system

Abstract

The system according to this embodiment aims to provide personalized advice that meets the individual needs of investors. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, an extraction unit, and an update unit. The collection unit collects information such as the user's investment history, risk tolerance, and goals. The analysis unit analyzes the information collected by the collection unit. The generation unit generates individually customized investment plans based on the analysis results obtained by the analysis unit. The extraction unit extracts market sentiment from news and social media. The update unit updates the advice based on the information extracted by the extraction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there were problems that it was difficult to provide advice according to individual investors' needs and that the provision of real-time information responding to market fluctuations was insufficient.

[0005] The system according to the embodiment aims to provide personalized advice according to individual investors' needs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an extraction unit, and an update unit. The collection unit collects information such as the user's investment history, risk tolerance, and goals. The analysis unit analyzes the information collected by the collection unit. The generation unit generates individually customized investment plans based on the analysis results obtained by the analysis unit. The extraction unit extracts market sentiment from news and social media. The update unit updates the advice based on the information extracted by the extraction unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized advice tailored to the individual needs of investors. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An investment advice system according to an embodiment of the present invention is a system that uses AI to provide personalized investment advice tailored to the individual needs of investors. This investment advice system collects information such as the user's investment history, risk tolerance, and goals, and the AI ​​analyzes this information. Next, the AI ​​generates an individually customized investment plan based on the analysis results. For example, it proposes a high-risk investment plan to users with high risk tolerance and a low-risk investment plan to users with low risk tolerance. Furthermore, it uses natural language processing to extract market sentiment in real time from news and social media, and the AI ​​updates the advice based on this information. For example, if the market fluctuates rapidly, the AI ​​immediately reflects this information and provides the user with the latest advice. This mechanism allows users to always receive personalized advice based on the latest market information, leading to faster and more appropriate investment decisions and improved risk management. Specifically, the steps are as follows: First, information such as the user's investment history, risk tolerance, and goals is collected. Next, the AI ​​analyzes this information and generates an individually customized investment plan. Furthermore, it uses natural language processing to extract market sentiment from news and social media, and the AI ​​updates the advice based on this information. Finally, the latest advice is provided to the user. This system targets individual investors and small and medium-sized institutional investors, and can generate revenue through subscription models and premium services in a growing market where advancements in AI and fintech are expected. Furthermore, it can be widely adopted by leveraging marketing strategies such as online advertising, SEO, PR through financial seminars and webinars, partnerships, and word-of-mouth marketing. This allows the investment advice system to provide users with personalized investment advice.

[0029] The investment advice system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an extraction unit, and an update unit. The collection unit collects information such as the user's investment history, risk tolerance, and goals. For example, the collection unit can collect information such as the user's past investment amount, investment destinations, and investment period. The collection unit can also collect survey results and past investment behavior in order to evaluate the user's risk tolerance. Furthermore, the collection unit can also collect information such as the user's short-term profit targets and long-term asset formation targets. For example, the collection unit can obtain the user's investment history from a database, conduct a survey to evaluate risk tolerance, and interview the user about their goals. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the user's investment history and evaluate their risk tolerance. Furthermore, the analysis unit can propose an appropriate investment plan based on the user's goals. Furthermore, the analysis unit can predict the user's investment behavior based on the collected information. For example, the analysis unit can evaluate the user's risk tolerance based on past investment history and propose an investment plan that matches their goals. The generation unit generates individually customized investment plans based on the analysis results obtained by the analysis unit. For example, the generation unit can propose high-risk investment plans to users with high risk tolerance. It can also propose low-risk investment plans to users with low risk tolerance. Furthermore, the generation unit can generate investment plans tailored to the user's goals. For example, it can propose high-risk, high-return investment plans to users with high risk tolerance and low-risk, low-return investment plans to users with low risk tolerance. The extraction unit extracts market sentiment from news and social media. For example, it can analyze the tone of news articles and the content of social media posts to extract market sentiment. The extraction unit can also filter market sentiment based on specific keywords. Furthermore, the extraction unit can improve the accuracy of extraction based on the reliability of news and social media. For example, it can analyze the tone of news articles to extract positive market sentiment and analyze social media posts to extract negative market sentiment.The update unit updates the advice based on the information extracted by the extraction unit. The update unit can, for example, immediately update the advice if the market fluctuates rapidly. The update unit can also update the advice based on specific market events. Furthermore, the update unit can adjust how the advice is displayed based on the user's sentiment. For example, if the market fluctuates rapidly, the update unit updates the advice based on the latest market information, and if a specific market event occurs, it updates the advice taking its impact into consideration. As a result, the investment advice system according to this embodiment can provide the user with personalized investment advice.

[0030] The data collection unit collects information such as users' investment history, risk tolerance, and goals. Specifically, it can collect detailed information such as the amount of money a user has invested in the past, the investment destinations, and the investment period. This includes information such as what asset classes the user has invested in, what kind of returns they have obtained, and how long they have continued investing. The data collection unit can also collect survey results and past investment behavior to assess the user's risk tolerance. For example, it can ask users questions about risk and quantify their risk tolerance based on their answers. Furthermore, the data collection unit can also collect information such as the user's short-term profit goals and long-term asset building goals. This includes how much wealth the user wants to build and over what period of time, specific monetary targets, and goals based on life events. For example, the data collection unit can retrieve the user's investment history from a database, conduct surveys to assess risk tolerance, and interview users about their goals. In this way, the data collection unit can centrally manage detailed information about users' investment behavior and goals and make it available to the analysis and generation units. Furthermore, the data collection unit can regularly update this information to respond to the user's latest situation. For example, if a user makes a new investment or their risk tolerance changes, this information is quickly collected and reflected throughout the system. This allows the data collection unit to always have up-to-date information on the user's investment behavior and goals, improving the accuracy and reliability of the entire system.

[0031] The analysis department analyzes the information collected by the data collection department. Specifically, it can analyze users' investment history and assess their risk tolerance. For example, it can analyze the user's investment behavior trends and quantify their risk tolerance based on data such as past investment amounts, investment destinations, and investment periods. The analysis department can also propose appropriate investment plans based on the user's goals. For example, it can propose optimal asset allocation and investment strategies according to the user's short-term profit targets and long-term asset formation goals. Furthermore, the analysis department can predict users' investment behavior based on the collected information. For example, it can predict future investment behavior and changes in risk preferences based on past investment history and risk tolerance, and provide advice accordingly. This allows the analysis department to conduct detailed analysis based on users' investment behavior and goals and provide individually customized investment plans. In addition, the analysis department utilizes AI to analyze data. For example, it uses machine learning algorithms to learn patterns in users' investment behavior and predict future investment behavior. It also uses natural language processing technology to analyze user survey results and interview content to accurately assess risk tolerance and goals. This allows the analysis department to perform advanced analysis of the collected information and provide users with optimal investment advice.

