to intuitively understand the received strategy.

[0311] Step 6:

[0312] Users input investment-related questions into a terminal, and the server uses a generative AI model to generate answers in natural language. The input is the user's question, and the output is the answer in natural language. This allows individual investors to receive appropriate feedback regarding their doubts and concerns.

[0313] (Application Example 1)

[0314] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0315] For individual investors, making appropriate investment decisions in a fluctuating market environment is a challenging task. In particular, quickly analyzing diverse data and interpreting the results requires specialized knowledge, placing a significant burden on individual investors. Therefore, it is necessary to provide real-time, personalized investment strategies and risk management to enable individual investors to make better investment decisions.

[0316] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0317] In this invention, the server includes means for acquiring data, means for analyzing the acquired data to identify trends, and means for generating strategies based on the investor's objectives and tolerance. This enables users to obtain the latest trends in financial markets and personalized strategies in real time, and to make optimal investment decisions quickly.

[0318] "Means of acquiring data" refers to a function that collects necessary information from external data sources and incorporates it into the system.

[0319] "Means of analyzing acquired data to identify trends" refers to the ability to analyze collected data and perform processing to clarify current market and economic trends.

[0320] "Means for generating strategies based on investor objectives and tolerance" refers to a function for creating customized investment strategies that take into account the investment objectives and risk tolerance of individual investors.

[0321] "Personalized delivery methods" refer to methods for adjusting the generated investment strategies to each investor's profile and delivering them directly.

[0322] A "means for generating answers in natural language" refers to a mechanism for receiving questions from investors and generating answers in natural language.

[0323] "A means of evaluating components and proposing adjustments as needed" refers to a function that evaluates each element within a portfolio and proposes adjustments if the risk increases.

[0324] "A means of providing real-time strategic advice using smartphones" refers to a mechanism that provides users with real-time investment strategies and advice through mobile devices.

[0325] This invention is a system that provides individual investors with real-time investment strategies utilizing financial market information. The system mainly consists of three elements: a server, a terminal, and a user.

[0326] The server acts as the central hub for information. Regarding data acquisition, it retrieves financial market data from diverse data sources. This involves using APIs via the internet to aggregate time-series data such as stock prices and economic indicators. Furthermore, the server analyzes this data and utilizes machine learning frameworks such as TensorFlow and PyTorch to perform time-series data modeling and anomaly detection in order to identify market trends.

[0327] The terminal acts as the interface between the user and the server. Using a portable information terminal such as a smartphone, it provides an application for users to input their investment objectives and risk tolerance. This application visualizes data and provides real-time notifications of investment strategies. Natural language processing, including Hugging Face Transformers, is used to receive instructions from the server and display responses to the user in natural language.

[0328] Users can access this system through their devices to obtain personalized investment strategies tailored to their individual profiles. For example, if a user enters a question via a smartphone application such as, "What is the recommended asset allocation based on current market conditions?", the server analyzes the latest market data and presents an appropriate portfolio. This process is rapid, allowing for timely investment decisions without delay.

[0329] By providing new investment insights using generative AI models, users can respond efficiently and effectively to fluctuating markets. An example of how the generative AI model supports effective strategic recommendations in this system is the prompt, "Based on the user's profile, please create a recommended portfolio based on market trends over the past month."

[0330] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0331] Step 1:

[0332] The server retrieves financial market data from data sources. It receives stock price and economic indicator data via APIs as input and stores it in its internal database. The data is managed as time-series data and used for future analysis.

[0333] Step 2:

[0334] The server uses machine learning models to analyze the acquired data. It takes time-series data as input and uses TensorFlow or PyTorch to detect data trends and anomalies. This analysis reveals trends in specific markets and generates predictions from the model. The output provides the analyzed market trend information.

[0335] Step 3:

[0336] Users use a terminal to input their investment objectives and risk tolerance. This becomes input data, and the terminal sends this information to the server. This input information is a crucial element when the server generates personalized strategies.

[0337] Step 4:

[0338] The server generates personalized investment strategies based on the user's investment objectives and risk tolerance. It receives analyzed market information and user profiles as input and uses a generating AI model to create strategies. As output, personalized investment strategies are generated and prepared for later distribution.

[0339] Step 5:

[0340] The terminal delivers investment strategies received from the server to the user. The terminal receives personalized strategies as input and presents them visually through the user interface. It also employs a real-time notification function, providing immediate notifications when important strategy updates occur.

[0341] Step 6:

[0342] The user enters investment-related questions through a terminal. The terminal sends the entered questions to a server, which generates appropriate answers using natural language processing.

[0343] Step 7:

[0344] The server uses a natural language processing engine to generate answers to user questions. It analyzes the user's question as input and uses a generative AI model to create answers based on market trend information. The output is the answer expressed in natural language, which is then provided to the user again via the terminal.

[0345] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0346] This invention is a system that uses an emotion engine to analyze a user's emotions and provide more effective investment strategies and advice. The following describes in detail the embodiments for carrying out this invention.

[0347] This invention involves three main elements: a server, a terminal, and a user. First, the server acquires financial market information from data sources and analyzes market trends based on this information. Machine learning models are used for the analysis to predict market trends and price fluctuations. Simultaneously, an emotion engine analyzes input from the terminal and estimates the user's emotions in real time.

[0348] Users input their investment goals and risk tolerance into the system via their device. Furthermore, through interaction with the user, the emotion engine identifies emotional states from text-based communication and recognizes emotional fluctuations that may influence investment decisions.

[0349] The server combines the results of sentiment analysis by an emotion engine with general market trend analysis to generate personalized investment strategies for each user. If stress or anxiety is detected in the user, the server will suggest investment options with relatively low risk, thus providing emotionally sensitive strategies.

[0350] The device displays personalized investment strategies delivered from the server to the user in real time. Furthermore, when the user enters investment-related questions or concerns, an emotion engine considers the tone and content to generate appropriate answers, which are then displayed on the device.

[0351] For example, if a user is anxious about fluctuations in the stock market, the emotion engine can identify that emotion, and the server may suggest investing in low-risk bonds after considering market analysis and the user's emotional state. Furthermore, when answering questions, the system can use language that alleviates the user's anxiety.

[0352] By implementing the invention in this way, users will be able to receive information and advice tailored to their individual emotional state, allowing them to make investment decisions with greater confidence.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The server retrieves financial market information in real time from data sources. This includes collecting economic data and news via APIs.

[0356] Step 2:

[0357] The server preprocesses the acquired data and performs analysis to identify market trends and movements. Machine learning models are used in the analysis to derive unique movements and predictions.

[0358] Step 3:

[0359] Users input information about their investment objectives and risk tolerance using their devices and send it to the system.

[0360] Step 4:

[0361] The device transmits user input to an emotion engine, which analyzes the user's emotional state from their text and voice. This allows the system to determine whether the user is feeling safe or anxious.

[0362] Step 5:

[0363] The server combines analyzed market trends with the user's emotional state to generate personalized investment strategies. If stress or anxiety is detected, the system prioritizes strategies with lower risk.

[0364] Step 6:

[0365] The server delivers the generated personalized investment strategy to the user's device using real-time notification technology. This notification is made immediate so that the user can check it in a timely manner.

[0366] Step 7:

[0367] The terminal displays investment information delivered from the server to the user. The user makes investment decisions based on this information.

[0368] Step 8:

[0369] Users enter their investment-related questions and concerns into their device and send them to the server.

[0370] Step 9:

[0371] The server uses natural language processing technology to analyze the user's question and, taking into account the emotional state generated by the emotion engine, generates an appropriate response. This response is crafted in a tone that provides a sense of calm and reassurance.

[0372] Step 10:

[0373] The server sends the generated response to the terminal in real time and displays it to the user. Based on this response, the user can decide on their next course of action after making an informed decision.

[0374] Through the process described above, this system links user emotions with market information, providing more flexible and accurate support in investment activities.

[0375] (Example 2)

[0376] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0377] Individual investors often find it difficult to grasp the complexities of the market and make appropriate investment decisions while considering their own emotional state. Furthermore, providing customized investment strategies that take into account emotional fluctuations and individual risk tolerances in real time is also challenging. This can lead to the risk of making inappropriate decisions in specific market conditions.

[0378] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0379] In this invention, the server includes means for acquiring market data from information sources, means for analyzing the acquired market data and identifying trends, and means for predicting market trends using machine learning algorithms. This makes it possible to analyze the sentiment of individual investors in real time and provide strategies that meet their individual needs.

[0380] "Information sources" refer to external data providers or services accessed to obtain market data.

[0381] "Market data" refers to information related to financial markets, such as prices, trading volume, and economic indicators.

[0382] "Analysis" refers to the process of analyzing acquired market data to derive specific patterns and trends.

[0383] "Trends" refer to the general trends in changes in prices and demand in the market.

[0384] A "machine learning algorithm" refers to a computational method that uses historical data to train a model and then makes predictions about unknown data.

[0385] A "trend" refers to the direction of change over time indicated by prices and other variables in the market.

