system

Abstract

The system according to this embodiment aims to enable individual investors to maintain calm investment decisions during sudden changes in the stock market. [Solution] The system according to the embodiment comprises a registration unit, a detection unit, a monitoring unit, and an intervention unit. The registration unit registers the user's investment strategy. The detection unit detects sudden changes in the stock market. The monitoring unit monitors the user's behavior. The intervention unit intervenes to prevent emotional decisions.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that individual investors may make emotional judgments during sudden changes in the stock market, making it difficult to maintain a calm investment judgment.

[0005] The system according to the embodiment aims to enable individual investors to maintain a calm investment judgment during sudden changes in the stock market.

Means for Solving the Problems

[0006] The system according to the embodiment includes a registration unit, a detection unit, a monitoring unit, and an intervention unit. The registration unit registers the investment strategy of the user. The detection unit detects sudden changes in the stock market. The monitoring unit monitors the behavior of the user. The intervention unit intervenes to prevent emotional judgments.

Effects of the Invention

[0007] The system according to this embodiment allows individual investors to maintain calm investment decisions during sudden changes in the stock market. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The AI ​​chat service for investment decision support according to an embodiment of the present invention is a system designed to prevent individual investors from making emotional decisions during sudden changes in the stock market. This system allows users to register their pre-set investment strategies with the AI. When the stock market undergoes a sudden change and the user attempts to buy or sell based on emotional judgment, the AI ​​detects this behavior and intervenes. Furthermore, the AI ​​analyzes the user's past trading data to show the consequences of emotional decisions. The AI ​​also analyzes the user's emotional state using natural language processing and provides appropriate advice. This mechanism allows users to avoid emotional decisions and make calm investment decisions. Psychological support is particularly important for novice investors, and the AI's role in this regard can increase the success rate of their investments. Additionally, AI support can be provided without incurring personnel costs, making it highly cost-effective. This service is particularly beneficial for novice investors in their 20s and 30s who have started investing using the new NISA program. They often gather information from social media and video-sharing sites and commonly use online securities companies. By using this service, they can avoid emotional decisions and make calm investment decisions. This allows the AI ​​chat service for investment decision support to prevent users from making emotional decisions and to promote calm investment decisions.

[0029] The investment decision support AI chat service according to the embodiment comprises a registration unit, a detection unit, a monitoring unit, and an intervention unit. The registration unit registers the user's investment strategy. The user's investment strategy includes, but is not limited to, stock investment, bond investment, and derivative trading. The registration unit, for example, inputs the user's pre-set investment strategy and registers it in the system. The detection unit detects sudden changes in the stock market. Sudden changes in the stock market are detected by, for example, a price fluctuation threshold or a surge in trading volume, but is not limited to, such criteria. The detection unit, for example, monitors market data in real time and detects sudden changes. The monitoring unit monitors the user's behavior. The user's behavior includes, for example, the timing of buying and selling and changes in trading volume, but is not limited to, such examples. The monitoring unit, for example, monitors the user's trading history in real time and detects abnormal behavior. The intervention unit intervenes to prevent emotional decisions. The intervention unit, for example, displays a message encouraging calm decision-making when the user is about to make a buy or sell decision based on emotional judgment. As a result, the investment decision support AI chat service according to the embodiment can register the user's investment strategy, detect sudden changes in the stock market, monitor the user's behavior, and prevent emotional decisions.

[0030] The registration section registers the user's investment strategy. This strategy may include, but is not limited to, stock investments, bond investments, or derivative transactions. For example, the registration section allows the user to input their pre-set investment strategy and register it in the system. Specifically, the user can input detailed information such as their investment goals, risk tolerance, and investment period through a dedicated interface. This allows the system to gain a detailed understanding of the user's investment strategy and provide support tailored to their individual needs. Furthermore, the registration section also analyzes the user's past trading history and market trends to provide feedback on the user's investment strategy. For example, based on past trading data, it can evaluate the success of the user's investment strategy and suggest areas for improvement. The registration section can also accommodate the user adding new investment strategies or modifying existing ones, allowing for flexible operation. This ensures that the user always maintains the optimal investment strategy in line with the latest market conditions. Additionally, the registration section has a function to encrypt and store the user's investment strategy to ensure security. This allows for secure management of investment strategies while protecting user privacy.

[0031] The detection unit detects sudden changes in the stock market. These sudden changes are detected based on criteria such as price fluctuation thresholds or surges in trading volume, but are not limited to these examples. The detection unit monitors market data in real time to detect sudden changes. Specifically, it collects market data in real time from multiple data sources and uses an AI algorithm to detect abnormal patterns. For example, it issues alerts when price fluctuation thresholds are exceeded or when trading volume surges. The AI ​​algorithm learns from historical market data and has the ability to identify patterns that differ from normal market trends. This allows the detection unit to quickly and accurately detect sudden market changes and notify the user. Furthermore, the detection unit continuously learns to improve the accuracy of anomaly detection. For example, each time new market data is added, the AI ​​algorithm learns from that data and improves detection accuracy. The detection unit can also provide customized alerts according to the user's investment strategy. For example, it can be configured to prioritize the detection of sudden changes in specific stocks or market segments, allowing for flexible responses to user needs. This allows the detection unit to help users respond quickly to sudden market changes and minimize investment risk.

[0032] The monitoring unit monitors user behavior. User behavior includes, but is not limited to, changes in trading timing and volume. For example, the monitoring unit monitors users' trading history in real time and detects abnormal behavior. Specifically, the monitoring unit collects user trading data and uses AI to detect abnormal patterns. For example, it issues alerts if there are large-scale trades that differ from normal trading patterns or frequent trades in a short period. The AI ​​learns from past trading data and understands the user's normal trading patterns, enabling it to quickly detect abnormal behavior. Furthermore, the monitoring unit also has the function of continuously monitoring user behavior and providing real-time feedback. For example, if abnormal behavior is detected, it immediately notifies the user and displays a message encouraging calm decision-making. The monitoring unit can also analyze user behavior data and suggest improvements to investment strategies. This allows users to review their trading behavior and build more effective investment strategies. In addition, the monitoring unit has the function of encrypting data and controlling access to protect user privacy and manage data securely. This allows the monitoring unit to effectively monitor user behavior and reduce investment risk.

[0033] The intervention unit intervenes to prevent emotional decisions. For example, when a user is about to make a trade based on emotional judgment, the intervention unit displays a message encouraging calm judgment. Specifically, the intervention unit monitors the user's trading behavior in real time and detects signs that emotional decisions are about to be made. For example, it displays a message encouraging calm judgment when there is an overreaction to a sudden price fluctuation or when frequent trading occurs in a short period of time. The AI ​​can analyze the user's past trading data and market trends to predict situations where emotional decisions are likely to be made. This allows the intervention unit to help users maintain calm judgment and prevent emotional trading. Furthermore, the intervention unit also has the function of providing specific advice and alternatives to the user. For example, when a user is about to make a trade based on emotional judgment, it displays a message encouraging calm judgment based on past data and market analysis results and proposes a specific action plan. In addition, the intervention unit can collect user feedback and continuously evaluate and improve the effectiveness of the intervention. This allows the intervention unit to support users in avoiding emotional decisions and executing more effective investment strategies. Furthermore, the intervention unit also has functions to encrypt data and control access to protect user privacy, ensuring secure data management. This allows the intervention unit to effectively support users' investment actions and reduce investment risks.

[0034] The analytics department analyzes past trading data. For example, it collects a user's trading data for the past year and analyzes trading patterns. Based on past trading data, the analytics department can gain a detailed understanding of the user's investment behavior. For instance, it calculates the success and failure rates of past trades and analyzes investment behavior trends. The analytics department can also evaluate the effectiveness of the user's investment strategy based on past trading data. In this way, analyzing past trading data allows for a more accurate understanding of the user's investment behavior.

