System

Abstract

An object of a system according to an embodiment is to accurately predict a market trend when an abnormal situation occurs.SOLUTION: A system includes an algorithm analysis unit, an abnormality detection unit, and a trend prediction unit. The algorithmic analyzer forecasts market trends based on historical market data. The abnormality detection part detects an abnormal situation from the market trend predicted by the algorithm analysis part. The trend prediction unit predicts a market trend after the abnormal situation detected by the abnormality detection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to accurately predict market trends when an abnormal situation occurs.

[0005] The system according to the embodiment aims to accurately predict market trends when an abnormal situation occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes an algorithm analysis unit, an anomaly detection unit, and a trend prediction unit. The algorithm analysis unit predicts market trends based on past market data. The anomaly detection unit detects abnormal situations from the market trends predicted by the algorithm analysis unit. The trend prediction unit predicts market trends after the abnormal situation detected by the anomaly detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately predict market trends when an abnormal situation occurs. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The market prediction system according to the embodiment of the present invention is a system that performs anomaly detection in addition to existing algorithmic analysis in predicting foreign exchange and stock markets, and adds trend prediction in the event of an abnormal situation. This enables the market prediction system to detect signs of an abnormal situation before it occurs and to predict market trends in advance.

[0029] A market prediction system according to an embodiment includes an algorithm analysis unit, an anomaly detection unit, and a trend prediction unit. The algorithm analysis unit predicts market trends based on past market data. For example, it predicts price fluctuations using technical indicators such as moving averages, Bollinger bands, and RSI (relative strength index). The algorithm analysis unit also uses a generation AI to perform pattern recognition based on past market data and optimize the performance of the algorithm under specific market conditions. For example, the generation AI analyzes market data from the past 10 years to optimize the performance of the algorithm under specific market conditions. The algorithm analysis unit also uses the generation AI to automatically generate combinations of different technical indicators and identify the most effective combination. For example, the generation AI combines technical indicators such as moving averages, Bollinger bands, and RSI and evaluates the performance of the combination. The anomaly detection unit detects abnormal events from the market trends predicted by the algorithm analysis unit. For example, it uses the generation AI to detect subtle patterns from past market data that are precursors to abnormal events. The anomaly detection unit also uses the generation AI to predict the probability of an abnormal event occurring and proposes a risk management strategy based on that probability. For example, the generation AI predicts the probability of an abnormal event occurring and proposes a risk management strategy based on that probability. Furthermore, the anomaly detection unit uses the emotion estimation function to monitor emotional fluctuations of market participants in real time when an abnormal event occurs, improving the accuracy of anomaly detection. For example, the emotion estimation function monitors emotional fluctuations of market participants in real time when an abnormal event occurs, and improves the accuracy of anomaly detection based on that data. The trend prediction unit predicts market trends after an abnormal event detected by the anomaly detection unit. For example, the generation AI performs a detailed analysis of data on past abnormal events to identify similar patterns. Furthermore, the trend prediction unit uses the generation AI to predict market trends after an abnormal event using multiple scenarios and proposes optimal investment strategies for each scenario. For example, the generation AI predicts market trends after an abnormal event using multiple scenarios and proposes optimal investment strategies for each scenario. Furthermore, the trend prediction unit uses the emotion estimation function to analyze emotional fluctuations of market participants after an abnormal event and predict the impact of those emotions on market trends.For example, the emotion estimation function analyzes the emotional fluctuations of market participants after an abnormal event and predicts the impact of those emotions on market trends. As a result, the market prediction system according to the embodiment can detect signs of an abnormal event before it occurs and predict market trends in advance. For example, investors can take appropriate measures before an abnormal event occurs. Furthermore, even if an abnormal event occurs, subsequent market trends can be predicted and appropriate investment strategies can be proposed.

[0030] The algorithm analysis unit can use the generative AI to recognize patterns based on past market data and optimize the performance of the algorithm under specific market conditions. For example, the algorithm analysis unit can use the generative AI to analyze market data from the past 10 years and optimize the performance of the algorithm under specific market conditions. For example, the algorithm analysis unit can learn market reactions when specific economic indicators are released and derive the optimal trading strategy under those conditions. The algorithm analysis unit can also use the generative AI to recognize patterns for different market segments (e.g., technology stocks, energy stocks, etc.) and develop algorithms that are optimal for each segment. This allows for providing trading strategies that respond to market trends for each segment. The algorithm analysis unit can also use the generative AI to analyze the impact of specific market events (e.g., central bank interest rate announcements) based on past market data and derive the optimal trading strategy when that event occurs. This allows for providing optimal algorithm performance according to market conditions.

[0031] The algorithmic analysis unit can use generative AI to automatically generate combinations of different technical indicators and identify the most effective combination. For example, the algorithmic analysis unit uses generative AI to combine technical indicators such as moving averages, Bollinger bands, and RSI, and evaluate the performance of that combination. For example, it identifies the optimal combination of indicators based on past data and develops a trading strategy using that combination. The algorithmic analysis unit also uses generative AI to automatically generate combinations of different technical indicators and simulate the performance of those combinations. For example, it evaluates the effectiveness of a trading strategy combining MACD and stochastics. The algorithmic analysis unit also uses generative AI to develop algorithms for optimizing combinations of technical indicators. For example, it identifies the most effective combination of indicators based on past market data and provides a trading strategy using that combination. This allows it to provide the optimal combination of technical indicators.