[0386] An "investor" refers to an individual or group that puts capital into the market with the aim of making a profit.

[0387] An "emotion analysis engine" refers to a technology that analyzes an individual's text and behavior to identify their emotional state.

[0388] "Natural language" refers to the language that humans use on a daily basis, and it is used in technologies that allow computers to understand and process it.

[0389] "Real-time notification technology" refers to communication methods and technologies for instantly transmitting information to users.

[0390] This system is primarily composed of interactions between servers, terminals, and users. The specific functions of each element are described below.

[0391] server

[0392] The server functions as a center for collecting market data. Specifically, it regularly acquires market data from multiple sources. The platforms used include financial data providers, and the data obtained from these is stored in a database. The server then analyzes the data using machine learning algorithms to predict market trends and developments. This process utilizes machine learning frameworks such as TensorFlow and PyTorch. The server also uses a sentiment analysis engine to analyze the sentiment from user text input.

[0393] terminal

[0394] The terminal provides an interface for users to interact with the server. Through the terminal, users can input their investment goals, risk tolerance, and opinions on the market. This information is transmitted to the server in real time. The terminal immediately displays analysis results and personalized investment strategies provided by the server to the user, helping them to make quick investment decisions.

[0395] User

[0396] Users input their financial situation and opinions on the market into the terminal. This allows users to clearly communicate their needs and concerns to the system. For example, they can enter prompts such as, "I'm concerned about the recent market volatility, so please suggest some safe investment options." Based on this, the system detects the user's emotional state and provides investment advice accordingly.

[0397] For example, if a user inputs "I feel anxious about the sharp decline in stock prices," the sentiment analysis engine identifies that emotion, and the server uses that data to suggest investing in high-safety bonds. This allows the user to make investment decisions with confidence, taking risk into consideration.

[0398] This system effectively integrates these components to provide users with timely and appropriate investment advice tailored to their individual emotions and market trends.

[0399] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0400] Step 1:

[0401] The server retrieves market data from multiple sources. It receives price information and economic indicators from financial data providers as input and stores this data in its database. After saving, the server performs data formatting processing so that it can be used in subsequent analysis processes.

[0402] Step 2:

[0403] Users input information about their investment goals and risk tolerance through their device. The device collects this data from the user in text format and immediately sends it to the server. The entered information serves as the basis for generating investment strategies.

[0404] Step 3:

[0405] The server uses an emotion analysis engine to analyze text data sent by the user. It receives the user's text message as input and extracts emotional characteristics using natural language processing techniques. As a result of the emotion analysis, the user's emotional state is output as "reassured," "anxious," "stressed," etc.

[0406] Step 4:

[0407] The server uses machine learning algorithms to analyze acquired market data and predict future market trends. Using the market data formatted in Step 1 as input data, the machine learning model is executed to obtain prediction results. The output of the prediction will provide information on future price fluctuations and trends.

[0408] Step 5:

[0409] The server combines sentiment analysis results with market trend forecasts to generate personalized investment strategies. Inputs include the user's emotional state and market forecast data, and an algorithm is used to select options suitable for the user. The resulting strategies include recommended investment options ranging from low-risk to high-risk.

[0410] Step 6:

[0411] The terminal presents personalized investment strategies provided by the server to the user in real time. It receives strategy data from the server as input and visualizes and displays it through a graphical interface. The user can then review the strategy details on the screen and decide on their next action.

[0412] Step 7:

[0413] If a user has additional questions or concerns regarding the strategy, they can inquire again through their device. The device sends the user's questions as text data to the server. The server reuses its sentiment analysis engine to generate appropriate answers and returns them to the device. This allows the user to continue making investment decisions with confidence.

[0414] (Application Example 2)

[0415] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0416] The aim is to solve the problem of difficulty in providing investment strategies that take into account the influence of emotions in individuals' financial activities, and to support individuals in making investment decisions with confidence. Furthermore, it aims to improve financial management skills by suggesting optimal payment methods tailored to each individual's emotional state.

[0417] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0418] In this invention, the server includes means for acquiring financial market information from data collection sources, means for analyzing the acquired financial market information and identifying market trends, means for generating investment strategies based on an individual's investment objectives and risk tolerance, means for personalizing the generated investment strategies and delivering them to the individual, means for generating responses in natural language to inquiries from the individual, means for evaluating the risk of the portfolio and proposing restructuring as necessary, and means for identifying an individual's emotional state using sentiment analysis technology and proposing payment methods appropriate to that emotional state. This makes it possible to provide investment strategies and payment methods that take into account an individual's emotional state.

[0419] A "data source" is an external or internal source of information used to collect information on financial markets.

[0420] "Financial market information" refers to data on conditions in financial markets, such as prices, trading volume, and trends.

[0421] "Market trends" is a concept that refers to price fluctuations, trends, and other market movements in financial markets.

[0422] "Individual" refers to an investor with specific investment objectives and risk tolerance.

[0423] "Investment objectives" refer to the goals that an individual aims to achieve when engaging in investment activities.

[0424] "Risk tolerance" is a measure that represents the range of risk an individual is willing to accept in investments.

[0425] An "investment strategy" is a plan or policy for conducting investment activities, formulated based on an individual's investment objectives and risk tolerance.

[0426] "Emotional analysis technology" is a method of identifying an individual's emotional state from their text, voice, and actions.

[0427] "Personalization" refers to customizing general information or strategies to suit the specific circumstances of an individual.

[0428] "Real-time notification technology" is a communication technology that transmits information to users instantly.

[0429] "Restructuring" refers to the act of changing the composition of a portfolio and adjusting its risk and profitability.

[0430] "Emotional state" refers to an individual's current psychological and emotional state.

[0431] "Payment method" refers to the means or process of settlement used by an individual when conducting financial transactions.

[0432] The system for realizing this invention mainly consists of three main elements: a server, a terminal, and a user.

[0433] The server acquires financial market information from data sources and analyzes market trends based on that information. Machine learning models are used for the analysis to predict market trends and price fluctuations. Furthermore, the server uses sentiment analysis technology to analyze text and voice input from users to provide a real-time estimation of the user's emotional state. For this purpose, it utilizes natural language processing libraries such as Transformers and the BERT model.

[0434] The terminal functions as an interface for the user to interact with the system, receiving the user's investment objectives and risk tolerance. It also displays personalized investment strategies and emotionally responsive payment method suggestions in real time. The server provides natural language answers to questions and concerns entered by the user through the terminal, assisting the user.

[0435] Users input information related to their investments into their device and receive suggestions from the server for the optimal investment strategy and payment method based on their emotional state. For example, if a user is feeling stressed, the server can suggest a low-risk payment plan.

[0436] Using a generative AI model, the server will provide personalized suggestions based on prompts such as: "The user is feeling anxious and needs low-risk financial advice. Please generate a secure payment plan to suggest to him."

[0437] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0438] Step 1:

[0439] The server retrieves financial market information from data sources. Specifically, it collects price, trading volume, and trend data using APIs such as the Yahoo Finance API. This data is used as input for subsequent market trend analysis.

[0440] Step 2:

[0441] The server analyzes acquired financial market information using a machine learning model to identify market trends. This analysis outputs market trends and price fluctuation predictions. Specifically, it uses the scikit-learn library in Python to perform regression analysis on historical data.

[0442] Step 3:

[0443] Users input their investment objectives and risk tolerance via a terminal. This input information is used as basic data for the server to generate appropriate investment strategies.

[0444] Step 4:

[0445] The server processes data extracted from text and audio using sentiment analysis technology to analyze the user's emotional state. Specifically, it uses the Transformers library and the BERT model to estimate the emotional state. This process outputs data related to the user's emotional state.

[0446] Step 5:

[0447] The server combines the market trend analysis results obtained in Step 2, the user's input information in Step 3, and their emotional state in Step 4 to generate a personalized investment strategy and payment method. As output, it presents low-risk or high-risk investment plans based on the user's emotional state.

[0448] Step 6:

[0449] The device displays the generated investment strategy and suggested payment methods to the user in real time. If the user asks questions about the presented plan, the device collects those questions.

[0450] Step 7:

[0451] The server analyzes the user's question using natural language processing techniques and generates an appropriate answer. This process also utilizes the BERT model to output answers that take the user's emotions into account.

[0452] Step 8:

[0453] The terminal presents the user with a response from the server. The user can then decide on further actions based on this response. The system functions in a loop, repeating this process.

[0454] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0455] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0456] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0457] [Third Embodiment]

[0458] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0459] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0460] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0461] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0462] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0463] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0464] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0465] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0466] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0467] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0468] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0469] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0470] This invention is a system that utilizes collected financial market information to provide effective investment strategies to individual investors. The following describes embodiments for carrying out this invention.

[0471] This system consists primarily of three elements: servers, terminals, and users.