[0035] The advisory unit provides appropriate advice. For example, the advisory unit provides advice such as risk management suggestions and investment recommendations. The advisory unit can provide optimal advice based on the user's investment strategy and emotional state. For example, if the user is taking on too much risk, the advisory unit will suggest an investment strategy to reduce risk. Also, if the user is unsure about where to invest, the advisory unit can recommend the optimal investment based on past trading data and market trends. In this way, it can support the user's investment decisions by providing appropriate advice. Some or all of the above processing in the advisory unit may be performed using, for example, generative AI, or without generative AI. For example, the advisory unit can provide advice using a generative AI model that takes the user's investment strategy and emotional state as input and outputs optimal advice.

[0036] The registration unit can analyze the user's past investment strategies and select the optimal registration method. For example, the registration unit may prioritize registering investment strategies that the user has previously succeeded with. The registration unit can also adjust the registration method to avoid investment strategies that the user has previously failed with. Furthermore, the registration unit can analyze the user's past investment patterns and suggest the most effective registration method. This allows the optimal registration method to be selected by analyzing the user's past investment strategies. Some or all of the above processing in the registration unit may be performed using, for example, generative AI, or without generative AI. For example, the registration unit can select a registration method using a generative AI model that takes the user's past investment strategies as input and outputs the optimal registration method.

[0037] The registration unit can filter investment strategies based on the user's current investment status and areas of interest when registering them. For example, the registration unit can prioritize registering highly relevant investment strategies based on the user's current portfolio. The registration unit can also register investment strategies focused on specific industries or regions based on the user's areas of interest. Furthermore, the registration unit can analyze the user's current investment status and suggest investment strategies to minimize risk. This allows for the registration of highly relevant investment strategies by filtering based on the user's current investment status and areas of interest. Some or all of the above processing in the registration unit may be performed using, for example, generative AI, or without generative AI. For example, the registration unit can perform filtering using a generative AI model that takes the user's current investment status and areas of interest as input and outputs the optimal investment strategy.

[0038] The registration unit can prioritize the registration of highly relevant strategies by considering the user's geographical location when registering investment strategies. For example, the registration unit can suggest highly relevant investment strategies by considering the economic conditions of the area where the user lives. The registration unit can also register optimal investment strategies based on market trends in areas the user frequently visits. Furthermore, the registration unit can suggest investment strategies to avoid region-specific risks based on the user's geographical location. In this way, highly relevant investment strategies can be registered by considering the user's geographical location. Some or all of the above processing in the registration unit may be performed using, for example, generative AI, or without generative AI. For example, the registration unit can perform filtering using a generative AI model that takes the user's geographical location as input and outputs the optimal investment strategy.

[0039] The registration unit can analyze a user's social media activity and register relevant strategies when registering investment strategies. For example, the registration unit can suggest investment strategies based on the opinions of investment experts the user follows. The registration unit can also register relevant investment strategies based on topics the user shows interest in on social media. Furthermore, the registration unit can analyze the user's social media activity and suggest investment strategies based on trends. In this way, relevant investment strategies can be registered by analyzing the user's social media activity. Some or all of the above processing in the registration unit may be performed using, for example, generative AI, or not using generative AI. For example, the registration unit can perform filtering using a generative AI model that takes the user's social media activity as input and outputs the optimal investment strategy.

[0040] The detection unit can improve the accuracy of its detection of sudden changes in the stock market by referring to past market data. For example, the detection unit can improve the accuracy of its detection by analyzing the current market situation based on data from past market changes. The detection unit can also predict sudden changes by referring to past market data and detecting specific patterns. Furthermore, the detection unit can detect abnormal trading activity based on past market data and issue a warning. In this way, the accuracy of sudden change detection can be improved by referring to past market data. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without generative AI. For example, the detection unit can take past market data as input and perform detection using a generative AI model that improves the accuracy of sudden change detection.

[0041] The detection unit can focus on specific industries or regions when detecting sudden changes in the stock market. For example, the detection unit can detect sudden changes in a specific industry and propose investment strategies related to that industry. It can also detect sudden market changes in a specific region and propose investment strategies related to that region. Furthermore, the detection unit can focus on specific industries or regions and propose measures to minimize the impact of sudden changes. This allows for the proposal of measures to minimize the impact of sudden changes by focusing on specific industries or regions. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without generative AI. For example, the detection unit can take data from a specific industry or region as input and perform detection using a generative AI model that detects sudden changes.

[0042] The detection unit can improve the accuracy of its detection of sudden changes in the stock market by referring to relevant news articles. For example, the detection unit can improve the accuracy of its detection by identifying the cause of the sudden change based on the latest news articles. The detection unit can also refer to news articles and analyze the impact of a specific event on the market. Furthermore, the detection unit can detect signs of a sudden change based on news articles and issue an early warning. In this way, the accuracy of sudden change detection can be improved by referring to relevant news articles. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without generative AI. For example, the detection unit can take news articles as input and perform detection using a generative AI model that improves the accuracy of sudden change detection.

[0043] The detection unit can determine detection priorities based on the user's investment portfolio when detecting sudden changes in the stock market. For example, the detection unit may prioritize detecting sudden changes in stocks that have a significant impact on the user's portfolio. It can also prioritize detecting sudden changes in industries or regions related to the user's portfolio. Furthermore, the detection unit can adjust the priority of sudden changes to minimize the risk to the user's portfolio. This allows for the priority detection of important sudden changes by determining detection priorities based on the user's investment portfolio. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without generative AI. For example, the detection unit can perform detection using a generative AI model that takes the user's investment portfolio as input and determines the priority of sudden change detection.

[0044] The monitoring unit can improve the accuracy of monitoring by referring to past behavioral data when monitoring user behavior. For example, the monitoring unit can detect abnormal behavior and issue warnings based on the user's past transaction data. The monitoring unit can also analyze the user's past behavioral patterns and predict and monitor abnormal behavior. Furthermore, the monitoring unit can improve the accuracy of monitoring by referring to the user's past behavioral data and detecting specific patterns. In this way, the accuracy of monitoring can be improved by referring to past behavioral data. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can take the user's past behavioral data as input and perform monitoring using a generative AI model that improves the accuracy of monitoring.

[0045] The monitoring unit can focus its monitoring on specific investment behaviors when monitoring user activity. For example, if a user frequently trades a particular stock, the monitoring unit will prioritize monitoring behaviors related to that stock. Similarly, if a user concentrates their investments in a particular industry, the monitoring unit can prioritize monitoring behaviors related to that industry. Furthermore, if a user employs a specific investment strategy, the monitoring unit can prioritize monitoring behaviors related to that strategy. This allows for priority monitoring of important behaviors by focusing on specific investment actions. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For instance, the monitoring unit can perform monitoring using a generative AI model that takes specific investment behaviors as input and determines monitoring priorities.

[0046] The monitoring unit can improve the accuracy of its monitoring by referring to relevant market data when monitoring user behavior. For example, the monitoring unit can analyze how user behavior is affected by market trends based on market data. The monitoring unit can also refer to market data and monitor user behavior based on abnormal market trends. Furthermore, the monitoring unit can analyze the impact of specific events on user behavior based on market data. This allows for improved monitoring accuracy by referring to relevant market data. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can perform monitoring using a generative AI model that takes market data as input and improves monitoring accuracy.

[0047] The monitoring unit can determine monitoring priorities based on the user's investment portfolio when monitoring user behavior. For example, the monitoring unit may prioritize monitoring behaviors that have a significant impact on the user's portfolio. It can also prioritize monitoring behaviors related to the user's portfolio in specific industries or regions. Furthermore, the monitoring unit can adjust monitoring priorities to minimize the risk to the user's portfolio. This allows for priority monitoring of important behaviors by determining monitoring priorities based on the user's investment portfolio. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can perform monitoring using a generative AI model that takes the user's investment portfolio as input and determines monitoring priorities.