[0032] The anomaly detection unit can use generative AI to detect subtle patterns that are precursors to abnormal events. For example, the anomaly detection unit uses generative AI to detect subtle patterns that are precursors to abnormal events from past market data. For example, it learns subtle price fluctuation patterns before the Lehman Shock and predicts abnormal events based on those patterns. The anomaly detection unit also uses generative AI to build a system that detects subtle patterns that are precursors to abnormal events in real time. For example, it detects sudden increases in trading volume and sudden price fluctuations and predicts the occurrence of abnormal events. The anomaly detection unit also uses generative AI to detect subtle patterns that are precursors to abnormal events and develops an algorithm that predicts abnormal events based on those patterns. For example, it identifies precursors to abnormal events based on past data and issues an alert when those precursors appear. This makes it possible to detect precursors to abnormal events.

[0033] The anomaly detection unit can use generative AI to predict the probability of an abnormal event occurring and propose a risk management strategy based on that probability. For example, the anomaly detection unit uses generative AI to build a system that predicts the probability of an abnormal event occurring and proposes a risk management strategy based on that probability. For example, it proposes a trading strategy for risk avoidance when the probability of an abnormal event occurring is high. The anomaly detection unit also uses generative AI to predict the probability of an abnormal event occurring in real time and proposes a risk management strategy based on that probability. For example, it proposes reducing positions when the probability of an abnormal event occurring increases. The anomaly detection unit also uses generative AI to develop an algorithm that predicts the probability of an abnormal event occurring and proposes a risk management strategy based on that probability. For example, it builds a system that automatically stops trading when the probability of an abnormal event occurring is above a certain level. This makes it possible to propose a risk management strategy based on the probability of an abnormal event occurring.

[0034] The anomaly detection unit can use the generative AI to expand the scope of anomaly detection and monitor other markets or sectors where abnormalities may occur. For example, the anomaly detection unit uses the generative AI to expand the scope of anomaly detection and build a system that monitors other markets (e.g., the cryptocurrency market) where abnormalities may occur. For example, it detects abnormalities in the Bitcoin market. The anomaly detection unit also uses the generative AI to expand the scope of anomaly detection and monitor other sectors (e.g., the energy sector) where abnormalities may occur. For example, it detects abnormalities in the crude oil market. The anomaly detection unit also uses the generative AI to expand the scope of anomaly detection and develop a system that monitors other markets or sectors where abnormalities may occur. For example, it analyzes correlations between different markets and predicts the occurrence of abnormalities. This makes it possible to monitor other markets or sectors where abnormalities may occur.

[0035] The anomaly detection unit can use the generation AI to link the results of anomaly detection with other risk management systems to perform comprehensive risk assessment. The anomaly detection unit, for example, uses the generation AI to link the results of anomaly detection with other risk management systems to build a system that performs comprehensive risk assessment. For example, the risk management strategy is automatically adjusted based on the results of anomaly detection. The anomaly detection unit also uses the generation AI to link the results of anomaly detection with other risk management systems to perform comprehensive risk assessment. For example, the risk of a portfolio is reevaluated based on the results of anomaly detection and an optimal asset allocation is proposed. The anomaly detection unit also uses the generation AI to develop an algorithm that links the results of anomaly detection with other risk management systems to perform comprehensive risk assessment. For example, the risk management system automatically adjusts transactions based on the results of anomaly detection. This allows the results of anomaly detection to be linked with other risk management systems to perform comprehensive risk assessment.

[0036] The trend prediction unit can use the generation AI to analyze data on past abnormal events in detail and identify similar patterns. For example, the trend prediction unit uses the generation AI to analyze data on past abnormal events (e.g., the Lehman Shock) in detail and identify similar patterns. For example, the trend prediction unit learns price fluctuation patterns before and after the Lehman Shock and predicts market trends when a similar abnormal event occurs. The trend prediction unit also uses the generation AI to analyze data on past abnormal events and build a system to identify similar patterns. For example, based on data on past abnormal events, it develops an algorithm that predicts market trends after an abnormal event. The trend prediction unit also uses the generation AI to analyze data on past abnormal events in detail and identify similar patterns, thereby predicting market trends after an abnormal event. For example, based on data on past abnormal events, it predicts price trends after an abnormal event. This makes it possible to analyze data on past abnormal events in detail and identify similar patterns.

[0037] The trend prediction unit can use the generation AI to predict market trends after an abnormal situation under multiple scenarios and propose optimal investment strategies for each scenario. For example, the trend prediction unit uses the generation AI to build a system that predicts market trends after an abnormal situation under multiple scenarios and proposes optimal investment strategies for each scenario. For example, it proposes investment strategies for a market recovery scenario and a market slump scenario after an abnormal situation. The trend prediction unit also uses the generation AI to predict market trends after an abnormal situation under multiple scenarios and proposes optimal investment strategies for each scenario. For example, it simulates market trends after an abnormal situation under multiple scenarios and proposes an investment strategy based on the results. The trend prediction unit also uses the generation AI to develop an algorithm that predicts market trends after an abnormal situation under multiple scenarios and proposes optimal investment strategies for each scenario. For example, it predicts market trends after an abnormal situation under multiple scenarios and optimizes investment strategies based on the scenarios. This makes it possible to predict market trends after an abnormal situation under multiple scenarios and propose optimal investment strategies for each scenario.