[0032] The generation unit generates individually customized investment plans based on the analysis results obtained by the analysis unit. Specifically, it can propose high-risk investment plans to users with a high risk tolerance. For example, it can generate plans that include investments in high-risk, high-return stocks and emerging markets. The generation unit can also propose low-risk investment plans to users with a low risk tolerance. For example, it can generate plans that include investments in bonds and index funds that are expected to provide stable returns. Furthermore, the generation unit can generate investment plans tailored to the user's goals. For example, it can propose investment plans that are expected to provide high returns in the short term to users with short-term profit targets, and investment plans that are expected to provide stable returns in the long term to users with long-term asset building targets. When generating these investment plans, the generation unit uses AI to analyze user information and automatically generate the optimal plan. For example, it can use machine learning algorithms to learn the optimal investment strategy from past data and generate the optimal investment plan for the user based on that. The generation unit can also visually display the generated investment plan using graphs and charts to make it easy for users to understand. This allows the generation unit to provide users with individually customized investment plans and support their investment decision-making.

[0033] The extraction unit extracts market sentiment from news and social media. Specifically, it can analyze the tone of news articles and the content of social media posts to extract market sentiment. For example, it can use natural language processing technology to analyze the content of news articles and identify positive and negative market sentiment. It can also analyze social media posts and filter market sentiment based on specific keywords. For example, it can analyze posts about specific stocks or market events and extract market sentiment from their tone and content. Furthermore, the extraction unit can improve the accuracy of its extraction based on the reliability of news and social media. For example, it can prioritize the analysis of posts from reliable news sources and influential social media accounts, and eliminate unreliable information. This allows the extraction unit to extract accurate and reliable market sentiment, improving the overall accuracy of the system. In addition, the extraction unit can update the extracted market sentiment in real time, responding to the latest market conditions. For example, if the market fluctuates rapidly or a significant market event occurs, it can immediately extract market sentiment and reflect it throughout the system. This allows the extraction unit to always grasp the latest market sentiment and provide appropriate investment advice to users.

[0034] The update unit updates advice based on information extracted by the extraction unit. Specifically, it can instantly update advice in the event of rapid market fluctuations. For example, if the stock market plummets, it will review high-risk investment plans and provide advice to reduce risk. The update unit can also update advice based on specific market events. For example, it will adjust investment plans based on events such as the release of important economic indicators or corporate earnings announcements. Furthermore, the update unit can adjust how advice is displayed based on the user's emotions. For example, if the market is unstable, it will display a message encouraging the user to make a calm judgment. Also, if the user is about to take on excessive risk, it will display a message warning about that risk. This allows the update unit to flexibly update advice according to the user's situation and market fluctuations, providing the user with optimal investment advice. In addition, the update unit can automate advice updates using AI. For example, it can use machine learning algorithms to learn market fluctuations and user behavior patterns and automatically update advice accordingly. This allows the update unit to always provide advice based on the latest information and support the user's investment decision-making.

[0035] The data collection unit can collect information such as the user's investment history, risk tolerance, and goals. For example, the data collection unit collects information such as the user's past investment amount, investment destinations, and investment period. The data collection unit can also collect survey results and past investment behavior to assess the user's risk tolerance. The data collection unit can also collect information such as the user's short-term profit targets and long-term asset formation targets. This allows the data collection unit to provide the basis for generating individually customized investment plans. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit may retrieve the user's investment history from a database, conduct a survey to assess risk tolerance, and interview the user about their goals.

[0036] The analysis unit can analyze the collected information and generate investment plans tailored to the user's risk tolerance. For example, the analysis unit can analyze the user's investment history and assess their risk tolerance. The analysis unit can also propose appropriate investment plans based on the user's goals. Based on the collected information, the analysis unit can also predict the user's investment behavior. This allows the analysis unit to provide investment advice that is appropriate for the user. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can assess the user's risk tolerance based on past investment history and propose an investment plan that meets their goals.

[0037] The generation unit can generate individually customized investment plans based on the analysis results. For example, the generation unit can propose high-risk investment plans to users with high risk tolerance. The generation unit can also propose low-risk investment plans to users with low risk tolerance. The generation unit can also generate investment plans tailored to the user's goals. In this way, the generation unit provides the user with optimal investment advice. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may propose high-risk, high-return investment plans to users with high risk tolerance, and low-risk, low-return investment plans to users with low risk tolerance.

[0038] The extraction unit can extract market sentiment from news and social media. For example, the extraction unit analyzes the tone of news articles and the content of social media posts to extract market sentiment. The extraction unit can also filter market sentiment based on specific keywords. The extraction unit can also improve the accuracy of the extraction based on the reliability of the news and social media. This allows the extraction unit to provide investment advice based on the latest market information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit may analyze the tone of news articles to extract positive market sentiment and analyze the content of social media posts to extract negative market sentiment.

[0039] The update unit can update the advice based on the extracted information. For example, if the market fluctuates rapidly, the update unit will immediately update the advice. The update unit can also update the advice based on specific market events. The update unit can also adjust how the advice is displayed based on the user's sentiment. In this way, the update unit provides the user with investment advice that reflects the latest market information. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, if the market fluctuates rapidly, the update unit will update the advice based on the latest market information, and if a specific market event occurs, it will update the advice taking its impact into consideration.

[0040] The data collection unit can analyze the user's past investment history and select the optimal collection method. For example, the data collection unit can prioritize collecting the types of investments the user has made frequently in the past. The data collection unit can also focus on collecting investments made during specific time periods from the user's past investment history. The data collection unit can also analyze the user's past investment history and prioritize collecting high-risk investments. This allows the data collection unit to collect information efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can retrieve the user's past investment history from a database and prioritize collecting the types of investments the user has made frequently.

[0041] The collection unit can filter investment history based on the user's current financial situation and areas of interest. For example, the collection unit can filter the investment history to be collected based on the user's current income. The collection unit can also prioritize the collection of relevant investment history based on the user's areas of interest. The collection unit can also collect low-risk investment history, taking into account the user's current financial situation. This allows the collection unit to collect highly relevant information. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can obtain the user's current income from a database and filter the investment history to be collected.

[0042] The collection unit can prioritize the collection of highly relevant investment history by considering the user's geographical location when collecting investment history. For example, the collection unit prioritizes the collection of relevant investment history based on the economic conditions of the area where the user lives. If the user is traveling, the collection unit can also collect investment history based on the economic conditions of the travel destination. The collection unit can also collect region-specific investment history by considering the user's geographical location. In this way, the collection unit collects region-specific information. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit obtains the user's geographical location information from a database and prioritizes the collection of relevant investment history.

[0043] The data collection unit can analyze a user's social media activity and collect relevant history when collecting investment history. For example, the data collection unit can collect history related to investments that the user has shown interest in on social media. The data collection unit can also collect history by referring to the investment activities of the user's social media followers. The data collection unit can also analyze the content of a user's social media posts and collect relevant investment history. In this way, the data collection unit collects information based on the user's interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can retrieve the user's social media activity from a database and collect relevant investment history.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the investment history. For example, the analysis unit can perform a detailed analysis on highly important investment history. The analysis unit can also perform a simplified analysis on less important investment history. The analysis unit can also adjust the depth of the analysis according to the importance of the investment history. This allows the analysis unit to perform a detailed analysis on important information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can obtain the importance of the investment history from a database and adjust the level of detail of the analysis according to the importance.