[0048] The intervention unit can improve the accuracy of its interventions by referring to past intervention data to prevent emotional judgments. For example, the intervention unit can select the most effective intervention method based on past intervention data. The intervention unit can also improve the accuracy of its interventions by referring to past intervention data and detecting specific patterns. Furthermore, the intervention unit can predict user responses based on past intervention data and perform appropriate interventions. In this way, the accuracy of interventions can be improved by referring to past intervention data. Some or all of the above processes in the intervention unit may be performed using, for example, generative AI, or without generative AI. For example, the intervention unit can take past intervention data as input and perform interventions using a generative AI model that improves the accuracy of the interventions.

[0049] The intervention unit can focus on specific investment behaviors to prevent emotional decisions. For example, if a user is emotionally inclined to buy or sell a particular stock, the intervention unit will prioritize intervening in that behavior. It can also prioritize intervening if a user is inclined to concentrate their investments in a particular sector. Furthermore, it can prioritize intervening if a user is emotionally inclined to change a particular investment strategy. This allows for prioritizing intervention in important behaviors by focusing on specific investment actions. Some or all of the above-described processes in the intervention unit may be performed using, for example, generative AI, or without generative AI. For example, the intervention unit can use a generative AI model that takes specific investment behaviors as input and determines the priority of interventions.

[0050] The intervention unit can improve the accuracy of its interventions by referring to relevant market data to prevent emotional judgments. For example, the intervention unit can analyze how user behavior is influenced by market trends based on market data. The intervention unit can also refer to market data and intervene in user behavior based on abnormal market trends. Furthermore, the intervention unit can analyze the impact of specific events on user behavior based on market data. This allows for improved accuracy of interventions by referring to relevant market data. Some or all of the above processing in the intervention unit may be performed using, for example, generative AI, or without generative AI. For example, the intervention unit can take market data as input and perform interventions using a generative AI model that improves the accuracy of the interventions.

[0051] The intervention unit can customize its intervention methods based on the user's investment portfolio to prevent emotional decisions. For example, the intervention unit may prioritize interventions that have a significant impact on the user's portfolio. It can also prioritize interventions related to industries or regions associated with the user's portfolio. Furthermore, the intervention unit can customize its intervention methods to minimize the risk to the user's portfolio. This allows for more effective interventions by customizing the intervention method based on the user's investment portfolio. Some or all of the above processing in the intervention unit may be performed using, for example, generative AI, or without generative AI. For example, the intervention unit can take the user's investment portfolio as input and perform interventions using a generative AI model that customizes the intervention method.

[0052] The analysis unit can focus its analysis on specific investment behaviors when analyzing historical trading data. For example, if a user frequently trades a particular stock, the analysis unit will prioritize analyzing trading data related to that stock. Similarly, if a user concentrates their investments in a particular industry, the analysis unit can prioritize analyzing trading data related to that industry. Furthermore, if a user employs a specific investment strategy, the analysis unit can prioritize analyzing trading data related to that strategy. This allows for the priority analysis of important trading data by focusing on specific investment behaviors. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For instance, the analysis unit can use a generative AI model that takes specific investment behaviors as input and determines the priority of the analysis.

[0053] The analysis unit can improve the accuracy of its analysis by referring to relevant market data when analyzing past trading data. For example, the analysis unit can analyze how a user's trading is affected by market trends based on market data. The analysis unit can also refer to market data and analyze trading data based on abnormal market trends. Furthermore, the analysis unit can analyze the impact of specific events on trading based on market data. This allows for improved accuracy of the analysis by referring to relevant market data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can take market data as input and perform analysis using a generative AI model that improves the accuracy of the analysis.

[0054] The emotion analysis unit can improve the accuracy of its analysis by referring to past emotion data when performing emotion analysis. For example, the emotion analysis unit can analyze the current emotional state in detail based on past emotion data. The emotion analysis unit can also improve the accuracy of its analysis by referring to past emotion data and detecting specific patterns. Furthermore, the emotion analysis unit can predict and analyze changes in the user's emotions based on past emotion data. In this way, the accuracy of the analysis can be improved by referring to past emotion data. Some or all of the above processing in the emotion analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the emotion analysis unit can take past emotion data as input and perform analysis using a generative AI model that improves the accuracy of the analysis.

[0055] The sentiment analysis unit can improve the accuracy of its analysis by referring to relevant market data when performing sentiment analysis. For example, the sentiment analysis unit can analyze how user sentiment is influenced by market trends based on market data. The sentiment analysis unit can also refer to market data and perform sentiment analysis based on abnormal market trends. Furthermore, the sentiment analysis unit can analyze the impact of specific events on user sentiment based on market data. In this way, the accuracy of the analysis can be improved by referring to relevant market data. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the sentiment analysis unit can take market data as input and perform analysis using a generative AI model that improves the accuracy of the analysis.

[0056] The advice unit can improve the accuracy of its advice by referring to past advice data when providing appropriate advice. For example, the advice unit can select the most effective advice method based on past advice data. The advice unit can also improve the accuracy of its advice by referring to past advice data and detecting specific patterns. Furthermore, the advice unit can predict user responses based on past advice data and provide appropriate advice. In this way, the accuracy of advice can be improved by referring to past advice data. Some or all of the above processes in the advice unit may be performed using, for example, generative AI, or without generative AI. For example, the advice unit can take past advice data as input and provide advice using a generative AI model that improves the accuracy of advice.

[0057] The advisory unit can improve the accuracy of its advice by referring to relevant market data when providing appropriate advice. For example, the advisory unit can analyze how user behavior is influenced by market trends based on market data. The advisory unit can also refer to market data and provide advice based on abnormal market trends. Furthermore, the advisory unit can analyze the impact of specific events on user behavior based on market data. This allows the advisory unit to improve the accuracy of its advice by referring to relevant market data. Some or all of the above processing in the advisory unit may be performed using, for example, generative AI, or without generative AI. For example, the advisory unit can take market data as input and provide advice using a generative AI model that improves the accuracy of the advice.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] An AI chat service for investment decision support can also include a predictive unit that forecasts the user's investment behavior. This predictive unit, for example, predicts future investment actions based on the user's past trading data and market trends. If a user tends to trade a particular stock frequently, the predictive unit can predict future trading timings for that stock. Furthermore, the predictive unit can predict how a user will behave under specific market conditions and provide appropriate advice. In addition, the predictive unit can anticipate changes in the user's investment strategy and offer proactive risk management suggestions. This allows for more effective support by predicting the user's investment behavior.

[0060] The AI ​​chat service for investment decision support can also include a simulation unit that simulates the user's investment actions. For example, the simulation unit can simulate the results of a user executing a specific investment strategy. The simulation unit can evaluate the risks and returns of a new investment strategy before the user tries it. It can also compare the performance of investment strategies under different market conditions. Furthermore, the simulation unit can reproduce the results of past trades the user has made and identify areas for improvement. This allows the user to evaluate the effectiveness of investment strategies in advance and minimize risk.

[0061] The AI ​​chat service for investment decision support can also include an evaluation unit that assesses the user's investment behavior. For example, the evaluation unit can evaluate the performance of the user's investment behavior based on their past trading data. It can calculate the success and failure rates of past trades and analyze trends in the user's investment behavior. Furthermore, the evaluation unit can evaluate the effectiveness of the user's investment strategy and suggest areas for improvement. In addition, the evaluation unit can compare the user's investment behavior with that of other investors and provide benchmarks. This allows the user to objectively evaluate and improve their own investment behavior.

[0062] The AI ​​chat service for investment decision support may also include a recording unit that tracks the user's investment behavior. This unit can, for example, record the user's trading history and investment strategies in detail. It can save details of past transactions for later reference. Furthermore, it can record the history of changes to the user's investment strategy and track its effectiveness. Additionally, it can analyze the user's trading history to identify patterns in investment behavior. This allows the user to record their investment actions in detail and analyze them later.