[0038] The trend prediction unit can use the generative AI to apply the post-emergency market trend prediction to other markets and asset classes. For example, the trend prediction unit uses the generative AI to build a system that applies the post-emergency market trend prediction to other markets (e.g., Asian markets and European markets). For example, it predicts trends in the Asian market after the Lehman Shock. The trend prediction unit also uses the generative AI to apply the post-emergency market trend prediction to different asset classes (e.g., stocks, bonds, commodities). For example, it predicts trends in the bond market after the Lehman Shock. The trend prediction unit also uses the generative AI to develop an algorithm that applies the post-emergency market trend prediction to other markets and asset classes. For example, it analyzes correlations between different markets and predicts market trends based on those relationships. This allows the post-emergency market trend prediction to be applied to other markets and asset classes.

[0039] The trend prediction unit can use the generation AI to update the market trend forecast after an abnormal event in real time and revise the forecast based on the latest market data. The trend prediction unit, for example, uses the generation AI to build a system that updates the market trend forecast after an abnormal event in real time and revise the forecast based on the latest market data. For example, the trend prediction unit monitors market trends after an abnormal event in real time and updates the forecast. The trend prediction unit also uses the generation AI to update the market trend forecast after an abnormal event in real time and revise the forecast based on the latest market data. For example, the trend prediction unit analyzes market trends after an abnormal event in real time and revise the forecast based on the results. The trend prediction unit also uses the generation AI to develop an algorithm that updates the market trend forecast after an abnormal event in real time and revise the forecast based on the latest market data. For example, the trend prediction unit predicts market trends after an abnormal event in real time and adjusts a trading strategy based on the prediction. This makes it possible to update the market trend forecast after an abnormal event in real time and revise the forecast based on the latest market data.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The market prediction system can further include a news analysis unit. The news analysis unit collects market-related news in real time, analyzes its content, and evaluates its impact on market trends. For example, it analyzes news such as the release of important economic indicators and corporate earnings announcements, and predicts the impact of that news on the market. The news analysis unit can also use generative AI to learn the relationship between past news data and market trends, and predict market reactions when similar news occurs. This enables market predictions that take the impact of news into account.

[0042] The market forecasting system can further include a weather data analysis unit. The weather data analysis unit collects weather data and evaluates the impact of that data on market trends. For example, it analyzes the tendency for agricultural product prices to rise when abnormal weather occurs. The weather data analysis unit can also use generative AI to learn the relationship between past weather data and market trends and predict market reactions when similar weather conditions occur. This enables market forecasts that take into account the impact of weather data.

[0043] The market prediction system can further include a geopolitical risk analysis unit. The geopolitical risk analysis unit collects information on international political and economic trends and evaluates the impact of this data on market trends. For example, it analyzes the tendency for energy prices to rise when conflicts or political upheavals occur in specific regions. The geopolitical risk analysis unit can also use generative AI to learn the relationship between past geopolitical risk data and market trends, and predict market reactions when similar risks occur. This makes it possible to make market predictions that take the impact of geopolitical risks into account.

[0044] The market prediction system can further include a consumer behavior analysis unit. The consumer behavior analysis unit collects consumer purchasing data and evaluates the impact of that data on market trends. For example, if a particular product begins to sell rapidly, it analyzes whether the stock prices of companies that sell that product tend to rise. The consumer behavior analysis unit can also use generative AI to learn the relationship between past consumer behavior data and market trends, and predict market reactions when similar purchasing patterns occur. This makes it possible to make market predictions that take the impact of consumer behavior into account.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The algorithmic analysis unit predicts market trends based on past market data. For example, it uses technical indicators such as moving averages, Bollinger bands, and RSI (Relative Strength Index) to predict price fluctuations. Generative AI then performs pattern recognition based on past market data to optimize the algorithm's performance under specific market conditions. Generative AI then automatically generates combinations of different technical indicators and identifies the most effective combination. Step 2: The anomaly detection unit detects abnormal situations from the market trends predicted by the algorithm analysis unit. For example, it uses generative AI to detect subtle patterns from past market data that may be precursors to abnormal situations. It also uses generative AI to predict the probability of an abnormal situation occurring and proposes a risk management strategy based on that probability. Furthermore, it uses an emotion estimation function to monitor emotional fluctuations among market participants in real time when an abnormal situation occurs, improving the accuracy of anomaly detection. Step 3: The trend forecasting unit predicts market trends following the abnormal event detected by the anomaly detection unit. For example, the generation AI is used to perform a detailed analysis of data on past abnormal events and identify similar patterns. The generation AI also predicts market trends following the abnormal event under multiple scenarios and proposes optimal investment strategies for each scenario. Furthermore, the emotion estimation function is used to analyze the emotional fluctuations of market participants following the abnormal event and predict the impact of those emotions on market trends.

