system

Abstract

The system according to this embodiment aims to analyze stock market data and determine the appropriate time to buy and sell investments. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a current analysis unit, and a decision unit. The collection unit collects historical stock market data. The analysis unit analyzes the data collected by the collection unit and identifies the reasons for past increases and decreases. The current analysis unit analyzes current market data based on the reasons identified by the analysis unit. The decision unit determines the appropriate time to buy and sell an investment based on the data analyzed by the current analysis unit.

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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to analyze data in the stock market to determine an appropriate timing for investment, and there are problems of excessive information and difficulty in judgment.

[0005] The system according to the embodiment aims to analyze data in the stock market and determine appropriate buying and selling times for investment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a current analysis unit, and a decision unit. The data collection unit collects historical stock market data. The analysis unit analyzes the data collected by the data collection unit and identifies the reasons for past increases and decreases. The current analysis unit analyzes current market data based on the reasons identified by the analysis unit. The decision unit determines the appropriate time to buy and sell an investment based on the data analyzed by the current analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze stock market data and determine the appropriate time to buy and sell investments. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] 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.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The investment decision system according to an embodiment of the present invention is a system that monitors corporate activities, management actions, performance, and the overall movement of the stock market every second, using the reasons for past rises and falls in the stock market as clues, to determine the optimal time to buy and sell an investment. This investment decision system solves the problem of information overload and difficulty in making decisions faced by individual investors and professional buyers, and achieves a level of decision-making speed that was previously impossible. First, the investment decision system collects past stock market data and analyzes it using AI. The AI ​​identifies the reasons for past rises and falls and uses this as clues to analyze current market data. Specifically, it monitors corporate activities, management actions, performance, and the overall movement of the stock market every second and integrates this data in real time. For example, it analyzes the impact of events such as new product announcements, changes in management, and earnings announcements on stock prices. Next, based on the analysis results, the AI ​​determines the optimal time to buy and sell an investment. For example, it finds specific patterns from past data and compares them with the current market situation to determine when to buy or sell. This judgment is provided to individual investors and professional buyers to support efficient investment. Furthermore, the investment decision system is also useful for companies. Companies can leverage historical market data to understand the impact of their activities on stock prices and market valuations. This supports more transparent business strategies and enables strategic decision-making that adapts to market expectations. For example, companies can use AI analysis results to understand which activities have a positive impact on stock prices and then determine their business strategies accordingly. This investment decision system will attract more investors, increase the assets of Japanese companies, and stimulate investment in new businesses. As a result, a virtuous cycle will be created where the economy prospers and GDP increases. This allows the investment decision system to analyze current market data based on historical stock market data to make optimal investment decisions.

[0029] The investment decision system according to this embodiment comprises a data collection unit, an analysis unit, a current analysis unit, and a decision unit. The data collection unit collects historical stock market data. Historical stock market data includes, but is not limited to, data from the past year, data from the past five years, etc. The data collection unit collects data from, for example, publicly available databases on the internet. The data collection unit can also collect data from corporate financial reports, market reports, etc. For example, the data collection unit automatically scans corporate financial reports and stores them in a database. The analysis unit analyzes the data collected by the data collection unit and identifies the reasons for past increases and decreases. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit uses regression analysis to identify factors influencing stock price fluctuations. The analysis unit can also classify data using clustering algorithms and find specific patterns. For example, the analysis unit uses K-means clustering to group data and identify patterns of increases and decreases. The current analysis unit analyzes current market data based on the reasons identified by the analysis unit. The current analysis unit collects and analyzes current market data using, for example, a real-time database. For example, the current analysis unit analyzes data in real time using streaming data processing technology. The current analysis unit can also compare current market data with historical data and analyze their interrelationships. For example, the current analysis unit can identify the relationship between current market data and historical data using correlation analysis. The decision unit determines the optimal time to buy and sell investments based on the data analyzed by the current analysis unit. The decision unit makes investment decisions using, for example, technical analysis or fundamental analysis. For example, the decision unit uses moving averages to determine the timing to buy and sell. The decision unit can also make investment decisions based on a company's financial indicators. For example, the decision unit makes investment decisions based on a company's PER (price-to-earnings ratio) or PBR (price-to-book ratio). As a result, the investment decision system according to this embodiment can analyze current market data based on historical stock market data and make optimal investment decisions. Some or all of the above-described processing in the decision unit may be performed using, for example, AI, or without using AI.For example, the decision-making unit can take the data currently analyzed by the analysis unit as input and use an AI model that outputs the optimal time to buy and sell investments to make investment decisions.

[0030] The data collection unit collects historical stock market data. This historical data includes, but is not limited to, data from the past year, the past five years, etc. The data collection unit collects data from publicly available databases on the internet, for example. Specifically, the data collection unit accesses multiple reliable databases to obtain data such as historical stock prices, trading volume, company financial status, and economic indicators. This includes methods such as automatically retrieving data using APIs and extracting necessary information using web scraping techniques. The data collection unit can also collect data from corporate financial reports and market reports. For example, the data collection unit automatically scans corporate financial reports and stores them in the database. This includes methods such as converting paper reports into digital data using OCR (optical character recognition) technology and extracting necessary information from PDF reports. Furthermore, the data collection unit can also collect unstructured data such as news articles and social media posts. This allows the data collection unit to collect a wide range of data from diverse sources and provide foundational data for investment decisions. The collected data is centrally stored in a database and managed so that the analysis unit and the current analysis unit can access it. The data collection unit includes processes to check the integrity and consistency of the data and remove inaccurate or missing data in order to ensure data quality. This allows the data collection unit to provide reliable data and improve the overall accuracy and reliability of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit to identify the reasons for past price increases and decreases. The analysis unit analyzes the data using, for example, statistical analysis and machine learning algorithms. Specifically, the analysis unit uses regression analysis to identify the factors influencing stock price fluctuations. Regression analysis models the relationship between independent variables (e.g., trading volume, corporate financial indicators, economic indicators, etc.) and the dependent variable (stock price) to explain stock price fluctuations. The analysis unit can also classify data using clustering algorithms to identify specific patterns. For example, the analysis unit uses K-means clustering to group data and identify patterns of increases and decreases. K-means clustering divides data points into K clusters and calculates the centroid of each cluster. This ensures that data points with similar characteristics belong to the same cluster. Furthermore, the analysis unit can analyze stock price trends and seasonality using time series analysis. For example, it uses the ARIMA model to analyze time series data of stock prices and predict future stock price trends. The analysis unit can also detect complex patterns using deep learning techniques. For example, an LSTM (Long Short-Term Memory) network is used to model the long-term dependencies of stock prices and predict future stock prices. This allows the analysis unit to analyze the collected data from multiple perspectives and identify the factors and patterns of past stock price fluctuations.

[0032] The current analysis unit analyzes current market data based on reasons identified by the unit. For example, the unit collects and analyzes current market data using real-time databases. Specifically, it analyzes data in real time using streaming data processing technology. This includes methods for collecting and processing data in real time using streaming platforms such as Apache Kafka and Apache Flink. The unit can also compare current market data with historical data and analyze their interrelationships. For example, it uses correlation analysis to identify the relationship between current market data and historical data. Correlation analysis calculates the correlation coefficient between two variables and evaluates the strength and direction of the relationship. This allows for determining how similar current market data is to historical data. Furthermore, the unit can use anomaly detection algorithms to detect unusual patterns and sudden fluctuations in current market data. For example, algorithms such as Isolation Forest and LOF (Local Outlier Factor) can be used to identify anomalous data points and issue early warnings. This allows the unit to analyze market data in real time and accurately understand the current market situation.

[0033] The decision-making unit determines the optimal buying and selling times for investments based on data currently analyzed by the analysis unit. The decision-making unit makes investment decisions using, for example, technical analysis and fundamental analysis. Specifically, it uses moving averages to determine buying and selling times. Moving averages calculate the average stock price over a certain period and visualize the trend. For example, it uses the intersection of short-term and long-term moving averages to determine buy and sell signals. Furthermore, the decision-making unit can also make investment decisions based on a company's financial indicators. For example, it uses a company's PER (price-to-earnings ratio) and PBR (price-to-book ratio) to make investment decisions. PER is an indicator showing how high a company's stock price is relative to its earnings, and PBR is an indicator showing how high a company's stock price is relative to its net assets. This allows the decision-making unit to evaluate a company's profitability and financial health and determine the appropriate timing for investment. In addition, the decision-making unit can also use AI to make investment decisions. For example, the decision-making unit can make investment decisions using an AI model that takes data analyzed by the analysis unit as input and outputs the optimal buying and selling times for investments. The AI ​​model employs technologies such as deep learning and reinforcement learning, learning from past data to predict future market trends. This allows the decision-making unit to make more accurate investment decisions by utilizing advanced analytical techniques.

[0034] The decision-making unit can identify specific patterns from past data and compare them with current market conditions to determine when to buy or sell. For example, the decision-making unit can analyze past data to find specific patterns. For example, the decision-making unit can identify patterns such as price trends or changes in trading volume. The decision-making unit can also compare current market conditions with past patterns to determine when to buy or sell. For example, the decision-making unit can determine when to buy or sell if current market conditions match a specific past pattern. This allows for the identification of specific patterns from past data and comparison with current market conditions to make optimal investment decisions. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can identify patterns using an AI model that takes past data as input and outputs specific patterns.