[0045] The analysis unit can apply different analysis algorithms depending on the investment category during the analysis. For example, for stock investments, the analysis unit can apply an analysis algorithm that emphasizes fluctuations in stock prices. For real estate investments, the analysis unit can also apply an analysis algorithm that emphasizes fluctuations in property value. For cryptocurrency investments, the analysis unit can also apply an analysis algorithm that emphasizes rapid price fluctuations. In this way, the analysis unit performs an appropriate analysis according to the category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit retrieves investment categories from a database and applies different analysis algorithms according to the category.

[0046] The analysis department can determine the priority of its analysis based on the submission date of investment history. For example, it may prioritize the analysis of recently submitted investment history. It may also postpone the analysis of older investment history. The analysis department may also adjust the order of analysis based on the submission date. This ensures that the analysis department prioritizes the analysis of the most recent information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department may retrieve the submission date of investment history from a database and determine the priority of analysis based on the submission date.

[0047] The analysis unit can adjust the order of analysis based on the relevance of investment history during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant investment history. The analysis unit may also postpone the analysis of less relevant investment history. The analysis unit can also adjust the order of analysis based on the relevance of investment history. This allows the analysis unit to prioritize the analysis of highly relevant information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may retrieve the relevance of investment history from a database and adjust the order of analysis based on relevance.

[0048] The analysis unit can adjust the order of analysis based on the relevance of investment history during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant investment history. The analysis unit may also postpone the analysis of less relevant investment history. The analysis unit can also adjust the order of analysis based on the relevance of investment history. This allows the analysis unit to prioritize the analysis of highly relevant information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may retrieve the relevance of investment history from a database and adjust the order of analysis based on relevance.

[0049] The generation unit can adjust the level of detail in the generated output based on the risk level of the investment plan. For example, the generation unit may generate an output with a detailed explanation for high-risk investment plans. For low-risk investment plans, the generation unit may also generate a simplified output. The generation unit can also adjust the depth of the output according to the risk level of the investment plan. This allows the generation unit to provide an appropriate investment plan according to the risk level. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may obtain the risk level of the investment plan from a database and adjust the level of detail in the output based on the risk level.

[0050] The generation unit can apply different generation algorithms depending on the investment category during generation. For example, for stock investments, the generation unit can apply a generation algorithm that emphasizes fluctuations in stock prices. For real estate investments, the generation unit can also apply a generation algorithm that emphasizes fluctuations in property value. For cryptocurrency investments, the generation unit can also apply a generation algorithm that emphasizes rapid price fluctuations. In this way, the generation unit provides appropriate investment plans according to the category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can obtain investment categories from a database and apply different generation algorithms according to the category.

[0051] The generation unit can determine the generation priority based on the submission date of the investment plan during generation. For example, the generation unit may prioritize the generation of recently submitted investment plans. The generation unit may also postpone the generation of older investment plans. The generation unit may also adjust the generation order based on the submission date. This ensures that the generation unit prioritizes the generation of the latest information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit may retrieve the submission date of the investment plan from a database and determine the generation priority based on the submission date.

[0052] The generation unit can adjust the generation order based on the relevance of the investment plans during generation. For example, the generation unit may prioritize generating highly relevant investment plans. The generation unit may also postpone the generation of less relevant investment plans. The generation unit can also adjust the generation order based on the relevance of the investment plans. This allows the generation unit to prioritize the generation of highly relevant information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may retrieve the relevance of investment plans from a database and adjust the generation order based on the relevance.

[0053] The extraction unit can improve the accuracy of its extraction based on the reliability of news and social media sources. For example, it may prioritize extracting information from highly reliable news sources. It can also filter out and extract information from less reliable social media sources. The extraction unit can also adjust the accuracy of its extraction based on the reliability of news and social media sources. This ensures that the extraction unit provides highly reliable information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit may obtain the reliability of news sources from a database and improve the accuracy of its extraction based on that reliability.

[0054] The extraction unit can filter market sentiment based on specific keywords during the extraction process. For example, the extraction unit can extract market sentiment based on keywords related to a specific company name. The extraction unit can also extract market sentiment based on keywords related to a specific industry. The extraction unit can also extract market sentiment based on keywords related to a specific event. This allows the extraction unit to provide highly relevant information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can retrieve specific keywords from a database and filter market sentiment based on those keywords.

[0055] The extraction unit can perform extraction while considering the geographical distribution of news and social media. For example, the extraction unit can prioritize the extraction of news and social media information related to a specific region. The extraction unit can also prioritize the extraction of market sentiment in geographically close regions. The extraction unit can also adjust the extraction priority based on geographical distribution. In this way, the extraction unit provides region-specific information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit can obtain the geographical distribution of news and social media from a database and perform extraction based on geographical distribution.

[0056] The extraction unit can improve the accuracy of the extraction by referring to relevant market data during the extraction process. For example, the extraction unit prioritizes extracting reliable information based on market data. The extraction unit can also filter out less relevant information by referring to market data. The extraction unit can also adjust the accuracy of the extraction based on market data. This ensures that the extraction unit provides reliable information. Some or all of the above processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit may obtain market data from a database and improve the accuracy of the extraction based on the market data.

[0057] The update unit can optimize its update algorithm by referring to past advice history during the update process. For example, the update unit analyzes past advice history and applies the optimal update algorithm. The update unit can also adjust the update algorithm based on user responses from past advice history. The update unit can also improve the accuracy of updates by referring to past advice history. This allows the update unit to provide the best possible advice to the user. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit retrieves past advice history from a database and optimizes the update algorithm.

[0058] The update unit can update the advice based on specific market events at the time of update. For example, the update unit will update the advice immediately when a market event occurs. The update unit can also adjust the content of the advice based on specific market events. The update unit can also update the advice taking into account the impact of market events. This allows the update unit to provide advice that is in line with the latest market conditions. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit may retrieve market event data from a database and update the advice based on specific market events.

[0059] The update unit can update advice while considering the user's geographical location information. For example, the update unit can update advice based on the economic conditions of the area where the user lives. If the user is traveling, the update unit can also update advice based on the economic conditions of the destination. The update unit can also provide region-specific advice while considering the user's geographical location information. In this way, the update unit provides region-specific information. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can obtain the user's geographical location information from a database and provide region-specific advice.

[0060] The update unit can improve the accuracy of its advice by referring to relevant market data during the update process. For example, the update unit provides reliable advice based on market data. The update unit can also filter out irrelevant advice by referring to market data. The update unit can also adjust the accuracy of its advice based on market data. This allows the update unit to provide reliable advice. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit retrieves market data from a database and improves the accuracy of its advice based on market data.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can evaluate a user's past investment performance and distinguish between successful and unsuccessful investments when gathering information such as the user's investment history, risk tolerance, and goals. For example, the data collection unit can identify the investment that yielded the highest return among the user's past investments and collect the characteristics of that investment. It can also identify the investment that incurred the largest loss among the user's past investments and collect the characteristics of that investment. Furthermore, the data collection unit can analyze the user's investment performance over time and calculate the success rate and failure rate of investments. This allows the data collection unit to understand the user's investment behavior trends and gather basic data to provide more accurate investment advice.