[0063] The AI ​​chat service for investment decision support can also include a sharing section for sharing users' investment actions. This sharing section could, for example, allow users to share trading information and investment strategies with other investors. Users could share their trading history and investment strategies with other investors and receive feedback. Furthermore, the sharing section could allow users to refer to and learn from the trading information and investment strategies of other investors. Additionally, the sharing section could enable users to participate in investment communities and exchange information. This allows users to share information with other investors and improve their investment decisions.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The registration section registers the user's investment strategy. The user's investment strategy includes stock investments, bond investments, derivative transactions, etc. The registration section inputs the investment strategy that the user has set up in advance and registers it in the system. Step 2: The detection unit detects sudden changes in the stock market. Sudden changes in the stock market are detected based on criteria such as price fluctuation thresholds and sudden increases in trading volume. The detection unit monitors market data in real time and detects sudden changes. Step 3: The monitoring unit monitors user behavior. User behavior includes the timing of buying and selling, and changes in trading volume. The monitoring unit monitors the user's trading history in real time and detects abnormal behavior. Step 4: The intervention unit intervenes to prevent emotional decisions. When a user is about to make a buy or sell decision based on emotions, the intervention unit displays a message encouraging them to make a calm decision.

[0066] (Example of form 2) The AI ​​chat service for investment decision support according to an embodiment of the present invention is a system designed to prevent individual investors from making emotional decisions during sudden changes in the stock market. This system allows users to register their pre-set investment strategies with the AI. When the stock market undergoes a sudden change and the user attempts to buy or sell based on emotional judgment, the AI ​​detects this behavior and intervenes. Furthermore, the AI ​​analyzes the user's past trading data to show the consequences of emotional decisions. The AI ​​also analyzes the user's emotional state using natural language processing and provides appropriate advice. This mechanism allows users to avoid emotional decisions and make calm investment decisions. Psychological support is particularly important for novice investors, and the AI's role in this regard can increase the success rate of their investments. Additionally, AI support can be provided without incurring personnel costs, making it highly cost-effective. This service is particularly beneficial for novice investors in their 20s and 30s who have started investing using the new NISA program. They often gather information from social media and video-sharing sites and commonly use online securities companies. By using this service, they can avoid emotional decisions and make calm investment decisions. This allows the AI ​​chat service for investment decision support to prevent users from making emotional decisions and to promote calm investment decisions.

[0067] The investment decision support AI chat service according to the embodiment comprises a registration unit, a detection unit, a monitoring unit, and an intervention unit. The registration unit registers the user's investment strategy. The user's investment strategy includes, but is not limited to, stock investment, bond investment, and derivative trading. The registration unit, for example, inputs the user's pre-set investment strategy and registers it in the system. The detection unit detects sudden changes in the stock market. Sudden changes in the stock market are detected by, for example, a price fluctuation threshold or a surge in trading volume, but is not limited to, such criteria. The detection unit, for example, monitors market data in real time and detects sudden changes. The monitoring unit monitors the user's behavior. The user's behavior includes, for example, the timing of buying and selling and changes in trading volume, but is not limited to, such examples. The monitoring unit, for example, monitors the user's trading history in real time and detects abnormal behavior. The intervention unit intervenes to prevent emotional decisions. The intervention unit, for example, displays a message encouraging calm decision-making when the user is about to make a buy or sell decision based on emotional judgment. As a result, the investment decision support AI chat service according to the embodiment can register the user's investment strategy, detect sudden changes in the stock market, monitor the user's behavior, and prevent emotional decisions.

[0068] The registration section registers the user's investment strategy. This strategy may include, but is not limited to, stock investments, bond investments, or derivative transactions. For example, the registration section allows the user to input their pre-set investment strategy and register it in the system. Specifically, the user can input detailed information such as their investment goals, risk tolerance, and investment period through a dedicated interface. This allows the system to gain a detailed understanding of the user's investment strategy and provide support tailored to their individual needs. Furthermore, the registration section also analyzes the user's past trading history and market trends to provide feedback on the user's investment strategy. For example, based on past trading data, it can evaluate the success of the user's investment strategy and suggest areas for improvement. The registration section can also accommodate the user adding new investment strategies or modifying existing ones, allowing for flexible operation. This ensures that the user always maintains the optimal investment strategy in line with the latest market conditions. Additionally, the registration section has a function to encrypt and store the user's investment strategy to ensure security. This allows for secure management of investment strategies while protecting user privacy.

[0069] The detection unit detects sudden changes in the stock market. These sudden changes are detected based on criteria such as price fluctuation thresholds or surges in trading volume, but are not limited to these examples. The detection unit monitors market data in real time to detect sudden changes. Specifically, it collects market data in real time from multiple data sources and uses an AI algorithm to detect abnormal patterns. For example, it issues alerts when price fluctuation thresholds are exceeded or when trading volume surges. The AI ​​algorithm learns from historical market data and has the ability to identify patterns that differ from normal market trends. This allows the detection unit to quickly and accurately detect sudden market changes and notify the user. Furthermore, the detection unit continuously learns to improve the accuracy of anomaly detection. For example, each time new market data is added, the AI ​​algorithm learns from that data and improves detection accuracy. The detection unit can also provide customized alerts according to the user's investment strategy. For example, it can be configured to prioritize the detection of sudden changes in specific stocks or market segments, allowing for flexible responses to user needs. This allows the detection unit to help users respond quickly to sudden market changes and minimize investment risk.

[0070] The monitoring unit monitors user behavior. User behavior includes, but is not limited to, changes in trading timing and volume. For example, the monitoring unit monitors users' trading history in real time and detects abnormal behavior. Specifically, the monitoring unit collects user trading data and uses AI to detect abnormal patterns. For example, it issues alerts if there are large-scale trades that differ from normal trading patterns or frequent trades in a short period. The AI ​​learns from past trading data and understands the user's normal trading patterns, enabling it to quickly detect abnormal behavior. Furthermore, the monitoring unit also has the function of continuously monitoring user behavior and providing real-time feedback. For example, if abnormal behavior is detected, it immediately notifies the user and displays a message encouraging calm decision-making. The monitoring unit can also analyze user behavior data and suggest improvements to investment strategies. This allows users to review their trading behavior and build more effective investment strategies. In addition, the monitoring unit has the function of encrypting data and controlling access to protect user privacy and manage data securely. This allows the monitoring unit to effectively monitor user behavior and reduce investment risk.

[0071] The intervention unit intervenes to prevent emotional decisions. For example, when a user is about to make a trade based on emotional judgment, the intervention unit displays a message encouraging calm judgment. Specifically, the intervention unit monitors the user's trading behavior in real time and detects signs that emotional decisions are about to be made. For example, it displays a message encouraging calm judgment when there is an overreaction to a sudden price fluctuation or when frequent trading occurs in a short period of time. The AI ​​can analyze the user's past trading data and market trends to predict situations where emotional decisions are likely to be made. This allows the intervention unit to help users maintain calm judgment and prevent emotional trading. Furthermore, the intervention unit also has the function of providing specific advice and alternatives to the user. For example, when a user is about to make a trade based on emotional judgment, it displays a message encouraging calm judgment based on past data and market analysis results and proposes a specific action plan. In addition, the intervention unit can collect user feedback and continuously evaluate and improve the effectiveness of the intervention. This allows the intervention unit to support users in avoiding emotional decisions and executing more effective investment strategies. Furthermore, the intervention unit also has functions to encrypt data and control access to protect user privacy, ensuring secure data management. This allows the intervention unit to effectively support users' investment actions and reduce investment risks.

[0072] The analytics department analyzes past trading data. For example, it collects a user's trading data for the past year and analyzes trading patterns. Based on past trading data, the analytics department can gain a detailed understanding of the user's investment behavior. For instance, it calculates the success and failure rates of past trades and analyzes investment behavior trends. The analytics department can also evaluate the effectiveness of the user's investment strategy based on past trading data. In this way, analyzing past trading data allows for a more accurate understanding of the user's investment behavior.