[0035] The data collection unit can continuously monitor corporate activities, management movements, performance, and overall stock market movements, and integrate this data in real time. For example, the data collection unit monitors events such as new product launches, management changes, and earnings announcements. For instance, it can monitor new product launches in real time and integrate the data. It can also monitor management movements and integrate data such as management changes. For example, it can monitor management changes in real time and integrate the data. Furthermore, it can monitor corporate earnings announcements and integrate the data. For example, it can monitor quarterly financial results in real time and integrate the data. This allows for monitoring corporate activities, management movements, performance, and overall stock market movements every second and integrating the data in real time, enabling a grasp of the latest market conditions. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data such as new product launches and management changes into a generating AI and have the generating AI perform the data integration.

[0036] The analysis unit can analyze the impact of at least one of the following events—a company's new product announcement, a change in management, or an earnings announcement—on stock prices. For example, the analysis unit can analyze the impact of a company's new product announcement on stock prices. For example, the analysis unit can analyze the fluctuations in stock prices after a new product announcement and identify the impact. The analysis unit can also analyze the impact of a change in management on stock prices. For example, the analysis unit can analyze the fluctuations in stock prices after a change in management and identify the impact. The analysis unit can also analyze the impact of a company's earnings announcement on stock prices. For example, the analysis unit can analyze the fluctuations in stock prices after a quarterly earnings announcement and identify the impact. In this way, by analyzing the impact of events such as a company's new product announcement, a change in management, and an earnings announcement on stock prices, the factors influencing stock price fluctuations can be understood. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data such as a company's new product announcement or a change in management into a generating AI and have the generating AI analyze the impact on stock prices.

[0037] The decision-making unit can be provided to at least one of the following: individual investors and professional buyers, and can support efficient investment. For example, the decision-making unit can be provided to individual investors to support efficient investment. For example, the decision-making unit can notify individual investors of the optimal time to buy and sell. The decision-making unit can also be provided to professional buyers to support efficient investment. For example, the decision-making unit can notify professional buyers of the optimal time to buy and sell. This improves the accuracy of investment decisions by supporting efficient investment for both individual investors and professional buyers. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input investment data from individual investors and professional buyers into a generating AI and have the generating AI execute the optimal investment decision.

[0038] The analysis unit can provide data to help companies understand the impact of their activities on at least one of the following: stock price and market valuation. For example, the analysis unit can analyze and provide data on the impact of a company's new product launch on its stock price. For example, the analysis unit can analyze fluctuations in stock price after a new product launch and identify the impact. The analysis unit can also analyze the impact of a company's marketing strategy on market valuation. For example, the analysis unit can analyze fluctuations in market valuation after a marketing campaign and identify the impact. The analysis unit can also analyze the impact of a company's financial reporting on its stock price. For example, the analysis unit can analyze fluctuations in stock price after quarterly earnings announcements and identify the impact. This supports companies' strategic decision-making by providing data to help them understand the impact of their activities on stock price and market valuation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data such as a company's new product launch or marketing strategy into a generating AI and have the generating AI analyze the impact on stock price and market valuation.

[0039] The decision-making unit can assist companies in making strategic decisions that align with market expectations. For example, the unit can assist companies in making decisions to develop new products that meet market expectations. For instance, the unit can propose a new product development plan based on market expectations. The decision-making unit can also assist companies in making decisions to formulate marketing strategies that meet market expectations. For example, the unit can propose a marketing campaign plan based on market expectations. The decision-making unit can also assist companies in formulating financial strategies that meet market expectations. For example, the unit can propose a financial plan based on market expectations. This enhances a company's competitiveness by supporting it in making strategic decisions that adapt to market expectations. Some or all of the processes described above in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input a company's market data into a generating AI and have the generating AI propose strategic decisions that align with market expectations.

[0040] The data collection unit can analyze past data collection history and select an appropriate collection method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI identify the optimal collection method. The data collection unit can also analyze past data collection history and optimize the collection frequency and timing. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI optimize the collection frequency and timing. The data collection unit can also determine the priority of data to be collected based on past data collection history. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI determine the priority of data to be collected. This improves the efficiency of data collection by analyzing past data collection history and selecting the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without using AI.

[0041] The data collection unit can filter data based on the company's current activities and market trends during data collection. For example, the data collection unit can filter data based on important events such as a company's new product launch or a change in management. For example, the data collection unit can input data such as a company's new product launch or a change in management into a generating AI and have the generating AI filter the important data. The data collection unit can also monitor market trends in real time and prioritize the collection of highly relevant data. For example, the data collection unit can input market trend data into a generating AI and have the generating AI prioritize the collection of highly relevant data. The data collection unit can also filter data based on a company's earnings announcement or market trends and extract important information. For example, the data collection unit can input company earnings announcement data into a generating AI and have the generating AI extract important information. This allows for the efficient collection of important information by filtering data based on the company's current activities and market trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0042] The data collection unit can prioritize the collection of highly relevant data based on geographical location information during data collection. For example, the data collection unit can collect highly relevant data based on the location and activity area of ​​a company. For example, the data collection unit can input company location data into a generating AI and have the generating AI collect highly relevant data. The data collection unit can also geographically analyze market trends and prioritize the collection of highly relevant data. For example, the data collection unit can input market trend data into a generating AI and have the generating AI collect highly relevant data. The data collection unit can also prioritize the collection of data from a specific region based on geographical location information. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI prioritize the collection of data from a specific region. This allows for the prioritization of data collection that takes geographical location information into consideration, thereby enabling an understanding of market trends in each region. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without using AI.

[0043] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the activities and trends of companies on social media and collect relevant data. For example, the data collection unit can input social media post data into a generating AI and have the generating AI analyze company activities and trends. The data collection unit can also analyze user reactions and comments on social media and collect relevant data. For example, the data collection unit can input social media comment data into a generating AI and have the generating AI analyze user reactions. The data collection unit can also analyze hashtags and keywords on social media and collect relevant data. For example, the data collection unit can input social media hashtag data into a generating AI and have the generating AI collect relevant data. By analyzing social media activity and collecting relevant data, it is possible to grasp the latest market trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0044] The analysis unit can optimize the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform detailed analysis on important data and simplified analysis on less important data. For example, the analysis unit can input the importance of the data into the generating AI and have the generating AI perform detailed analysis on the important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit can input the importance of the data into the generating AI and have the generating AI determine the priority of the analysis. The analysis unit can also create detailed reports on important data and simplified reports on less important data. For example, the analysis unit can input the importance of the data into the generating AI and have the generating AI create detailed reports. In this way, by adjusting the level of detail of the analysis based on the importance of the data, detailed analysis can be performed on important data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit can apply different analysis algorithms depending on the data classification during analysis. For example, the analysis unit can apply a specific analysis algorithm to corporate activity data and a different analysis algorithm to management activity data. For example, the analysis unit inputs corporate activity data into a generating AI and has the generating AI apply a specific analysis algorithm. The analysis unit can also apply a detailed analysis algorithm to performance data and a simplified analysis algorithm to overall stock market movement data. For example, the analysis unit inputs performance data into a generating AI and has the generating AI apply a detailed analysis algorithm. Furthermore, the analysis unit can select and apply the optimal analysis algorithm depending on the data classification. For example, the analysis unit inputs the data classification into a generating AI and has the generating AI select the optimal analysis algorithm. This allows for the provision of optimal analysis results by applying different analysis algorithms depending on the data classification. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0046] The analysis unit can set analysis priorities based on the data submission date during analysis. For example, the analysis unit can prioritize the analysis of the latest data and postpone older data. For example, the analysis unit can input the data submission date into the generating AI and have the generating AI prioritize the analysis of the latest data. The analysis unit can also adjust the analysis schedule based on the data submission date. For example, the analysis unit can input the data submission date into the generating AI and have the generating AI adjust the analysis schedule. The analysis unit can also prioritize the analysis of data with a recent submission date and postpone data with a later submission date. For example, the analysis unit can input the data submission date into the generating AI and have the generating AI prioritize the analysis of data with a recent submission date. In this way, by determining the analysis priority based on the data submission date, the latest data can be analyzed preferentially. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0047] The analysis unit can optimize the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. For example, the analysis unit can input the data relevance into a generating AI and have the generating AI prioritize the analysis of highly relevant data. The analysis unit can also adjust the analysis schedule based on the data relevance. For example, the analysis unit can input the data relevance into a generating AI and have the generating AI adjust the analysis schedule. The analysis unit can also prioritize the analysis of highly relevant data and simplify the analysis of less relevant data. For example, the analysis unit can input the data relevance into a generating AI and have the generating AI simplify the analysis of less relevant data. By adjusting the order of analysis based on the data relevance, highly relevant data can be prioritized for analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0048] The current analysis unit can improve the accuracy of its analysis of current market data based on its interrelationship with historical data. For example, the current analysis unit can improve the accuracy of its analysis by comparing historical and current data and analyzing their interrelationships. For example, the current analysis unit can input historical data into a generating AI and have the generating AI analyze its interrelationship with the current data. The current analysis unit can also improve the accuracy of its analysis by predicting the trend of current data based on historical data. For example, the current analysis unit can input historical data into a generating AI and have the generating AI predict the trend of current data. The current analysis unit can also correct the analysis results of current data by considering the interrelationship with historical data. For example, the current analysis unit can input historical data into a generating AI and have the generating AI correct the analysis results of current data. By doing so, the current analysis unit can provide more accurate analysis results by improving the accuracy of its analysis by considering the interrelationship with historical data. Some or all of the above processes in the current analysis unit may be performed using AI, for example, or without using AI.