[0063] The analytics department can identify patterns in users' investment behavior and predict future investment actions when analyzing collected information. For example, it can analyze what investment actions users tend to take under specific market conditions. It can also predict how users will react to investments at specific risk levels. Furthermore, based on patterns in users' investment behavior, the analytics department can simulate how users will react to future market fluctuations. This allows the analytics department to provide users with more appropriate investment advice.

[0064] The generation unit, when generating individually customized investment plans based on analysis results, can propose investment plans tailored to the user's life stage. For example, it can propose investment plans aimed at long-term asset building to younger users. It can also propose investment plans that consider the balance between risk and return to middle-aged users. Furthermore, it can propose investment plans aimed at stable returns with reduced risk to older users. In this way, the generation unit can provide optimal investment advice tailored to the user's life stage.

[0065] The extraction unit can focus on extracting market sentiment from specific regions or countries when extracting market sentiment from news and social media. For example, the extraction unit can prioritize extracting economic news from a particular country that the user is interested in. It can also analyze social media posts from a specific region and extract market sentiment from that region. Furthermore, the extraction unit can extract market sentiment based on economic indicators and market data from a specific region. This allows the extraction unit to accurately grasp market sentiment in regions and countries that the user is interested in, enabling it to provide more accurate investment advice.

[0066] The update unit can adjust the frequency of advice updates based on extracted information, according to the user's investment style. For example, it can update advice more frequently for users who prefer short-term investments, and less frequently for users who prefer long-term investments. Furthermore, the update unit can adjust the content of the advice according to the user's investment style. This allows the update unit to provide advice at the optimal time for each user's investment style.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects information such as the user's investment history, risk tolerance, and goals. For example, it collects information such as the user's past investment amount, investment destinations, and investment period, and collects survey results and past investment behavior to assess risk tolerance. It also collects information such as the user's short-term profit goals and long-term asset building goals. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the user's investment history, assesses their risk tolerance, and proposes an appropriate investment plan based on the user's goals. It also predicts the user's investment behavior based on the collected information. Step 3: The generation unit generates individually customized investment plans based on the analysis results obtained by the analysis unit. For example, it proposes high-risk investment plans to users with high risk tolerance and low-risk investment plans to users with low risk tolerance. It also generates investment plans tailored to the user's goals. Step 4: The extraction unit extracts market sentiment from news and social media. For example, it analyzes the tone of news articles and the content of social media posts to extract market sentiment. It also filters market sentiment based on specific keywords and improves the accuracy of the extraction based on the reliability of the news and social media. Step 5: The update unit updates the advice based on the information extracted by the extraction unit. For example, it immediately updates the advice if the market fluctuates rapidly or if a specific market event occurs. It also adjusts how the advice is displayed based on the user's sentiment.

[0069] (Example of form 2) An investment advice system according to an embodiment of the present invention is a system that uses AI to provide personalized investment advice tailored to the individual needs of investors. This investment advice system collects information such as the user's investment history, risk tolerance, and goals, and the AI ​​analyzes this information. Next, the AI ​​generates an individually customized investment plan based on the analysis results. For example, it proposes a high-risk investment plan to users with high risk tolerance and a low-risk investment plan to users with low risk tolerance. Furthermore, it uses natural language processing to extract market sentiment in real time from news and social media, and the AI ​​updates the advice based on this information. For example, if the market fluctuates rapidly, the AI ​​immediately reflects this information and provides the user with the latest advice. This mechanism allows users to always receive personalized advice based on the latest market information, leading to faster and more appropriate investment decisions and improved risk management. Specifically, the steps are as follows: First, information such as the user's investment history, risk tolerance, and goals is collected. Next, the AI ​​analyzes this information and generates an individually customized investment plan. Furthermore, it uses natural language processing to extract market sentiment from news and social media, and the AI ​​updates the advice based on this information. Finally, the latest advice is provided to the user. This system targets individual investors and small and medium-sized institutional investors, and can generate revenue through subscription models and premium services in a growing market where advancements in AI and fintech are expected. Furthermore, it can be widely adopted by leveraging marketing strategies such as online advertising, SEO, PR through financial seminars and webinars, partnerships, and word-of-mouth marketing. This allows the investment advice system to provide users with personalized investment advice.

[0070] The investment advice system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an extraction unit, and an update unit. The collection unit collects information such as the user's investment history, risk tolerance, and goals. For example, the collection unit can collect information such as the user's past investment amount, investment destinations, and investment period. The collection unit can also collect survey results and past investment behavior in order to evaluate the user's risk tolerance. Furthermore, the collection unit can also collect information such as the user's short-term profit targets and long-term asset formation targets. For example, the collection unit can obtain the user's investment history from a database, conduct a survey to evaluate risk tolerance, and interview the user about their goals. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the user's investment history and evaluate their risk tolerance. Furthermore, the analysis unit can propose an appropriate investment plan based on the user's goals. Furthermore, the analysis unit can predict the user's investment behavior based on the collected information. For example, the analysis unit can evaluate the user's risk tolerance based on past investment history and propose an investment plan that matches their goals. The generation unit generates individually customized investment plans based on the analysis results obtained by the analysis unit. For example, the generation unit can propose high-risk investment plans to users with high risk tolerance. It can also propose low-risk investment plans to users with low risk tolerance. Furthermore, the generation unit can generate investment plans tailored to the user's goals. For example, it can propose high-risk, high-return investment plans to users with high risk tolerance and low-risk, low-return investment plans to users with low risk tolerance. The extraction unit extracts market sentiment from news and social media. For example, it can analyze the tone of news articles and the content of social media posts to extract market sentiment. The extraction unit can also filter market sentiment based on specific keywords. Furthermore, the extraction unit can improve the accuracy of extraction based on the reliability of news and social media. For example, it can analyze the tone of news articles to extract positive market sentiment and analyze social media posts to extract negative market sentiment.The update unit updates the advice based on the information extracted by the extraction unit. The update unit can, for example, immediately update the advice if the market fluctuates rapidly. The update unit can also update the advice based on specific market events. Furthermore, the update unit can adjust how the advice is displayed based on the user's sentiment. For example, if the market fluctuates rapidly, the update unit updates the advice based on the latest market information, and if a specific market event occurs, it updates the advice taking its impact into consideration. As a result, the investment advice system according to this embodiment can provide the user with personalized investment advice.

[0071] The data collection unit collects information such as users' investment history, risk tolerance, and goals. Specifically, it can collect detailed information such as the amount of money a user has invested in the past, the investment destinations, and the investment period. This includes information such as what asset classes the user has invested in, what kind of returns they have obtained, and how long they have continued investing. The data collection unit can also collect survey results and past investment behavior to assess the user's risk tolerance. For example, it can ask users questions about risk and quantify their risk tolerance based on their answers. Furthermore, the data collection unit can also collect information such as the user's short-term profit goals and long-term asset building goals. This includes how much wealth the user wants to build and over what period of time, specific monetary targets, and goals based on life events. For example, the data collection unit can retrieve the user's investment history from a database, conduct surveys to assess risk tolerance, and interview users about their goals. In this way, the data collection unit can centrally manage detailed information about users' investment behavior and goals and make it available to the analysis and generation units. Furthermore, the data collection unit can regularly update this information to respond to the user's latest situation. For example, if a user makes a new investment or their risk tolerance changes, this information is quickly collected and reflected throughout the system. This allows the data collection unit to always have up-to-date information on the user's investment behavior and goals, improving the accuracy and reliability of the entire system.