[0073] The emotion analysis unit analyzes the user's emotional state. For example, it estimates emotions from the user's facial expressions and voice using an emotion recognition algorithm. The emotion analysis unit can analyze the user's emotional state in real time and take appropriate action to prevent emotional judgments. For example, if the user is stressed, the emotion analysis unit displays a message encouraging calm judgment. It can also display a message encouraging quick action if the user is relaxed. This allows for appropriate action to prevent emotional judgments by analyzing the user's emotional state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The advisory unit provides appropriate advice. For example, the advisory unit provides advice such as risk management suggestions and investment recommendations. The advisory unit can provide optimal advice based on the user's investment strategy and emotional state. For example, if the user is taking on too much risk, the advisory unit will suggest an investment strategy to reduce risk. Also, if the user is unsure about where to invest, the advisory unit can recommend the optimal investment based on past trading data and market trends. In this way, it can support the user's investment decisions by providing appropriate advice. Some or all of the above processing in the advisory unit may be performed using, for example, generative AI, or without generative AI. For example, the advisory unit can provide advice using a generative AI model that takes the user's investment strategy and emotional state as input and outputs optimal advice.

[0075] The registration unit can estimate the user's emotions and adjust the timing of investment strategy registration based on the estimated emotions. For example, if the user is stressed, the registration unit can delay registration to encourage registration when the user is calm. Conversely, if the user is relaxed, the registration unit can register immediately, enabling a quick response. Furthermore, if the user is agitated, the registration unit can temporarily suspend registration and wait until the user calms down. This allows for registration when the user is calm by adjusting the timing of investment strategy registration according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The registration unit can analyze the user's past investment strategies and select the optimal registration method. For example, the registration unit may prioritize registering investment strategies that the user has previously succeeded with. The registration unit can also adjust the registration method to avoid investment strategies that the user has previously failed with. Furthermore, the registration unit can analyze the user's past investment patterns and suggest the most effective registration method. This allows the optimal registration method to be selected by analyzing the user's past investment strategies. Some or all of the above processing in the registration unit may be performed using, for example, generative AI, or without generative AI. For example, the registration unit can select a registration method using a generative AI model that takes the user's past investment strategies as input and outputs the optimal registration method.

[0077] The registration unit can filter investment strategies based on the user's current investment status and areas of interest when registering them. For example, the registration unit can prioritize registering highly relevant investment strategies based on the user's current portfolio. The registration unit can also register investment strategies focused on specific industries or regions based on the user's areas of interest. Furthermore, the registration unit can analyze the user's current investment status and suggest investment strategies to minimize risk. This allows for the registration of highly relevant investment strategies by filtering based on the user's current investment status and areas of interest. Some or all of the above processing in the registration unit may be performed using, for example, generative AI, or without generative AI. For example, the registration unit can perform filtering using a generative AI model that takes the user's current investment status and areas of interest as input and outputs the optimal investment strategy.

[0078] The registration unit can estimate the user's emotions and determine the priority of investment strategies to register based on those emotions. For example, if the user is feeling anxious, the registration unit will prioritize registering low-risk investment strategies. Conversely, if the user is confident, the registration unit may prioritize registering high-risk but high-return investment strategies. Furthermore, if the user is in a neutral emotional state, the registration unit may prioritize registering balanced investment strategies. This allows for the registration of more appropriate investment strategies by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The registration unit can prioritize the registration of highly relevant strategies by considering the user's geographical location when registering investment strategies. For example, the registration unit can suggest highly relevant investment strategies by considering the economic conditions of the area where the user lives. The registration unit can also register optimal investment strategies based on market trends in areas the user frequently visits. Furthermore, the registration unit can suggest investment strategies to avoid region-specific risks based on the user's geographical location. In this way, highly relevant investment strategies can be registered by considering the user's geographical location. Some or all of the above processing in the registration unit may be performed using, for example, generative AI, or without generative AI. For example, the registration unit can perform filtering using a generative AI model that takes the user's geographical location as input and outputs the optimal investment strategy.

[0080] The registration unit can analyze a user's social media activity and register relevant strategies when registering investment strategies. For example, the registration unit can suggest investment strategies based on the opinions of investment experts the user follows. The registration unit can also register relevant investment strategies based on topics the user shows interest in on social media. Furthermore, the registration unit can analyze the user's social media activity and suggest investment strategies based on trends. In this way, relevant investment strategies can be registered by analyzing the user's social media activity. Some or all of the above processing in the registration unit may be performed using, for example, generative AI, or not using generative AI. For example, the registration unit can perform filtering using a generative AI model that takes the user's social media activity as input and outputs the optimal investment strategy.

[0081] The detection unit can estimate the user's emotions and adjust the criteria for detecting sudden changes in the stock market based on the estimated emotions. For example, if the user is feeling anxious, the detection unit can tighten the criteria for detecting sudden changes and issue an early warning. Conversely, if the user is confident, the detection unit can loosen the criteria for detecting sudden changes and delay the warning. Furthermore, if the user is in a neutral emotional state, the detection unit can apply the standard criteria for detecting sudden changes. This allows for warnings to be issued at a more appropriate time by adjusting the criteria for detecting sudden changes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The detection unit can improve the accuracy of its detection of sudden changes in the stock market by referring to past market data. For example, the detection unit can improve the accuracy of its detection by analyzing the current market situation based on data from past market changes. The detection unit can also predict sudden changes by referring to past market data and detecting specific patterns. Furthermore, the detection unit can detect abnormal trading activity based on past market data and issue a warning. In this way, the accuracy of sudden change detection can be improved by referring to past market data. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without generative AI. For example, the detection unit can take past market data as input and perform detection using a generative AI model that improves the accuracy of sudden change detection.

[0083] The detection unit can focus on specific industries or regions when detecting sudden changes in the stock market. For example, the detection unit can detect sudden changes in a specific industry and propose investment strategies related to that industry. It can also detect sudden market changes in a specific region and propose investment strategies related to that region. Furthermore, the detection unit can focus on specific industries or regions and propose measures to minimize the impact of sudden changes. This allows for the proposal of measures to minimize the impact of sudden changes by focusing on specific industries or regions. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without generative AI. For example, the detection unit can take data from a specific industry or region as input and perform detection using a generative AI model that detects sudden changes.

[0084] The detection unit can estimate the user's emotions and adjust the order in which it displays the results of the sudden change detection based on the estimated emotions. For example, if the user is feeling anxious, the detection unit will display the most important information first. If the user is relaxed, the detection unit can also display detailed information sequentially. Furthermore, if the user is in a hurry, the detection unit can prioritize displaying concise information. In this way, by adjusting the order in which the sudden change detection results are displayed according to the user's emotions, important information can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The detection unit can improve the accuracy of its detection of sudden changes in the stock market by referring to relevant news articles. For example, the detection unit can improve the accuracy of its detection by identifying the cause of the sudden change based on the latest news articles. The detection unit can also refer to news articles and analyze the impact of a specific event on the market. Furthermore, the detection unit can detect signs of a sudden change based on news articles and issue an early warning. In this way, the accuracy of sudden change detection can be improved by referring to relevant news articles. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without generative AI. For example, the detection unit can take news articles as input and perform detection using a generative AI model that improves the accuracy of sudden change detection.

[0086] The detection unit can determine detection priorities based on the user's investment portfolio when detecting sudden changes in the stock market. For example, the detection unit may prioritize detecting sudden changes in stocks that have a significant impact on the user's portfolio. It can also prioritize detecting sudden changes in industries or regions related to the user's portfolio. Furthermore, the detection unit can adjust the priority of sudden changes to minimize the risk to the user's portfolio. This allows for the priority detection of important sudden changes by determining detection priorities based on the user's investment portfolio. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without generative AI. For example, the detection unit can perform detection using a generative AI model that takes the user's investment portfolio as input and determines the priority of sudden change detection.