[0472] The server acquires information about financial markets from diverse data sources, integrates it, and stores it in a database. Specific data sources include stock price information, economic indicators, and international news feeds. Furthermore, the server uses this data to analyze overall market trends and trends related to specific stocks. This involves the use of advanced machine learning models for time-series data analysis, anomaly detection, and predictive modeling.

[0473] For users, the first step is to register with the system via a terminal and provide information about their investment objectives and risk tolerance. This allows the server to create and update user profiles and prepare to propose personalized investment strategies.

[0474] The terminal acts as the interface with the user, visually displaying investment information transmitted from the server. Furthermore, the terminal allows users to input any questions or concerns they may have regarding investments, and this information is then sent to the server.

[0475] The server utilizes natural language processing technology to quickly generate and respond to user inquiries with appropriate advice. In this process, the natural language processing engine analyzes the user's question and creates an answer based on relevant analysis results and past market trends.

[0476] In the investment strategy proposal section, the server optimizes the portfolio and selects specific stocks based on the user's profile and market analysis results. Furthermore, from a risk management perspective, it continuously assesses the portfolio's risk and proposes rebalancing as needed. For example, if risk becomes excessively concentrated, it may recommend reinvesting in bonds or other low-risk assets.

[0477] This allows individual investors to gain clear and timely strategic insights, effectively respond to fluctuating markets, and make decisions to achieve their investment goals.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] The server retrieves real-time financial market information from data sources. This includes collecting stock price data, economic indicators, and news articles via APIs.

[0481] Step 2:

[0482] The server preprocesses the acquired raw data. Specifically, it cleanses and normalizes the data and converts it into a format suitable for analysis.

[0483] Step 3:

[0484] The server analyzes pre-processed data using machine learning models to identify market trends and developments. This includes detecting outliers and predicting future price fluctuations.

[0485] Step 4:

[0486] Users input their investment objectives and risk tolerance using a terminal and send this information to the system.

[0487] Step 5:

[0488] The server creates a user profile based on information provided by the user and generates a personalized investment strategy. The user profile is stored in a database and updated as needed.

[0489] Step 6:

[0490] The server delivers the generated investment strategies to the user's device in real time. This delivery uses technologies such as WebSocket and push notifications.

[0491] Step 7:

[0492] The user enters investment-related questions via their device and sends them to the server.

[0493] Step 8:

[0494] The server uses natural language processing technology to analyze the user's question, generate an appropriate answer, and send it back to the terminal. During this process, it provides a detailed, contextual explanation, referencing past analysis results and market trends.

[0495] Step 9:

[0496] The server continuously monitors the user's portfolio and performs a risk assessment of the portfolio. It proposes rebalancing as needed and notifies the user of the proposed changes.

[0497] These steps work together to enable users to receive highly accurate investment information in real time, helping them make informed and informed investment decisions.

[0498] (Example 1)

[0499] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0500] One of the challenges individual investors face when developing effective investment strategies in financial markets is the need to properly analyze large amounts of market data and generate strategies that match their investment objectives and risk tolerance. Furthermore, obtaining real-time information and optimizing portfolios in a rapidly changing market environment is difficult. The inability to obtain investment advice in natural language also poses a communication barrier.

[0501] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0502] In this invention, the server includes means for acquiring data from information sources, means for analyzing the acquired data to identify market trends, and means for generating investment plans according to an individual's investment objectives and risk tolerance. This enables individual investors to efficiently analyze large amounts of data, receive investment strategies tailored to their needs in real time, and respond quickly to market changes through natural language support.

[0503] "Information sources" refer to a variety of sources that provide data related to financial markets. Specific examples include stock price information, economic indicators, and news feeds.

[0504] "Data analysis" refers to all techniques used to process acquired data and identify market trends and patterns. This includes statistical methods and machine learning algorithms.

[0505] A "personalized investment plan" refers to an investment strategy tailored to an individual's investment objectives and risk tolerance. This plan proposes specific actions based on market analysis results.

[0506] "Natural language processing" refers to the technology that enables computers to understand and generate human language. This makes it possible to generate appropriate answers in human language to user inquiries.

[0507] A "generative AI model" refers to a predictive model that uses machine learning algorithms to create new data and information. In particular, in language generation, it can provide natural dialogue in response to user prompts.

[0508] "Portfolio adjustment" refers to the process of reviewing and optimizing the allocation of investment assets in response to risk assessments and market fluctuations. This adjustment allows investors to pursue the minimization of risk and the maximization of returns.

[0509] This invention is a system that effectively collects and analyzes financial market information and provides personalized investment strategies to individual investors. Specific embodiments for implementing this invention are described below.

[0510] The system primarily consists of three elements: servers, terminals, and users. The servers acquire financial market data from information sources. They utilize API connections and web scraping techniques to collect data from sources such as stock prices, economic indicators, and news feeds. This allows the latest market information to be stored in the database.

[0511] The server utilizes machine learning algorithms to analyze collected data and understand overall market trends. Specifically, it employs techniques such as LSTM networks and ARIMA models to perform time-series forecasting and anomaly detection. This makes it possible to quickly capture fluctuating market trends.

[0512] The terminal acts as the interface with the user, visually displaying analysis results and investment strategies sent from the server. This includes features that present information clearly using charts and graphs. Users input their investment objectives and risk tolerance through the terminal, allowing the server to build a personalized investment plan.

[0513] Users can input questions and investment-related topics in natural language into their terminals, and the server uses a generative AI model to generate appropriate advice in response to these inquiries. Prompts can include specific questions such as, "How can I maximize profits while minimizing risk?" or "What investment strategies are you suggesting based on current market conditions?"

[0514] This system allows individual investors to adapt to fluctuating market conditions and obtain clear and timely investment strategies. Through the integration of terminals and servers, support for individual investors is enhanced, enabling risk management and optimized portfolio construction.

[0515] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0516] Step 1:

[0517] The server retrieves data from financial market-related sources. Specifically, it uses data retrieval APIs and web scraping to collect stock price information, economic indicators, and news feeds. Inputs are source URLs and API keys, and output is financial information in raw data format. This allows the server to obtain the necessary market data in real time.

[0518] Step 2:

[0519] The server analyzes the acquired raw data. It uses machine learning algorithms, specifically LSTM and ARIMA, to analyze time-series patterns. The input is raw data, and the output is analysis results showing market trends. This process extracts trends for specific stocks or market segments and predicts future market trends.

[0520] Step 3:

[0521] Users input their investment objectives and risk tolerance via a terminal. This input is in the form of numerical or multiple-choice information. The server then creates an investment profile for the individual investor. The output is the user's profile data, which is used later for developing investment strategies.

[0522] Step 4:

[0523] The server matches user profiles with analysis results to generate personalized investment strategies. The input is the user's profile and market analysis results, and the output is an optimized investment strategy. Specifically, the AI ​​model constructs a recommended portfolio and creates investment scenarios from a risk management perspective.

[0524] Step 5:

[0525] The terminal visually displays investment strategies received from the server. The input is the recommended investment strategy from the server, and the output is visual information (graphs, charts) for the user. This allows the user to intuitively understand the received strategy.

[0526] Step 6:

[0527] Users input investment-related questions into a terminal, and the server uses a generative AI model to generate answers in natural language. The input is the user's question, and the output is the answer in natural language. This allows individual investors to receive appropriate feedback regarding their doubts and concerns.

[0528] (Application Example 1)

[0529] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0530] For individual investors, making appropriate investment decisions in a fluctuating market environment is a challenging task. In particular, quickly analyzing diverse data and interpreting the results requires specialized knowledge, placing a significant burden on individual investors. Therefore, it is necessary to provide real-time, personalized investment strategies and risk management to enable individual investors to make better investment decisions.

[0531] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0532] In this invention, the server includes means for acquiring data, means for analyzing the acquired data to identify trends, and means for generating strategies based on the investor's objectives and tolerance. This enables users to obtain the latest trends in financial markets and personalized strategies in real time, and to make optimal investment decisions quickly.

[0533] "Means of acquiring data" refers to a function that collects necessary information from external data sources and incorporates it into the system.

[0534] "Means of analyzing acquired data to identify trends" refers to the ability to analyze collected data and perform processing to clarify current market and economic trends.

[0535] "Means for generating strategies based on investor objectives and tolerance" refers to a function for creating customized investment strategies that take into account the investment objectives and risk tolerance of individual investors.

[0536] "Personalized delivery methods" refer to methods for adjusting the generated investment strategies to each investor's profile and delivering them directly.

[0537] A "means for generating answers in natural language" refers to a mechanism for receiving questions from investors and generating answers in natural language.

[0538] "A means of evaluating components and proposing adjustments as needed" refers to a function that evaluates each element within a portfolio and proposes adjustments if the risk increases.

[0539] "A means of providing real-time strategic advice using smartphones" refers to a mechanism that provides users with real-time investment strategies and advice through mobile devices.

[0540] This invention is a system that provides individual investors with real-time investment strategies utilizing financial market information. The system mainly consists of three elements: a server, a terminal, and a user.