[0072] The analysis department analyzes the information collected by the data collection department. Specifically, it can analyze users' investment history and assess their risk tolerance. For example, it can analyze the user's investment behavior trends and quantify their risk tolerance based on data such as past investment amounts, investment destinations, and investment periods. The analysis department can also propose appropriate investment plans based on the user's goals. For example, it can propose optimal asset allocation and investment strategies according to the user's short-term profit targets and long-term asset formation goals. Furthermore, the analysis department can predict users' investment behavior based on the collected information. For example, it can predict future investment behavior and changes in risk preferences based on past investment history and risk tolerance, and provide advice accordingly. This allows the analysis department to conduct detailed analysis based on users' investment behavior and goals and provide individually customized investment plans. In addition, the analysis department utilizes AI to analyze data. For example, it uses machine learning algorithms to learn patterns in users' investment behavior and predict future investment behavior. It also uses natural language processing technology to analyze user survey results and interview content to accurately assess risk tolerance and goals. This allows the analysis department to perform advanced analysis of the collected information and provide users with optimal investment advice.

[0073] The generation unit generates individually customized investment plans based on the analysis results obtained by the analysis unit. Specifically, it can propose high-risk investment plans to users with a high risk tolerance. For example, it can generate plans that include investments in high-risk, high-return stocks and emerging markets. The generation unit can also propose low-risk investment plans to users with a low risk tolerance. For example, it can generate plans that include investments in bonds and index funds that are expected to provide stable returns. Furthermore, the generation unit can generate investment plans tailored to the user's goals. For example, it can propose investment plans that are expected to provide high returns in the short term to users with short-term profit targets, and investment plans that are expected to provide stable returns in the long term to users with long-term asset building targets. When generating these investment plans, the generation unit uses AI to analyze user information and automatically generate the optimal plan. For example, it can use machine learning algorithms to learn the optimal investment strategy from past data and generate the optimal investment plan for the user based on that. The generation unit can also visually display the generated investment plan using graphs and charts to make it easy for users to understand. This allows the generation unit to provide users with individually customized investment plans and support their investment decision-making.

[0074] The extraction unit extracts market sentiment from news and social media. Specifically, it can analyze the tone of news articles and the content of social media posts to extract market sentiment. For example, it can use natural language processing technology to analyze the content of news articles and identify positive and negative market sentiment. It can also analyze social media posts and filter market sentiment based on specific keywords. For example, it can analyze posts about specific stocks or market events and extract market sentiment from their tone and content. Furthermore, the extraction unit can improve the accuracy of its extraction based on the reliability of news and social media. For example, it can prioritize the analysis of posts from reliable news sources and influential social media accounts, and eliminate unreliable information. This allows the extraction unit to extract accurate and reliable market sentiment, improving the overall accuracy of the system. In addition, the extraction unit can update the extracted market sentiment in real time, responding to the latest market conditions. For example, if the market fluctuates rapidly or a significant market event occurs, it can immediately extract market sentiment and reflect it throughout the system. This allows the extraction unit to always grasp the latest market sentiment and provide appropriate investment advice to users.

[0075] The update unit updates advice based on information extracted by the extraction unit. Specifically, it can instantly update advice in the event of rapid market fluctuations. For example, if the stock market plummets, it will review high-risk investment plans and provide advice to reduce risk. The update unit can also update advice based on specific market events. For example, it will adjust investment plans based on events such as the release of important economic indicators or corporate earnings announcements. Furthermore, the update unit can adjust how advice is displayed based on the user's emotions. For example, if the market is unstable, it will display a message encouraging the user to make a calm judgment. Also, if the user is about to take on excessive risk, it will display a message warning about that risk. This allows the update unit to flexibly update advice according to the user's situation and market fluctuations, providing the user with optimal investment advice. In addition, the update unit can automate advice updates using AI. For example, it can use machine learning algorithms to learn market fluctuations and user behavior patterns and automatically update advice accordingly. This allows the update unit to always provide advice based on the latest information and support the user's investment decision-making.

[0076] The data collection unit can collect information such as the user's investment history, risk tolerance, and goals. For example, the data collection unit collects information such as the user's past investment amount, investment destinations, and investment period. The data collection unit can also collect survey results and past investment behavior to assess the user's risk tolerance. The data collection unit can also collect information such as the user's short-term profit targets and long-term asset formation targets. This allows the data collection unit to provide the basis for generating individually customized investment plans. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit may retrieve the user's investment history from a database, conduct a survey to assess risk tolerance, and interview the user about their goals.

[0077] The analysis unit can analyze the collected information and generate investment plans tailored to the user's risk tolerance. For example, the analysis unit can analyze the user's investment history and assess their risk tolerance. The analysis unit can also propose appropriate investment plans based on the user's goals. Based on the collected information, the analysis unit can also predict the user's investment behavior. This allows the analysis unit to provide investment advice that is appropriate for the user. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can assess the user's risk tolerance based on past investment history and propose an investment plan that meets their goals.

[0078] The generation unit can generate individually customized investment plans based on the analysis results. For example, the generation unit can propose high-risk investment plans to users with high risk tolerance. The generation unit can also propose low-risk investment plans to users with low risk tolerance. The generation unit can also generate investment plans tailored to the user's goals. In this way, the generation unit provides the user with optimal investment advice. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may propose high-risk, high-return investment plans to users with high risk tolerance, and low-risk, low-return investment plans to users with low risk tolerance.

[0079] The extraction unit can extract market sentiment from news and social media. For example, the extraction unit analyzes the tone of news articles and the content of social media posts to extract market sentiment. The extraction unit can also filter market sentiment based on specific keywords. The extraction unit can also improve the accuracy of the extraction based on the reliability of the news and social media. This allows the extraction unit to provide investment advice based on the latest market information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit may analyze the tone of news articles to extract positive market sentiment and analyze the content of social media posts to extract negative market sentiment.

[0080] The update unit can update the advice based on the extracted information. For example, if the market fluctuates rapidly, the update unit will immediately update the advice. The update unit can also update the advice based on specific market events. The update unit can also adjust how the advice is displayed based on the user's sentiment. In this way, the update unit provides the user with investment advice that reflects the latest market information. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, if the market fluctuates rapidly, the update unit will update the advice based on the latest market information, and if a specific market event occurs, it will update the advice taking its impact into consideration.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of investment history collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may temporarily delay the collection of investment history. If the user is relaxed, the data collection unit may immediately collect the investment history. If the user is excited, the data collection unit may collect the investment history more frequently. This allows the data collection unit to reduce the user's stress and collect information at the appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can analyze the user's emotion data in real time and immediately grasp changes in emotions.

[0082] The data collection unit can analyze the user's past investment history and select the optimal collection method. For example, the data collection unit can prioritize collecting the types of investments the user has made frequently in the past. The data collection unit can also focus on collecting investments made during specific time periods from the user's past investment history. The data collection unit can also analyze the user's past investment history and prioritize collecting high-risk investments. This allows the data collection unit to collect information efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can retrieve the user's past investment history from a database and prioritize collecting the types of investments the user has made frequently.