[0087] The monitoring unit can estimate the user's emotions and adjust monitoring criteria based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can tighten monitoring criteria and issue an early warning. Conversely, if the user is relaxed, the monitoring unit can loosen monitoring criteria and delay the warning. Furthermore, if the user is in a neutral emotional state, the monitoring unit can apply standard monitoring criteria. This allows for more appropriate timing of warnings by adjusting monitoring criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The monitoring unit can improve the accuracy of monitoring by referring to past behavioral data when monitoring user behavior. For example, the monitoring unit can detect abnormal behavior and issue warnings based on the user's past transaction data. The monitoring unit can also analyze the user's past behavioral patterns and predict and monitor abnormal behavior. Furthermore, the monitoring unit can improve the accuracy of monitoring by referring to the user's past behavioral data and detecting specific patterns. In this way, the accuracy of monitoring can be improved by referring to past behavioral data. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can take the user's past behavioral data as input and perform monitoring using a generative AI model that improves the accuracy of monitoring.

[0089] The monitoring unit can focus its monitoring on specific investment behaviors when monitoring user activity. For example, if a user frequently trades a particular stock, the monitoring unit will prioritize monitoring behaviors related to that stock. Similarly, if a user concentrates their investments in a particular industry, the monitoring unit can prioritize monitoring behaviors related to that industry. Furthermore, if a user employs a specific investment strategy, the monitoring unit can prioritize monitoring behaviors related to that strategy. This allows for priority monitoring of important behaviors by focusing on specific investment actions. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For instance, the monitoring unit can perform monitoring using a generative AI model that takes specific investment behaviors as input and determines monitoring priorities.

[0090] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise display method. This allows for prioritizing the display of important information by adjusting the display method of monitoring results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The monitoring unit can improve the accuracy of its monitoring by referring to relevant market data when monitoring user behavior. For example, the monitoring unit can analyze how user behavior is affected by market trends based on market data. The monitoring unit can also refer to market data and monitor user behavior based on abnormal market trends. Furthermore, the monitoring unit can analyze the impact of specific events on user behavior based on market data. This allows for improved monitoring accuracy by referring to relevant market data. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can perform monitoring using a generative AI model that takes market data as input and improves monitoring accuracy.

[0092] The monitoring unit can determine monitoring priorities based on the user's investment portfolio when monitoring user behavior. For example, the monitoring unit may prioritize monitoring behaviors that have a significant impact on the user's portfolio. It can also prioritize monitoring behaviors related to the user's portfolio in specific industries or regions. Furthermore, the monitoring unit can adjust monitoring priorities to minimize the risk to the user's portfolio. This allows for priority monitoring of important behaviors by determining monitoring priorities based on the user's investment portfolio. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can perform monitoring using a generative AI model that takes the user's investment portfolio as input and determines monitoring priorities.

[0093] The intervention unit can estimate the user's emotions and adjust its intervention method based on the estimated emotions. For example, if the user is feeling anxious, the intervention unit will intervene with calm words to encourage calm judgment. If the user is agitated, the intervention unit can intervene by presenting specific data to help them calm down. Furthermore, if the user is in a neutral emotional state, the intervention unit can apply a standard intervention method. This allows for more effective intervention by adjusting the intervention method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The intervention unit can improve the accuracy of its interventions by referring to past intervention data to prevent emotional judgments. For example, the intervention unit can select the most effective intervention method based on past intervention data. The intervention unit can also improve the accuracy of its interventions by referring to past intervention data and detecting specific patterns. Furthermore, the intervention unit can predict user responses based on past intervention data and perform appropriate interventions. In this way, the accuracy of interventions can be improved by referring to past intervention data. Some or all of the above processes in the intervention unit may be performed using, for example, generative AI, or without generative AI. For example, the intervention unit can take past intervention data as input and perform interventions using a generative AI model that improves the accuracy of the interventions.

[0095] The intervention unit can focus on specific investment behaviors to prevent emotional decisions. For example, if a user is emotionally inclined to buy or sell a particular stock, the intervention unit will prioritize intervening in that behavior. It can also prioritize intervening if a user is inclined to concentrate their investments in a particular sector. Furthermore, it can prioritize intervening if a user is emotionally inclined to change a particular investment strategy. This allows for prioritizing intervention in important behaviors by focusing on specific investment actions. Some or all of the above-described processes in the intervention unit may be performed using, for example, generative AI, or without generative AI. For example, the intervention unit can use a generative AI model that takes specific investment behaviors as input and determines the priority of interventions.

[0096] The intervention unit can estimate the user's emotions and determine the priority of interventions based on the estimated emotions. For example, if the user is feeling anxious, the intervention unit will prioritize the most important interventions. If the user is relaxed, the intervention unit can also provide detailed information and intervene. Furthermore, if the user is in a hurry, the intervention unit can prioritize concise interventions. This allows for prioritizing important interventions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The intervention unit can improve the accuracy of its interventions by referring to relevant market data to prevent emotional judgments. For example, the intervention unit can analyze how user behavior is influenced by market trends based on market data. The intervention unit can also refer to market data and intervene in user behavior based on abnormal market trends. Furthermore, the intervention unit can analyze the impact of specific events on user behavior based on market data. This allows for improved accuracy of interventions by referring to relevant market data. Some or all of the above processing in the intervention unit may be performed using, for example, generative AI, or without generative AI. For example, the intervention unit can take market data as input and perform interventions using a generative AI model that improves the accuracy of the interventions.

[0098] The intervention unit can customize its intervention methods based on the user's investment portfolio to prevent emotional decisions. For example, the intervention unit may prioritize interventions that have a significant impact on the user's portfolio. It can also prioritize interventions related to industries or regions associated with the user's portfolio. Furthermore, the intervention unit can customize its intervention methods to minimize the risk to the user's portfolio. This allows for more effective interventions by customizing the intervention method based on the user's investment portfolio. Some or all of the above processing in the intervention unit may be performed using, for example, generative AI, or without generative AI. For example, the intervention unit can take the user's investment portfolio as input and perform interventions using a generative AI model that customizes the intervention method.

[0099] The analytics unit can estimate the user's emotions and adjust the analysis method of trading data based on the estimated emotions. For example, if the user is feeling anxious, the analytics unit will prioritize analyzing low-risk trading data. Conversely, if the user is confident, the analytics unit can prioritize analyzing high-risk trading data. Furthermore, if the user is in a neutral emotional state, the analytics unit can apply a standard analysis method. This allows for more appropriate analysis by adjusting the analysis method of trading data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The analysis unit can focus its analysis on specific investment behaviors when analyzing historical trading data. For example, if a user frequently trades a particular stock, the analysis unit will prioritize analyzing trading data related to that stock. Similarly, if a user concentrates their investments in a particular industry, the analysis unit can prioritize analyzing trading data related to that industry. Furthermore, if a user employs a specific investment strategy, the analysis unit can prioritize analyzing trading data related to that strategy. This allows for the priority analysis of important trading data by focusing on specific investment behaviors. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For instance, the analysis unit can use a generative AI model that takes specific investment behaviors as input and determines the priority of the analysis.

[0101] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The analysis unit can improve the accuracy of its analysis by referring to relevant market data when analyzing past trading data. For example, the analysis unit can analyze how a user's trading is affected by market trends based on market data. The analysis unit can also refer to market data and analyze trading data based on abnormal market trends. Furthermore, the analysis unit can analyze the impact of specific events on trading based on market data. This allows for improved accuracy of the analysis by referring to relevant market data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can take market data as input and perform analysis using a generative AI model that improves the accuracy of the analysis.

[0103] The emotion analysis unit can estimate the user's emotions and adjust the emotion analysis method based on the estimated emotions. For example, if the user is feeling anxious, the emotion analysis unit can increase the accuracy of the emotion analysis and perform a more detailed analysis. Conversely, if the user is relaxed, the emotion analysis unit can lower the accuracy of the emotion analysis and perform a simpler analysis. Furthermore, if the user is in a neutral emotional state, the emotion analysis unit can apply a standard emotion analysis method. This allows for more appropriate analysis by adjusting the emotion analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The emotion analysis unit can improve the accuracy of its analysis by referring to past emotion data when performing emotion analysis. For example, the emotion analysis unit can analyze the current emotional state in detail based on past emotion data. The emotion analysis unit can also improve the accuracy of its analysis by referring to past emotion data and detecting specific patterns. Furthermore, the emotion analysis unit can predict and analyze changes in the user's emotions based on past emotion data. In this way, the accuracy of the analysis can be improved by referring to past emotion data. Some or all of the above processing in the emotion analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the emotion analysis unit can take past emotion data as input and perform analysis using a generative AI model that improves the accuracy of the analysis.