[0541] The server acts as the central hub for information. Regarding data acquisition, it retrieves financial market data from diverse data sources. This involves using APIs via the internet to aggregate time-series data such as stock prices and economic indicators. Furthermore, the server analyzes this data and utilizes machine learning frameworks such as TensorFlow and PyTorch to perform time-series data modeling and anomaly detection in order to identify market trends.

[0542] The terminal acts as the interface between the user and the server. Using a portable information terminal such as a smartphone, it provides an application for users to input their investment objectives and risk tolerance. This application visualizes data and provides real-time notifications of investment strategies. Natural language processing, including Hugging Face Transformers, is used to receive instructions from the server and display responses to the user in natural language.

[0543] Users can access this system through their devices to obtain personalized investment strategies tailored to their individual profiles. For example, if a user enters a question via a smartphone application such as, "What is the recommended asset allocation based on current market conditions?", the server analyzes the latest market data and presents an appropriate portfolio. This process is rapid, allowing for timely investment decisions without delay.

[0544] By providing new investment insights using generative AI models, users can respond efficiently and effectively to fluctuating markets. An example of how the generative AI model supports effective strategic recommendations in this system is the prompt, "Based on the user's profile, please create a recommended portfolio based on market trends over the past month."

[0545] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0546] Step 1:

[0547] The server retrieves financial market data from data sources. It receives stock price and economic indicator data via APIs as input and stores it in its internal database. The data is managed as time-series data and used for future analysis.

[0548] Step 2:

[0549] The server uses machine learning models to analyze the acquired data. It takes time-series data as input and uses TensorFlow or PyTorch to detect data trends and anomalies. This analysis reveals trends in specific markets and generates predictions from the model. The output provides the analyzed market trend information.

[0550] Step 3:

[0551] Users use a terminal to input their investment objectives and risk tolerance. This becomes input data, and the terminal sends this information to the server. This input information is a crucial element when the server generates personalized strategies.

[0552] Step 4:

[0553] The server generates personalized investment strategies based on the user's investment objectives and risk tolerance. It receives analyzed market information and user profiles as input and uses a generating AI model to create strategies. As output, personalized investment strategies are generated and prepared for later distribution.

[0554] Step 5:

[0555] The terminal delivers investment strategies received from the server to the user. The terminal receives personalized strategies as input and presents them visually through the user interface. It also employs a real-time notification function, providing immediate notifications when important strategy updates occur.

[0556] Step 6:

[0557] The user enters investment-related questions through a terminal. The terminal sends the entered questions to a server, which generates appropriate answers using natural language processing.

[0558] Step 7:

[0559] The server uses a natural language processing engine to generate answers to user questions. It analyzes the user's question as input and uses a generative AI model to create answers based on market trend information. The output is the answer expressed in natural language, which is then provided to the user again via the terminal.

[0560] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0561] This invention is a system that uses an emotion engine to analyze a user's emotions and provide more effective investment strategies and advice. The following describes in detail the embodiments for carrying out this invention.

[0562] This invention involves three main elements: a server, a terminal, and a user. First, the server acquires financial market information from data sources and analyzes market trends based on this information. Machine learning models are used for the analysis to predict market trends and price fluctuations. Simultaneously, an emotion engine analyzes input from the terminal and estimates the user's emotions in real time.

[0563] Users input their investment goals and risk tolerance into the system via their device. Furthermore, through interaction with the user, the emotion engine identifies emotional states from text-based communication and recognizes emotional fluctuations that may influence investment decisions.

[0564] The server combines the results of sentiment analysis by an emotion engine with general market trend analysis to generate personalized investment strategies for each user. If stress or anxiety is detected in the user, the server will suggest investment options with relatively low risk, thus providing emotionally sensitive strategies.

[0565] The device displays personalized investment strategies delivered from the server to the user in real time. Furthermore, when the user enters investment-related questions or concerns, an emotion engine considers the tone and content to generate appropriate answers, which are then displayed on the device.

[0566] For example, if a user is anxious about fluctuations in the stock market, the emotion engine can identify that emotion, and the server may suggest investing in low-risk bonds after considering market analysis and the user's emotional state. Furthermore, when answering questions, the system can use language that alleviates the user's anxiety.

[0567] By implementing the invention in this way, users will be able to receive information and advice tailored to their individual emotional state, allowing them to make investment decisions with greater confidence.

[0568] The following describes the processing flow.

[0569] Step 1:

[0570] The server retrieves financial market information in real time from data sources. This includes collecting economic data and news via APIs.

[0571] Step 2:

[0572] The server preprocesses the acquired data and performs analysis to identify market trends and movements. Machine learning models are used in the analysis to derive unique movements and predictions.

[0573] Step 3:

[0574] Users input information about their investment objectives and risk tolerance using their devices and send it to the system.

[0575] Step 4:

[0576] The device transmits user input to an emotion engine, which analyzes the user's emotional state from their text and voice. This allows the system to determine whether the user is feeling safe or anxious.

[0577] Step 5:

[0578] The server combines analyzed market trends with the user's emotional state to generate personalized investment strategies. If stress or anxiety is detected, the system prioritizes strategies with lower risk.

[0579] Step 6:

[0580] The server delivers the generated personalized investment strategy to the user's device using real-time notification technology. This notification is made immediate so that the user can check it in a timely manner.

[0581] Step 7:

[0582] The terminal displays investment information delivered from the server to the user. The user makes investment decisions based on this information.

[0583] Step 8:

[0584] Users enter their investment-related questions and concerns into their device and send them to the server.

[0585] Step 9:

[0586] The server uses natural language processing technology to analyze the user's question and, taking into account the emotional state generated by the emotion engine, generates an appropriate response. This response is crafted in a tone that provides a sense of calm and reassurance.

[0587] Step 10:

[0588] The server sends the generated response to the terminal in real time and displays it to the user. Based on this response, the user can decide on their next course of action after making an informed decision.

[0589] Through the process described above, this system links user emotions with market information, providing more flexible and accurate support in investment activities.

[0590] (Example 2)

[0591] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0592] Individual investors often find it difficult to grasp the complexities of the market and make appropriate investment decisions while considering their own emotional state. Furthermore, providing customized investment strategies that take into account emotional fluctuations and individual risk tolerances in real time is also challenging. This can lead to the risk of making inappropriate decisions in specific market conditions.

[0593] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0594] In this invention, the server includes means for acquiring market data from information sources, means for analyzing the acquired market data and identifying trends, and means for predicting market trends using machine learning algorithms. This makes it possible to analyze the sentiment of individual investors in real time and provide strategies that meet their individual needs.

[0595] "Information sources" refer to external data providers or services accessed to obtain market data.

[0596] "Market data" refers to information related to financial markets, such as prices, trading volume, and economic indicators.

[0597] "Analysis" refers to the process of analyzing acquired market data to derive specific patterns and trends.

[0598] "Trends" refer to the general trends in changes in prices and demand in the market.

[0599] A "machine learning algorithm" refers to a computational method that uses historical data to train a model and then makes predictions about unknown data.

[0600] A "trend" refers to the direction of change over time indicated by prices and other variables in the market.

[0601] An "investor" refers to an individual or group that puts capital into the market with the aim of making a profit.

[0602] An "emotion analysis engine" refers to a technology that analyzes an individual's text and behavior to identify their emotional state.

[0603] "Natural language" refers to the language that humans use on a daily basis, and it is used in technologies that allow computers to understand and process it.

[0604] "Real-time notification technology" refers to communication methods and technologies for instantly transmitting information to users.

[0605] This system is primarily composed of interactions between servers, terminals, and users. The specific functions of each element are described below.

[0606] server

[0607] The server functions as a center for collecting market data. Specifically, it regularly acquires market data from multiple sources. The platforms used include financial data providers, and the data obtained from these is stored in a database. The server then analyzes the data using machine learning algorithms to predict market trends and developments. This process utilizes machine learning frameworks such as TensorFlow and PyTorch. The server also uses a sentiment analysis engine to analyze the sentiment from user text input.

[0608] terminal

[0609] The terminal provides an interface for users to interact with the server. Through the terminal, users can input their investment goals, risk tolerance, and opinions on the market. This information is transmitted to the server in real time. The terminal immediately displays analysis results and personalized investment strategies provided by the server to the user, helping them to make quick investment decisions.

[0610] User

[0611] Users input their financial situation and opinions on the market into the terminal. This allows users to clearly communicate their needs and concerns to the system. For example, they can enter prompts such as, "I'm concerned about the recent market volatility, so please suggest some safe investment options." Based on this, the system detects the user's emotional state and provides investment advice accordingly.

[0612] For example, if a user inputs "I feel anxious about the sharp decline in stock prices," the sentiment analysis engine identifies that emotion, and the server uses that data to suggest investing in high-safety bonds. This allows the user to make investment decisions with confidence, taking risk into consideration.

[0613] This system effectively integrates these components to provide users with timely and appropriate investment advice tailored to their individual emotions and market trends.