[0083] The collection unit can filter investment history based on the user's current financial situation and areas of interest. For example, the collection unit can filter the investment history to be collected based on the user's current income. The collection unit can also prioritize the collection of relevant investment history based on the user's areas of interest. The collection unit can also collect low-risk investment history, taking into account the user's current financial situation. This allows the collection unit to collect highly relevant information. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can obtain the user's current income from a database and filter the investment history to be collected.

[0084] The data collection unit can estimate the user's emotions and determine the priority of investment history to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit may prioritize collecting low-risk investment history. If the user is confident, the data collection unit may also prioritize collecting high-risk investment history. If the user is excited, the data collection unit may also prioritize collecting recent investment history. In this way, the data collection unit prioritizes collecting information that is important to the user. 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. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit analyzes the user's emotion data in real time and determines the priority of investment history to collect.

[0085] The collection unit can prioritize the collection of highly relevant investment history by considering the user's geographical location when collecting investment history. For example, the collection unit prioritizes the collection of relevant investment history based on the economic conditions of the area where the user lives. If the user is traveling, the collection unit can also collect investment history based on the economic conditions of the travel destination. The collection unit can also collect region-specific investment history by considering the user's geographical location. In this way, the collection unit collects region-specific information. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit obtains the user's geographical location information from a database and prioritizes the collection of relevant investment history.

[0086] The data collection unit can analyze a user's social media activity and collect relevant history when collecting investment history. For example, the data collection unit can collect history related to investments that the user has shown interest in on social media. The data collection unit can also collect history by referring to the investment activities of the user's social media followers. The data collection unit can also analyze the content of a user's social media posts and collect relevant investment history. In this way, the data collection unit collects information based on the user's interests. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can retrieve the user's social media activity from a database and collect relevant investment history.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit provides a simple and easy-to-understand analysis result. If the user is feeling confident, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually appealing analysis result. In this way, the analysis unit provides analysis results that are easy for the user to understand. 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit analyzes the user's emotion data in real time and adjusts the presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the investment history. For example, the analysis unit can perform a detailed analysis on highly important investment history. The analysis unit can also perform a simplified analysis on less important investment history. The analysis unit can also adjust the depth of the analysis according to the importance of the investment history. This allows the analysis unit to perform a detailed analysis on important information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can obtain the importance of the investment history from a database and adjust the level of detail of the analysis according to the importance.

[0089] The analysis unit can apply different analysis algorithms depending on the investment category during the analysis. For example, for stock investments, the analysis unit can apply an analysis algorithm that emphasizes fluctuations in stock prices. For real estate investments, the analysis unit can also apply an analysis algorithm that emphasizes fluctuations in property value. For cryptocurrency investments, the analysis unit can also apply an analysis algorithm that emphasizes rapid price fluctuations. In this way, the analysis unit performs an appropriate analysis according to the category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit retrieves investment categories from a database and applies different analysis algorithms according to the category.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is excited, the analysis unit can also provide a visually appealing analysis. In this way, the analysis unit provides appropriate analysis results according to the user's situation. 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit analyzes the user's emotion data in real time and adjusts the length of the analysis.

[0091] The analysis department can determine the priority of its analysis based on the submission date of investment history. For example, it may prioritize the analysis of recently submitted investment history. It may also postpone the analysis of older investment history. The analysis department may also adjust the order of analysis based on the submission date. This ensures that the analysis department prioritizes the analysis of the most recent information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department may retrieve the submission date of investment history from a database and determine the priority of analysis based on the submission date.

[0092] The analysis unit can adjust the order of analysis based on the relevance of investment history during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant investment history. The analysis unit may also postpone the analysis of less relevant investment history. The analysis unit can also adjust the order of analysis based on the relevance of investment history. This allows the analysis unit to prioritize the analysis of highly relevant information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may retrieve the relevance of investment history from a database and adjust the order of analysis based on relevance.

[0093] The analysis unit can adjust the order of analysis based on the relevance of investment history during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant investment history. The analysis unit may also postpone the analysis of less relevant investment history. The analysis unit can also adjust the order of analysis based on the relevance of investment history. This allows the analysis unit to prioritize the analysis of highly relevant information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may retrieve the relevance of investment history from a database and adjust the order of analysis based on relevance.

[0094] The generation unit can estimate the user's emotions and adjust the presentation of the investment plan it generates based on the estimated emotions. For example, if the user is feeling anxious, the generation unit can generate a simple and easy-to-understand investment plan. If the user is confident, the generation unit can also generate a detailed investment plan. If the user is excited, the generation unit can also generate a visually appealing investment plan. In this way, the generation unit provides an investment plan that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can analyze the user's emotion data in real time and adjust the presentation of the investment plan it generates.

[0095] The generation unit can adjust the level of detail in the generated output based on the risk level of the investment plan. For example, the generation unit may generate an output with a detailed explanation for high-risk investment plans. For low-risk investment plans, the generation unit may also generate a simplified output. The generation unit can also adjust the depth of the output according to the risk level of the investment plan. This allows the generation unit to provide an appropriate investment plan according to the risk level. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may obtain the risk level of the investment plan from a database and adjust the level of detail in the output based on the risk level.

[0096] The generation unit can apply different generation algorithms depending on the investment category during generation. For example, for stock investments, the generation unit can apply a generation algorithm that emphasizes fluctuations in stock prices. For real estate investments, the generation unit can also apply a generation algorithm that emphasizes fluctuations in property value. For cryptocurrency investments, the generation unit can also apply a generation algorithm that emphasizes rapid price fluctuations. In this way, the generation unit provides appropriate investment plans according to the category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can obtain investment categories from a database and apply different generation algorithms according to the category.

[0097] The generation unit can estimate the user's emotions and adjust the length of the investment plan it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise investment plan. If the user is relaxed, the generation unit can also generate a longer investment plan with detailed explanations. If the user is excited, the generation unit can also generate a visually appealing investment plan. In this way, the generation unit provides an appropriate investment plan according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit analyzes the user's emotion data in real time and adjusts the length of the investment plan it generates.

[0098] The generation unit can determine the generation priority based on the submission date of the investment plan during generation. For example, the generation unit may prioritize the generation of recently submitted investment plans. The generation unit may also postpone the generation of older investment plans. The generation unit may also adjust the generation order based on the submission date. This ensures that the generation unit prioritizes the generation of the latest information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit may retrieve the submission date of the investment plan from a database and determine the generation priority based on the submission date.

[0099] The generation unit can adjust the generation order based on the relevance of the investment plans during generation. For example, the generation unit may prioritize generating highly relevant investment plans. The generation unit may also postpone the generation of less relevant investment plans. The generation unit can also adjust the generation order based on the relevance of the investment plans. This allows the generation unit to prioritize the generation of highly relevant information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may retrieve the relevance of investment plans from a database and adjust the generation order based on the relevance.