[0049] The current analysis unit can perform analysis based on company attribute information when analyzing current market data. For example, the current analysis unit adjusts the analysis method based on the industry and size of the company. For example, the current analysis unit inputs company industry data into the generating AI and has the generating AI adjust the analysis method. The current analysis unit can also prioritize the analysis of highly relevant data based on company attribute information. For example, the current analysis unit inputs company attribute information into the generating AI and has the generating AI prioritize the analysis of highly relevant data. The current analysis unit can also correct the analysis results by taking company attribute information into consideration. For example, the current analysis unit inputs company attribute information into the generating AI and has the generating AI correct the analysis results. By performing analysis while considering company attribute information, it is possible to provide analysis results that are tailored to the characteristics of each company. Some or all of the above processes in the current analysis unit may be performed using AI, for example, or without using AI.

[0050] The current analysis unit can perform analysis based on geographical distribution when analyzing current market data. For example, the current analysis unit prioritizes the analysis of highly relevant data based on geographical distribution. For example, the current analysis unit inputs geographical distribution data into a generating AI and has the generating AI prioritize the analysis of highly relevant data. The current analysis unit can also correct the analysis results considering geographical distribution. For example, the current analysis unit inputs geographical distribution data into a generating AI and has the generating AI correct the analysis results. The current analysis unit can also adjust the analysis schedule based on geographical distribution. For example, the current analysis unit inputs geographical distribution data into a generating AI and has the generating AI adjust the analysis schedule. This allows for an understanding of regional market trends by performing analysis while considering geographical distribution. Some or all of the above processes in the current analysis unit may be performed using AI, for example, or without using AI.

[0051] The current analysis unit can improve the accuracy of its analysis based on relevant literature when analyzing current market data. For example, the current analysis unit can correct the analysis results by referring to relevant literature. For example, the current analysis unit can input relevant literature data into a generating AI and have the generating AI correct the analysis results. The current analysis unit can also adjust the analysis method based on relevant literature. For example, the current analysis unit can input relevant literature data into a generating AI and have the generating AI adjust the analysis method. The current analysis unit can also improve the accuracy of its analysis by referring to relevant literature. For example, the current analysis unit can input relevant literature data into a generating AI and have the generating AI improve the accuracy of its analysis. By improving the accuracy of the analysis by referring to relevant literature, it is possible to provide more accurate analysis results. Some or all of the above processes in the current analysis unit may be performed using AI, for example, or without using AI.

[0052] The decision-making unit can analyze past investment behavior and select an appropriate decision-making method when determining the optimal time to buy or sell an investment. For example, the decision-making unit can analyze past investment behavior, identify successful patterns, and select the optimal decision-making method. For example, the decision-making unit can input past investment behavior data into a generating AI and have the generating AI identify successful patterns. The decision-making unit can also select a decision-making method to avoid failed patterns based on past investment behavior. For example, the decision-making unit can input past investment behavior data into a generating AI and have the generating AI identify failed patterns. The decision-making unit can also analyze past investment behavior and select the optimal decision-making method adapted to the current market conditions. For example, the decision-making unit can input past investment behavior data into a generating AI and have the generating AI select the optimal decision-making method adapted to the current market conditions. By analyzing past investment behavior and selecting the optimal decision-making method, the accuracy of investment decisions is improved. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without using AI.

[0053] The decision-making unit can optimize its decision-making methods based on current market conditions when determining the optimal time to buy or sell an investment. For example, the decision-making unit can analyze current market conditions in real time and provide the optimal decision-making methods. For example, the decision-making unit can input current market condition data into a generating AI and have the generating AI provide the optimal decision-making methods. The decision-making unit can also customize the decision-making methods by considering current market trends. For example, the decision-making unit can input current market trend data into a generating AI and have the generating AI customize the decision-making methods. The decision-making unit can also provide decision-making methods to minimize risk based on current market conditions. For example, the decision-making unit can input current market condition data into a generating AI and have the generating AI provide decision-making methods to minimize risk. This makes it possible to make investment decisions with minimized risk by customizing the decision-making methods based on current market conditions. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without using AI.

[0054] The decision-making unit can select an appropriate decision-making method based on geographical location information when determining the optimal time to buy or sell an investment. For example, the decision-making unit can provide a decision-making method that takes into account market trends in each region based on geographical location information. For example, the decision-making unit inputs geographical location information data into a generating AI and causes the generating AI to provide a decision-making method that takes into account market trends in each region. The decision-making unit can also provide a decision-making method to minimize the risks in a specific region based on geographical location information. For example, the decision-making unit inputs geographical location information data into a generating AI and causes the generating AI to provide a decision-making method to minimize the risks in a specific region. The decision-making unit can also provide optimal investment decisions for each region, taking geographical location information into consideration. For example, the decision-making unit inputs geographical location information data into a generating AI and causes the generating AI to provide optimal investment decisions for each region. By selecting the optimal decision-making method that takes geographical location information into consideration, it becomes possible to make investment decisions that take into account market trends in each region. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI.

[0055] The decision-making unit can analyze social media activity and propose decision-making methods when determining the optimal time to buy or sell an investment. For example, the decision-making unit can analyze trends on social media and provide optimal investment decisions. For example, the decision-making unit can input social media trend data into a generating AI and have the generating AI provide optimal investment decisions. The decision-making unit can also analyze user reactions on social media and propose decision-making methods. For example, the decision-making unit can input social media user reaction data into a generating AI and have the generating AI propose decision-making methods. The decision-making unit can also analyze hashtags and keywords on social media and propose decision-making methods. For example, the decision-making unit can input social media hashtag data into a generating AI and have the generating AI propose decision-making methods. By analyzing social media activity and proposing decision-making methods, it becomes possible to make investment decisions that take into account the latest market trends. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze past data collection history and select an appropriate collection method. For example, it can identify and apply the most efficient collection method from past data collection history. For instance, the data collection unit inputs past data collection history into a generating AI to identify the optimal collection method. The data collection unit can also analyze past data collection history and optimize collection frequency and timing. For example, it inputs past data collection history into a generating AI to optimize collection frequency and timing. Furthermore, the data collection unit can determine the priority of data to be collected based on past data collection history. For example, it inputs past data collection history into a generating AI to determine the priority of data to be collected. This improves the efficiency of data collection by analyzing past data collection history and selecting the optimal collection method.

[0058] The analysis unit can optimize the level of detail in its analysis based on the importance of the data. For example, it can perform detailed analysis on important data and simplified analysis on less important data. For instance, the analysis unit can input the data importance into the generating AI and have the generating AI perform detailed analysis on the important data. The analysis unit can also determine the priority of analysis based on the data importance. For example, the analysis unit can input the data importance into the generating AI and have the generating AI determine the priority of analysis. Furthermore, the analysis unit can create detailed reports on important data and simplified reports on less important data. For example, the analysis unit can input the data importance into the generating AI and have the generating AI create detailed reports. This allows for detailed analysis of important data by adjusting the level of detail based on the data importance.

[0059] The current analysis unit can improve the accuracy of its analysis of current market data by analyzing its interrelationships with historical data. For example, it can improve the accuracy of its analysis by comparing historical and current data and analyzing their interrelationships. For instance, the current analysis unit inputs historical data into a generating AI and has the AI ​​analyze its interrelationships with current data. Furthermore, the current analysis unit can improve the accuracy of its analysis by predicting trends in current data based on historical data. For example, it inputs historical data into a generating AI and has the AI ​​predict trends in current data. In addition, the current analysis unit can correct the analysis results of current data by considering its interrelationships with historical data. For example, it inputs historical data into a generating AI and has the AI ​​correct the analysis results of current data. By improving the accuracy of the analysis by considering its interrelationships with historical data, it can provide more accurate analysis results.

[0060] The decision-making unit can analyze past investment behavior to select an appropriate decision-making method when determining the optimal time to buy or sell an investment. For example, it can analyze past investment behavior, identify successful patterns, and select the optimal decision-making method. For example, the decision-making unit inputs past investment behavior data into a generating AI and has the generating AI identify successful patterns. The decision-making unit can also select a decision-making method to avoid failed patterns based on past investment behavior. For example, the decision-making unit inputs past investment behavior data into a generating AI and has the generating AI identify failed patterns. Furthermore, the decision-making unit can analyze past investment behavior and select the optimal decision-making method adapted to the current market conditions. For example, the decision-making unit inputs past investment behavior data into a generating AI and has the generating AI select the optimal decision-making method adapted to the current market conditions. As a result, the accuracy of investment decisions is improved by analyzing past investment behavior and selecting the optimal decision-making method.