[0614] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0615] Step 1:

[0616] The server retrieves market data from multiple sources. It receives price information and economic indicators from financial data providers as input and stores this data in its database. After saving, the server performs data formatting processing so that it can be used in subsequent analysis processes.

[0617] Step 2:

[0618] Users input information about their investment goals and risk tolerance through their device. The device collects this data from the user in text format and immediately sends it to the server. The entered information serves as the basis for generating investment strategies.

[0619] Step 3:

[0620] The server uses an emotion analysis engine to analyze text data sent by the user. It receives the user's text message as input and extracts emotional characteristics using natural language processing techniques. As a result of the emotion analysis, the user's emotional state is output as "reassured," "anxious," "stressed," etc.

[0621] Step 4:

[0622] The server uses machine learning algorithms to analyze acquired market data and predict future market trends. Using the market data formatted in Step 1 as input data, the machine learning model is executed to obtain prediction results. The output of the prediction will provide information on future price fluctuations and trends.

[0623] Step 5:

[0624] The server combines sentiment analysis results with market trend forecasts to generate personalized investment strategies. Inputs include the user's emotional state and market forecast data, and an algorithm is used to select options suitable for the user. The resulting strategies include recommended investment options ranging from low-risk to high-risk.

[0625] Step 6:

[0626] The terminal presents personalized investment strategies provided by the server to the user in real time. It receives strategy data from the server as input and visualizes and displays it through a graphical interface. The user can then review the strategy details on the screen and decide on their next action.

[0627] Step 7:

[0628] If a user has additional questions or concerns regarding the strategy, they can inquire again through their device. The device sends the user's questions as text data to the server. The server reuses its sentiment analysis engine to generate appropriate answers and returns them to the device. This allows the user to continue making investment decisions with confidence.

[0629] (Application Example 2)

[0630] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0631] The aim is to solve the problem of difficulty in providing investment strategies that take into account the influence of emotions in individuals' financial activities, and to support individuals in making investment decisions with confidence. Furthermore, it aims to improve financial management skills by suggesting optimal payment methods tailored to each individual's emotional state.

[0632] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0633] In this invention, the server includes means for acquiring financial market information from data collection sources, means for analyzing the acquired financial market information and identifying market trends, means for generating investment strategies based on an individual's investment objectives and risk tolerance, means for personalizing the generated investment strategies and delivering them to the individual, means for generating responses in natural language to inquiries from the individual, means for evaluating the risk of the portfolio and proposing restructuring as necessary, and means for identifying an individual's emotional state using sentiment analysis technology and proposing payment methods appropriate to that emotional state. This makes it possible to provide investment strategies and payment methods that take into account an individual's emotional state.

[0634] A "data source" is an external or internal source of information used to collect information on financial markets.

[0635] "Financial market information" refers to data on conditions in financial markets, such as prices, trading volume, and trends.

[0636] "Market trends" is a concept that refers to price fluctuations, trends, and other market movements in financial markets.

[0637] "Individual" refers to an investor with specific investment objectives and risk tolerance.

[0638] "Investment objectives" refer to the goals that an individual aims to achieve when engaging in investment activities.

[0639] "Risk tolerance" is a measure that represents the range of risk an individual is willing to accept in investments.

[0640] An "investment strategy" is a plan or policy for conducting investment activities, formulated based on an individual's investment objectives and risk tolerance.

[0641] "Emotional analysis technology" is a method of identifying an individual's emotional state from their text, voice, and actions.

[0642] "Personalization" refers to customizing general information or strategies to suit the specific circumstances of an individual.

[0643] "Real-time notification technology" is a communication technology that transmits information to users instantly.

[0644] "Restructuring" refers to the act of changing the composition of a portfolio and adjusting its risk and profitability.

[0645] "Emotional state" refers to an individual's current psychological and emotional state.

[0646] "Payment method" refers to the means or process of settlement used by an individual when conducting financial transactions.

[0647] The system for realizing this invention mainly consists of three main elements: a server, a terminal, and a user.

[0648] The server acquires financial market information from data sources and analyzes market trends based on that information. Machine learning models are used for the analysis to predict market trends and price fluctuations. Furthermore, the server uses sentiment analysis technology to analyze text and voice input from users to provide a real-time estimation of the user's emotional state. For this purpose, it utilizes natural language processing libraries such as Transformers and the BERT model.

[0649] The terminal functions as an interface for the user to interact with the system, receiving the user's investment objectives and risk tolerance. It also displays personalized investment strategies and emotionally responsive payment method suggestions in real time. The server provides natural language answers to questions and concerns entered by the user through the terminal, assisting the user.

[0650] Users input information related to their investments into their device and receive suggestions from the server for the optimal investment strategy and payment method based on their emotional state. For example, if a user is feeling stressed, the server can suggest a low-risk payment plan.

[0651] Using a generative AI model, the server will provide personalized suggestions based on prompts such as: "The user is feeling anxious and needs low-risk financial advice. Please generate a secure payment plan to suggest to him."

[0652] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0653] Step 1:

[0654] The server retrieves financial market information from data sources. Specifically, it collects price, trading volume, and trend data using APIs such as the Yahoo Finance API. This data is used as input for subsequent market trend analysis.

[0655] Step 2:

[0656] The server analyzes acquired financial market information using a machine learning model to identify market trends. This analysis outputs market trends and price fluctuation predictions. Specifically, it uses the scikit-learn library in Python to perform regression analysis on historical data.

[0657] Step 3:

[0658] Users input their investment objectives and risk tolerance via a terminal. This input information is used as basic data for the server to generate appropriate investment strategies.

[0659] Step 4:

[0660] The server processes data extracted from text and audio using sentiment analysis technology to analyze the user's emotional state. Specifically, it uses the Transformers library and the BERT model to estimate the emotional state. This process outputs data related to the user's emotional state.

[0661] Step 5:

[0662] The server combines the market trend analysis results obtained in Step 2, the user's input information in Step 3, and their emotional state in Step 4 to generate a personalized investment strategy and payment method. As output, it presents low-risk or high-risk investment plans based on the user's emotional state.

[0663] Step 6:

[0664] The device displays the generated investment strategy and suggested payment methods to the user in real time. If the user asks questions about the presented plan, the device collects those questions.

[0665] Step 7:

[0666] The server analyzes the user's question using natural language processing techniques and generates an appropriate answer. This process also utilizes the BERT model to output answers that take the user's emotions into account.

[0667] Step 8:

[0668] The terminal presents the user with a response from the server. The user can then decide on further actions based on this response. The system functions in a loop, repeating this process.

[0669] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0670] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0671] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0672] [Fourth Embodiment]

[0673] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0674] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0675] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0676] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0677] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0678] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0679] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0680] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0681] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0682] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0683] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0684] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0685] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0686] This invention is a system that utilizes collected financial market information to provide effective investment strategies to individual investors. The following describes embodiments for carrying out this invention.

[0687] This system consists primarily of three elements: servers, terminals, and users.

[0688] The server acquires information about financial markets from diverse data sources, integrates it, and stores it in a database. Specific data sources include stock price information, economic indicators, and international news feeds. Furthermore, the server uses this data to analyze overall market trends and trends related to specific stocks. This involves the use of advanced machine learning models for time-series data analysis, anomaly detection, and predictive modeling.

[0689] For users, the first step is to register with the system via a terminal and provide information about their investment objectives and risk tolerance. This allows the server to create and update user profiles and prepare to propose personalized investment strategies.

[0690] The terminal acts as the interface with the user, visually displaying investment information transmitted from the server. Furthermore, the terminal allows users to input any questions or concerns they may have regarding investments, and this information is then sent to the server.

[0691] The server utilizes natural language processing technology to quickly generate and respond to user inquiries with appropriate advice. In this process, the natural language processing engine analyzes the user's question and creates an answer based on relevant analysis results and past market trends.

[0692] In the investment strategy proposal section, the server optimizes the portfolio and selects specific stocks based on the user's profile and market analysis results. Furthermore, from a risk management perspective, it continuously assesses the portfolio's risk and proposes rebalancing as needed. For example, if risk becomes excessively concentrated, it may recommend reinvesting in bonds or other low-risk assets.

[0693] This allows individual investors to gain clear and timely strategic insights, effectively respond to fluctuating markets, and make decisions to achieve their investment goals.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] The server retrieves real-time financial market information from data sources. This includes collecting stock price data, economic indicators, and news articles via APIs.

[0697] Step 2:

[0698] The server preprocesses the acquired raw data. Specifically, it cleanses and normalizes the data and converts it into a format suitable for analysis.

[0699] Step 3:

[0700] The server analyzes pre-processed data using machine learning models to identify market trends and developments. This includes detecting outliers and predicting future price fluctuations.

[0701] Step 4:

[0702] Users input their investment objectives and risk tolerance using a terminal and send this information to the system.

[0703] Step 5:

[0704] The server creates a user profile based on information provided by the user and generates a personalized investment strategy. The user profile is stored in a database and updated as needed.