[0100] The extraction unit can estimate the user's emotions and determine the priority of market emotions to extract based on the estimated user emotions. For example, if the user is feeling anxious, the extraction unit may prioritize extracting low-risk market emotions. If the user is confident, the extraction unit may also prioritize extracting high-risk market emotions. If the user is excited, the extraction unit may also prioritize extracting recent market emotions. In this way, the extraction unit prioritizes extracting information that is important to the user. 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. Some or all of the above processing in the extraction unit may be performed using AI or not using AI. For example, the extraction unit analyzes the user's emotion data in real time and determines the priority of market emotions to extract.

[0101] The extraction unit can improve the accuracy of its extraction based on the reliability of news and social media sources. For example, it may prioritize extracting information from highly reliable news sources. It can also filter out and extract information from less reliable social media sources. The extraction unit can also adjust the accuracy of its extraction based on the reliability of news and social media sources. This ensures that the extraction unit provides highly reliable information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit may obtain the reliability of news sources from a database and improve the accuracy of its extraction based on that reliability.

[0102] The extraction unit can filter market sentiment based on specific keywords during the extraction process. For example, the extraction unit can extract market sentiment based on keywords related to a specific company name. The extraction unit can also extract market sentiment based on keywords related to a specific industry. The extraction unit can also extract market sentiment based on keywords related to a specific event. This allows the extraction unit to provide highly relevant information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can retrieve specific keywords from a database and filter market sentiment based on those keywords.

[0103] The extraction unit can estimate the user's emotions and adjust the display method of the extracted market emotions based on the estimated user emotions. For example, if the user is feeling anxious, the extraction unit provides a simple and easy-to-understand display method. If the user is feeling confident, the extraction unit can also provide a display method that includes detailed information. If the user is excited, the extraction unit can also provide a visually appealing display method. In this way, the extraction unit provides information that is easy for the user to understand. 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. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit analyzes the user's emotion data in real time and adjusts the display method of the extracted market emotions.

[0104] The extraction unit can perform extraction while considering the geographical distribution of news and social media. For example, the extraction unit can prioritize the extraction of news and social media information related to a specific region. The extraction unit can also prioritize the extraction of market sentiment in geographically close regions. The extraction unit can also adjust the extraction priority based on geographical distribution. In this way, the extraction unit provides region-specific information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit can obtain the geographical distribution of news and social media from a database and perform extraction based on geographical distribution.

[0105] The extraction unit can improve the accuracy of the extraction by referring to relevant market data during the extraction process. For example, the extraction unit prioritizes extracting reliable information based on market data. The extraction unit can also filter out less relevant information by referring to market data. The extraction unit can also adjust the accuracy of the extraction based on market data. This ensures that the extraction unit provides reliable information. Some or all of the above processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit may obtain market data from a database and improve the accuracy of the extraction based on the market data.

[0106] The update unit can estimate the user's emotions and adjust the frequency of advice updates based on the estimated emotions. For example, if the user is feeling anxious, the update unit will update the advice more frequently. If the user is feeling confident, the update unit can also reduce the frequency of advice updates. If the user is excited, the update unit can even update the advice in real time. This ensures that the update unit provides advice at the appropriate time for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit may analyze the user's emotion data in real time and adjust the frequency of advice updates.

[0107] The update unit can optimize its update algorithm by referring to past advice history during the update process. For example, the update unit analyzes past advice history and applies the optimal update algorithm. The update unit can also adjust the update algorithm based on user responses from past advice history. The update unit can also improve the accuracy of updates by referring to past advice history. This allows the update unit to provide the best possible advice to the user. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit retrieves past advice history from a database and optimizes the update algorithm.

[0108] The update unit can update the advice based on specific market events at the time of update. For example, the update unit will update the advice immediately when a market event occurs. The update unit can also adjust the content of the advice based on specific market events. The update unit can also update the advice taking into account the impact of market events. This allows the update unit to provide advice that is in line with the latest market conditions. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit may retrieve market event data from a database and update the advice based on specific market events.

[0109] The update unit can estimate the user's emotions and adjust how advice is displayed based on the estimated emotions. For example, if the user is feeling anxious, the update unit provides a simple and easy-to-understand display. If the user is feeling confident, the update unit can also provide a display that includes detailed information. If the user is excited, the update unit can also provide a visually appealing display. In this way, the update unit provides advice that is easy for the user to understand. 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. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit analyzes the user's emotion data in real time and adjusts how advice is displayed.

[0110] The update unit can update advice while considering the user's geographical location information. For example, the update unit can update advice based on the economic conditions of the area where the user lives. If the user is traveling, the update unit can also update advice based on the economic conditions of the destination. The update unit can also provide region-specific advice while considering the user's geographical location information. In this way, the update unit provides region-specific information. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can obtain the user's geographical location information from a database and provide region-specific advice.

[0111] The update unit can improve the accuracy of its advice by referring to relevant market data during the update process. For example, the update unit provides reliable advice based on market data. The update unit can also filter out irrelevant advice by referring to market data. The update unit can also adjust the accuracy of its advice based on market data. This allows the update unit to provide reliable advice. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit retrieves market data from a database and improves the accuracy of its advice based on market data.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The data collection unit can evaluate a user's past investment performance and distinguish between successful and unsuccessful investments when gathering information such as the user's investment history, risk tolerance, and goals. For example, the data collection unit can identify the investment that yielded the highest return among the user's past investments and collect the characteristics of that investment. It can also identify the investment that incurred the largest loss among the user's past investments and collect the characteristics of that investment. Furthermore, the data collection unit can analyze the user's investment performance over time and calculate the success rate and failure rate of investments. This allows the data collection unit to understand the user's investment behavior trends and gather basic data to provide more accurate investment advice.

[0114] The analytics department can identify patterns in users' investment behavior and predict future investment actions when analyzing collected information. For example, it can analyze what investment actions users tend to take under specific market conditions. It can also predict how users will react to investments at specific risk levels. Furthermore, based on patterns in users' investment behavior, the analytics department can simulate how users will react to future market fluctuations. This allows the analytics department to provide users with more appropriate investment advice.

[0115] The generation unit, when generating individually customized investment plans based on analysis results, can propose investment plans tailored to the user's life stage. For example, it can propose investment plans aimed at long-term asset building to younger users. It can also propose investment plans that consider the balance between risk and return to middle-aged users. Furthermore, it can propose investment plans aimed at stable returns with reduced risk to older users. In this way, the generation unit can provide optimal investment advice tailored to the user's life stage.

[0116] The extraction unit can focus on extracting market sentiment from specific regions or countries when extracting market sentiment from news and social media. For example, the extraction unit can prioritize extracting economic news from a particular country that the user is interested in. It can also analyze social media posts from a specific region and extract market sentiment from that region. Furthermore, the extraction unit can extract market sentiment based on economic indicators and market data from a specific region. This allows the extraction unit to accurately grasp market sentiment in regions and countries that the user is interested in, enabling it to provide more accurate investment advice.

[0117] The update unit can adjust the frequency of advice updates based on extracted information, according to the user's investment style. For example, it can update advice more frequently for users who prefer short-term investments, and less frequently for users who prefer long-term investments. Furthermore, the update unit can adjust the content of the advice according to the user's investment style. This allows the update unit to provide advice at the optimal time for each user's investment style.

[0118] The data collection unit can estimate the user's emotions and adjust the method of collecting investment history based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect investment history in a non-interactive manner. If the user is relaxed, the data collection unit can also collect investment history in an interactive manner. If the user is agitated, the data collection unit can broaden the range of information it collects. This allows the data collection unit to collect information in an appropriate manner according to the user's emotional state.