[0047] (Example 2) The market prediction system according to the embodiment of the present invention is a system that performs anomaly detection in addition to existing algorithmic analysis in predicting foreign exchange and stock markets, and adds trend prediction in the event of an abnormal situation. This enables the market prediction system to detect signs of an abnormal situation before it occurs and to predict market trends in advance.

[0048] A market prediction system according to an embodiment includes an algorithm analysis unit, an anomaly detection unit, and a trend prediction unit. The algorithm analysis unit predicts market trends based on past market data. For example, it predicts price fluctuations using technical indicators such as moving averages, Bollinger bands, and RSI (relative strength index). The algorithm analysis unit also uses a generation AI to perform pattern recognition based on past market data and optimize the performance of the algorithm under specific market conditions. For example, the generation AI analyzes market data from the past 10 years to optimize the performance of the algorithm under specific market conditions. The algorithm analysis unit also uses the generation AI to automatically generate combinations of different technical indicators and identify the most effective combination. For example, the generation AI combines technical indicators such as moving averages, Bollinger bands, and RSI and evaluates the performance of the combination. The anomaly detection unit detects abnormal events from the market trends predicted by the algorithm analysis unit. For example, it uses the generation AI to detect subtle patterns from past market data that are precursors to abnormal events. The anomaly detection unit also uses the generation AI to predict the probability of an abnormal event occurring and proposes a risk management strategy based on that probability. For example, the generation AI predicts the probability of an abnormal event occurring and proposes a risk management strategy based on that probability. Furthermore, the anomaly detection unit uses the emotion estimation function to monitor emotional fluctuations of market participants in real time when an abnormal event occurs, improving the accuracy of anomaly detection. For example, the emotion estimation function monitors emotional fluctuations of market participants in real time when an abnormal event occurs, and improves the accuracy of anomaly detection based on that data. The trend prediction unit predicts market trends after an abnormal event detected by the anomaly detection unit. For example, the generation AI performs a detailed analysis of data on past abnormal events to identify similar patterns. Furthermore, the trend prediction unit uses the generation AI to predict market trends after an abnormal event using multiple scenarios and proposes optimal investment strategies for each scenario. For example, the generation AI predicts market trends after an abnormal event using multiple scenarios and proposes optimal investment strategies for each scenario. Furthermore, the trend prediction unit uses the emotion estimation function to analyze emotional fluctuations of market participants after an abnormal event and predict the impact of those emotions on market trends.For example, the emotion estimation function analyzes the emotional fluctuations of market participants after an abnormal event and predicts the impact of those emotions on market trends. As a result, the market prediction system according to the embodiment can detect signs of an abnormal event before it occurs and predict market trends in advance. For example, investors can take appropriate measures before an abnormal event occurs. Furthermore, even if an abnormal event occurs, subsequent market trends can be predicted and appropriate investment strategies can be proposed.

[0049] The algorithm analysis unit can use the generative AI to recognize patterns based on past market data and optimize the performance of the algorithm under specific market conditions. For example, the algorithm analysis unit can use the generative AI to analyze market data from the past 10 years and optimize the performance of the algorithm under specific market conditions. For example, the algorithm analysis unit can learn market reactions when specific economic indicators are released and derive the optimal trading strategy under those conditions. The algorithm analysis unit can also use the generative AI to recognize patterns for different market segments (e.g., technology stocks, energy stocks, etc.) and develop algorithms that are optimal for each segment. This allows for providing trading strategies that respond to market trends for each segment. The algorithm analysis unit can also use the generative AI to analyze the impact of specific market events (e.g., central bank interest rate announcements) based on past market data and derive the optimal trading strategy when that event occurs. This allows for providing optimal algorithm performance according to market conditions.

[0050] The algorithmic analysis unit can use generative AI to automatically generate combinations of different technical indicators and identify the most effective combination. For example, the algorithmic analysis unit uses generative AI to combine technical indicators such as moving averages, Bollinger bands, and RSI, and evaluate the performance of that combination. For example, it identifies the optimal combination of indicators based on past data and develops a trading strategy using that combination. The algorithmic analysis unit also uses generative AI to automatically generate combinations of different technical indicators and simulate the performance of those combinations. For example, it evaluates the effectiveness of a trading strategy combining MACD and stochastics. The algorithmic analysis unit also uses generative AI to develop algorithms for optimizing combinations of technical indicators. For example, it identifies the most effective combination of indicators based on past market data and provides a trading strategy using that combination. This allows it to provide the optimal combination of technical indicators.

[0051] The algorithm analysis unit can use the emotion estimation function to collect investor emotion data and analyze the impact of that emotion on market trends. For example, the algorithm analysis unit uses the emotion estimation function to collect investor emotion data from social media and news articles and analyze the impact of that emotion on market trends. For example, it analyzes the tendency for the market to rise when positive emotion is strong. The algorithm analysis unit also uses the emotion estimation function to collect investor emotion data in real time and predict market trends based on that data. For example, it analyzes the tendency for the market to fall when negative emotion becomes stronger. The algorithm analysis unit also uses the emotion estimation function to collect investor emotion data and optimize trading strategies based on that data. For example, it uses the emotion data to predict turning points in the market and trades based on that prediction. This makes it possible to analyze the impact of investor emotion on market trends.