[0061] The decision-making unit can optimize its decision-making methods based on current market conditions when determining the optimal time to buy or sell an investment. For example, it can analyze current market conditions in real time and provide the optimal decision-making method. For example, the decision-making unit inputs current market condition data into a generating AI and has the generating AI provide the optimal decision-making method. The decision-making unit can also customize the decision-making method by considering current market trends. For example, the decision-making unit inputs current market trend data into a generating AI and has the generating AI customize the decision-making method. Furthermore, the decision-making unit can provide decision-making methods to minimize risk based on current market conditions. For example, the decision-making unit inputs current market condition data into a generating AI and has the generating AI provide decision-making methods to minimize risk. As a result, by customizing the decision-making method based on current market conditions, it becomes possible to make investment decisions that minimize risk.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects historical stock market data. This historical data includes, for example, data from the past year, data from the past five years, etc. The data collection unit collects data from publicly available databases on the internet, corporate financial reports, market reports, etc. For example, the data collection unit can automatically scan corporate financial reports and store them in the database. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the reasons for past increases and decreases. The analysis unit analyzes the data using statistical analysis and machine learning algorithms. For example, it may use regression analysis to identify factors influencing stock price fluctuations, or use K-means clustering to group the data and identify patterns of increases and decreases. Step 3: The current analysis unit analyzes current market data based on the reasons identified by the analysis unit. The current analysis unit collects current market data using a real-time database and analyzes the data in real time using streaming data processing technology. It also compares current market data with historical data and uses correlation analysis to identify the relationship between current market data and historical data. Step 4: The Decision Unit determines the optimal buying and selling times for an investment based on the data currently analyzed by the Analysis Unit. The Decision Unit makes investment decisions using technical analysis and fundamental analysis. For example, it may use moving averages to determine buying and selling times, or make investment decisions based on a company's financial indicators (PER and PBR). The Decision Unit can also make investment decisions using an AI model that takes the data currently analyzed by the Analysis Unit as input and outputs the optimal buying and selling times for an investment.

[0064] (Example of form 2) The investment decision system according to an embodiment of the present invention is a system that monitors corporate activities, management actions, performance, and the overall movement of the stock market every second, using the reasons for past rises and falls in the stock market as clues, to determine the optimal time to buy and sell an investment. This investment decision system solves the problem of information overload and difficulty in making decisions faced by individual investors and professional buyers, and achieves a level of decision-making speed that was previously impossible. First, the investment decision system collects past stock market data and analyzes it using AI. The AI ​​identifies the reasons for past rises and falls and uses this as clues to analyze current market data. Specifically, it monitors corporate activities, management actions, performance, and the overall movement of the stock market every second and integrates this data in real time. For example, it analyzes the impact of events such as new product announcements, changes in management, and earnings announcements on stock prices. Next, based on the analysis results, the AI ​​determines the optimal time to buy and sell an investment. For example, it finds specific patterns from past data and compares them with the current market situation to determine when to buy or sell. This judgment is provided to individual investors and professional buyers to support efficient investment. Furthermore, the investment decision system is also useful for companies. Companies can leverage historical market data to understand the impact of their activities on stock prices and market valuations. This supports more transparent business strategies and enables strategic decision-making that adapts to market expectations. For example, companies can use AI analysis results to understand which activities have a positive impact on stock prices and then determine their business strategies accordingly. This investment decision system will attract more investors, increase the assets of Japanese companies, and stimulate investment in new businesses. As a result, a virtuous cycle will be created where the economy prospers and GDP increases. This allows the investment decision system to analyze current market data based on historical stock market data to make optimal investment decisions.

[0065] The investment decision system according to this embodiment comprises a data collection unit, an analysis unit, a current analysis unit, and a decision unit. The data collection unit collects historical stock market data. Historical stock market data includes, but is not limited to, data from the past year, data from the past five years, etc. The data collection unit collects data from, for example, publicly available databases on the internet. The data collection unit can also collect data from corporate financial reports, market reports, etc. For example, the data collection unit automatically scans corporate financial reports and stores them in a database. The analysis unit analyzes the data collected by the data collection unit and identifies the reasons for past increases and decreases. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit uses regression analysis to identify factors influencing stock price fluctuations. The analysis unit can also classify data using clustering algorithms and find specific patterns. For example, the analysis unit uses K-means clustering to group data and identify patterns of increases and decreases. The current analysis unit analyzes current market data based on the reasons identified by the analysis unit. The current analysis unit collects and analyzes current market data using, for example, a real-time database. For example, the current analysis unit analyzes data in real time using streaming data processing technology. The current analysis unit can also compare current market data with historical data and analyze their interrelationships. For example, the current analysis unit can identify the relationship between current market data and historical data using correlation analysis. The decision unit determines the optimal time to buy and sell investments based on the data analyzed by the current analysis unit. The decision unit makes investment decisions using, for example, technical analysis or fundamental analysis. For example, the decision unit uses moving averages to determine the timing to buy and sell. The decision unit can also make investment decisions based on a company's financial indicators. For example, the decision unit makes investment decisions based on a company's PER (price-to-earnings ratio) or PBR (price-to-book ratio). As a result, the investment decision system according to this embodiment can analyze current market data based on historical stock market data and make optimal investment decisions. Some or all of the above-described processing in the decision unit may be performed using, for example, AI, or without using AI.For example, the decision-making unit can take the data currently analyzed by the analysis unit as input and use an AI model that outputs the optimal time to buy and sell investments to make investment decisions.

[0066] The data collection unit collects historical stock market data. This historical data includes, but is not limited to, data from the past year, the past five years, etc. The data collection unit collects data from publicly available databases on the internet, for example. Specifically, the data collection unit accesses multiple reliable databases to obtain data such as historical stock prices, trading volume, company financial status, and economic indicators. This includes methods such as automatically retrieving data using APIs and extracting necessary information using web scraping techniques. The data collection unit can also collect data from corporate financial reports and market reports. For example, the data collection unit automatically scans corporate financial reports and stores them in the database. This includes methods such as converting paper reports into digital data using OCR (optical character recognition) technology and extracting necessary information from PDF reports. Furthermore, the data collection unit can also collect unstructured data such as news articles and social media posts. This allows the data collection unit to collect a wide range of data from diverse sources and provide foundational data for investment decisions. The collected data is centrally stored in a database and managed so that the analysis unit and the current analysis unit can access it. The data collection unit includes processes to check the integrity and consistency of the data and remove inaccurate or missing data in order to ensure data quality. This allows the data collection unit to provide reliable data and improve the overall accuracy and reliability of the system.

[0067] The analysis unit analyzes the data collected by the data collection unit to identify the reasons for past price increases and decreases. The analysis unit analyzes the data using, for example, statistical analysis and machine learning algorithms. Specifically, the analysis unit uses regression analysis to identify the factors influencing stock price fluctuations. Regression analysis models the relationship between independent variables (e.g., trading volume, corporate financial indicators, economic indicators, etc.) and the dependent variable (stock price) to explain stock price fluctuations. The analysis unit can also classify data using clustering algorithms to identify specific patterns. For example, the analysis unit uses K-means clustering to group data and identify patterns of increases and decreases. K-means clustering divides data points into K clusters and calculates the centroid of each cluster. This ensures that data points with similar characteristics belong to the same cluster. Furthermore, the analysis unit can analyze stock price trends and seasonality using time series analysis. For example, it uses the ARIMA model to analyze time series data of stock prices and predict future stock price trends. The analysis unit can also detect complex patterns using deep learning techniques. For example, an LSTM (Long Short-Term Memory) network is used to model the long-term dependencies of stock prices and predict future stock prices. This allows the analysis unit to analyze the collected data from multiple perspectives and identify the factors and patterns of past stock price fluctuations.

[0068] The current analysis unit analyzes current market data based on reasons identified by the unit. For example, the unit collects and analyzes current market data using real-time databases. Specifically, it analyzes data in real time using streaming data processing technology. This includes methods for collecting and processing data in real time using streaming platforms such as Apache Kafka and Apache Flink. The unit can also compare current market data with historical data and analyze their interrelationships. For example, it uses correlation analysis to identify the relationship between current market data and historical data. Correlation analysis calculates the correlation coefficient between two variables and evaluates the strength and direction of the relationship. This allows for determining how similar current market data is to historical data. Furthermore, the unit can use anomaly detection algorithms to detect unusual patterns and sudden fluctuations in current market data. For example, algorithms such as Isolation Forest and LOF (Local Outlier Factor) can be used to identify anomalous data points and issue early warnings. This allows the unit to analyze market data in real time and accurately understand the current market situation.