[0705] Step 6:

[0706] The server delivers the generated investment strategies to the user's device in real time. This delivery uses technologies such as WebSocket and push notifications.

[0707] Step 7:

[0708] The user enters investment-related questions via their device and sends them to the server.

[0709] Step 8:

[0710] The server uses natural language processing technology to analyze the user's question, generate an appropriate answer, and send it back to the terminal. During this process, it provides a detailed, contextual explanation, referencing past analysis results and market trends.

[0711] Step 9:

[0712] The server continuously monitors the user's portfolio and performs a risk assessment of the portfolio. It proposes rebalancing as needed and notifies the user of the proposed changes.

[0713] These steps work together to enable users to receive highly accurate investment information in real time, helping them make informed and informed investment decisions.

[0714] (Example 1)

[0715] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] One of the challenges individual investors face when developing effective investment strategies in financial markets is the need to properly analyze large amounts of market data and generate strategies that match their investment objectives and risk tolerance. Furthermore, obtaining real-time information and optimizing portfolios in a rapidly changing market environment is difficult. The inability to obtain investment advice in natural language also poses a communication barrier.

[0717] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0718] In this invention, the server includes means for acquiring data from information sources, means for analyzing the acquired data to identify market trends, and means for generating investment plans according to an individual's investment objectives and risk tolerance. This enables individual investors to efficiently analyze large amounts of data, receive investment strategies tailored to their needs in real time, and respond quickly to market changes through natural language support.

[0719] "Information sources" refer to a variety of sources that provide data related to financial markets. Specific examples include stock price information, economic indicators, and news feeds.

[0720] "Data analysis" refers to all techniques used to process acquired data and identify market trends and patterns. This includes statistical methods and machine learning algorithms.

[0721] A "personalized investment plan" refers to an investment strategy tailored to an individual's investment objectives and risk tolerance. This plan proposes specific actions based on market analysis results.

[0722] "Natural language processing" refers to the technology that enables computers to understand and generate human language. This makes it possible to generate appropriate answers in human language to user inquiries.

[0723] A "generative AI model" refers to a predictive model that uses machine learning algorithms to create new data and information. In particular, in language generation, it can provide natural dialogue in response to user prompts.

[0724] "Portfolio adjustment" refers to the process of reviewing and optimizing the allocation of investment assets in response to risk assessments and market fluctuations. This adjustment allows investors to pursue the minimization of risk and the maximization of returns.

[0725] This invention is a system that effectively collects and analyzes financial market information and provides personalized investment strategies to individual investors. Specific embodiments for implementing this invention are described below.

[0726] The system primarily consists of three elements: servers, terminals, and users. The servers acquire financial market data from information sources. They utilize API connections and web scraping techniques to collect data from sources such as stock prices, economic indicators, and news feeds. This allows the latest market information to be stored in the database.

[0727] The server utilizes machine learning algorithms to analyze collected data and understand overall market trends. Specifically, it employs techniques such as LSTM networks and ARIMA models to perform time-series forecasting and anomaly detection. This makes it possible to quickly capture fluctuating market trends.

[0728] The terminal acts as the interface with the user, visually displaying analysis results and investment strategies sent from the server. This includes features that present information clearly using charts and graphs. Users input their investment objectives and risk tolerance through the terminal, allowing the server to build a personalized investment plan.

[0729] Users can input questions and investment-related topics in natural language into their terminals, and the server uses a generative AI model to generate appropriate advice in response to these inquiries. Prompts can include specific questions such as, "How can I maximize profits while minimizing risk?" or "What investment strategies are you suggesting based on current market conditions?"

[0730] This system allows individual investors to adapt to fluctuating market conditions and obtain clear and timely investment strategies. Through the integration of terminals and servers, support for individual investors is enhanced, enabling risk management and optimized portfolio construction.

[0731] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0732] Step 1:

[0733] The server retrieves data from financial market-related sources. Specifically, it uses data retrieval APIs and web scraping to collect stock price information, economic indicators, and news feeds. Inputs are source URLs and API keys, and output is financial information in raw data format. This allows the server to obtain the necessary market data in real time.

[0734] Step 2:

[0735] The server analyzes the acquired raw data. It uses machine learning algorithms, specifically LSTM and ARIMA, to analyze time-series patterns. The input is raw data, and the output is analysis results showing market trends. This process extracts trends for specific stocks or market segments and predicts future market trends.

[0736] Step 3:

[0737] Users input their investment objectives and risk tolerance via a terminal. This input is in the form of numerical or multiple-choice information. The server then creates an investment profile for the individual investor. The output is the user's profile data, which is used later for developing investment strategies.

[0738] Step 4:

[0739] The server matches user profiles with analysis results to generate personalized investment strategies. The input is the user's profile and market analysis results, and the output is an optimized investment strategy. Specifically, the AI ​​model constructs a recommended portfolio and creates investment scenarios from a risk management perspective.

[0740] Step 5:

[0741] The terminal visually displays investment strategies received from the server. The input is the recommended investment strategy from the server, and the output is visual information (graphs, charts) for the user. This allows the user to intuitively understand the received strategy.

[0742] Step 6:

[0743] Users input investment-related questions into a terminal, and the server uses a generative AI model to generate answers in natural language. The input is the user's question, and the output is the answer in natural language. This allows individual investors to receive appropriate feedback regarding their doubts and concerns.

[0744] (Application Example 1)

[0745] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] For individual investors, making appropriate investment decisions in a fluctuating market environment is a challenging task. In particular, quickly analyzing diverse data and interpreting the results requires specialized knowledge, placing a significant burden on individual investors. Therefore, it is necessary to provide real-time, personalized investment strategies and risk management to enable individual investors to make better investment decisions.

[0747] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0748] In this invention, the server includes means for acquiring data, means for analyzing the acquired data to identify trends, and means for generating strategies based on the investor's objectives and tolerance. This enables users to obtain the latest trends in financial markets and personalized strategies in real time, and to make optimal investment decisions quickly.

[0749] "Means of acquiring data" refers to a function that collects necessary information from external data sources and incorporates it into the system.

[0750] "Means of analyzing acquired data to identify trends" refers to the ability to analyze collected data and perform processing to clarify current market and economic trends.

[0751] "Means for generating strategies based on investor objectives and tolerance" refers to a function for creating customized investment strategies that take into account the investment objectives and risk tolerance of individual investors.

[0752] "Personalized delivery methods" refer to methods for adjusting the generated investment strategies to each investor's profile and delivering them directly.

[0753] A "means for generating answers in natural language" refers to a mechanism for receiving questions from investors and generating answers in natural language.

[0754] "A means of evaluating components and proposing adjustments as needed" refers to a function that evaluates each element within a portfolio and proposes adjustments if the risk increases.

[0755] "A means of providing real-time strategic advice using smartphones" refers to a mechanism that provides users with real-time investment strategies and advice through mobile devices.

[0756] This invention is a system that provides individual investors with real-time investment strategies utilizing financial market information. The system mainly consists of three elements: a server, a terminal, and a user.

[0757] The server acts as the central hub for information. Regarding data acquisition, it retrieves financial market data from diverse data sources. This involves using APIs via the internet to aggregate time-series data such as stock prices and economic indicators. Furthermore, the server analyzes this data and utilizes machine learning frameworks such as TensorFlow and PyTorch to perform time-series data modeling and anomaly detection in order to identify market trends.

[0758] The terminal acts as the interface between the user and the server. Using a portable information terminal such as a smartphone, it provides an application for users to input their investment objectives and risk tolerance. This application visualizes data and provides real-time notifications of investment strategies. Natural language processing, including Hugging Face Transformers, is used to receive instructions from the server and display responses to the user in natural language.

[0759] Users can access this system through their devices to obtain personalized investment strategies tailored to their individual profiles. For example, if a user enters a question via a smartphone application such as, "What is the recommended asset allocation based on current market conditions?", the server analyzes the latest market data and presents an appropriate portfolio. This process is rapid, allowing for timely investment decisions without delay.

[0760] By providing new investment insights using generative AI models, users can respond efficiently and effectively to fluctuating markets. An example of how the generative AI model supports effective strategic recommendations in this system is the prompt, "Based on the user's profile, please create a recommended portfolio based on market trends over the past month."

[0761] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0762] Step 1:

[0763] The server retrieves financial market data from data sources. It receives stock price and economic indicator data via APIs as input and stores it in its internal database. The data is managed as time-series data and used for future analysis.

[0764] Step 2:

[0765] The server uses machine learning models to analyze the acquired data. It takes time-series data as input and uses TensorFlow or PyTorch to detect data trends and anomalies. This analysis reveals trends in specific markets and generates predictions from the model. The output provides the analyzed market trend information.

[0766] Step 3:

[0767] Users use a terminal to input their investment objectives and risk tolerance. This becomes input data, and the terminal sends this information to the server. This input information is a crucial element when the server generates personalized strategies.