[0052] The anomaly detection unit can use generative AI to detect subtle patterns that are precursors to abnormal events. For example, the anomaly detection unit uses generative AI to detect subtle patterns that are precursors to abnormal events from past market data. For example, it learns subtle price fluctuation patterns before the Lehman Shock and predicts abnormal events based on those patterns. The anomaly detection unit also uses generative AI to build a system that detects subtle patterns that are precursors to abnormal events in real time. For example, it detects sudden increases in trading volume and sudden price fluctuations and predicts the occurrence of abnormal events. The anomaly detection unit also uses generative AI to detect subtle patterns that are precursors to abnormal events and develops an algorithm that predicts abnormal events based on those patterns. For example, it identifies precursors to abnormal events based on past data and issues an alert when those precursors appear. This makes it possible to detect precursors to abnormal events.

[0053] The anomaly detection unit can use generative AI to predict the probability of an abnormal event occurring and propose a risk management strategy based on that probability. For example, the anomaly detection unit uses generative AI to build a system that predicts the probability of an abnormal event occurring and proposes a risk management strategy based on that probability. For example, it proposes a trading strategy for risk avoidance when the probability of an abnormal event occurring is high. The anomaly detection unit also uses generative AI to predict the probability of an abnormal event occurring in real time and proposes a risk management strategy based on that probability. For example, it proposes reducing positions when the probability of an abnormal event occurring increases. The anomaly detection unit also uses generative AI to develop an algorithm that predicts the probability of an abnormal event occurring and proposes a risk management strategy based on that probability. For example, it builds a system that automatically stops trading when the probability of an abnormal event occurring is above a certain level. This makes it possible to propose a risk management strategy based on the probability of an abnormal event occurring.

[0054] The anomaly detection unit can use the emotion estimation function to monitor emotional fluctuations of market participants in real time when an abnormal event occurs, thereby improving the accuracy of anomaly detection. For example, the anomaly detection unit uses the emotion estimation function to monitor emotional fluctuations of market participants in real time when an abnormal event occurs, and improves the accuracy of anomaly detection based on the data. For example, it predicts the occurrence of an abnormal event when negative emotions suddenly increase. The anomaly detection unit also uses the emotion estimation function to collect emotional fluctuations of market participants in real time when an abnormal event occurs, and improves the anomaly detection algorithm based on the data. For example, it uses the emotion data to identify precursors to an abnormal event. The anomaly detection unit also uses the emotion estimation function to monitor emotional fluctuations of market participants when an abnormal event occurs, and builds a system that improves the accuracy of anomaly detection based on the data. For example, it predicts the probability of an abnormal event occurring based on the emotion data. In this way, the accuracy of anomaly detection is improved by monitoring emotional fluctuations of market participants when an abnormal event occurs.

[0055] The anomaly detection unit can use the generative AI to expand the scope of anomaly detection and monitor other markets or sectors where abnormalities may occur. For example, the anomaly detection unit uses the generative AI to expand the scope of anomaly detection and build a system that monitors other markets (e.g., the cryptocurrency market) where abnormalities may occur. For example, it detects abnormalities in the Bitcoin market. The anomaly detection unit also uses the generative AI to expand the scope of anomaly detection and monitor other sectors (e.g., the energy sector) where abnormalities may occur. For example, it detects abnormalities in the crude oil market. The anomaly detection unit also uses the generative AI to expand the scope of anomaly detection and develop a system that monitors other markets or sectors where abnormalities may occur. For example, it analyzes correlations between different markets and predicts the occurrence of abnormalities. This makes it possible to monitor other markets or sectors where abnormalities may occur.

[0056] The anomaly detection unit can use the generation AI to link the results of anomaly detection with other risk management systems to perform comprehensive risk assessment. The anomaly detection unit, for example, uses the generation AI to link the results of anomaly detection with other risk management systems to build a system that performs comprehensive risk assessment. For example, the risk management strategy is automatically adjusted based on the results of anomaly detection. The anomaly detection unit also uses the generation AI to link the results of anomaly detection with other risk management systems to perform comprehensive risk assessment. For example, the risk of a portfolio is reevaluated based on the results of anomaly detection and an optimal asset allocation is proposed. The anomaly detection unit also uses the generation AI to develop an algorithm that links the results of anomaly detection with other risk management systems to perform comprehensive risk assessment. For example, the risk management system automatically adjusts transactions based on the results of anomaly detection. This allows the results of anomaly detection to be linked with other risk management systems to perform comprehensive risk assessment.