[0069] The decision-making unit determines the optimal buying and selling times for investments based on data currently analyzed by the analysis unit. The decision-making unit makes investment decisions using, for example, technical analysis and fundamental analysis. Specifically, it uses moving averages to determine buying and selling times. Moving averages calculate the average stock price over a certain period and visualize the trend. For example, it uses the intersection of short-term and long-term moving averages to determine buy and sell signals. Furthermore, the decision-making unit can also make investment decisions based on a company's financial indicators. For example, it uses a company's PER (price-to-earnings ratio) and PBR (price-to-book ratio) to make investment decisions. PER is an indicator showing how high a company's stock price is relative to its earnings, and PBR is an indicator showing how high a company's stock price is relative to its net assets. This allows the decision-making unit to evaluate a company's profitability and financial health and determine the appropriate timing for investment. In addition, the decision-making unit can also use AI to make investment decisions. For example, the decision-making unit can make investment decisions using an AI model that takes data analyzed by the analysis unit as input and outputs the optimal buying and selling times for investments. The AI ​​model employs technologies such as deep learning and reinforcement learning, learning from past data to predict future market trends. This allows the decision-making unit to make more accurate investment decisions by utilizing advanced analytical techniques.

[0070] The decision-making unit can identify specific patterns from past data and compare them with current market conditions to determine when to buy or sell. For example, the decision-making unit can analyze past data to find specific patterns. For example, the decision-making unit can identify patterns such as price trends or changes in trading volume. The decision-making unit can also compare current market conditions with past patterns to determine when to buy or sell. For example, the decision-making unit can determine when to buy or sell if current market conditions match a specific past pattern. This allows for the identification of specific patterns from past data and comparison with current market conditions to make optimal investment decisions. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can identify patterns using an AI model that takes past data as input and outputs specific patterns.

[0071] The data collection unit can continuously monitor corporate activities, management movements, performance, and overall stock market movements, and integrate this data in real time. For example, the data collection unit monitors events such as new product launches, management changes, and earnings announcements. For instance, it can monitor new product launches in real time and integrate the data. It can also monitor management movements and integrate data such as management changes. For example, it can monitor management changes in real time and integrate the data. Furthermore, it can monitor corporate earnings announcements and integrate the data. For example, it can monitor quarterly financial results in real time and integrate the data. This allows for monitoring corporate activities, management movements, performance, and overall stock market movements every second and integrating the data in real time, enabling a grasp of the latest market conditions. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data such as new product launches and management changes into a generating AI and have the generating AI perform the data integration.

[0072] The analysis unit can analyze the impact of at least one of the following events—a company's new product announcement, a change in management, or an earnings announcement—on stock prices. For example, the analysis unit can analyze the impact of a company's new product announcement on stock prices. For example, the analysis unit can analyze the fluctuations in stock prices after a new product announcement and identify the impact. The analysis unit can also analyze the impact of a change in management on stock prices. For example, the analysis unit can analyze the fluctuations in stock prices after a change in management and identify the impact. The analysis unit can also analyze the impact of a company's earnings announcement on stock prices. For example, the analysis unit can analyze the fluctuations in stock prices after a quarterly earnings announcement and identify the impact. In this way, by analyzing the impact of events such as a company's new product announcement, a change in management, and an earnings announcement on stock prices, the factors influencing stock price fluctuations can be understood. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data such as a company's new product announcement or a change in management into a generating AI and have the generating AI analyze the impact on stock prices.

[0073] The decision-making unit can be provided to at least one of the following: individual investors and professional buyers, and can support efficient investment. For example, the decision-making unit can be provided to individual investors to support efficient investment. For example, the decision-making unit can notify individual investors of the optimal time to buy and sell. The decision-making unit can also be provided to professional buyers to support efficient investment. For example, the decision-making unit can notify professional buyers of the optimal time to buy and sell. This improves the accuracy of investment decisions by supporting efficient investment for both individual investors and professional buyers. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input investment data from individual investors and professional buyers into a generating AI and have the generating AI execute the optimal investment decision.

[0074] The analysis unit can provide data to help companies understand the impact of their activities on at least one of the following: stock price and market valuation. For example, the analysis unit can analyze and provide data on the impact of a company's new product launch on its stock price. For example, the analysis unit can analyze fluctuations in stock price after a new product launch and identify the impact. The analysis unit can also analyze the impact of a company's marketing strategy on market valuation. For example, the analysis unit can analyze fluctuations in market valuation after a marketing campaign and identify the impact. The analysis unit can also analyze the impact of a company's financial reporting on its stock price. For example, the analysis unit can analyze fluctuations in stock price after quarterly earnings announcements and identify the impact. This supports companies' strategic decision-making by providing data to help them understand the impact of their activities on stock price and market valuation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data such as a company's new product launch or marketing strategy into a generating AI and have the generating AI analyze the impact on stock price and market valuation.

[0075] The decision-making unit can assist companies in making strategic decisions that align with market expectations. For example, the unit can assist companies in making decisions to develop new products that meet market expectations. For instance, the unit can propose a new product development plan based on market expectations. The decision-making unit can also assist companies in making decisions to formulate marketing strategies that meet market expectations. For example, the unit can propose a marketing campaign plan based on market expectations. The decision-making unit can also assist companies in formulating financial strategies that meet market expectations. For example, the unit can propose a financial plan based on market expectations. This enhances a company's competitiveness by supporting it in making strategic decisions that adapt to market expectations. Some or all of the processes described above in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input a company's market data into a generating AI and have the generating AI propose strategic decisions that align with market expectations.

[0076] The data collection unit can estimate the user's emotions and optimize the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection and collect only important data. For example, the data collection unit can estimate the user's stress level using an emotion engine or generative AI and adjust the frequency of data collection. The data collection unit can also increase the frequency of data collection and collect more detailed data if the user is relaxed. For example, the data collection unit can estimate the user's relaxation level using an emotion engine or generative AI and adjust the frequency of data collection. Furthermore, if the user is in a hurry, the data collection unit can collect data in real time and provide it quickly. For example, the data collection unit can estimate the user's hurried state using an emotion engine or generative AI and adjust the timing of data collection. This reduces the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without using AI.

[0077] The data collection unit can analyze past data collection history and select an appropriate collection method. For example, the data collection unit can identify and apply the most efficient collection method from past data collection history. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI identify the optimal collection method. The data collection unit can also analyze past data collection history and optimize the collection frequency and timing. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI optimize the collection frequency and timing. The data collection unit can also determine the priority of data to be collected based on past data collection history. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI determine the priority of data to be collected. This improves the efficiency of data collection by analyzing past data collection history and selecting the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without using AI.

[0078] The data collection unit can filter data based on the company's current activities and market trends during data collection. For example, the data collection unit can filter data based on important events such as a company's new product launch or a change in management. For example, the data collection unit can input data such as a company's new product launch or a change in management into a generating AI and have the generating AI filter the important data. The data collection unit can also monitor market trends in real time and prioritize the collection of highly relevant data. For example, the data collection unit can input market trend data into a generating AI and have the generating AI prioritize the collection of highly relevant data. The data collection unit can also filter data based on a company's earnings announcement or market trends and extract important information. For example, the data collection unit can input company earnings announcement data into a generating AI and have the generating AI extract important information. This allows for the efficient collection of important information by filtering data based on the company's current activities and market trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0079] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. For example, the data collection unit will estimate the user's stress level using an emotion engine or generative AI and prioritize collecting important data. The data collection unit can also prioritize collecting detailed data if the user is relaxed. For example, the data collection unit will estimate the user's relaxation level using an emotion engine or generative AI and prioritize collecting detailed data. The data collection unit can also prioritize collecting important data in real time if the user is in a hurry. For example, the data collection unit will estimate the user's hurried state using an emotion engine or generative AI and prioritize collecting important data. This allows for data collection tailored to the user's needs by determining the priority of data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without using AI.

[0080] The data collection unit can prioritize the collection of highly relevant data based on geographical location information during data collection. For example, the data collection unit can collect highly relevant data based on the location and activity area of ​​a company. For example, the data collection unit can input company location data into a generating AI and have the generating AI collect highly relevant data. The data collection unit can also geographically analyze market trends and prioritize the collection of highly relevant data. For example, the data collection unit can input market trend data into a generating AI and have the generating AI collect highly relevant data. The data collection unit can also prioritize the collection of data from a specific region based on geographical location information. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI prioritize the collection of data from a specific region. This allows for the prioritization of data collection that takes geographical location information into consideration, thereby enabling an understanding of market trends in each region. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without using AI.

[0081] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the activities and trends of companies on social media and collect relevant data. For example, the data collection unit can input social media post data into a generating AI and have the generating AI analyze company activities and trends. The data collection unit can also analyze user reactions and comments on social media and collect relevant data. For example, the data collection unit can input social media comment data into a generating AI and have the generating AI analyze user reactions. The data collection unit can also analyze hashtags and keywords on social media and collect relevant data. For example, the data collection unit can input social media hashtag data into a generating AI and have the generating AI collect relevant data. By analyzing social media activity and collecting relevant data, it is possible to grasp the latest market trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0082] The analysis unit can estimate the user's emotions and optimize the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. For example, the analysis unit can estimate the user's stress level using an emotion engine or generative AI and provide simple analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can estimate the user's relaxation level using an emotion engine or generative AI and provide detailed analysis results. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, the analysis unit can estimate the user's hurried state using an emotion engine or generative AI and provide concise analysis results. By adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without using AI.