[0105] The emotion analysis unit can estimate the user's emotions and adjust the display method of the emotion analysis results based on the estimated emotions. For example, if the user is feeling anxious, the emotion analysis unit can provide a simple and highly visible display method. If the user is relaxed, the emotion analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the emotion analysis unit can provide a concise display method. This allows for prioritizing the display of important information by adjusting the display method of the emotion analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The sentiment analysis unit can improve the accuracy of its analysis by referring to relevant market data when performing sentiment analysis. For example, the sentiment analysis unit can analyze how user sentiment is influenced by market trends based on market data. The sentiment analysis unit can also refer to market data and perform sentiment analysis based on abnormal market trends. Furthermore, the sentiment analysis unit can analyze the impact of specific events on user sentiment based on market data. In this way, the accuracy of the analysis can be improved by referring to relevant market data. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the sentiment analysis unit can take market data as input and perform analysis using a generative AI model that improves the accuracy of the analysis.

[0107] The advice unit can estimate the user's emotions and adjust its advice based on those emotions. For example, if the user is feeling anxious, the advice unit will provide advice in calm language to encourage rational judgment. If the user is agitated, the advice unit can also provide advice by presenting specific data to help them calm down. Furthermore, if the user is in a neutral emotional state, the advice unit can apply a standard advice method. This allows for more effective advice by adjusting the advice method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The advice unit can improve the accuracy of its advice by referring to past advice data when providing appropriate advice. For example, the advice unit can select the most effective advice method based on past advice data. The advice unit can also improve the accuracy of its advice by referring to past advice data and detecting specific patterns. Furthermore, the advice unit can predict user responses based on past advice data and provide appropriate advice. In this way, the accuracy of advice can be improved by referring to past advice data. Some or all of the above processes in the advice unit may be performed using, for example, generative AI, or without generative AI. For example, the advice unit can take past advice data as input and provide advice using a generative AI model that improves the accuracy of advice.

[0109] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is feeling anxious, the advice unit will prioritize providing the most important advice. If the user is relaxed, the advice unit can provide more detailed information and advice. Furthermore, if the user is in a hurry, the advice unit can prioritize providing concise advice. This allows for the prioritization of important advice based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The advisory unit can improve the accuracy of its advice by referring to relevant market data when providing appropriate advice. For example, the advisory unit can analyze how user behavior is influenced by market trends based on market data. The advisory unit can also refer to market data and provide advice based on abnormal market trends. Furthermore, the advisory unit can analyze the impact of specific events on user behavior based on market data. This allows the advisory unit to improve the accuracy of its advice by referring to relevant market data. Some or all of the above processing in the advisory unit may be performed using, for example, generative AI, or without generative AI. For example, the advisory unit can take market data as input and provide advice using a generative AI model that improves the accuracy of the advice.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] An AI chat service for investment decision support can also include a predictive unit that forecasts the user's investment behavior. This predictive unit, for example, predicts future investment actions based on the user's past trading data and market trends. If a user tends to trade a particular stock frequently, the predictive unit can predict future trading timings for that stock. Furthermore, the predictive unit can predict how a user will behave under specific market conditions and provide appropriate advice. In addition, the predictive unit can anticipate changes in the user's investment strategy and offer proactive risk management suggestions. This allows for more effective support by predicting the user's investment behavior.

[0113] The AI ​​chat service for investment decision support can also include a simulation unit that simulates the user's investment actions. For example, the simulation unit can simulate the results of a user executing a specific investment strategy. The simulation unit can evaluate the risks and returns of a new investment strategy before the user tries it. It can also compare the performance of investment strategies under different market conditions. Furthermore, the simulation unit can reproduce the results of past trades the user has made and identify areas for improvement. This allows the user to evaluate the effectiveness of investment strategies in advance and minimize risk.

[0114] The AI ​​chat service for investment decision support can also include an evaluation unit that assesses the user's investment behavior. For example, the evaluation unit can evaluate the performance of the user's investment behavior based on their past trading data. It can calculate the success and failure rates of past trades and analyze trends in the user's investment behavior. Furthermore, the evaluation unit can evaluate the effectiveness of the user's investment strategy and suggest areas for improvement. In addition, the evaluation unit can compare the user's investment behavior with that of other investors and provide benchmarks. This allows the user to objectively evaluate and improve their own investment behavior.

[0115] The AI ​​chat service for investment decision support may also include a recording unit that tracks the user's investment behavior. This unit can, for example, record the user's trading history and investment strategies in detail. It can save details of past transactions for later reference. Furthermore, it can record the history of changes to the user's investment strategy and track its effectiveness. Additionally, it can analyze the user's trading history to identify patterns in investment behavior. This allows the user to record their investment actions in detail and analyze them later.

[0116] The AI ​​chat service for investment decision support can also include a sharing section for sharing users' investment actions. This sharing section could, for example, allow users to share trading information and investment strategies with other investors. Users could share their trading history and investment strategies with other investors and receive feedback. Furthermore, the sharing section could allow users to refer to and learn from the trading information and investment strategies of other investors. Additionally, the sharing section could enable users to participate in investment communities and exchange information. This allows users to share information with other investors and improve their investment decisions.

[0117] The AI ​​chat service for investment decision support can further estimate the user's emotions and adjust the risk level of investment strategies based on those emotions. For example, if the user is feeling anxious, it can suggest a low-risk investment strategy. Conversely, if the user is confident, it can suggest a high-risk but high-return investment strategy. Furthermore, if the user is in a neutral emotional state, it can suggest a balanced investment strategy. In this way, by adjusting the risk level of investment strategies according to the user's emotions, it can provide more appropriate investment strategies.

[0118] The AI ​​chat service for investment decision support can further estimate the user's emotions and adjust the content of investment advice based on those emotions. For example, if the user is feeling anxious, it can provide advice to mitigate risk. If the user is confident, it can suggest an aggressive investment strategy. Furthermore, if the user is in a neutral emotional state, it can provide balanced advice. In this way, by adjusting the content of investment advice according to the user's emotions, it can provide more effective advice.

[0119] The AI ​​chat service for investment decision support can further estimate the user's emotions and provide feedback on investment actions based on those emotions. For example, if the user is feeling anxious, it can provide reassurance by showing past success stories. If the user is confident, it can encourage calm judgment by showing past failure stories. Furthermore, if the user is in a neutral emotional state, it can provide feedback based on objective data. This allows for more appropriate support by providing feedback on investment actions according to the user's emotions.

[0120] The AI ​​chat service for investment decision support can further estimate the user's emotions and assess the risk of investment actions based on those emotions. For example, if the user is feeling anxious, it can warn against high-risk actions. Conversely, if the user is confident, it can recommend low-risk actions. Furthermore, if the user is in a neutral emotional state, it can perform a standard risk assessment. This allows for more appropriate risk management by assessing the risk of investment actions in accordance with the user's emotions.