[0057] The anomaly detection unit can use the emotion estimation function to collect emotional data of market participants when an abnormal event occurs and improve the anomaly detection algorithm based on the data. For example, the anomaly detection unit can use the emotion estimation function to collect emotional data of market participants when an abnormal event occurs and improve the anomaly detection algorithm based on the data. For example, the anomaly detection unit can predict the occurrence of an abnormal event when negative emotions suddenly increase. The anomaly detection unit can also use the emotion estimation function to collect emotional data of market participants when an abnormal event occurs in real time and build a system that improves the anomaly detection algorithm based on the data. For example, the emotion data can be used to identify precursors to an abnormal event. The anomaly detection unit can also use the emotion estimation function to collect emotional data of market participants when an abnormal event occurs and develop an algorithm that improves the anomaly detection algorithm based on the data. For example, the probability of an abnormal event occurring can be predicted based on the emotion data. This allows the anomaly detection algorithm to be improved based on the emotional data of market participants when an abnormal event occurs.

[0058] The trend prediction unit can use the generation AI to analyze data on past abnormal events in detail and identify similar patterns. For example, the trend prediction unit uses the generation AI to analyze data on past abnormal events (e.g., the Lehman Shock) in detail and identify similar patterns. For example, the trend prediction unit learns price fluctuation patterns before and after the Lehman Shock and predicts market trends when a similar abnormal event occurs. The trend prediction unit also uses the generation AI to analyze data on past abnormal events and build a system to identify similar patterns. For example, based on data on past abnormal events, it develops an algorithm that predicts market trends after an abnormal event. The trend prediction unit also uses the generation AI to analyze data on past abnormal events in detail and identify similar patterns, thereby predicting market trends after an abnormal event. For example, based on data on past abnormal events, it predicts price trends after an abnormal event. This makes it possible to analyze data on past abnormal events in detail and identify similar patterns.

[0059] The trend prediction unit can use the generation AI to predict market trends after an abnormal situation under multiple scenarios and propose optimal investment strategies for each scenario. For example, the trend prediction unit uses the generation AI to build a system that predicts market trends after an abnormal situation under multiple scenarios and proposes optimal investment strategies for each scenario. For example, it proposes investment strategies for a market recovery scenario and a market slump scenario after an abnormal situation. The trend prediction unit also uses the generation AI to predict market trends after an abnormal situation under multiple scenarios and proposes optimal investment strategies for each scenario. For example, it simulates market trends after an abnormal situation under multiple scenarios and proposes an investment strategy based on the results. The trend prediction unit also uses the generation AI to develop an algorithm that predicts market trends after an abnormal situation under multiple scenarios and proposes optimal investment strategies for each scenario. For example, it predicts market trends after an abnormal situation under multiple scenarios and optimizes investment strategies based on the scenarios. This makes it possible to predict market trends after an abnormal situation under multiple scenarios and propose optimal investment strategies for each scenario.

[0060] The trend prediction unit uses the emotion estimation function to analyze emotional fluctuations of market participants after an abnormal event and predict the impact of those emotions on market trends. The trend prediction unit, for example, uses the emotion estimation function to analyze emotional fluctuations of market participants after an abnormal event and builds a system to predict the impact of those emotions on market trends. For example, it analyzes the tendency for the market to fall as negative emotions intensify. The trend prediction unit also uses the emotion estimation function to collect emotional fluctuations of market participants after an abnormal event in real time and predict market trends based on that data. For example, it analyzes the tendency for the market to rise as positive emotions intensify. The trend prediction unit also uses the emotion estimation function to analyze emotional fluctuations of market participants after an abnormal event and develops an algorithm to predict the impact of those emotions on market trends. For example, it predicts market trends after an abnormal event based on emotion data. This makes it possible to analyze emotional fluctuations of market participants after an abnormal event and predict the impact of those emotions on market trends.

[0061] The trend prediction unit can use the generative AI to apply the post-emergency market trend prediction to other markets and asset classes. For example, the trend prediction unit uses the generative AI to build a system that applies the post-emergency market trend prediction to other markets (e.g., Asian markets and European markets). For example, it predicts trends in the Asian market after the Lehman Shock. The trend prediction unit also uses the generative AI to apply the post-emergency market trend prediction to different asset classes (e.g., stocks, bonds, commodities). For example, it predicts trends in the bond market after the Lehman Shock. The trend prediction unit also uses the generative AI to develop an algorithm that applies the post-emergency market trend prediction to other markets and asset classes. For example, it analyzes correlations between different markets and predicts market trends based on those relationships. This allows the post-emergency market trend prediction to be applied to other markets and asset classes.

[0062] The trend prediction unit can use the generation AI to update the market trend forecast after an abnormal event in real time and revise the forecast based on the latest market data. The trend prediction unit, for example, uses the generation AI to build a system that updates the market trend forecast after an abnormal event in real time and revise the forecast based on the latest market data. For example, the trend prediction unit monitors market trends after an abnormal event in real time and updates the forecast. The trend prediction unit also uses the generation AI to update the market trend forecast after an abnormal event in real time and revise the forecast based on the latest market data. For example, the trend prediction unit analyzes market trends after an abnormal event in real time and revise the forecast based on the results. The trend prediction unit also uses the generation AI to develop an algorithm that updates the market trend forecast after an abnormal event in real time and revise the forecast based on the latest market data. For example, the trend prediction unit predicts market trends after an abnormal event in real time and adjusts a trading strategy based on the prediction. This makes it possible to update the market trend forecast after an abnormal event in real time and revise the forecast based on the latest market data.