[0083] The analysis unit can optimize the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform detailed analysis on important data and simplified analysis on less important data. For example, the analysis unit can input the importance of the data into the generating AI and have the generating AI perform detailed analysis on the important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit can input the importance of the data into the generating AI and have the generating AI determine the priority of the analysis. The analysis unit can also create detailed reports on important data and simplified reports on less important data. For example, the analysis unit can input the importance of the data into the generating AI and have the generating AI create detailed reports. In this way, by adjusting the level of detail of the analysis based on the importance of the data, detailed analysis can be performed on important data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0084] The analysis unit can apply different analysis algorithms depending on the data classification during analysis. For example, the analysis unit can apply a specific analysis algorithm to corporate activity data and a different analysis algorithm to management activity data. For example, the analysis unit inputs corporate activity data into a generating AI and has the generating AI apply a specific analysis algorithm. The analysis unit can also apply a detailed analysis algorithm to performance data and a simplified analysis algorithm to overall stock market movement data. For example, the analysis unit inputs performance data into a generating AI and has the generating AI apply a detailed analysis algorithm. Furthermore, the analysis unit can select and apply the optimal analysis algorithm depending on the data classification. For example, the analysis unit inputs the data classification into a generating AI and has the generating AI select the optimal analysis algorithm. This allows for the provision of optimal analysis results by applying different analysis algorithms depending on the data classification. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0085] The analysis unit can estimate the user's emotions and optimize the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis result. For example, the analysis unit can estimate the user's stress level using an emotion engine or generative AI and provide a short analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can estimate the user's relaxation level using an emotion engine or generative AI and provide a detailed analysis result. The analysis unit can also provide a quick analysis result if the user is in a hurry. For example, the analysis unit can estimate the user's hurried state using an emotion engine or generative AI and provide a quick analysis result. By adjusting the length of the analysis based on the user's emotions, the analysis result can be tailored to the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without using AI.

[0086] The analysis unit can set analysis priorities based on the data submission date during analysis. For example, the analysis unit can prioritize the analysis of the latest data and postpone older data. For example, the analysis unit can input the data submission date into the generating AI and have the generating AI prioritize the analysis of the latest data. The analysis unit can also adjust the analysis schedule based on the data submission date. For example, the analysis unit can input the data submission date into the generating AI and have the generating AI adjust the analysis schedule. The analysis unit can also prioritize the analysis of data with a recent submission date and postpone data with a later submission date. For example, the analysis unit can input the data submission date into the generating AI and have the generating AI prioritize the analysis of data with a recent submission date. In this way, by determining the analysis priority based on the data submission date, the latest data can be analyzed preferentially. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0087] The analysis unit can optimize the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. For example, the analysis unit can input the data relevance into a generating AI and have the generating AI prioritize the analysis of highly relevant data. The analysis unit can also adjust the analysis schedule based on the data relevance. For example, the analysis unit can input the data relevance into a generating AI and have the generating AI adjust the analysis schedule. The analysis unit can also prioritize the analysis of highly relevant data and simplify the analysis of less relevant data. For example, the analysis unit can input the data relevance into a generating AI and have the generating AI simplify the analysis of less relevant data. By adjusting the order of analysis based on the data relevance, highly relevant data can be prioritized for analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0088] The current analysis unit can estimate the user's emotions and optimize the analysis method of current market data based on the estimated user emotions. For example, if the user is stressed, the current analysis unit can provide a simple and easy-to-understand analysis method. For example, the current analysis unit can estimate the user's stress level using an emotion engine or generative AI and provide a simple analysis method. The current analysis unit can also provide a detailed analysis method if the user is relaxed. For example, the current analysis unit can estimate the user's relaxation level using an emotion engine or generative AI and provide a detailed analysis method. The current analysis unit can also provide a concise analysis method if the user is in a hurry. For example, the current analysis unit can estimate the user's hurried state using an emotion engine or generative AI and provide a concise analysis method. By adjusting the analysis method of current market data based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Currently, some or all of the above-mentioned processes in the analysis unit may be performed using AI, for example, or without using AI.

[0089] The current analysis unit can improve the accuracy of its analysis of current market data based on its interrelationship with historical data. For example, the current analysis unit can improve the accuracy of its analysis by comparing historical and current data and analyzing their interrelationships. For example, the current analysis unit can input historical data into a generating AI and have the generating AI analyze its interrelationship with the current data. The current analysis unit can also improve the accuracy of its analysis by predicting the trend of current data based on historical data. For example, the current analysis unit can input historical data into a generating AI and have the generating AI predict the trend of current data. The current analysis unit can also correct the analysis results of current data by considering the interrelationship with historical data. For example, the current analysis unit can input historical data into a generating AI and have the generating AI correct the analysis results of current data. By doing so, the current analysis unit can provide more accurate analysis results by improving the accuracy of its analysis by considering the interrelationship with historical data. Some or all of the above processes in the current analysis unit may be performed using AI, for example, or without using AI.

[0090] The current analysis unit can perform analysis based on company attribute information when analyzing current market data. For example, the current analysis unit adjusts the analysis method based on the industry and size of the company. For example, the current analysis unit inputs company industry data into the generating AI and has the generating AI adjust the analysis method. The current analysis unit can also prioritize the analysis of highly relevant data based on company attribute information. For example, the current analysis unit inputs company attribute information into the generating AI and has the generating AI prioritize the analysis of highly relevant data. The current analysis unit can also correct the analysis results by taking company attribute information into consideration. For example, the current analysis unit inputs company attribute information into the generating AI and has the generating AI correct the analysis results. By performing analysis while considering company attribute information, it is possible to provide analysis results that are tailored to the characteristics of each company. Some or all of the above processes in the current analysis unit may be performed using AI, for example, or without using AI.

[0091] The current analysis unit can estimate the user's emotions and optimize the order in which it displays the analysis results of current market data based on the estimated user emotions. For example, if the user is stressed, the current analysis unit will prioritize displaying important analysis results. For example, the current analysis unit will estimate the user's stress level using an emotion engine or generative AI and prioritize displaying important analysis results. Also, if the user is relaxed, the current analysis unit can display detailed analysis results in a sequential manner. For example, the current analysis unit will estimate the user's relaxation level using an emotion engine or generative AI and prioritize displaying detailed analysis results. Furthermore, if the user is in a hurry, the current analysis unit can prioritize displaying concise analysis results. For example, the current analysis unit will estimate the user's hurried state using an emotion engine or generative AI and prioritize displaying concise analysis results. In this way, by adjusting the order in which analysis results are displayed based on the user's emotions, information important to the user can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI.

[0092] The current analysis unit can perform analysis based on geographical distribution when analyzing current market data. For example, the current analysis unit prioritizes the analysis of highly relevant data based on geographical distribution. For example, the current analysis unit inputs geographical distribution data into a generating AI and has the generating AI prioritize the analysis of highly relevant data. The current analysis unit can also correct the analysis results considering geographical distribution. For example, the current analysis unit inputs geographical distribution data into a generating AI and has the generating AI correct the analysis results. The current analysis unit can also adjust the analysis schedule based on geographical distribution. For example, the current analysis unit inputs geographical distribution data into a generating AI and has the generating AI adjust the analysis schedule. This allows for an understanding of regional market trends by performing analysis while considering geographical distribution. Some or all of the above processes in the current analysis unit may be performed using AI, for example, or without using AI.

[0093] The current analysis unit can improve the accuracy of its analysis based on relevant literature when analyzing current market data. For example, the current analysis unit can correct the analysis results by referring to relevant literature. For example, the current analysis unit can input relevant literature data into a generating AI and have the generating AI correct the analysis results. The current analysis unit can also adjust the analysis method based on relevant literature. For example, the current analysis unit can input relevant literature data into a generating AI and have the generating AI adjust the analysis method. The current analysis unit can also improve the accuracy of its analysis by referring to relevant literature. For example, the current analysis unit can input relevant literature data into a generating AI and have the generating AI improve the accuracy of its analysis. By improving the accuracy of the analysis by referring to relevant literature, it is possible to provide more accurate analysis results. Some or all of the above processes in the current analysis unit may be performed using AI, for example, or without using AI.

[0094] The decision-making unit can estimate the user's emotions and optimize the method for determining the optimal buying and selling times for investments based on the estimated emotions. For example, if the user is stressed, the decision-making unit can provide a simple and easy-to-understand decision method. For example, the decision-making unit can estimate the user's stress level using an emotion engine or generative AI and provide a simple decision method. The decision-making unit can also provide a detailed decision method if the user is relaxed. For example, the decision-making unit can estimate the user's relaxation level using an emotion engine or generative AI and provide a detailed decision method. Furthermore, if the user is in a hurry, the decision-making unit can provide a concise decision method. For example, the decision-making unit can estimate the user's state of urgency using an emotion engine or generative AI and provide a concise decision method. In this way, by adjusting the method for determining the optimal buying and selling times for investments based on the user's emotions, it is possible to provide decision results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the decision-making unit may be performed using AI, for example, or without using AI.