[0119] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on those emotions. For example, if the user is feeling anxious, the analysis unit can perform a rapid analysis and provide the results. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can provide visually appealing analysis results. This allows the analysis unit to provide analysis results at the appropriate time according to the user's emotional state.

[0120] The generation unit can estimate the user's emotions and adjust the risk level of the investment plan it generates based on those emotions. For example, if the user is feeling anxious, the generation unit will generate a low-risk investment plan. If the user is confident, the generation unit can also generate a high-risk investment plan. If the user is excited, the generation unit can generate an investment plan that considers the balance between risk and return. In this way, the generation unit can provide the optimal investment plan according to the user's emotional state.

[0121] The extraction unit can estimate the user's emotions and adjust the types of market emotions extracted based on the estimated emotions. For example, if the user is feeling anxious, the extraction unit will prioritize extracting positive market emotions. If the user is confident, the extraction unit may also prioritize extracting negative market emotions. If the user is excited, the extraction unit may also prioritize extracting the latest market emotions. This allows the extraction unit to provide appropriate market emotions according to the user's emotional state.

[0122] The update function can estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is feeling anxious, the update function will provide advice that minimizes risk. If the user is confident, the update function may also provide advice that encourages taking risks. If the user is excited, the update function may also provide advice that considers investment diversity. In this way, the update function can provide optimal advice tailored to the user's emotional state.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects information such as the user's investment history, risk tolerance, and goals. For example, it collects information such as the user's past investment amount, investment destinations, and investment period, and collects survey results and past investment behavior to assess risk tolerance. It also collects information such as the user's short-term profit goals and long-term asset building goals. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the user's investment history, assesses their risk tolerance, and proposes an appropriate investment plan based on the user's goals. It also predicts the user's investment behavior based on the collected information. Step 3: The generation unit generates individually customized investment plans based on the analysis results obtained by the analysis unit. For example, it proposes high-risk investment plans to users with high risk tolerance and low-risk investment plans to users with low risk tolerance. It also generates investment plans tailored to the user's goals. Step 4: The extraction unit extracts market sentiment from news and social media. For example, it analyzes the tone of news articles and the content of social media posts to extract market sentiment. It also filters market sentiment based on specific keywords and improves the accuracy of the extraction based on the reliability of the news and social media. Step 5: The update unit updates the advice based on the information extracted by the extraction unit. For example, it immediately updates the advice if the market fluctuates rapidly or if a specific market event occurs. It also adjusts how the advice is displayed based on the user's sentiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, extraction unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information such as the user's investment history, risk tolerance, and goals using the control unit 46A of the smart device 14. The analysis unit analyzes the collected information using the specific processing unit 290 of the data processing unit 12. The generation unit generates individually customized investment plans using the specific processing unit 290 of the data processing unit 12. The extraction unit extracts market sentiment from news and social media using the control unit 46A of the smart device 14. The update unit updates advice based on the information extracted by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, extraction unit, and update unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information such as the user's investment history, risk tolerance, and goals by the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected information by the specific processing unit 290 of the data processing unit 12. The generation unit generates individually customized investment plans by the specific processing unit 290 of the data processing unit 12. The extraction unit extracts market sentiment from news and social media by the control unit 46A of the smart glasses 214. The update unit updates advice based on the information extracted by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, extraction unit, and update unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information such as the user's investment history, risk tolerance, and goals using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected information using the specific processing unit 290 of the data processing unit 12. The generation unit generates individually customized investment plans using the specific processing unit 290 of the data processing unit 12. The extraction unit extracts market sentiment from news and social media using the control unit 46A of the headset terminal 314. The update unit updates advice based on the information extracted by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, extraction unit, and update unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information such as the user's investment history, risk tolerance, and goals by the control unit 46A of the robot 414. The analysis unit analyzes the collected information by, for example, the specific processing unit 290 of the data processing unit 12. The generation unit generates individually customized investment plans by, for example, the specific processing unit 290 of the data processing unit 12. The extraction unit extracts market sentiment from news and social media by, for example, the control unit 46A of the robot 414. The update unit updates advice based on the information extracted by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A collection unit that collects information such as the user's investment history, risk tolerance, and goals, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit generates an individually customized investment plan based on the analysis results obtained by the analysis unit, An extraction unit that extracts market sentiment from news and social media, The system includes an update unit that updates the advice based on the information extracted by the extraction unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect information such as the user's investment history, risk tolerance, and goals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected information is analyzed to generate an investment plan tailored to the user's risk tolerance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate individually customized investment plans based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The extraction unit is Extracting market sentiment from news and social media. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned update unit is, Update the advice based on the extracted information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of investment history collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past investment history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting investment history, filtering is performed based on the user's current financial situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's sentiment and determines the priority of investment history to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting investment history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting investment history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the investment history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, different analytical algorithms are applied depending on the investment category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on when the investment history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of investment history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of investment history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is We estimate the user's emotions and adjust how investment plans are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, adjust the level of detail based on the risk level of the investment plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, different generation algorithms are applied depending on the investment category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the length of the investment plan generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the generation priority is determined based on when the investment plan was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the generation order is adjusted based on the relevance of the investment plan. The system described in Appendix 1, characterized by the features described herein. (Note 26) The extraction unit is It estimates user sentiment and determines the priority of market sentiment to extract based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The extraction unit is During extraction, the accuracy of the extraction is improved based on the reliability of news and social media. The system described in Appendix 1, characterized by the features described herein. (Note 28) The extraction unit is During extraction, market sentiment is filtered based on specific keywords. The system described in Appendix 1, characterized by the features described herein. (Note 29) The extraction unit is We estimate user sentiment and adjust how market sentiment is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The extraction unit is During the extraction process, the geographical distribution of news and social media content is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The extraction unit is During extraction, we refer to relevant market data to improve the accuracy of the extraction. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned update unit is, It estimates the user's emotions and adjusts the frequency of advice updates based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned update unit is, During updates, the update algorithm is optimized by referring to past advice history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned update unit is, When updating, the advice will be updated based on specific market events. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned update unit is, It estimates the user's emotions and adjusts how advice is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned update unit is, When updating, the advice will be updated taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned update unit is, When updating, we refer to relevant market data to improve the accuracy of our advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 collection unit that collects information such as the user's investment history, risk tolerance, and goals, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit generates an individually customized investment plan based on the analysis results obtained by the analysis unit, An extraction unit that extracts market sentiment from news and social media, The system includes an update unit that updates the advice based on the information extracted by the extraction unit. A system characterized by the following features.

2. The aforementioned analysis unit is The collected information is analyzed to generate an investment plan tailored to the user's risk tolerance. The system according to feature 1.

3. The generating unit is Generate individually customized investment plans based on the analysis results. The system according to feature 1.

4. The extraction unit is Extracting market sentiment from news and social media. The system according to feature 1.

5. The aforementioned update unit is Update the advice based on the extracted information. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of investment history collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past investment history and select the optimal data collection method. The system according to feature 1.

Images