[0121] The AI ​​chat service for investment decision support can further estimate the user's emotions and enhance their motivation for investment actions based on those emotions. For example, if the user is feeling anxious, it can boost their motivation by showing them success stories. If the user is confident, it can maintain their motivation by setting challenging goals. Furthermore, if the user is in a neutral emotional state, it can set goals to maintain the status quo. In this way, by increasing motivation for investment actions according to the user's emotions, it can promote more proactive investment behavior.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The registration section registers the user's investment strategy. The user's investment strategy includes stock investments, bond investments, derivative transactions, etc. The registration section inputs the investment strategy that the user has set up in advance and registers it in the system. Step 2: The detection unit detects sudden changes in the stock market. Sudden changes in the stock market are detected based on criteria such as price fluctuation thresholds and sudden increases in trading volume. The detection unit monitors market data in real time and detects sudden changes. Step 3: The monitoring unit monitors user behavior. User behavior includes the timing of buying and selling, and changes in trading volume. The monitoring unit monitors the user's trading history in real time and detects abnormal behavior. Step 4: The intervention unit intervenes to prevent emotional decisions. When a user is about to make a buy or sell decision based on emotions, the intervention unit displays a message encouraging them to make a calm decision.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the registration unit, detection unit, monitoring unit, intervention unit, analysis unit, sentiment analysis unit, and advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14, which inputs the user's investment strategy and registers it in the system. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which monitors market data in real time and detects sudden changes. The monitoring unit is implemented by the control unit 46A of the smart device 14, which monitors the user's trading history in real time and detects abnormal behavior. The intervention unit is implemented by the identification processing unit 290 of the data processing unit 12, which displays a message encouraging calm judgment when the user is about to make a buy or sell decision based on emotional judgment. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which collects the user's trading data for the past year and analyzes trading patterns. The sentiment analysis unit is implemented by the control unit 46A of the smart device 14, which estimates emotions from the user's facial expressions and voice. The advice section is implemented by the specific processing unit 290 of the data processing device 12, and provides optimal advice based on the user's investment strategy and emotional state. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the registration unit, detection unit, monitoring unit, intervention unit, analysis unit, sentiment analysis unit, and advice unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214, which inputs the user's investment strategy and registers it in the system. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which monitors market data in real time and detects sudden changes. The monitoring unit is implemented by the control unit 46A of the smart glasses 214, which monitors the user's trading history in real time and detects abnormal behavior. The intervention unit is implemented by the identification processing unit 290 of the data processing unit 12, which displays a message encouraging calm judgment when the user is about to make a buy or sell decision based on emotional judgment. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which collects the user's trading data for the past year and analyzes trading patterns. The emotion analysis unit is implemented by the control unit 46A of the smart glasses 214, which estimates emotions from the user's facial expressions and voice. The advice unit is implemented by the identification processing unit 290 of the data processing device 12, which provides optimal advice based on the user's investment strategy and emotional state. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the registration unit, detection unit, monitoring unit, intervention unit, analysis unit, sentiment analysis unit, and advice unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314, which inputs the user's investment strategy and registers it in the system. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which monitors market data in real time and detects sudden changes. The monitoring unit is implemented by the control unit 46A of the headset terminal 314, which monitors the user's trading history in real time and detects abnormal behavior. The intervention unit is implemented by the identification processing unit 290 of the data processing unit 12, which displays a message encouraging calm judgment when the user is about to make a buy or sell decision based on emotional judgment. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which collects the user's trading data for the past year and analyzes trading patterns. The emotion analysis unit is implemented by the control unit 46A of the headset terminal 314, which estimates emotions from the user's facial expressions and voice. The advice unit is implemented by the identification processing unit 290 of the data processing device 12, which provides optimal advice based on the user's investment strategy and emotional state. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the registration unit, detection unit, monitoring unit, intervention unit, analysis unit, sentiment analysis unit, and advice unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414, which inputs the user's investment strategy and registers it in the system. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which monitors market data in real time and detects sudden changes. The monitoring unit is implemented by the control unit 46A of the robot 414, which monitors the user's trading history in real time and detects abnormal behavior. The intervention unit is implemented by the identification processing unit 290 of the data processing unit 12, which displays a message encouraging calm judgment when the user is about to make a buy or sell decision based on emotional judgment. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which collects the user's trading data for the past year and analyzes trading patterns. The sentiment analysis unit is implemented by the control unit 46A of the robot 414, which estimates emotions from the user's facial expressions and voice. The advice section is implemented by the specific processing unit 290 of the data processing device 12, and provides optimal advice based on the user's investment strategy and emotional state. The correspondence between each section and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A registration section for registering the user's investment strategy, A detection unit that detects sudden changes in the stock market, A monitoring unit that monitors user behavior, It includes an intervention unit to prevent emotional judgments. A system characterized by the following features. (Note 2) It has an analysis department that analyzes past transaction data. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an emotion analysis unit that analyzes the user's emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with an advisory department to provide appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned registration unit is It estimates the user's emotions and adjusts the timing of investment strategy registration based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned registration unit is We analyze the user's past investment strategies and select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is When registering an investment strategy, filtering is performed based on the user's current investment status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is It estimates user sentiment and determines the priority of investment strategies to register based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is When registering an investment strategy, the system prioritizes registering strategies that are highly relevant to the user, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is When registering an investment strategy, the system analyzes the user's social media activity and registers relevant strategies. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is We estimate user sentiment and adjust the criteria for detecting sudden changes in the stock market based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit is When detecting sudden changes in the stock market, historical market data is used to improve the accuracy of the detection. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit is When detecting sudden changes in the stock market, the detection process focuses on specific industries or regions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is The system estimates the user's emotions and adjusts the order in which sudden change detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is When detecting sudden changes in the stock market, we improve the accuracy of the detection by referring to relevant news articles. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is When detecting sudden changes in the stock market, the system prioritizes detection based on the user's investment portfolio. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned monitoring unit, We estimate user sentiment and adjust monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned monitoring unit, When monitoring user behavior, we improve the accuracy of monitoring by referring to past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, When monitoring user behavior, focus the monitoring on specific investment actions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, When monitoring user behavior, we improve the accuracy of monitoring by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, When monitoring user behavior, the monitoring priorities are determined based on the user's investment portfolio. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned intervention unit is It estimates the user's emotions and adjusts the intervention method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned intervention unit is To prevent emotionally driven decisions, we refer to past intervention data to improve the accuracy of interventions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned intervention unit is To prevent emotional decisions, interventions are focused on specific investment behaviors. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned intervention unit is The system estimates the user's emotions and determines the priority of interventions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned intervention unit is To prevent emotionally driven decisions, we refer to relevant market data to improve the accuracy of interventions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned intervention unit is To prevent emotional decisions, the method of intervention is customized based on the user's investment portfolio. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis method of transaction data based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned analysis unit is When analyzing historical trading data, the analysis focuses on specific investment behaviors. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned analysis unit is When analyzing historical trading data, referencing relevant market data improves the accuracy of the analysis. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned emotion analysis unit, The system estimates the user's emotions and adjusts the emotion analysis method based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned emotion analysis unit, When performing sentiment analysis, past sentiment data is referenced to improve the accuracy of the analysis. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts how the emotion analysis results are displayed based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned emotion analysis unit, When performing sentiment analysis, we refer to relevant market data to improve the accuracy of the analysis. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned advice section, It estimates the user's emotions and adjusts the advice given based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned advice section, When providing appropriate advice, we refer to past advice data to improve the accuracy of the advice. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned advice section, When providing appropriate advice, we refer to relevant market data to improve the accuracy of that advice. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A registration section for registering the user's investment strategy, A detection unit that detects sudden changes in the stock market, A monitoring unit that monitors user behavior, It includes an intervention unit to prevent emotional judgments. A system characterized by the following features.

2. It has an analysis department that analyzes past transaction data. The system according to feature 1.

3. It includes an emotion analysis unit that analyzes the user's emotional state. The system according to feature 1.

4. Equipped with an advisory department to provide appropriate advice. The system according to feature 1.

5. The aforementioned registration unit is It estimates the user's emotions and adjusts the timing of investment strategy registration based on the estimated user emotions. The system according to feature 1.

6. The aforementioned registration unit is We analyze the user's past investment strategies and select the optimal registration method. The system according to feature 1.

7. The aforementioned registration unit is When registering an investment strategy, filtering is performed based on the user's current investment status and areas of interest. The system according to feature 1.

8. The aforementioned registration unit is It estimates user sentiment and determines the priority of investment strategies to register based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned registration unit is When registering an investment strategy, the system prioritizes registering strategies that are highly relevant to the user, taking into account the user's geographical location. The system according to feature 1.

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