[0095] The decision-making unit can analyze past investment behavior and select an appropriate decision-making method when determining the optimal time to buy or sell an investment. For example, the decision-making unit can analyze past investment behavior, identify successful patterns, and select the optimal decision-making method. For example, the decision-making unit can input past investment behavior data into a generating AI and have the generating AI identify successful patterns. The decision-making unit can also select a decision-making method to avoid failed patterns based on past investment behavior. For example, the decision-making unit can input past investment behavior data into a generating AI and have the generating AI identify failed patterns. The decision-making unit can also analyze past investment behavior and select the optimal decision-making method adapted to the current market conditions. For example, the decision-making unit can input past investment behavior data into a generating AI and have the generating AI select the optimal decision-making method adapted to the current market conditions. By analyzing past investment behavior and selecting the optimal decision-making method, the accuracy of investment decisions is improved. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without using AI.

[0096] The decision-making unit can optimize its decision-making methods based on current market conditions when determining the optimal time to buy or sell an investment. For example, the decision-making unit can analyze current market conditions in real time and provide the optimal decision-making methods. For example, the decision-making unit can input current market condition data into a generating AI and have the generating AI provide the optimal decision-making methods. The decision-making unit can also customize the decision-making methods by considering current market trends. For example, the decision-making unit can input current market trend data into a generating AI and have the generating AI customize the decision-making methods. The decision-making unit can also provide decision-making methods to minimize risk based on current market conditions. For example, the decision-making unit can input current market condition data into a generating AI and have the generating AI provide decision-making methods to minimize risk. This makes it possible to make investment decisions with minimized risk by customizing the decision-making methods based on current market conditions. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without using AI.

[0097] The decision-making unit can estimate the user's emotions and, based on the estimated emotions, prioritize the optimal buying and selling times for investments. For example, if the user is stressed, the decision-making unit will prioritize providing important investment decisions. For example, the unit will estimate the user's stress level using an emotion engine or generative AI and prioritize providing important investment decisions. The decision-making unit can also provide detailed investment decisions if the user is relaxed. For example, the unit will estimate the user's relaxation level using an emotion engine or generative AI and provide detailed investment decisions. The decision-making unit can also quickly provide important investment decisions if the user is in a hurry. For example, the unit will estimate the user's hurried state using an emotion engine or generative AI and quickly provide important investment decisions. In this way, by determining the optimal buying and selling times for investments based on the user's emotions, important investment decisions can be prioritized for the user. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the decision-making unit may be performed using AI, for example, or without using AI.

[0098] The decision-making unit can select an appropriate decision-making method based on geographical location information when determining the optimal time to buy or sell an investment. For example, the decision-making unit can provide a decision-making method that takes into account market trends in each region based on geographical location information. For example, the decision-making unit inputs geographical location information data into a generating AI and causes the generating AI to provide a decision-making method that takes into account market trends in each region. The decision-making unit can also provide a decision-making method to minimize the risks in a specific region based on geographical location information. For example, the decision-making unit inputs geographical location information data into a generating AI and causes the generating AI to provide a decision-making method to minimize the risks in a specific region. The decision-making unit can also provide optimal investment decisions for each region, taking geographical location information into consideration. For example, the decision-making unit inputs geographical location information data into a generating AI and causes the generating AI to provide optimal investment decisions for each region. By selecting the optimal decision-making method that takes geographical location information into consideration, it becomes possible to make investment decisions that take into account market trends in each region. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI.

[0099] The decision-making unit can analyze social media activity and propose decision-making methods when determining the optimal time to buy or sell an investment. For example, the decision-making unit can analyze trends on social media and provide optimal investment decisions. For example, the decision-making unit can input social media trend data into a generating AI and have the generating AI provide optimal investment decisions. The decision-making unit can also analyze user reactions on social media and propose decision-making methods. For example, the decision-making unit can input social media user reaction data into a generating AI and have the generating AI propose decision-making methods. The decision-making unit can also analyze hashtags and keywords on social media and propose decision-making methods. For example, the decision-making unit can input social media hashtag data into a generating AI and have the generating AI propose decision-making methods. By analyzing social media activity and proposing decision-making methods, it becomes possible to make investment decisions that take into account the latest market trends. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The decision-making unit can estimate the user's emotions and, based on those emotions, optimize the notification method for the optimal buying and selling times for investments. For example, if the user is stressed, it can provide a simple and highly visible notification method. For instance, the decision-making unit can estimate the user's stress level using an emotion engine or generative AI and provide a simple notification method. Similarly, if the user is relaxed, it can provide a detailed notification method. For example, the decision-making unit can estimate the user's relaxation level using an emotion engine or generative AI and provide a detailed notification method. Furthermore, if the user is in a hurry, it can provide a concise notification method. For example, the decision-making unit can estimate the user's hurried state using an emotion engine or generative AI and provide a concise notification method. By adjusting the notification method based on the user's emotions, the system can provide notification results that are easy for the user to understand.

[0102] The data collection unit can estimate the user's emotions and prioritize data collection based on those emotions. For example, if a user is stressed, it can prioritize collecting only important data. For instance, the data collection unit estimates the user's stress level using an emotion engine or generative AI and prioritizes collecting important data. Similarly, if a user is relaxed, it can prioritize collecting detailed data. For example, the data collection unit estimates the user's relaxation level using an emotion engine or generative AI and prioritizes collecting detailed data. Furthermore, if a user is in a hurry, it can prioritize collecting important data in real time. For example, the data collection unit estimates the user's hurried state using an emotion engine or generative AI and prioritizes collecting important data. This allows for data collection tailored to the user's needs by determining data collection priorities based on the user's emotions.

[0103] The analysis unit can estimate the user's emotions and optimize the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, it can provide simple and easy-to-understand analysis results. For instance, the analysis unit estimates the user's stress level using an emotion engine or generative AI and provides simple analysis results. Similarly, if the user is relaxed, it can provide detailed analysis results. For example, the analysis unit estimates the user's relaxation level using an emotion engine or generative AI and provides detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. For example, the analysis unit estimates the user's hurried state using an emotion engine or generative AI and provides concise analysis results. By adjusting the display method of the analysis results based on the user's emotions, the system can provide analysis results that are easy for the user to understand.

[0104] The current analysis unit can estimate user emotions and optimize the analysis method of current market data based on the estimated user emotions. For example, if a user is stressed, it can provide a simple and easy-to-understand analysis method. For instance, the current analysis unit estimates the user's stress level using an emotion engine or generative AI and provides a simple analysis method. Similarly, if a user is relaxed, it can provide a detailed analysis method. For example, the current analysis unit estimates the user's relaxation level using an emotion engine or generative AI and provides a detailed analysis method. Furthermore, if a user is in a hurry, it can provide a concise analysis method. For example, the current analysis unit estimates the user's hurried state using an emotion engine or generative AI and provides a concise analysis method. By adjusting the analysis method of current market data based on user emotions, it is possible to provide analysis results that are easy for users to understand.

[0105] The decision-making unit can estimate the user's emotions and, based on those emotions, prioritize the optimal buying and selling times for investments. For example, if the user is stressed, it can prioritize important investment decisions. For instance, the decision-making unit estimates the user's stress level using an emotion engine or generative AI and prioritizes important investment decisions. Similarly, if the user is relaxed, it can provide detailed investment decisions. For example, the decision-making unit estimates the user's relaxation level using an emotion engine or generative AI and provides detailed investment decisions. Furthermore, if the user is in a hurry, it can quickly provide important investment decisions. For example, the decision-making unit estimates the user's hurried state using an emotion engine or generative AI and quickly provides important investment decisions. This allows the system to prioritize important investment decisions for the user by determining the optimal buying and selling times based on their emotions.

[0106] The data collection unit can analyze past data collection history and select an appropriate collection method. For example, it can identify and apply the most efficient collection method from past data collection history. For instance, the data collection unit inputs past data collection history into a generating AI to identify the optimal collection method. The data collection unit can also analyze past data collection history and optimize collection frequency and timing. For example, it inputs past data collection history into a generating AI to optimize collection frequency and timing. Furthermore, the data collection unit can determine the priority of data to be collected based on past data collection history. For example, it inputs past data collection history into a generating AI to determine the priority of data to be collected. This improves the efficiency of data collection by analyzing past data collection history and selecting the optimal collection method.

[0107] The analysis unit can optimize the level of detail in its analysis based on the importance of the data. For example, it can perform detailed analysis on important data and simplified analysis on less important data. For instance, the analysis unit can input the data importance into the generating AI and have the generating AI perform detailed analysis on the important data. The analysis unit can also determine the priority of analysis based on the data importance. For example, the analysis unit can input the data importance into the generating AI and have the generating AI determine the priority of analysis. Furthermore, the analysis unit can create detailed reports on important data and simplified reports on less important data. For example, the analysis unit can input the data importance into the generating AI and have the generating AI create detailed reports. This allows for detailed analysis of important data by adjusting the level of detail based on the data importance.