[0063] The trend prediction unit can use the emotion estimation function to propose an emotion-based investment strategy based on the emotion data of market participants after an abnormal situation. The trend prediction unit, for example, uses the emotion estimation function to collect emotion data of market participants after an abnormal situation and builds a system that proposes an emotion-based investment strategy based on the data. For example, the trend prediction unit analyzes the tendency for the market to rise when positive emotions increase and proposes an investment strategy based on that trend. The trend prediction unit also uses the emotion estimation function to collect emotion data of market participants after an abnormal situation in real time and proposes an emotion-based investment strategy based on that data. For example, the trend prediction unit analyzes the tendency for the market to fall when negative emotions increase and proposes an investment strategy based on that trend. The trend prediction unit also uses the emotion estimation function to collect emotion data of market participants after an abnormal situation and develops an algorithm that proposes an emotion-based investment strategy based on that data. For example, the trend prediction unit predicts market trends after an abnormal situation based on the emotion data and proposes an investment strategy based on the prediction. This makes it possible to propose an emotion-based investment strategy based on the emotion data of market participants after an abnormal situation.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The market prediction system can further include a news analysis unit. The news analysis unit collects market-related news in real time, analyzes its content, and evaluates its impact on market trends. For example, it analyzes news such as the release of important economic indicators and corporate earnings announcements, and predicts the impact of that news on the market. The news analysis unit can also use generative AI to learn the relationship between past news data and market trends, and predict market reactions when similar news occurs. This enables market predictions that take the impact of news into account.

[0066] The market prediction system can further include a social media analysis unit. The social media analysis unit collects market-related posts from social media such as Twitter and Facebook, analyzes their content, and evaluates their impact on market trends. For example, if there is an increase in positive posts about a specific stock, it predicts an increase in the price of that stock. The social media analysis unit can also use generative AI to learn the relationship between past social media data and market trends, and predict market reactions when similar posts increase. This makes it possible to make market predictions that take the influence of social media into account.

[0067] The market forecasting system can further include a weather data analysis unit. The weather data analysis unit collects weather data and evaluates the impact of that data on market trends. For example, it analyzes the tendency for agricultural product prices to rise when abnormal weather occurs. The weather data analysis unit can also use generative AI to learn the relationship between past weather data and market trends and predict market reactions when similar weather conditions occur. This enables market forecasts that take into account the impact of weather data.

[0068] The market prediction system can further include a geopolitical risk analysis unit. The geopolitical risk analysis unit collects information on international political and economic trends and evaluates the impact of this data on market trends. For example, it analyzes the tendency for energy prices to rise when conflicts or political upheavals occur in specific regions. The geopolitical risk analysis unit can also use generative AI to learn the relationship between past geopolitical risk data and market trends, and predict market reactions when similar risks occur. This makes it possible to make market predictions that take the impact of geopolitical risks into account.

[0069] The market prediction system can further include a consumer behavior analysis unit. The consumer behavior analysis unit collects consumer purchasing data and evaluates the impact of that data on market trends. For example, if a particular product begins to sell rapidly, it analyzes whether the stock prices of companies that sell that product tend to rise. The consumer behavior analysis unit can also use generative AI to learn the relationship between past consumer behavior data and market trends, and predict market reactions when similar purchasing patterns occur. This makes it possible to make market predictions that take the impact of consumer behavior into account.

[0070] The market prediction system can further use an emotion estimation function to collect investor emotion data and analyze the impact of that emotion on market trends. For example, investor emotion data can be collected from social media and news articles, and the impact of that emotion on market trends can be analyzed. For example, the tendency for the market to rise when positive emotion is strong can be analyzed. The emotion estimation function can also be used to collect investor emotion data in real time and predict market trends based on that data. For example, the tendency for the market to fall when negative emotion becomes stronger can be analyzed. The emotion estimation function can also be used to collect investor emotion data and optimize trading strategies based on that data. For example, the emotion data can be used to predict turning points in the market, and trading can be performed based on that prediction. This makes it possible to analyze the impact of investor emotion on market trends.

[0071] The market prediction system can further use an emotion estimation function to monitor emotional fluctuations of market participants in real time when an abnormal event occurs, thereby improving the accuracy of anomaly detection. For example, the emotion estimation function can be used to monitor emotional fluctuations of market participants in real time when an abnormal event occurs, and the accuracy of anomaly detection can be improved based on the data. For example, the occurrence of an abnormal event can be predicted when negative emotions suddenly increase. The emotion estimation function can also be used to collect emotional fluctuations of market participants in real time when an abnormal event occurs, and the anomaly detection algorithm can be improved based on the data. For example, the emotion data can be used to identify precursors to an abnormal event. The emotion estimation function can also be used to monitor emotional fluctuations of market participants when an abnormal event occurs, and a system can be constructed to improve the accuracy of anomaly detection based on the data. For example, the probability of an abnormal event occurring can be predicted based on the emotion data. In this way, the accuracy of anomaly detection can be improved by monitoring emotional fluctuations of market participants when an abnormal event occurs.