[0768] Step 4:

[0769] The server generates personalized investment strategies based on the user's investment objectives and risk tolerance. It receives analyzed market information and user profiles as input and uses a generating AI model to create strategies. As output, personalized investment strategies are generated and prepared for later distribution.

[0770] Step 5:

[0771] The terminal delivers investment strategies received from the server to the user. The terminal receives personalized strategies as input and presents them visually through the user interface. It also employs a real-time notification function, providing immediate notifications when important strategy updates occur.

[0772] Step 6:

[0773] The user enters investment-related questions through a terminal. The terminal sends the entered questions to a server, which generates appropriate answers using natural language processing.

[0774] Step 7:

[0775] The server uses a natural language processing engine to generate answers to user questions. It analyzes the user's question as input and uses a generative AI model to create answers based on market trend information. The output is the answer expressed in natural language, which is then provided to the user again via the terminal.

[0776] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0777] This invention is a system that uses an emotion engine to analyze a user's emotions and provide more effective investment strategies and advice. The following describes in detail the embodiments for carrying out this invention.

[0778] This invention involves three main elements: a server, a terminal, and a user. First, the server acquires financial market information from data sources and analyzes market trends based on this information. Machine learning models are used for the analysis to predict market trends and price fluctuations. Simultaneously, an emotion engine analyzes input from the terminal and estimates the user's emotions in real time.

[0779] Users input their investment goals and risk tolerance into the system via their device. Furthermore, through interaction with the user, the emotion engine identifies emotional states from text-based communication and recognizes emotional fluctuations that may influence investment decisions.

[0780] The server combines the results of sentiment analysis by an emotion engine with general market trend analysis to generate personalized investment strategies for each user. If stress or anxiety is detected in the user, the server will suggest investment options with relatively low risk, thus providing emotionally sensitive strategies.

[0781] The device displays personalized investment strategies delivered from the server to the user in real time. Furthermore, when the user enters investment-related questions or concerns, an emotion engine considers the tone and content to generate appropriate answers, which are then displayed on the device.

[0782] For example, if a user is anxious about fluctuations in the stock market, the emotion engine can identify that emotion, and the server may suggest investing in low-risk bonds after considering market analysis and the user's emotional state. Furthermore, when answering questions, the system can use language that alleviates the user's anxiety.

[0783] By implementing the invention in this way, users will be able to receive information and advice tailored to their individual emotional state, allowing them to make investment decisions with greater confidence.

[0784] The following describes the processing flow.

[0785] Step 1:

[0786] The server retrieves financial market information in real time from data sources. This includes collecting economic data and news via APIs.

[0787] Step 2:

[0788] The server preprocesses the acquired data and performs analysis to identify market trends and movements. Machine learning models are used in the analysis to derive unique movements and predictions.

[0789] Step 3:

[0790] Users input information about their investment objectives and risk tolerance using their devices and send it to the system.

[0791] Step 4:

[0792] The device transmits user input to an emotion engine, which analyzes the user's emotional state from their text and voice. This allows the system to determine whether the user is feeling safe or anxious.

[0793] Step 5:

[0794] The server combines analyzed market trends with the user's emotional state to generate personalized investment strategies. If stress or anxiety is detected, the system prioritizes strategies with lower risk.

[0795] Step 6:

[0796] The server delivers the generated personalized investment strategy to the user's device using real-time notification technology. This notification is made immediate so that the user can check it in a timely manner.

[0797] Step 7:

[0798] The terminal displays investment information delivered from the server to the user. The user makes investment decisions based on this information.

[0799] Step 8:

[0800] Users enter their investment-related questions and concerns into their device and send them to the server.

[0801] Step 9:

[0802] The server uses natural language processing technology to analyze the user's question and, taking into account the emotional state generated by the emotion engine, generates an appropriate response. This response is crafted in a tone that provides a sense of calm and reassurance.

[0803] Step 10:

[0804] The server sends the generated response to the terminal in real time and displays it to the user. Based on this response, the user can decide on their next course of action after making an informed decision.

[0805] Through the process described above, this system links user emotions with market information, providing more flexible and accurate support in investment activities.

[0806] (Example 2)

[0807] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0808] Individual investors often find it difficult to grasp the complexities of the market and make appropriate investment decisions while considering their own emotional state. Furthermore, providing customized investment strategies that take into account emotional fluctuations and individual risk tolerances in real time is also challenging. This can lead to the risk of making inappropriate decisions in specific market conditions.

[0809] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0810] In this invention, the server includes means for acquiring market data from information sources, means for analyzing the acquired market data and identifying trends, and means for predicting market trends using machine learning algorithms. This makes it possible to analyze the sentiment of individual investors in real time and provide strategies that meet their individual needs.

[0811] "Information sources" refer to external data providers or services accessed to obtain market data.

[0812] "Market data" refers to information related to financial markets, such as prices, trading volume, and economic indicators.

[0813] "Analysis" refers to the process of analyzing acquired market data to derive specific patterns and trends.

[0814] "Trends" refer to the general trends in changes in prices and demand in the market.

[0815] A "machine learning algorithm" refers to a computational method that uses historical data to train a model and then makes predictions about unknown data.

[0816] A "trend" refers to the direction of change over time indicated by prices and other variables in the market.

[0817] An "investor" refers to an individual or group that puts capital into the market with the aim of making a profit.

[0818] An "emotion analysis engine" refers to a technology that analyzes an individual's text and behavior to identify their emotional state.

[0819] "Natural language" refers to the language that humans use on a daily basis, and it is used in technologies that allow computers to understand and process it.

[0820] "Real-time notification technology" refers to communication methods and technologies for instantly transmitting information to users.

[0821] This system is primarily composed of interactions between servers, terminals, and users. The specific functions of each element are described below.

[0822] server

[0823] The server functions as a center for collecting market data. Specifically, it regularly acquires market data from multiple sources. The platforms used include financial data providers, and the data obtained from these is stored in a database. The server then analyzes the data using machine learning algorithms to predict market trends and developments. This process utilizes machine learning frameworks such as TensorFlow and PyTorch. The server also uses a sentiment analysis engine to analyze the sentiment from user text input.

[0824] terminal

[0825] The terminal provides an interface for users to interact with the server. Through the terminal, users can input their investment goals, risk tolerance, and opinions on the market. This information is transmitted to the server in real time. The terminal immediately displays analysis results and personalized investment strategies provided by the server to the user, helping them to make quick investment decisions.

[0826] User

[0827] Users input their financial situation and opinions on the market into the terminal. This allows users to clearly communicate their needs and concerns to the system. For example, they can enter prompts such as, "I'm concerned about the recent market volatility, so please suggest some safe investment options." Based on this, the system detects the user's emotional state and provides investment advice accordingly.

[0828] For example, if a user inputs "I feel anxious about the sharp decline in stock prices," the sentiment analysis engine identifies that emotion, and the server uses that data to suggest investing in high-safety bonds. This allows the user to make investment decisions with confidence, taking risk into consideration.

[0829] This system effectively integrates these components to provide users with timely and appropriate investment advice tailored to their individual emotions and market trends.

[0830] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0831] Step 1:

[0832] The server retrieves market data from multiple sources. It receives price information and economic indicators from financial data providers as input and stores this data in its database. After saving, the server performs data formatting processing so that it can be used in subsequent analysis processes.

[0833] Step 2:

[0834] Users input information about their investment goals and risk tolerance through their device. The device collects this data from the user in text format and immediately sends it to the server. The entered information serves as the basis for generating investment strategies.

[0835] Step 3:

[0836] The server uses an emotion analysis engine to analyze text data sent by the user. It receives the user's text message as input and extracts emotional characteristics using natural language processing techniques. As a result of the emotion analysis, the user's emotional state is output as "reassured," "anxious," "stressed," etc.

[0837] Step 4:

[0838] The server uses machine learning algorithms to analyze acquired market data and predict future market trends. Using the market data formatted in Step 1 as input data, the machine learning model is executed to obtain prediction results. The output of the prediction will provide information on future price fluctuations and trends.

[0839] Step 5:

[0840] The server combines sentiment analysis results with market trend forecasts to generate personalized investment strategies. Inputs include the user's emotional state and market forecast data, and an algorithm is used to select options suitable for the user. The resulting strategies include recommended investment options ranging from low-risk to high-risk.

[0841] Step 6:

[0842] The terminal presents personalized investment strategies provided by the server to the user in real time. It receives strategy data from the server as input and visualizes and displays it through a graphical interface. The user can then review the strategy details on the screen and decide on their next action.

[0843] Step 7:

[0844] If a user has additional questions or concerns regarding the strategy, they can inquire again through their device. The device sends the user's questions as text data to the server. The server reuses its sentiment analysis engine to generate appropriate answers and returns them to the device. This allows the user to continue making investment decisions with confidence.

[0845] (Application Example 2)

[0846] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0847] The aim is to solve the problem of difficulty in providing investment strategies that take into account the influence of emotions in individuals' financial activities, and to support individuals in making investment decisions with confidence. Furthermore, it aims to improve financial management skills by suggesting optimal payment methods tailored to each individual's emotional state.