[0108] The current analysis unit can improve the accuracy of its analysis of current market data by analyzing its interrelationships with historical data. For example, it can improve the accuracy of its analysis by comparing historical and current data and analyzing their interrelationships. For instance, the current analysis unit inputs historical data into a generating AI and has the AI ​​analyze its interrelationships with current data. Furthermore, the current analysis unit can improve the accuracy of its analysis by predicting trends in current data based on historical data. For example, it inputs historical data into a generating AI and has the AI ​​predict trends in current data. In addition, the current analysis unit can correct the analysis results of current data by considering its interrelationships with historical data. For example, it inputs historical data into a generating AI and has the AI ​​correct the analysis results of current data. By improving the accuracy of the analysis by considering its interrelationships with historical data, it can provide more accurate analysis results.

[0109] The decision-making unit can analyze past investment behavior to select an appropriate decision-making method when determining the optimal time to buy or sell an investment. For example, it can analyze past investment behavior, identify successful patterns, and select the optimal decision-making method. For example, the decision-making unit inputs past investment behavior data into a generating AI and has the generating AI identify successful patterns. The decision-making unit can also select a decision-making method to avoid failed patterns based on past investment behavior. For example, the decision-making unit inputs past investment behavior data into a generating AI and has the generating AI identify failed patterns. Furthermore, the decision-making unit can analyze past investment behavior and select the optimal decision-making method adapted to the current market conditions. For example, the decision-making unit inputs past investment behavior data into a generating AI and has the generating AI select the optimal decision-making method adapted to the current market conditions. As a result, the accuracy of investment decisions is improved by analyzing past investment behavior and selecting the optimal decision-making method.

[0110] The decision-making unit can optimize its decision-making methods based on current market conditions when determining the optimal time to buy or sell an investment. For example, it can analyze current market conditions in real time and provide the optimal decision-making method. For example, the decision-making unit inputs current market condition data into a generating AI and has the generating AI provide the optimal decision-making method. The decision-making unit can also customize the decision-making method by considering current market trends. For example, the decision-making unit inputs current market trend data into a generating AI and has the generating AI customize the decision-making method. Furthermore, the decision-making unit can provide decision-making methods to minimize risk based on current market conditions. For example, the decision-making unit inputs current market condition data into a generating AI and has the generating AI provide decision-making methods to minimize risk. As a result, by customizing the decision-making method based on current market conditions, it becomes possible to make investment decisions that minimize risk.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects historical stock market data. This historical data includes, for example, data from the past year, data from the past five years, etc. The data collection unit collects data from publicly available databases on the internet, corporate financial reports, market reports, etc. For example, the data collection unit can automatically scan corporate financial reports and store them in the database. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the reasons for past increases and decreases. The analysis unit analyzes the data using statistical analysis and machine learning algorithms. For example, it may use regression analysis to identify factors influencing stock price fluctuations, or use K-means clustering to group the data and identify patterns of increases and decreases. Step 3: The current analysis unit analyzes current market data based on the reasons identified by the analysis unit. The current analysis unit collects current market data using a real-time database and analyzes the data in real time using streaming data processing technology. It also compares current market data with historical data and uses correlation analysis to identify the relationship between current market data and historical data. Step 4: The Decision Unit determines the optimal buying and selling times for an investment based on the data currently analyzed by the Analysis Unit. The Decision Unit makes investment decisions using technical analysis and fundamental analysis. For example, it may use moving averages to determine buying and selling times, or make investment decisions based on a company's financial indicators (PER and PBR). The Decision Unit can also make investment decisions using an AI model that takes the data currently analyzed by the Analysis Unit as input and outputs the optimal buying and selling times for an investment.

[0113] 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.

[0114] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, current analysis unit, decision unit, and sentiment estimation function, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects data from publicly available databases on the internet and corporate financial reports. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data using statistical analysis and machine learning algorithms. The current analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes current market data using a real-time database. The decision unit is implemented by the specific processing unit 290 of the data processing device 12 and makes investment decisions using technical analysis and fundamental analysis. The sentiment estimation function is implemented by the control unit 46A of the smart device 14 and estimates the user's sentiment and optimizes the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0120] 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.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0122] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] 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.

[0124] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, current analysis unit, decision unit, and sentiment estimation function, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data from publicly available databases on the internet and corporate financial reports. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data using statistical analysis and machine learning algorithms. The current analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes current market data using a real-time database. The decision unit is implemented by the specific processing unit 290 of the data processing device 12 and makes investment decisions using technical analysis and fundamental analysis. The sentiment estimation function is implemented by the control unit 46A of the smart glasses 214 and estimates the user's sentiment and optimizes the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0138] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] 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.

[0140] 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.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, current analysis unit, decision unit, and sentiment estimation function, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data from publicly available databases on the internet and corporate financial reports. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data using statistical analysis and machine learning algorithms. The current analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes current market data using a real-time database. The decision unit is implemented by the specific processing unit 290 of the data processing device 12 and makes investment decisions using technical analysis and fundamental analysis. The sentiment estimation function is implemented by the control unit 46A of the headset terminal 314 and estimates the user's sentiment and optimizes the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0154] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] 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.

[0156] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] 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.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the data collection unit, analysis unit, current analysis unit, decision unit, and sentiment estimation function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects data from publicly available databases on the internet and corporate financial reports. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using statistical analysis and machine learning algorithms. The current analysis unit is implemented by the control unit 46A of the robot 414 and analyzes current market data using a real-time database. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes investment decisions using technical analysis and fundamental analysis. The sentiment estimation function is implemented by the control unit 46A of the robot 414 and estimates the user's sentiment and optimizes the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0166] 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.

[0167] Figure 9 shows the 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.

[0168] 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.

[0169] 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.

[0170] 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, and motorcycles, 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 based, for example, 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.

[0171] 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."

[0172] 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.

[0173] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] 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 other things 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.

[0183] 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.

[0184] (Note 1) The data collection department collects historical stock market data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the reasons for past increases and decreases, The current analysis unit analyzes current market data based on the reasons identified by the aforementioned analysis unit, Based on the data analyzed by the aforementioned analysis unit, a judgment unit determines the appropriate time to buy and sell an investment. including A system characterized by the following features. (Note 2) The unit that makes the determination said, By identifying specific patterns from past data and comparing them with current market conditions, we can determine the best time to buy or sell. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Continuously monitor corporate activities, management actions, performance, and overall stock market movements, and integrate this data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, This study analyzes the impact of at least one of the following corporate events—new product announcements, management changes, or earnings announcements—on stock prices. The system described in Appendix 1, characterized by the features described herein. (Note 5) The unit that makes the determination said, Offered to at least one of the following: individual investors and professional buyers, supporting efficient investing. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, To provide companies with data to understand the impact their activities have on at least one of the following: stock price and market valuation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The unit that makes the determination said, Supporting companies in making strategic decisions that align with market expectations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates user sentiment and optimizes the timing of data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past data collection history and select the appropriate collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the company's current activities and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user sentiment and prioritizes the data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and optimizes the representation of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, optimize the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data classification. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and optimizes the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is optimized based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is, We estimate user sentiment and optimize the analysis method of current market data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is, When analyzing current market data, we improve the accuracy of the analysis based on its correlation with historical data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is, Current market data analysis is performed based on company attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is, It estimates user sentiment and optimizes the order in which it displays the analysis results of current market data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is, Current market data analysis is performed based on geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is, When analyzing current market data, we aim to improve the accuracy of the analysis based on relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 26) The unit that makes the determination said, It estimates user sentiment and optimizes the method for determining the optimal buying and selling times for investments based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The unit that makes the determination said, When determining the optimal time to buy or sell an investment, analyze past investment behavior to select the appropriate decision-making method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The unit that makes the determination said, When determining the optimal time to buy or sell an investment, optimize the decision-making process based on current market conditions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The unit that makes the determination said, It estimates user sentiment and sets priorities for the optimal buying and selling times for investments based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The unit that makes the determination said, When determining the optimal time to buy or sell an investment, the system selects the appropriate decision-making method based on geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The unit that makes the determination said, This service analyzes social media activity to propose methods for determining the optimal time to buy and sell investments. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects historical stock market data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the reasons for past increases and decreases, The current analysis unit analyzes current market data based on the reasons identified by the aforementioned analysis unit, Based on the data analyzed by the aforementioned analysis unit, a judgment unit determines the appropriate time to buy and sell an investment. including A system characterized by the following features.

2. The unit that makes the determination said, By identifying specific patterns from past data and comparing them with current market conditions, we can determine the best time to buy or sell. The system according to feature 1.

3. The aforementioned collection unit is Continuously monitor corporate activities, management actions, performance, and overall stock market movements, and integrate this data in real time. The system according to feature 1.

4. The aforementioned analysis unit, This study analyzes the impact of at least one of the following corporate events—new product announcements, management changes, or earnings announcements—on stock prices. The system according to feature 1.

5. The unit that makes the determination said, Offered to at least one of the following: individual investors and professional buyers, supporting efficient investing. The system according to feature 1.

6. The aforementioned analysis unit, To provide companies with data to understand the impact their activities have on at least one of the following: stock price and market valuation. The system according to feature 1.

7. The unit that makes the determination said, Supporting companies in making strategic decisions that align with market expectations. The system according to feature 1.

8. The aforementioned collection unit is It estimates user sentiment and optimizes the timing of data collection based on the estimated user sentiment. The system according to feature 1.

Images