[0072] The market prediction system can further use an emotion estimation function to analyze the emotional fluctuations of market participants after an abnormal event and predict the impact of those emotions on market trends. For example, the emotion estimation function can be used to analyze the emotional fluctuations of market participants after an abnormal event and build a system to predict the impact of those emotions on market trends. For example, the tendency for the market to fall when negative emotions intensify can be analyzed. The emotion estimation function can also be used to collect emotional fluctuations of market participants after an abnormal event in real time and predict market trends based on that data. For example, the tendency for the market to rise when positive emotions intensify can be analyzed. The emotion estimation function can also be used to analyze the emotional fluctuations of market participants after an abnormal event and develop an algorithm to predict the impact of those emotions on market trends. For example, market trends after an abnormal event can be predicted based on emotion data. This makes it possible to analyze the emotional fluctuations of market participants after an abnormal event and predict the impact of those emotions on market trends.

[0073] The market prediction system can further use an emotion estimation function to propose an emotion-based investment strategy based on the emotion data of market participants after an abnormal event. For example, a system can be constructed that uses the emotion estimation function to collect emotion data of market participants after an abnormal event and propose an emotion-based investment strategy based on that data. For example, a system can be constructed that uses the emotion estimation function to collect emotion data of market participants after an abnormal event in real time and propose an emotion-based investment strategy based on that data. For example, a system can be constructed that analyzes the tendency for the market to rise when positive emotion increases and proposes an investment strategy based on that trend. Furthermore, the emotion estimation function can be used to collect emotion data of market participants after an abnormal event in real time and propose an emotion-based investment strategy based on that data. For example, a system can be constructed that analyzes the tendency for the market to fall when negative emotion increases and proposes an investment strategy based on that trend. Furthermore, the emotion estimation function can be used to collect emotion data of market participants after an abnormal event and develop an algorithm that proposes an emotion-based investment strategy based on that data. For example, a system can be constructed that predicts market trends after an abnormal event based on the emotion data and proposes an investment strategy based on that prediction. In this way, an emotion-based investment strategy can be proposed based on the emotion data of market participants after an abnormal event.

[0074] The market prediction system can further use an emotion estimation function to collect emotional data of market participants after an abnormal event and improve the anomaly detection algorithm based on that data. For example, the emotion estimation function can be used to collect emotional data of market participants after an abnormal event and improve the anomaly detection algorithm based on that data. For example, the occurrence of an abnormal event can be predicted when negative emotions suddenly increase. The emotion estimation function can also be used to collect emotional data of market participants after an abnormal event in real time and build a system that improves the anomaly detection algorithm based on that data. For example, the emotion data can be used to identify precursors to an abnormal event. The emotion estimation function can also be used to collect emotional data of market participants after an abnormal event and develop an algorithm that improves the anomaly detection algorithm based on that data. For example, the probability of an abnormal event occurring can be predicted based on the emotion data. In this way, the anomaly detection algorithm can be improved based on the emotional data of market participants after an abnormal event.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: The algorithmic analysis unit predicts market trends based on past market data. For example, it uses technical indicators such as moving averages, Bollinger bands, and RSI (Relative Strength Index) to predict price fluctuations. Generative AI then performs pattern recognition based on past market data to optimize the algorithm's performance under specific market conditions. Generative AI then automatically generates combinations of different technical indicators and identifies the most effective combination. Step 2: The anomaly detection unit detects abnormal situations from the market trends predicted by the algorithm analysis unit. For example, it uses generative AI to detect subtle patterns from past market data that may be precursors to abnormal situations. It also uses generative AI to predict the probability of an abnormal situation occurring and proposes a risk management strategy based on that probability. Furthermore, it uses an emotion estimation function to monitor emotional fluctuations among market participants in real time when an abnormal situation occurs, improving the accuracy of anomaly detection. Step 3: The trend forecasting unit predicts market trends following the abnormal event detected by the anomaly detection unit. For example, the generation AI is used to perform a detailed analysis of data on past abnormal events and identify similar patterns. The generation AI also predicts market trends following the abnormal event under multiple scenarios and proposes optimal investment strategies for each scenario. Furthermore, the emotion estimation function is used to analyze the emotional fluctuations of market participants following the abnormal event and predict the impact of those emotions on market trends.

[0077] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0078] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0079] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0082] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0083] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0084] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0085] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0086] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0087] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0088] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0089] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0090] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0091] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0097] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 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.

[0112] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0118] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0127] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0128] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0129] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0130] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0131] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0132] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0133] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0134] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0135] 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.

[0136] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0137] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0138] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0139] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0140] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0141] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0142] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0143] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0144] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An algorithmic analysis department that predicts market trends based on past market data; an anomaly detection unit that detects an abnormal situation from the market trend predicted by the algorithm analysis unit; a trend prediction unit that predicts market trends after the abnormal situation detected by the abnormality detection unit. A system characterized by:

2. The algorithm analysis unit Generative AI is used to perform pattern recognition based on historical market data to optimize algorithm performance under specific market conditions.

2. The system of claim 1.

3. The algorithm analysis unit Generative AI automatically generates the combinations of different technical indicators and identifies the most effective combinations.

2. The system of claim 1.

4. The algorithm analysis unit Collect investor sentiment data and analyze the impact of that sentiment on said market trends 2. The system of claim 1.

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