[0848] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0849] In this invention, the server includes means for acquiring financial market information from data collection sources, means for analyzing the acquired financial market information and identifying market trends, means for generating investment strategies based on an individual's investment objectives and risk tolerance, means for personalizing the generated investment strategies and delivering them to the individual, means for generating responses in natural language to inquiries from the individual, means for evaluating the risk of the portfolio and proposing restructuring as necessary, and means for identifying an individual's emotional state using sentiment analysis technology and proposing payment methods appropriate to that emotional state. This makes it possible to provide investment strategies and payment methods that take into account an individual's emotional state.

[0850] A "data source" is an external or internal source of information used to collect information on financial markets.

[0851] "Financial market information" refers to data on conditions in financial markets, such as prices, trading volume, and trends.

[0852] "Market trends" is a concept that refers to price fluctuations, trends, and other market movements in financial markets.

[0853] "Individual" refers to an investor with specific investment objectives and risk tolerance.

[0854] "Investment objectives" refer to the goals that an individual aims to achieve when engaging in investment activities.

[0855] "Risk tolerance" is a measure that represents the range of risk an individual is willing to accept in investments.

[0856] An "investment strategy" is a plan or policy for conducting investment activities, formulated based on an individual's investment objectives and risk tolerance.

[0857] "Emotional analysis technology" is a method of identifying an individual's emotional state from their text, voice, and actions.

[0858] "Personalization" refers to customizing general information or strategies to suit the specific circumstances of an individual.

[0859] "Real-time notification technology" is a communication technology that transmits information to users instantly.

[0860] "Restructuring" refers to the act of changing the composition of a portfolio and adjusting its risk and profitability.

[0861] "Emotional state" refers to an individual's current psychological and emotional state.

[0862] "Payment method" refers to the means or process of settlement used by an individual when conducting financial transactions.

[0863] The system for realizing this invention mainly consists of three main elements: a server, a terminal, and a user.

[0864] The server acquires financial market information from data sources and analyzes market trends based on that information. Machine learning models are used for the analysis to predict market trends and price fluctuations. Furthermore, the server uses sentiment analysis technology to analyze text and voice input from users to provide a real-time estimation of the user's emotional state. For this purpose, it utilizes natural language processing libraries such as Transformers and the BERT model.

[0865] The terminal functions as an interface for the user to interact with the system, receiving the user's investment objectives and risk tolerance. It also displays personalized investment strategies and emotionally responsive payment method suggestions in real time. The server provides natural language answers to questions and concerns entered by the user through the terminal, assisting the user.

[0866] Users input information related to their investments into their device and receive suggestions from the server for the optimal investment strategy and payment method based on their emotional state. For example, if a user is feeling stressed, the server can suggest a low-risk payment plan.

[0867] Using a generative AI model, the server will provide personalized suggestions based on prompts such as: "The user is feeling anxious and needs low-risk financial advice. Please generate a secure payment plan to suggest to him."

[0868] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0869] Step 1:

[0870] The server retrieves financial market information from data sources. Specifically, it collects price, trading volume, and trend data using APIs such as the Yahoo Finance API. This data is used as input for subsequent market trend analysis.

[0871] Step 2:

[0872] The server analyzes acquired financial market information using a machine learning model to identify market trends. This analysis outputs market trends and price fluctuation predictions. Specifically, it uses the scikit-learn library in Python to perform regression analysis on historical data.

[0873] Step 3:

[0874] Users input their investment objectives and risk tolerance via a terminal. This input information is used as basic data for the server to generate appropriate investment strategies.

[0875] Step 4:

[0876] The server processes data extracted from text and audio using sentiment analysis technology to analyze the user's emotional state. Specifically, it uses the Transformers library and the BERT model to estimate the emotional state. This process outputs data related to the user's emotional state.

[0877] Step 5:

[0878] The server combines the market trend analysis results obtained in Step 2, the user's input information in Step 3, and their emotional state in Step 4 to generate a personalized investment strategy and payment method. As output, it presents low-risk or high-risk investment plans based on the user's emotional state.

[0879] Step 6:

[0880] The device displays the generated investment strategy and suggested payment methods to the user in real time. If the user asks questions about the presented plan, the device collects those questions.

[0881] Step 7:

[0882] The server analyzes the user's question using natural language processing techniques and generates an appropriate answer. This process also utilizes the BERT model to output answers that take the user's emotions into account.

[0883] Step 8:

[0884] The terminal presents the user with a response from the server. The user can then decide on further actions based on this response. The system functions in a loop, repeating this process.

[0885] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0886] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0887] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0888] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0889] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0890] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0891] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0892] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0893] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0894] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0895] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0896] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0897] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0898] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0899] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0900] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0901] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0902] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0903] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0904] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0905] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0906] The following is further disclosed regarding the embodiments described above.

[0907] (Claim 1)

[0908] Means for obtaining financial market information from data sources,

[0909] A means of analyzing acquired financial market information to identify market trends,

[0910] A means of generating investment strategies based on the investment objectives and risk tolerance of individual investors,

[0911] A means of distributing the generated investment strategies to individual investors,

[0912] A means of generating natural language answers to questions from individual investors,

[0913] A means to assess portfolio risk and propose rebalancing as needed,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, which uses a machine learning model for analysis to identify market trends.

[0917] (Claim 3)

[0918] The system according to claim 1, which uses real-time notification technology to deliver personalized investment strategies.

[0919] "Example 1"

[0920] (Claim 1)

[0921] Means of obtaining data on financial markets from information sources,

[0922] A means of analyzing acquired data to identify market trends,

[0923] A means of generating an investment plan according to an individual's investment objectives and risk tolerance,

[0924] A means of personalized and delivering the generated investment plans,

[0925] A natural language processing method for generating answers in natural language to individual questions,

[0926] A means of assessing risk and proposing portfolio adjustments as needed,

[0927] A means of generating responses in response to user requests using a generative AI model,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, which uses a machine learning algorithm for data analysis to identify market trends.

[0931] (Claim 3)

[0932] The system according to claim 1, which uses real-time notifications for the delivery of personalized investment plans.

[0933] "Application Example 1"

[0934] (Claim 1)

[0935] Means of acquiring data,

[0936] A means of analyzing acquired data to identify trends,

[0937] Means for generating strategies based on investors' objectives and tolerances,

[0938] A means of distributing the generated strategies in an individualized manner,

[0939] A means of generating natural language answers to questions from investors,

[0940] A means of evaluating the components and proposing adjustments as necessary,

[0941] A means of providing real-time strategic advice using smartphones,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] The system according to claim 1, which uses a model for analysis.

[0945] (Claim 3)

[0946] The system according to claim 1, which uses notification technology to deliver personalized strategies.

[0947] "Example 2 of combining an emotion engine"

[0948] (Claim 1)

[0949] Means of obtaining market data from information sources,

[0950] A means of analyzing acquired market data and identifying trends,

[0951] A method for predicting market trends using machine learning algorithms,

[0952] Means for generating strategies based on individual goals and risk tolerance,

[0953] A means of personalizing the generated strategies and delivering them to individuals,

[0954] A means of identifying an individual's emotional state using an emotion analysis engine,

[0955] A means of adjusting strategies based on emotional states and market trends,

[0956] A means of generating responses in natural language to inquiries from individuals,

[0957] Means of using technology to present information to individuals in real time,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] The system according to claim 1, which utilizes a generative AI model for identifying and predicting market trends.

[0961] (Claim 3)

[0962] The system according to claim 1, which uses real-time notification technology to deliver personalized strategies.

[0963] "Application example 2 when combining with an emotional engine"

[0964] (Claim 1)

[0965] Means for obtaining financial market information from data sources,

[0966] A means of analyzing acquired financial market information to identify market trends,

[0967] A means of generating investment strategies based on an individual's investment objectives and risk tolerance,

[0968] A means of personalizing the generated investment strategies and delivering them to individuals,

[0969] A means of generating responses in natural language to inquiries from individuals,

[0970] A means to assess the risks of a portfolio and propose restructuring as needed,

[0971] A means of identifying an individual's emotional state using emotion analysis technology and proposing a payment method appropriate to that emotional state,

[0972] A system that includes this.

[0973] (Claim 2)

[0974] The system according to claim 1, which utilizes machine learning technology for analysis to identify market trends.

[0975] (Claim 3)

[0976] The system according to claim 1, which uses real-time notification technology to deliver personalized investment strategies. [Explanation of Symbols]

[0977] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of acquiring data, A means of analyzing acquired data to identify trends, Means for generating strategies based on investors' objectives and tolerances, A means of distributing the generated strategies in an individualized manner, A means of generating natural language answers to questions from investors, A means of evaluating the components and proposing adjustments as necessary, A means of providing real-time strategic advice using smartphones, A system that includes this.

2. The system according to claim 1, which uses a model for analysis.

3. The system according to claim 1, which uses notification technology to deliver personalized strategies.

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